esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Cohesive Properties on Low-Velocity Impact Simulations of Woven Composite Shells
## 2 Finite Element Models
### Finite Element Modelling Approach  
Nine plain weaves of glass fibres at 0 and 90 degrees with \(98\pm 4\%\) g/m\({}^{2}\) in each direction were combined with an AROPOL FS 1962 polyester resin and a MEKP-50 hardener to produce the semicylindrical composite shells shown in Figure 1. This figure also shows a schematic representation of the shell layup generated with continuum shell elements. Theseelements provide an efficient and accurate way to simulate the behaviour of shell structures under various loading conditions, making them suitable to study composite shells structures. Moreover, they are computationally efficient when compared to solid elements.  
To model the interlaminar behaviour, cohesive surfaces were added to the interfaces of adjacent layers. The cohesive behaviour was applied to the nodes that were initially in contact, meaning that any new contact that occurred during the analysis did not experience cohesive constrain forces. The cohesive surfaces are directly related to the shell elements and share the same FE mesh size. In this way, they provide a simple way to model the interlaminar connection in negligible small interface thicknesses. The use of this strategy for modelling the interlaminar damage implies that the S-BCM must be defined as a surface interaction property and not as a material property. The main advantage of this approach is the quick and easy modelling of the cohesive connection. Moreover, it often allows for higher stable time increments than cohesive elements and does not add any mass to the FE model.  
The simulations were conducted using a semiautomatic mass-scaling technique that was uniformly applied to the entire FE model. A target time increment of \(1\times 10^{-7}\) was employed during the simulations. This approach resulted in an overall mass increase of 1.8%. However, it also provided a notable benefit in terms of computational efficiency, reducing the computational cost of the solution by approximately 50%.
## 3 Results and Discussion
### SOC Estimation Results
If the SOC estimation model and HPPC parameters are scaled up without considering the balancing mechanisms, the deviations between individual cells' SOC values can accumulate, leading to inaccurate pack-level SOC predictions. Figure 16 illustrates the variation in electrical parameters (R\({}_{0}\), R\({}_{1}\), R\({}_{2}\), C\({}_{1}\), and C\({}_{2}\)) of ECM as a function of SoC for three different temperature conditions: -10 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C. It is evident from the plots that temperature significantly affects the parameters, particularly at lower SoC levels, with R\({}_{0}\) and R\({}_{1}\) demonstrating more pronounced fluctuations under different temperatures. The capacitive components, C\({}_{1}\) and C\({}_{2}\), exhibit relatively larger magnitudes but appear to be less sensitive to temperature variations compared to the resistive components. Figure 16: The determined RC parameters for optimal conditions.
# FODIT: A Filter-Based Module for Optimizing Data Storage in B5G IoT Environments
## 2 Background
### Filters
To do this, Algorithm 2 is used, where the fingerprint of the element is computed using Equation (1). Subsequently, the positions of the candidate bins are calculated using Equations (2) and (3). If the obtained bins have any entry with the fingerprint \(f_{x}\), then the membership query returns true with a probability of false positive; otherwise, it returns false. Finally, in the deletion operation to remove an element \(x\) from the cuckoo filter \(CF[b]\), the process is similar to the search. Using the function \(SF(x)\) in Algorithm 2, the item \(x\) is searched. If there exists an entry that contains the fingerprint \(f_{x}\) in the obtained bins, then a copy of it is deleted as shown in Algorithm 3. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature** & **Bloom Filter** & **Counting Bloom Filter** & **Sliding Bloom Filter** & **Cuckoo Filter** \\ \hline
**Supports Deletions** & No & Yes & No & Yes \\
**False Positives** & \(\left(1-e^{-\frac{h}{2}}\right)^{k}\) & \(\left(1-e^{-\frac{h}{2}}\right)^{k}\) & n \(\left\lceil\frac{\alpha}{n}\right\rceil\leq e\)\(u+n\) & \(1-\left(1-\frac{1}{2^{7}}\right)^{2b}=\frac{2b}{2^{7}}\) \\
**Insertion Speed** & \(\mathcal{O}(k)\) & \(\mathcal{O}(k)\) & \(\mathcal{O}(k)\) & \(\mathcal{O}(\log n)\) \\
**Query Speed** & \(\mathcal{O}(k)\) & \(\mathcal{O}(k)\) & \(\mathcal{O}(k)\) & \(\mathcal{O}(1)\) \\
**Deletion Speed** & Not supported & \(\mathcal{O}(k)\) & Not supported & \(\mathcal{O}(1)\) \\
**Implementation** & Simple & Moderate (counter & Complex (window & Moderate (cuckoo hashing) \\
**Scalability** & Limited (fixed size) & Limited (counter overhead) & Better (sliding window) & Best (resizable, low overhead) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of Probabilistic Filters.
