esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### XRFS, RS, and LIBS Analysis  
XRFS analyses were undertaken using a SciAps X-250 handheld analyzer. The X-250 utilizes a Rh X-ray tube with 4 mm spot size and 20 mm silicon drift detector (SSD) with 135 eV resolution that has a FWHM of 5.95 keV for the Mn K-alpha line. An energy-to-channel calibration was performed at the beginning of each day of operation and approximately every 30 minutes thereafter. Three separate sequential measurements of 40 seconds were made with preset beam parameters (50 keV, 60 mA, no filter; 40 keV, 60 mA, no filter; and 15 keV, 80 mA, 100 mm Al filter) at each of three locations on each soil pellet using the SciAps internal'soil mode' calibration based on the analysis of 40 certified reference materials (CRMs). This provided quantitative data for 17 elements (P, S, K, Ca, Ti, Fe, V, Cr, Mn, Ni, Zn, Rb, Sr, Zr, Sn, Sb, Ba), with some other elements of interest (As, Co, Mo, Cd, Tl, Pb, U, Th) consistently below the limit of detection in the soil suites analyzed.  
RS analyses were undertaken with a SciAps Chem-200 Raman analyzer. This instrument utilizes a 785 nm laser that produces 500 mW (maximum power). Spectra were collected over a wavenumber range of 200-1800 cm\({}^{-1}\) using fifteen 1.2-second accumulations at three locations on each soil pellet after calibration using a benzonitrile standard. Raw, unprocessed (i.e., non-baseline corrected) spectra were used for the data analysis.  
LIBS analyses were undertaken using a SciAps Z-300 LIBS instrument which contains a 1064 nm Nd-YAG laser that produces a 50-100 um diameter beam on the sample surface. The laser was operated at 5-6 mJ, a 1 nsec pulse duration, and a 10 Hz firing rate. The three spectrometer-CCD (charge-coupled device) pairs, covering the spectral range of spectral range of 190 to 950 nm (FWHM = 0.20 nm at \(<\)365 nm, 0.25 nm from 365 to 620 nm, and 0.40 nm at \(>\)620 nm) had a gate width of 1 millisecond a gate delay of \(\sim\)650 ns. Analyses were collected under Ar gas flow using three data collection laser pulses after two cleaning pulses in each location of three \(4\times 2\) grids with 0.5 mm spot spacing on each soil pellet to generate 72 spectra with 23,431 data points per spectrum. The instrument was wavelength calibrated at the beginning of operation and periodically thereafter at approximately 30-min intervals.
## 4 Experiments and Results  
### Data Pre-Processing  
As the number of samples in the rock image dataset is too small and the quantity distribution among various types is not balanced, the model trained with such a dataset has low accuracy and a risk of overfitting [30]. In order to solve the above problems and make the trained model have higher classification accuracy and stronger generalization ability, it is necessary to augment the number of samples in the dataset. In addition, in order to meet the needs of model training, verification, and testing, the augmented dataset needs to be partitioned into the training set, the verification set, and the test set in a certain proportion.
