esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Sampling and Analytical Methods
### D-O and S-Pb Isotopic Analyses  
Thirteen typical samples, from the gold-bearing disseminated and stockwork-style fine-grained granitic ones, were crushed to 40-60 mesh in order to separate the pyrite and quartz. For the quartz and pyrite, \(>\)99% purity was obtained by handpicking under a binocular microscope, and they were then used for D-O and S-Pb isotopic studies, respectively, at the Analytical Laboratory of the Beijing Research Institute of Uranium Geology.  
For D isotopic analysis, quartz grains were heated to over 600 \({}^{\circ}\)C in an induction furnace to release the water from fluid inclusions inside the quartzes, which was then converted to H\({}_{2}\) by reacting with zinc powder at ca. 400 \({}^{\circ}\)C. Regarding the O isotopic analysis, the quartz grains were first reacted with BrF5 at 500-600 \({}^{\circ}\)C for ca. 14 h to generate O\({}_{2}\), and then reacted with graphite to generate CO\({}_{2}\) at ca. 700 \({}^{\circ}\)C. The resultant H\({}_{2}\) and CO\({}_{2}\) were measured by the Finnigan MAT-253 mass spectrometer to determine the D-O isotopic composition, which was recorded relative to the Standard Mean Ocean Water (SMOW) with a precision of \(\pm\)1% [49].  
For S isotopic analysis, under a vacuum pressure of 2 \(\times\) 10\({}^{-2}\) Pa at ca. 980 \({}^{\circ}\)C, the pyrite grains were reacted with cuprous oxide to generate SO\({}_{2}\), which was then measured using a Delta V Plus mass spectrometer. The S isotope composition adopted the Canyon Diablo Troilite (CDT) standards with a precision of \(\pm\)0.2% [49]. For Pb isotopic analysis, pyrite grains were first dissolved by HNO\({}_{3}\) and HCl, then passed through an anion exchange resin to extract the Pb, which, after drying, was diluted by 1% HNO\({}_{3}\) for testing by the ISOPROBE-T Thermal Ionization Mass Spectrometer. Pb isotope results are reported in terms of normalization to the accepted ratio for NBS-981: \({}^{208}\)Pb/\({}^{204}\)Pb = 36.7119 \(\pm\) 0.0331, and \({}^{207}\)Pb/\({}^{204}\)Pb = 15.4968 \(\pm\) 0.0107, \({}^{206}\)Pb/\({}^{204}\)Pb = 16.9386 \(\pm\) 0.0131 (\(\pm\)2\(\sigma\)).
# Evaluation of the Effects of Hidden Node Problems in IEEE 802.15.7 Uplink Performance
## 3 IEEE 802.15.7 Overview
### PHY Layer  
The IEEE 802.15.7 standard PHY layer is responsible for several functions, such as transmission and reception via the optical medium, activation and deactivation of the optical transceiver, and detection of the state of the transmission channel (idle/busy).  
Figure 4: Example of scenario with hidden nodes in a VLC network.  
Figure 5: Network topologies defined in IEEE 802.15.7.
# A Sensitivity Analysis of a Computer Model-Based Leak Detection System for Oil Pipelines
## 4 Conclusions  
This study investigated how various sources of uncertainty affect leak detection with the widely used SPS Statefinder leak detection software [15]. Uncertainties in viscosity, bulk modulus, polling time, time skew, and data noise were investigated. It was found that the SPS Statefinder leak detection system can cope better with errors in viscosity than in the bulk modulus. Leak detectability was unaffected by viscosity errors as long as Statefinder was configured to allow it to fully correct the friction factor. In the case of bulk modulus errors, full correction was only achievable when the bound of the bulk modulus correction coefficient was set equal to the exact amount of required correction and when the pressure wave propagated in a certain pattern. That is, when there is a positive error in the bulk modulus and the transient causes the pressure to increase in the pipeline or there is negative error in the bulk modulus and the transient causes pressure to decrease in the pipeline. A longer polling time alone did not affect leak detectability, but time skew caused a significant degradation of leak detectability during both steady and transient operating conditions when the \"measured\" data contained Gaussian noise. Therefore, a general ranking of the importance of the uncertainty sources (from high to low) is: (1) time skew, as it always causes degraded leak detectability; (2) bulk modulus error, as it may be incorrectly adjusted in some cases, and the required amount of correction is often not known; (3) viscosity error, as it only has an impact when full correction is not allowed and the data are noisy; and (4) polling time, as it does not affect leak detectability for the cases tested. The probabilistic effect of random uncertainties of all sources caused delayed leak detection in all cases. It was also shown that in general, systems with a lower \(R\) factor were less sensitive to uncertainties compared to systems with high \(R\) factors. These findings are applicable to a wide category of real-time transient model-based leak detection systems that utilize optimization algorithms to minimize the difference between the modelled and measured pipeline states.  