# Measurement and Analysis of Vibration Levels for Truck Transport Environment in Korea
## 2 Experiment Design
### Measurements and Recording Parameters  
A data recorder (SAVER 3 \(\times\) 90, Lansmont Corporation, Monterey, CA, USA) with built-in triaxial acceleration sensors was used to record the vibration signals in a traveling truck. The instrument was held securely by double-faced adhesive tape and blue tape at the center position at the rear end of the load platform of the truck, where the vibration level is relatively large [11; 15; 29; 33] (Figure 3).  
Before starting the measurement, the data recorder was configured with the following settings: an event trigger threshold of 0.5 G, a sampling rate of 1000 Hz, a record time of 2 s, and a signal pre-trigger of 50% for signal-triggered data, and a sampling rate of 1000 Hz and record time of 2 s for timing-triggered data.
# Gaussian Process Modeling of Specular Multipath Components
## 2 System and Signal Models  
We consider indoor environments where fixed anchors communicate with a mobile agent by means of radio signals. Figure 1 illustrates the considered scenario, where two physical anchor positions are shown as blue crosses at positions \(\mathbf{a}_{1}^{(1)}\) and \(\mathbf{a}_{1}^{(2)}\) and the agent positions \(\mathbf{p}\) are selected along a segmented trajectory. Furthermore, some exemplary virtual anchors (VAs) are shown. The VA positions are mirror images of the physical anchor positions that are induced by reflections at flat surfaces--typically walls--and thus depend on the surrounding environment (the floor plan) [27]. The position of the \(k\)-th VA of the \(j\)th physical anchor at position \(\mathbf{a}_{1}^{(j)}\) is denoted as \(\mathbf{a}_{k}^{(j)}\). Note that in this work we consider horizontal propagation only. For brevity, we neglect the anchor index \(j\) from now on. Also, we denote \(\mathcal{L}\) as the set of measurement points.  
Figure 1: Floor plan of the evaluation scenario. Bold black lines denote walls, thick gray lines represent glass windows, other lines illustrate other materials. Two blue crosses represent the physical anchors; orange circles denote virtual anchors (VAs) which were considered in the experimental validation. An agent moves along a trajectory segmented into seven parts indicated with distinct colors. Capital letters (with or without mark \(*\) or \(\dagger\)) refer to sub-segments of different materials along each wall.
# Simple Approximate Formulas for Postbuckling Deflection of Heavy Elastic Columns
## 2 Method  
### Governing Equation  
We consider a vertically built heavy elastic column with a free end at the tip. Figure 1a shows the configuration of a column subjected to downward concentrated load at the tip as well as uniformly distributed weight. Under the combined load condition, the local bending moment, \(M(\ell)\), satisfies the relation of  
\[\frac{dM}{d\ell}=\left\{F+\rho(L-\ell)\right\}\sin\theta(\ell). \tag{1}\]  
Here, \(F\) is the concentrated downward load applied at the tip, \(\rho\) is the self-weight of the column per axial length, and \(L\) is the column length; \(\ell\) is the arc length measured from the bottom end, and \(\theta\) is the local tilt angle. Angle \(\theta\) is defined such that \(\theta=0\) in the vertical direction and \(\theta=\pi/2\) in the horizontal; see Figure 1b.  