## 2 Theoretical Foundations
### SWS Principle and Design
Furthermore, a normalization layer applies layer normalization to the feedforward layer's output, thereby expediting training and enhancing model performance while facilitating learning and generation of the corresponding output sequence [29]. Building on this, the SWS module introduces a slicing operation that partitions the feature map into multiple non-overlapping blocks. This slicing operation enables the module to compute local statistics instead of global statistics, which is particularly advantageous for emphasizing small objects that may be overlooked within these blocks. Each block independently calculates its average pixel variation, effectively capturing the distinctiveness of small targets within that specific block. Small targets exhibiting deviations from the local average are assigned higher weights, thereby enhancing their features. Subsequently, after computing the weight for each block, the feature map is reassembled by applying these weights to ensure enhanced representation of small targets while maintaining or further amplifying features of larger targets as required.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 3 Factors in the Synthesis of a Mesoporous Material
### Temperature  
The final properties of mesoporous materials depend mainly on the temperature under which they were created. Mesoporous materials are synthesized at temperatures between 10 to 130 \({}^{\circ}\)C, with 25 \({}^{\circ}\)C being considered reasonable. The critical micelle temperature (CMT) and the cloud point (CP) are two factors to consider in terms of the temperature [76]. Typically, the selected temperature should be higher than the CMT of the surfactants. For cationic surfactants, CMTs are relatively low. Under these conditions, the rate of accumulation of surfactant temptation decreases with decreasing synthesis temperatures. As a result of this process, high-quality mesoporous materials will be achieved. However, if a _non_-ionic surfactant is utilized as a template, the reaction temperature should be highdue to increased CMT [76]. In the preparation of SBA-15, the synthesis temperature needed for the complete dissolution of the surfactant and the CMT is slightly higher than the ambient temperature. It lies within the range of 40-35 \({}^{\circ}\)C [78]. Hence, the diameters of the SBA-15 pores change according to the conversion conditions. In the range of 40-90 \({}^{\circ}\)C, there are micropores in the system, and all types of pores have been observed at temperatures of more than 100 \({}^{\circ}\)C. Hence, by increasing the temperature, the cavity sizes grow and the thickness of the walls is reduced in SBA-15 mesoporous materials, thus reducing the fraction of cavities that are micropores [78]. The CP is the point at which liquid-liquid phase crystal separation occurs directly on the EO (ethoxy) chain in block copolymers. Therefore, any change in this important point should be considered in the synthesis of silicon _meso_-structures. If the CP decreases, then the lengths of the EO hydrophilic chains will decrease, while the lengths of these chains will increase with increasing CP. Hence, the length of the EO chain varies directly with the CP. In addition, enhancing the CP creates different geometric structures, ranging from lamellar to cubic. A parameter called the critical packing parameter (CPP) is involved in their construction [79]:  
Therefore, based on the CMT and CPP mechanisms, we can determine the formation of mesoporous nanostructures and their shapes.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Result Analysis
#### 4.3.2 Performance Comparison in Terms of Varying Network Sector Density  
In 5G V2X networks, the network density, which is the number of SCs per sector, has a significant impact on the efficiency of the handover process. A higher density of SCs improves network coverage and capacity. However, it also creates challenges such as unnecessary handovers and ping-pong effects.  
This analysis evaluated three handover mechanisms for network sector densities ranging from 50 to 250 SCs per sector at a vehicle velocity of 80 km/h. Figures 8-10 show the individual simulation results of three algorithms relative to SC density changes.  
More frequent switches occur in ACHQ owing to the higher number of SCs per sector and the lack of ST in its implementation, leading to excessive HO signaling. CAP meaningfully decreases unnecessary HOs by ensuring a minimum ST before switching, which ensures an efficient handover decision in IHD-V2X that is further intelligently optimized by Q-Learning HO attempts by intelligently learning from past HOs. Recurrent and unnecessary HOs lead to higher failure rates, and the network struggles to process the increased volume of HOs. On the other hand, ACHQ exhibits the highest success rate because of the high occurrence of ping-pong HO; hence, it remains inefficient in highly dense situations. CAP exhibits a considerable increase in successful handovers by optimizing the duration for which a UE stays connected to a cell before switching. IHD-V2X attains the highest success rate, exceeding 80% for the necessary HOs, owing to its adaptive learning approach that selects the optimal HO and minimizes failures. At higher SC densities, handover delays become a serious issue for ACHQ because the network struggles with excessive HOs. CAP notably lessens delays by guaranteeing more stable connectivity before handover. The best performance is shown by IHD-V2X, which intelligently adjusts to network conditions and minimizes HO delays while optimizing the HOs. Similarly, ping-pong HOs were highest in ACHQ, modest in CAP, and lowest in OHD-V2X. To aggregate, as shown in Figure 11, ACHQ slightly lessens unnecessary HOs, but does not completely remove them, leading to a high network bottleneck and inefficient resource usage. The CAP notably improves this aspect by implementing a minimum stay period before HO initiation. The most significant improvement was observed for IHD-V2X. By dynamically predicting  
Figure 7: Average results analysis for different algorithms for UEs moving at various velocities.
## 3 Methodology
### Benchmarking Scenario  
This research encompasses four categories of benchmarking scenarios. Details for each scenario are provided below:  
1. **Detection performance**: The first scenario involves benchmarking the performance of the FL-DNN model to test it using testing data from all devices. The parameters analyzed in this measurement are accuracy, precision, recall, and F1-score.
