esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Proposed Approach
### Setup Phase  
In this section, we describe the setup phase, along with the smart contract functionalities we employ in our approach. This smart contract ensures that both publishers and subscribers can verify that the sent/received messages have not been tampered with and that the broker/publisher cannot repudiate having sent those messages. We report the code of the smart contract in Listing 1. In the following, we refer to the notation described in the previous section. To initialize the whole system, \(B\) deploys the smart contract. Thus, \(B\) represents the owner of the smart contract. Upon authenticating \(P\), \(B\) adds its address to the smart contract via the addClient function. This way, the list of all the publishers in the system is stored on the clients array. Similarly, the list of all the subscribers interested in the topics of the publisher is stored on this array. When a subscriber wants to join a topic, it sends its Ethereum address to the broker, who then adds it to the clients array using the addClient function. The addRootIPFSAddress function allows \(B\) to map an MHT root to its corresponding IPFS address. This function can be invoked only by the contract owner (i.e., \(B\)) who locally computes the MHT root of the data received/sent. The getIPFSAddress function allows for retrieving the IPFS address associated with a given MHT root. If the MHT root, provided as input, is not found in the mapping, then this function returns 0 as the default value. As we explain in the following sections, this function can be invoked by both publishers and subscribers to check the integrity of the data published/received. Specifically, a publisher can input an MHT root to this function that is locally computed on all the data sent during a temporal round. Subscribers input this function to an MHT root that is provided by the broker. If this function returns the default value, this is a clear indication that the integrity of the data was violated in a temporal round, either in the publisher-broker segment or in the subscriber-broker segment. Then, our contract provides a function, called pullTheAlarm, through which both authorized publishers and/or subscribers can pull the alarm to let others know about the data flow integrity violation. Both publishers and subscribers can implement mechanisms to capture and react to these events emitted by the smart contract. This can be achieved using libraries such as web3j (for Java) or web3 js (for JavaScript), which provide tools for interacting with smart contracts on the Ethereum blockchain ([PERSON], 2018). As explained above, when the pullTheAlarm emits an event, both publishers and subscribers are alerted in real-time. Additionally, in this case, if this function is called by a legitimate publisher or subscriber, the function emits a ClientAlert event. To be considered legitimate, the blockchain address of a publisher/subscriber should be present in the clients array. To perform this check, the pullTheAlarm function invokes the isClientAddress internal function.
## 4 Experiments and Analysis
### Edge Computing Configuration  
The proposed framework was implemented on the authors' laboratory edge server. The server was equipped with a 13 th Gen Intel(R) Core(TM) i7-13700k CPU processor (Intel Corporation, Santa Clara, CA, USA) and 64 GB of memory. For the time-series forecasting model training, an NVIDIA GeForce RTX 4090 (NVIDIA Corporation, Santa Clara, CA, USA) with 24 GB of memory was used. The edge server created docker environments for each model run and allocated the available CPU resources to each docker container. In this paper, the computation overhead of container generation was not considered because it was negligible compared to the execution time of the time-series forecasting models.  
The execution time of the models was measured in docker environments on Ubuntu 24.04 (Canonical Ltd., London, UK). CPU resources were allocated to the dockers at levels ranging from 1% to 100% by increasing 1% on each run.
planes when a sufficiently long undulator is used due to the gain-guiding [6]. This means that only a single diffraction-limited radiation mode is amplified to saturation in the FEL process; however, starting from the initial shot noise of the electron beam, SASE FEL radiation shows a chaotic behavior from the stochastic nature of spontaneous radiation and exhibits inherent spectral and intensity instability. Self-seeding FEL was proposed to solve the problem by introducing a coherent light pulse that produces micro-bunching in the electron beam in an internal way [7]. In particular, self-seeding FEL showed that the spectral bandwidth can be reduced by more than an order of magnitude close to the transform-limited case [8].  
Since being opened to users, significant progress has been made at the PAL-XFEL. In terms of the accelerator, the FEL pulse energy has been continuously increased to more than 1.2 mJ in all photon energy ranges. The FEL photon energy was doubled from 12 keV to 20 keV for various experiments in the beamline. To improve the temporal coherency of the SASE FEL, self-seeding was evaluated from the very beginning, and the brightest FEL in the world was achieved at the end of 2020. To explore the new era of the X-ray pump-probe experiment, two-color FEL was implemented, showing promising results. In this paper, we present the features of the PAL-XFEL and describe the recent progress of the PAL-XFEL in detail.