Figure 1: Configuration diagram of a column under combined loads: (**a**) Concentrated load \(F\) at the tip and uniformly distributed load \(\rho\) caused by the self-weight per length. (**b**) Tilt angle \(\theta(\ell)\) and tip angle \(\theta_{0}\) at \(\ell=L\).  
The column is sufficiently slender such that the local curvature is proportional to the local bending moment \(M(\ell)\) as  
\[EI\frac{d\theta(\ell)}{d\ell}=-M(\ell), \tag{2}\]  
where \(E\) is the Young's modulus and \(I\) is the second moment of area of the column. For convenience, we introduce three dimensionless parameters [18]:  
\[\alpha=\frac{FL^{2}}{EI},\quad\beta=\frac{\rho L^{3}}{EI},\quad s=1-\frac{\ell} {L}, \tag{3}\]  
where \(\alpha\) and \(\beta\) represent the relative importance of the concentrated load \(F\) and self-weight \(\rho L\), respectively, to the flexural rigidity \(EI\). The normalized arc length \(s\) is defined such that \(s=0\) at the tip and \(s=1\) at the bottom. Using these three dimensionless parameters, the governing equation is derived from Equations (1) and (2) as  
\[\frac{d^{2}\theta(s)}{ds^{2}}=-(\alpha+\beta s)\sin\theta(s), \tag{4}\]  
and the associated boundary conditions are  
\[\theta(s)=0\;\;\text{at}\;\;s=1\;\;\text{(i.e., at the bottom)}, \tag{5}\] \[\frac{d\theta(s)}{ds}=0\;\;\text{at}\;\;s=0\;\;\text{(i.e., at the tip)}. \tag{6}\]
## 4 The Proposed DSB Workflow Scheduling Algorithm
### Proposed Algorithm
#### 4.4.5 Elastic Resource Provisioning  
The resource provisioning phase is based on the optimization of a QoS metric cost and used to dynamically adjust the number of required VM instances to ensure the completion of workflow within the deadline. Its primary purpose is to prioritize the reuse of already leased VMs by utilizing idle times on previous rented intervals when possible instead of renting a new VM instance. When all the tasks in a BoT are ready for execution, they are put in the execution queue from the priority queue. Tasks in a BoT can be scheduled one by one to an elastic number of VMs for parallel, or serial executions. Scheduling plan is created based on tasks in the same BoT to decide the number and type of VM instances that can be launched to schedule the BoT. The plan needs to be made once for all the tasks in each BoT using MIP. The MIP problem was formulated to provide an estimate of the number and types of VMs that can optimize total cost of the workflow execution with the given deadline. It can be formally modeled as follows:  
\[\text{minimize}\sum_{v_{i}\in V}\sum_{m_{r}\in VM}EC_{i}^{r} \tag{17}\]  
subject to:  
\[x_{i}^{r}=1,\qquad x_{i}^{r}\in\{0,\ 1\},\qquad\forall v_{i}\in V,\qquad\forall m _{r}\in VM \tag{18}\]  
\[FT_{i}^{r}\leq FT_{j}^{r}-ET_{j}^{r},\qquad\forall(v_{i},v_{j})\in V,\qquad i<j \tag{19}\]  
\[\sum_{m_{r}\in VM,u\in m_{r}}y_{r,u}=n_{bot}^{r,u},\ \forall bot\in BoT \tag{20}\]  
\[DL_{bot\in BoT}\leq DL_{W} \tag{21}\]  
\[FT_{sink}^{r}\leq DL_{W} \tag{22}\]  
Constraint (18) is a binary variable and ensures that each task is assigned to one and only one VM type. Constraint (19) ensures the precedence constraint. Constraint (20) ensures that the number of tasks of a BoT assigned to the \(u^{th}\) VM of type \(r\) process all the tasks. Constraint (21) guarantees that the sum of sub-deadlines of all BoTs is not greater than the workflow deadline. Constraint (22) ensures that the deadline is met. The problem is solved in CPLEX solver and a scheduling plan \(\mathcal{SP}_{q}^{r}=(m_{r},n_{q}^{r},k_{botq}^{m_{r}})\) is obtained for each BoT to obtain VMs that can process the BoTs while meeting the deadline and with minimum cost. \(n_{q}^{r}\) corresponds to the maximum number of tasks in the BoT \(bot_{q}\) that can be executed by the VM type \(m_{r}\) without exceeding the deadline and \(k_{bot_{bot_{bot_{bot_{bot_{bot_{bot_{bot_{bot_{bot_{bot_{bot_ _{{bot___can meet the BoT's sub-deadline. In extreme cases in which no such VM type exist that can complete the tasks within deadline, a fastest VM type can be rented. Whenever a BoT completes its execution, the sub-deadline of all the remaining BoTs is updated dynamically. Since all the tasks within a BoT are homogeneous, so they have the same VM type preference. Therefore, the current VM type \(m_{sel}\) can be assigned to all tasks in BoT. The algorithms for this phase are given in Algorithms 2-4.
