esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Aerobic Capacity and Second Vertilatory Threshold  
Maximal oxygen uptake was measured by a direct method using a graded test. Based on the results of this test, the first (VT1) and second (VT2) ventilatory thresholds were also determined for each subject individually. The test was performed on a bicycle ergometer (Ergoline Ergoselect 100, GE, Bitz, Germany). Breath-by-breath gas analysis was performed using a Metalyzer 3B ergospirometer (Cortex, Leipzig, Germany). Each time before the test, the ergospirometer was calibrated, according to the manufacturer's requirements (volume and gas calibration). The test began with a resting recording of the indicators tested for two minutes and then the subjects performed a 4 min warm-up with a load of 60 watts and a cadence of 60 revolutions per minute. Then, every 2 min, the exercise power was increased by 30 watts until a subjective volitional exhaustion. During the test, the participant was vigorously verbally cheered on. The following indices were measured in the test: heart rate (HR), oxygen uptake (VO2), pulmonary ventilation (VE), minute carbon dioxide production (VCO2), respiratory rate (RER), percentage of oxygen in exhaled air (FEO2), percentage of carbon dioxide in exhaled air (FECO2), ventilation equivalents for oxygen (VE/VO2) and carbon dioxide (VE/VCO2), and exercise power (P). After the test, the data obtained were analyzed; maximum oxygen uptake (VO2 max) and ventilation thresholds (VT1 and VT2) were determined. The following criteria were used to determine VO2 max: RER > 1.1, HR close to the age-predicted HRmax (\(\pm\)5 beats/minute), and no increase in VO2 despite increasing load (plateau in VO2). All participants met the first two criteria. If no plateau was observed, but the other criteria were met, VO2 peak was taken as VO2 max [27]. Based on changes in measured physiological indices with increasing exercise power, ventilatory thresholds were determined. Vertilatory thresholds were determined using the respiratory equivalents method [28; 29]. The second ventilatory threshold was determined at the intensity at which VE/VCO2 reached a minimum value and FECO2 reached a maximum value, and a second breakdown of the linearity of pulmonary ventilation was observed.
# Semantic Segmentation of Brain Tumors Using a Local-Global Attention Model
## 4 Experiments and Results
### Comparison with State-of-the-Art Methods
#### 4.4.1 Comparison on the BraTS2024-GLI Dataset  
The segmentation results of each model for the BraTS2024-GLI dataset are presented in Table 2. Among these models, our LG UNETR achieves Dice scores of 88.50%, 78.25%, and 80.80% for WT, TC, and ET, respectively, significantly outperforming all other methods for the given segmentation targets. The average Dice score of LG UNETR is 82.51%, representing a 1.39% increase over the original Swin UNETR and a 1.06% increase over the second-best 3D UX-NET. In terms of the \(HD_{95}\), LG UNETR achieves the lowest value of 8.02 mm, followed by 3D UX-NET with 8.40 mm. Regarding computational consumption, MedNeXt exhibits the lowest, closely followed by our model, with only a marginal disparity between them. Compared with the original Swin UNETR, LG UNETR not only achieves a significant improvement in performance but also reduces the number of parameters and FLOPs. Figure 8 presents the prediction comparisons between them. It can be seen that our model can provide more accurate predictions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Params (M) (\(\downarrow\))**} & \multirow{2}{*}{**GFLOPs (\(\downarrow\))**} & \multicolumn{4}{c}{**Dice (\%) (\(\uparrow\))**} \\ \cline{3-7}  & & & **ET** & **TC** & **WT** & **Avg.** & \\ \hline MedNeXt [47] & **61.7** & **1079.9** & 74.61 & 71.49 & 85.45 & 77.19 & 12.24 \\ UNETR [46] & 173.6 & 2095.4 & 78.92 & 75.44 & 86.16 & 80.17 & 8.42 \\ UNETR+ [48] & 280.7 & 1912.6 & 79.22 & 75.77 & 87.19 & 80.72 & 8.45 \\ Swin UNETR [21] & 97.1 & 1234.5 & 79.14 & 76.36 & 87.39 & 80.96 & 8.42 \\
3D UX-NET [53] & 82.9 & 2362.4 & 80.18 & 76.96 & 87.22 & 81.45 & 8.40 \\ LG UNETR (ours) & 83.9 & 1150.9 & **80.80** & **78.25** & **88.50** & **82.51** & **8.02** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with state-of-the-art methods on the BraTS2024-GLI dataset. The best results are in **bold** and the second best underlined. The up arrow indicates that the higher the better and the down arrow indicates that the lower the better.  
