esa-sceva/satcom-chunk-collection huggingface dataset:
# Logogram VR: Treadmill-Coupled VR with Word Reflective Content for Embodied Logogram Learning
## 3 Methods
### Implementation
#### 3.1.2 Program Flow  
The system is designed to test a proof-of-concept at a design exhibition. With three characters to learn in such a densely populated environment, the demonstration scenario should last 5-10 min. The duration was set to approximately seven minutes after experiment 1, which assessed the desirable playtime and number of characters for each scenario for the participating learners.  
We set Hanja, the Korean rendering of Chinese characters, as the type of logogram, considering its importance to the Korean language. Among thousands of Hanja characters, the words rain, cloud, and thunder were selected as representative scenarios for learning (Figure 2). The character for rain belongs to a hieroglyphic logogram, whereas cloud and thunder incorporate rain in their strokes.  
Figure 3 illustrates the program flow diagram. When a learner puts on the device and launches the program, the VR media begins with an environment that includes a forest and thunder. The user is free to move around within the virtual reality environment, and the virtual agent (VA) guides them to a waterfall. They are shown a brief description of the characters that they have to learn, and the virtual agent recommends that the user writes and speaks as the topic is explained. When the user finishes, the program also checks their writing and speech. Specifically, while they are writing the letter, the user can see a guide to learn the stroke sequence. When the user finishes each stroke using a VR controller, the Unity 3D colliders embedded in the guide for the strokes measure whether they achieved the correct stroke order. The speaking part is recognized by the computer using a microphone that is embedded in the HMD and Google Speech-to-Text API. The word keys were established through our initial test to guarantee recognition accuracy. Once the user's writing and speech have been successfully verified, the meaning of the letter is displayed within the media content of the virtual reality environment. For example, when the user completes the word rain, rain starts falling in the forest. These stages of the VA's explanation, the user's repetition, and the reflection in VR are repeated, and the program ends when the user completes the words in each scenario.  
Figure 1: (**A**) Hardware settings; (**B**) example set-up at a design exhibition. Logogram VR comprises a head-mounted display, VR controllers, a treadmill, and a computer to execute the software.  
Figure 2: Exemplary characters in the Logogram VR’s user scenarios. (**A**) Rain, (**B**) cloud, and (**C**) thunder.
## 4 Topology Optimization in Weight Reduction  
The topology optimization method is a mathematical method for structural optimization of material distribution in a given area according to the given load, constraints, and objectives. For the components of the aircraft in this paper, which were analyzed as a continuum structure, the topology optimization that could be used included the homogenization method, variable density method, and level set method [18]. The basic method is to divide the design region into finite elements to form a new continuum by removing the regions within the preset safety region through several iterations according to the preset conditions in order to achieve weight reduction [19]. Topology optimization studies the fundamental problem of material arrangement in engineering [20]. The mathematical model of the topological optimization consists of the design variables, the optimized objective function, and the constraint function.  
\[\begin{cases}f=\text{min}f(x)\quad(\ x=\{x_{1},x_{2},\cdots,x_{n}\}\ )\\ \text{s.t.}\quad\quad g_{i}(x)\leq 0\quad\quad\quad i=1,2,\cdots,I\\ \quad\quad h_{j}(x)\leq 0\quad\quad\quad j=1,2,\cdots,J\\ \quad\quad x_{kL}\leq x_{k}\leq x_{kU}\quad\quad k=1,2,\cdots,n\end{cases} \tag{2}\]  
where \(x=\{x_{1},x_{2},\cdots,x_{n}\}\), representing that there are n design variables; \(f(x)\) is the optimized objective function; \(g_{i}(x)\) and \(h_{j}(x)\) are the constraint functions of the variables; and \(x_{kL}\leq x_{kU}\) is the value range of the variables [21].
## 3 Results and Discussion
### Results of Test under S3 and S4 Conditions
#### 3.2.2 Rainfall Test of S4  
After 1 h 55 min, the soil at the dam-slope toe was first saturated. A large number of gullies began to appear at the dam-slope toe, as shown in Figure 10a; after 2 h 10 min, the gullies on the dam-slope surface developed rapidly. The gullies at the dam-slope toe cut the dam body, accompanied by a small-scale local flow slide, resulting in serious erosion of the dam-slope toe. At the same time, a transverse crack appeared 20 cm away from the dam-slope crest, as shown in Figure 10b. After 2 h 15 min, the transverse cracks in the upper part of the dam-slope surface provided a channel for rainwater infiltration, and the crack width increased rapidly. After 2 h 55 min, the runoff erosion of the lower part of the dam slope was serious, the original slope toe basically disappeared, and the erosion failure surface gradually developed upstream (dam-slope crest). The dam slope was seriously damaged, then the test was over. The final erosion failure of the dam slope is shown in Figure 10c. Overall, S4 had no sliding failure, and the dam-slope surface was mainly eroded by runoff, accompanied by small-scale local flow-slide failure. Unlike S2, S4 had no large-scale flow-slide or sliding failure.