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Difficulties of Sensing Additional Snow Parameters  
SWE and SD are two commonly desired snow parameters as both of them can be used to estimate the amount of water stored within a snowpack. So far, there are numerous studies aiming to identify the relationship between the information stored in SAR data and SWE as well as SD. At present, however, there is no solid conclusion to solve this challenge since various other factors affect the interactions between SAR signals and both, SWE and SD. The wetness of the snowpack, for example, would change the relative surface roughness and further affect SAR backscatter [106]. The correlation between backscattering and SWE would therefore vary with the conditions of snow, soil [223], and the surface roughness [92]. The relationship between SD and SAR also links to grain size, snow density, melting conditions, and temperature [188; 220]. Simulations suggest that the backscattering would increase with SD in a coarse-grained snowpack, but decrease in a fine-grained snowpack [96]. Consequently, it is unreliable to derive SWE or SD merely from SAR information as it either requires inversions or empirical models [98; 152; 224; 225].
# Efficient Transmit Antenna Subset Selection for Multiuser Space-Time Line Code Systems
## 2 System Model of MU-STLC with TAS Selection
By the simple STLC combining procedure at the receiver described in [14], the multiuser combined-STLC received signal vector can be given as  
\[\mathbf{y}=\tilde{\mathbf{H}}_{S}\tilde{\mathbf{W}}_{S}\mathbf{x}+\mathbf{z}^ {{}^{\prime}}\in C^{2K\times 1} \tag{8}\]  
where  
\[\tilde{\mathbf{H}}_{S}\triangleq\left[\mathbf{H}_{S}\,\,\mathbf{Q}_{2K}^{T} \mathbf{H}_{S}^{*}\right]\in C^{2K\times 2N_{S}} \tag{9}\]  
\[\tilde{\mathbf{W}}_{S}\triangleq\left[\mathbf{W}_{S}^{T}\,\,\mathbf{Q}_{2K}^{ T}\mathbf{W}_{S}^{H}\right]^{T}\in C^{2N_{S}\times 2K} \tag{10}\]  
and the combined AWGN vector \(\mathbf{z}^{{}^{\prime}}\in C^{2K\times 1}\) follows the distribution \(\mathcal{CN}(0_{2K},\,2\sigma_{z}^{2}\mathbf{I}_{2K})\). Here, the MU-STLC precoding matrix \(\tilde{\mathbf{W}}_{S}\) can be given by  
\[\tilde{\mathbf{W}}_{S}=\widetilde{\beta}_{S}\mathbf{H}_{S}^{H}\,\tilde{ \mathbf{V}}_{S} \tag{11}\]  
where the power normalization factor related to the selected TAS is given as  
\[\widetilde{\beta}_{S}=\frac{1}{\left\|\tilde{\mathbf{H}}_{S}^{H}\,\tilde{ \mathbf{V}}_{S}\right\|_{F}} \tag{12}\]  
and \(\tilde{\mathbf{V}}_{S}\) is determined by ZF and MMSE precoders, respectively, as  
\[\tilde{\mathbf{V}}_{ZF,S}=\left(\tilde{\mathbf{H}}_{S}\tilde{\mathbf{H}}_{S}^{ H}\right)^{-1} \tag{13}\]  
\[\tilde{\mathbf{V}}_{MMSE,S}=\left(\tilde{\mathbf{H}}_{S}^{H}\tilde{\mathbf{H} }_{S}^{H}+\frac{2K\sigma_{z}^{2}}{\sigma_{x}^{2}}\mathbf{I}_{2K}\right)-1 \tag{14}\]
# Enhancing the 3 GPP V2X Architecture with Information-Centric Networking
## 5 3 GPP V2X-NDN Forwarding Strategy
### Interest Processing at the Transmitting UE  
A consumer, i.e., a UE wishing to retrieve a content, creates an Interest packet, including the content name built as specified above, and sends it to its local NDN forwarding engine. The forwarding strategy at the consumer has to select the _outgoing face(s)_ over which sending the Interest. The decision consists in selecting either the PC5 or the LTE-Uu interface.  