## 6 Results and Discussion
### Practical Implications and Industrial Applications  
The results clearly show that the newly introduced approach can contribute to technology development in a number of industrial applications. The proposed simulation framework for estimating electric vehicle (EV) energy consumption has significant implications for both traditional industrial applications and the emerging field of software-defined vehicles (SDVs). By enabling accurate modeling of energy consumption across subsystems like HVAC, lighting and steering, the framework supports the design and optimization of EV components, improving the energy efficiency and vehicle range. Its integration into energy management systems can enhance real-time monitoring, predictive maintenance and eco-routing, while its utility in fleet management and charging infrastructure planning contributes to operational efficiency and cost reduction. Moreover, the framework aligns closely with the principles of SDVs, where the software governs most vehicle functionalities. It facilitates the development of digital twins for EVs, providing a platform for continuous monitoring, simulation and optimization of subsystem performance. This capability enables modular software updates, over-the-air improvements and dynamic energy management tailored to user preferences or specific driving conditions. Furthermore, by supporting the integration of advanced driver assistance systems (ADAS) and autonomous driving features, the framework ensures efficient resource allocation, even under high computational loads. As a standardized tool, it fosters collaboration across the automotive ecosystem and accelerates the adoption of SDVs by enabling faster innovation cycles, lower development costs, and greater adaptability to regulatory and consumer demands.
## 2 Experimental Program
### Measurement and Loading Plan  
Experimental measurement was performed in a total of three stages. First of all, an assessment was conducted to check the performance of the existing bridge before strengthening of the demolished bridge. The second step is the application of the external prestressing method, which strengthens the structure by prestressing external tendons. Finally, after strengthening, a loading test was performed to verify the performance. As shown in Figures 5 and 6, two actuators with a capacity of 5000 kN were utilized, and for safety reasons, a four-point loading test was performed with a displacement control method at a speed of 2 mm/min. The boundary condition was a simple beam using a hinge and roller at 250 mm from both ends, and the load was set such that the length of the pure bending section was 6 m with a width of 3 m from the center of the specimen to both sides. In the before strength phase, the load was applied only until the crack occurred, and in the after strength phase, the load was applied until the point of destruction of the structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Duct No.**} & \multirow{2}{*}{**Distance 1 (m)**} & \multirow{2}{*}{**Tendon No.**} & \multicolumn{2}{c}{**Measured**} \\ \cline{4-5}  & & & & **Force (kN)** & **Stress (MPa)** \\ \hline \multirow{2}{*}{1} & \multirow{2}{*}{5} & 1 & 30.8 & 613 \\  & & 2 & 32.7 & 650 \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{9} & 1 & \multicolumn{2}{c}{Tension released} \\  & & 2 & 33.4 & 664 \\  & & 3 & 25.1 & 499 \\ \hline \multirow{2}{*}{3} & \multirow{2}{*}{23} & 1 & \multicolumn{2}{c}{Tension released} \\  & & 2 & 2 & \multicolumn{2}{c}{Tension released} \\ \hline \hline \end{tabular} \({}^{\dagger}\)Distance from left end.  
\end{table}
Table 1: Result of internal tendon force measurement.  
Figure 4: Evaluation of internal tendon force.  
Sensors were installed around the displacement transducer in the middle of the span and at 1/4 and 3/4 of the span to measure the behavior of the structure and the activity of the anchorage during loading. In addition, strain gauges were installed on the internal tendons and the concrete surface. A displacement transducer that can measure the activity of the anchorage during and after strengthening was installed, and a load cell was installed at the end of the external tendon to check the change in the introduced tension.
## 4 Conclusions  
This study investigated leak detection in water-filled small-diameter plastic pipes with AE methods. Experiments were carried out in a test bed that simulated the water leaks typically occurring in service pipes of the water supply network. In particular, the possibility of detecting leaks without altering the typical operational conditions of the network was assessed.  
The tests provided moderately satisfactory results. The AE technique showed an acceptable sensitivity to the presence of active leaks in the unburied pipe. The trend of at least one AE parameter, namely the Cumulative Counts parameter, exhibited a direct correlation with both the leak distance and the leaking flow rate.  
Tests performed on the buried pipe confirmed the possibility of detecting leaks within a range of 8 m, which is basically sufficient for the application of interest. However, leak detection could not be achieved with a satisfactory reliability. In particular, the experiments showed that the pipe material and the presence of soil as surrounding medium caused a significant attenuation of the AE signal, which hampered the detection even for leaks that were rather close to the sensor location. It is worth noting that the investigation of more advanced signal processing methods (e.g., analysis of the AE waveforms in the frequency domain or even pattern recognition techniques) may be worthy of interest to possibly enhance the results. However, advanced techniques would be more computationally demanding. This would result in higher costs of the hardware components, thus being potentially incompatible with the requirements of the desired leak detection system.  
