esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Review of Literature and Standards
### MES from IT Standpoint
The Open Platform Communications Unified Architecture (_OPC UA_) is a product of the OPC Foundation ([51]), whose aim is to provide a service-oriented, platform-independent standard whose specification is organized into several documents, ranging from concepts and security model to services, data access, alarms, and so forth. OPC UA defines the relations and semantics of services that servers can provide, mapped onto different communication and transport protocols. Interoperability is enabled by mapping concepts between property sets from different domains and/or frameworks. There is an interesting research work presented in [16], which shows a service model similarity evidence and an interoperability affinity between OPC UA and RAMI 4.0 Administration Shell concepts.
# Autonomous Active Tag Using Energy Harvesting Strategies
## 1 Introduction
Other systems mix more than one energy harvesting technique [14; 15], such as solar and wind-based, in order to get enough energy to meet the demand of remote sensors and small-scale portable electronics; while solar and radio-frequency can be used to supply wireless sensor networks using dual storage with a supercapacitor and a battery. There is even a system-on-chip that implements all the techniques contemplated in this study to feed wireless sensor networks using a battery or supercapacitor [16]. Table 1 shows an overview of the previous energy harvesting techniques, where the energy is stored, and the application of the study. The main contribution of this paper is to ensure that an active tag can work autonomously, using power consumption management strategies and energy harvesting techniques to do so. The active tag itself is composed of a memory, a microcontroller and a transceiver. The three components together have the functionalities of saving, receiving, and transmitting data. In all these processes, the tag is able to write data in a non-volatile memory unit, which means that the processing of the data can be done in the tag. The objective is to gather enough energy to maintain the standby operation of the device and support all the wireless communication. This is achieved thanks to the integration of the energy harvesting techniques, the management of the recollected power and the low power consumption techniques, which allowed the autonomous system to be created. In other words, the creation of an autonomous active tag is based on the integration of different techniques from different fields to achieve the objective. These tags can be used in isolated places without the interaction of anybody as they recharge themselves and send the required information that the final application will need. They stay on for as long as the application needs. This will be very useful, for example, to know the state of traffic signals without staff interaction on a road, by simply getting close to the tag with a reader inside a car while driving. This is called an interaction infrastructure-vehicle, which is the application selected for testing. Another example would be for a connected house, storing information of the environmental parameters or habits.
# Corrosion and Scaling in Geothermal Heat Exchangers
## 4 Corrosion in Geothermal Environments
### Mechanically Assisted Corrosion  
Mechanically assisted corrosion refers to a phenomenon where corrosion processes are accelerated due to the influence of mechanical forces, usually tensile stress. This acceleration can take different forms depending on the nature of the mechanical load. When the mechanical load involves erosive forces, it is specifically termed erosion-corrosion. Conversely, when cyclic stress is a contributing factor, it is referred to as corrosion fatigue. In systems where passivation is a key element of corrosion protection, mechanically assisted corrosion processes are believed to disrupt the protective passive film, thereby increasing the corrosion rate. In erosion-corrosion scenarios, corrosion is expedited by the relative motion between the metallic surface and the corrosive medium. This medium can take various forms, including fluids carrying suspended solid particles and/or bubbles, steam, or combinations thereof. This relative motion continually removes the passive film or corrosion products, exposing fresh metal surfaces to the corrosive medium [36]. Consequently, areas with higher flow velocity experience a faster rate of erosion-corrosion. In geothermal systems, erosion-corrosion occurs in high-velocity and pressure fluid conditions and may lead to distortion of heat exchanger tube shapes [12].  
Corrosion fatigue, on the other hand, results from the combined effect of alternating stresses and exposure to a corrosive environment [37; 38]. This mechanism is particularly significant in passivating metals, where stresses can facilitate pit formation. These pits act as stress concentrators and initiation sites for fatigue cracks. Corrosion fatigue typically leads to brittle fractures through the growth of transgranular cracks. In geothermal systems, sources of cyclic stress contributing to corrosion fatigue have been identified, such as steam turbine vibrations and the periodic expansion/contraction of pipelines due to fluctuations in the temperature of the transported steam or fluid [10].
