esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Pavement Performance Based on Rheological Tests
#### 4.4.2. Putting Property  
Figure 16 shows the cumulative strain curve of specimens under two different stresses (0.1 kPa and 3.2 kPa). It can be seen that the cumulative deformation of 0%WRA is the minimum. The cumulative deformation of virgin asphalt is far greater than that of 0%WRA, because the aging effect improves the non-deformability of asphalt. The addition of W-oil increases the cumulative deformation of aged asphalt. The higher dosage of W-oil is, the higher the deformability of the rejuvenated asphalt. It is concluded that the deformation recovering capacity is improved by the aging effect, while the addition of W-oil lowers the deformation recovering capacity of aged asphalt. When the stress is 0.1 kPa, 15%WRA has a cumulative deformation curve close to that of virgin asphalt, while in the case of 3.2 kPa, 15%WRA has a cumulative deformation curve with a certain difference from that of virgin asphalt, which demonstrates that the trend of cumulative deformation under different stresses varies.  
Figure 16. Multiple Stress Creep Recovery (MSCR) Curve of Virgin, Aged, and Rejuvenated Asphalt.  
Figure 15. Mixing and Compacting Temperature of Virgin, Aged, and Rejuvenated Asphalt.  
Non-recoverable creep compliance (\(J_{nr}\)) can characterize the anti-rutting property at high temperature well. The larger \(J_{nr}\) is, the poorer the anti-rutting is; the smaller \(J_{nr}\) is, the better the anti-rutting. The non-recoverable creep compliance (\(J_{nr}\)) of each cycle under each stress can be calculated as Equation (1):  
\[J_{nr}=\frac{\gamma_{nr}}{\tau} \tag{1}\]  
where \(\tau\) is the loading stress for each cycle, kPa; and \(\gamma_{nr}\) is the non-recoverable strain of the cycle.  
According to Figure 17, it can be seen that the \(J_{nr}\) of 0%WRA is far smaller than that for virgin asphalt; therefore, the aging effect improves the rutting resistance of asphalt. The larger the dosage of W-oil is, the larger \(J_{nr}\) value of the rejuvenated asphalt; it is concluded that W-oil lowers the rutting property of aged asphalt. There are differences in the \(J_{nr}\) values of specimens under different stresses, but no obvious rules are found.
## 2 Materials and Methods
### Concurrent Dilated Residual Convolution Module  
The concurrent dilated residual convolution module is a core component of the encoding process in MSACN. CDRCM plays a pivotal role in the enhancement of our proposed method. During the encoding phase, it systematically incorporates concurrent dilated convolutions with varying dilation factors, forming a pyramid-like structure. This design is strategically implemented to capture features at multiple scales and enrich the model's receptive field. Typically, the concurrent dilated residual convolution module is used to be integrated into the residual basic block. The concatenated output from these dilated convolutional channels is further processed through residual modules to facilitate effective feature encoding. The design of dilation factors considers the relatively high spatial resolution of the raw images, making it unsuitable to employ smaller dilation sizes for establishing sparse convolutional kernels. The expansion of the basic block is set to 1, so the shape of the feature channel is the same as the input. The bottleneck has an extra convolution layer on the right side of the basic block, and expansion is set to 4, which means that the size of input and output maps is different at this time. As illustrated in Figure 2, after the skip connection is matched with the shape of the cloud semantic prediction module, a full convolution layer is introduced to the middle part of ResNet50 to change the channel-matching shape. To better receive the validated information of the cloud, we enlarge the field of view and maintain the resolution features of the input channel, and replace the first convolution with the dilation convolution of dalition = 4, as illustrated in Figure 3a, kernel1. It is the dilated convolution of dalition = 2, while kernel2 is an ordinary convolution. Through a series of residual blocks and convolutional layers, the dimensions of channels gradually increase, the shape gradually decreases, and finally a preliminary effective feature layer with a shape of [2048; 8; 8] is obtained.  
In the part of the remaining encoding process, apart from a series of convolutions, BN, ReLU, and MaxPooling to obtain output, the basic residual block includes multiple bottlenecks and the basic block module is shown in (b) and (c) of Figure 3. The inclusion of these two modules addresses the vanishing gradient problem inherent in DCNNs by introducing cross-layer shortcut connections, making the network easier to train and optimize.  
Figure 2: Illustration of MSACN with encoding and decoding structures.
