esa-sceva/satcom-chunk-collection huggingface dataset:
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 5 Experiments
### Parameter Analysis  
We first investigate the impact of the layer number \(K\), which is a key hyper-parameter in GCN-based recommendation models. A larger value of \(K\) indicates that the model will leverage more embedding propagation layers stacked to generate node representations. We vary the value of \(K\) in the range of \(\{1,2,3,4\}\) and present the corresponding results with respect to accuracy and diversity in Figure 3. The metrics of HR@10 and CC@10 are used to evaluate the recommendation accuracy and diversity. Note that using only one layer though the proposed DTGCF surpasses ACF and DSPR in both accuracy and diversity, which verifies the benefit of aggregating rich information from neighbors. It can be observed that, as the layer number increases, the recommendation accuracy of the model is obviously improved, but the significant advancement diminishes when the layer number is larger than 3. However, with the increase of the number of layers, the diversity of the proposed model decreases relatively slowly compared to the speed of accuracy change. It justifies the rationality and effectiveness of our diversity-promoted strategies.  
Then, we conduct experiments to examine the impact of the hyper-parameter \(\alpha_{0}\), which is used to control the probability of selecting similar but negative samples. Specifically, we search the value of \(\alpha_{0}\) in \(\{0.02,0.04,0.06,\ldots,0.30\}\) with an increment of 0.02. The experimental results are shown in Figure 4. We can see that the diversity of model is continuously improved with the increase of \(\alpha_{0}\). Since more negative items are chosen from positive categories, the trained model will learn users' interest across categories and recommend items from more diverse categories. In particular, with a relatively small \(\alpha_{0}\), the recommendation accuracy is quite stable and even gains a slightly improvement. However, the accuracy declines sharply when \(\alpha_{0}\) becomes too large to capture reasonable representations. Therefore, a smaller value of \(\alpha_{0}\) is recommended to balance the recommendation accuracy and diversity.  
Figure 3: Results of different number layers on the Movielens 10M dataset in terms of HR@10 and CC@10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Recall@10** & **HR@10** & **CC@10** & **ILD@10** & **F1@10** \\ \hline DPP & 0.0438 & 0.1412 & 0.5672 & 0.8072 & 0.2261 \\ DGCN & 0.0912 & 0.2476 & 0.5524 & 0.7768 & 0.3419 \\ \hline tag-aware GCN & 0.0924 & 0.2882 & 0.5136 & 0.7330 & 0.3692 \\ personalized negative sampling & 0.0918 & 0.2534 & 0.5424 & 0.7694 & 0.3454 \\ adversarial learning & 0.0813 & 0.2403 & 0.5854 & 0.8164 & 0.3407 \\ two-way TransTag & 0.1124 & 0.3156 & 0.5187 & 0.7485 & 0.3924 \\ DTGCF & 0.1086 & 0.3124 & 0.5821 & 0.8124 & 0.4065 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study on the Movielens 10M dataset.
###### Abstract  
With the increasing share of variable and limitedly predictable renewable energy in power systems worldwide, ensuring reserve capacity to maintain the balance of supply and demand becomes more important. On the other hand, the development of the virtual power plant model (VPP) allows renewable sources and energy storage to participate in reserve service. This paper addresses the optimal reserve bidding strategy problem of a VPP comprising of renewable energy resources (RESs), energy storage systems (ESSs), and several customers. The VPP participates in balance capacity (BC), day-ahead (DA), and intra-day (ID) markets. The scheduling problem is formulated as a two-stage chance-constrained optimization model taking the uncertainty of RESs production, load consumption, and probability of reserve activation into account. The response of VPP after its reserve capacity is called and generated is also considered to increase the operational flexibility of VPP. The proposed model is implemented on a test VPP system, and the effects of RESs sizing, ESSs sizing, and the probability of reserve activation are analyzed. Results indicate that the proposed model can perform well under real-world conditions.  
chance-constrained programming; day-ahead scheduling; energy storage system; intra-day scheduling; reserve market; renewable energy source; virtual power plant Article
## 3 Active Disturbance Rejection Control Design  
The proposed control strategy is illustrated in Figure 2. In order to actively compensate for the effects of both lumped mismatched and matched uncertainties and the lack of angular velocity measurement, the two observers, i.e., an ESMO and a LESO, are constructed. Consequently, the estimated velocity and generalized disturbances are fed back into the main controller whose input generated from a trajectory planner. Inherited from [19], a nonlinear variable structure filter-based trajectory planner with minor modifications is developed to relax the demand on smooth reference trajectory of the conventional backstepping technique by considering the kinematic constraints of the physical system. The designs of observers, trajectory planner, and main control strategy will be deliberately presented in the following sections.
