esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tutorial on Retroreflectors and Arrays Used in Satellite and Lunar Laser Ranging
###### Abstract  
The present paper discusses the basics of retroreflector theory and the manner in which they are combined in arrays to service the laser tracking of artificial satellites and the Moon. We begin with a discussion of the relative advantages and disadvantages of solid versus hollow cube corners and the functional dependence of their optical cross-sections and far-field patterns on cube diameter. Because of velocity aberration effects, the design of an array for a particular space mission depends on many factors, including the desired range accuracy and the satellite's orbital altitude, velocity, and pass geometry relative to the tracking station. This generally requires the individual retroreflectors in the array to be \"spoiled\" by perturbing one or more of the 90-degree angles that define a perfect cube corner, or alternatively, by adding a curved surface to a hollow cube. In order to obtain adequate return signal strengths from all points along the satellite path, the rotational orientation of the retroreflectors within the array may need to be varied or \"clocked\". Possible approaches to developing millimeter-accuracy arrays with both large cross-sections and ultrashort satellite signatures are discussed, as are new designs proposed to replace aging reflectors on the Moon. Finally, we briefly discuss methods for laser ranging beyond the Moon.  
Keywords:retroreflectors; cube corners; Satellite Laser Ranging; Lunar Laser Ranging; velocity aberration; spoiling; clocking; cross-section +
Footnote †: journal: _Photonics_
## 4 COIN-LEO Algorithm
### Deep Reinforcement Learning Using the PPO Algorithm
#### 4.2.3 Reward  
The reward function R(S, A) is used to evaluate the actions A and the state S taken by the agent in reinforcement learning and provide corresponding rewards or penalties. This reward function considers two factors: data flow delay and model inference time.  
Specifically, the reward function R(S, A) is defined as follows:  
\[\text{R}(\text{S},\text{A})=\left\{\begin{aligned} \frac{1}{dp^{i}_{p+1 _{a}+d_{i,a}}}if\;i<3\\ \frac{1}{dp^{i}_{p+1_{a}+d_{i,a}+dp^{i}_{a+1,L}}}if\;i=3\;and\;d^{ stream}\leq d^{stream}_{max}\\ -d^{stream}if\;i=3\;and\;d^{stream}>d^{stream}_{max}\end{aligned}\right. \tag{1}\]  
In the first case (\(i<3\)), the reward function considers a combination of data flow delay and model inference time to encourage early completion of the inference task and reduce latency. In the last splitting case (\(i=3\)), two submodels are generated, and the second case takes this scenario into account. In the third case (\(i=3\)), if \(d^{stream}>d^{stream}_{max}\), the reward function provides a negative reward, i.e., a penalty, to avoid exceeding the maximum allowable data flow delay and ensure the effectiveness and real-time nature of the inference task.  
Through the design of the reward function, the agent can choose the optimal actions in different states to efficiently complete the inference task and maximize cumulative rewards, which aims to optimize inference performance and latency effects. In this way, the agent can gradually learn and optimize strategies during the training process of reinforcement learning to adapt to different environments and task requirements.
## 2 Test Materials and Physical Properties
### Particle Gradation Analysis  
Referring to the Geotechnical Test Method Standard (GB/T 50123-2019) [16], particle analysis tests were conducted on the tailing sand, as shown in Figures 2 and 3 for the original particle gradation curves of different sampling points.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Number** & \(m_{0}\) **(g)** & \(m_{d}\) **(g)** & \(\omega\) **(\%)** \\ \hline
**I\({}_{1}\)** & 22.25 & 20.87 & 6.6 \\
**I\({}_{2}\)** & 21.07 & 19.49 & 8.1 \\
**I\({}_{3}\)** & 19.06 & 16.72 & 14.0 \\
**I\({}_{4}\)** & 19.61 & 17.12 & 14.5 \\
**II\({}_{1}\)** & 23.11 & 21.30 & 8.5 \\
**II\({}_{2}\)** & 23.13 & 20.74 & 11.5 \\
**II\({}_{3}\)** & 19.82 & 15.78 & 25.6 \\
**II\({}_{4}\)** & 19.23 & 17.39 & 10.6 \\
**II\({}_{5}\)** & 21.33 & 18.40 & 15.9 \\ \hline \end{tabular}
\end{table}
Table 1: Moisture content record table.  