# Optimization of Active Distribution Network Operation with SOP Considering Reverse Power Flow
## 3 Actual Maximum Transfer Capacity of Reverse Power Flow in the Distribution Network
### Objective Function  
The objective function aims to minimize the sum of the daily comprehensive operating costs of multiple distribution networks after joint operation. The daily comprehensive operating costs include: network loss cost \(\mu_{loss}^{loss}\), \(P_{m,t}^{loss}\), wind and solar curtailment cost \(\mu_{DG}\)\(P_{DG,m,t}^{loss}\), and device (SOP, CB, OLTC) operation cost \(C_{OP,m,t}\), as expressed in the following equation:  
\[minF_{m}=\sum_{m=1}^{\Omega}\sum_{t=1}^{T}\left(\mu_{loss}P_{m,t}^{loss}+\mu_ {DG}P_{DG,m,t}^{loss}+C_{OP,m,t}\right) \tag{34}\]  
\[P_{m,t}^{loss}=\sum_{j=\Omega_{B}}I_{m,ij,t}^{2}r_{m,ij}^{*} \tag{35}\]  
\[P_{m,DG,t}^{*loss}=\sum_{i=1}^{N}\left(P_{m,i,t}^{*DC}\ +P_{m,i,t}^{*SOP}\ -P_{m,i,t}^{*L}\right)-P_{m,t}^{*loss}-cos\varphi_{m}S_{m,r, max}^{*} \tag{36}\]  
\[C_{m,OP,t}=C_{m,OP,t}^{SOP}+C_{m,OP,t}^{OLTC}+C_{m,OP,t}^{CB} \tag{37}\]  
In this context, \(P_{m,t}^{*loss}\) and \(P_{m,DG,t}^{*loss}\) denote the per unit values of active network losses and curtailed wind and solar resources, respectively, for distribution network m during period t. The coefficients \(\mu_{loss}\) and \(\mu_{DG}\) represent the cost weights for network losses and renewable energy curtailment. \(P_{m,DG,t}^{*}\) and \(P_{m,load,t}^{*}\) denote the dispatched power from distributed generation and load demand at node i in distribution network m during period t. Costs \(C_{OP,m,t}^{SOP}\), \(C_{OP,m,t}^{OLTC}\), and \(C_{OP,m,t}^{CB}\) correspond to the operational actions of the SOP, OLTC, and CB, respectively.
## 2 Theory of Stability, and Approach Pursued  
Although both graphite and CB have a hydrophobic surface, they differ in their atomic structure and surface properties [31]. The purer the CB, the greater its properties, in particular its surface charge and non-polarity, approach those of graphite [31]. While graphite displays a homogeneous surface, that of CB is more heterogeneous. Depending on the degree of purity, it can contain a variety of different functional groups, such as carboxyl and hydroxyl groups, phenolic groups, and lactols [5; 32]. Depending on the type of functional group, they display either an acidic or alkaline behavior in water [32].  
Substances with non-polar surfaces can be stabilized by non-ionic surfactants, such as PVP and TX-100. The hydrophobic tail of the surfactant adsorbs at the non-polar surfaces, while the hydrophilic component extends into the liquid medium. For non-ionic surfactants, the stabilization mechanism is primarily based on electrosteric effects (a combination of electrostatic and steric stabilization).  
The phenomena occurring in the diffusive ion layer dominate electrostatic stabilization. Equally charged ion layers repel each other, thus preventing the formation of aggregates and stabilizing the dispersion. The zeta potential is often used as an indicator of the magnitude of the electrostatic forces between colloidal particles and, thus, is determined by the surface charge density [33]. Consequently, the zeta potential is a function of the surface or particle charge, which is significantly influenced by the pH value [1]. According to [PERSON] et al. [26], the higher the absolute value of the zeta potential, the greater the electrostatic stabilization of a dispersion.  
Steric stabilization is achieved when molecules adsorb at the particle surface and prevent the particles from approaching each other. A sufficient quantity of molecules must be adsorbed for the layer formed to be dense enough to shield the Van-der-Waals forces. Unlike electrostatic stabilization, steric stabilization is not reflected in zeta potential measurements [1].  
