esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The degradation caused by each cycle of the loading spectrum is computed and accumulated one after another. The degradation takes into account the current cycle loading characteristics as well as previous loading history. Considerable efforts have been spent to develop the models to study the fatigue behavior of glass-fiber- and carbon-fiber-reinforced plastic composites under constant amplitude loads. Experts and scholars have made great efforts in the study of the fatigue behavior of glass-fiber-reinforced materials under variable amplitude fatigue loading for more than three decades now. Based on the standardized fatigue spectra such as WISPR (Wind turbine reference SPEctRum), FALSTAFF (Fighter Aircraft Loading STandard For Fatigue evaluation), experiments and theoretical predictions have been reported by several researchers [19]. Thermal analyses of composite structures, which have been utilized in high-temperature environments due to the high temperature resistance, are essential. In order to ensure the structural design and safety of such materials, especially during service condition, a sophisticated understanding of the heat transfer mechanisms is becoming more and more important. Much effort has been made to solve various problems of heat transfer and fluid flow using numerical methods and analytical methods [20; 21]. Analytical methods have the advantages of being more accurate [22], more efficient, stable for multi-dimensional heat conduction, and providing deep insights into the fundamentals. Numerical methods have the advantage of a wider range of use. Owing to the designability of thermal conductivity of functionally graded material (FGM), FGM as an important composite material has been widely used as a high temperature material in aerospace. Studies [23; 24] of the heat conduction of FGM have been performed to understand the fundamentals and select the optimum material. This paper develops a methodology for determining the residual strength of C/SiC ceramic matrix composite panels subjected to combined thermal-acoustic loadings. The influence of the thermal-acoustic loadings on the evolution of the residual strength of the thin-walled structures is divided into two parts in this paper. The thermal environment has an important effect on the dynamical properties of the panel structures (elastic constant, thermal stress, thermal buckling and geometric nonlinearity) and the material mechanical properties (static strength, fatigue life and residual strength). Large mean stresses caused by thermal stresses and high alternating stresses make it different from the fatigue of panels subjected to acoustic loading alone. the static strength, residual strength and fatigue life of the material for fiber direction, which are determined under tensile and compression at elevated temperature, are the input of the methodology. Following this, the residual strength of panels under combined thermal-acoustic loadings can be predicted. The methodology is validated with the residual strength test of 2D plain-woven C/SiC composite panel subjected to combined thermal-acoustic loadings.
## 2 Materials and Methods
### Experimental Research
\[v=\frac{Q_{t}^{x}}{t*5}\left(\frac{kg}{m^{2}*ml}\right) \tag{9}\]  
where Q\({}^{x}\) is the surface water absorption (kg/m\({}^{2}\)) at step p (1, 2, 3, 4, 5, 6, 7, 8, 9, 10) which corresponds to the time t (2, 4, 6, 8, 10, 12, 14, 16, 18, 20 min) recorded from the start of the test, and corresponding to a certain volume of sprayed water on the surface (5, 10, 15, 20, 25, 30, 35, 40, 45, 50 mL). The parameter thus determined, namely the evolution rate of the water absorption process, v, characterizes the process of water absorption on the surface, and indirectly the surface hydrophilicity evaluation from a kinetic point of view. For each case of activation, with or without UV exposure, indicators for the efficiency of the addition of TiO\({}_{2}\) in the cementitious matrix on the surface water absorption have been properly defined:  
* The Evolution Index (EI) of surface hydrophilicity represents the percentage variation (increase) of water absorption of the surface at time t, in relation to water absorption of the specimen previously determined (Equation (10)): \[EI=\frac{Q_{t}^{xUV}-Q_{t-2}^{xUV}}{Q_{t-2}^{xUV}}*100\ (\%)\] (10) where, Q\({}^{xUV}\) is the water absorption to the surface of the specimen at time t (4, 6, 8, 10, 12, 14, 16, 18, 20 min) from the start of the test; Q\({}_{t-2}\)\({}^{xUV}\) is the surface water absorption of the specimen measured at the previous stage (t = 2 min); and x is the number of hours of UV exposure (0 h, 1 h or 1 + 24 h). A faster increase in the Evolution Index is an indicator of the effectiveness of the addition of nanoparticles on surface hydrophilicity. * The Efficiency Index of Nanoparticles (EIN) represents the percentage variation (increase) of surface total water absorption (time 20 min, volume of water 50 mL), of the specimen of the cementitious composite with TiO\({}_{2}\) nanoparticles addition, in relation to surface total water absorption, (time 20 min, volume of water 50 mL) of the control cementitious composite samples (without TiO\({}_{2}\) nanoparticles) (Equation (11)): \[EIN=\frac{Q_{20}^{xUV}-Q_{20}^{control\ sample\ xUV}}{Q_{20}^{control\ sample\ xUV}}*100\ (\%)\] (11) where, \(Q_{20}^{xUV}\) is the surface water absorption of the cementitious composite specimen with TiO\({}_{2}\) nanoparticles addition at t = 20 min after the start of the test (50 mL water); \(Q_{20}^{control\ sample\ xUV}\) is the surface water absorption of the control specimen (without TiO\({}_{2}\) nanoparticles addition) at t = 20 min after the start of test (50 mL water); and x is the number of UV exposure hours (0 h, 1 h or 1 + 24 h). The higher the Efficiency Index of Nanoparticles, the higher the surface hydrophilicity is; furthermore, the cementitious mixture with the corresponding TiO\({}_{2}\) nanoparticles addition is considered to be more efficient in assessing the self-cleaning performance.
