esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Program  
### Exposure Conditions  
As explained earlier, the experimental program subjected the 28-day cured cylindrical specimens to prolonged exposure to ambient and marine conditions for a period of three months. Thus, 27 specimens were subjected to extended ambient exposure after the 28-day curing for a period of three months. Similarly, another set of 27 specimens was subjected to marine exposure conditions for a period of three months after the 28-day ambient curing, making it a hybrid cured specimen set. The ambient conditions refer to exposing the test specimens in laboratory conditions, which were mainly in the range of 23 \(\pm\) 2 \({}^{\circ}\)C for an extended period of three months, as shown in Figure 1 below.  
The seawater conditions were simulated under laboratory environments for the marine conditions with reference to the previous literature [3, 32, 33, 34, 35]. Various researchers have simulated the marine exposure conditions by adopting different wet-dry cycles, high salt content, and higher water temperatures [32, 33, 36, 37, 38]. Introducing wet-dry cycles and adding salt into artificial water tanks better simulate tidal and marine environments [33, 36, 37, 39]. Thus, this study adopted an accelerated weathering tank (also called aging tanks) to better represent the harsh marine conditions, as shown in Figures 2-4 below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mix Proportion of SCGC (Kg/m\({}^{3}\))**[3, 28] & \\ \hline Fly Ash & 480 \\ \hline Slag & 360 \\ \hline Micro Fly Ash & 120 \\ \hline Sodium metasilicate (Anhy.) Alkali Activator & 96 \\ \hline Fine Aggregate & 763 \\ \hline Coarse Aggregate & 677 \\ \hline \multicolumn{2}{c}{**Workability Properties of fresh SCGC**} \\ \hline Slump Flow (mm) & 670 \\ \hline T500 (sec) & 4.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mix proportion and workability properties of self-compacting geopolymer concrete.  
Figure 1: Specimens under ambient exposure conditions.  
An electronically automated tidal cycle comprising 6 h wet followed by a 6 h dry cycle was set to simulate the tidal conditions. Thus, after every 6 h, the seawater was pumped from one tank to the other, as shown in Figure 3. Hence, the specimens were subjected to the tidal cycle. The initial conditions were set to simulate the tidal conditions.  
Figure 4: Automated tidal cycle settings.  
Figure 3: Control panel of the accelerated weathering tank.  
Figure 2: Accelerated weathering tanks.  
to a three-month exposure period of 6 h wet-6 h dry cycles. Again, the maximum cycle temperatures were set at 50 \(\pm\) 2 \({}^{\circ}\)C, and the weathering tank's salt content had 5% (mass fraction) of NaCl, simulating the salinity of seawater. All the specimens used in this study were completely immersed in the water tanks during the wet cycles, and dried during the dry periods with the help of fans and vents provided in the weathering tanks.
# River Water Quality Monitoring Using LoRa-Based IoT
## 4 System Design
### Sensors and LoRa Module  
For a better understanding of the sensors and LoRa module used in this prototype, images with the main characteristics and sensor calibration procedure are shown and explained in this section.  
The temperature sensor used is the DFRobot DFR0198 [24], which is a waterproof sensor based on the DS18B20 [25]. It communicates with the Arduino board via the 1-Wire [26] protocol in digital pin D3. Figure 11 shows the parameters of this sensor.  
Figure 10: System block diagram of the developed prototype, with the two supporting, IoT Node and Gateway.  
This temperature sensor, DS18B20, does not require calibration, as it is calibrated at the source.  
The pH sensor used is the DFRobot SEN0161-V2 [27], which sends a voltage value to the analog input A1 of the Arduino board, and with software, converts it to the pH value of the solution being tested. The parameters of the pH sensor are shown in Figure 12.  
This sensor is calibrated with software adopting the two-point calibration method using two standard buffer solutions (pH = 7.0 and pH = 4.0). This calibration procedure is shown in Figure 13.  
The conductivity sensor used is the DFRobot DFR0300 [28], which sends a voltage value to the analog input A2 of the Arduino board, and with software, converts it to the conductivity value of the solution being tested. Figure 14 shows the conductivity sensor parameters.  