# On the Melting Thresholds of Semiconductors under Nanosecond Pulse Laser Irradiation
## 3 Results and Discussion
### Germanium  
The next set of simulations has been carried out for germanium for the conditions of the experiments reported in [10]: wavelength 694 nm and pulse duration 70 ns. Time-resolved reflectivity measurements using a probe 1.06 \(\upmu\)m laser wavelength identified the energy density of 400 mJ/cm\({}^{2}\) as a value, at which the rise of reflectivity was detected corresponding to the observable melting. Numerical simulations using the finite difference method were also carried out in Ref. [10], and the obtained melting threshold was claimed to be \"practically identical\" to the measured one (although the method for threshold determination in the simulation was not specified). The authors used experimentally measured values of reflectivity and absorption from Refs. [9; 47], which are in good agreement with the optical constants we derived from measurements reported in [48] and also confirmed in [49]. Our calculations give \(F_{\mathrm{th}}\) = 370 mJ/cm\({}^{2}\) (Figure 1a, Table 1), which is in good agreement with the data [10], particularly taking into account that the increase in reflectivity detected in [10] assumes a significant fraction of molten germanium and thus a slightly higher fluence than that needed to reach the melting temperature at the surface. Near the melting threshold, the calculated melt fraction reaches a maximum after a delay of approximately 20 ns relative to the moment of laser peak intensity (Figure 1a), which is also in agreement with the measurements [10]. With the known properties of liquid Ge (Tables A7 and A8), we have performed simulations for \(F>F_{\mathrm{th}}\), which are again in good agreement with the measured durations of a high reflectivity stage corresponding to molten germanium [10] (Figure 1b). It should be mentioned that different values are reported for the thermal conductivity of liquid Ge. In the modeling, we use the value of 29.7 W/(m\(\cdot\)K) [50], while in Ref. [51], \(\kappa\) = 43 W/(m\(\cdot\)K) was measured. The calculations performed for various pulse durations demonstrate a stronger \(\tau_{\mathrm{L}}\) dependence than that for silicon, close to the \(\propto\tau_{\mathrm{L}}\)\({}^{0.5}\) dependence (Table 1).  
Figure 1: (a) Surface temperature (solid lines) and molten fraction (dot-dashed lines) of germanium obtained in the modelling for different laser fluences (694-nm, 70-ns pulse). The laser temporal profile is shown by the dashed line. (b) Comparison of the duration of increased reflectivity (data from experiment [10]) and the melt duration obtained in our simulations.
## 4 A Look Toward Privacy in 6G Core Networks
### Trustworthiness in 6G Networks
Other aspects of 6G communications that add to the trustworthiness and performance of the network are discussed in [72]. The main topic included in this work is trustworthy Task-Oriented Semantic Communication (ToSC), which advocates for only sending pertinent information within the network. This technique potentially increases user identity protection and privacy and also increases the efficiency of the network if less information is being processed. In mMTC, this strategy could be incorporated to relieve stress on the interfaces between devices, freeing up resources while still maintaining privacy. Deep learning (DL) is discussed as the foundation of their approach to ToSC, extracting only the pertinent information in a query, and the inclusion of differential privacy to the communication contents. It is also mentioned that a balance between utility, efficiency, and privacy is important in 6G networks to maintain extremely high bandwidth and ultra-low latency. Sensors are another area where 6G network interfaces will be impacted by new types of data and strict performance requirements, especially within the context of self-driving vehicles, autonomous robotics, etc. The authors in [73] address how sensor data integration into 6G technology causes many more attack vectors, along with furthering privacy concerns. They bring forward the idea of adding a Sensing Policy, Consent, and Transparency Management (SPCTM) function. The SPCTM function would manage privacy policies, user consent, and transparency, ensuring compliance and awareness. An approach that adds a network function is usually an accepted method for additional functionality and extensibility, but it can also act as a double-edged sword by increasing the size of the attack surface. A survey on trust anchor technologies [74] addresses the importance of the terms and visions of trust as a resource in 6G systems alongside the usage of AI technologies and cooperation with third-party vendors. The concept of \"trust anchors\" as a foundational service in 6G networks has the following functional and architectural requirements, such as are brought forward to ensure a trustworthy network ecosystem:  
* Non-Repudiation;
* Immutability;
* Transparency;
* Programmability;
* Modularity;
* Integration. In the example of Core-to-Core communications in 6G networks, where multiple independent network providers require the establishment of a trusted platform, a third  
Figure 5: The core tenets of trustworthiness in a 6G network ecosystem. party needs to provide a trust anchor to all parties. In similar approaches where a network provider utilizes a third party for enhancements in a specific VNF, the network itself provides the trust foundation to all application providers and consumers. This creates a two-way agreement for unified trust management in 6G interfaces. The authors believe their work can be a starting point for creating Trusted Infrastructure architectures for trust anchors in 6G communications.