Figure 8: Prediction visualization of different models on the test set of the BraTS2024-GLI dataset. Existing methods face challenges in accurately segmenting small tissue regions (marked in the dashed box). Best viewed in zoom.
## 3 Experimental Section
### Desorption Experiments  
The adsorption-desorption cycles were repeated consecutively four times to determine the reusability of sorbents. For adsorption experiments, 0.05 g of EDA-MPMs was loaded with Cr(VI) ions using 50 mL (200 mg/L) solution at 298 K, pH 2.0 and contact time of 8 h. The agitation rate was fixed at 150 rpm. After adsorption experiments, the Cr(VI)-loaded EDA-MPMs were separatedfrom the solution by an external magnetic field; and then desorption of Cr(VI) was performed using 10 mL 0.1 mol/L NaOH. The final Cr(VI) concentration was determined by ICP-OES. After each cycle of adsorption-desorption, sorbent was washed with distilled water and used in the succeeding cycle.
## 2 Materials and Methods
### System Model  
Assuming that RSUs are deployed at both ends of each street segment, as shown in the Figure 2, the RSUs are standalone, i.e., not directly connected to other RSUs by wire, but they can communicate indirectly through the base station. For the P2P multimedia data exchange between the vehicle nodes in VANET[28], the traffic cost through cellular communication alone is very expensive. Therefore, the data packets can be transmitted through the multi-hop routing between the vehicles, and some basic control information can be transmitted using cellular communication. When the vehicle node needs to send data, it only needs to attach the necessary control information to the header of the data packet and deliver it to the nearby RSU, which is responsible for forwarding packets. The communication between adjacent RSUs needs to forward packets through multi-hop routing through the vehicle nodes on the street segments. In addition, each vehicle is equipped with digital maps and on-board navigation systems, such as Beidou Navigation or GPS.  
The architecture of a software-defined VANET in this paper is shown in Figure 3. The RSU can communicate with the SDN server in the simulation area in both directions. Each RSU periodically sends the connectivity quality evaluation values of its adjacent street segments to the SDN server, so that the SDN server holds the global information of all the street segments in the simulation area and can calculate an optimized route.
## 5 Simulations and Performance Evaluations  
We carried out extensive simulation of the system, and adopted the strategy of averaging multiple sets of random data to eliminate the measurement errors in the simulation process. We have adopted two contrasting strategies, one of which is the strategy without data cache, and the other is the strategy of neither data cache nor computation offloading. The three strategies are compared to analyze the effect of each strategy on the response time delay of the system from (1) \(M\), the number of MTs; (2) \(R\), the number of a \(m_{i}\)'s \(r_{j}\); (3) \(K\), the number of MEC-NBS; (4) \(\phi_{k}\), the service rate of \(n_{k},k\in\mathcal{K}\); (5) \(s_{k}^{\text{C2 NBS}},\forall k\in\mathcal{K}\), data transmission rate between C and MEC-NBS; (6) \(s_{k}^{\text{NNS2S}},\forall k\in\mathcal{K}\), data transmission rate between MEC-NBS and C; (7) \(s_{k}^{\text{BS2 NBS}}\) and \(s_{k}^{\text{NNS2 BS}},\forall k\in\mathcal{K}\), data transmission rate between MEC-BS and MEC-NBS. The result in each of the 8 scenarios is obtained from 50 repeated simulations using different random seeds. The simulation results verify the effectiveness of our strategy. It can well meet the requirements of minimizing the service response delay and improving the service quality of the resource scheduling.  
