esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 4 Intelligent Edge Computing
### Slice Composition with Machine Learning
When deploying the performance achieved in training the network, the author concluded with the need to generate more data to supply the LSTM network. [PERSON] [71] explores a variation of the LSTM architecture together with Convolutional Neural Networks (CNN) in a slice context approach for vehicular networks (V2X). The neural network training used a mobile network traffic dataset from the city of Milan, Italy [72], which contained data from the following three categories: SMS, telephone, and web browsing. For the simulation, each category considered a slice to be allocated through the model which, in turn, presented a satisfactory performance in establishing the connection between the customer and the slice that would serve it. The LSTM network is of the Encoder/Decoder type, and in the project in question, the encoder was used to predict the network traffic, while the decoder obtained the optimal slice. The entire datasetwas used for network training, with the input in the network being an image matrix. Despite the satisfactory result reported, the SLA was not presented, or the parameterization of the dataset attributes that was used. Therefore, replicating the procedure to validate the performance of the network would not be feasible.
## 5 Case Study
### Discussion  
Practically, this smart traffic event detection sensor dramatically reduces data throughput requirements for research projects looking to analyse urban phenomena. The rapid implementation of this real-world application following REIP's workflow and using the REIP SDK shows promise for its use across diverse applications of sensor networks. With a given set of application requirements, a pipeline can be designed that abstracts away a large amount of technical detail typically required to develop software that could perform this set of complex, interacting operations.  
A particularly challenging aspect of real-time sensing is moving data between processes that operate on different schedules. This is commonly addressed with ad hoc solutions and application-specific code that are not generalisable or re-configurable. When projects are under tight deadlines, it often does not make sense to do more than that; the project just needs code that will perform the required task, and re-usability and portability are not the primary motivators. The REIP SDK addresses these common computational problems in a general sense so that when a research problem comes along, an experiment can be up and running as quickly as possible using pre-existing functional blocks. Indeed, we were able to reuse many of the blocks used during evaluation in Section 4 to build the data acquisition and processing pipeline of the given case study (Figure 5).  
Another significant challenge in remote sensing is hardware and software integration, which is subject to a number of constraints including: compute resources available, hardware I/O offered, sensing options, inter-process data rates and available remote connectivity options. With an application pipeline defined using the REIP SDK, this integration process becomes less of a challenge, as the blocks chosen dictate the minimal hardware platform that can support it. The presented process of manually benchmarking possible hardware platforms (Section 4.2.2) is not ideal but is a precursor to our planned simulation and optimisation tool for a pipeline evaluation stage, where optimal hardware platforms will be matched to an application pipeline in an automatic way subject to user constraints, such as maximum memory usage, data output rate, etc.  
Figure 7: Car and bicycle pass-by frame from sensor’s camera overlaid with bounding boxes and labels from object detection block for illustration. Green region on inset audio amplitude plot shows time period where car is detectable within video frames, with red region showing longer periods of audio-based detection of a car horn. The blue portions signify periods when no audio event was detected.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.2 Collision Avoidance  
In [70], the authors introduce a strategy to avoid collisions to improve robot navigation in scenarios with numerous pedestrians. Their approach improves scalability and resilience as the number of agents increases. The authors aimed to develop a technique that can manage a broad range of non-communicating agents or pedestrians that display unexpected behaviors, outperforming previous collision avoidance approaches. Their collision avoidance technique was learned directly from the received data, allowing robots to adopt to human behavior and crowded situations.  
The authors in [70] used GA3C-CADRL to incorporate observations from a large number of other agents. They trained the collision avoidance policy by comparing the robot's observations of the environment to a probability distribution across various actions. The reward function of the policy provides incentives to achieve targets and punish collisions or near collisions. Throughout the training, the policy acquires state measurements to generate commands, while the environment provides subsequent states and incentives, as shown in Figure 8. The authors used an ROS to execute a collision avoidance strategy in simulated and real-world settings. They used a pretrained policy called \"_cadrl-ros_\" [70] that integrates seamlessly into existing robotic systems, facilitating communication between various components of the robot's navigation system.  
The above-mentioned work made substantial progress in developing a comprehensive and scalable collision avoidance system. It exhibits improved performance in simulations  
Figure 8: System architecture for simulation-in-loop training. For more information, please refer to Table 4.  
involving up to forty agents and shows the effectiveness of their approach in real-world environment using both multiagent and ground robot platforms. This work has important implications for diverse applications, including autonomous vehicles, delivery robots, and other autonomous systems that function in dynamic environments.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 2 Background and Related Works
### Background
#### 2.1.3 Types of Blockchain  
The blockchain architecture defines the relation for the transaction or validation of nodes running on the network. In the case that the blockchain nodes members are known to the network, then blockchains such as Hyperledger Fabric [16] and Ripple [17] are referred to as permissioned. When an architecture is available to the public, a single node or organization, such as Ethereum and Bitcoin, may be part of the network, so this blockchain is public [2].  