# Additive Manufacturing in Underwater Applications
## 3 Materials
### Ceramics, Concrete, Geopolymers, and Similar
The results confirm the good mixture efficiency in terms of washout resistance in both freshwater and seawater. However, seawater shows higher resistance to washout than freshwater [33]. No information on such tests for 3D-printed materials is available in the literature. Moreover, some comparative works have been provided on the topic of underwater applications of geopolymers and cement concrete for 3D-printed artificial reefs [27; 34]. Besides the two most popular materials, others have been tested for the 3D printing of artificial reefs--for example, calcium carbonate, plasters, sandstone powder, ceramic clay, terracotta, and diatomic sand with magnesium oxide--by other research groups [16; 35]. The possibility of the usage of in situ resources such as seawater and marine sediments for AM technology was investigated by [PERSON] et al. [31]. The provided research confirms the possibility of obtaining mortar/cement by using coral sand and seawater with some additives. The properties of the prepared mixtures, especially those reinforced by glass or basalt fibers, were acceptable for the design of cement-based materials for construction in remote islands and coastal areas [31]. Currently, trials in this area have been conducted \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Material** & **Investigated Properties** & **Main Findings** & **Reference** \\ \hline \multirow{4}{*}{5} & \multirow{4}{*}{\begin{tabular}{c} Seawater and marine \\ sediments + glass/basalt fibers \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Possibility to use for in situ 3D \\ printing of marine resources for \\ design mortar or cement. \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} Composites exhibited good \\ buildability. \\ properties that those produced using \\ traditional casting technology; \\ however, they had sufficient \\ properties for use in remote areas for \\ infrastructural projects. \\ \end{tabular} } & \multirow{4}{*}{[31]} \\  & \multirow{4}{*}{\begin{tabular}{c} [31] \\ 30-printed samples and samples \\ in a water environment had 20\% and \\ 15.1\% lower CS, compared to air-cast \\ samples. \\ Samples 3D-printed in the air and \\ underwater had different anisotropic \\ variations. \\ \end{tabular} } & \multirow{4}{*}{\begin{tabular}{c} [18] \\ 32]} \\ \end{tabular} \\  & \multirow{4}{*}{\begin{tabular}{c} [18] \\ 33-printed samples. \\ \end{tabular} } \\  & \multirow{4}{*}{\
\begin{tabular}{c} [19] \\ 34-printed samples. \\ about 72\% of that of the air samples. \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} [19] \\ 35-printed samples. \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._only in air. It is worth considering the implementation of this approach for underwater 3D printing, especially for significant depths, where the usage of in situ materials will be particularly valuable.
## 2 System Frequency Constraint Model  
### Comprehensive Frequency Response Model  
This paper represents the speed control system of coal-fired units using a first-order model [22] and combines it with the frequency response model of wind turbines [18] to form a comprehensive frequency response model of the system. The first-order equivalent model of the speed control system is shown in Equation (1), and the frequency response model of wind turbines is shown in Equation (2). The frequency response model of wind power units consistsof two parts: virtual inertia and droop control. The frequency response model of photovoltaic units is similar to that of wind power units [14] and will not be elaborated further.  
\[\Delta P_{G}=\frac{k_{G}}{1+T_{G}s}\Delta f \tag{1}\]  
In Equation (1), \(\Delta P_{G}\) represents the power change in the coal-fired unit; \(k_{G}\) is the gain coefficient; \(T_{G}\) is the time constant of the speed controller for the coal-fired unit; and \(\Delta f\) is the frequency deviation.  
\[\Delta P_{W}=k_{W}(2H_{w}s+k_{w})\Delta f \tag{2}\]  
In Equation (2), \(\Delta P_{W}\) represents the power change in the wind turbine; \(k_{Wc}\) is the gain coefficient; \(H_{w}\) is the virtual inertia constant of the wind turbine; and \(k_{w}\) is the droop control coefficient of the wind turbine.  