## Abbreviations  
ALL  
acute lymphoblastic leukemia  
AML  
acute myeloid leukemia  
Ang-1  
angiopoietin-1  
BM  
bone marrow  
BMP
bone morphogenetic protein  
CAFs  
cancer-associated fibroblasts  
CAR  
CXCL12-abundant reticular cells  
CCL22  
C-C motif chemokine ligand 22  
CCR4  
C-C chemokine receptor type 4  
CDH1  
E-cadherin gene  
CML  
chronic myeloid leukemia  
CXCR-4  
C-X-C chemokine receptor type 4  
Dll4  
Delta-like ligand 4  
EC  
endothelial cells  
ECM  
extracellular matrix  
EMT  
epithelial-to mesenchymal transition  
EPCs  
endothelial precursor cells  
FGFR1  
fibroblast growth factor receptor 1  
G-CSF  
granulocyte colony-stimulating factor  
HA  
hyaluronic acid  
HGF  
hepatocyte growth factor  
HSC  
hematopoietic stem cells  
IGF1 and IGF2  
insulin-like growth factor 1 and 2  
IL-6  
interleukin-6  
JAG1  
jagged canonical Notch ligand 1  
COX  
lysyl oxidase  
MET  
mesenchymal-to-epithelial transitions  
MMPs  
matrix metalloproteinase  
MSCs
mesenchymal stem cells  
OBs  
osteoblasts  
OPN  
osteopontin  
PDGFB  
platelet-derived growth factor receptor beta  
PIGF  
placental growth factor  
PMNs  
pre-metastatic niches  
PSA  
prostate-specific antigen  
PSGL-1  
glycoprotein ligand-1  
PTH  
parathyroid hormone  
PTHrP  
parathyroid hormone-related peptide  
RANKL  
receptor activator of nuclear factor-\(\kappa\)B ligand  
SDF-1  
stromal cell-derived factor-1  
SNO  
spindle-shaped N-cadherin+ CD45 osteoblastic cells  
TGF-\(\beta\)  
transforming growth factor-\(\beta\)  
TSP1  
thrombospondin
###### Abstract  
Objective: The objective of this study was to investigate the association between BMI, days spent in hospital, blood loss, and surgery time in patients who suffered from isolated pelvic fractures and pelvic fractures with concomitant injuries (polytrauma patients). Methods: This study included 76 consecutive patients who were admitted for pelvic ring fracture surgery between 2017 and 2022. The inclusion criteria were pelvic fractures and indications for operative treatment (LC II and III, APC II and III, and VS). The exclusion criteria were non-operative treatment for pelvic ring fractures, acetabular fractures and fractures requiring primary total hip arthroplasty (THA), and periprosthetic acetabular fractures. Demographic data were collected, including age (in years), sex, type of fracture according to Young-Burgess, date of injury and surgery, surgical approach and stabilization methods, mechanism of trauma, concomitant trauma in other regions, body mass index (BMI), blood transfusions, number of days spent in the hospital, and surgery duration. Results: Patients who suffered from a pelvic ring injury with concomitant injuries had a significantly greater amount of blood units transferred (1.02 units vs. 0.55 units), and the length of hospital stay was also longer compared to the mean results (5.84 days vs. 3.58 days), \(p=0.01\) and \(p=0.001\), respectively. Moreover, patients with a higher BMI had more frequent APC II and APC III fractures (\(p=0.012\)). Conclusions: This study demonstrates that polytrauma patients who suffered from pelvic ring injury are, indeed, at risk of blood transfusion in terms of greater units of blood and a longer duration of hospital stay. Moreover, BMI has an impact on pelvic ring fracture morphology. However, there is no doubt that there is an absolute need for further studies and investigations to provide better overall management of polytrauma patients with pelvic fractures.  