Figure 1: Field sampling.  
From the sieving results, it is concluded that the mass of particles with a particle size greater than 0.074 mm in the whole tailings exceeds 50% of the total mass, which belongs to the tailing chalk sand. From Figures 2 and 3, it can be obtained that the average particle size of tailings gradually decreases and the percentage of powder particle content increases from the tailing pond release position to the downstream position. The original average moisture content of the experimentally measured specimen is 12%, due to the limitation of the sampling depth, the measured moisture content is small compared with the actual value, so this paper selects the effect of particle size on tailing strength under the condition of 15% moisture content.  
According to Table 2, with the increase in the distance from the discharge port, the effective particle size d\({}_{10}\), the average particle size d\({}_{50}\) and the restricted particle size d\({}_{60}\) all show a decreasing trend, indicating that the further away from the discharge port the  
Figure 3: Ore drawing mouth 2.  
Figure 2: Ore drawing mouth 1.  
smaller the particle size is; the upper particle size of measurement line 2 is generally larger than the lower particle size, and the discharged tailings are continuously deposited with the growth of time, and the fine particles continuously penetrate the lower layer, explaining the reason for the stratification phenomenon; C\({}_{\text{u}}\) and C\({}_{\text{c}}\) are quantitative indexes used to describe particle gradation, which represent the coefficient of uniformity and the coefficient of curvature, respectively. The results of particle analysis at different sampling points show that the C\({}_{\text{u}}\) is about 3.765 and C\({}_{\text{c}}\) is about 0.932. When C\({}_{\text{u}}\geq\) 5, 1 \(\leq\) C\({}_{\text{c}}\leq\) 3 is satisfied at the same time, the tailing grading is good. As can be seen from the table, the grading is generally poor.
# Automatic Translation between Mixtec to Spanish Languages Using Neural Networks
## 2 Theoretical Foundation
### Recurrent Neural Network  
Recurrent Neural Networks (RNNs) are networks that generate sequences, such as texts and videos. RNNs maintain the context of previous sequences and the subsequent sequence, making them an ideal technique for text analysis. Additionally, RNNs have long short-term memory (LSTM) capabilities, which aid in handling sequences.  
There are three types of recurrent neural networks: One-to-Many, Many-to-One, and Many-to-Many. The Many-to-Many type allows for handling sequences of inputs and outputs, enabling tasks such as automatic translation, text-to-speech conversion, or speech-to-text.  
RNNs are suitable for generating short sequences with a limited number of characters, which is known as short-term memory. This poses a challenge when dealing with long sequences, as RNNs may not effectively manage large amounts of data. To address this issue, LSTM [10] was developed to overcome the limitations of recurrent networks. Figure 1 explains the architecture of an LSTM neural network.  
The architecture allows a constant error flow through the units. It includes a multiplicative input unit to preserve the memory data stored in \(j\) from alterations. Another multiplicative output unit protects the irrelevant data stored in \(j\).
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 2 Background
### GNSS Fundamentals
#### 2.3.2 Radio Frequency Drone Jamming  
RF jamming disrupts communication between a drone and the GS, affecting its ability to receive instructions. GNSS signals are particularly vulnerable due to their weakness, and GPS jammers can prevent the drone's return-to-home feature, leading to malfunctions or crashes [27]. The main RF jamming techniques include [5; 20; 21]:  
* **Noise/Barrage Jamming**: Wideband noise reduces the Signal-to-Noise Ratio (SNR), increasing error rates and decreasing channel capacity.
* **Sweep Jamming**: A narrowband signal sweeps across frequencies, simulating the effects of barrage jamming at high sweep rates.
* **Tone Jamming**: Discrete tones target specific frequencies, effective against FHSS signals.
* **Follower Jamming**: Tracks frequency changes in FHSS systems to jam newly selected frequencies.