## 3 LPR Cell Design
### Device Simulations  
Device simulations have been conducted to measure the response of the charge sensing node to the light pulse. Figure 14 shows simulated potential profiles for turning on (125 ns and 1 \(\upmu\)s from the beginning when the light is on) and off. Both potentials of photodiode and charge sensing node are going up for turning off and going down for turning on. The response of the photodiode potential is not so much different from that of the charge sensing node. Therefore, the capacitance of the photodiode influences to the response while the model of the transient analysis in Section 3.2 considers the charge sensing node capacitance only. The influence of the photodiode capacitance can be translated into the increase of the equivalent charge sensing node capacitance. Figure 14(a,b) show the simulated waveform of photocurrent and voltage response of V\({}_{\text{CS}}\) node in light on and off conditions. For the turning-on response, \(y_{\text{on}}(t)\) defined as follows is introduced.  
\[y_{\text{on}}(t)=\ln\left\{\frac{I_{p}}{I_{d,\text{on}}(t)}-1\right\} \tag{24}\]  
Using Equation (7), \(y_{\text{on}}(t)\) is given by:  
\[y_{\text{on}}(t)=\ln\left\{\frac{I_{p}}{I_{d\text{M}}}-1\right\}-\frac{t}{\tau} \tag{25}\]  
Therefore, \(y_{\text{on}}(t)\) is a linear function of \(t\) and the time constant \(\tau\) can be obtained by the gradient of the plot of \(y_{\text{on}}(t)\).  
For the turning-off response, \(y_{\text{off}}(t)\) defined as follows is introduced:  
\[y_{\text{off}}(t)=\frac{I_{p}}{I_{d,\text{off}}(t)} \tag{26}\]  
Using Equation (11), \(y_{\text{off}}(t)\) is expressed as:  
\[y_{\text{off}}(t)=1+\left(t-T_{\text{{}_{C}}}/2\right)/\tau \tag{27}\]  
Therefore, \(y_{\text{off}}(t)\)is also a linear function of \(t\) and the time constant \(\tau\) can also be obtained by the gradient of the plot of \(y_{\text{off}}(t)\).  
Figure 15(a,b) are plots of \(y_{\text{on}}(t)\) and \(y_{\text{off}}(t)\), respectively, which are calculated from the simulation results of Figure 16(a,b). As shown in Figure 16, both \(y_{\text{on}}(t)\) and \(y_{\text{off}}(t)\) are approximately linear functions of \(t\). The summary of the simulation results is shown in Table 1. The time constants for turning on and turning off calculated by the gradient of the plots are 14.0 ns and 14.2 ns, respectively. Since the plot of y\({}_{\text{on}}\) has a slight non-linearity, the time constant is calculated by the time range of 10 to 30 ns. From these simulation results, the charge sensing node capacitances for turning on and turning off are 6.8 fF and 6.9 fF, respectively.  
Figure 16: Simulated waveforms.  
Figure 14: Potential profile during turning on and off.  
Figure 15: Measured of time constant \(\tau\) from simulation result **(a)** Light ON phase and **(b)** Light OFF phase.
