esa-sceva/satcom-chunk-collection huggingface dataset:
# Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware
## 2 Representative Architectures for Secure Boot
### Signed Certificate in the Immutable Bootloader (E)
It is an important consideration, however, that a revoked or retired key pair and certificate must be treated as revoked by the device as well. For this purpose, an approach like prior architecture (C) can be used where, preferably, a monotonic counter using an OTP memory region is provisioned with several half-word OTP slots, all hydrated with the value of 0 xff, which indicates that the slot is writable (or empty) [22]. The device maker can configure additional metadata within the certificate at the time of generating a certificate signing request. As an example, a common name field, abbreviated as \"CN\", can be leveraged for this purpose. The device maker can define a naming convention scheme to provide all the information necessary for this field to be meaningful (e.g., CN: package-name:2, where the number 2 is an incrementing identification of the certificate and means any previous certificates with the number less than 2 are deemed retired). The device maker must ensure that this number always increments from its prior value. It can start with 0, which means that no OTP memory slots are consumed (i.e., non-0 xff value). Referring to Figure 4, the incoming signing certificate's CN value is xxx:2 (where xxx is the package name and the number 2 is the identifier of the certificate), whereas the monotonic counter value is 1, which means that the current certificate used is of version 1 and one slot from the monotonic counter would be burned. The CN has version number as 2, which is less than that of the identifier from the certificate. The bootloader, after all the integrity verification, increments the monotonic counter to the next number by burning the next slot within the OTP memory, thus making the number of slots used equal to the identification number provided by the certificate metadata. Figure 4: X.509 for firmware signing. Considering the attributes in Table 1, the limitations in using the above architecture come from the device limitations of processing capacity, available memory, and utilization requirements of how much delay incurred in cryptographic checks is acceptable. While the architecture supports an unlimited number of key rotations throughout the life of the product, it still places a limitation on the number of rotations (and revocations thereof) by the number of available slots in the OTP memory, also known as the ceiling of the monotonic counter. However, it will certainly be a much larger number, as storing half-words requires much less memory as compared to storing full-length cryptographic keys or their 128-bit hash values. Another important design time consideration of this system is to have the life (longer expiry) of the root certificate and the issuing certificate be long enough to outlive the device lifetime. Refer to Table 6 for a summary of tradeoffs when using this architecture.
## 5 Conclusions  
Most of the soil testing labs, especially for agriculture purposes, cannot afford expensive analytical instruments in developing countries. MP-AES is a relatively cheaper multi-element instrumental technique. The results of this study indicate that MP-AES can be a cost-effective analytical tool for the routine and direct analysis of biosludge samples for critical micronutrients. The methodology developed in this work is simple and efficient, and provides high sample throughput. ANOVA tests demonstrated that there are no significant differences between the analytical results obtained using MP-AES and ICP-OES for the determination of B, P, and Mo content in biosludge samples. Compared to ICP-OES, MP-AES is more economical because of its lower cost and the use of an easily sourceable nitrogen gas, which can be generated using an air compressor and a nitrogen generator. In addition, LODs and LOQs determined for B, P, and Mo using MP-AES are comparable to the well-established ICP-OES technique, and are significantly superior to those obtained with FAAS. In fact, analyses of B, P, and Mo, which are difficult using FAAS, can be carried out using the MP-AES technique in a single run. In addition, this technique has several advantages, such as the possibility of deployment even in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Elements** & **MP-AES** & **ICP-OES** & **FAAS** \\ \hline B & 0.001 & 0.001 & 1 \\ P & 0.025 & 0.030 & 50 \\ Mo & 0.002 & 0.003 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of limit of detection (LOD) in mg/L of different techniques.  
Figure 1: Agreement between certified values and the values obtained in this study by MP-AES and ICP-OES for B, P and Mo in SRM-Sewage Sludge EnviroMAT (BE-1).  
remote locations where access to gases such as argon can be difficult. This study reveals that MP-AES is cost-effective as well as accurate for determining the content of B, P, and Mo in biosludge samples and is a convenient replacement for FAAS and ICP-OES. The procedure developed in this work will be beneficial for analytical applications in the future.  
[PERSON] appreciates the support of the Deanship of Scientific Research at King Saud University for the Prolific Research Group PRG-1437-31. One of the authors ([PERSON]) would like to thank CSIR, New Delhi, India, for financial support in the form of the Emeritus Scientist Scheme.  
[PERSON], [PERSON] and [PERSON] conceived, designed and supervised the experiments and put together the initial draft of the paper; [PERSON] performed the experiments; [PERSON] and [PERSON] carried out the literature review; [PERSON] and [PERSON] analyzed the data, finalized the paper and [PERSON] was the main corresponding author during the review process.  
The authors declare no conflict of interest.
# A Study on the Surrogate-Based Optimization of Flexible Wings Considering a Flutter Constraint +
## 2 Methodology
### Surrogate Model Training, Validation, and Testing
#### 2.2.1 Training and Validation  
The model was trained using the dataset, focusing on hyperparameter tuning. Hyperparameters were carefully adjusted to ensure model accuracy and prevent overfitting. The optimal settings were identified via a Grid Search, using RMSE and R\({}^{2}\) for selection. The model's hyperparameters analyzed were the number of trees in the ensemble, maximum tree depth, minimum samples at a leaf node, and minimum samples for a node split [34]. The objective during training is to minimize a loss function for improved prediction based on input data, ensuring that hyperparameters are set for both known (training set) and unseen (validation set) data.