p pacs: pelvic fracture; pelvis; BMI; transfusion; Young-Burgess; trauma; polytrauma
## 2. **Breadboard Design and Implementation**
### Limiting Magnitude of the Sensor and Visual Magnitude of Debris
From (10), its estimated diameter is 0.88 m. For an altitude of 1000 km, with a magnitude 8.7 and the same configuration, the distance is 1429 km, and the estimated diameter is 1.03 m. Analogously, for an orbit at 2000 km altitude and observer distance of 2706 km, a magnitude 8.9 leads to an estimated diameter of 1.79 m. For an optimistic phase angle of 20 deg, for the 800 km, 1000 km, and 2000 km orbits, the diameters are 0.30 m, 0.35 m, and 0.60 m, respectively (note that the observed magnitude strongly depends on the phase angle). The different observable minimum sizes of debris at several orbiting altitudes, corresponding to different orbital velocities, observed at phase angles of 120 deg and 20 deg, are shown in Figure 7.  
Figure 7 shows an expected behavior, where the rough size of the detectable debris decreases with its distance to the sensor (which in our simulated scenario is in a ground-based location) but taking into account that with lesser distance comes a faster orbit and a more largely spread streak. Note that for an optimistic phase angle of 20 deg, in the hypothetical scenario of almost non-trailing objects, where all of their signals would be integrated roughly over the same pixels, our sensor would detect debris of 10 cm at a  
Figure 7: Debris size in the function of the distance from a ground-based sensor and their corresponding orbital velocity, assumed to be circular, for phase angles of 120 deg and 20 deg. 316 km distance. Such configuration is not achievable with ground-based observations, like those shown in Figure 7, but it can be achieved with a sensor in orbit. In this sense, our camera in space can detect under-catalogued debris in almost co-orbital orbits at about 300 km distance, i.e., space debris moving almost parallel to the camera leaving virtually no streak at all, being therefore detected at the sensor's limit magnitude of 10.2 for 1 s exposure as a point-source. It is evident that without a noticeable streak in the image, we would have to use alternative identification algorithms for the debris, but for such an ideal scenario, this limit is indeed reachable. Also note that with ground-based observations tracking the sky at non-sidereal rates, a piece of debris moving in the sky in an orbit such that matches the tracking rate of the sensor would present itself as a point-source, whereas all stars and other debris with distinct moving rates would be recorded as streaks.
# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 5 Problem Solution  
The optimal offloading decisions are obtained by employing a Lyapunov algorithm and greedy algorithm under the condition of ensuring the stability of task computation queue and meeting the deadline of task execution.
###### Abstract  
Research in the training simulation sector to improve the realism and immersive experience of operator training simulators (OTSs) entails combining cutting-edge technologies such as virtual reality (VR) and augmented reality (AR). Although most of the existing studies has been about troubleshooting training, research into the response to chemical accidents through mutual cooperation between the participants has been insufficient. Therefore, we developed an immersive OTS that can facilitate mutual cooperation. Training processes to educate trainees in general chemical facilities were selected, while changes that can occur in facilities during an accident and the corresponding responses in various scenarios were used as the training content. A communication system that relays information between the worksite and the control room was implemented using a distributed control system (DCS) and AR technology. We installed a pilot plant and developed a DCS, thereby establishing an infrastructure that allows the boardman and field operator to cooperate during accident scenarios. Furthermore, we developed an OTS that allowed trainees to learn prompt and accurate responses to chemical accidents through operation of the actual equipment. The training effect of the OTS was found to be approximately 4.5 times better than traditional training methods. It is, therefore, anticipated that the developed OTS will minimize losses or damage caused by chemical accidents.  
operator training simulator (OTS); mutual cooperation system; chemical accident response; process safety; digital platform
## 3 Methods
### Optimization of the Number of Vessels and Travel Speed  
This study seeks to find the optimum number of large and small vessels and determine the most economical travel speed to reduce the total costs. The developed optimization model is incorporated into the simulation model to prescribe such decisions while satisfying all the required system constraints. Table 1 defines all notations used in the optimization model.  