To enhance the efficiency of system frequency calculations, this paper employs a parameter equivalence aggregation method to convert the multi-machine comprehensive system frequency response (SFR) model into an equivalent system frequency response (ESFR) model [18], as shown in Figure 1.  
Here, \(T_{Gc}\) represents the time constant of the speed controller for the coal-fired unit, as shown in Equation (3); \(k_{Gc}\) is the mechanical power gain coefficient of the coal-fired unit, as shown in Equation (4); \(k_{Wc}\) is the mechanical power gain coefficient of the wind turbine, as shown in Equation (5).  
\[T_{Gc}=\frac{\sum\limits_{i=1}^{N_{G}}P_{Gi}T_{Gi}v_{i,t}}{\sum\limits_{i=1}^{ n}P_{Ni}v_{i,t}} \tag{3}\]  
In Equation (3), \(N_{G}\) is the number of coal-fired units capable of participating in frequency regulation; \(v_{i,t}\) is the operational state variable of the unit at time \(t\); \(P_{Ni}\) is the power of unit \(i\); \(P_{Gi}\) is the power of coal-fired unit \(i\); \(n\) is the total number of units in the system.  
\[k_{Gc}=\frac{\sum\limits_{i=1}^{N_{G}}P_{Gi}v_{i,t}k_{Gi}}{\sum\limits_{i=1}^{ n}P_{Ni}v_{i,t}}, \tag{4}\]  
\[k_{Wc}=\frac{\sum\limits_{i=1}^{N_{W}}P_{Wi}v_{i,t}}{\sum\limits_{i=1}^{n}P_{ Ni}v_{i,t}} \tag{5}\]  
In Equation (4), \(k_{Gi}\) is the gain coefficient of coal-fired unit \(i\); \(P_{Wi}\) is the power of wind turbine \(i\); \(N_{W}\) is the number of wind turbines capable of participating in frequency regulation.  
Figure 1: Equivalent system frequency response model.
## 3 Control Method
### Threshold-Based Control  
In scenarios where discrete, rule-based decisions are sufficient, the threshold-based control strategy offers an efficient way to trigger module actuation. It continuously monitors parcel features (such as identification markers) and activates the relevant module as soon as a preset condition is met (Figure 4), making it ideal for tasks like sorting and basic orientation. With reference to the experimental setup described in Section 2.1, a camera system captures images of the parcels as they travel along the surface. These images are processed in real time by a dedicated software environment, where a defined region of interest (ROI) is analyzed for markers, such as _AprilTags_, that identify each package. When a marker is detected and its identification code is retrieved, the control system compares the information against preset thresholds corresponding to the desired operation.  
The selection of activated modules depends on both the final objective and the nature of the threshold condition met. Different types of thresholds can be defined. For instance, in one category of tests, the control system directs parcels along different trajectories using predefined module orientations based solely on their identifying IDs. In these tests, the threshold condition is simply the package's \"name.\" In another set of experiments, the system is used for package reorientation. Here, the primary goal is to adjust the orientation of a parcel by specific angles as it passes through the smart surface. Consequently, in this case, the threshold variable is the angle, requiring more extensive and continuous monitoring.  
In summary, threshold-based control for this technology relies on a real-time monitoring framework that couples image processing with targeted actuator commands. By tailoring the actuation logic to the specific task, whether diverting a parcel along a different path or re-orienting it by a defined angle, the method demonstrates a simple, yet versatile and robust approach for managing automated material handling processes. The subsequent sections will delve deeper into each application, providing further experimental insights and quantitative evaluations.  
