esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Although fractional order property can be observed in a wide range of mechanical operation systems and some good prediction results were obtained by the FOC models, there are two potential problems that the above mentioned references have not solved:  
1. If the fractional order or LRD property of the health indicator time series (HITS) is too weak (e.g., the Hurst exponent approaching 0.5), especially the time series at the stationary operation stage, the \(f\)-ARIMA and FBM models might become unusable and the prediction accuracy reduced dramatically if the classical FOC model lacks the optimal solution. 2. Similar to the physics-based model, the model parameters of the classical LRD model such as \(f\)-ARIMA cannot be adjusted with the evolution of an actual degeneration trend of the HITS, and the prediction accuracies are also greatly decreased. 3. If some sharp transition points (STPs) are contained in the health indicator time series (HITS), especially the time series at the incipient/severe fault phase, the traditional \(f\)-ARIMA and FOC approaches treat all time series values equally, which ignore the fact that the STPs value should be preserved at a larger weight, thus limiting their effectiveness in practical application. To overcome these issues and improve the prediction accuracy, this paper proposes a new approach for degradation prediction based on the long-range dependence (LRD) and particle filter (PF) algorithm, using EVI and kurtosis health indicators of a rolling bearing as the tested time series. The proposed method presents a prognostic tool by recursively updating the LRD model parameters with online measurements based on the particle filter (PF) algorithm. The initial parameters in the LRDmodel are described as probability distribution functions to incorporate the FOC property of rotating machinery degradation trend. Then, the PF is introduced to recursively estimate and update the model parameters, in which the small weighted particles will be removed in order to overcome the degeneracy phenomenon of particles, meanwhile, the updated parameters are fed into the established LRD model to predict the future health indicators step-by-step. The proposed technique is implemented to the accelerated life test of rolling bearing, which achieves a higher prognostic accuracy compared with some state-of-the-arts models such as the autoregressive-moving-average ARMA, \(f\)-ARIMA and FBM. The remainder of this paper is organized as follows: Section 2 introduces the algorithm and theoretical derivation of the fractional Gaussian noise and LRD model. Section 3 describes the algorithm of a particle filter frame. In Section 4, the results and discussion of the proposed method are presented. Finally, conclusions and future research objectives are drawn in Section 5.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Security Attributes
#### 7.1.5 Verifiability  
The PVPBC voting system supports individual and universal verifiability features because it incorporates the Selene voting scheme in its design. Specifically, the proposed system enables universal verifiability because all of the required information is published on the ledger. This allows every independent person to verify cryptographic operations performed at every stage of the election. Some examples of the types of verifications performed are listed. They include verifying the requirements that the tracker numbers are unique, and obtaining proof that shuffling and decryption have all been carried out correctly. Individual verifiability is achieved in the proposed system because voters are able to verify their vote on the DLT after receiving their \(\alpha\) term and by reconstructing their tracking number. However, this does not offer the same level of individual verifiability provided by the Selene protocol. This is because the front end has control over the voter's private key. This is in contrast to the Selene protocol where voters control their private key. Consequently, they control the \(\alpha\) term and are able to generate a fake \(\alpha\). We believe that sacrificing this Selene protocol feature in the PVPBC voting system is a feature worth sacrificing, given that it helps to preserve the voter usability.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 5 Physical Layer  
In digital communications, single carrier (SC) and OFDM waveforms are used to transmit information at a high data rate, and they have similar theoretical spectrum efficiency [20]. Both waveforms are impacted by the channel selectivity on time and frequency domains. For dealing with these channel impairments, specific signal processing techniques are required at the receiver side.  
The underwater acoustic channel is highly time-variant, and its tracking can be a difficult task [21]. With this knowledge, it would be desirable that the selected waveform could cope with this inherent channel feature. The usage of a differential OFDM system does not require any channel equalization, decreasing the computational complexity at the receiver side [21]. Therefore, differential OFDM was selected for this project due to its  
Figure 6: Network usage rate for \(T_{\text{frame}}=2\) s.  
Figure 7: Frame structure.  
robustness. Another motivation for this choice is the possibility of changing its parameters according to the application, and to the environmental conditions.  