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
###### Abstract  
The added value of combining InSAR and GNSS data, characterized by good spatial coverage and high temporal resolution, respectively, is evaluated based on a specific event: the propagation of the magma intrusion leading to the 26 May 2016 eruption at Piton de la Fournaise volcano (Reunion Island, France). Surface displacement is a non linear function of the geometry and location of the pressurized source of unrest, so inversions use a random search, based on a neighborhood algorithm, combined with a boundary element modeling method. We first invert InSAR and GNSS data spanning the whole event (propagation phase and eruption) to determine the final geometry of the intrusion. Random search conducted in the inversion results in two best-fit model families with similar data fits. Adding the same time-period GNSS dataset to the inversions does not significantly modify the results. Even when weighting data to provide even contributions, the fit is systematically better for descending than ascending interferograms, which might indicate an eastward flank motion. Then, we invert the GNSS time series in order to derive information on the propagation dynamics, validating our approach using a SAR image acquired during the propagation phase. We show that the GNSS time series can only be used to correctly track the magma propagation when the final intrusion geometry derived from InSAR and GNSS measurements is used as an a priori. A new method to extract part of a mesh, based on the representation of meshes as graphs, better explains the data and better accounts for the opening of the eruptive fissure than a method based on the projection of a circular pressure sources. Finally, we demonstrate that the temporal inversion of GNSS data strongly favors one family of models over an other for the final intrusion, removing the ambiguity inherent in the inversion of InSAR data.  
InSAR; GNSS; Piton de la Fournaise; Mike propagation; inversion; joint inversion 26 August 2019; Accepted: 20 September 2019; Published: 25 September 2019
# TSCH Multiflow Scheduling with QoS Guarantees:
###### Abstract  
Industrial Wireless Sensor Networks (IWSN) are becoming increasingly popular in production environments due to their ease of deployment, low cost and energy efficiency. However, the complexity and accuracy demanded by these environments requires that IWSN implement quality of service mechanisms that allow them to operate with high determinism. For this reason, the IEEE 802.15.4e standard incorporates the Time Slotted Channel Hopping (TSCH) protocol which reduces interference and increases the reliability of transmissions. This standard does not specify how time resources are allocated in TSCH scheduling, leading to multiple scheduling solutions. Schedulers can be classified as autonomous, distributed and centralised. The first two have prevailed over the centralised ones because they do not require high signalling, along with the advantages of ease of deployment and high performance. However, the increased QoS requirements and the diversity of traffic flows that circulate through the network in today's Industry 4.0 environment require strict, dynamic control to guarantee parameters such as delay, packet loss and deadline, independently for each flow. That cannot always be achieved with distributed or autonomous schedulers. For this reason, it is necessary to use centralised protocols with a disruptive approach, such as Software Defined Networks (SDN). In these, not only is the control of the MAC layer centralised, but all the decisions of the nodes that make up the network are configured by the controller based on a global vision of the topology and resources, which allows optimal decisions to be made. In this work, a comparative analysis is made through simulation and a testbed of the different schedulers to demonstrate the benefits of a fully centralized approach such as SDN. The results obtained show that with SDN it is possible to simplify the management of multiple flows, without the problems of centralised schedulers. SDN maintains the Packet Delivery Ratio (PDR) levels of other distributed solutions, but in addition, it achieves greater determinism with bounded end-to-end delays and Deadline Satisfaction Ratio (DSR) at the cost of increased power consumption.  
AMUS; IWSN; Orchestra; QoS; SDN; TSCH
## 2 Related Works
AEDP [6] is an adaptive energy detection protocol for LPL, which dynamically adjust a node's CCA threshold to improve network reliability and duty cycle based on application-specified bounds. AEDP can effectively mitigate the impact of noise on radio duty cycles while maintaining satisfactory link reliability. [PERSON] et al. present interference aware adaptive clear channel assessment (IAACCA), which more proactively contends for channel access by replacing the standard CCA [26]. In this method, unlike the Zigbee that performs single CCA, the sequence of CCAs is performed until the channel is found to be clear. Under Wi-Fi interference, IAACCA reduces packet loss compared to standard CSMA mechanism. In IAACCA, a policy decision is adapted after collision with interference. In the other proposed method, ZiSense [27] reduces false WakeUps by using an active scanning technique in duty cycling MAC protocols. The authors in Zisense present one approach to realizing DCCA by RSSI sampling at high frequency, listening for timing and spectral characteristics indicative of 802.15.4.P-DCCA [25] proposed an optimized approach to differentiating clear channel assessment (DCCA) so that a variety of output power is considered in the transmission mode. A P-DCCA check indicates two states, when the transmission medium is occupied by another WSN node, and when the channel is occupied by external interference. This method is based on ContikiMAC, the radio duty cycle protocol. The above methods are either hardware-based or in some case, they are based on WiFi radio. In some methods, they are based on a single CCA in every WakeUp as opposed to the ContikiMAC protocol. Nevertheless, some of them have high complexity in terms of code and also dependent on the sink coordination, routing control, and signal modulation. In the current paper, LW-CCA (light weight clear channel assessment) is presented as an extension of ContikiMAC. It is designed to reduce energy consumption caused by idle listenings and false WakeUps in the ContikiMAC concerning the high packet delivery rate (PDR) [28]. LW-CCA mechanism is a non-complex method, and it is independent to sink and to route mechanism by minimum complexity in programming. The LW-CCA, through focusing on the time factors in radio wake-up, offers a method to reduce the percentage of radio duty cycles in false WakeUps and idle listenings. Finally, LW-CCA is compared via simulation to the basic ContikiMAC. The proposed method can be used on low power IOT platforms based on 802.15.4 radio and Contiki OS, such as Z1, Tmote-SKY, and Micaz [29].