Figure 11: DFRobot DFR0198, temperature sensor, parameters (adapted from [24]).  
Figure 12: DFRobot SEN0161-V2, pH sensor, parameters (adapted from [27]).  
This sensor is calibrated with software, adopting the two-point calibration method using two standard buffer solutions (EC = 12.88 mS and EC = 1413 \(\upmu\)S). This calibration procedure is shown in Figure 15.  
The turbidity sensor used is the Seed Studio 101020752 [29], which sends a voltage value inversely proportional to the turbidity value to an analog input A3 of the Arduino. This voltage is converted to turbidity values (0 to 3000 NTU) with software. The parameters of this sensor are shown in Figure 16.  
No calibration process was performed on this sensor, but the voltage-to-turbidity conversion formula (Figure 17) provided by the manufacturer was used.  
Figure 16: Seed Studio 101020752, turbidity sensor, parameters (adapted from [29]).  
Figure 14: DFRobot DFR0300, conductivity sensor, parameters (adapted from [28]).  
The LoRa module used in both subsystems is based on Semtech chip SX1276 [20], a LoRa half-duplex transceiver that operates in the ISM frequency band. The basic characteristics are shown in Figure 18.  
In both subsystems, IoT Node and Gateway, this LoRa module interconnects with the controller board (Arduino and Raspberry Pi) by SPI for communication and other auxiliary pins for control and interrupting handling.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 1 Introduction
A critical challenge remains in adapting models from a laboratory to a workshop environment, where noise, external disturbances, and varying operational settings significantly affect fault diagnosis performance. This research aims to analyze the lab-to-field generalization gap and evaluate the effectiveness of TL in improving the adaptability of diagnostic models trained on labeled lab-recorded data (source domain) when applied to unlabeled data from emulated industrial conditions (target domain). To reflect the complexities of a workshop environment, synthetic noise is embedded into lab-recorded data across various operating scenarios, enabling a systematic assessment of TL's ability to mitigate domain shifts and enhance model robustness against real-world variations. This study identifies the strengths and limitations of TL through the case of rotor unbalance detection through bearing vibration signals, a common problem in rotating machinery [19]. Even a minor unbalance can generate excessive vibrations and dynamic forces, leading to bearing wear, increased energy consumption, and system failure [20]. While frequency-domain analysis is commonly used for unbalance detection, variable-speed applications introduce additional challenges due to shifting frequency components. For example, in machine tool spindles, rotational speeds fluctuate based on the machining requirements, making it challenging to extract consistent unbalance signatures. This study used a binary classification approach to distinguish between balanced and unbalanced cases to assess model performance. This provides insights into the feasibility and effectiveness of TL in overcoming ML generalization challenges for maintenance applications. The main contributions of this study are as follows:  
* A comparative evaluation of ML and TL models for rotor unbalance classification under variable speed conditions. * Model performance benchmark on source-to-source (lab-to-lab) and source-to-target (lab-to-emulated workshop) transfer tasks. * Analysis of the influence of noise type, severity, and data availability on ML and TL classification accuracy across 24 distinct configurations, each representing a unique combination of these factors. * Practical insights into the strengths and limitations of domain adaptation techniques for industrial maintenance applications. The remainder of this paper is organized as follows. Section 2 details the materials and methods, covering experimental setup, data description, preprocessing, description of analyzed configurations, and modeling of both ML and TL approaches. Section 3 presents results and discussions, comparing ML and TL models through overall classification results, accuracy analysis per noise type, sensitivity-specificity trade-off, and detailed evaluations across unbalance levels and rotation speeds. Finally, Section 4 summarizes key findings, highlights limitations, and suggests future research directions.
