esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Correlation between Operating Conditions and Conveying Capacity  
The relevant coefficients have been calculated to determine the relationship between the operating variables and the mass flow. In the backward-fitting model, the sensitivity of the time scale affecting the mass flow rate is significant. The absolute value of the relevant coefficient is \(-0.93\). The effect of screw speed is also more sensitive to mass flow. The relevant coefficient is \(0.98\). The sensitivity of the screw outer diameter to the effect of mass flow is relatively low, with a relevant coefficient of \(0.28\). This shows that the mass flow rate is negatively correlated with the screw's outer diameter. However, the magnitude of the change is relatively small.  
In the forward-fitting model, the increase in particle viscosity can be seen as the mass flow rate decreases at constant speed. This also means that the production cycle affects the physical properties of the material. Additionally, the increased resistance to the interaction of particles with the blade. It is possible to modify key parameters in the control system to ensure stable and accurate casting based on the forward and backward-fitted models.
## 3 Results and Discussion  
### Influence of the Stirring Time and the Rotational Rate  
Figure 2A shows the particle size distribution (DSP) for the fresh 50P/50E lemon essential oil emulgel as a function of the stirring time, being constant the rotational rate (400 rpm). As can be observed, a monomodal distribution was obtained with a population peak about 0.12 \(\upmu\)m and, in the time interval studied (from 1 to 7 min), the DSP was not influenced by this variable. Additionally, in Figure 2B, the mean diameters (Sauter mean diameter, \(D_{3,2}\) and mean volume diameter, \(D_{4,3}\)) and the span as a function of the stirring  
time are shown, corroborating that this variable does not influence them. This state was also corroborated by ANOVA analysis.  
The physical stability of these emulgels is shown in Figure 3. The stirring time does not influence the stability of the emulgel significantly, although a slightly lower TSI value was found for 3 min. For this reason, this time, 3 min, was selected as the stirring time.  
In Figure 4, the DSP (Figure 4A) and the mean diameters and span (Figure 4B) for the fresh 50P/50E lemon essential oil emulgel as a function of rotational rate at the stirring time previously set are shown. All emulgels showed the same particle size distribution and similar to previous samples, they were monomodal with a population peak about 0.11 \(\upmu\)m. This fact can also be observed by analyzing the values of mean diameters and span, and in addition, was confirmed by ANOVA analysis. Therefore, it can be concluded that, within the interval of times and rates studied, the mixing protocol used to obtain the emulgels does not influence their final size.  
Figure 3: Time evolution of the Turbiscan Stability Index, calculated for the entire height of the measuring cell, with aging time (15 days) for 50P/50E emulgels as a function of the stirring time.  
Figure 2: (**A**) Particle size distributions for fresh 50P/50E emulgels at different stirring times. (**B**) Mean diameters and spans as a function of the stirring time. The bars correspond to the standard deviation. The tests were carried out at an ambient temperature.  
The evolution of TSI with aging time is shown in Figure 5. All rates exhibited a similar evolution of the TSI and there was no defined trend between the TSI values and the rotational rate. However, 200 rpm showed the lower values of this parameter, namely a higher stability; therefore, this rotational rate was selected for the next stage.
## 3 Simulation Results
In order to highlight the performance of the proposed bipolar LVDC distribution compared to the other exiting works, an open-circuit fault is applied to the DC load 1 at the instant t = 7 h. Figure 23 illustrates the DC load 1 and 2 voltages taking into account the occurrence of this open-circuit fault. Based on this figure, it is obvious that in the event of an OCF, the voltage of DC load 1 stays at 320 V while the voltage of DC load 2 falls to 0. Since the DC load 1 was still receiving power as a result, this suggested the architecture's performance was greatly improved even with an OCF. The voltage and current of the AC load 1 in the case of an occurrence of an OCF in the LVAC distribution are shown in Figure 24. In order to confirm the impact of this fault, a THD of the current signal is also displayed as seen in Figure 25. With reference to Figure 25, it should be noted that the THD rises by around 0.02% while being below 5%, demonstrating the proposed architecture's improved efficiency even in the presence of an OCF. Following the fault's occurrence, this fault may result in a secondary failure that can render this proposed architecture completely dysfunctional. According to the absolute average value of the DC load 1 or 2 voltage, a recommended diagnostic approach is presented. As shown in Figure 26, the fault variables FD1 and FD2 are used to identify the OCF. When the voltage's absolute average value is zero, the fault variable is equal to one. Since the absolute average voltage value is not zero in the healthy case, the fault variable is equal to zero. Referring to Figure 26, this fault is seen being detected and identified at 7.034 h by the suggested diagnostic approach, which enhances the high performance and robustness of this method. Figure 24: AC load 1 voltage (**a**) and current (**b**) values in the LVAC distribution under OCF. Figure 16:0 THD obtained for the AC load 1 current under OCF.
