esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Motivation  
The future of transportation is on the edge of a significant transformation. As technology continues to advance at a rapid pace, the vision of fully autonomous vehicles on our roads is becoming a closer reality. In particular, the trucking industry stands to gain substantially from this revolution. Trucks play a pivotal role in our economy, moving goods across vast distances; hence, their automation could usher in a new era of transportation. Developing an autonomous truck trailer (semi-truck) could lead to safer, more efficient, and sustainable transportation. Self-driving trucks can operate continuously and take the most efficient routes, leading to reduced fuel consumption and transportation costs. Additionally, they can enhance supply chain efficiency by speeding up transportation and reducing costs, which can boost the economy. Currently, several companies are working on developing technologies that enable fully autonomous trucks. One of the challenging tasks to be automated is backing up a truck and trailer or driving in reverse. For human drivers, the challenge of this task arises for several reasons, including limited visibility, different turning points, oversteering, length and weight of the trailer, and lack of experience. Thus, backing up a truck and trailer requires careful attention, patience, and practice. It is important to take your time and be aware of your surroundings to avoid accidents or damage to your vehicle or the trailer.
# A Novel Wearable Foot and Ankle Monitoring System for the Assessment of Gait Biomechanics
###### Abstract
A comparison of the Oxford and the Rizzoli foot models considering normal gait and voluntarily pathological gait was conducted in [32; 35], concluding that the kinematic output (both static and dynamic) of the Rizzoli foot model exhibits more informational content regarding plantar and dorsal flexion. Regarding the employment of the plantar pressure distribution in the analysis the gait kinematics, [PERSON] et al. studies the correlation between the plantar pressures and the Oxford foot model in [36]. On the other hand, [PERSON] et al. studies and validates the anatomical masking of footprint pressures based on the Oxford foot model [37]. Regarding the employment of EMG assessment in conjunction with foot kinematics was reported by [PERSON] et al. for treadmill running in [38] or by [PERSON] et al. for professional football players in [39]. To make our literature review more comprehensive, we present the studied literature in a comparative fashion in Table 3, listing a brief overview of the study, the number of participants involved in the study, the hardware used for data acquisition, the number of sensors for plantar pressure assessment, the availability of muscular activity assessment and a brief review of the results. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Muscles** & n.a. & n.a. \\ \hline
**No of Plantar Sensors** & 85–99 & 960 \\ \hline
**Plantar Data Regions** & 8: (1) heel, (2) medial arch, (3) lateral arch, (4) medial metatarsal, (5) central metataralsals, (6) lateral metataralsals, (7) great toe, and (8) little toes & 10: (1) interphalangeal joint (IPJ), (2) lesser toe, (3) metatarosphalangeal joint 1 (MPJ1), (4) MPJ2, (5) MPJ3; (6) MPJ4, (7) MPJ5, (8) midfoot, (9) medial heel, and (10) lateral heel \\ \hline
**Hardware Used for Data Collection** & Pedar-X system pressure insoles placed inside shoes & F-scan in-shoe pressure measurement system (Tekscan, MA, USA) \\ \hline
**No. of Participants** & 20 females & 51 (31 healthy and 20 with foot pathology) \\ \hline
**Reference** & Ref. [4] compares the plantar pressure distribution of the foot between different incline and speed during treadmill jogging by using plantar inside measurement system & Ref. [5] identifies foot pathologies based on plantar pressure asymmetry \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative presentation of the studied literature in a tabular form.
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
## 2 Materials and Methods  
The samples used in this study were prepared from a natural soil from Gothenburg, south west coast of Sweden. The soil consists of 55% silt, 29% fine sand and 16% clay. The natural water content is 30%, the liquid limit 37% and the plastic limit 20%. The optimal moisture content is 12%, and the proctor density is 1.97 t/m\({}^{3}\). The ignition test showed that the soil has 4% organic content. Therefore, according to the Swedish standard, natural soil is classified as sandy clayey silt (sacSi) with low organic content [26; 27; 28].  
Natural soil was firstly homogenized and then mixed with Petrit T as well as the cement in various percentage from 1 to 7% as dry mass of soil and cured for 7, 14, 28, 60 and 90 days before testing.  
Unconfined compressive tests (UCS) were used as an indicator to predict the soil strength enhancement before and after treatment. A series of consistency limits tests were performed on the treated soil after various curing times to investigate the effect of binder types and amounts on the soil plasticity index. Laser particle size analyzer tests were conducted to study the effects of binder types on the particle size distribution (PSD) of the soil before and after treatment. These were measured by using CILAS 1064 laser particle size analyzer in the liquid mode with a measurement range from 0.04 to 500 \(\upmu\)m. The pH tests were used as an indicator of soil-binder reaction by measuring the pH value of the soil after treated with a binder and cured for various curing periods.  