# Decentralized Identity Authentication with Auditability and Privacy
## 5 Privacy-Preserving Protocol
### Intermediate Protocol
#### 5.1.3 Discussion  
The use of a VRF guarantees that the service can verify that the tokens were computed by the given identity holder and that they are consecutive, while submitted tokens do not reveal corresponding identities or previous tokens to the public, since VRF hashes (i.e., tokens) cannot be verified without their corresponding proofs (we also note that the token set acts as the anonymity set in this case). Moreover, tokens are deterministic; thus, clients can easily check for potential credential misuses by using blockchain lookups that query their next token values.  
Unfortunately, the protocol assumes that the participating services are honest as they verify the continuity between tokens (the blockchain platform checks only for the token uniqueness in the token set). To illustrate this, let us assume that an adversary compromises the client's secret key \(sk\) and a service controlled by the adversary (in fact, nothing prevents the adversary from acting as a service, too). In such a case, the adversary would produce and insert into the blockchain's token set a token \(tkn^{\prime}\) computed from \(\langle tkn^{\prime},\bot\rangle\gets VRF_{sk}(k)\), where \(k\) is significantly larger than the client's counter \(cnt\). With this malicious token inserted, the adversary can keep authenticating with honest services by using consecutive tokens (starting from \(k+1\)). However, since the client would be auditing the blockchain for \(cnt\)'s consecutive tokens, they would not be able to detect the malicious token (at least for a long time period).  
Below, we show how this intermediate protocol can be turned into the final protocol, where the linkage between tokens and identities is verified by the blockchain (i.e., services do not have to be trusted in that scope) while not revealing the linkage to the public, thus preventing the described attack.
# Enhanced Efficiency and Reduced Side Lobe Level Convex Conformal Reflectarray
###### Abstract  
In this work, the design of a convex conformal reflectarray (CRA), suitable to be used for high gain on board antenna in mobile communication systems, is presented. The reflector is not planar, as in usual reflectarray configurations, but the re-radiating elements are printed on a surface conformed to a cylinder to mimic a curved surface, such as the fuselage of an airplane, on which the reflectarray could be mounted. To compensate for the loss in efficiency due to the curvature, an ad hoc feed is designed; the results obtained with the simulation of the entire structure and the measurements of its prototype prove the effectiveness of the design procedure and its capability in enhancing the CRA features, in comparison with similar but more conventional solutions.  
reflectarray; conformal reflectarray; millimeter wave; 3D printing; feed horn; selective laser melting Article
# Dual-Polarization Conversion and Coding Metasurface for Wideband Radar Cross-Section Reduction
## 7 Conclusions  
This study introduces a dual-polarization conversion metasurface capable of achieving both cross-polarization and circular polarization conversion. It efficiently transforms linearly polarized electromagnetic waves into their orthogonal LP counterparts, achieving over 90% efficiency within single frequency bands ranging from 8.53 to 11.63 GHz, positioned at the center of LP-CP bands. Moreover, the metasurface achieves highly efficient LP-CP conversion within two sidebands spanning frequency ranges of 12.40 to 17.56 GHz. The aperiodic arrangement of meta-atoms within the coding metasurface minimizes backward scattering by redistributing reflected energies in various directions through reorientation. The proposed CM demonstrates consistent performance across different polarization states, exhibiting polarization insensitivity. This study introduces a concept for achieving LP-LP and LP-CP conversion, along with radar cross-section (RCS) reduction, showcasing potential applications in radar technology.  
Not applicable.
# Data Synchronization: A Complete Theoretical Solution for Filesystems
## 3 Definitions
### Namespace and Filesystems  
Our filesystem model is arguably simplistic, nevertheless it captures all important aspects of real-word implementations. In spirit, it is a mixture of _identity_- and _path-based_ models [10; 16]. Objects are stored in nodes, which are uniquely identified by fixed and predetermined _paths_. The set of available nodes is fixed in advance, and no path operations are considered. Filesystems are required to have the _tree-property_ at all times: in a given filesystem along any branch starting from any of the root nodes, there must be zero or more _directories_, zero or one _file_, followed by _empty_ nodes. Our model does not support _links_ (but see Section 7.4), thus the namespace--the set of available nodes or paths--forms a collection of rooted trees. Filesystems are defined over and populate this fixed namespace.  