## 5 Results
### User Spacecraft Orbit Determination Results  
The previous sections have served to establish the range of reasonable uncertainties for TT&C-only TDRS orbits. The main objective of this section is to analyze the impact of DOWD OD precision under circumstances where TDRS precision is solely determined by TT&C tracking in the absence of BRTS replacement (see Section 2). To achieve this objective, four distinct sets of satellites with varying characteristics were utilized in the investigation (see Section 4.3). Two separate inquiries were conducted to thoroughly explore the issue.  
The first inquiry involved establishing user spacecraft orbits using TDRS orbits based on Scenario 1 and Scenario 2 of Section 5.1, where the TDRS orbits were derived from operational values. In the second inquiry, a cluster of TDRS ephemerides obtained from scenario 3 of Section 5.1 was used to obtain a more comprehensive understanding of the relationship between user spacecraft orbit error and TDRS orbit error evolution.
# Focusing 3D Small Objects with Object Matching Set Abstraction
## 3 Methodology  
The presence of small objects significantly affects the performance of 3D object detection. This work proposes a point-wise methodology for object detection. The method contains a representation learning block and a prediction head. In the first block, the SA module is usually used to carry out the structured learning of features, significantly reducing the computational cost. Typically, this module employs task-agnostic schemes, such as random sampling or FPS, for data downsampling and utilizes ball query to extract the nearest neighboring points. First, small objects have significantly fewer points than large objects. Therefore, in this process, the feature learning of small objects is greatly affected by downsampling. Second, the geometric difference between the ball and the objects (pedestrians and cyclists) is large, inevitably introducing more noise points. Therefore, our target is to learn excellent features in the representation learning process and reduce information loss. We introduce an efficient point-based detector utilizing object matching set abstraction, as depicted in Figure 1. The representation learning block extracts structured features from the point clouds. Subsequently, the features are fed to the detection and segmentation modules. The detection head outputs predictions, and the segmentation head is used to provide semantic guidance for feature learning. This work focuses on the representation learning block. The block mainly comprises three components: (a) focus-based sampling, (b) cube query, and (c) MLP. Focus-based sampling raises the probability of informative points in small objects. Cube query leverages geometric priors to extract spatial points that exhibit higher conformity with the objects' morphological characteristics. MLP employs stacked \(1\times 1\) convolutional layers with BatchNorm to encode geometric patterns in local neighborhoods, thereby enhancing robustness against point cloud permutation invariance and density variations via hierarchical multi-scale feature aggregation. It is noted that the number of SA layers is greater than or equal to two.
