esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Robotic Peg-in-Hole Assembly Method Based on Variable Admittance
## 2 Materials and Methods
### TD3 Algorithm Design
#### 2.3.2 State and Action Design  
In this instance of peg-in-hole assembly, reinforcement learning was employed to control the contact force within a smaller range to ensure safe contact, while also accelerating the entire assembly process.  
The interaction between the robot and the environment primarily relied on data from a six-axis force/torque sensor. For this task, the robot mainly performed insertion along the Z-direction and rotational motion around the \(X\)-axis within the hole. Consequently, the force \(F_{Z}\) and torque \(T_{X}\) were the key data considered in this study.  
The assembly time was closely related to the amplitude of the robot's movements in each motion cycle. Excessive movement amplitude could lead to an overload of force, causing the assembly to fail. Conversely, a reasonable movement amplitude could accelerate the assembly speed. Therefore, to expedite the assembly process, the single-step displacement \(H_{t}\) and single-step rotation angle \(\theta_{t}\) in each motion cycle were taken into account. Displacement referred to the motion along the Z+ direction, while the single-step rotation angle referred to the rotational motion around the \(X\)-axis.  
Section 2 analyzes how virtual damping parameters could enhance the admittance effect. The action design involved the damping parameter B1 associated with displacement along the Z-direction and the damping parameter B2 associated with rotation around the tool \(X\)-axis direction.  
Elucidating the Rationale: In this assembly experiment, the displacement in the X-direction was minimal, offering limited room for improvement. To expedite the model training process, the damping value in the X-direction was not considered. The displacement in the \(Y\)-direction remained relatively small but it was highly correlated with the torque acting around the tool \(X\)-axis. In order to optimize the rotational motion around the \(X\)-axis and accelerate the training speed, the optimization of the Y-direction displacement damping parameter was abandoned, and the damping parameter B associated with rotation around the \(X\)-axis was directly trained. The \(Z\)-direction was the direction of insertion, and optimizing the force and motion amplitude in this direction was crucial for improving the assembly process.  
Therefore, for the peg-in-hole assembly problem addressed in this study, the state space S for the agent was set as \(S=(F_{z},T_{x},H_{t},\theta_{t})\), and the action space A was set as \(A=(\text{B1},\text{B2})\).
## 4 The Seismic Performance Parameters of RCS Composite Joints Under Floor Effect Are Studied
### The Influence of Bolt Preload on Seismic Performance of Nodes
#### 4.3.2 Curve Characteristics and Ductility  
The increase in the high-strength bolt preload significantly optimizes the hysteresis curve of the RCS composite joint and improves the ultimate bearing capacity and seismic performance of the joint.  
Increasing the preload of the high-strength bolt can significantly improve the initial stiffness and ultimate bearing capacity of the RCS composite node, as shown in Figure 22.  
An excessively high preload on the high-strength bolts can reduce the ductility of the RCS composite joint when the slab interaction effects are considered. Specifically, the ductility of joint FJD-10 is found to be 26.3% and 30.7% lower than that of joints FJD-0 and FJD-9, respectively (Table 6). Therefore, in practical engineering applications, it is essential to adjust the bolt preload appropriately to balance the joint's stiffness, load capacity, and ductility, thereby optimizing its seismic performance.
# Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures
## 2 Problem Formulation and Simulation Methodology
### The Planning Method
#### 2.2.1 Propagation Analysis  
Various studies have been conducted in projects like METIS, MiWEBA, ITU-R M, mmMAGIC etc. in order to deploy channel models in higher frequency bands, such as millimeter waves, to support 5G use cases [8; 11; 12; 13]. The path loss model that was adopted here was the Alpha-Beta-Gama (ABG) empirical path loss model:  
\[PL(dB)=10\ a\ log_{10}(d)+\beta+10\gamma log_{10}(f_{\rm GHz}) \tag{1}\]  
The model has three parameters to describe mean path loss over frequency and distance, which derive from the statistical process of measurements. In our analysis, at urban environments at 28 GHz, these parameters took the following values [13]:  
\[a=3.5,\ \beta=24.4,\ \gamma=1.9 \tag{2}\]  
The propagation losses of this model vs. distance for \(f\) = 28 GHz are depicted in Figure 2 along with the losses of the WINNER model for microcells at the 2 GHz band of LTE [14].  