Formally, the _namespace_ is a set \(\mathbb{N}\) endowed with the partial function \(\uparrow:\mathbb{N}\to\mathbb{N}\) returning the parent of every non-root node (it is not defined on roots). If \(n=\uparrow m\) then \(n\) is the parent of \(m\), and \(m\) is a child of \(n\). For two nodes \(n,m\in\mathbb{N}\) we say that \(n\) is _above_\(m\), or \(n\) is an ancestor of \(m\), and write \(n\prec m\) if \(n=\uparrow^{i}(m)\) for some \(i\geq 1\). As usual, \(n\prec m\) means \(n\prec m\) or \(n=m\). As the parent function induces a tree-like structure, \(\preccurlyeq\) is a partial order. Two nodes \(n,m\in\mathbb{N}\) are _comparable_ if either \(n\preccurlyeq m\) or \(m\preccurlyeq n\), and they are _uncomparable_ or _independent_ otherwise.  
A _filesystem_\(\Phi\) populates the nodes of the namespace with values. The value stored at node \(n\in\mathbb{N}\) is denoted by \(\Phi(n)\). This value can be \(\mathbb{O}\) indicating that the node is _empty_ (no content, not to be confused with the empty file); can be \(\mathbb{D}\) indicating that the node is a _directory_; otherwise it is a _file_ storing the complete content (which could happen to be \"no content\"). We use \(\mathbb{O}\), \(\mathbb{D}\) and \(\mathbb{F}\) to denote the _type_ of the content corresponding to these possibilities. While there is only one value of type \(\mathbb{O}\) and one value of type \(\mathbb{D}\) (see Section 7.3 for a discussion on lifting this limitation), there are many different file values of type \(\mathbb{F}\) representing different file contents.
# Synergistic Effect and Phase Behavior of SCG-CAPB-H\({}_{2}\)O
## 3 Results and Discussion
### Synergistic Effect of SCG/CAPB Ternary Compound System
#### 3.1.2 Kraft Point of Sodium Cocyl Glycinate (SCG)  
The solubility of surfactants in water varies with temperature. As the temperature increases to a certain point, the solubility of the surfactant sharply increases. This temperature is known as the Krafft point. To measure the lowest temperature at which micelle formation occurs, the sum of the amounts of dissolved and undissolved surfactant in the system must exceed the critical micelle concentration.  
The experiment was conducted to determine the Krafft point of sodium cocoyl glycinate solution at a concentration of 1.0 \(\times\) 10\({}^{-2}\) mol/L, slightly above the critical micelle concentration. After maintaining the solution at various temperatures for 30 min, the transmittance of red light in the visible spectrum was measured using a UV-visible spectrophotometer. The results are shown in Figure 3. It can be observed from the graph that at  
\begin{table}
\begin{tabular}{c c c c c} \hline \(X_{1}\) & _CMC_ (mmol\(\cdot\)L\({}^{-1}\)) & _CMC\({}^{*}\)_ (mmol\(\cdot\)L\({}^{-1}\)) & _\(\gamma_{CMC}\)_ (mN\(\cdot\)m\({}^{-1}\)) & _pC\({}_{20}\)_ \\ \hline
0 & 0.974 & - & 32.8 & 4.69 \\
0.2 & 0.172 & 2.124 & 25.9 & 4.21 \\
0.4 & 0.148 & 1.640 & 26.8 & 4.36 \\
0.6 & 0.152 & 1.336 & 26.7 & 4.38 \\
0.8 & 0.185 & 1.127 & 22.4 & 4.41 \\
1 & 3.013 & - & 29.3 & 3.62 \\ \hline \end{tabular}
\end{table}
Table 1: CMC, CMC*, \(\gamma_{CMC}\), and pC\({}_{20}\) values for different molar fractions (X\({}_{1}\)) of the SCG/CAPB mixed system.  