## 2 Hyaluronan Role in Wound Healing
Phase IV- Remodeling, or scar formation: ECM remodeling requires the up-regulation of matrix metalloproteinases (MMPs), including collagenase-1 (MMP-1), stomelysin (MMP-3), gelatinase B (MMP-9), and macrophage elastase (MMP-12), which are secreted by many cells, including fibroblasts, vascular smooth muscle (VSM), and leukocytes, and contribute to tissue remodeling and repair. MMPs are up-regulated during wound healing in epidermal and dermal cells, but they are dysregulated in chronic wounds. HMW-HA increases the expression of collagen III and increases the activity of anti-fibrotic transforming growth factor (TGF)-\(\beta\)[46]. TGF-\(\beta\) receptor signaling is the most potent pathway, which stimulates fibroblast to myofiblast differentiation [47]. The remodeling phase can last over a year and consists of wound contraction, vascularization, and cellularity decline. As described above, the molecular weight is a critical factor of HA biological properties (see Table 1). Molecular weight affects HA's interactions with other biomolecules and its diffusion coefficient; thus its tortuosity and convection-diffusion rates can be influenced or impeded due to steric hindrance of the macromolecule. HA with different molecular weights presents different effects in normal tissue compared to after injury or disease. As a result, it is important to accurately characterize the molecular weight and molecular weight distribution of HA during the research and development phase of new products [48]. Using validated and reproducible methods that use adequate standards could determine the molecular distribution of HA [49]. Understanding the biological effect and metabolism of HA is crucial to regulating drug-receptor interactions, cell therapies, and wound healing treatments. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Size** & **Function** & **Characteristics** & **Ref.** \\ \hline vHMW-HA (2.7 \(\times\) 10\({}^{6}\) g/mol) & Improve re-epithelial & \(\uparrow\)TGF-\(\beta\)1, VEGF, IL-6, IL-1\(\beta\), IL-8, and MMPs & [50] \\ \hline  & & & \(\uparrow\)TGF-\(\beta\)1, VEGF, Arg1, and IL-10. Increased the healing of diabetic foot ulcers. Increases proliferation, migration and tube formation in endothelial cells, and protects against apoptosis. Inhibition of NO production. & [53; 54] \\ \hline LMW-HA (\(\leq\)1.2 \(\times\) 10\({}^{5}\)) & Pro-inflammatory & \(\downarrow\)IL-1\(\beta\), IL-8 & [53; 54] \\ \hline  & Increased inflammation & Enhanced fibroblast migration and wound repair. Increased proliferation, migration, and tube formation of endothelial cells under high glucose conditions. & [33] \\ Fragmented HA (o-HA) & Increased angiogenesis in diabetic healing & Increased proliferation, migration, and tube formation of endothelial cells under high glucose conditions. & [55] \\ \hline  & & & \\ \end{tabular}
\end{table} 
Table 1: Effects of exogenous HA for wound healing.
## 2 Internal Leakage Minimization Based on Sealing Technology
### Variable Gap Sealing
#### 2.3.1 Variable Gap Sealing Based on Structural Design  
[PERSON] et al. [27] adopted the theory of elastoplastic deformation in metal materials and proposed a variable gap sealing technology based on the principle of automatic pressure compensation. When the pressure rises in the high-pressure working chamber of the cylinder, the lip will be slightly deformed under the action of the oil pressure difference between the inner and outer rings, which will reduce the sealing gap between the cylinder barrel and the piston, thereby reducing the leakage, as shown in Figure 7a. A pressure compensation mechanism was used in the hydraulic cylinder to establish a gap sealing flow field that can automatically adapt to pressure changes, reducing the pressure to 95 mL/min under 31.5 MPa.  
Subsequently, the variable lip structure was improved by [PERSON] et al. [28], shown in Figure 7b, and a method was proposed to change the sealing gap by radially expanding the outer surface of the piston, which was close to the inner wall of the cylinder. The thin-walled sleeve was used as the deformed structure and fixed in the middle of the piston. Then, a cavity was formed, and the outer piston was tightly sleeved on the outside of the thin-walled sleeve. When hydraulic oil entered the cavity, the thin-walled sleeve expanded and deformed under the condition of increased pressure, which would cause the outer piston to deform. Therefore, the gap between the piston and the inner wall of the cylinder was reduced for less leakage.  
However, the two kinds of variable gap structure seals mentioned above often failed due to plastic deformation. Meanwhile, the service life was short due to errors in the manufacturing and assembly. Thus, some scholars began to explore the possibility of achieving the variable gap sealing function from a material point of view. We introduce the realization of gap compensation from the material properties in the next section.
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 3 Signal Design Scheme
### Frequency Selection  
Although frequency can be selected in the L-band, S-band, and C-band from the perspective of LEO/GNSS joint receiving, selecting in the GNSS L-band or adjacent frequency bands is a priority direction.  