This research was funded by a grant from Enbridge Pipelines Inc. and by the Natural Sciences and Engineering Research Council of Canada Collaborative Research Development grant, both of which are gratefully acknowledged.  
[PERSON] and [PERSON] conceived and designed the study; [PERSON] performed the study and wrote the draft paper; [PERSON] and [PERSON] revised and edited the manuscript.  
The authors declare no conflict of interest.
## 5 Towards a Standardized Framework for Digital TPM Implementation
### Technological Choices and Integration Logic  
Starting first with the technological level, a standardized framework should be built on a modular and layered architecture that supports both real-time operations and advanced analytics. In most cases, as reported in the literature, implementations follow a four-layer model comprising data acquisition, preprocessing, analysis, and decision support. This structure enables the seamless integration of several critical technological enablers.  
First, IoT devices and smart sensors play a central role by enabling condition monitoring through measurements of vibration, temperature, and pressure. Next, communication gateways ensure interoperability with existing legacy systems by supporting widely adopted protocols such as OPC-UA, BLE, and LIN. In addition, cloud platforms contribute by offering remote access, scalability, and centralized data analytics capabilities. Furthermore, AI algorithms enhance the framework's intelligence, enabling functions such as failure prediction, anomaly detection, and root cause analysis. Finally, digital twins and simulation models provide tools for virtually testing maintenance strategies and conducting scenario planning  
To ensure broader accessibility, especially for SMEs, particular emphasis must be placed on low-cost and open-source solutions such as the Arduino, Raspberry Pi, and open-source SCADA systems. Moreover, the framework should incorporate interoperability guidelines and include technology maturity checklists to support consistent and reliable implementation across different contexts.
# Fed-Hetero: A Self-Evaluating Federated Learning Framework for Data Heterogeneity
## 2 Related Works
### Algorithms for Handling Data Heterogeneity
The FedLAW [5] approach uses a weight shrinking concept which is applied to the aggregation weights used in federated learning. These weights determine the contribution of each client's model updates during the aggregation process to form the global model. This adjustment impacts the influence of individual client updates on the global model by excluding the clients with lesser weights. FLAMA (Federated Learning with Adaptive Weighted Model Aggregation) [6] dynamically adjusts the model aggregation weights in each federated learning training round, considering the number of useful data samples contributed by each client and the performance of the global model. Clients with fewer or less useful data samples might receive lower aggregation weights, potentially marginalizing under-represented data distributions. The one-pass distribution sketch [22] analyzes the variations in data distributions among clients, selects clients based on these differences, and personalizes tasks accordingly. The client selection strategy excludes the clients with limited data. Federated daisy chaining [23] enables clients to share information with other clients via a server, which raises privacy concerns. Table 1 presents the summary of the federated learning algorithms and their limitations. The observations from the related works are given below:  
* The main challenges in federated learning are data heterogeneity, communication overhead, and privacy concerns. * Data heterogeneity can occur due to various factors, including quantity imbalance, label distribution imbalance, feature variability, and image variation. * Various algorithms are proposed to address the quantity skew, label distribution, and feature skew data heterogeneity. * In most existing studies, the same datasets are shared among different clients, which does not accurately reflect real-world scenarios. The limitations of the existing works are as follows:
* The literature reveals that most existing studies tend to exclude the clients with limited data by using strategies such as weighted averaging, importance-based aggregation, or adaptive weighting. * Most approaches use an information-sharing strategy that compromises privacy and performs client selection and personalization based on data distributions. * The existing works have not fully explored the potential of a server-based feedback system to notify clients with reduced performance.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