a temperature of 36 \({}^{\circ}\)C, there is a significant increase in the transmittance of sodium cocoyl glycinate solution, reaching 100%. This indicates complete dissolution of the solute in the solution, indicating the formation of micellar solution in water, significantly increasing the solubility of sodium cocoyl glycinate in water. Therefore, the Krafft point of sodium cocoyl glycinate was determined to be 36 \({}^{\circ}\)C. Notably, this is the first report for the Krafft temperature of sodium cocoyl glycinate.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 4 Performance Evaluation
### Simulation Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FRR Methods** & **Experiment 1** & **Experiment 2** & **Experiment 3** & **Experiment 4** \\ \hline Proposed SRDT-FRR & 0.052201 & 0.051386 & 0.050082 & 0.05000 \\ BP-LSRFD & 0.910058 & 0.810284 & 0.892125 & 0.909243 \\ BP-BFD & 0.105135 & 0.104157 & 0.100978 & 0.100082 \\ baseline SS-FRR & 0.901417 & 0.910221 & 0.906797 & 0.903618 \\ LSRFD & 0.884349 & 0.90000 & 0.891359 & 0.896332 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The impact scores for fault scenario C.  
Figure 9: Primary-backup path switching results for fault scenario C.  
In all fault scenarios, the SRDT-FRR scheme maintains routing interruption times around 100 ms, significantly outperforming the other schemes. Particularly in transient and mixed fault scenarios, its fault recovery efficiency is markedly superior, demonstrating high sensitivity and responsiveness to transient faults. The SRDT-FRR scheme, with its backup path activation and deactivation timer mechanisms, effectively prevents frequent switching during transient fault conditions. As a result, the number of primary-backup path switches is significantly lower than in the comparison schemes, minimizing the switching overhead. Whether under stable or transient faults, the SRDT-FRR scheme ensures rapid recovery, protecting business traffic from interruptions while minimizing additional network resource overhead. Its high efficiency and robustness across various fault scenarios validate the practicality and reliability of the solution. The experimental results clearly demonstrate that the SRDT-FRR scheme significantly enhances the routing reliability and fault recovery efficiency in satellite networks under different fault conditions. The experiment shows that SRDT-FRR maintains a stable routing interruption time of 100 ms across three fault scenarios (Figures 4-9), significantly outperforming the comparative schemes, with BP-LSRFD exhibiting 3100 ms and BP-BFD 550 ms. Furthermore, the average impact scores of SRDT-FRR (Tables 1-6) for the three scenarios are 0.103050, 0.044111, and 0.067514, respectively, representing a reduction of at least 80% compared to other schemes. The \(p\)-value is less than 0.001, indicating that there are significant differences in the routing interruption time and switching overhead between the proposed SRDT-FRR and the other strategies. The SRDT-FRR scheme's millisecond-level fault detection capability and flexible path switching strategy outperform the comparison schemes in key performance metrics such as routing interruption time, primary-backup path switching overhead, and network stability, offering an effective solution to the fast rerouting challenge in high-dynamics satellite networks.
## 5 Experimental Results  
In general, our calibration and compensation method is carried out with the following steps:  
1. Calibration of the gyro bias along the azimuth direction  
The first step of our correction method is to calibrate the gyro bias along the azimuth direction. In order to eliminate the gyro bias on roll/pitch direction, an IMU is mounted on a turntable, with the azimuth gyro direction coinciding along the rotational angle of the turntable. While the turntable rotates, the IMU works in a conventional way, and the attitude can be calculated using the inertial navigation algorithm (introduced in Section 3). During such process, both the navigation algorithm and the rotational turntable would provide an attitude information of the IMU, and the difference in these two azimuth angles could be compared to calibrate the gyro bias. The detailed theory can be found in Section 4.2.  
2. Experimental verification after compensating gyro bias  
After calibrating the gyro bias along the azimuth direction. The gyro bias could be compensated for in the subsequent experiments. To verify the effectiveness of our method, we conducted several individual experiments. The original data and the compensatedangle were used to construct the navigation algorithm. The calculated position of the RINS is compared with GNSS signal, and the effectiveness of our method could be observed.