At present, the main frequencies of GNSS civilian signals are L1 and L5, which are also the interoperability frequencies. GPS, Galileo, and BeiDou-3 all broadcast signals on L1 and L5, as shown in Table 1.  
In addition, Japan's QZSS also broadcasts signals on L1 and L5 frequencies, India's NavIC also broadcasts signals on L5 frequency, and NavIC plans to broadcast MBOC signals on L1. The L1 and L5 frequencies are very crowded. If LEO navigation augmentation signals are broadcast on L1 and L5 frequencies, it will face stricter compatibility requirements. However, L1 and L5 are the main GNSS civilian frequencies that can share the RF channel with LEO L1 and L5 signals, reducing the cost and complexity of the receiving terminal and making them a good frequency choice.
## 6 Evaluation Issues
### Validation example
#### 6.1.2 Protected assets  
The first step of the security problem definition is the identification of assets that should be protected. Assets may be placed within the TOE or in its environment. The main asset are the patient's medical data sampled by the mote, which can be expressed by a previously defined enhanced generic, here refined. Moreover, a new asset-type generic, _i.e._, the TOE provided services, is defined, related to the sensor operation, _i.e._, measuring, sampling, processing, transferring data, and displaying the mote status.  
DTO.SensorData. Data measured, stored, processed, or transmitted by the intelligent sensor and data related to the network services.  
Refinement: Medical data of the monitored patient.  
DTO.SensorService. Services provided by a sensor: measuring, sampling, processing, transferring data, and displaying its status.  
Please note that the patient's medical data are very sensitive, e.g., they may be misused by insurance companies, their counterfeiting may cause a wrong medical diagnosis which can put the patient's life in danger, _etc_.  
The TOE also has security-related data used for the biometric identification of the monitored person. The -closest\" generic which can be used is DTO.Password but it does not concern biometrics. For this reason, the previously defined set of enhanced generics is supplemented by a new item:  
DTO.BioData. Biometric or physiological data used for the identification of a person.  
Figure 3: IMBMS—Intelligent Mote-Based Medical Sensor as the TOE.  
Three standard enhanced generics related to the resource limitation are added as auxiliary but important items (patient's monitoring should be long-term and permanent):  
DTO.NodePowerRes. Energy supplying a sensor node.  
DTO.NodeProcessRes. Node processing ability.  
DTO.NodeTransmRes. Node data transmission ability.  
Because such an IT product as IMBMS protects sensitive medical data, the evaluation assurance level (not discussed there) declared for it will be probably EAL3 or above and then the security of the development process ought to be considered as well (please note the ALC_DVS assurance family--[1]/Part 3). For this reason, all design related data should be precisely identified and expressed, e.g., with the use of the previously define item:  
DAP.DesignData. Sensitive project data and documentation.  
Refinement: Logical, physical design data of the TOE hardware, software specifications, code and other related documentation, development aids, test data, user data related documentation, material for software development and manufacturing process.  
Careful and rigorous design, deployment and management of medical monitoring systems are very important for the patients and medical personnel.  
The more co-operates directly with the base station, which can be also distinguished as an asset.  
DIT.BaseStation. A distinguished node of a wireless sensor network (WSN) used to control the network or as a gateway intermediating between WSN and other network.