By using the notations defined in Table 1, we formulate an optimization model as follows.  
\[\begin{split}&\text{Minimize z}=CR_{L}x_{L}+CR_{S}x_{S}+CP_{L}t_{L}+ CP_{S}t_{S}+\{CV_{L}(2+S0*VR*\ (s_{F}-1)+\\ & S0*VR*\ (s_{B}-1))\}t_{L}+\{CV_{S}(2+S0*VR*\ (s_{F}-1)+S0*VR*\ (s_{B}-1))\}t_{S}\end{split} \tag{1}\]  
Subject to:  
\[y\geq LT \tag{2}\]  
\[0\leq x_{L}\leq MN_{L} \tag{3}\]  
\[0\leq x_{S}\leq MN_{S} \tag{4}\]  
\[SF_{l}\leq s_{F}\leq SF_{m} \tag{5}\]  
\[SF_{l}\leq s_{B}\leq SF_{m} \tag{6}\]  
\[x_{L},x_{S}:\ integer \tag{7}\]  
\[s_{F},s_{B}:\ 0.1*\ integer \tag{8}\]The objective function (1) is to minimize the sum of three types of costs: rental costs, port dues, and the operating costs of large and small vessels. The first two terms are the rental costs of large and small vessels. The third and fourth terms are port dues of large and small vessels. The last two terms are the voyage costs, which are the sum of the forward and backward voyage costs. The voyage cost is a function of the number of trips and speed factors. A speed factor (\(s_{F}\) and \(s_{B}\)) of 1.0 means the speed at S0.  
Constraint (2) restricts the amount of product transported to be at least the target volume. Constraints (3) and (4) impose the possible range of the number of large and small vessels, respectively. Constraint (5) specifies the range of the forward speed factor, and Constraint (6) specifies the range of the backward speed factor. Constraints (7) and (8) invoke integer restrictions on decision variables. Note that the decision variables \(s_{F}\) and \(s_{B}\) have discrete values with a step size of 0.1.
## 3 Optimization of the Conversion Efficiency  
The optical conversion efficiency of THz generation via rectification in GaP is typically on the order of \(10^{-7}\)-\(10^{-5}\)[29; 30; 31; 32; 33]. For maximal THz generation, the pulse intensity in the crystal should be optimized by targeting the saturation intensity, where nonlinear losses quench any further increase in the conversion efficiency at increasing pump intensities. In our experimental setting, this condition translates into finding the beam size for which the conversion efficiency saturates at the maximum available pump energy, i.e., 300 uJ, corresponding to 30 W at our fixed 100 kHz.  
To this end, the rectification conversion efficiency was recorded at increasing pump powers and fixed beam size. This process was repeated for three different \(1/e^{2}\) beam diameters: 0.88 mm, 1.05 mm, and 3.50 mm. The THz power was recorded with a pyroelectric detector (Gentec THZ5I-BL-BNC, Gentec-EO, Quebec City, QC, Canada) while the pump power was measured with a thermal detector (Gentec UP25N-100H-H9-D0, Gentec-EO, Quebec City, QC, Canada).  
It was found that, for our GaP crystal, (1 mm thick, 110-cut, antireflection-coated GaAs, UniversityWafer) pumped by 1030 nm pulses with a duration of 245 fs at a repetition rate of 100 kHz (Light Conversion Carbide CB3-40W, Light Conversion, Vilnius, Lithuania), the conversion efficiency saturated at \(\simeq\)\(7\times 10^{-7}\) for a 30 W pump power when using an  
Figure 1: Conventional THz-TDS beamline. BS: Beamsplitter; OR: Optical rectification via GaP crystal; EO: electro-optic wafer; QWP: quarter-wave plate; PBS: polarising beam splitter (Wollaston prism); BD: balanced detector.  
optimal beam diameter of around 3.5 mm \(1/e^{2}\), as shown in Figure 2. We note that the conversion efficiency in our experiment is lower than what is reported in record-breaking experiments, e.g., [32], as our laser pulses are longer and the efficiency scales with the driving pulse duration \(\tau\) as \(1/\tau^{2}\)[34, 35].
## 7 Simulation Results and Discussion
### Analysis of Proposed Scheme Results against Existing State of the Art
#### 7.2.2 Depth Adjustment  
At low network density, distance between void nodes is high. Figure 9 depicts the displacement of void nodes in GDGOR-IA and GEDAR. It can be seen that at node number 200, 15% of network nodes are void nodes. As node number in the network field increases, the displacement of void nodes decreases. This is because of increase in node density, the fraction of void nodes decreases as shown in Figure 8.