* **Smart Jamming**: Selectively targets critical signals in DSSS and FHSS systems.
## 6 Experiment and Results  
In this study, a series of target-detection-comparison experiments and indoor target-tracking experiments were designed for verification. A visual motion-capture system was employed to calibrate the true value of the target's trajectory. The motion-capture system (MCS) was composed of eight cameras. It can cover the space experiment range of 4.7 m \(\times\) 3.7 m \(\times\) 2.6 m and can achieve posture tracking and data recording with a refresh rate of 260 Hz. The moving targets in the experiment were rectangular and circular targets, as exhibited in Figure 11.
## 2 Related Work: A Review of Relevant Survey Papers
In addition to these survey articles, there are some other articles that provide extensive reviews of HO and mobility management in HetNet and UD-HetNet. The authors in [38] comprehensively review the challenges and solutions related to mobility management in advanced communication networks such as 5G and B5G. They cover various existing HO and mobility management schemes, including traditional and advanced approaches such as SDN and NFV. Furthermore, future challenges such as managing a large number of devices, ensuring seamless handovers, addressing security and privacy concerns, and improving resource utilization are also discussed. However, the paper does not specifically address challenges related to high speed, load balancing, and ICI, and focuses primarily on SDN and NFV as the advanced mobility management schemes. In [39], the authors thoroughly reviewed the HO mechanisms in mobile HetNets, with a focus on providing a detailed and latest analysis of the current state of HO management. The authors discuss the basic procedures for HOs, the impact of HCPs on KPIs, self-optimization techniques, and the major challenges faced in HO management. Additionally, the authors categorized existing HO decision algorithms, such as ML/DL, RSRP, FLC, and velocity-assisted HO techniques, and reviewed evaluation methods for performance. A survey of SDN-enabled architectures and HO optimization techniques in multi-RAT networks is conducted in [40]. The advanced SDN-enabled architectures for radio access networks (RANs), offloading techniques, and implementation strategies are investigated in this paper. Moreover, the authors discuss the challenges and limitations of SDN-based HO optimization approaches and suggest potential areas for future research. The article [41] provides a state-of-the-art survey of MRO studies that utilize ML techniques, addressing the methodology used, HO criteria, HCPs, KPIs, simulators, and accomplishments. The study also discusses MRO challenges for intra-system and inter-system mobility and highlights issues for future investigations. A brief description of survey articles from [28; 29; 30; 31; 32; 33; 34; 35; 36; 37] is shown in Table 1, whereas Table 2 summarizes the scenarios, applied schemes, and focused challenges discussed in the related surveys.
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 3 Ray Tracing Simulations  
As support for the UMi measurements, RT simulations were performed for the OS-CC and SC-CC environments. The polarimetric RT tool, which has been developed by the Fraunhofer Heinrich Hertz Institute (Berlin, Germany) based on NVIDIA's OptiX engine, makes use of parallelized processing on the graphics processing unit (GPU) [27]. It applies geometrical optics (GO) and the uniform theory of diffraction (UTD) for reflections on convex surfaces. The measurement environments are incorporated in form of 3D models from Leipziger Platz (Figure 2a) and Potsdamer Strasse (Figure 2b). They were manually extended to include street furniture and tree trunks [28].  
All building surfaces were assumed to be glass with relative permittivity \(\varepsilon_{r}=8.9-j0.13\)[29], where \(j\) is the imaginary unit. The ground was modeled with \(\varepsilon_{r}=6.14-j0.30\), the value defined for concrete in [30], since the sidewalk is paved with concrete sets. The trees were assumed to be smooth, dielectric cylinders with \(\varepsilon_{r}=1.57-j0.096\), the value for wood in [30]. The lamp posts were modeled as smooth and perfectly electrically conducting cylinders. Up to one transmission and four reflections were admitted. Isotropic, vertically polarized antennas were used, which has been found to well reflect the omnidirectional measurement antennas as long as only distances above 5 m are considered and their gain is subtracted. The RX measurement tracks with 0.4 mm spacing were fully re-simulated with the ray tracer. For each simulated RX point, a discrete equivalent baseband CIR was composed from the simulated rays based on their complex amplitude and propagation delay by binning (small bin size of 0.4 ns). Rays falling in one delay bin were added coherently. To provide fully comparable results, the simulated CIRs were band limited to the measurement bandwidth of 250 MHz, and the same post-processing was applied.
