esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Framework
### Dataset Collection  
Real-world electricity demand data for an entire year are obtained and linearly scaled to match the capacity of the target power system. This scaled demand serves as the historical load profile. To avoid infeasible solutions caused by randomly initialized generator states (e.g., on/off statuses and continuous uptime/downtime), we sequentially solve the UCP for each day in the historical load profile. The final generator states (including continuous operating hours and power outputs) at the end of each day are preserved and used as the initial state for the next day. This ensures feasible and realistic transitions between instances.  
Figure 3 shows how the dataset was collected. A subset of the load profiles is used as the test set, while the remaining profiles form the basis of the training set. For each training-day load, load\({}^{i}\), we apply slight perturbations to generate multiple variations denoted as load\({}^{i}_{1},\ldots,\)load\({}^{i}_{m}\). Each perturbed load, load\({}^{i}_{j}\), is paired with the same initial generator state, initial\({}^{i}\), producing a set of UCP instances \(M^{i}_{1},\ldots M^{i}_{m}\). Both the training and validation sets undergo perturbations to expand the dataset. Models trained on the training set are validated on the validation set, and the model exhibiting the best performance on the validation set is retained for testing on the test set. For the test set, instances are generated using the original training-day loads to evaluate the performance of the proposed framework in the numerical experiment section.  
For each instance \(M^{i}_{j}\), a bipartite graph \(\mathbb{G}^{i}_{j}\) is constructed following the method described in Section 2.2, and its corresponding stability vector stable\({}^{i}_{j}\) is calculated as described in Section 2.3. It is worth noting that it is impossible to collect all feasible solutions from the B&B search tree, so all feasible solutions explored during the solver's process are used as a substitute.  
Figure 3: Dataset flowchart.
# Revisiting the Design of Parallel Stream Joins on Trusted Execution Environments
## 3 Challenges of Parallelizing Stream Joins on TEEs
### Distributed Scheduling Strategies  
Whilst dynamic scale-out servers for SGX are not yet supported by cloud service providers, we believe that the technology could greatly benefit from horizontal scaling, especially given the currently imposed limitation on enclave sizes. Scale-out systems come in different flavors, but they essentially consist of an interconnected cluster of nodes distributing the processing load across multiple machines following user directives. We consider MPICH [48], a Message Passing Interface (MPI) implementation that combines in its design goals: wide portability and high performance. MPI is a message-passing API that provides abstractions for processes by providing them with ranks according to the communication groups they belong to, enabling a variety of virtual topologies that organize the application's semantics efficiently. It is considered the de-facto programming system on supercomputers and provides a natural interface for easier adoption by existing HPC applications [49]. In Algorithm 1, we elaborate on our suggested static initialization method in distributing the workload of secure join operations. The method assumes a constant tuple arrival rate, based on which optimal configuration parameters, such as the number of enclaves and join-matrix dimensions, are calculated as detailed in Section 6. Although inflexible to changes in the rate of arrival, this distribution method has the benefit of minimizing all TEE-induced overheads. In contrast, the adaptive distribution strategy periodically recalculates all parameters based on the varying arrival rate, which optimizes hardware resources utilization. However, this induces recurring enclave destruction and re-initialization. In both approaches, data orchestration is managed by a single central machine that receives the original streams and manages inter-machine communications using MPI.  
Figure 4: Join execution order under the Join-Matrix Scheme.  
```
1 call MPI_Init():
2 call MPI_Comm_size();
3 call MPI_Comm_rank();
4 //Initialize Enclaves on all machines
5 whileData_is_availabledo
6 ifworld_rank==0 then
7\(inputs\gets Buffer\_stream(M_{S},M_{R})\);
8\(output\_stream\leftarrowSerialize(inputs)\);
9\(string\gets output\_stream.str()\);
10\(length\gets string\_length()\);
11 call MPI_Send(&Length);
12 call MPI_Send(&inputs);
13
14 end if
15 Set \(count\) to 0;
16 ifcount %\(wold\_size==world\_rank\)then
17 call MPI_Recv(&Length);
18 Declare char buffer[Length];
19\(\text{call MPI\_Recv(\&buffer)}\);
20\(//Process\) join on local Enclave
21\(count++\);
22
23 end if
24
25 end while
```  
**Algorithm 1**Pseudo-code for an MPI Join distribution
## 2 Materials and Methods
### Extending WEave for Circular Economy Traceability
#### 2.2.1 Extended Network Topology  
We add new organizational roles to represent end-of-life processing entities, namely recycling (RC) and waste disposal (WD), for our apparel supply chain simulation. The RC could be a third-party textile recycler or the brand's own take-back program unit. This organization will participate in relevant channels to record post-consumer processes. An open question is whether consumers themselves should interact with the blockchain (likely not directly, to avoid complicating the network with thousands of individual end-users). Instead, the retailer (RE) or recycler (RC) can act on behalf of the consumer to log return events.  