So, in this section, we present the physical layer aspect regarding the AUV's communication. We first describe the OFDM modem embedded in each AUV [22; 23; 24]. We then present a method that takes advantage of the knowledge of the network geometry to automatically adapt some of the OFDM parameters.
# Dynamic Information Flow Tracking: Taxonomy, Challenges, and Opportunities
## 3 Hardware-Assisted Dynamic Information Flow Tracking  
In this section, we discuss the hardware-based DIFT designs, including gate-level DIFT designs and micro-architecture-level DIFT designs. The gate-level DIFT designs include gate-level netlist and RTL designs. For micro-architecture-level design, we mainly focus on in-core DIFT (the tag-related operations are integrated into the processor pipeline) and off-core DIFT (the tag-related operations are packaged at the back end of the commit stage in the processor pipeline). We discuss the offload DIFT, wherein target applications and DIFT applications run on different processor cores, in Sections 4 and 5.
## 4 System Architecture
### PGDIN Components
Further, it stores implicit information expressed in the ontology, custom power network operation rules (expressed in SWRL), and new assertions or knowledge (e.g., about certain problems, assets behaviour, _etc._) that has been automatically generated at the inference process. This technology is implemented by means of the components: Pellet, Jess, and Jena Ontology API. * _Agent Platforms_: The agents hosted in the platform present the ability to follow certain behaviours and to react upon specific situations. Moreover, they may communicate with other agents to collaboratively reach a certain goal (e.g., anticipate incorrect behaviour of the network, restoring the supply, issue a consumption forecast, coordinate distributed generation, _etc._) There are several approaches and alternatives to implement agents following the FIPA specifications, and the PGDIN implements the JADE bundle (eventually, depending on the final hardware platform and its performance, some PGDINs may alternatively implement a light-weight agent platform such as LEAP). Further information on the PGDIN design can be found in [21]. This technology is implemented by means of the components: JADE and OSGi. * An _OSGi_ (Open Services Gateway initiative) _container_: It allows to apply life-cycle management (with functions such as install, stop, or start) though a set of well-known APIs, facilitates the detection, addition and removal of services, and allows them to react in consequence. * _SOA_ (Service-Oriented Architecture) _interface_: SOA helps to encapsulate legacy applications (e.g., SCADA, EMS or DMS applications) as services. This technology is not implemented in PGDIN 1.0. * _Message-Oriented Middleware_ (MOM): MOM enables bidirectional real-time reliable communication, which allows handling large amounts of information and can adapt to changes. More accurately, PGDINs implement the OMG DDS (Object Managing Group's Data Distribution Service standard) 1.2 standard. Similarly, DDS allows real-time data subscribing and publishing with parameterisable quality of service. This technology is implemented by means of the components: DDS and API DDS. * _ESP_ (Event Stream Processing) / _CEP_ (Complex Event Processing): ESP/CEP enables processing events (e.g., route, aggregate, filter) and allows complex calculations and execution of rules at runtime. This technology is implemented by means of the components: Esper and Oracle EP.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
###### Abstract  
Bearings play a crucial role in mitigating loads, maintaining stability, and transferring forces between superstructures and substructures. However, bearing failures caused by external factors can compromise structural safety. Therefore, continuous monitoring of bearing displacement is essential, yet current inspection methods are labor-intensive and unsuitable for long-term management. To address this, researchers have proposed systems such as Linear Variable Differential Transformers (LVDTs) and computer vision-based monitoring methods to track bearing displacement over time. However, reliance on external power sources and complex installation processes has limited their widespread application. This paper proposes an automated monitoring system integrating low-power IoT sensors, computer vision, and cloud computing. The system features an event-driven power mechanism to minimize energy consumption and utilizes vision-based displacement measurement techniques, providing both portability and efficiency. Applied in a real-world setting for nine months, the system successfully enabled the long-term monitoring of bridge bearings. The results demonstrate its effectiveness in overcoming traditional limitations and highlight its potential in supporting automated, data-driven assessments of structural stability.  
automated bearing monitoring; low-power sensing; computer-vision; SHM Article
## 3 The Performed Test Procedure and Apparatus
### Sample Preparation  
For these tests, samples consisting of Iddefjord granite were used. Iddefjord granite was used due to its homogenous appearance, and the NTNU/SINTEF laboratory has a comprehensive UCS database for this specific rock type to facilitate the comparison of the test results. To some extent, one can say that Iddefjord granite constitutes a reference rock type for our laboratory.  
