esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results  
In this section, we report the results of the trajectory tracking and disturbance rejection tests carried out with the dynamic simulator described in Section 4. We first show the performance of the velocity-level algorithm and then the performance of the acceleration-level one. The results for the trajectory tracking tests are shown for both an aerial manipulator with a two-DOF arm (as described in Section 4) and one with a three-DOF arm. In the latter case, one link is added with the same geometrical and inertial characteristics as the other two. The three-DOF manipulator is kinematically redundant since the task involves the tracking of a two-DOF trajectory with the end-effector (its orientation is not controlled).  
For each of the two kinematic control algorithms, we first show their performance in an open-loop scenario (joint reference velocity or acceleration profiles given by the inverse kinematics solution) and then in the case with the feedback, introduced to reduce the end-effector error to zero. To make the results comparable, we assume that the aerodynamic disturbances have the same behavior (depicted in the first graph of Figure 6) in all the simulations carried out. In all of them, we also assume that the kinematic controller uses inertial parameters of the UAV that differ from the nominal ones as described in Section 4.3 (the maximum positive variation is always considered).  
In the disturbance rejection tests, the only unknown force acting on the system is the impulsive force on the end-effector. Experiments are carried out only for a two-DOF arm.  
For each experiment, we report the temporal evolution of the norm of the task-space error (the end-effector error) and the trajectories of the end-effector and the base COG. The joint position, velocity, or acceleration limits are not considered in this work, and the inclusion of them could be part of future work.  
\begin{table}
\begin{tabular}{c c} \hline
**Measured Variable** & **Std. Deviation, \(\sigma\)** \\ \hline Base position, \(\left\{x_{b},z_{b}\right\}^{T}\) & 0.005 m \\ \hline Base linear velocities, \(\left\{v_{b,v},v_{b,z}\right\}^{T}\) & 0.005 m/s \\ \hline Base pitch angle, \(\theta_{b}\) & 0.001 rad \\ \hline Base angular velocity, \(\omega_{b}\) & 0.005 rad/s \\ \hline \end{tabular}
\end{table}
Table 4: Standard deviations of the distributed noise added to the considered measurements.  
\begin{table}
\begin{tabular}{c c} \hline
**Inertial Parameter** & **Assumed Variation of the Parameter** \\ \hline UAV mass (\(m_{B}\)) & \(+/-15\%\) \\ \hline Links mass (\(m_{i}\)) & \(+/-2\%\) \\ \hline UAV moment of inertia (\(I_{B}\)) & \(+/-15\%\) \\ \hline Links moment of inertia (\(I_{G_{i}}\)) & \(+/-5\%\) \\ \hline \end{tabular}
\end{table}
Table 5: Assumed variation in the inertial parameters used in the kinematic controller compared to those used in the dynamic simulations (reference values).
# Transmitters and Receivers for High Capacity Indoor Optical Wireless Communication
## 2 Beam-Steering for Optical Wireless Communications Transmitter  
### Introduction  
One of the main challenges in OWC systems is achieving precise beam steering while maintaining high data rates and signal integrity. Established beam steering approaches show limited throughput, steering speeds, optical bandwidth, reliability, and miniaturaz  
Figure 1: Indoor optical wireless communication scenario. Data comes to the home via fiber-to-the-home (FTTH) which is then redistributed to separate rooms and wirelessly transmitted to the mobile devices.  
tion potential. In this section, we present the implementation of C-Band beam steering for an indoor OWC system using silicon micro-lenses integrated with piezoelectric Micro Electro-Mechanical systems (MEMs) actuators to achieve bidirectional beam steering with 51.3\({}^{\circ}\) horizontal and 22.4\({}^{\circ}\) vertical angular ranges, while maintaining an error-free transmission rate of 10 Gbps.  
Although primarily designed for indoor environments such as homes and offices, the architecture can be adapted for terrestrial applications by adjusting the beam divergence and receiver specifications. Another advantage is that the data rate of the system is not dependent on the steering mechanism, being primarily limited by the bandwidth of transmitter components (modulators, drivers) and receiver characteristics (photodiode bandwidth, aperture size and field-of-view (FoV)).