## 4 Experimental Results and Analysis
### Estimation of Channel Characteristics
#### 4.1.2 Results and Analysis  
Real calibration satellite echoes are used to estimate the channel characteristics. We used 1000 periods of real calibration satellite echoes to improve the SNR in coherent accumulation process. Figure 5 is the waveform and spectrum of a real calibration satellite echo signal in one period. The signal was submerged in noise, since the RCS of LCS4 is only 0.947 m\({}^{2}\) and the transmitting and receiving distance was around 1800 km. Figure 6 shows the waveform and spectrum of the signal, which was coherently accumulated by 1000 consecutive periods of real calibration satellite echoes in the time domain. The signal and noise can be distinguished in Figure 6a. As described in Section 3, the SNR in was improved to 12 dB and more accurate information of channel characteristic can be obtained via coherently accumulating in the time domain. Moreover, it can be seen that the waveform and spectrum of echoes are not ideal LFM signals, due to the modulation of the non-ideal channel characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter Name** & **Value** & **Parameter Name** & **Value** \\ \hline Carrier frequency & 10 GHz & Sample frequency & 1.8 GHz \\ Signal bandwidth & 1.2 GHz & PRF & 100 Hz \\ Pulse width & 10 us & Accumulated pulses & 1000 \\ \hline \end{tabular}
\end{table}
Table 1: System parameters of the bistatic ISAR.  
\begin{table}
\begin{tabular}{c c c} \hline
1 05398U 71067E & 18222.85046121 +.00000123 +00000-0 +37305-4 0 & 9996 \\ \hline
2 05398 087.6191 050.7604 0062825 345.3222 014.6148 14.33716076462694 \\ \hline \end{tabular}
\end{table}
Table 2: TLE of RIGIDSPHERE 2 (LCS4) satellite (10 August 2018).
# When Social Networks Meet D2D Communications: A Survey
## 3 How Social Ties Can Improve D2D Communications
### Relay Discovery and Peer Selection
[25] introduced the concept of sociality to identify the presence of malicious node in a network by making use of the concepts of reliability and reputation to model the level of trust among the devices involved in a D2D transmission. Similarly, the authors in [26] proposed a coalition formation game to enhance content uploading services through an opportunistic hop-by-hop forwarding scheme based on the trust of each hop. However, even if in its infancy, the research towards social networking concepts applied to D2D communications is progressing really quickly, so that some real applications can already be found. In this context, one of the first applications proposed is SoCast [27], which is a video multiCast system that uses social-aware D2D communications. Among the main features of SoCast, the system considers an incentive for the user cooperating in the communication based on the social ties between peers, which is a similar feature to [7] where the relay discovery is based on the concept of reciprocal cycles. Finally, [PERSON] et al. [28] proposed an innovative approach for D2D communications applied to a real indoor environment, based on a Social IoT (SIoT) architecture [4] able to involve all participating objects in a twofold procedure, gathering both Quality of Service (QoS) and spectrum sensing data and weighting the received information using a social trustworthiness algorithm. Figure 2 shows our classification tree for the peer selection approaches based on the two main issues addressed in this context, namely mobility and trust, with the root of the tree aggregating general works applying social concept to the topic.
## 3 Results
### System Calibration  
Manual placement of the transducers in the hemisphere resulted in small deviations in the distance between each sensing surface and the target. Post-processing was performed to shift the signals of each channel in time domain. Using an optimization algorithm and an appropriate cost function, an automatic calibration can be performed. In this study, we used the continuous sequential (CS) optimization algorithm [60]. According to the Nyquist theorem, the step size should be smaller than half the wavelength. Considering the central frequency of the transducers, i.e., 5 MHz, its bandwidth and the speed of sound of 1500 m/s, an optimization step size of 100 \(\upmu\)m is adequate which is equivalent to three samples with a 50 MS/s DAQ sampling frequency. A flowchart of the continuous sequential algorithm is shown in Figure 3a. In this optimization algorithm (see Figure 3a), each channel was shifted with an increment of 100 \(\upmu\)m to a maximum displacement of 1.5 mm forward and 1.5 mm backward, creating 31 possible sets of shifted data per channel. These data were then reconstructed using the DMAS algorithm. Due to the very large search space, i.e., 31\({}^{50}\) combinations and lack of a reference image, an automatic optimization process will take a very long time. Therefore, the optimality of the channel data shift was determined by the user through visually assessing reduction in background signal and alignment of imaged object features. The best reconstructed image out of 31 for each of the 50 channels was chosen sequentially and the corresponding signal shift was stored. The reconstructed images before and after optimization are shown in Figure 3b(i,ii), and the quantification of the CNR for each volume is shown in Figure 3c. As seen in Figure 3c, CNR was improved by 30% after optimization.