## 3 Results
### Experiment Verification and Analysis
#### 3.3.2 Analyzing the Compression and Expansion of the Original-Size Stent  
A tapered rod with an external diameter of 5.6 mm was used to expand the 3D-printed stent, originally 4 mm in diameter to 6 mm (Figure 13a). The expanded stent showed no visible partial distortion or fractures, confirming that the original-sized stent met the design's predicted deformations after expansion. Next, a stent compressor was used to compress the stent and insert it into a silicone tube with an inner diameter of 2 mm (Figure 13b). The results indicated that when the stent was compressed into the silicone tube, the struts remained intact without overlapping or structural fracturing. However, the crown at the terminal end of the stent showed slight skewing and compression. This uneven compression may be due to the inherent variability in the 3D-printed stent, which cannot be perfectly uniform.  
Furthermore, after the PLA stent was compressed and placed into the silicone tube, it had to remain within the elastic deformation range of the PLA filament. If the stent had entered the plastic deformation range, it would not have been able to revert from 2 mm to its original diameter. To confirm this, the compressed stent was ejected from the silicone tube using a metal rod to observe whether it would return to its original dimensions. As shown in Figure 14a-d, a metal rod with a diameter of 2 mm was used to eject the stent, which immediately expanded to a diameter of 3.98 mm, very close to its original diameter of 4 mm (Figure 14e,f). This confirmed that throughout the expansion, compression, and release process, the PLA stent's structural deformation remained within the elastic deformation range, consistent with the stent deformation predicted by the finite element model. Figure 15 presents the simulation analysis results for compression displacement, compression force, and expansion displacement of the original-sized stent. Based on the compression displacement, compression force, and radial expansion relationships calculated by the finite element model, appropriate elastic bands could be designed and added to the PLA stent to achieve the radial expansion and radial force required for the self-expanding AVF stent.
## 2 Related Work
### Detailed Structure of SE-GNNRM
#### 3.2.2 Session Embedding Layer  
Although the item embedding layer of the model learns the transition dependencies of items in the session, it does not contain any recurrent or convolutional mechanisms, resulting in the model not fully considering the sequentiality of the session and therefore not knowing the position of the previous items in the session [37]. In session recommendation, each session is a sequence combination of a set of ordered items, where the position order encoding takes into account the different effects of relative positions between different elements in a sentence or a sequence [15; 38]. In the model proposed in this paper, in order to strengthen the sequentiality of the learned session embedding, the reverse position information is incorporated into the session embedding through the position matrices \(P=[p_{1},p_{2},p_{3},\ldots,p_{n}]\), where \(p_{i}\in R^{d}\) is the position vector of position \(i\). The session sequence \(O=[o_{s,1},o_{s,2},o_{s,3},\ldots o_{s,n}]\) is output by the joint function in the item embedding layer. After \(O\) is added to the position information, the \(i\)-th item is represented as:  
\[z_{i}=\tanh(W_{3}[o_{s,i}\|p_{n-i+1}]+b_{6}) \tag{9}\]  
where \(W_{3}\in R^{d\times 2d}\) is the weight matrices that can be learned, \(b_{6}\in R^{d}\) is the bias vector, \(\tanh(\cdot)\) represents a bitangent activation function, \(\|\) represents the splicing operation, and the item represents \(v_{o,i}\) is concatenated with the position vector \(p_{n-i+1}\), where \(n\) is the length of the current session. Inspired by STAMP and NARM, the session embedding that fuses the long-term and short-term interest preferences of a session can better reflect the characteristics of the current session. The purpose is to emphasize the importance of the current preference to adapt to the complex and changeable interaction behavior of users. Next, the soft attention mechanism is used to calculate the weighted summation of the attention coefficient \(\alpha_{i}\) of the item in the session and the item embedding \(z_{i}\) to obtain the current session embedding \(h_{c}\), which is described as follows.  