Table 1 lists the settings of the parameters used in simulations. Parameter values are configured based on the table unless stated clearly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Value** & **Name** & **Value** \\ \hline \(M\) & \([30,100]\) & \(R\) & \([1,8]\) \\ \hline \(K\) & \([2,9]\) & \(p_{ij}\) & \([0,1]\) \\ \hline \(d\) & \(2.4\times 10^{7}\) MB & \(d_{k}\) & \([2.2,2.4]\times 10^{7}\) MB \\ \hline \(e_{i}\) & \([0.1,1]\) /s & \(c_{i,j}\) & \([5,9]\times 10^{4}\) MB \\ \hline \(v_{j}\) & \([10,20]\) MB & \(w_{j}\) & \([10,20]\) MB \\ \hline \(z_{j}\) & \([30,55]\) MB & \(q_{j}\) & \([150,360]\) MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters used in simulations and their default values.  
Table 2 lists the three schemes in MATLAB simulation. We use genetic algorithm to get the minimum system delay of OMEC, and simulate the three strategies mentioned above. The results of simulating are shown in the figures.
## 4 Proposed Architecture
### Hashing of Merkle Tree  
Hashed trees are generated using the Merkle hashing technique, where the direction of the trees goes upward. This tree is called the Merkle hash tree (MHT), wherein the leaves of the tree represent the hash of the values of the data or the ordered elements of a set. Let this authentic ordered set of elements for MHT be \(x_{0,0}\), \(x_{0,1}\), \(x_{0,2}\), \(\ldots\), \(x_{0,n}\); therefore, the leaf node of the element \(x_{0,i}\) will be the hash of that element. Let this leaf node be represented by \(x_{1,i}\), where \(x_{1,i}=H(x_{0,i})\) and \(H()\) is a function that is cryptographically hashed one way.  
A node in the MHT contains multiple incoming edges; the value of a node is the combined or concatenated hash [41] of its preceding nodes, also known as child nodes, where the sequence of the nodes is maintained. An internal node or a non-leaf node \(x_{2,0}\) with child nodes \(x_{1,0}\) and \(x_{1,1}\) hence contains the value \(x_{2,0}=H(x_{1,0}||x_{1,1})\). The MHT and a verification object that contains a set of nodes can be used to demonstrate the existence of an element. The root of the MHT [42] can be recomputed by the verifier by using the verification object and a set of nodes that are contained within it. The verifier compares the recomputed root using the verification object, with the publicly known root that the tree generates. For instance, consider the element \(x_{0,0}\) in the MHT shown in Figure 9; the verification object consists of the values of the nodes \(x_{0,0},x_{1,1}\), and \(x_{2,1}\). \(x_{1,0}=H(x_{0,0})\), \(x_{2,0}=H(x_{1,0}||x_{1,1})\) and conclusively, \(\mathit{root}=H(y_{2,0}||x_{2,1})\) is constructed by the verifier. Once this verification object is constructed, the verifier can compare the computed root with the publicly known root and verify the value.
## 5 Conclusions  
This work introduced SDKU-Net, a novel architecture specifically designed for shadow detection in remote sensing images. The model features a dynamic kernel adjustment mechanism that facilitates multi-level feature extraction through progressive kernel size scaling. Additionally, a combined loss function effectively addresses the class imbalance issue commonly found in remote sensing images, particularly in shadow detection tasks. The proposed optimizer-switching mechanism enables a refined fine-tuning phase, incorporating an early stopping strategy that reduces training time. These contributions, guided by IoU validation monitoring, allow the model to dynamically adjust training hyperparameters according to process requirements. Importantly, data leakage is prevented as the validation data are exclusively used for kernel size and optimizer adjustments, not for model training.  
Comprehensive evaluations on the AISD dataset demonstrated that SDKU-Net outperforms existing methods, achieving the highest scores in IoU, F1-Score, and OA. These results highlight the model's effectiveness in accurately segmenting intricate shadow details in remote sensing scenarios. Despite its strengths, SDKU-Net has some limitations, such as occasional over-segmentation and reduced effectiveness in close-range images. Future research should focus on refining the dynamic kernel adjustment mechanism and exploring strategies to enhance performance in close-up scenes. Expanding evaluations to include diverse datasets could further validate the model's generalization capabilities. In summary, SDKU-Net represents a significant advancement in shadow detection methodologies. Its robust, efficient, and adaptable design makes it a valuable tool for applications in urban planning, environmental monitoring, and infrastructure analysis, contributing to more precise and reliable insights from remote sensing imagery. While SDKU-Net was designed  
Figure 12: Visualization of the performance obtained by the generalization of the SDKU-Net which leads the detection of shadows absent from ground truth annotations.  
with efficiency and adaptability in mind, future work could explore the integration of this methodology with more complex architectures such as UNET++ for applications that demand higher model capacity, such as detailed urban planning analysis. This could provide further insights into the trade-offs between computational cost and segmentation accuracy in diverse scenarios.  