Since the content name captures information about the application type and the associated data features (e.g., the geographical scope), specific forwarding policies can be designed according to the name components. We assume that Interests for retrieving locally-available information are sent over the PC5 interface, because this information can be very likely provided by neighboring vehicles. For example, information concerning the status of the road segment where the consumer is traveling (this can be inferred from its on board positioning system) are sent on the PC5. Conversely, Interests related to information available far away from the current UE position--e.g., fees of charging stations for electric vehicles and traffic conditions along the road of a pre-planned journey--are transmitted over the LTE-Uu interface to more likely reach the far away provider(s). The latter can be located in the same cell where the consumer is located or not.  
Once the outgoing interface and the corresponding mode (only for PC5) have been selected, the UE acts as follows. It marks the packet in order to be treated as non-IP traffic by the 3 GPP interfaces. To this aim, the Type field of the 3 GPP frame header is set to the legacy _non-IP_ value when passed to layer 2. We propose adding a value for the one-byte legacy Message Family field to identify NDN traffic (e.g., value 5), besides the already specified IEEE 1609 (value 1), ISO (value 2), and ETSI-ITS (value 4) in [39]. Then, if the Interest must be sent over the PC5 interface through Mode 4, the consumer simply broadcasts the Interest on the sidelink. In the other cases (PC5 Mode 3 and LTE-Uu), the consumer has to send a resource grant request to the eNodeB, prior to packet transmission. We assume that such request piggybacks additional information to allow the eNodeB to efficiently allocate resources, as detailed in the following.
## 3 Experimental Results
### Preliminary Considerations
Therefore, the frictional pressure drops, which will be reported later at the corresponding mean vapor quality in the test section, were not affected by the heat transfer occurring inside the microfin tube, as also shown in [28], where frictional pressure gradients during adiabatic flow (i.e., with a negligible momentum contribution) overlapped the values carried out during two-phase heat transfer. The percentage of the momentum pressure drop on the total pressure drop depends on the mass velocity, on the vapor quality, and on the applied heat flux: this percentage is higher at low mass velocity and high heat flux (because the vapor quality difference between inlet and outlet is higher), whereas it is lower at high mass velocity and low heat flux (because the vapor quality difference between inlet and outlet is lower). At mass velocity \(G\) = 400 kg m\({}^{-2}\) s\({}^{-1}\), with an imposed heat flux \(HF\) = 10 kW m\({}^{-2}\), the momentum pressure drops accounted for 1.6-3.7% of the total pressure drops, whereas at mass velocity \(G\) = 100 kg m\({}^{-2}\) s\({}^{-1}\), with an imposed heat flux \(HF\) = 50 kW m\({}^{-2}\), they accounted for 21.3-24.8%. Following the procedure suggested by [PERSON] and [PERSON] [26], neglecting the accuracy of the correlation implemented for the evaluation of the void fraction, the mean uncertainty on the frictional pressure drop was \(\pm\)0.5%, with a minimum and maximum value of \(\pm\)0.1% and \(\pm\)1.9%, respectively. Following the procedure suggested by [PERSON] and [PERSON] [26], considering an accuracy on the calculated void fraction equal to \(\pm\)0.05, the mean uncertainty on the frictional