We considered two approaches:  
* Single Production Channel with Extended Roles: Simply include RC (and possibly the retail customer interface) on the existing production channel (PC). Under this scheme, after the product is sold to the consumer (which could be recorded as a final transaction from RE to a \"Consumer\" identity or simply marked as sold), any returns would be processed by RE/RC on PC. The RC would create a new asset instance (e.g., a RecycledMaterial batch) linked to the returned product, and then that asset flows to RM/TX for use in new production.
* Separate Reverse Channel: Create a new, third Fabric channel dedicated to reverse logistics and circular flows, say an \"End-of-Life Channel (EC)\", where RE, RC, and possibly AU participate. This channel could handle return authorizations, recycling work orders, etc., which might be sensitive (e.g., volumes of returns could be confidential).  
For simplicity and traceability integrity, our design opts for the single production channel approach with extended roles--using the existing production channel to log recycling activities--so that the full lineage of an item (from virgin raw materials through any recycling events and the associated testing or refurbishing lifecycles) can be queried in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
one place. Thus, Figure 2a extends the original network diagram (Appendix A.1) to include the recycler (RC) and waste disposal (WD) nodes connected on the production channel. Our updated topology aligns with concepts in closed-loop supply chain literature [26], where a reverse logistics loop connects end customers back to suppliers or manufacturers. Similarly, Figure 2b depicts the network topology for the furniture supply chain simulation. The notable difference between the two centers is the replacement of a buying agent in the former with a refurbisher in the latter; this new agent embodies the various actions required when refurbishing materials prior to reuse. We should note that this extension could also be useful in apparel chains that include resale of \"gently used\" clothing, given the higher likelihood of furniture being refurbished and resold as furniture compared to apparel, a case we had not previously considered.
## 3 Test Results and Discussion
### Duncan-Chang Model Parameters  
In 1970, [PERSON] and [PERSON] proposed the E-\(\upmu\) model based on the assumption of the Konder stress-strain hyperbola, and then [PERSON] et al. (1980) proposed a modified Duncan-Chang E-B model. The two models better reflect the nonlinear behavior of soil with a clear concept. They are widely used in the deformation and stability analysis of earth-rockfill dams.  
In the Duncan-Chang E-\(\upmu\) model, the tangential elastic modulus \(E_{t}\) of soil and the tangent Poisson's ratio \(\mu_{t}\) can be calculated as follows:  
\[E_{t}=Kp_{a}\left(\frac{\sigma_{3}}{p_{a}}\right)^{n}\left[1-\frac{R_{f}( \sigma_{1}-\sigma_{3})(1-\sin\varphi)}{2c\cos\varphi+2\sigma_{3}\sin\varphi} \right]^{2}, \tag{4}\]  
\[\mu_{t}=\frac{G-\text{Flg}\left(\frac{\sigma_{3}}{p_{a}}\right)}{\left(1-A \right)^{2}}, \tag{5}\]  
\[A=\frac{D(\sigma_{1}-\sigma_{3})}{Kp_{a}\left(\frac{\sigma_{3}}{p_{a}}\right) \left[1-\frac{R_{f}(\sigma_{1}-\sigma_{3})(1-\sin\varphi)}{2c\cos\varphi+2 \sigma_{3}\sin\varphi}\right]}, \tag{6}\]  
where \(R_{f}\) is the failure ratio, \(K\) and \(n\) are the experimental constants of the tangential elastic modulus, and \(G\), \(F\), and \(D\) are the experimental constants of the tangent Poisson's ratio.  
In the Duncan-Chang E-B model, the tangent elastic modulus \(E_{t}\) is calculated in the same way as in the E-\(\upmu\) model. The tangent bulk modulus \(B_{t}\) is calculated as follows:  
\[B_{t}=K_{b}p_{a}\left(\frac{\sigma_{3}}{p_{a}}\right), \tag{7}\]  
where \(K_{b}\) and \(m\) are the tangent bulk modulus test constants.  