Figure 4: Threshold-based method logic flowchart.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.4. RSAR [48]  
To minimize data loss and improve energy usage in UWSNs, the authors suggested a novel idea of reliable and stability-aware routing (RSAR) protocol. The energy assignment to a node in RSAR is dependent on its depth, and the network's five energy grades are constructed from top to bottom. Based on the node's energy grade, residual energy, and depth, the proposed protocol picks the optimal forwarder node. RSAR forwards packets across a single connection. The cooperative reliable and stability-aware routing (CoRSAR) protocol is suggested to address the problem, selecting two relays closest to the destination to ensure reliable communications.
# Application of Riemannian Seismic Ray Path Tracing in Salt Dome Prospecting
## 2 Materials and Methods (Theoretical Background)
### Differential Manifold
#### 2.1.2 Geodesic Equations  
A _geodesic_ is the generalisation of the straight line in a curved manifold, and is defined as:  
**Definition 3**.: _A curve \(\kappa:I\subset\mathbb{R}\to\mathbb{M}\) is a geodesic curve in \((\mathbb{M})\) if the covariant derivative of its parametric velocity is zero, i.e.,_  
\[\frac{D\kappa}{D\lambda}=0. \tag{5}\]  
But since \(\kappa:=\vec{U}=\frac{d\vec{q}}{d\lambda}\), in the coordinate system \(\left\{q^{i}\right\}\), this condition is equivalent to  
\[\frac{Dq^{i}}{D\lambda}=\frac{d}{d\lambda}\left(\frac{dq^{i}}{d\lambda}\right) +\sum_{k=1}^{d}\Gamma^{k}_{mn}\frac{dq^{m}}{d\lambda}\frac{dq^{n}}{d\lambda}=0 \tag{6}\]  
and since the Christoffel symbols \(\Gamma^{l}_{mn}\) are known functions of the \(\{q^{i}\}\) coordinates, this is a nonlinear (quasi-linear) second-order differential equation for \(q^{i}=q^{i}(\lambda)\)[57].  
Although for this work it is sufficient and necessary to know the formalism of the geodesic equations, below are the tools of differential geometry that deal with the concepts of intrinsic curvature. These will allow us to gain better intuition of the effect of the curvature in the seismic rays.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.2.1 Protein-Based Hydrogels  
Proteins have many desirable functional and structural characteristics, which contribute to their ability to form hydrogels, including biocompatibility and biodegradability [26]. To create a gel network, protein aggregation or physical crosslinking acts as the main mechanism contributing to protein gelation. The resulting network can sustain large volumes of water, and is stabilized by the presence of non-covalent crosslinking, such as hydrophobic interactions, van der Waals forces, electrostatic interactions and hydrogen bonding. Properties of the protein-based hydrogel such as bioactivity and hydrophobicity are dictated by the primary amino acid sequence of the protein itself [26].  
Collagen is a naturally derived protein found in skin, bone and tendons. It is commonly used to create biocompatible hydrogels, providing structural support and promotion of stem cell differentiation [27]. Fibrin is another natural protein derived from fibrinogen in human and animal blood. Its ability to form a fibrous network during clot formation makes it an attractive protein for tissue engineering and wound healing applications [28]. [PERSON] et al. [29] reported that regeneration at infarction sites was more significant in the presence of a fibrin matrix. [29].
## 1 Introduction  
Steganography provides means for hiding information, not just making it intelligible by encrypting it. The concealment of a message at all can be the difference between life and death in cases when the very sending of a message (encrypted or not) is considered a crime and a threat to the authorities. The techniques of steganography have sometimes been criticized for serving criminals seeking to operate outside the law, but the use of it for whistleblowers and for freedom fighters (e.g., [1]) who are in countries with authoritarian regimes is well documented.  
The technique of steganography does not by itself change a message, but merely hides its existence in other information. This is what distinguishes steganography from cryptography. Nevertheless, steganography is very often used in combination with cryptography, by first encrypting a message and then hiding it. This combination makes a very strong protection against revealing a secret message since upon looking for hidden messages it may be impossible to perform cryptanalysis on all possibly hidden data found in any of a great number of files. Thus, in effect, adding an encryption step can greatly improve security aspects of the message exchange--the content of the communication is not only secret but even the very existence that any kind of communication took place is unknown. This is an additional property that can be crucial in some circumstances where the occurrence of encrypted messages can draw attention from the authorities. Since the percentage of users of steganography is unknown to a much larger extent than is the case for e.g., cryptography, it is harder to motivate its relevance [2]. This is the reason why there are few reports on the numbers of use of such methods. However, this does not mean that methods of steganography are not used.  