# Self-Organizing and Scalable Routing Protocol (SOSRP) for Underwater Acoustic Sensor Networks
## 5 Simulation Results
### Network Scalability  
In a real UASN, when it is needed to increase the number of nodes, it would be very expensive and illogical to collect out of the water those in service and make a new deployment again one by one until the total number of sensors is completed. Instead of this, a more realistic task would be to perform a new deployment of only the new nodes needed, maintaining the topology of the previous network.  
Considering the above discussion, SOSRP was tested for network scalability by deploying the 50 nodes at first and simulating them for different ranges (70 to 100 meters). Ten new nodes were randomly added to the network in each simulation, keeping the previous location of deployed nodes. The process was repeated until the network size reached 100 nodes. This approach is more realistic and allows each newly deployed node to connect with the network using the local information from neighbor nodes. Moreover, the number of operations decreased in the network (e.g., calculating new routes) by keeping the routes stable with minimum changes when few nodes were added to the existing network. It  
The results show the number of hops in all paths generated for different network sizes (50, 70, and 100). It is quite evident from the results that by adding new nodes in the network, the total number of paths is increased because of the addition of ten nodes in each run. However, the maximum number of hops (max: hops = 6) in any path are the same for different network sizes, as shown in Figure 19. This proves that performance metrics of SOSRP remain stable irrespective of increasing network size.  
In order to validate the observed results, multiple simulations were performed. The results of two simulations (Test 1 and Test 2) are shown in Figure 20 for end-to-end delay obtained in longest path. Comparing the obtained results, it can be seen in Figure 20a that with increasing network size, the delay  
Figure 19: Number of hops in each path vs transmission range for three network sizes: (a) 50, (b) 70, (c) 100.  
Figure 18: Average end-to-end delay (a) and end-to-end delay for longest path (b).  
for longest path decreases for a transmission range of 70 meters. An opposite effect can be observed in Figure 20b, where delay is increasing up to a network size of 80 nodes, keeping the transmission range 70 meters. This is because of random placement of new nodes, increasing the number of hops in the route selected. However, the maximum longest path delay obtained in Test 2 (approximately 1.45 s) is still less than maximum delay in Test 1. The stability is obtained in longest path delay by increasing the transmission range. The effect is noticeable in results for different transmission ranges (80, 90 and 100).
## 7 Dialogue Management and Large Language Models  
The dialogue manager of the system utilizes the Dialogue Selector and Dialogue Trees proposed by [2]. However, the development of Barista opened a new problem of how to incorporate LLMs into dialogue management. While human-designed dialogue trees offer engaging conversations that are meticulously crafted by dialogue designers, they lack the flexibility of LLMs to respond to unforeseen inputs.  
To enhance the system's abilities, we proposed four approaches for incorporating generative models into dialogue management: a handling of the out-of-domain (OOD) inputs; a handling of proactive questions; hybrid dialogues, and inserting LLMs into dialogue trees. The LLM loop is central to all four strategies, managing the conversational flow for several turns. The loop comprises the user input, the generated response, and a terminating function, the latter of which determines whether the loop continues or ends (see Figure 11). The terminating function operates with the following three criteria.  
* **Turn Counting:** This criterion keeps track of the number of turns handled by the LLM. If this count exceeds a predefined threshold, the loop is terminated to prevent the situation in which the topic of conversation wanders off the original topic. Also, the probability of a nonsensical response increases with the number of generated responses in a row. Thus, the turn-counting mechanism ensures that the conversation remains coherent and does not deviate from the topic.
* **Regex Matching:** The function detects phrases such as \"Alright\", \"I don't know\", or \"I want to chat about something else\", which suggest user disinterest in the current topic of conversation. If any of these phrases or their variations are detected, the loop is terminated to allow for a change in conversational direction and tomaintain user interest.