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 3 System Design  
Since the release of Bluetooth mesh networking specifications in July 2017, Bluetooth has three topology options: point-to-point, broadcast, and mesh [31]. Point-to-point connections are commonly used for audio streaming or data exchange between a smartphone and a headset or a smart watch. Broadcast topologies offer one-to-many device communications, which is well-suited for beacon solutions. Mesh topologies allow many-to-many device communications, which is ideal for building sensor network applications. In this work, we combine the latter two topology options--broadcast and mesh--to extend the applicability of beacon solutions. Specifically, the unique feature of our system is that we augment the BLE beacon devices with the capability of forming a mesh network, so interactive applications inside the beacon mesh network can be fulfilled solely based on the transport of the BLE technology. In the following, we will describe the system architecture, the principle of operation, and the design of messages in the beacon network.
## 3 Numerical Method and Solutions
### Discretization Scheme
We move back into frequency space in order to compute \(\
abla\cdot(\
abla(u_{i}\cdot(K*u_{i})))\), \(\
abla\cdot(\
abla(u_{i}\cdot(K*u_{j})))\), and \(\
abla\cdot(u\
abla(U))\), using property (4). For example,  
\[\left(\widehat{\
abla\cdot(u_{i}\
abla(K*u_{i}))}\right)_{kv}^{n}=ik\left( \overline{(u_{i}\partial_{x}(K*u_{i}))}\right)_{kv}^{n}+iv\left(\overline{(u_{ i}\partial_{y}(K*u_{i}))}\right)_{kv}^{n}.\]  
Finally, we use the IFFT to move back to physical space. This gives us a discrete representation of the right-hand side for group \(i\) at time \(t=n\Delta t\),  
\[\partial_{t}\mathbf{u}^{i,n}=f(\mathbf{u}^{i,n}),i=1,...,N, \tag{8}\]  
where \(f\) is the process of computing the right-hand side using the FFT and IFFT, as described above. We can solve for equilibrium solutions directly by setting \(\partial_{t}u^{i,n}=0\) in (8) for \(i=1,...,N\). To investigate the dynamics of the system, we can time step until we reach equilibrium. One can use Equation (8) and an ODE solver to find \(u^{i,n+1}\). In particular, we use the Runge-Kutta method in MatLab. We determine error at time \(n\Delta t\) by computing the \(L^{2}\)-norm of \(f(u^{i,n})\) for \(i=1,...,N\). For an equilibrium solution, this value should be zero. We continue time-stepping until this value is below a chosen tolerance. computations for higher values of \(N\). When the parameter \(b\) is larger, indicating stronger aggregation within groups, a higher value of \(\eta\) helps balance this effect. An observation from Table 2 is that lower \(\eta\) values take more time to compute when \(b=0.25\), compared to when \(b=0.2\). In cases where \(\eta\) and \(b\) are well-balanced, the expense of adding groups remains relatively constant. However, in situations where \(\eta\) is small relative to \(b\), adding groups becomes more computationally expensive. A factor influencing the computational duration is the proximity of the initial data to the equilibrium solution. Additionally, we observe that when the value of \(b\) was excessively large compared to \(\eta\), or if the value of \(a\) was too large, our numerical techniques exhibited instability and failed to reach convergence. The balance between \(\eta\) and \(b\) not only impacts the computational cost of solving the system, but also influences the behavior of solutions. For instance, Figure 1 displays equilibrium solutions for the scenario when \(b=a=0.25\), corresponding to the bottom-left quadrant of Table 2. In this context, the first column (representing more aggregated groups) takes the longest time to compute compared to other simulations with the same number of groups, while the second and third columns are the fastest, but similar to the remaining four. Note that the increase in the parameter \(\eta\) leads to more overlap between territories of competing groups.