Therefore, this paper investigates the following hypothese:  
**Hypothesis 1**.: _Graphite and CB nanoparticles can be stabilized in aqueous formulations, such that the dispersions meet the requirements for both printability and final cell performance._  
**Hypothesis 2**.: _Even though CB is graphite-based, graphite and CB cannot be stabilized by the same non-ionic dispersants._  
The approach pursued in this paper is as follows. First, the cause-effect relationships between the indicators of instability in a dispersion and the measured quantities are analyzed. The predominant cause-effect relationships were experimentally investigated for graphite and CB dispersions containing either PVP or TX-100. Based on the findings, a systematic methodology is then derived, which aids in the utilization of dispersants in printable electrode dispersions for drop-on-demand JJP processes.
## 3 Numerical Simulation  
### One-Dimensional Numerical Simulation  
#### 3.1.1 Building the One-Dimensional Numerical Simulation Model  
In this study, CMG-STARS software was used to develop a numerical model to simulate the H\({}_{2}\)S generation during the one-dimensional CSS experiment. According to the size of the one-dimensional sandpack model, a one-dimensional numerical simulation model was established, as shown in Figure 5. Reasonable grid size and step size can not only approach reality, but also improve the operational speed of the grid model. Therefore, the one-dimensional numerical simulation model included 48 \(\times\) 19 \(\times\) 19 (17328) grids, and the grid size was 1 cm (X) \(\times\) 0.2 cm (Y) \(\times\) 0.2 cm (Z). The reservoir properties (porosity, permeability, oil saturation) were obtained from the experimental data, and Table 4 shows the other reservoir and rock properties used in the model.  
In this study, the fluid model was set up with three components: water, heavy oil, and H\({}_{2}\)S. The density and oil volume factors of heavy oil are 955.977 (kg/m\({}^{3}\)) and 1.035, respectively. The viscosity-temperature curve of heavy oil, oil-water relative permeability curve, and gas-liquid relative permeability curve adopted in the model are shown in Figures 6 and 7.  
The kinetic parameters (activation energy and frequency factor) used in the numerical simulation model were obtained from the aforementioned studies (Figure 3), and the following reaction Equation (6) was applicable to describe the generation of H\({}_{2}\)S during the CSS process.  
\[\text{Heavy Oil}\rightarrow\text{H}_{2}\text{S} \tag{6}\]  
The production parameters of wells in the model are consistent with the operating parameters during the one-dimensional CSS experiment.
## 4 Generic Context Aware Cross-Layer Framework
### Ontology-Based AmI Context Model  
Representing context information requires a modeling method to standardize and formalize information, through which a common understanding of the exchanged (global) AmI context by all sensor nodes can be achieved. In this paper, the representation of common AmI contexts such as user location and activity is shown in Figure 5, which is based on an ontology model derived from our previous work [16].  
This ontology model supports context representation for common AmI scenarios. The ontology captures the basic attributes of a user-relevant event, including attributes of the event itself and those of the user (personal and activity attributes) during the event. The class _UserEvent_ is the root entity. Each instance of _UserEvent_ represents a detected event relating to/affecting a particular user, and is generated by an intelligent agent on behalf of the user. The class _UserEvent_ has three descendant classes: _Event_, _Personal,_ and _Activity Attributes_. The _Event Attributes_ class presents the facts of the event, such as the  
Figure 5: Ontology model for representation of common AmI contexts.  
location and time of event, the event radius (or perimeter), and the status of the objects within the event radius. The _Personal Attributes_ class captures the personal facts of the user, such as age, gender, health, and disability status. The _Activity_ class encompasses the details associated with the physical activities of the user, such as the type, duration, and frequency of the user's activity (e.g., walk, run, sleep, talk), and the input data (e.g., sensor readings) required for the inference of the activity.  
A representation of this Ontology can be described, for example using RDF/XML serialization:  
<owl:Class rdf:ID=\"UserEvent\"> <owl:Class rdf:ID=\"Event Attributes\"> <rdfs:subClassOf rdf:resource=\"#UserEvent\"/> </owl:Class>... <owl:Class rdf:ID=\"Personal Attributes\"> <rdfs:subClassOf rdf:resource=\"#UserEvent\"/> </owl:Class> <owl:ObjectProperty rdf:ID=\"age\"> <rdfs:domain rdf:resource=\"#Personal Attributes\"> <rdf:range rdf:resource=\"xsd:integer\"> </owl:ObjectProperty> <owl:ObjectProperty rdf:ID=\"disability\"> <rdfs:domain rdf:resource=\"#Personal Attributes\"> <rdf:range rdf:resource=\"xsd:disabilitystate\"> </owl:ObjectProperty> <owl:Class rdf:ID=\"Activity Attributes\"> <rdfs:subClassOf rdf:resource=\"#UserEvent\"/> </owl:Class>... </owl:Class>  
The context information of a user-relevant event can be represented by one instance of this ontology. It is important not to make this context model too complex as the sensor nodes are resource-constrained and lack the capacity for complex processing.