The core samples were drilled from a cubical rock block of Iddefjord granite of about 30 cm in each dimension. The rock block was intact, without any visible cracks. The rock material is, in this context, considered relatively homogenous, as shown in Figure 3.  
After drilling, the cores were controlled to check the intactness and smoothness of the core wall. The qualified long cores were then divided into shorter sections of approximately 120 mm. The two ends of the short cores were flattened to an accuracy of 0.02 mm and perpendicular to the core axis. The preparation of the cores was performed according to sample preparation recommendations in the ISRM's _Suggested Methods for Determining the Strength of Rock Materials in Triaxial Compression_[2]. The sample was prepared and tested as soon as possible without any further drying of saturation. Thus, it can be stated that the samples were normal air-dried samples.  
Samples A, B, and C after preparation are shown in Figure 4. A sample with installed sensors and ready for testing is presented in Figure 5.  
Figure 3: Granite core samples were drilled from an intact and homogenous rock block (Iddefjord granite). The arrows are just to mark the direction of the cores in relation to the block. The arrows are not important to the test.  
the Schmidt hammer N-type was not used on the rock cores as the energy impact is strong (stronger than the L-type) and may destroy the rock core. The rebound value L-type was then estimated using a correlation proposed by [PERSON] and [PERSON] [40], which is \(Rn(\text{N})=7.124+1.249 Rn(\text{L})\). Results of the measurements and non-destructive tests are presented in Table 3.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 2 Related Works
The Tree Construction Request is then re-broadcast to other possible two-hop devices. Two-hop devices evaluate the received Tree Construction Requests to decide their parents. All end devices are required to transmit traffic periodically to maintain the topology. The proposal was evaluated in a testbed containing one gateway and ten nodes, which were located manually to have two-hop nodes in the topology. The work in [22; 23] proposed a multi-hop communication protocol based on a centralized MAC LoRaWAN instead of the MAC mechanisms used by standard LoRaWAN, such as Listen Before Talk and ALOHA. It uses Time Division Multiple Access (TDMA) as the medium access strategy to target real-time IoT applications. The main goal of the proposed system was to decrease the Time on Air (ToA) by utilizing a multi-hop technique to allow the LoRa device to use a smaller spreading factor. The proposed solution adopted a hierarchical topology approach. The end devices are organized in advance of the network operation; hence, the network topology is assumed to be priorly known. The whole topology is divided into several layers, where the first layer constitutes devices with one hop distance from the network gateway. Relaying of messages is restricted to devices that belong to adjacent layers. The system assumes the usage of devices that exclusively work as relay devices. It also uses frequent Bacon broadcasting to maintain node synchronization in the network. The proposed system was evaluated in a simulation environment using OMNeT++ with three layers of tree topology containing up to 54 nodes.
## 2 Background  
### Self-Attention  
The SAM is primarily used for handling complex data. The SAM can capture the relationships between the overall feature sequence and the current moment's features, assigning different attention weights to input data features [17]. Compared to the attention mechanism, SAM not only swiftly captures internal feature relationships in data, thus reducing dependence on external information, but also allows the model to attend to all positions within the input sequence simultaneously during processing.  
The three crucial vectors comprising the self-attention mechanism are \(Q_{i}\), \(K_{i}\), and \(V_{i}\) vectors. These vectors are obtained through linear transformations of the input data \(X=\{x_{1},x_{2},\cdots,x_{i}\}\) using corresponding learned weight matrices \(W_{q}\), \(W_{k}\), and \(W_{p}\).  