Table 1 summarizes typical values used in the performed simulations for generic mmWave operational scenarios.  
We considered the downlink of a 5G network since it is usually the bottleneck for such a system. The Signal to Interference plus Noise Ratio (SINR) at the \(k\)th CN is:  
\[SINR_{k}=\frac{P_{tx}^{b}A_{0}^{b}A_{\phi\to k}^{b}G_{bk}^{ch}}{\sum_{i\in[b \backslash b]}P_{tx}^{i}A_{0}^{i}A_{\phi\to k}^{i}G_{i\to k}^{ch}+P_{N}} \tag{3}\]  
where \(b\) is the serving gNB for the \(k\)th CN, \(P_{tx}^{b}\) and \(A_{0}^{b}\) are the transmitted power and maximum antenna gain for the \(b\)th gNB, respectively, while \(A_{\phi\to k}^{b}\) is the normalized antenna gain of the \(b\)th gNB towards the \(k\)th CN and \(G_{bk}^{ch}\) is the channel gain between them. Similarly, for the interference calculation, \(A_{\phi\to k}^{i}\) is the normalized antenna gain of the \(i\)th interfering gNB towards the served CN. Lastly, \(P_{N}\) is the noise power at the receiver. The micro gNBs employ either omnidirectional antennas (\(A^{b}=1\)) or directional \(120^{\circ}\) sector antennas [15] with \(A_{0}=18\) dBi (see Table 1) and \(A^{b}=-\text{min}(12(\phi/\phi_{3 dB})^{2},25)\), where \(\phi_{3 dB}=70^{\circ}\). It has to be noted here that the Effective Isotropic Radiated Power (EIRP) of the transmitting BS antennas remains the same with sector and omnidirectional antennas, i.e., 60 dBm.  
Figure 2: Path loss vs. distance at 28 GHz and 2 GHz bands.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 3 Static-CAPIC: Cache Based on Popularity and Class with the Static Cache Proportion
### Mathematical Analysis for Static-CAPIC
This is in accordance with Figure 4, with each class having different request characteristics according to Table 1. Assuming a service of class \(k\) has a different average rate request, \(\lambda_{k}\) with \(k=(1,2,\ldots,K)\), the cache hit ratio for the content store which is partitioned into class \(k\) with the cache size \(c_{k}\), the pattern of request arrival per time unit follows the Poisson distribution with rates \(\lambda_{k}\) and request patterns follow Zipf-Mandelbrot with content access probabilities \(i\), \(p(i)\), can be written as:  
\[CHR(C)=\frac{\sum_{i=1}^{C1}\left(p(i)\cdot\lambda_{1}\right)+\ldots+\sum_{i=1 }^{K}\left(p(i)\cdot\lambda_{k}\right)}{\sum_{i=1}^{K}\lambda_{k}} \tag{4}\]  
and  
\[c_{1}+c_{2}+\ldots+\ c_{k}=C\]  
where:  
\(k=\) (1, 2, 3,..., \(K\)) = content class,  
\(p(i)=\) hit probability for content \(i\),  
\(c_{k}=\) cache size for content class \(k\),  
\(\lambda_{k}=\) average number of request per time unit, \(>\)0,  
\(C=\) total size of content store. When the average request rate for each content class is stated as \(\lambda_{1}<\ \lambda_{2}<\ \lambda_{3}\), and each content class has an identical Zipf-Mandelbrot distribution pattern, the base on the Equation (4), is used to get the largest \(CHR(C)\) value. Furthermore, the class with the largest average interest rate, \(\lambda_{k}\), needs to be given the biggest proportion of cache \(c_{k}\), followed by a smaller proportion for classes with smaller average interest arrivals. However, consumers demand can change at any time. For instance, they can ask for specific content in one class more than the other, which changes with time. The number of consumers' requests fluctuates in a limited time, and in a long period, it produces an average value of demand following Poisson distribution. This condition needs to be accounted for to provide better performance. Therefore, based on this mathematical analysis, for Static-CAPIC, the cache proportion is determined in accordance with the approximate consumer interest rate. Naturally,  
Figure 4: Illustration of \(K\) number of class partitions on the content store. the class with a higher interest rate needs more cache proportion to acquire the high cache hit ratio. The simulation result shown in Section 5.2 indicates that the network cache hit ratio is more significant than the LCD+sharing scheme, which means that the goal of Static-CAPIC has been reached. However, in detail, the first class has the smaller cache hit ratio compared to the sharing scheme, whereas it has to be prioritized compared to other class due to delay and jitter requirement. This result will be explained in more detail in Section 5.2.