## 3 Results and Analyses
### RA-AF Analysis Results

Table 3: Energy and amplitude ranges for each cluster. Figure 14: _Cont._  
Figure 12: Probability density of RA-AF by GMM. Figure 13: RA-AF distribution calculated by K-means. Figure 14: RA-AF probability density for each loading stage. Figure 15: RA-AF distribution for each loading stage. As demonstrated, the characteristics of acoustic emission (AE) signals vary across different damage stages, and the RA-AF values obtained through clustering analysis exhibit distinct patterns. This enables the systematic classification of damage mechanisms and facilitates the establishment of damage-specific labels for experimental data, which can be utilized to develop predictive models. Based on the aforementioned analysis, AE signals are categorized into seven distinct classes based on their underlying damage mechanisms, as summarized in Table 4.
# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 4 Experiment Results and Analysis
### Ablation Experiment  
This paper uses YOLOv11n as the baseline to conduct ablation experiments on the improved algorithm (all experiments are performed on the pre-trained model generated by the ADA-StyleGAN3 dataset) and verifies the effectiveness of each improvement in sonar target detection by sequentially adding different improvements to the model.  
Firstly, as shown in Table 2, when replacing the original detection head with the FASFF four-head detection head, it can be seen from the table below that GFLOPs increase significantly, and the number of parameters increases by about 1.4 M, but the Pre, R, and mAP50 only increase by 0.2%, 2.8%, and 0.4%, respectively. This is because sonar target detection is not simply small-scale target detection. Sonar images suffer from low resolution and strong interference. Relying solely on FASFF's feature fusion and small-target detection head may introduce computational complexity, harming performance. Still, FASFFHead is crucial. When used alone, its performance gain is not obvious compared to the increased computation, but when combined with our network, it significantly enhances feature fusion and detection stability. As shown in experiments 6 and 8, using FASFFHead brings a qualitative improvement. Also, Table 3 indicates that models with more parameters than ours without FASFFHead do not match its performance.  
Secondly, when adding the EEA attention mechanism or the C3K2_Sc module separately, it can be seen that compared to the YOLOv11n baseline model, the number of parameters decreases while Pre, R, and mAP50 are significantly improved. This indicates that both the EEA attention mechanism and the C3K2_Sc module have the ability to reduce data redundancy and show greater sensitivity to sonar targets and more excellent feature extraction capabilities for sonar images. Then, when adding these two improvement methods simultaneously, it can be seen that groups 5, 6, and 7 all exhibit better performances, with Pre and mAP50 in each group reaching over 80%. Notably, the accuracy of group 7 reaches 88.4%, which is 12.2% higher than the baseline.  