To represent the attention level between each pair of input sequence elements \(x_{i}\) and \(x_{j}\), attention scores need to be computed. These scores are initially calculated as the dot product of the \(Q_{i}\) and the \(K_{i}\), which is divided by a scaling factor. According to reference [18], the calculation formula is as follows:  
\[score(Q_{i},K_{i})=\frac{Q_{i}K_{j}}{\sqrt{d_{k}}} \tag{1}\]  
In the equation, \(d_{K}\) denotes the dimensionality of the \(K\) vector.  
To normalize the computed attention scores using function softmax--transforming them into values between 0 and 1 such that their sum equals 1--we obtain attention weight values. The calculation formula is  
\[w_{ij}=softmax(score(Q_{i},K_{i}))=softmax\bigg{(}\frac{Q_{i}K_{j}}{\sqrt{d_{k }}}\bigg{)} \tag{2}\]  
In the equation, \(W_{ij}\) represents the attention weight.  
We calculate the final output result:  
\[z_{i}=\sum_{j=1}^{n}w_{ij}V_{j} \tag{3}\]  
In the equation, \(Z_{i}\) represents the final output result.  
The principle structure diagram of self attention mechanism is shown in Figure 1[19]. Introducing self-attention mechanisms in neural networks effectively addresses the issue of information overload in predictive models [20]. Self-attention allows the model to dynamically adjust attention distributions when processing input sequences, thereby reducing reliance on the entire sequence and improving computational efficiency.
# Recent Advances in Black Silicon Surface Modification for Enhanced Light Trapping in Photodetectors
## 3 Dry Methods for Black Silicon Fabrication
### Femtosecond Laser Ablation
demonstrated that optimizing the roughness and aspect ratio of porous nanostructures on BSi surfaces resulted in near-perfect absorption across a broad spectrum [69]. Moreover, sub-wavelength nanostructures (with features smaller than the wavelength of light) on BSi contribute to effective light absorption by suppressing light reflection through destructive interference of reflected waves. According to the Effective Medium Theory (EMT), when surface features are much smaller than the wavelength of incident light, the material acts as an effective medium with a smoothly varying refractive index. This leads to minimal reflection and increased light absorption over a wide spectral range, making BSi highly effective in applications requiring broadband absorption [69]. Surface morphology significantly influences light-trapping efficiency. Tailoring the surface features, such as creating spiked or porous structures, enhances the material's ability to trap light at specific wavelengths, particularly in the infrared region. For example, [PERSON] et al. demonstrated that Se-doped BSi surfaces, processed using laser ablation techniques, achieved broadband light absorption (400-2200 nm) by optimizing the surface morphology, resulting in improved light trapping efficiency and photodetector performance [23].
## 4 Case Study  
### Problem Definition  
The real-world case employed in this study was solicited from the air conditioning division of a global manufacturing firm based in Korea. Globally renowned for the high quality of its air conditioners, washing machines, refrigerators, smart TVs, and mobile phones, this firm has extensive global production and sales activities, with global sales worth USD 57.7 billion in 2017. Further, this firm is highly connected to a complex global supplier network, and consequently operates and assigns designated supply network strategy teams to each division for risk and sustainability management. Based on interviews with general managers from the supply management and procurement divisions of home appliance department, a final network of a representative product that consists of 19 suppliers and their network structures were identified (Figure 1). To gain a better understanding regarding the conceptual design of supplier network from a practitioner's perspective, this study initially adopted prior theoretical knowledge on supply network design and requested practitioners' comments based on their experiences. Interview topics included a general question around what is the most common supplier network structure that is observed among business divisions that make transactions on both global and local level. Once the dominant suppliers and its interrelationships are identified regarding its degree of connectivity, then the practitioners were requested to evaluate the level of transaction value and the size of each firm.  
The suppliers of the focal firm are mainly composed of manufacturers (MFG) and assembly factories which have their own network of suppliers. Manufacturers are represented by the main parts they are responsible for: component parts (Part), motor system (motor), product frame (frame), jet engine (jet), integrated circuit chip (IC chip), pipe components (pipe), and main final product (main). The characteristics of a network structure includes a mixture of centralized and potential block-diagonal relationships. Suppliers are divided into three tiers. The first tier consists of the firm's own local and global assembly and production sites. Notably, suppliers 8 and 15 are significantly larger than the others as they primarily serve as main 'hubs', acting as moderators in the network. Suppliers 9-11 and 12-14 are located near each other, displaying potential business or social relationships.
# Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot
## 2 Methods
### Sensing System Design
#### 2.3.2 Trunk Posture and Hip Joint Acceleration Measurements  
The human body is firmly connected to the exoskeleton via the trunk and feet, and a unique motion mapping from the exoskeleton to the human body is ensured by APAL kinematics and its structure design. Thus, we are able to derive motion data of the human body from the embedded sensing system in the exoskeleton. An IMU is adopted to measure the trunk posture and acceleration of the hip joints. As shown in Figure 4, the axis of the IMU is set parallel to the body coordinate axis, so IMU directly provides the attitude (\({}^{W}\mathbf{\theta}_{IMU}=\{\theta_{roll},\theta_{pitch},\theta_{yaw}\}\)) in the world coordinate system (WCS), and the angular velocity (\({}^{B}\mathbf{\dot{\theta}}_{IMU}=\{\omega_{roll},\omega_{pitch},\omega_{yaw}\}\)) and acceleration (\({}^{B}\mathbf{a}_{IMU}=\{^{B}\bar{x}_{IMU},^{B}\bar{y}_{IMU},^{B}\bar{x}_{IMU}\}\)) of the body coordinate system (BCS). The angular acceleration (\({}^{B}\mathbf{\dot{\theta}}_{IMU}\)) of the IMU in the BCS can be obtained via the first-order difference of \({}^{B}\mathbf{\dot{\theta}}_{IMU}\). To improve the smoothness and reduce the high-frequency noise induced by quantization error, interpolation and a low pass filter are used.  
For the trunk posture, since the IMU is fixed in the exoskeleton back frame which is securely connected to the human trunk, the posture of component \(B\) equals \({}^{W}\mathbf{\theta}_{IMU}\). The acceleration (\({}^{W}\ddot{x}_{H},^{W}\ddot{y}_{H},^{W}\ddot{z}_{H})\) of the hip joint cannot be measured directly in the WCS by the IMU because \(O_{1}\) and \(O_{2}\) do not coincide with \(O_{B}\), but they can be derived through kinematic relations. The acceleration (\({}^{B}\mathbf{a}_{IMU}\)) obtained by the IMU is first converted from the BCS to the WCS as \({}^{W}\mathbf{a}_{IMU}={}^{B}\mathbf{b}^{W}\mathbf{R}\cdot{}^{B}\mathbf{a}_{IMU}\)  
Figure 3: Hardware system and the active power-assist lower limb (APAL) prototype.  
where \({}^{BtoW}\mathbf{R}\) is the rotation matrix for the BCS-WCS conversion. Let the coordinates of the IMU in the BCS be \({}^{B}\mathbf{P}_{IMU}\), and those in the WCS be \({}^{W}\mathbf{P}_{IMU}\). Thus, the coordinates of the left hip joint in the BCS are \({}^{B}\mathbf{P}_{H}\), and those in the WCS should be \({}^{W}\mathbf{P}_{H}={}^{BtoW}\mathbf{R}\cdot(^{B}\mathbf{P}_{H}-{}^{B}\mathbf{P}_{IMU})+{}^{W} \mathbf{P}_{IMU}\). The acceleration in the WCS is \({}^{W}\mathbf{a}_{H}={}^{W}\tilde{\mathbf{P}}_{H}={}^{BtoW}\tilde{\mathbf{R}}\cdot(^{B}\mathbf{ P}_{H}-{}^{B}\mathbf{P}_{IMU})+{}^{W}\mathbf{a}_{IMU}\). Here, \({}^{BtoW}\tilde{\mathbf{R}}\) is the second-order differential of the rotation matrix \((^{BtoW}\mathbf{R})\). The required \({}^{W}\ddot{x}_{H}\) and \({}^{W}\ddot{z}_{H}\) are components of \({}^{W}\mathbf{a}_{H}\) on axes \(x_{W}\) and \(z_{W}\), respectively.