The 10 parameters of the Duncan-Chang E-B (E-\(\upmu\)) models are \(c\), \(\varphi\) (\(\varphi_{0}\), \(\Delta\varphi\)), \(R_{f}\), \(K\), \(n\), \(K_{b}\), \(m\), \(G\), \(F\), and \(D\). The parameters of the E-B (E-\(\upmu\)) model and the shear strength index according to the test curve are shown in Table 3. According to Table 3, the strength index of the sample with the end restraint was slightly larger than that of the end-free sample, indicating that the end restraint had a limited effect on the strength of the coarse-grained soil. The \(K\) and \(K_{b}\) values of the sample with the end restraint were greater than those of the end-free sample, indicating that the end restraint had a significant influence on the deformation characteristics of the coarse-grained soil.
## 5 Results and Discussion  
The numerical investigation which pertains to the aerodynamic and aeroacoustic analyses of propeller arrays, assesses the interaction aerodynamic effects on the emitted noise. It is worth notingthat, a validation analysis of the presented results has not been performed in this work because of the lack of data to compare with. Indeed, at the Authors knowledge, the most similar results in the literature are those by [PERSON] [22], which however concern the analysis of UAV (Unmanned Aerial Vehicle) rotors in hovering conditions. Anyway, it should be emphasized that both the aerodynamic and the aeroacoustic code used for the present numerical analysis have been widely validated in the past by comparison with numerical and experimental data available in the literature [38; 39].  
Two different configurations are analysed, with two and three identical propellers respectively, and their results are compared with those of an isolated rotor, which provides the same thrust of one single rotor of the array. The analysis is aimed at characterising the noise emitted by one of the propellers of the array by taking into account the aerodynamic influence of the others. It is aimed at defining an equivalent noise source of the single propeller to be used within a tool for near-field (and subsequently far-field) propagation. The blade of each rotor is characterised by a rectangular shape with its airfoil profile being a NACA 65A010; the geometric parameters are given in Table 2. The sixteen rotor blades are mounted on a cylindrical hub having radius \(r_{0}^{H}=0.124\) m and elliptical nose and tail cones with the principal semi-axes of 0.2 m and 0.5 m long, respectively.  
The hub distance between two adjacent propellers in the array is defined as the sum of the rotor diameter, \(d\), and the tip-clearance, _dist_, as shown in Figure 3. For every analysed configuration, the rotor (#1) spins counterclockwise (if viewed from the front), whereas the other rotors (#2 for the twin-propeller array and #2 and #3 for the three-propeller one) rotate clockwise. In order to perform a sensitivity analysis on the effect of the tip-clearance (and hence of the aerodynamic interaction effects) on the emitted noise, for the twin-propeller configuration four analyses with increasingly tip-clearance distance from 0.02 m to 0.02 m + _dist_, with _dist_ = {0, 0.125d, 0.25d, 0.5d}, are performed, whereas the three-rotor array is analysed only for the tip-clearance \(dist=0\). Note that, the additional distance of 0.02 m is introduced to avoid tips impingement due to the rectangular shape of the blades. All the analysed configurations are in axisymmetric flight condition, with an advance speed of 164 m/s, a blade rotational velocity \(\Omega=2500\) rpm, and provide a thrust of 3700 N.
## 4 LEO Satellite Mega-Constellation Network Caching Policy  
### RF Algorithm  
RF is one of the combined algorithms for data mining, reaching well-documented levels of accuracy and processing speed, and frequently appearing in new research [55]. RF as a supervised machine learning algorithm, uses a series of decision trees to distinguish between different classes of input data [35; 56]. RF begins at the top of a decision tree and branches through a series of binary decisions, eventually leading to the input sample being defined as one of the possible categories [35], as shown in Figure 9. The process of establishing a random forest is as follows:  
Step 1: The sample set is sampled \(n\) times to form a new sample set.  
Step 2: Feature subsets are built by randomly selecting features (e.g., cache rate, replacement rate, and popularity).  
Step 3: On the new sample set and feature subset, the best segmentation attribute is found, and the decision tree is established.  
Step 4: \(H\) decision trees are constructed by repeating Steps 1-3 with \(m\) times.  
Step 5: The random forest model is built by combining the results of \(H\) decision trees.  
Figure 8: Caching process in LEO satellite mega-constellation networks.