* **ODES Classifier**[50]: The class predicted by the ODES classifier serves as another criterion for terminating the LLM loop. If the classifier predicts one of the classes _USER_DISINTREST_, _USER_INITATED_TOPIC_SWITCH_, or _USER_REQUEST_STOP_, indicating that the conversation is no longer engaging to the user, the loop is then terminated. This ensures that the SocialBot's responses remain engaging and adaptive to user preferences.  
Figure 11: The LLM loop.
## 3 The SOMM Model
### Achieving Flexibility via Virtual Machine  
As mentioned, due to the large number of nodes and unique constraints of a GWMSN, alteration is a natural characteristic of these networks. New nodes may be added to network or existing nodes may be removed. Also, because these networks are multi-purpose, new functionalities may be added or existing implementation of a service may be changed during lifetime of network. Current operating systems of WSNs support these kinds of changes poorly. To solve this problem, some virtual machines are developed for WSNs which have been described in Section 2. Employing a virtual machine as a middleware decrease the application code size; in addition, it simplifies the node reprogramming and as a result, increases the network flexibility. Portability is another advantage of virtual machine. A virtual machine lies as a layer between applications and the underlying operating system and makes them independent of operating systems. Having different implementations of virtual machine for various operating systems allows us to have multiple operating systems in a GWMSN at the same time. This is useful when using different operating systems for different nodes based on the node capabilities is needed. A virtual machine can also provide code mobility which is useful in application development for a GWMSN. Mobile agents allow a GWMSN to do multiple tasks and serve multiple clients at the same time. For these reasons, SOMM proposes a virtual machine with support for code mobility as its core.
## 2 Industry 4.0 Background
### Machine-to-Machine Communication  
The Industry 4.0 paradigm is based on machines, devices, logistics and humans connected to each other to exchange data, process them and make decisions based upon them, while appropriately coordinating in a ubiquitous and ad hoc manner thanks to M2M communications [15]. According to this vision, objects are equipped with communication capabilities that enable them to be autonomous and self-organizing [18]. As discussed in the previous subsection, Internet access is going to be ubiquitously available in industrial environments, thus providing connectivity to all the involved objects. Nevertheless, not only smart objects are going to be part of Industry 4.0. Thanks to the evolution of Micro-Electro-Mechanical Systems (MEMS) technology, tiny low-cost low-power sensors with communication interfaces have started to be embedded into the most various objects. Some examples are: RFID tags that are placed on products for supply chain management and energy harvesting sensors supplied by machinery vibration, used to monitor manufacturing processes. Since they are developed to energy-efficiently transmit data, they are not provided with high computational capabilities, thus relying on other objects' intelligence for their cooperation- and organization-related tasks. For this reason, M2M communication is ensured not only among physical objects but also among their digital counterparts residing in the cloud [19]. Data are then stored and/or processed in the cloud, which provides to the system the resources that it needs. In this case, since data cannot be immediately associated with the object that produced them, and their location and time, appropriate mechanisms have to be applied to ensure that integrity and consistency are maintained [17].  
The choice of using local or remote resources basically depends on the application that has to be performed by the system and the location of the required resources, i.e., sensing and/or processing capabilities, and storage capacity [20]. If the required resources can be provided by objects that are located close to each other, objects can be able to form a connected group, preferably relying on short-range communication technologies. In this case, the management of resources should be carried out as close as possible to the point where they are used. On the other hand, if the objects that are involved in the application execution cannot directly contact local objects that can provide the required resources, other remote objects can be contacted. Depending on the position of these remote objects with respect to the core network, we can refer to fog/edge computing [21] or cloud computing technologies.
## 2 Materials and Methods
### High Accuracy Prediction Model Based on WTA Ensemble Learning
#### 2.4.1 Combined Features of Voltage Amplitude and Electromagnetic Power  
In order to meet the fast and accurate prediction requirement online, the two types of superior features with higher accuracy are selected according to the mRMR ranking result in Section 2.3, namely the generator electromagnetic power \(P_{e}\) and bus voltage magnitude \(V\).