Data curation, G.A.-R. and C.G.M.-P.; formal analysis, [PERSON]-R.; investigation, [PERSON]-P. and [PERSON]-D.; methodology, G.A.-R.; software, [PERSON]-R. and [PERSON]; supervision, [PERSON]-D.; validation, I.A.E.-V. and J.J.S.-D.; visualization, I.A.E.-V. and C.G.M.-P.; writing--original draft, G.A.-R.; writing--review and editing, J.J.S.-D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data are unavailable due to privacy.
## 2 Materials and Methods
### Performance Evaluation of Studied Asphalt Mixtures  
The performance related tests included the determination of the water sensitivity, rutting resistance, stiffness modulus and fatigue resistance of the studied asphalt mixtures.  
The design of a bituminous road pavement involves the definition of a structure that ensures the capacity of withstanding the loads applied by vehicles and by weather conditions. The mechanical properties of the bituminous mixtures to be used in the road structure should be evaluated, such as the stiffness modulus, phase angle, and pavement resistance to failure mechanisms such as cracking due to fatigue and permanent deformation.  
Among the resistance to the weather conditions, the evaluation of asphalt mixture water sensitivity is very important, since this property is directly related to the performance of the road pavement. This property was determined by the EN 12697-12 standard. The indirect tensile strength test was carried out according to the EN 12697-23 standard, after a volumetric characterization of the specimens to determine the air void content, which significantly influences the results.  
The rutting resistance of the mixtures was assessed by means of the wheel tracking test (WTT), according to the EN 12697-22 standard, using the procedure B (in air), with a standard wheel load of approximately 700 N. The test was carried at a temperature of 50 \({}^{\circ}\)C, being representative of our country's hotter summer days.  
The stiffness modulus test was carried out on prismatic specimens, using the four-point bending configuration (4 PB-PR), according to the EN 12697-26 standard. The test was carried out at 10, 20,  
Figure 3: Asphalt mixtures with PEX developed in this work: (**a**) Production stage applying the dry method of incorporation; (**b**) Tests samples with PEX particles clearly visible.  
30 and 40 \({}^{\circ}\)C for a range of frequencies (0.1, 0.2, 0.5, 1, 2, 5, 8 and 10 Hz). Based on the principle of superposition time temperature, used to relate the equivalence between frequency and temperature, it was possible to construct master curves, in this case using 20 \({}^{\circ}\)C as temperature of reference. The results are represented by the stiffness modulus and the phase angle, which are the most relevant properties of viscoelastic materials such as asphalt mixtures.  
The fatigue resistance of the studied mixtures was also determined using the four-point bending beam test procedure, according to the EN 12697-24 standard. The tests were carried out at 20 \({}^{\circ}\)C, in strain control mode, and using a frequency of 10 Hz. The fatigue laws of the studied mixtures can be represented by Equation (1):  
\[N=a\times(1/\varepsilon_{t})^{b}, \tag{1}\]  
which relates the level of extension applied in the test (\(\varepsilon_{t}\)) with the number load cycles (\(N\)) that cause test specimen failure (reduction of stiffness to half of its initial value), and where a, b are coefficients determined experimentally.
## 2 Methodology
### Virtual part  
The virtual part, grounded in the DT system, is systematically executed through four sequential phases: Construction of LPTN-based VML, parameter classification, parameter identification, and thermal prediction.  
Figure 3: Flowchart of digital twin-driven virtual–real hybrid framework for bearing thermal prediction.  
Step 1: Construction of LPTN-based VML  
An LPTN model is established based on the structural dimensions of the investigated deep groove ball bearing, grease property parameters, and heat transfer theory. Concurrently, a frictional heat generation model for bearing surfaces is developed using tribological principles. These dual models collectively constitute the thermal analysis VML space.  
Step 2: Parameter classification  
All parameters involved in heat source quantification and TRs calculation within the LPTN model are classified into constant parameters and time-varying/ambiguous parameters (e.g., convective TRs). The intrinsic relationships between these unknown parameters and operational variables (time, rotational speed, and applied loads) are analytically derived to simplify heat source/resistance formulations, thereby defining parameters that need to be identified for subsequent step.  