## 3 Measurement Results
### Further Research  
#### 3.2.1 Flow Meter (Static Calibration)--Bronkhorst L01  
The Bronkhorst \(\upmu\)-FLOW L01 is a liquid mass flow meter for ultra-low flow rates. In addition, the flow meter has a very low internal volume and provides a fast response flow signal. The \(\upmu\)-FLOW L01 mass flow meter is basically a straight sensor tube without any moving parts or built-in obstructions. The heating/sensor unit works on the principle of heat transfer and is arranged around the pipe. As the flow passes through the flow meter, the temperature difference is determined from the upstream and downstream temperature sensors. The determined temperature difference is a function of the flow rate and the heat capacity of the liquid to be measured. The measured and processed values can be output via the analogue interface or via RS232. Bronkhorst provides its own software for this purpose (FlowDDE for communication with the flow meter and provision of measurement data and FlowPlot for visualisation and communication with FlowDDE). The flow meter is specified for a flow range of 5 mg h\({}^{-1}\) to 100 mg h\({}^{-1}\) (about 5 mL h\({}^{-1}\) to 100 mL h\({}^{-1}\) for water) with a pressure rating up to 400 bar.  
Figure 10 shows a typical calibration measurement at a flow rate of 1000 nL min\({}^{-1}\) (60 mL h\({}^{-1}\)). The red dots are the flow values (around 10 Hz) indicated by the Bronkhorst flow meter and the blue line is the measured weight taken by the weighing scale (1 Hz) over a time period of more than 7 h. At this flow rate, the determined measurement deviation was less than 1.5%.  
Figure 9: Measurement results of the Medtronic insulin pump for measurements over one day (24 h) for a preset flow rate of 0.025 U h\({}^{-1}\) (0.25 mL h\({}^{-1}\)).
## 1 Nanomaterials and Devices
### Multifunctional Magnetic Nanostructures for Enhanced Hyperthermia Therapy  
Raja Das  
Faculty of Materials Science and Engineering and Phenikaa Institute for Advanced Study (PIAS), Phenikaa University, Hanoi 10000, Vietnam  
Phenikaa Research and Technology Institute (PRATI), A&A Green Phoenix Group, 167 Hoang Ngan, Hanoi 10000, Vietnam  
Despite their great potential in magnetic hyperthermia therapy for cancer treatment, spherical magnetite (Fe\({}_{3}\)O\({}_{4}\)) or maghemite (\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)) nanoparticles, which are the most commonly employed and only FDA approved materials, yield limited heating capacity. Therefore, there is an increasing need for new strategies to improve the heating efficiency (evaluated by the specific absorption rate (SAR)) of these nanostructures. By utilizing the proven advantages of one dimensional (1D) nanostructures over their spherical and cubic counterparts, such as larger surface area, multisegmented capabilities, enhanced blood circulation time, and prolonged retention in tumors, we have demonstrated that the SAR of iron oxide nanostructures can be manipulated by altering their aspect ratio [1]. Calorimetric and AC magnetometry experiments performed on highly crystalline Fe\({}_{3}\)O\({}_{4}\) nanorods consistently show large SAR values, which are superior to their spherical and cubic nanoparticles of similar volume. In this line of interest, we have advanced to synthesize 1D hollow magnetic nanotubes of Fe\({}_{3}\)O\({}_{4}\) for dual-purpose applications in hyperthermia and drug delivery due to their high effective anisotropy and enhanced surface areas (inner and outer surfaces) [2]. Even though the 1D nanostructures show superior heating efficiency at the high field region (\(>\)400 Oe) due to their enhanced effective anisotropy, they do possess relatively poor heating efficiency at the low field region (\(<\)400 Oe, Brezovich safe limit). To overcome this, we have proposed a novel approach with which a synergistic exploitation of the magnetic and photothermal properties of the plasmonic metals and magnetic iron oxide, such as Ag(core)/Fe\({}_{3}\)O\({}_{4}\)(shell) nanoflowers, can reduce the magnetic field and laser intensities that are required in the case that both external stimuli are applied separately [3]. Our study establishes a key step towards optimizing the hyperthermia therapy through a combined multifunctional magnetic and photothermal treatment and improving our understanding of the therapeutic process to specific applications. These results pave a new pathway for the design of multifunctional magnetic nanostructures for advanced hyperthermia.