Figure 11: AF vs. RA, at the 26 db threshold level, comparison between conditions _NLb_ and: **(a)**_Lk-1b;_ **(b)**_Lk-2b._  
Figure 12: AF vs. RA, at the 27 db threshold level, comparison between conditions _NLb_ and: **(a)**_Lk-1b;_ **(b)**_Lk-2b._Based on the results provided by the experimental campaign, the authors suggest that monitoring of AE with basic signal processing techniques in the tested frequency range does not appear to be a suitable tool to detect the target leaking flow in buried polyethylene service pipes with small diameter.  
[PERSON] and [PERSON] conceived, designed, and performed the experiments; [PERSON] analyzed the data and wrote the paper; [PERSON] and [PERSON] supervised the research and revised the manuscript.  
The authors declare no conflict of interest.
## 2 Literature Review
### Hybrid Swarm-Gradient Descent Algorithms  
[PERSON] et al. [16] introduced a gradient-based PSO algorithm in which the global best solution performs localized searches using gradient descent. This hybrid approach improves solution quality but significantly increases execution time due to the combined computational demands of random searching and gradient descent steps.  
[PERSON] et al. [15] proposed a swarm-based gradient descent algorithm for global optimization. The algorithm initializes a swarm of potential solutions, which communicate and update their positions through gradient descent. As iterations progress, less promising solutions are removed, and closely positioned solutions are merged, reducing computational costs. However, the algorithm remains computationally intensive due to the need for step-size optimization at every iteration, its complex implementation, and extensive hyperparameter tuning requirements.  
[PERSON] et al. [43] developed a compact cat swarm optimization algorithm based on a Small Sample Probability Model (SSPCCSO). This algorithm combines swarm and gradient-based methods by using a compact representation of the swarm, where only a single cat move is stored at each iteration according to gradient descent principles. New cats are sampled using a probability model that adjusts based on fitness evaluations. Despite its promise for computational efficiency, SSPCCSO has yet to be tested on real-world applications, such as neural network training.  
From our narrative literature review, we can conclude the following:  
* Gradient-based algorithms are among the most practical optimization methods for high-dimensional problems; however, they often fail to reach the global optimal solution.
* Adaptive learning rates and momentum in gradient-based algorithms have been employed to mitigate suboptimal solutions but still leave room for further enhancement.
* Restart-based gradient algorithms incur higher computational costs, particularly in the case of a cold restart, where optimization begins from a completely new initial point.
* Multi-start algorithms in the literature are often complex to implement due to additional sub-procedures, such as clustering, fitting surrogate models, and memory management.  
* Swarm-based algorithms often struggle with the curse of dimensionality, making them impractical for high-dimensional problems like neural network training.
* Hybrid swarm-gradient algorithms in the literature may offer improved global optimization capabilities but often come with higher computational costs, greater implementation complexity, and numerous hyperparameters that are difficult to tune.
# Modernising Receiver Operating Characteristic (ROC) Curves +
Footnote †: The justification for making a measurement can be sought in asking what decisions are based on measurement, such as in assessing the compliance of a quality characteristic of an entity in relation to a specification limit, _SL_. The relative performance of testing devices and classification algorithms used in assessing compliance is often evaluated using the venerable and ever popular receiver operating characteristic (ROC). However, the ROC tool has potentially all the limitations of classic test theory (CTT) such as the non-linearity, effects of ordinality and confounding task difficulty and instrument ability. These limitations, inherent and often unacknowledged when using the ROC tool, are tackled here for the first time with a modernised approach combining measurement system analysis (MSA) and item response theory (IRT), using data from pregnancy testing as an example. The new method of assessing device ability from separate Rasch IRT regressions for each axis of ROC curves is found to perform significantly better, with correlation coefficients with traditional area-under-curve metrics of at least 0.92 which exceeds that of linearised ROC plots, such as [PERSON]'s, and is recommended to replace other approaches for device assessment. The resulting improved measurement quality of each ROC curve achieved with this original approach should enable more reliable decision-making in conformity assessment in many scenarios, including machine learning, where its use as a metric for assessing classification algorithms has become almost indispensable.