Step 3: Parameter identification  
Treating all parameters that need to be identified as independent variables, a fitness function is formulated by aggregating cumulative errors between virtual datasets from the VML and sample datasets from the PEL. This transforms the identification task into a minimization problem seeking optimal parameter values that maximize virtual and sample datasets' congruence. To accelerate convergence, the Nutcracker Optimizer Algorithm (NOA) is implemented for fitness function optimization.  
Step 4: Thermal prediction  
Post-identification, the refined LPTN-based VML twin structure is completed. Under current working conditions, thermal twin data are generated, with virtual temperature predictions under high-risk behaviors rigorously validated against data validation.  
This completes the digital twin-driven virtual-real hybrid framework for bearing thermal prediction. Subsequent chapters provide a detailed explanation of the constituent models and methods involved in each step.
# A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems
## 3 Flowchart of the Proposed Heuristic Algorithm and Problem Assumptions  
In this research, based on the following 8 steps of Figure 4, a novel heuristic algorithm is proposed to solve a novel NWOSP-SCSP approach based on an \(O|TTJD,MT|C_{max}\) problem:  
Figure 4: Flowchart for 8 steps of the suggested heuristic algorithm to solve the \(O|TTJD,MT|C_{max}\).  
It is included in the assumptions that all surgical operations are available for processing at zero time. All ORs are available throughout production. ORs have no breakdown. There is no maintenance activity. Each OR can simultaneously operate at least one surgical operation. Each surgery operation can be processed at a maximum of one OR at a time. All surgery operations are processed precisely once per OR. After the end of each operation, the surgery operations are released and can perform the next operation. All surgery operations are independent (sequencing does not affect processing time). The surgery operations are non-preemptive.  
This study applies MILP for the NWOSP-SCSP approach in the suggested model. By presenting the problem in three fields of \(\gamma|\beta|\alpha\) is in the form of \(O|TT|D,MT|C_{max}\), the concept of \(TT\,JD\) in the field \(\beta\) means the transportation time which depends on the type of the surgical operation, and MT represents the multi transportation system. Many researchers have used heuristics to solve OSSP. Some of them have been mentioned in the literature review. The NP-hardness of the open shop scheduling problems has led the researchers to use heuristics to solve problems. Our study solves MILP with small instances using the GAMS BONMIN solver. In addition to the models, a novel metaheuristic hybrid SA algorithm is proposed to solve large-scale problems by applying MATLAB in a good computational time.
# A 12-20 GHz Wideband High-Power SP2T Switch Based on Gap Waveguide Technology
## 3 Switch Design and Simulation Results
Figure 5: The simulated S-parameters of the microwave switch in (**a**) State A and (**b**) State B. In practical situations and due to mechanical tolerances, the position of the sliding layer may have slight displacements. Therefore, it is critical to investigate the effects of the sliding part displacement on switch performance. Accordingly, displacements in all directions are applied to the sliding part, and the input matching of the switch is simulated, shown in Figure 7. The switch performance is disturbed by distortions in different axes, but the performance is acceptable for a displacement of 1 mm on the X-axis, 0.5 mm on the Y-axis, and 0.1 mm on the Z-axis. The X-axis is the direction of the switch sliding, and Z-displacement (d\({}_{\mathrm{z}}\)) is identical to the air gap changes. The simulated insertion loss of the switch for different d\({}_{\mathrm{z}}\) is plotted in Figure 7d. As can be seen in Figure 7d, when d\({}_{\mathrm{z}}\) increases, the insertion loss aggravates, but it still is less than 0.5 dB, which is acceptable. In addition, the dispersion diagram of the period pins is simulated when the air gap is 0.3 mm (equal to 0.2 mm Z-axis displacement), shown in Figure 7e. Although the bandgap is decreased (11 GHz to 30 GHz), it covers the whole frequency bandwidth, and therefore, no wave leakage occurs. As a result, the switch is not too sensitive to the sliding part movements, and it can be manufactured using a low-cost method. Figure 6: The simulated electric field distribution by excitation of the pole-port in (a) State A and (b) State B.  
Figure 7: _Cont._