2. **Transaction performance**: The second scenario aims to evaluate the performance of the DLT when handling transactions in the proposed CIDS model. The parameter analyzed in this measurement is transactions per second (TPS).
3. **Network latency performance**: The third scenario is calculating network latency in an edge-fog-cloud architecture. This scenario involves assessing the time it takes for data to traverse the communication layers within the system. Beginning with edge latency, the duration for data to move from edge devices to local processing units is measured, considering local network dynamics. Moving to fog latency, the time taken for data processing at fog nodes, which offer additional computational capabilities closer to the edge, is evaluated. Cloud latency assessment follows, considering the round-trip time for data to travel from the edge and fog layers to centralized cloud servers and back. End-to-end latency encapsulates the total time for data or tasks to traverse the entire architecture, encompassing edge, fog, and cloud layers. Network latency, an essential component, examines the delays introduced during data transmission between these layers.
4. **Resource consumption performance**: The fourth testing scenario involves measuring resource consumption. The performance of resource consumption focuses on CPU and memory consumption during the training process. In this research, we compare the learning process between centralized learning and federated learning when distributed in edge computing. We monitor the CPU and memory consumption every second during the learning process, then average all the captured values from CPU and memory consumption to calculate the CPU and memory consumption in a single learning process. In FL based on edge-fog-cloud architecture, we monitor the learning process in 5 rounds.
## 4 Numerical Results
### Orbits and Pad Motions  
Figures 11-14 provide representative examples of the response of the rotor-TPJB that might demonstrate the characteristics of the design parameter to the effects of pivot friction. The results are shown in the orbit, frequency, phase portrait, and Poincare attractor formats, which correspond to the selected conditions in Figures 7-10. Figure 11a shows that the response is 1/3 sub-synchronous motion based on the attractor and frequency spectrum. It can be seen that when the radius of the pivot was increased (\(R_{\text{pyt}}\) = 0.01 0.02), under the corresponding operating conditions, the response changed to quasi-periodic type with a higher amplitude, as can be seen in Figure 11b. Figure 12 effectively shows the effect of pad preload. An appropriately increased preload (\(m_{\text{p}}\) = 1/2 3/4) can alter the limit cycle, as shown in Figure 12a, to the equilibrium state, as in Figure 12b; it contributes to the improvement in rotordynamic stability. Figure 13 shows that the quasi-periodic motion, as seen in Figure 13a, changes to a quenched 1x-synchronous response with a lower amplitude, as shown in Figure 13b, if the pivot offset is applied properly, e.g., \(\chi_{\text{p}}\)/\(\chi\) = 0.5 0.6, even in a large unbalanced eccentricity on the disc. Lastly, Figure 14 shows an example where the extended fluid film layer (\(L\)/\(D\) = 0.5 1.0) stabilizes the friction-induced quasi-periodic motions not substantially but partially to 1/2\(\times\)-subsynchronous response.  
Figure 12: Comparisons of orbits and pad motions at 16,000 rpm with various pad preloads (other parameters: \(e_{\rm imb}=0.00\)\(C_{\rm b}\), \(R_{\rm pvt}=0.015\) m, \(\chi_{\rm p}/\chi=0.5\), I/D = 0.5): (**a**) \(m_{\rm p}=1/2\); (**b**) \(m_{\rm p}=3/4\).  
Figure 13: Comparisons of orbits and pad motions at 15,000 rpm with different pivot offset (other parameters: \(e_{\text{imb}}\) = 0.20 \(C_{\text{b}}\), \(R_{\text{pvt}}\) = 0.015 m, \(m_{\text{P}}\) = 0.5, L/D = 0.5): (**a**) \(\chi_{\text{p}}\)/\(\chi\) = 0.5; (**b**) \(\chi_{\text{p}}\)/\(\chi\) = 0.6.