## 2 Materials and Methods
### Implementation Steps  
A quasi-experimental design was used. All participants underwent two learning modalities: CBCT image analysis and AR-based tooth model exploration. A four-week interval was introduced between the two sessions to minimize carryover effects. Participants viewed CBCT images of selected teeth on a 2D monitor. Following each scan, they completed a questionnaire assessing their understanding of root canal morphology, adapted from a previous study [7], with some modifications. For each tooth, three questions were asked: (1) the number of root canals present, (2) detecting merging/branching of canals, and (3) [PERSON]'s classification of a root canal configuration.  
Participants interacted with 3D virtual tooth models with the instructor in an immersive AR environment using HMDs (Figure 2). Then, they answered identical anatomical identification questions previously posed during the CBCT exercise. Students did not know the teeth used in the AR model.  
Students were given a timer to record the time needed to answer the three questions related to each tooth using CBCT or AR models. In addition to completing the questionnaire, students were asked to fill out the following survey after completing each session:  
Figure 2: Screen captures illustrate the virtual learning environment (**a**), where students collaborate with a virtual instructor to explore root canal system complexities (**b**). Student performance is assessed through timed tasks and subsequent knowledge-based questions (**c**,**d**).  
From a didactic point of view, did the VR simulation result in a better or worse comprehension of the root canal anatomy than CBCT? Or did both methods perform the same? Did you face dizziness or disorientation during the VR simulation?
## 1 Introduction
Most of these devices are now marketed with integrated rotary encoders [32], and some manufacturers have introduced models equipped with integrated force platforms. In contrast, conical shaft devices offer less resistance during movement compared to cylindrical shaft systems [26; 30; 33]. Due to their conical shape, the instantaneous radius varies throughout the range of motion [30]. These systems are particularly suited for lateral displacements, changes in direction, and movements involving diverse force vectors [21; 34]. Although conical systems are also monitored with rotary encoders, estimating power and force is more complex due to the variability in instantaneous radius, with most software using an average radius based on the selected configuration [35]. Although most strength and conditioning coaches and therapists recognize the advantages of isoinertial devices in professional practice, barriers such as high cost, space requirements, and limited time often restrict their use [28; 36; 37]. The HandyGym system represents a new generation of portable isoinertial training devices, featuring compact dimensions (10 \(\times\) 12 \(\times\) 20 cm) and a lightweight (0.9 kg) design, making it easily transportable in a backpack [38]. It is also more affordable compared to other commercially available systems and integrates a wireless rotary encoder. Additionally, its design allows for various strength exercises across multiple vectors, similar to conical axis isoinertial systems [21]. To the authors' knowledge, HandyGym is the first fully portable isoinertial training system available on the market, with no other manufacturers currently offering similar features. These characteristics make HandyGym a promising solution to address some of the limitations practitioners encounter when implementing this technology in professional practice. To the best of our knowledge, no scientific publication in major databases has investigated the use of this training system for exercise interventions or assessed the validity of the measurements obtained through its integrated encoder. Therefore, this study aims to evaluate the validity of the HandyGym sensor for measuring force and power during isoinertial exercises. We hypothesize that the HandyGym sensor will provide valid measurements of force and power, albeit with some bias and estimation errors that may vary depending on load conditions. Furthermore, we expect these measurements to closely correlate with those from a reference system, thereby demonstrating the sensor's suitability for practical application.
# Performance Analysis of Partitioned Step Particle Swarm Optimization in Function Evaluation
## 1 Introduction
### Particle Swarm Optimization Components
#### 1.2.1 Velocity Rule  
The velocity rule is how the particles of the algorithm determine their next update or next step. Originally, PSO [9] uses three components for the step; the previous velocity, the particle best (_pbest_), and the global best (_gbest_). The inertia weight variant later introduced by [PERSON] and [PERSON] [14] is the recent widely used variant that follows the equation  
\[vel_{new}=\omega vel_{old}+c_{1}rand(pbest-x)+c_{2}rand(gbest-x) \tag{1}\]  
where \(c_{1}\) and \(c_{2}\) are the cognitive and cooperative constants, respectively; \(x\) is the particle position, \(\omega\) is the inertia weight, and \(vel\) is the velocity of the particle.  