pressure drop was \(\pm\)5.2%, with a minimum and maximum value of \(\pm\)0.5% and \(\pm\)8.4%, respectively. Preliminary tests were run during R1234 ze(E) liquid forced convection in order to verify the accuracy of the new test section. The aim of these tests was to evaluate the heat flow rate on the water side and compare it against that on the refrigerant side. The two calculated heat flow rates were within \(\pm\)3 W. Furthermore, experimental heat transfer coefficients were obtained by applying Equation (1) with the mean saturation temperature replaced by the mean refrigerant temperature. The obtained experimental heat transfer coefficients were compared against the values predicted by the correlation of [PERSON] [29] multiplied by the area enhancement ratio, and with the procedure suggested by [PERSON] et al. [30]. The area enhancement ratio for the present microfin tube was 1.63, which is the ratio between the total inner surface area of the microfin tube and the inner area of a smooth tube having an inner diameter equal to the fin tip diameter. The mean relative deviation of [PERSON]'s model [29] was 9.1%, whereas the mean deviation of [PERSON] et al. 's model [30] was \(-\)1.0%. Further details about the validation of the tested tube can be found in [PERSON] et al. [31].
## 4 Results and Discussion
### Effect of Integral Paths  
Since the new semi-analytic method contains an additional integration of the crack surface part (integral paths \(\Phi_{1}\) and \(\Phi_{2}\)) compared to the modified semi-weight function method, it is necessary to investigate the effect of the integral paths on the calculation results.  
Figure 4(a) shows a square plate (\(L\)\(\times\)\(L\) = 100 mm \(\times\) 100 mm) with a central co-linear double hydraulic crack of length \(a\) (\(a\) = 20 mm) and spacing \(b\) (\(b\) = \(a/2\)) under hydraulic pressure (\(P_{h}\) = 10 MPa) and far-field tensile stress (\(\sigma_{y}\) = 5 MPa); the material parameters are the same as those applied in Example 2. Figure 4(b) shows the corresponding finite element model, and Table 4 lists five groups of calculated SIFs (\(K_{1}\)) for different external circular integral radii \(r_{2}\) (i.e., different integral lengths for integral paths \(\Phi_{1}\) and \(\Phi_{2}\) in Equation (23)).  
It can be found that the \(K_{1}\) of the non-adjacent crack tip (point A) remains almost the same, while the \(K_{1}\) of the adjacent crack tip (point B) is almost unchanged in the range of \(r_{2}\)\(<\)\(b/2\) and slightly increased in \(r_{2}\)\(>\)\(b/2\). This is because when the external integral path \(\Gamma_{2}\) of point B is closer to the other crack tip (point C) in the range of \(r_{2}\)\(>\)\(b/2\), the stress and displacement on the outer integral path are more affected by the singularities of the other  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **\(b/6\)** & **\(b/3\)** & **\(b/2\)** & **2\(b/3\)** & **5b/6** \\ \hline Point A & 92.151 & 92.153 & 92.150 & 92.152 & 92.152 \\ Point B & 99.259 & 99.257 & 99.260 & 100.924 & 101.812 \\ \hline \end{tabular}
\end{table}
Table 4: Hydro-mechanical coupling SIFs (\(K_{1}\)) for different external integral radii (\(r_{2}\)).  
Figure 5: Calculation model.  
crack tip (point C). Furthermore, point C has a greater effect on the SIFs of point B than that of point A, which is similar to the results available in the literature [25]. Therefore, to obtain more effective results, a reasonable range of the external integral radius (\(r_{2}\)) for solving the multi-crack problem through the use of the extended semi-weight function method is \(r_{2}<b/2\).