# Joint Radar-Communication Optimization of Distributed Airborne Radar for AOA Localization
## 3 JRCO Algorithm
### Initialization of System State  
This module initializes the system state when \(t=0\). Based on the definition of the system state in Equation (23), it is necessary to specify values for \(\overline{\mathbf{p}}\), \(\mathbf{\omega}^{0}\), and \(\mathbf{C}_{R}^{0}\). For simplicity, all values in \(\mathbf{\omega}^{0}\) can be made equal, i.e., \(\omega_{1}^{0}=\omega_{2}^{0}=\cdots=\omega_{M}^{0}=\omega_{ini}\). \(\omega_{ini}\) represents the initial value of the communication coverage range for all UAVs. Regarding \(\overline{\mathbf{p}}^{0}\), it is believed that when \(t=0\), all UAVs are distributed in a two-dimensional departure area with a side length of \(b\). For \(i=1,2,\cdots,M\), \(\overline{x}_{i}^{0}\), \(\overline{y}_{i}^{0}\) and \(\overline{z}_{i}^{0}\) follows a uniform-distribution on the interval \([0,b]\). The value in \(\mathbf{C}_{R}^{0}\) can be specified based on the actual scenario of the UAV.
###### Abstract  
Real-time command and data transfer are essential for autonomous underwater vehicle (AUV) motion control in underwater missions. Due to the limitations of underwater acoustic communication, which has a low data rate, this paper introduces a new control structure using underwater wireless optical communication (UWOC) to enable effective real-time command and data transfer. In this control structure, control inputs for the AUV attitude from outside of the water are transferred to the AUV for motion control, while its orientation data and visual images from the AUV camera are sent to the control station outside the water via the UWOC system. For demonstrating the performance of control action and data monitoring, an AUV is built with a constructed UWOC system, two vertical thrusters, and two horizontal thrusters. For attitude control of the AUV, an attitude heading reference system (AHRS) and a depth sensor are installed. Bi-directional communication in the UWOC system is achieved using a return-to-zero (RZ) modulation scheme for faster, longer-range data transfer. A signal processor converts sensor data received from the transmitted data. Finally, the hovering control performance of the AUV equipped with the UWOC system was experimentally evaluated in a water tank, achieving average root mean square errors (RMSEs) of 4.82\({}^{\circ}\) in roll, 2.49\({}^{\circ}\) in pitch, and 1.99 mm in depth, while simultaneously transmitting real-time motion data at 21.2 FPS with VGA-resolution images (640 \(\times\) 480 pixels) at a communication rate of 1 Mbps.  
autonomous underwater vehicle; hovering control of AUV; real-time commands; underwater wireless optical communication +
Footnote †: journal: Applied sciences
# Ankle Joint Dynamic Stiffness in Long-Distance Runners: Effect of Foot Strike and Shoes Features
## 1 Introduction
Although using these criteria runners may have been misclassified [25], according to [PERSON] and [PERSON] [5], habitual forefoot strikers exhibited a more compliant ankle and absorbed more (negative) work than habitual rearfoot strikers when running with their preferred foot strike pattern (forefoot); however, no differences were found with habitual rearfoot strikers running with a forefoot strike pattern (nonpreferred mode). It is common for studies concerning running and ankle stiffness to simplify the loading phase of the moment-angle loop by representing the linear slope from initial foot contact to peak moment [5; 26; 27; 15; 28] (Figure 1, dashed line). This approach overlooks the potentially meaningful details occurring within the loading phase. For instance, at initial foot contact, the ankle joint moment increases nonlinearly with the change in angle (red portion in Figure 1). This state represents the ankle joint response to foot/ground initial loading. Thereafter, the ankle dorsiflexes slowly, while the ankle moment increases fast (blue portion in Figure 1). This is a less compliant condition, representing the loading of the passive structures of the muscle-tendon units, and can be seen as a different state [29]. Another interesting phase that deserves attention is the unloading phase (light-blue portion in Figure 1), where the movement of the joint reverses to plantar flexion and the joint moment decreases. To the best of our knowledge, no studies have investigated the stance phase of running by considering these three task-relevant subphases of the moment-angle loop, which we expect to yield a more sensitive insight of the differences between habitual rearfoot and forefoot strikers. The aim of this study was to investigate if foot strike loading technique has an effect on the ankle moment-angle dynamics during the stance phase of running. We expected forefoot strikers to have lower dynamic stiffness during the loading phase, based on previous findings [5]. We also expected forefoot strikers to have a higher proportion of negative work relative to positive work because of their loading technique that allows them to store and return elastic potential energy in the foot-ankle structure. To test whether habitual foot strike loading technique compromises the control of ankle stiffness, shoes with different assistive constructs were considered. We expected differences in ankle stiffness and work ratio between groups to be greater in minimally assistive shoes, due to the unfamiliarity of rearfoot strikers to this condition. Because we expected forefoot strikers have a greater intrinsic foot-ankle adaptability to external loading (i.e., greater ability to control ankle stiffness), we expected their (forefoot strikers) ankle stiffness to have a higher time-dependency. That is, ankle stiffness during unloading will depend more on ankle stiffness during loading in forefoot strikers.