However, as discussed in [PERSON] and [PERSON] [15], the weighted velocity variant discussed above is locally convergent and may suffer from premature convergence. Other papers tried to mitigate this problem by changing the value of the weight and/or changing the velocity rule. One notable method is removing the _pbest_ component and just use the previous velocity and the global best for the update [16; 17] which showed better results, especially in multimodal functions.  
Another unique variant uses an additional component to the original PSO. Weight-Improved Crazy PSO (WICPSO) [18] uses a bad experience component (_pworst_) to help the particle move away from the not so good position.
## 5 Conclusions  
Both the pre-compensation from the transmitter's FIR filter and the post-compensation from the receiver's FIR filter are demonstrated to compensate the bandwidth narrowing effect. The bandwidth of the received coherent signal is estimated from the raw output of the ADC with the improved accuracy by using Savitzky-Golay digital filter. No coherent receiver locking or data recovery is needed. The adaptive equalization of the FIR filter can improve the system's tolerance to the bandwidth narrowing effect. This process can be done in real time with running traffic. The proposed method is critical for the coherent signal with high baud rate and high spectral efficiency, such as a single-carrier 64-GBd 16-QAM signal running with 400-Gb/s information bits.
## 4 Results  
Based on the maximum reach experiment results, we confirmed that the signal reach failed from 69 cm. Figure 3 shows the measured value and average value of RSSI for each installation depth simultaneously. The measurement error was less than 2 dBm. All indoor experiments had the same measurement error. The average RSSI at the shallowest depth (30 cm) was \(-\)65.8 dBm, and the average RSSI at the depth at which the signal finally reached 68 cm was \(-\)77.2 dBm. The signal attenuation width decreased with increasing installation depth.  
Based on the comparison results of signal attenuation according to the filler, attenuation in subgrade soil was the largest (Figure 4). For sand, the average RSSI at the shallowest depth (35 cm) was \(-\)58.6 dBm, and the average RSSI at the deepest depth (60 cm) was \(-\)71.0 dBm. In the case of the graded gravel, the average RSSI at the shallowest and deepest depths were \(-\)54.2 dBm and \(-\)68.2 dBm, respectively.  
As shown in Figure 5, we observed the signal attenuation based on the moisture content. The RSSI was \(-\)68.2 dBm in the dry state and \(-\)70.0 dBm in the saturated state. We confirmed the effect of moisture on signal attenuation. We observed a difference of 1.8 dBm depending on the amount of moisture in the 25 cm deep subgrade soil.  
Figure 4: Comparison of signal attenuation of different materials.  
Figure 3: Experimental results of the maximum depth of signal.  
Figure 6 presents the results of the surface layer thickness change experiment in preparation for construction errors. Regardless of the type of medium, the signal attenuation width was large at a shallow depth, and signal intensity converged at a deep depth. Assuming the depth sum of the two layers to be 40 cm when the road surface layer was 20 cm and the asphalt layer was 20 cm, the signal strength was -71.8 dBm. When the asphalt layer was 5 cm and the road surface layer was 35 cm, the signal strength was \(-\)70.0 dBm. Based on these results, we can infer that the asphalt layer has a more significant effect on signal attenuation. Construction errors occurring in the field may affect signal attenuation. The depth of installation from the surface is most important.
## 2 Materials and Methods
### Imaging System Setup  
Custom-designed OCT has been used for an in vivo imaging study of mouse retina [22; 23]. Figure 1 shows the schematic diagram of the system used for this study. A broadband (M-T-850-HP-I, Superlum, Cork, Ireland, \(\lambda_{center}=850\) nm, \(\Delta\lambda=165\) nm) superluminescent diode (SLD) was used as the light source. A fiber coupler (TW850R5A2, Thorlabs, Newton, NJ, USA; 75:25) divided the OCT light to the sample and reference arms. A custom-designed spectrometer was constructed with a line CCD camera (OCTOPLUS, e2v, Chelmsford, UK; 2048 pixels) and a transmission grating (Wasatch Photonics, West Logan, UT, USA; 1200 line/mm). The axial and lateral resolution of the system was theoretically estimated at 1.4 and 3 \(\upmu\)m, respectively. The pixel resolution is 1.2 \(\upmu\)m in both axial and lateral dimensions.