Steganography may be deployed in many respects, but in modern times it usually means involving computer files. This study focuses on musical steganography through the MIDI format. The MIDI format is a standard music protocol used worldwide to create music and to facilitate its accessibility.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 5 Experimental Verification
### Wave Aberration Test of Optical System  
Wave aberration of an optical system is a critical technical index to measure the imaging quality of an optical system. The parabolic off-axis reflection optical system's wave aberration is tested using a four-dimensional interferometer and standard mirror. The selected four-dimensional interferometer model is PhaseCam 4020, as shown in Figure 13, and its technical specifications are shown in Table 8.  
Figure 12: Experimental platform: (**a**) physical picture of the primary mirror; (**b**) physical picture of the secondary mirror; (**c**) physical picture of the optical system.  
\begin{table}
\begin{tabular}{c c} \hline
**Technical Index** & **Technical Parameter** \\ \hline Acquisition speed & \textgreater{}10 frames/s \\ Minimum exposure time & 35 ms \\ Sample reflectance & 1\textasci{}100\% \\ RMS repeatability & \textless{}0.001\(\lambda\) \\ RMS accuracy & \textless{}0.002\(\lambda\) \\ \hline \end{tabular}
\end{table}
Table 8: Technical specifications of PhaseCam 4020 interferometer.  
The principle of wave aberration measurement of optical system is as follows: the interferometer provides a plane or spherical test wave (determined by the optical system to be measured), generates a plane wave through the measured optical system, the light is vertically incident on the standard plane mirror, the wave surface self-collimation reflection, returns to the interferometer, forms the wave surface of the optical system to be measured, and thus obtains the wave aberration of the optical system [30]. The wave aberration test principle diagram is shown in Figure 14, and the wave aberration diagram obtained by the test is shown in Figure 15. The actual wave aberration of the optical system has been measured four times, and the measurement results are shown in Table 9.  
Figure 14: Wave aberration test schematic.  
Figure 13: PhaseCam 4020 interferometer.  
Figure 15: Wave aberration diagram of the actual measurement of the optical system.  
According to Table 9, it can be seen that the average value of wave aberration of the optical system is 0.0337\(\lambda\), and the maximum value is 0.0359\(\lambda\), which is less than \(1/25\lambda\) and meets the requirements of the system index.
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 3 Detailed Description of the Method
### RANSAC-P3P and Maximum Likelihood Estimator  
Almost all robust SVO methods employ the RANSAC scheme for motion estimation when there are noise and outliers with the feature detector, matcher, and tracker. In this paper, ego-motion is estimated through the RANSAC-P3P reported by [PERSON] and [PERSON] in [19], where a set of closed-form hypotheses on the minimum number of data needed to obtain a solution is solved, and the hypothesis that shows the highest consensus with the other data is selected as an initial solution. Then, the MLE [9,10] is applied to produce a final, corrected ego-motion estimation between the two consecutive frames.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## 6 The Proposed Power Processing Circuit Performance
The energy stored in the capacitor is estimated by using \(E=1/2 CV^{2}\), and the value is reported in Figure 13d. The RF-to-DC conversion efficiency for the proposed system can be plotted as a function of DC output power over the radio frequency input power, and the result is illustrated in Figure 14. From the figure, it is noticed that the proposed system gives an efficiency of more than 1% for an input power of \(-50\) dBm, \(-40\) dBm, \(-30\) dBm and \(0\) dBm. However, for an input power of \(-20\) dBm and \(-10\) dBm the efficiency is less than 1%. The RF-to-DC conversion efficiency values show that the proposed system is suitable for working with low and medium input power. This condition is not much different with another type of harvesting systems which are normally optimised to work with low input power. Figure 14: The harvesting antenna and proposed power processing circuit RF-to-DC conversion efficiency versus time. Figure 13: The measurement results of (**a**) Voltage across \(C_{STORE}\); (**b**) Current through \(C_{STORE}\); (**c**) Output power and (**d**) Stored energy.