The specimens for particle size distribution tests (PSD) were prepared and cured in an identical way to the UCS specimens. Binder content was added as dry material with 4% and 7% by dry weight of soil. After 28 days of curing time, the specimens were pressed out of the tubes with the help of a mechanical jack directly before testing. Prior to the PSD test, both stabilized and unstabilized soil specimens were air-dried and ground through 0.5-0.063 mm sieves. Then, about 5 g of that passing from 0.063 mm sieve was mixed with sufficient distilled water and subjected to the PSD analyzer (CILAS 1064).  
The soil and the binders as well as the specimen preparation and testing methodology are described in more detail in [18; 29; 30].
## 4 Experimental Section
### Findings and Limitations in This Experiment
#### 4.4.2 Real Word Validation  
The validation results from both simulation and real-world environments clearly demonstrate that the proposed method (OURS) offers substantial improvements over the Vision-Based Policy Gradient (VPG) [13] and the normal mask approach for pushing and grasping tasks using the DQN model.  
Task completion: In the simulation environment, our method (OURS) achieved a task completion rate of 90.7%, surpassing both VPG [13] (80.2%) and the normal mask approach (82.7%). This enhancement in task completion is further corroborated by the real-world validation results, where our method achieved a task completion rate of 88.0%, compared to 78.3% for VPG [13]. These findings suggest the superior effectiveness of our approach in diverse pushing and grasping scenarios.  
Grasp success: The proposed method demonstrates a significant improvement in grasp success rates, attaining 85.7% in the simulation, which is higher than VPG [13] (75.3%) and the normal mask method (78.6%). In real-world scenarios, the grasp success rate of our method is 81.4%, also outperforming VPG [13] (72.1%). These results indicate a marked enhancement in the model's ability to accurately identify and select optimal grasp points, likely due to the more efficient use of image masks.  
Action efficiency: Our method also exhibits superior action efficiency, achieving higher efficiency rates both in simulations (70.3%) and in real-world scenarios (68.2%) compared to alternative methods. Notably, our method requires significantly fewer steps to achieve an 80% success rate (600 steps), in contrast to VPG [13] (1700 steps) and the normal mask approach (1250 steps). This demonstrates that our model is more efficient, completing tasks with reduced time and energy consumption.  
Overall, these validation outcomes underscore the advantages of the proposed method (OURS) across multiple performance metrics in both simulated and real-world conditions. The superiority of our approach reflects the benefits of leveraging a collaborative framework combining deep reinforcement learning and image mask techniques to enhance the execution of robotic tasks in complex environments.
# Dynamic Carrier-Sense Threshold Selection for Improving Spatial Reuse in Dense Wireless LANs
## 2 Related Work
The problem of DSC is also shown in the performance evaluation section, where we compare the proposed protocol to DSC, as well as the conventional 802.11 Distributed Coordination Function (DCF). Several proposals have been made to address the problem of DSC. [PERSON] et al., [15] proposed a technique where RTS/CTS (Request-To-Send/Clear-To-Send) is adaptively used to protect cell-edge users. RTS/CTS is used only for the nodes that show high frame error rate (FER), to protect the nodes from hidden terminals. [PERSON] et al., [16] proposed a scheme where CST is decided based on location information of nodes. Potential hidden terminals are identified using location information, and CST is set so that the carrier-sense range covers the hidden terminals. [PERSON] et al., [17] proposed a technique where transmit power and CST are adjusted based on Expected Transmission Count (ETX). The basic idea is to first calculate transmit power proportional to ETX. A high ETX means the node is experiencing bad channel condition, so the node increases transmit power to increase SNR at the receiver. Then, CST is set proportional to the transmit power. [PERSON] et al., [18] proposed a scheme where CST is calculated based on not only from RSSI of the associated AP, but also RSSI of neighboring APs. If the neighboring APs are closely located, the node uses a low CST to protect its transmission. Although these techniques may improve throughput and fairness of DSC, they require additional message overhead [15] or special hardware such as camera or GPS [16]. Adjusting CST based on ETX [18] may mitigate packet loss due to collisions, but edge nodes may get less chance of accessing the channel due to low CST. There are other existing works that try to improve spatial reuse by changing parameters other than the CST such as transmit power [19] and modulation and coding scheme (MCS) [20]. Also, some works use special hardware such as directional antenna [21] or full-duplex capability [22] to improve system spectral efficiency of the system. Compared to the existing protocols, the proposed protocol uses dual CST to effectively protect the packets that are concurrently transmitted. When there is an on-going packet, a node can only start transmitting its packet concurrently if it is confident that its own packet as well as the on-going packet will be successfully received at their respective receivers. In the next section, we describe the proposed protocol in detail.