# Waste Silt as Filler in Hot Mix Asphalt: A Laboratory Characterization
## 1 Introduction
Moreover, samples produced with 100% ceramic waste had improved moisture damage resistance and better fatigue life performance when compared with the control sample. The influence of several waste filler types on different properties of asphalt pavements was reviewed by various authors [6; 17; 18]. In line with the same trend, the effect of clay materials as filler replacements was studied regarding pavement properties. For example, natural filler was substituted with 15% bentonite clay [19]. The partial substitution led to an increased indirect tensile strength and flow values of the HMA mixtures. Similarly, bentonite clay was used as a filler in hot mix asphalt samples [20]. However, the clay was used in both normal and thermally treated conditions. The clay was calcined at different temperatures ranging from 400-800 \({}^{\circ}\)C, where the best treatment was selected based on Atterberg limits while aiming for minimized plasticity and swelling of the bentonite clay. Overall, the 100% substitution of the natural filler with the thermally treated bentonite clay showed an increase in several mechanical properties of HMA. The results from the two different studies show that the thermal treatment of the waste filler could result in better performance of HMA mixtures. A similar trend was observed when the use of normal kaolin and thermally treated kaolin clay as filler in the HMA mixture was investigated [21]. Samples produced with 100% thermally treated clay were shown to have better performance in terms of Marshall stability and indirect tensile strength (ITS) compared with those of normal kaolin. The current study aimed at recycling waste silt obtained from a local aggregate production plant in Bologna (Italy). The waste silt will be used in two different forms, namely, untreated and thermally treated as a filler, totally replacing limestone. Such an application would be beneficial for a company contributing to a circular economy.
## 4 Component Modelling and Interaction Analysis
### Analytical Study Using Hanssen'S Interaction Formula  
For the study of the interaction effect and the fitting of Equation (1), four samplings were run using a Latin hypercube sampling strategy [44] to characterise the behaviour of both cross-sections with foam and cork cores. The number of simulations was set to 80 for cork-filled specimens and 120 for those filled with foam, as its density was variable. Once the results from the sampling were obtained, the parameters from Equation (1) were fitted to the data. The values of the parameters were bounded as \(0\leq\alpha\leq 1\) and \(0\leq\beta\leq 2\) so that no negative exponents appeared in the interaction term from Equation (1).  
Two additional 13-simulation samplings covering the design space in Table 1 were used to estimate the mean crushing force of the empty extrusions \(F_{\text{avg}}^{0}\) included in Equation (1). The empirical expression for the average crushing force of empty circular tubes proposed by [PERSON] et al. [45] was fitted to the simulation results by identifying the best-fitting parameters, giving  
\[F_{\text{avg}}^{0}=63.39(D/t)^{0.35}M_{0}, \tag{18}\]  
where \(M_{0}\) is the fully plastic bending moment per unit length, defined as \(M_{0}=\sigma_{\text{y}}\ t^{2}/4\). The yield stress of the aluminium alloy \(\sigma_{\text{y}}\) is taken as the average of the initial yield stress and the ultimate tensile strength, thus \(\sigma_{\text{y}}=221.55\) MPa.  