## 3 Proposed ADR-Detection Algorithm
A primary concern during this implementation of the ADR-Det algorithm and the application included was to achieve close to real-time performance. Therefore, the datasets selected for the CNNs and the algorithms had to be as lightweight as possible. Using those requirements, memory usage during execution does not exceed 1.5 GB of RAM. CPU resources are used to the maximum extent, utilizing multiple cores and vectorization capabilities. Such provisions have been considered to provide a portable version. Moreover, CPU consumption of the algorithm that will detect dangerous goods in real-time may also provide Augmented Reality (AR) meta-information or even feed this information directly to a database. To use the ADR-Det algorithm as a real-time detection tool, at least 8 CPU cores are required, processing up to 0.86 images/s. Nevertheless, in order to provide portable and real-time ADR detection functionality, ARM portable devices (RPi Broadcom and Nvidia Tegra SoCs), the ADR-Det algorithm has been excluded by the HMRT-EF application and has been cloud ported. That is, offered as a service using an appropriate HMRT-EF application for portable embedded devices and as a standalone application for PCs (\(\times\)64) for non-cloud post-processing cases. The HMRT-EF standalone application functionality and logic follow.
# Supply-Scalable High-Speed I/O Interfaces
## 3 Design Considerations of Supply-Scalable I/O
### Clock Generation  
As mentioned briefly in the introduction, multiple PLLs (or multiple oscillators in a single PLL) are frequently implemented in wide-range transceivers to cover the entire frequency range [4; 8; 10; 11; 53]. It is mainly because LC oscillators are popular for high-speed I/O applications because of its superior phase noise performance and high-speed capability compared to ring oscillators, and they are also less sensitive to supply voltage variation. However, LC oscillators occupy a much larger area, so using multiple LC oscillators is an expensive solution. The survey results given in Figures 10 and 11 validate such trade-off between LC-based PLL and ring-based PLL. Figure 10 illustrates a scatter plot of figure-of-merit (FoM) of PLL versus operating frequency of PLL designs presented in IEEE International Solid-State Circuits Conference (ISSCC) from 2010 to 2019 [19]. The FoM of the PLL is defined as  
\[FoM_{PLL}\ =\ 10\log\left(\frac{\sigma_{J}}{1s}\right)^{2}\cdot\left(\frac{P_{ PLL}}{1 mW}\right)\cdot \tag{5}\]  
where \(\sigma_{J}\) and \(P_{PLL}\) denote the absolute jitter and power consumption of a PLL, respectively, which is widely used to evaluate the PLL jitter performance with equalized power consumption [54]. The trend shown in Figure 10 implies that the LC-based PLLs generally exhibit better jitter performance and higher operating frequency than the ring-based counterpart. On the other hand, we can find that the ring-based PLL occupies much less silicon area from Figure 11, where the FoM versus silicon area is plotted. In addition, the tuning range of the ring is much wider than LC [19]. As a result, the design trade-off between LC- and ring-based PLLs in wide-range I/O can be summarized as follows: the LC PLL offers better jitter performance; however, its range is limited so that no less than two LC PLLs are required to cover a wide range, which results in significant area consumption. On the other hand, the ring PLL achieves wide range with a small area; however, its higher phase noise and supply sensitivity results in worse performance. Conventionally, using multiple PLLs has been a majority option for wide-range I/O transceivers [8; 10; 11; 55; 56]. Recently, there have been some ring-based works by introducing some circuit techniques to overcome the limit of ring PLLs; for example, a two-stage ring oscillator [23], clock multiplying delay-locked loop (MDLL) [31], and multi-phase calibration [57]. For specific applications where only discrete rates are used, a single PLL with a configurable division ratio can be also used [16; 58].  