###### Abstract  
A two-dimensional space turntable system has been used to ensure that the Solar X-ray and Extreme Ultraviolet Imager (X-EUVI) can track the Sun stably, and the prediction of the two-dimensional turntable trajectory is an important part of payload health management. Different from the dynamic model using traditional trajectory prediction, we propose a new method for predicting the pitch axis trajectory of the turntable based on the sun vector and a deep learning CNN-LSTM model. First, the ideal solar position of the pitch axis was calculated using the sun vector. Then, the ideal solar position was combined with the running turntable pitch axis motor speed, current, and solar position error signal as the CNN-LSTM model input data. The model parameters were trained and adjusted through test data simulation using Fengyun-3E satellite orbit data. Finally, the next position of the pitch axis was predicted. The test results showed that in the sun vector and CNN-LSTM model, the RMSE value was 0.623 and the MSE value was 0.388. It was better than the LSTM model or CNN model alone and could accurately predict the pitch axis position.  
two-dimensional turntable; pitch axis trajectory; sun vector; CNN-LSTM model; deep learning
## 5 Conclusions  
In this paper, ANC based on the use of a single circular array and 2D spatial information is proposed. In terms of wave field reproduction, the ANC performance for different radii of the microphone, loudspeaker arrays, and reference circle was investigated. In the experiment, a 20-dB average noise reduction was shown in the ROI when the radius of the microphone array was 0.5 m and that of the loudspeaker array was 1.5 m for a noise source transmitting a periodic signal at 200 Hz. In the proposed system, the number of speakers and microphones is considerably reduced compared with using the existing ANC system with 3D spatial information. In addition, using only 2D spatial information, we are one step closer to the commercialization of global ANC. Moreover, the ANC performance according to the noise source angle was also investigated, and we found that the SPL of the ROI increased when the noise source was beyond a certain angle. This disadvantage of the circular array can be reduced by installing sound-absorbing materials on the floor and ceiling of the room or using an additional microphone to achieve the vertical component of the wave field [17]. Furthermore, as the actual room has a square shape, research on ANC in terms of wave field reproduction based on the shape of various microphone arrays should be conducted in reverberant rooms [27]. The findings of this study, along with further research, demonstrate that global ANC commercialization can be achieved through a simple system.  
Conceptualization, S.H.; Formal analysis, S.H.; Funding acquisition, H.-g.K. and S.W.; Investigation, S.H. and J.K.; Methodology, S.H.; Project administration, H.-g.K.; Software, S.H.; Supervision, S.W.; Visualization, S.H. and J.K.; Writing--original draft, S.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant-in-aid HANWHA SYSTEMS.  
Not applicable.  
All data in this paper are available from the corresponding author upon reasonable request. There are no additional data.  
The authors declare no conflict of interest.
# Railway Cloud: Management and Orchestration Functionality Designed as Microservices
###### Abstract
Railway functions that could be virtualised include train signalling and control, monitoring and surveillance, diagnostics and maintenance, passenger information systems, ticketing, and communication systems. Virtualisation provides a flexible solution for all types of railway applications, enabling upgrades with minimal effort [22; 23]. Virtualisation, with its modular software architecture, could contribute to improvements in the safety of mission critical functions. Different tasks can be executed independently of each other, having access to their own partition and own sets of resources, enabling the assignment of different trust levels and the application of appropriate security policies. The real-time requirements of safety-critical functions can be met by the assignment of the necessary computing resources. In the authors' previous research, a cloud-based railway control system architecture, which enables the use of AI and function virtualisation, was proposed [24; 25]. The main advantages of this proposed architecture are its modular structure and the decoupling of time-sensitive control functions from time-tolerant control functions. In this paper, more details of the railway cloud platform are discussed, and cloud management functionality is considered. Also, an approach to design microservices for railway cloud orchestration is presented. The remainder of this paper is organised as follows. The next section presents works related to railway cloud architecture, management, and applications and highlights the novelty of this paper. Section 3 briefly describes the proposed cloud-based railway control system architecture, with an emphasis on the railway cloud platform and its management functionality. Section 4 is devoted to explaining railway cloud management use cases, which are the basis for synthesising the functionality of management microservices. Section 5 presents the proposed approach for the design of railway cloud orchestration microservices, including the verification and validation of microservices. Some issues related to safety and security in cloudified railway systems are discussed in Section 6. Finally, the conclusion summarises the benefits of this approach and points out the inherent disadvantages of microservices that cloud be mitigated through proper planning and management.