A similar procedure was followed for the square tube, using the empirical formula from [PERSON] and [PERSON] [46] as a baseline and adjusting its parameters to match the results from the simulations. The resulting expression reads  
\[F_{\text{avg}}^{0}=56.15(C/t)^{0.33}M_{0}. \tag{19}\]  
The plateau stress \(\sigma_{\text{r}}\) of the reinforcing materials was also required for Equation (1), which was estimated for this case as the average engineering stress up to a compressive engineering strain of 70%. This gave \(\sigma_{\text{r}}=2.13\) MPa for the cork and \(\sigma_{\text{r}}=2.38\) MPa for the foam.  
The values of the parameters after fitting Equation (1) to the simulation results were presented in Table 5. The fitness of the adjustments was assessed by calculating the root-mean-squared error (RMSE) and the R\({}^{2}\) between the sampling values and the proposed formulas, delivering an acceptable agreement with R\({}^{2}\geq 0.975\) for all scenarios. The results showed that \(C_{\text{avg}}\) took lower values for the  
Figure 7: Force-displacement curves for an empty circular tube with \(D=130\) mm and \(t=1.9\) mm. Experimental and numerical results.
# Design and Implementation of a RFID Reader/Router in RFID-WSN Hybrid System
## 4 Software Design of RFID Reader System
### Design of RFID Reader Software System  
On the VS2008 platform, the host computer reader software based on serial communication is developed. The serial communication control MSCOMM in Microsoft Foundation Classes (MFC) is used to set the serial number, baud rate, parity bit and stop bit of serial. The application software can realize the function of reading the electronic tag number, reading the data block of the specified sector, and modifying the data block of the specified sector. The flow chart of the communication function between RFID reader and host computer is demonstrated in Figure 8.  
The host computer application software sends control instructions to the ZigBee coordinator through the host computer serial port, and the coordinator transmits the control instructions to ZigBee router nodes. Router nodes core controller CC2530 determines inside procedures according to the control commands, and then controls registers in RF chip MF RC522, and sends out the corresponding coded instructions through an antenna. Electronic tags within the range of reader perform operations according to the commands, and then return the appropriate data to the reader. ZigBee router nodes parse out the data returned from the electronic tag and send the electronic tag information to the ZigBee coordinator. Then the coordinator transmits information through UART serial port to the host computer software and the data is displayed on the software interface. Its basic program flow is shown in Figure 9.  
Figure 8: The flow chart of the communication function between RFID reader and host computer.
# Comprehensive Review of Storage Optimization Techniques in Blockchain Systems
## 4 Methods
### Data Compression
Compared with traditional decentralized storage schemes, EDCOMA's dual compression method reduces the quantity and size of data authenticators, decreasing storage costs by an average of about 89.9%. This improvement in storage efficiency is notable across various data scales, especially in resource-limited storage node environments, where dual compression effectively reduces storage burdens. Furthermore, experiments analyzed the storage effects of different compression ratios, showing that as D-compression and P-compression ratios increase, the number of data authenticators progressively decreases, further reducing node storage requirements. This optimization enables EDCOMA to be more scalable and efficient in handling large-scale data storage. To further reduce the storage burden on the blockchain, EDCOMA introduces a random challenge mechanism, storing only compressed authenticators and audit states on the blockchain. By using smart contracts to randomly select partial data blocks for verification, it avoids the need to store and transfer complete data, thereby reducing on-chain storage redundancy. This mechanism performed well in experiments, effectively maintaining data integrity while significantly reducing on-chain resource consumption, making it suitable for large-scale decentralized storage scenarios. In summary, EDCOMA's dual compression and random challenge mechanism optimizations on storage costs make it highly practical in decentralized storage systems. [PERSON] et al. [101] primarily explore a binary matrix-based data representation method for data compression within blockchain systems. By converting blockchain data into binary matrix form, this approach significantly reduces data storage requirements. Leveraging the efficient representation and operational characteristics of binary matrices, the original data are encoded into matrix form, compressed, and then stored on the blockchain. This method not only enhances storage efficiency but also accelerates data transmission and processing speed, thereby optimizing the overall performance of the blockchain system. Through this innovative compression approach, blockchain systems can manage and store data more efficiently, advancing blockchain technology's applicability and development across various fields. Table 6 illustrates the effectiveness of using data compression techniques to reduce storage redundancy in blockchain systems.