\[h_{c}=\sum_{i=1}^{n}\alpha_{i}z_{i} \tag{10}\]  
\[\alpha_{i}=q^{T}\sigma\big{(}W_{4}z_{i}+W_{5}z_{n}+W_{6}z_{agg}+b_{7}\big{)} \tag{11}\]  
\[z_{agg}=\frac{1}{n}\sum_{i=1}^{n}z_{i} \tag{12}\]  
where \(\sigma(\cdot)\) is a sigmoid function, where \(W_{4}\), \(W_{5}\), \(W_{6}\in R^{d\times d},q\in R^{d}\) is the weight matrices used to calculate the attention coefficient \(\alpha_{i}\), and \(b_{7}\in R^{d}\) is the partial set vector. \(h_{c}\) is obtained by the weighted summation of the calculated attention coefficient \(\alpha_{i}\) of the item in the session and the item embedding \(z_{i}\). The target embedding \(z_{n}\) is defined as the last item of the input session sequence, \(z_{agg}\) is the average value represented by the items in the session, which represents a central feature of the session [39].
# An Improved RAIM Availability Assessment Method Based on the Characteristic Slope
## 6 Conclusions  
According to the theory and simulation analysis, the RAIM availability might be optimistically or conservatively assessed using the classic-PL-based method and might only be conservatively assessed using the enhanced-PL-based method. Using the ideal-PL-based method, both optimistic and conservative assessment can be prevented. However, the calculation of the ideal PL brings a heavy computational burden to the GNSS receiver or the onboard computer. The slope-based method has the same RAIM availability assessment result as the ideal-PL-based method. Because the ideal slope threshold is only related to one geometric parameter, it can be calculated offline and searched online. Thus, the on-board calculation burden can be reduced using the slope-based method. This improved method can be used in RAIM for single GNSS constellation. Further, a semi-physical simulation experiment will be implemented using the common on-board processor to verify the real-time performance of the slope-based method. Moreover, the RAIM availability assessment performance will be verified using massive actual measurement data.  
Conceptualization, D.S.; Methodology, J.Z.; Data curation, J.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded jointly by the National Key R&D Program, China (No. 2022 YFB3904400).  
Figure 13: Conservative assessment rate using the enhanced-PL-based method.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Gradient Correlation Functions in Digital Image Correlation
## 3 Evaluation of Correlation Bases
### Experiments  
The experimental set-up is sketched in Figure 4. The set-up consists of a 10 mW continuous wave He-Ne laser (632.8 nm wavelength) as illumination source, a white-painted aluminum plate, an \(f=55\) mm Mikro-Nikkor objective and a monochrome Dalsa nano camera (3.5 \(\upmu\)m pixel size, resolution \(2056\times 2464\) pixels). The magnification was \(m=0.9\) which translates into 3.9 \(\upmu\)m/pixel in object coordinates. One acquired speckle image with aperture setting \(f/32\) is seen to the right in Figure 4. The experiments were performed with aperture settings \([f/22,f/16,f/11,f/8]\), which resulted in average speckle sizes \([7.6,5.5,3.8,2.7]\) pixels, respectively. Ten consecutive in-plane translations are performed using a fine-pitch micrometer. Each incremental translation was approximately 2.5 \(\upmu\)m, which translates into approximately 0.65 pixels on the detector. The total translation between the first and last image is therefore approximately 25 \(\upmu\)m, which translates  
Figure 3: Results from the simulation. The plots show, row-wise, random errors from the different types of correlation functions defined by Equations (1) and (3)-(8) as a function of intensity correlation value for speckle sizes \(S=3\), 4, 5, 6, and 7 pixels, respectively. The lower right plot shows the reliability of the evaluations as a function of intensity correlation.  
into 6.5 pixels in detector coordinates. The result from the experiment is summarized in Figure 5. The reliability was unity for each of the analyzed image pairs, and is not presented separately.
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 4 Simulation and Performance Analysis  
This section describes the evaluation of the performance of the algorithm proposed in this article for different system parameters. To verify the effectiveness and convergence of the DRL-SAR, we used the throughput and load balance index of the SMSCS, the path transmission loss of the short-message terminal, and the terminal service satisfaction index as the algorithm evaluation criteria. At the same time, the DRL-SAR was compared with the DQN, GA, and TS-IHA [24]. An Intel(R) Xeon(R) W-2104 CPU @3.20 Hz, 16 GB RAM computer was used for the simulation experiments conducted in this work. The simulation platform was based on Python 3.6, and the neural network in the DRL-SAR was built through TensorFlow.