# Experimental Verification of the CFD Model of the Squeeze Film Lifting Effect
## 1 Introduction  
Levitation is a phenomenon when a body is suspended in an environment without any mechanical support. The effect is achieved by arranging a system counteracting a gravity pull. The balancing forces can be induced with the utilization of multiple methods, i.e., magnetic levitation, electrostatic levitation, aerodynamic levitation, acoustic levitation, etc. Two distinct methods can be identified under the term acoustic levitation. Standing-wave acoustic levitation (SWAL) and near-field acoustic levitation (NFAL) are also referred to as squeeze film acoustic levitation. SWAL is based on the effect of the standing wave; the levitating object can be located in the nodes of the acoustic pressure field lying between the acoustic source and the reflector [1]. In the NFAL, the reflector simultaneously plays the role of the suspended object. Moreover, both types of levitation can be identified through the dominant direction of the gas movement in the film. The main gas velocity direction normal to the plane of the inducer is characteristic of the SWAL, whereas, in NFAL, the primal velocity component is parallel to the length of the film, similar to the case of the squeeze film effect.
## 5 Security Issues and Cyber Attacks in Wireless Sensor Networks
### Attacks on Various Network Layers
#### 5.3.3 Selective Forwarding  
Selective forwarding is a network layer attack. In this type of attack, a counterfeit node acts like an actual node and divert the packets to a wrong path but selectively drops some of the packets sothat it becomes difficult to identify the intrusion. Acknowledgement based routing, multi data flow and detection based on neighboring information can be used to detect this type of intrusion [70; 71; 72; 73; 74; 75].
## 2 Materials and Methods
### Planning Computed Tomography  
The thymoma patients were computed tomography (CT) scanned for radiation therapy planning. Patients were in the supine position during scanning. Contiguous 5 mm CT slices were acquired. The CT data were transferred to a treatment planning workstation (Monaco v. 5.11.03, Elekta AB, Stockholm, Sweden). A radiation oncologist determined the target volumes. The gross target volume (GTV) was delineated to encompass all visibly evident disease. In the context of postoperative radiotherapy, the clinical target volume (CTV) was defined to encompass the entirety of the tumor bed along with surgical clips [4]. Regional lymph nodes were not encompassed within the CTV. Expansion to create the planning target volume (PTV) involved the addition of a 0.5 cm margin to the CTV and 1-1.5 cm to the GTV, accounting for uncertainties in patient motion and treatment delivery. All patients received treatment utilizing an image-guided radiation therapy technique. The delineation of the surrounding organs-at-risk (OARs), comprising the breasts, lungs, esophagus, thyroid, heart and spinal cord, was conducted manually based on the planning CT scans. Additionally, the left anterior descending artery (LAD) and the left ventricle (LV) were delineated following guidelines provided by a published atlas [15].
# Parameter Study for Child Booster Seats in Frontal Collisions
## 6 Value Determination of the Selected Layout Parameters  
After determining the influential factors of shoulder belt guide position (\(x_{1}\)) and backrest stiffness (\(x_{2}\)) from the above sensitivity analysis, a full factorial experiment can be directly utilized to determine the design value of shoulder belt guide position and backrest stiffness. The experimental scheme and the simulation results are shown in Table 2. The minimal WIC, 0.660, appears in the ninth column. The best backrest stiffness is 30,400 Nm/rad. The headrest height relevant to the reference model is 30 mm and the corresponding shoulder belt guide is 48 mm along the seat backrest. Further, the values of WIC and each injury index in the final design model are compared with those values in the reference model, as illustrated in Table 3. The WIC is significantly reduced, and its value is 19.6%. Meanwhile, \(a_{H}\), \(a_{C}\), \(F_{Z}\) and \(x_{H}\) of the child dummy in the final design model are reduced by 18.9%, 19.9%, 20.3% and 10.1%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{\(x_{1}\) (mm)} & \(x_{2}\) & \(a_{H}\) & \(a_{c}\) & \(F_{z}\) & \multirow{2}{*}{**WIC**} \\  & & **(Nm/rad)** & **(g)** & **(g)** & **(kN)** & \\ \hline
1 & 20 & 20,267 & 51.4 & 34.4 & 2.2 & 0.6713 \\
2 & 25 & 20,267 & 51.3 & 34.3 & 2.19 & 0.6693 \\
3 & 30 & 20,267 & 51.2 & 34.8 & 2.18 & 0.6701 \\
4 & 20 & 25,333 & 47.5 & 32.6 & 2.11 & 0.6308 \\
5 & 25 & 25,333 & 47.6 & 32.5 & 2.10 & 0.6301 \\
6 & 30 & 25,333 & 48.0 & 32.4 & 2.07 & 0.6295 \\
7 & 20 & 30,400 & 47.3 & 32.5 & 2.12 & 0.6300 \\
8 & 25 & 30,400 & 47.6 & 32.4 & 2.10 & 0.6296 \\
9 & 30 & 30,400 & 47.9 & 32.2 & 2.08 & 0.6288 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental scheme and simulation results.  