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 4 Vehicular Environment Identification Methodology
### Choice of Loss Function and Its Impact  
In this study, our CNN model is designed to identify different vehicular environments, which involves a typical classification problem. Therefore, choosing an appropriate loss function is crucial for ensuring effective model training and optimal performance.  
For the multi-class classification problem, we have selected the Categorical Cross-Entropy Loss as it is highly effective in dealing with probability distributions across multiple categories. This loss function measures the disparity between the probability distribution predicted by the model and the actual distribution of the target labels [29]. The formula is given by:  
\[L=-\sum_{c=1}^{M}y_{o,c}\log(p_{o,c}) \tag{17}\]  
where \(M\) is the total number of classes, \(y_{o,c}\) is the true label for class \(c\), and \(p_{o,c}\) is the probability predicted by the model for class \(c\).  
The Categorical Cross-Entropy Loss function enhances classification accuracy by penalizing incorrect predictions and encouraging the model to lean toward the correct class probabilities. This loss function is particularly suitable for classification tasks as it provides a clear mathematical path aimed directly at optimizing classification accuracy.  
Additionally, this loss function helps address issues of class imbalance by adjusting the weight of each class, which allows the model to pay more attention to minority classes during training, thus improving the model's overall recognition capabilities across all categories.
## 5 Scheme Construction
### Decryption  
When a user submits their data access request to the cloud server along with their secret keys, the decrypt algorithm is executed as follows:  
Decrypt(CT, GPP, \(\mathrm{FK}_{gid}\))\(\rightarrow\)(K): When the set of attribute keys of the user (\(\mathrm{S}_{gid}\)) matches any of the conjunction or sub-policy \((W,~{}p,~{}Z)\), then the symmetric key \(\mathrm{K}\) can be retrieved by the following steps:  
Step 1: Compute \(CK=\prod_{i\in SAA}\mathrm{K}_{2,\mathrm{i}}\), and choose constants \(\mu_{x}\in Z_{N}\), such that \(\sum_{(x)\in\mathrm{S}_{gid}}\mu_{x}W_{x}=(1,0,\ldots,0)\). Then compute  
\[e(~{}CK,~{}C_{0})=e\Big{(}\prod_{k=1}^{l}g^{\alpha_{k}}g^{r_{gid}\beta_{k}} \mathrm{R}^{\prime}_{gid,k},~{}g^{s}\Big{)}\!=e(g,g)^{s\sum_{k-1}^{l}\alpha_{k }}e(g,g)^{r_{gid}\beta^{s}\sum_{k-1}^{l}\beta_{k}} \tag{12}\]  
Step 2: For all attribute-related keys for which \(\rho(x)\in\mathrm{S}_{gid}\), compute  
\[\begin{array}{l}\prod_{\rho(x)\in\mathrm{S}_{gid}}\ \big{[}e(C_{1,x},K_{1})e(C_{2,x},K_{3, \mathrm{i}})\big{]}^{\mu_{x}}=\\ \prod_{\rho(x)\in\mathrm{S}_{gid}}\Big{[}e\Big{(}g^{\sum_{k\in SAA}\beta_{k} \mathrm{Ax.}}\Big{(}g^{r_{g(x)}}{}^{-r_{g}},g^{r_{gid}}\mathrm{R}_{gid} \Big{)}\ e\Big{(}g^{r_{g}},~{}T_{p(x)}^{2}r_{gid}\mathrm{R}^{\prime}_{gid,k, \mathrm{i}}\Big{)}\Big{]}^{\mu_{x}}\\ \qquad\qquad\qquad=\prod_{\rho(x)\in\mathrm{S}_{gid}}\big{[}\ e\big{(}g^{ \sum_{k\in SAA}\beta_{k}\mathrm{Ax.}},g^{r_{gid}}\big{)}\big{]}^{\mu_{x}}=e(g, g)^{r_{gid}\sum_{k\in SAA}\beta_{k}}\end{array} \tag{13}\]  
Step 3: After dividing the result of step 1 by step 2, we obtain \(e(g,g)^{s\sum_{k-1}^{l}\alpha_{k}}\).  
Step 4:  
\[C/~{}e(g,g)^{s\sum_{k-1}^{l}\alpha_{k}}\!=K\cdot e(g,g)^{s\sum_{k\in SAA}\alpha _{k}}/~{}e(g,g)^{s\sum_{k-1}^{l}\alpha_{k}}=K \tag{14}\]  
The user can recover the data by using the symmetric key \(\mathrm{K}\).