1. **Public blockchain network** The data in a public blockchain network are accessible to the public where participants can be part of the consensus without the need for permissions. Transactions with some anonymities are available to all nodes [18]. The participant, based on a consensus algorithm, may therefore verify a transaction and engage in the approval process, as in Proof of Stake (PoS) and Proof of Work (PoW) [2]. The system is fully secure by repeating synchronous public blockchains with each network miner. This architecture is employed in cryptocurrency networks such as Bitcoin and Ethereum; however, it raises privacy issues.
2. **Private blockchain (permissioned)** This kind of limited blockchain permits the return of an intermediary. Private blockchains strictly control the data access permission of a network. Any transaction in the network can only be verified and validated by companies or organizations with a high level of efficiency. The failure to provide a decentralized infrastructure for secure databases is a drawback of private blockchains, which are provided by public blockchains [19]. Private blockchains can be recognized correctly as conventional centralized networks but with a strong cryptographic model for network transactions to be verified and validated [2]. Each hospital keeps EHRs in its private blockchain, which offers the benefits of speed, privacy, cheap cost, and improved security [20].  
3. **Consortium or federated blockchain** A consortium is a combination of private and public blockchains that can be thought of as partly decentralized. The nodes have the authority to be chosen ahead of time and the transaction can be made private or public [2]. In addition, the hospitals are structured to create a blockchain consortium that keeps searchable PHI indexes. The physician can find the indexes in the consortium blockchain to obtain the originals by visiting the associated private hospital blockchain [20].
# Feasibility Analysis of Adopting the Hydrogen Hydrostatic Thrust Bearing
## 4 Application Parameters
### Circumferential Groove  
To improve the performance of the HHTB, a circumferential groove was arranged near the locating circle of the orifice to throttle the gas in the secondary shallow cavity. The static parameters of the HHTB with different groove depths are presented in Figure 10. Other parameters remained unchanged, as shown in Table 3. The bearing with a circumferential groove had a greater load capacity than the bearing without a groove. With deepening grooves, the load capacity of the HHTB gradually increased and tended toward a certain value. Furthermore, the static stiffness of the HHTB was also enhanced after setting a circumferential groove. The film clearance corresponding to the peak static stiffness gradually increased and stabilized with growing circumferential groove depth.  
Figure 10: Static parameters of the HHTB under different groove depths: (**a**) load capacity and (**b**) static stiffness.  
Figure 9: Static parameters of the HHTB using different numbers of orifices: (**a**) load capacity and (**b**) static stiffness.  
The load capacities of HHTBs for various groove widths are presented in Figure 11. The load capacity of the HHTB initially increased and later decreased as the groove width increased. When the circumferential groove width was small, the load capacity increased because of the increased throttling effect of the shallow secondary cavity. However, the excessive width of the circumferential groove in the later stage was equivalent to increased gas film clearance, which deteriorated the bearing's capacity.
## 9 Optimization Potential  
Our results indicate significant optimization potential through path compression. Based on the observed distributions, we estimate that optimized node layouts could reduce average proof sizes through the following:  
\[R=1-\frac{\sum\limits_{k=2}^{41}k\cdot P_{opt}(k)}{\sum\limits_{k=2}^{41}k \cdot P_{current}(k)}\approx 0.70,\]  
where \(P_{opt}(k)\) and \(P_{current}(k)\) are optimized and current path length probabilities. This suggests potential proof size reductions of up to 70% through structural optimization.  
We base our claim of 70% structural optimization potential on two key factors:  
1. Theoretical Analysis:  
Our probabilistic model demonstrates that for highly skewed access patterns, where a small subset of addresses accounts for the majority of transactions (as observed in Ethereum network data), the average path length can be reduced by up to 70% compared to balanced trees. This is derived from the entropy-based lower bound of path lengths in adaptive trees versus fixed-length paths in balanced trees.  
2. Empirical Validation:  
In our previous work on adaptive Merkle trees ([PERSON] et al. (2018)), we demonstrated that for real-world blockchain workloads with Zipf-like distribution of address access frequencies:* The most frequently accessed 20% of addresses can achieve path length reductions of 65-70%;
* The overall average path length reduction across all addresses reaches 30-35%;
* These results were validated using historical Ethereum transaction data over multiple timeframes.  
The 70% figure represents the maximum theoretical optimization potential achievable in ideal conditions, while practical implementations typically achieve 30-35% improvement due to implementation constraints and the need to maintain tree balance for less frequently accessed addresses [8].  