Figure 16: Maximum sensitivity analysis of each parameter: A = Position of the shoulder belt guides; B = Position of the lap belt guides; C = Belt webbing stiffness; D = Initial tension of shoulder belt; E = Seat backrest stiffness; F = Cushion angle.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \(a_{H}\) & \(a_{c}\) & \(F_{z}\) & \(x_{H}\) & \multirow{2}{*}{**WIC**} \\  & **(g)** & **(g)** & **(kN)** & **(mm)** & \\ \hline Original model & 59.1 & 40.2 & 2.61 & 285.3 & 0.7819 \\ Final design model & 47.9 & 32.2 & 2.08 & 256.6 & 0.6288 \\ Reduced injury (\%) & 18.9 & 19.9 & 20.3 & 10.1 & 19.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of child dummy injury indices between the final design model and the original model.
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Experimental Equipment  
To evaluate the performance of the proposed C-STS mechanism, we integrated it into a curved-spoke wheel and constructed a test platform, as shown in Figure 10b. For comparison, a platform without the suspension was also built (Figure 10a). The curved-spoke wheel geometry used in the experiment was defined by \(r=87\) mm and \(\theta=50^{\circ}\). Both platforms share a symmetrical design, featuring two DC motors, motor drivers, a single MCU, a Li-Po battery, and a joystick controller. The test stairway consists of four steps, each measuring 300 mm \(\times\) 160 mm, as shown in Figure 10c. To ensure that only the effect of the C-STS mechanism on climbing performance is observed, the platform's tail was extended to 1200 mm, preventing it from contacting the steps during ascent. A marker was attached to the wheel's center of rotation (CoR), and 4K video at 60 fps was recorded for trajectory analysis. Table 4 summarizes the specifications and equipment used in these experiments.  
Figure 10: Experimental equipment. (**a**) Basic (without suspension) platform configuration. (**b**) Compliant (with suspension) platform configuration. (**c**) Stair used on the experiment (300 mm \(\times\) 160 mm).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(k_{t,spring}\) & **P1** & **P2** & **P3** \\ \cline{2-5} \(k_{t,spoke}\) [Nm/rad] & & 52.31 & 1068 & 26,364 \\ \hline \multirow{3}{*}{\(k_{t,total}\)} & 0.206 & 0.20519 (0.39\%) & 0.20596 (0.019\%) & 0.20600 (0\%) \\ \cline{2-5}  & 0.547 & 0.54134 (1.03\%) & 0.54672 (0.051\%) & 0.54699 (0.0018\%) \\ \cline{2-5}  & 0.909 & 0.89347 (1.71\%) & 0.90823 (0.085\%) & 0.90897 (0.0033\%) \\ \hline \end{tabular}
\end{table}
Table 3: Spoke torsional stiffness calculation based on SolidWorks simulation.
## 5 Conclusions
\begin{tabular}{l l} MTC & Machine Type Communication \\ M2M & Machine-to-Machine \\ IRS & Intelligent Reflecting Surface \\ FD & Full Duplex \\ HD & Half Duplex \\ BER & Bit Error Rate \\ SNR & Signal-to-Noise Ratio \\ mm & Millimetre \\ IoT & Internet of Things \\ URLLC & Ultra-Reliable Low-Latency Communications \\ STAR & Simultaneous Transmission And Reflection \\ UL & Uplink \\ DL & Downlink \\ PDF & Probability Density Function \\ MC & Monte Carlo \\ BLER & Block Error Rate \\ SI & Self Interference \\ BS & Base Station \\ D2D & Device-to-Device \\ SINR & Signal-to-Interference Noise Ratio \\ MIMO & Multiple Input Multiple Output \\ LoS & Line of Sight \\ BPSK & Binary Phase Shift Keying \\ AWGN & Additive White Gaussian Noise \\ CDF & Cumulative Density Function \\ IBFD & In-Band Full Duplex \\ \end{tabular}