## 4 Materials and Methods  
### Proposed SMSRS Model  
The proposed SMSRS model incorporates SRA with the MSRS algorithm of [11] to generate a resource schedule with a more realistic project completion duration. In the SMSRS model, we developed an SRA model with the aid of Microsoft excel using triangular probability distribution and Monte Carlo simulation. Thereafter, the SRA model was integrated with the MSRS algorithm.  
Figure 1 shows an overview of the methodology for the existing MSRS and the proposed SMSRS models. To demonstrate the integration procedure, a sample project (project A) obtained from [11] was used to set up the model. Note that, the daily resource availability of the project was modified in order to formulate a suitable resource substitution rule adopted for the project. Figure 2 depicts the network diagram of project A, while Table 2 shows the project information which includes the activities, precedence relationships, activity durations, activity resource requirements, daily resources availability, and the resource substitution rule for the project. First, SRA was conducted to generate realistic activity durations. Then, the results obtained are used to conduct MSRS for the project so that realistic project completion time can be determined considering activity duration uncertainty.  
Figure 1: Overview of research methodology.  
Figure 2: Network diagram for project A.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Activity**} & \multirow{2}{*}{**Predecessor**} & \multicolumn{2}{c}{**Duration**} & \multicolumn{2}{c}{**Daily Resource Requirements**} \\ \cline{3-6}  & & **(Days)** & **R1** & **R2** & **R3** \\ \hline A & - & 3 & 4 & 4 & 2 \\ B & - & 4 & 3 & 4 & 1 \\ C & - & 5 & 1 & 3 & 2 \\ D & A & 2 & 1 & 0 & 0 \\ E & A & 3 & 2 & 1 & 0 \\ F & B & 4 & 2 & 2 & 1 \\ G & D,F & 3 & 3 & 1 & 2 \\ H & C & 6 & 4 & 4 & 4 \\ I & C & 4 & 3 & 2 & 1 \\ J & E,H & 3 & 1 & 4 & 5 \\ \hline \hline \multicolumn{6}{c}{Daily Resources Availability} & 7 & 7 & 10 \\ \hline \multicolumn{6}{c}{Substitution rule: 2R3 = 1R1 and 2R3 = 1R2.} \\ \end{tabular}
\end{table}
Table 2: Project information for project A.
## 6 The Case for Cross-Layer Design  
### Introduction  
Cross-layer design is the concept of merging two or more layers within the protocol stack to improve the efficiency of the interaction between the protocols. The Open Systems Interconnection (OSI) model is a protocol stack abstraction that was developed to standardize communications by developing the concept of logical layers. The protocols at each layer hide the complexity of the layer below and provide services to the layer above. The protocols that fit into this type of model do not need to be aware of other protocols in the layers above or below. This offers many advantages in terms of scalability, compatibility, and flexibility of network design.  
Resource efficiency can be gained by exploring a more unified scheme that merges common layer protocol functionalities into a cross-layer module for resource constrained nodes. However this type of structure, whilst possessing many advantages, cannot offer a consistent one size fitting solution for all network design problems. Protocol schemes that move away from the traditional layered model, whilst identifying significant advantages, must counter these improvements in network performance with a balanced view of the potential drawbacks of scalability or compatibility that may be introduced with the implementation of cross-layer solutions. The interaction between the layers can be varied but the justification to move away from the traditional model must be based on key benefits with a careful assessment of any downsides.