While our approach demonstrates significant potential for proof size reduction, it is important to address the computational overhead implications. The optimized node layout strategy introduces additional processing requirements during trie construction and updates. However, this overhead is offset by reduced network bandwidth requirements and faster proof verification, resulting in a net positive impact on overall transaction throughput. The trade-off becomes particularly advantageous for stateless clients, where reduced proof sizes directly translate to improved transaction processing capacity.
# Experimental Study of the Dynamic Characteristics of PVA-Amended Expansive Soils
###### Abstract  
Expansive soils are distributed across a wide area in China, and land transport and surface construction will inevitably involve these soils. To mitigate the deficiencies of single-method expansive soil modification, it is highly important to adopt the use of polyvinyl alcohol (PVA) to improve expansive soils and enhance the strength and toughness of modified soils. In addition, solidification technology can be utilized for the resource utilization of expansive soils. In this study, triaxial testing is employed to evaluate the mechanical properties of solidified soil. When the confining pressure is the same and with increasing PVA content, soil particles and PVA combine to form a cemented substance, which fills the internal pores of the soil samples, enhances the cohesion between soil particles, and improves the bearing capacity of the soil. The stress-strain curve for the modified soil first increases and then decreases. The shear strength peaks at a PVA content of 3%. Based on the improved soil with a 3% PVA content, GDS dynamic triaxial tests were carried out to investigate the effects of different confining pressures and frequencies on the dynamic stress-strain curves, dynamic modulus of elasticity, and variation rule for the damping ratio of the improved soil. The results show that the dynamic stress-strain curve for the improved soil increases with increasing confining pressure and frequency and that the dynamic stress-strain backbone curves exhibit significant nonlinearities at different frequencies and circumferential pressures. The dynamic elastic modulus increases with increasing confining pressure and frequency and decreases gradually with increasing dynamic strain. The initial dynamic modulus of elasticity increases with increasing envelope pressure and frequency but is less affected by frequency, and the damping ratio decreases with increasing confining pressure and frequency. Soil treatment can improve the pore distribution, inhibit the extension of soil cracks, and enhance soil compactness.  
polyvinyl alcohol; expansive soil; static/dynamic triaxial test; dynamic behavior Article
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 7 UOC Near-Future Network Scenarios and Research Challenges
### UOC--Today's Research Challenges
#### 7.2.3 Random Air-Water Interface Optical Propagation  
One of the most difficult and complex problems in underwater UOC is optical beam propagation via a random air-water interface [494]. When a laser beam propagates through air, via the air-water interface, and finally through the water to an underwater terminal, it suffers from geometric phase aberrations caused by the random motion of the water surface wave. This results in a significant reduction and a random fluctuation in the received communication signal, which limits the data rate that may be achieved. Recently the beam-wandering effects and detailed derivations of probabilistic models have been reported [494]. The following are the primary findings in both the transmission and reflection scenarios. In the reflection case, the PDF of an emerging angle's angular variations is given (assuming the PDF for the slope wave, PDFS(\(\theta_{S}\)) is known) as  
\[PDF_{D}(\theta_{D})=\frac{1}{2}PDF_{S}\bigg{(}\frac{\theta_{D}}{2}\bigg{)}, \tag{53}\]  
where \(\theta_{D}\) is the angle by which its reflection departs from its original position on a calm, flat surface, and \(\theta_{S}\) is slope of the time-varying surface at the end moment of contact. In the transmission case, the PDF for the beam wander can be estimated as  
\[PDF_{D}(\theta_{D})=4 PDF_{S}(-4\theta_{D}), \tag{54}\]  
Based on the results of the beam-wandering effects for both transmission and reflection, underwater optical wireless communication performance parameters, such as SNR and BER can be evaluated. A new technique for mitigating laser propagating fluctuations through a random air-water interface using an AO method was proposed [495]. The authors demonstrated the AO concept to correct the distortions using a laboratory setup of underwater communication. The AO system consisted of a deformable mirror (DM), Shack-Hartmann WFS) and FSM, and their results show several metrics, such as Strehl ratio, relevant to underwater UOC system performance operating through air and water terminals.
## 3 Proposed PEADO-OFDM Scheme and Full-Duplex Communication
### The Proposed PEADO-OFDM Transmitter with Low Complexity  
The PEADO-OFDM scheme removes the clipping noise at the transmitter side but requires an additional FFT operation. Therefore, we propose another PEADO-OFDM transmitter with low complexity by utilizing the internal characteristics of clipped ACO-OFDM signals.  