Figure 10: Comparison of ring-based and LC-based clock generators in terms of figure-of-merit and frequency, based on the works presented in International Solid-State Circuits Conference (ISSCC) 2010–2019 [19].
## 2 Materials and Methods
### Perceived Risk Evaluation  
The survey consisted of a straightforward evaluation designed to gauge pedestrians' perceived risk based on street images. To facilitate user-friendly responses and ensure efficient data storage, a custom-built image evaluation program was developed using Python 3.12, as shown in Figure 3. Using this program, participants reviewed a total of 61 images and provided their responses in a consistent format. Referring to questions used in previous studies [32; 33; 34], only one standardized question was presented for each image: \"How would you feel walking along the sidewalk shown in this image?\" Response options were displayed using a five-point Likert scale: (1) very safe, (2) safe, (3) neither safe nor dangerous, (4) dangerous, or (5) very dangerous. The scale reflects the extent to which participants perceived the sidewalk as unsafe; thus, higher scores indicate a more dangerous walking environment. All images were displayed individually at the same size, and participants could either manually save each response or enable an auto-save option. To minimize ordering effects, the image sequence was randomized for each participant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Items** & **Categories** & **Frequency** & **Percentage (\%)** \\ \hline \multirow{3}{*}{Gender} & Male & 33 & 78.6 \\  & Female & 9 & 21.4 \\  & 20–29 & 37 & 88.1 \\ \multirow{3}{*}{Age} & 30–39 & 4 & 9.5 \\  & 40–49 & 1 & 2.4 \\ \multirow{3}{*}{Experience *} & Yes & 42 & 100.0 \\  & No & 0 & 0.0 \\ \multirow{3}{*}{Perceived sidewalk constraint **} & Strongly disagree & 0 & 0.0 \\  & Disagree & 8 & 19.0 \\ \multirow{3}{*}{\
\begin{tabular}{c} Perceived sidewalk \\ constraint ** \\ \end{tabular} } & Neutral & 7 & 16.7 \\  & Agree & 21 & 50.0 \\ \multirow{3}{*}{
\begin{tabular}{c} Strategy agree \\ \end{tabular} } & Strongly agree & 6 & 14.3 \\ \hline \hline \end{tabular}  
* Based on the question, “Have you had experience walking along sidewalks adjacent to construction sites in urban areas?”. ** Based on the question, “Have you ever felt that the sidewalk became narrow due to construction work?”.  
\end{table}
Table 1: Demographic characteristics of survey participants (\(n=42\)).  
Figure 3: A custom-built image evaluation program developed for this study.
# Effect of Different pH Beverages on the Color Stability of Smart Monochromatic Composite
## 1 Introduction  
Composite resin is considered one of the best direct restorative materials because it mimics the aesthetics of natural tooth tissue [1, 2, 3]. It is widely used to restore the broken-down teeth that are either carious or fractured, the buildup of congenitally malformed teeth, veneering of disclosed natural teeth and cementation of the orthodontic appliances on the teeth [4]. The clinical success of dental composites depends upon their physical, chemical, and mechanical properties, which are greatly influenced by the characteristics of the oral environment and properties of the resin materials [5, 6].  
One of the physical properties of composites that is of the utmost importance is the color stability of the material. The color stability of resin composites can greatly affect their clinical longevity and performance within the oral cavity [7] and is reported as one of the main reasons for replacing the restorations [8]. Manufacturers have focused on making thismaterial's color stable over time. Various etiological factors are responsible for the color changes in resin composites. Extrinsic discoloration may occur due to oral hygiene habits, or the presence of certain chromogens in the diet, tobacco, water absorption, and surface roughness of the material [9; 10], whereas intrinsic factors occur due to the physiomechanical reaction within the resin material, depending upon the type of resin matrix, which ultimately affects the hydrophilic/hydrophobic nature of the material, filler weight, size and distribution, the type of photoinitiator used and the degree of polymerization [11; 12].  
The human oral cavity is exposed to a variety of dietary beverages throughout the day. The most commonly consumed beverages in our population are milk tea, coffee, and carbonated soft drinks. All of these have been shown to have a significant staining effect on the resin composites. Previous studies have indicated the deleterious effect of these beverages on conventional and bulk-fill composites [7; 13; 14; 15].  