## 2 Materials and Methods
### Global Precipitation Measurement Spaceborne Radar (GPM-DPR) Data  
The GPM-DPR consists essentially of two radars operated in tandem and with similar antenna radiation pattern. The Ku-band (13.54 GHz) radar is very similar to that found on TRMM and scans a swath that is approximately 245 km wide across the satellite track, measuring some 49 footprints of approximately 5 km in diameter and with 250 m vertical resolution at the nadir. Figure 1 in [24] showsthe shape of such footprints as well as their coarsening in terms of vertical resolution with increasing distance from the nadir (the vertical resolution degrades from 250 m to \(\sim\)1700 m at 17\({}^{\circ}\) from the nadir). The Ka-band (35 GHz) radar matches the scan of the Ku-band radar across the central 125 km of its swath, allowing for the application of dual-frequency algorithms (obviously reducing the coverage to the central swath). Hence, on the one hand, the use of the dual-frequency algorithm and observations would limit the number of available overpasses to 393, as can be seen in [25] (Table 2, p. 1252), whereas, on the other hand, the use of the second frequency slightly improves the GPM precipitation estimates in the Swiss Alps and plateau, as can be seen in [25] Table A1 (p. 1266) and A2 (p. 1267). Consequently, in this study we opted to maximize the number of overpasses and synchronous observations (528, see Table 2 in [25]), and this implies that the 35 GHz measurements are not considered.  
The GPM data used in this study are the Ku-band level 2 data, version V04A and V05A. As for TRMM, the GPM level 2 data include many variables, but the key variable evaluated in this work is the estimated surface precipitation rate (\"precipRateESurface\"), although, in practice, there is little difference between this variable and the classical TRMM variable (\"precipRateNearSurface\"). The GPM level 2-DPR product is unlikely to be as broadly used as ground-based measurements or level 3 satellite precipitation accumulation products [26; 27; 28; 29; 30]. However, it is vitally important to the ground validation study using dense radar and gauge network (like the present study) and to the generation of the level-3 products.  
The location and vertical resolution of GPM level 2 cells are different from one overpass to another. Using a nearest-neighbor approach, each estimated surface precipitation rate was simply assigned to a GPM-like 25 km\({}^{2}\) reference pixel (see, for instance, Figures S2-S4) based on a Cartesian grid that is coincident with the Swiss national coordinate system (CH1903). In brief, the Swiss coordinates result from a conformal mapping from the Bessel reference ellipsoid onto a longitudinal cylinder, tangential in Bern, having the cross section correspondent to the great circle of the approximating sphere perpendicular to the Earth meridian passing across Bern. The tangential point has coordinates (600, 200; km).
# CoSiWiNeT: A Clock Synchronization Algorithm for Wide Area IIoT Network
## 5 CoSiWiNeT Evaluation
### Results and Validity  
The clock synchronization algorithm's essential function is to reduce the variability in raw offsets introduced by packet delay variation in a network. Therefore, a better-performing clock synchronization algorithm is the one that curbs this variability more effectively. For performance comparison of different clock synchronization methods, various parameters such as (i) predicted clock offset, (ii) predicted frequency offset, (iii) clock offset error, and (iv) algorithm execution time were used. The results of network trace 1 and 2 reveal that CoSiWiNeT performs better than state-of-the-art KF algorithm, LS algorithm, and state-of-the-practice NTP protocol on the first three parameters. The results of traces 3 and 4 reveal that the performance of CoSiWiNeT is almost comparable with state-of-the-art KF algorithm, LS algorithm, and state-of-the-practice NTP protocol on the first three parameters. However, the fourth parameter, execution time, indicates that CoSiWiNeT achieves better performance at the cost of a longer execution time (18.4 ms) compared to KF, LS, and NTP (maximum 61.53 us).  
The evaluation of of a new clock synchronization algorithm could be done in various ways. First, using test data obtained from live network characterization as a stimulus to the Matlab program and measuring the time metrics; second, using live data obtained from real industrial sites as a stimulus to Matlab program and measuring the time metrics and thirdly implementing the software solution on a hardware platform and using the same in real industrial networks and measuring the time metrics. All the above evaluation methods of a new clock synchronization algorithm vary in terms of their degree of confidence. However, practical difficulties such as availability of industrial network data or access to industrial sites for experimentation, can act as bottlenecks. The possible approach of comparing performance of various state-of-the-art algorithms with CoSiWiNeT is to run these algorithms simultaneously on different devices in the same network. However, in this case, network conditions and host device activities experienced by all algorithms would possibly not be the same at all time and thus the comparison may not be fair. To deal with the above-mentioned practical difficulties, the raw and uncorrected offset data obtained from a real WAN was used as a stimulus to validate the performance of CoSiWiNeT and compare it with KF and LS methods. Without theoretical analysis of the network, the generalization of results based on experimental evaluation can be limited. To increase the confidence in results, the longer network traces with different degrees of network conditions were used for evaluation. Such analysis of realistic case studies reduced the threats to the validity of results.