## 3 Field Data Acquisition and Processing
### Site Description and Blasting Practice
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Mines**} & \multicolumn{2}{c}{**A**} & \multicolumn{2}{c}{**B**} & \multicolumn{2}{c}{**C**} & \multicolumn{2}{c}{**D**} & \multicolumn{2}{c}{**E**} \\ \hline \multirow{4}{*}{**Rock Mass Type**} & \multicolumn{2}{c}{**Barakar Formation Overburden Rock**} & \multicolumn{2}{c}{**Barakar Formation Overburden Rock**} & \multicolumn{2}{c}{**Lower Landau Overburden**} & \multirow{2}{*}{**Comspect**} & \multirow{2}{*}{**Limestone**} \\  & & & & & & & & \\ \cline{1-1} \cline{6-10}  & & & & & & & \\ \hline
**Rock Mass** & **Unit** & **Symbol** & & & & & \\ \cline{1-1} \cline{6-10} \multirow{2}{*}{**Properties**} & Bulk density & (g/cc) & \(\rho_{r}\) & 2.65 & 2.43 & 1.96 & 4.46 & 4.32 & 2.69 \\ Uniaxial Compressive Strength & (MPa) & \(\sigma_{c}\) & 65.80 & 43.50 & 39.70 & 152.29 & 202.85 & 116.23 \\ Tensile Strength & (MPa) & \(\sigma_{t}\) & 7.25 & 3.93 & 3.41 & 23.07 & 17.09 & 10.89 \\ Young’s modulus & (G/a) & \(E_{t}\) & 12.356 & 8.237 & 6.276 & 33.980 & 30.200 & 37.000 \\ Poisson’s ratio & \(v_{s}\) & 0.12 & 0.15 & 0.15 & 0.33 & 0.34 & 0.18 \\ P-wave velocity & (m/s) & \(\sigma_{p}\) & 2900 & 2730 & 2106 & 4786 & 4659 & 4256 \\ S-wave velocity & (m/s) & \(\sigma_{c}\) & 1910 & 1750 & 1630 & 2573 & 2420 & 2887 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of the tests to determine physicochemical properties of the rock mass specimen. using the equation \(W=q\times W_{e}\), allowing for the inclusion of varying energy content in the predictive modeling process.
## 6 Discussion
### Challenges and Future Directions
#### 6.3.2 Long-Term Dependencies of Traffic Data in Short-Term Traffic Prediction  
Long-term dependencies in traffic data are patterns and trends that extend over a significant time span and influence short-term traffic conditions. By incorporating these dependencies, short-term traffic prediction models can more accurately forecast immediate traffic conditions by considering historical data and broader temporal patterns. When using RNNs for extracting long-term dependencies of traffic data in short-term prediction, issues such as vanishing or exploding gradients often lead to training difficulties. This is because, during backpropagation, gradients can quickly diminish or grow exponentially with each layer, especially when dealing with long sequences. Additionally, capturing long-term dependencies is challenging with RNNs due to the difficulty in capturing long-term de pendencies. In long sequences, the current output may depend on inputs from a long time ago, and due to the vanishing gradient problem, these long-term dependencies cannot be effectively learned through backpropagation through time. The network struggles to understand these critical historical dependencies, leading to decreased prediction performance. This limits the model's ability to predict over long periods, while the information contained in long time series is crucial for predicting future traffic conditions. As the prediction period increases, effectively utilizing historical long-term data to maintain prediction accuracy becomes a major challenge.  
In future research, further investigation and development of innovative deep learning architectures will be crucial for addressing long-term dependency issues. For instance, novel RNN layers, such as TTT [1], employ new model designs to compress and represent inputs, dynamically adjusting the internal structure of the model to accommodate input data and capture long-term dependencies. Such innovative approaches offer promising directions for better capturing and utilizing long-term dependencies.
## 3 Experimental Results
### Optimization Algorithm  
The function of the optimization algorithm is to minimize or to maximize the loss function by improving the training method. When adjusting the weighting and deviation parameters for model updating, a suitable optimization algorithm can make the model achieve better and faster results. Due to the choice of the DeepLabv3+ model, three optimization algorithms were available: \"SGDM\", \"RMSprop\" and \"Adam\". Details of all optimization algorithms can be found in [18].  
To investigate the influence of the pre-trained network on the prediction result, a certain data set of micrographs was used with a variation of the network. From the neural network training results in Table 4, it can be seen that the optimization algorithm of SGDM was more suitable for this project requirements, so SGDM was finally adopted.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Weights** & **Backbone** & **Image Resolution** & **mIoU** \\ \hline
1, 1, 1, 1, 1 & Xception & 2048 \(\times\) 1024 & 60.96\% \\
0.95, 1.14, 0.15, 0.014, 0.062 & Xception & 2048 \(\times\) 1024 & 76.88\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of training results with different initial weights.