Nowadays, the trend of using the smart monochromatic shade of composite is booming. Dentists are more inclined towards using smart chromatic technology owing to the fact that it reduces the need for a variety of composite shades in inventory, minimizes the wastage of unused composite shades, minimizes chaiside time, eliminates the need for shade selection, and reduces reliance on shade-matching procedures [16]. Omnichroma (OM) is one of the smart chromatic materials and is believed to attain the shade of the surrounding tooth structure in which it is placed. However, knowledge regarding the color stability of this material is scarce in scientific literature [17; 18]. Therefore, it is worthwhile to investigate the color stability of Omnichroma in comparison to micro-hybrid composite.
# Leveraging Hybrid RF-VLP for High-Accuracy Indoor Localization with Sparse Anchors
## 4 Results and Discussions
### Impact of Receiver Arrangement on Localization Accuracy  
To verify the versatility of the positioning system in real-world environments, we analyze its positioning performance under different receiver arrangements. The detailed arrangements are shown in Figure 8, square and triangle receiver arrangements are tested as two typical scenarios. And, to better demonstrate localization accuracy in more general scenarios, we also include a random receiver arrangements and evaluate their localization accuracy. In each experiment, 12 receivers are positioned at a fixed coordinate, and the BLE transmitting power is set to \(-3\) dBm. A total of 500 experiments are conducted for each arrangement. The positioning results of each experiment are shown in Figure 8, with different colored dots representing the positioning results of receivers within the coverage areas of different LEDs, and the positioning error is shown in Figure 9. Overall, the RMSE of the positioning accuracy for all nodes under the square arrangement is 26.1 cm, with an average error of 21.7 cm. Additionally, it is observed that the positioning results for receivers closer to the center of the positioning area are more accurate, while those near the edges are more dispersed. This is due to the presence of walls and other obstructions at the edges, which interfere with Bluetooth and optical signals, leading to a lower signal-to-noise ratio and a slight increase in positioning error in those areas. Under different receiver arrangements, the square array exhibits the best localization performance. Although the localization accuracy under random arrangement is slightly lower than that of the  
Figure 7: Localization error of the hybrid system under different BLE transmission power levels.  
square array, it still achieves an average localization error of approximately 23 cm. In the triangular arrangement, the proximity of receivers to each other lead to interference from Bluetooth signals, resulting in relatively lower localization accuracy. Nonetheless, in typical application scenarios, the receivers are often randomly distributed, yet reliable localization accuracy remains achievable.
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods
### Implementation Strategy
#### 2.1.3 Data Provenance  
Metadata, tags, and attributes that track data provenance are critical to big data systems. Data provenance is defined as the record of the source, processing, and overall lineage of the data. Traditionally, data provenance is associated with audit logs and debugging; however, we propose that it provides an important role in AC. Data provenance can be expressed using a data model, business vocabulary, or other directed acyclic graphic terms. Making big datasets available for analytics requires tracking when processes are executed that reduce the data sensitivity and then updating the data provenance attribute in a trustworthy manner. Research has been published that describes using metadata tags to track processing provenance in this manner (e.g., sanitization history) [37].  
[PERSON] et al. [38] introduce three key sources for metadata, which they titled \"A-B-C's of data context,\" i.e.,  
A = application or programs run against the data;  
B = data source and usage over time (who used it);  
C = changes and version history.  
Several techniques to protect and reduce data sensitivity have been identified as Privacy-Preserving Data Mining (PPDM) techniques in the ABAC process. Privacy-preserving data protections are integral to AC services and enable linear performance scaling. This implies distributed AC policy decision points (PDP) and the execution of distributed PPDM algorithms [39; 40; 41].  
Attributes (metadata tags) track application privacy-preserving algorithms on sensitive datasets. The attributes can be used to ensure processing only on authorized computers in the cluster. For example, some policies may require sensitive data storage and processing only in private computing systems, whereas other non-sensitive data in the dataset can be stored and processed in a public cloud service. This type of tag, along with Hadoop rack awareness features and data locality controls, can be used to further control access. This is achieved by incorporating designated data zone considerations, e.g., raw data, private/classified subsets, sanitized subsets, and summary reports, into metadata tags. [PERSON] et al. provide an example of the flexibility that is possible when incorporating metadata tags into big data AC [42].