In ACO-OFDM, the input symbols of IFFT can be represented as:  
\[X=[0,X_{1},0,X_{3},\ldots,0,X_{N-1}], \tag{4}\]  
where \(N\) is the size of IFFT. We denote the output signals of IFFT and the clipped ACO-OFDM signals as \(x_{n}\) and \(x_{n,c}\), respectively. \(x_{n,c}\) can be described by \(x_{n}\), which is given by:  
\[x_{n,c}=\frac{1}{2}(x_{n}+|x_{n}|),\ 0\leq n<N, \tag{5}\]  
Applying the FFT operation to both sides of Equation (4), we can obtain:  
\[X_{c}=\frac{1}{2}X+\frac{1}{2}FFT(|x_{n}|), \tag{6}\]  
where \(X_{c}\) represents the FFT of clipped ACO-OFDM signals \(x_{n,c}\). In addition, \(X_{c}\) can be divided into even and odd parts, i.e.,:  
\[X_{c}=X_{odd,c}+X_{even,c}, \tag{7}\]  
\[X_{odd,c}=\left\{\begin{array}{cl}0,&if\ k\ is\ even,\\ X_{c},&if\ k\ is\ odd,\end{array}\right. \tag{8}\]  
\[X_{even,c}=\left\{\begin{array}{cl}X_{c},&if\ k\ is\ even,\\ 0,&if\ k\ is\ odd,\end{array}\right. \tag{9}\]  
where \(X_{odd,c}\) and \(X_{even,c}\) represent the odd part and even part of the \(X_{c}\), respectively.  
It is obvious that \(X_{even,c}\) represents the frequency domain form of the clipping noise because the clipping noise only falls on the even subcarrier. For ACO-OFDM signals, it has been proven that after clipping, the symbols on the odd subcarriers are only half of the original symbols in the frequency domain. Thus, \(X_{odd,c}\) can be rewritten as:  
\[X_{odd,c}=\frac{1}{2}X, \tag{10}\]  
Combining Equations (6), (7) and (10), it is easy to derive that:  
\[X_{even,c}=\frac{1}{2}FFT(|x_{n}|), \tag{11}\]  
Equation (11) reveals that the frequency domain form of the clipping noise \(X_{even,c}\) and the absolute value of the output signals of IFFT are a pair of Fourier transforms. Therefore, we can obtain the clipping noise by employing an absolute operation in the time domain rather than an FFT operation, resulting in lower complexity. The obtained time-domain clipping noise can be used as pre-distortion signals, which will be subtracted by the DCO-OFDM branch after the IFFT operation. The diagram of new PEADO-OFDM transmitter with a dual-branch structure is shown in Figure 3.
# In-Vivo Quantification of Knee Deep-Flexion in Physiological Loading Condition trough Dynamic MRI
## 1 Introduction  
Musculoskeletal disorders are the second most common cause of disability worldwide, exceeded only by traffic-related injuries, and they are responsible for the 21.3% of the total years lived with disability [1, 2]. Among human articulations, the knee is one of the most susceptible to ligament injuries and to the risk of osteoarthritis development [3]. Understanding and identifying a patient's normal and pathological joint function is, therefore, a high clinical priority.  
Static morphological imaging helps the diagnosis and the etiology identification of these disorders [4]. However, the functional characterization of musculoskeletal system in physiological conditions still relies on clinician experience [5]. Indeed, the relation between anatomical structures that can be observed during static imaging may significantly differ from what is measured during dynamic musculoskeletal tasks [6, 7, 8]. Several studies showed that evaluating a patient by means of static, non-weight-bearing scans alone may result in misdiagnoses [6, 7, 8, 9, 10]. In-vivo imaging of joint motion may fill the gap, providing a tool to better understand the normal joint physiology, investigating the etiology of musculoskeletal diseases, and designing more effective treatments.  
Currently, in-vivo analysis of articular motion can be performed by several techniques [11]: ultrasonography [12], fluoroscopy [13], computed tomography (CT) [14], and Magnetic Resonance Imaging (MRI) [6]. Ultrasonography, however, is limited to the evaluation of soft tissues around the joint. Fluoroscopy and CT expose the patient to ionizing radiation and do not allow the direct observation of soft tissues. On the other hand, MRIreturns information of both bones and soft tissues without known risk for the patient. This, together with the recent advances in dynamic sequences, boosted the application of Dynamic MRI (DMRI) to the investigation of joint behavior [15].  
The goal of this work is to present an experimental-computational procedure for the investigation of the knee deep-flexion under physiological loads. The procedure reconstructs the spatial kinematics of the knee from dynamic planar MR images. To this aim, we employed a weight-bearing MR scanner, in combination with a custom MR compatible rig, to guide the knee flexion during the dynamic scan. Finally, we developed a new registration algorithm to reconstruct the three-dimensional tibio-femoral kinematics from DMRI.