# A Review of Training and Guidance Systems in Medical Surgery
## 3 Methods
### Synthesis of the Selected Works
#### 3.8.9 Radiology  
Virtual environments provide new training alternatives in radiotherapy (RT). [PERSON] et al. created MITIE, an imaging training immersive environment [97]. The application allows RT students to practice, outside the usual radiography laboratory, radiographic procedures. MITIE uses the Quest3D library for rendering the virtual scene. By using a tutor-determined gold standard, MITIE enables students to compare their performance and knowledge using a PBL pedagogy. The system provides automated feedback, such as playback. Moreover, a video is used to highlight errors and point potential improvements. On the other hand, [PERSON] et al. programmed a virtual treatment room, RTStar, that provides a range of simulations and visualization found in radiology [45]. RTStar was built using C++ and OpenGL for graphic rendering. RTStar initial use was for education and interactive, immersive radiology; however, the authors extended the system to support RT planning. Currently, RTStar provides a visual warning when a collision between the gantry of the linear accelerator, the couch, and the patient is about to happen in the simulation.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 2 Related Works
### Research Works
However, existing blockchain platforms such as Ethereum and Hyperledger Fabric already encrypt users' data using a secure cryptosystem. Therefore, adding a new cryptographic layer has no added value and could degrade the performance of the proposed solution, mainly for IoT devices with their related characteristics in terms of low processing and battery capacity. We can also observe that private communication between two parties while enabling full access to user data is not addressed by most of the existing approaches. This is important, especially in healthcare applications where a physician may need to know every single piece of data detected by the IoT device to better diagnose the patient's health condition. Furthermore, in terms of scalability, one common pattern of the proposed solutions is that they transmit data received by the sensors directly without selecting urgent data to be assigned a high priority. Routine data could be transmitted in a predefined timestamp to minimize the blockchain network transaction load. Compared to similar research in Table 1, our proposed model distinguishes itself by defining user privacy policies to manage predefined smart contracts and utilizing Hyperledger Fabric's private channels to create secure channels. We focus on reducing, as much as possible, the encryption layers and external storage, and maintaining a single-layer architecture optimized for scalability and simplicity to fit with IoT devices' limited resources, unlike other models, such as [32], proposing a user-centric but non-blockchain-specific control, DISP's M2M and M2H focus [23], or [PERSON] et al. 's hybrid blockchain structure [27]. We avoid the complexity of multi-tiered approaches seen in [39], and the local-only data-sharing control models, such as in [20], since our model connects with external service providers in a permissioned network. [PERSON] et al. [38] use smart contracts for access control management only; however, our model defines policies to enable the user to control the shared data according to their preferences using predefined smart contracts. We progress beyond the existing approaches introduced above by using a blockchain-based solution without adding a new cryptographic layer to facilitate its integration with a low cost and reduced processing capacity and time.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 4 Multiple-Observations Helper Data Scheme
### Description of the Scheme  
First, a binary secret key \(s\in\{0,1\}^{\log_{2}|\mathcal{S}|}\) is generated uniformly at random, such that \(H(S)=\log_{2}|\mathcal{S}|\), with \(\log_{2}|\mathcal{S}|\) an integer. The key \(s\) is then encoded using a linear error-correcting code into a codeword \(c^{n}\). The codeword is XORed with all \(t\) enrollment vectors \(\left(x_{1}^{n},x_{2}^{n},\ldots,x_{t}^{n}\right)\), resulting in \(t\) sequences \(\left(w_{1}^{n},w_{2}^{n},\ldots,w_{t}^{n}\right)\), with  
\[w_{i}^{n}=c^{n}\oplus x_{i}^{n},\quad\text{ for }i=1,2,\ldots,t. \tag{17}\]  
Please note that \(w_{i}^{n}\) corresponds to a helper data sequence of the classic fuzzy commitment scheme [6]. The helper data of the MO helper data scheme is constructed by adding all these sequences together, i.e., the helper data are  
\[m_{t}^{n}\overset{\Delta}{=}w_{1}^{n}+w_{2}^{n}+\cdots+w_{t}^{n}=\sum_{i=1}^{t }(c^{n}\oplus x_{i}^{n}). \tag{18}\]  
The helper data are stored on the device or in a database, and they are considered public information.  
When the key must be recovered, the decoder observes the helper data \(m_{t}^{n}\) and another observation vector \(y^{n}\) of the SRAM-PUF. A decoder function maps each pair \((m_{t}^{n},y^{n})\) to a corresponding estimate \(\hat{s}\) of the original secret. Ideally, the reconstructed secret \(\hat{s}\) is equal to the original secret. Furthermore, an attacker who can observe the helper data \(m_{t}^{n}\) should not obtain information about the secret.