## 3 Materials and Methods
### Pseudo-Label Generation Algorithm Based on SAM  
The Segment Anything Model (SAM) [14; 34; 35] is a foundation segmentation model pre-trained on over 1 billion high-quality masks across 11 million diverse, high-resolution images. While SAM offers strong generalization capabilities, its direct application to domain-specific tasks--such as face segmentation in new media content--often lacks task-aware precision, especially under weak supervision [36; 37; 38].  
To address this challenge, we propose a prompt-guided pseudo-label generation strategy that integrates SAM into our weakly supervised face segmentation framework. Given a training image \(I\), we extract a set of positive point prompts \(\mathcal{P}=\{p_{1},p_{2},\ldots,p_{n}\}\) based on sparse facial annotations (e.g., facial landmarks or center points). Specifically, for the point selection, frontal faces use the nasal tip (defined as the facial centroid) as the primary positive prompt and profile views utilize the central cheek region aligned with the zygomatic arch. As for occluded or extreme-angle cases, we adopt a multi-point ensemble (e.g., eye corner + mouth corner) to ensure coverage. This anatomically guided prompting ensures cross-image consistency while minimizing manual intervention (only 1-3 clicks per subject). The selected landmarks are validated through anatomical priors and demonstrate robustness across diverse scenarios (e.g., rapid camera movements in variety shows, intricate period costumes). For each image, the points and the image data are input into SAM to generate an initial segmentation mask.  
\[\bar{M}=\text{SAM}(I,\mathcal{P})\]  
To enhance the quality of the pseudo-labels, we apply a lightweight post-processing step that removes small, irrelevant regions based on area thresholding. The final refined mask is computed as  
\[M=\mathcal{R}(\bar{M})=\{\bar{m}_{i}\in\bar{M}\mid\text{Area}(\bar{m}_{i})>\tau\}\]  
Figure 1: Overall learning framework. It comprises three learning steps, including (**a**) the pseudo-label generation using SAM, (**b**) coarsely training the segmentation model using the generated labels, and (**c**) refining the segmentation model using twin architecture.  
where \(\hat{m}_{i}\) represents a connected component in \(\hat{M}\), and \(\tau\) is a predefined area threshold (set to 100 as default). This simple yet effective filtering process ensures that the retained pseudo-labels correspond to dominant facial regions, suppressing noisy segments caused by background clutter or SAM over-segmentation.  
As illustrated in Figure 2, this strategy enables SAM to produce high-quality, task-relevant pseudo-labels from minimal annotation, significantly reducing manual labeling costs while providing robust supervision signals for subsequent model training.
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 9 Preliminary Analysis C: Evaluating the Hotspot Scenario
### Simulation Results  
Figure 16 presents the simulation results in terms of the 10 th percentile of throughput. The dashed lines represent the scenario with hotspots (Scenario 3). The simulations were executed using the Strict FR algorithm, with the variation of its relevant parameters, according to Section 8 (RsrqThreshold and BandwidthDistribution). The former ranges between 30 and 33, and the latter is evaluated in two configurations. The first allocates 52 RBs to the common sub-band and 16 RBs to the private sub-band. The second allocates 28 RBs to the common sub-band and 24 RBs to the private sub-band.  
The 10 th percentile of throughput (Figure 16) indicates a significant performance loss due to the hotspots. The curves with the same distribution (curves 2, 4, and 5) show that the scenario with hotspots has lower throughput, especially if compared to curve 4, which has fewer users.  
Even though the performance loss is expected, given the increase in the number of users, both cases discussed above are important. Section 2 discussed scenarios naturally characterized by hotspots. In most of these scenarios, the hotspots also increase the total number of active users in the system, such as football games or music festivals. However, even if the change is only in user concentration, there is a relevant performance loss, as presented in Figure 16, given that curve 2 has the worst performance than curve 5.