Finally, when integrating the four improvement methods to form the SFE-YOLO model, the final accuracy reaches 92%, the recall rate reaches 90.3%, and mAP50 is 12.7% higher than the baseline, reaching 89.7%. This demonstrates the superior performance of SFE-YOLO in sonar target detection. In conclusion, the improved network structure achieves significant improvements in all evaluation metrics, and the number of parameters and computational cost remain within a practical and controllable range.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 2 Related Work
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Voting Scheme** & **EI** & **Pr** & **Vr** & **Us** & **Limitations** & **Strengths** \\ \hline Pret a Voter [12] & ✓ & ✓ & ✓ & \(\times\) & Secrecy of the election is at risk when the election authority is compromised. Uses cut-and-choose protocols which require complex interaction with the voter. & Offers verifiable elections. \\ \hline Wombat [13] & \(\times\) & ✓ & ✓ & \(\times\) & Paper-and-cryptographic-based voting system. Usability issues identified during the trials which arise due to the ballot design. & Overcomes the privacy issues associated with demand print ballots. \\ \hline Scantegrity II [14] & \(\times\) & ✓ & ✓ & \(\times\) & Does not support verifiability in remote voting. Uses cut-and-choose protocols which require complex interaction with the voter. & Individual verifiability is achieved by a cut-and-choose mechanism. \\ \hline Helios [11] & \(\times\) & ✓ & \(\times\) & \(\times\) & Does not support verifiability in remote voting. Requires a separated public authentication-mechanism service. It is vulnerable to ballot stuffing. \\ \hline Pretty Good Democracy [18] & ✓ & \(\times\) & ✓ & \(\times\) & Privacy is at risk as the election system has knowledge of the voting codes associated with the verifiability process. & Individual verifiability is achieved by making use of a code-vote approach. \\ \hline Belenios [15] & ✓ & ✓ & ✓ & \(\times\) & Not suitable for use in high-stake elections as it is not coercion-resistant. & Offers verifiable online elections. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Motivating the need for PVPBC by comparing the strengths and shortcomings of existing e-voting systems under the headings: Eligibility (EI), Privacy (Pr), Verifiability (Vr) and Usability (Us).
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 3 Method
### Loss Function  
In the training of deep learning networks, the design of the loss function is crucial for model performance. This section's loss function consists of two parts, mainly used for edge-guided supervision and dual-path decoder prediction generation. The former learns edge information from feature maps, while the latter continuously updates the generated predictions. The overall loss function of this network is:  
\[L=L_{E}+L_{pre} \tag{20}\]  
\(L_{\text{E}}\) is the edge supervision loss in the edge guidance module, and \(L_{\text{pre}}\) is the predicted loss instance activation loss. \(L_{\text{E}}\) is defined as:  
\[L_{E}=\lambda_{\text{edge}}L_{\text{edge}} \tag{21}\]  
\(\lambda_{\text{edge}}\) and \(L_{\text{edge}}\) are the cross-entropy losses of two edge stages. The predicted loss \(L_{pre}\) of the dual-path decoder is defined as:  
\[L_{pre}=\sum_{i=0}^{D}(\lambda_{ce}L_{ce}^{i}+\lambda_{dice}L_{dice}^{i})+ \lambda_{cls}L_{cls}^{i} \tag{22}\]  
In the context of the Transformer decoder, the variable D represents the number of layers. The value of i = 0 denotes the prediction loss of the IA bootstrap query prior to its integration into the Transformer decoder. The variables \(L_{ce}^{i}\) and \(L_{dice}^{i}\) refer to the binary cross-entropy loss and dice loss, respectively, associated with the segmentation mask. \(L_{cls}\) represents the cross-entropy loss for object categorization, with a \"no object\" weight of 0.1. \(\lambda_{ce}\), \(\lambda_{dice}\), and \(\lambda_{cls}\) are hyperparameters that serve to balance the three losses. Similarly, the Hungarian algorithm is employed to search for the optimal two-part match for target assignment. Additionally, an additional location cost, \(\lambda_{loc}\)\(L_{loc}\), is incorporated for each query, in order to account for the cost of the location of the query.