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 6 Prototype Implementation and Evaluations
### Experimental Evaluations
#### 6.3.2 Performance of Project Creation and Session Joining  
Whenever a new project is created, the initialization process involves uploading of the initial source code copy. Whenever a member joins an ongoing real-time collaboration session, the joining process involves downloading of the source code copy (for the new member only). These processing times are important to the user experience. To this end, we conducted 12 sets of experiments to measure the average processing times of project creation and session joining based on the six selected projects, and the experimental results are presented in Table 4. The experimental PC's configuration is the same as that in Section 6.3.1 (i.e., Intel Core i7 @ 2.2 GHz processor, 16 GB of RAM, macOS 10.14.6 operating system).  
It can be observed that even for large-size projects, the time costs for project creation and session joining were still acceptable. More importantly, such time cost for joining a session was only incurred at the new member's client, as the source code of the project is directly fetched from the server without involving other existing members.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multicolumn{2}{c}{**Local Editing Operation**} & \multicolumn{2}{c}{**Remote Editing Operation**} \\ \cline{2-5}  & **2C Session** & **3C Session** & **2C Session** & **3C Session** \\ \hline PythonCrawler & 802 μs & 803 μs & 2.262 ms & 0.833 ms \\ koa & 876 μs & 864 μs & 4.650 ms & 4.803 ms \\ flask & 859 μs & 817 μs & 6.095 ms & 5.940 ms \\ sharedb & 852 μs & 905 μs & 2.801 ms & 2.830 ms \\ python-twitter & 853 μs & 836 μs & 8.080 ms & 5.869 ms \\ react & 867 μs & 834 μs & 2.044 ms & 4.294 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average processing times of local and remote editing operations in two-collaborator sessions (2C sessions) and three-collaborator sessions (3C sessions).
## 3 Results and Discussion
### Seasonal Variability
However, shorter-term temporal variations are masked in monthly values, and as it will be discussed later in Section 3.4, differences between azimuths can be larger when shorter time intervals are considered. The seasonal cycles of the surface concentrations retrieved by the in situ and the MAX-DOAS measurements are in very good agreement. The lower NO\({}_{2}\) levels during all summer periods, the significant reduction in January 2021 and the peaks during the cold periods (e.g., November, December 2020; February, November 2021; and October, November 2022) are well captured by both instruments. The median NO\({}_{2}\) VCD for the period of study retrieved by the MAX-DOAS is 5.94 \(\pm\) 4.90 \(\times\) 10\({}^{15}\) molecules/cm\({}^{2}\) and the median NO\({}_{2}\) surface concentration is 0.61 \(\pm\) 0.83 \(\times\) 10\({}^{11}\) molecules/cm\({}^{3}\). The lowermost monthly median VCDs and surface concentrations were reported in July 2020 (4.16 \(\times\) 10\({}^{15}\) molecules/cm\({}^{2}\) and 0.31 \(\times\) 10\({}^{11}\) molecules/cm\({}^{3}\), respectively), while the highest were found in November 2021 (10.73 \(\times\) 10\({}^{15}\) molecules/cm\({}^{2}\) and 1.61 \(\times\) 10\({}^{11}\) molecules/cm\({}^{3}\), respectively). The monthly variability (i.e., the interquartile range), presented by the error bars, is significant, reaching maximum values in November 2021. The monthly variability captured by the in situ data is even larger, since the site is affected more by local emissions. The monthly median of the in situ data in May 2022 was unusually high, affected by days that were characterized by extreme NO\({}_{2}\) levels, probably due to very local events and activities that could not be captured by the MAX-DOAS. The relatively lower NO\({}_{2}\) concentrations that were reported by both systems during summer are related to the reduced emission sources (e.g., absence of domestic heating) and to the shorter lifetime of NO\({}_{2}\) caused by the enhanced photolysis rate due to stronger solar radiation.