## 3 UWOC System Model
### The Transmitter Unit  
The transmitter unit utilises a beam-shaping optical unit to interface with the water propagation channel. The transmitter unit's modulator provides the needed modulation shaping characteristics to generate the information bit stream. Moreover, in UWOC systems, the driver circuit is another crucial part of the transmitter unit [66]. The main job of this device is to convert the electrical signal from the modulator into an optical signal that can be transmitted through the water channel. The driver circuit typically consists of a laser or LED driver that provides the necessary current to the light source, which emits the optical signal \(s(t)\). The selection of the light source and driver circuit is determined by the particular system requirements, such as the desired data rate, transmission distance, power consumption [67], and optical characteristics of the water channel [68]. In the UWOC system, the LED setup is more affordable and straightforward, but the connection range is very constrained because of the incoherent optical beam and light spread in all directions [4]. Laser diodes are often used as the light source in UWOC systems due to their long ranges, high intensity of output power, improved collimation characteristics, narrow beam divergence [69], high efficiency, small size [70], high data rates, and low latencies [4]. The high-quality output of the coherent laser beam is quickly degraded by turbulence and underwater scattering. The laser-based UWOC system may reach a link distance of 100 m in clear water and 30 to 50 m in turbid water, while the LED-based UWOC may cover a linkspan of no more than 50 m [69].
## 3 Data Source and Data Preprocessing
### Along-Track Filtering  
After data screening and main magnetic field correction, several non-lithospheric signals that were not modeled remain in the observations. These residual signals are dominated by long-wave components of magnetospheric contributions [20; 48]. Another important source of residuals is the ionospheric disturbance magnetic field, and the sources of secondary importance are the induced magnetic field and the secular variation error from the main magnetic field. Along-track high-pass filtering of fast Fourier transform is adopted in the current study to eliminate the large-scale residual signals. The challenge in employing this method is the selection of the cut-off wavelength. The magnetic field contributions from wavelength signals greater than 1200 km are reduced with high pass filtering by using a Hamming window function (filter of order 128). In the study, a lithospheric field (MF7) is removed before performing along-track filtering for the residual signals in the actual filtering process to lower the level of spectral leakage [49]. Finally, a residual analysis is conducted for the values after filtering, and magnetic anomaly values greater than \(\pm\)25 nT are eliminated. At satellite altitude, the intensity of lithospheric magnetic anomaly is generally less than 25 nT [50].
# Quantifying Moss Response to Metal Contaminant Exposure Using Laser-Induced Fluorescence
## 3 Results
### Quantitative Analysis of LIF Image Color Components  
#### 3.4.1 Interpreting Trends  
The above presented methods of expressing changes in color distributions in scaled histograms, and the red versus green color comparisons, indicate that LIF could be used effectively to indicate moss exposure to Cu. The focus then turned to determining which, if any, approach is preferred to quantify changes between trials, as well as having the lowest threshold for Cu exposure identification using this LIF technique.
## 4 Conclusions  
This paper presents a model governing the tilting and impact dynamics of a spinning splined disc with axial-fixed boundaries. The tilting and axial movement of different deformed discs are simulated via the state-space model in Simulink. Both time domain and frequency domain results are analyzed, and the results show the importance of disc deformation on the dynamic behaviors of spline-guided spinning discs. The analysis also considers cofactors such as boundary distance, initial impulse force and rotation speed. Several conclusions are drawn based on the results and analyses:  
1. Regardless of the deformation, the disc tilting and axial motion are highly correlated. Both motions either show strong nonlinearity or stop immediately after the impulse, and when one motion stops the other stops soon. Also the final position of axial displacement is hard to predict, it varies as long as the condition changes.
2. The deformation of the disc increases the complexity of the tilting and axial motion of spinning discs. The dynamic responses of discs with larger deformation angles are more dramatic and longer than those of less-deformed discs, because the deformation of the disc increases the possibility of impact between the disc and boundary.
3. The effect of increasing the impulse force is similar to reducing the boundary distance; they both increase the intensity of the motions. The difference is that, narrowing the boundary distance could restrict the maximum tilting angle, and larger impulsion increase the tilting frequency. In addition, the rotation speed can also extend the time of tilting.  
Further planned investigations will focus on the response of various types of excitation force, as well as developing multi-disc model to simulate the wet clutch system.  
Conceptualization, J.X., B.M. and M.C.; Methodology, L.Y. and J.X.; Simulation, J.X. and L.Y.; Software, J.X.; Validation, L.Z. and J.X.; Visualization, J.X. and L.Z.; Supervision, B.M. and M.C.; Funding acquisition, M.C.; Writing, J.X. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant Nos. 51975047 and 51775045).  
Not applicable.  
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.  
The authors declare no conflict of interest.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.3 Control Plane  
The control plane is dispersed among network devices in conventional networking. Despite the logical centralization of the control plane in SDN, the controller's decisions are not guided by knowledge but rather by the policies of the network applications. In KDN, the control plane is both logically centralized and self-learning, as control is based on both network policies and knowledge generated by AI and machine learning techniques.
# Pyramid Feature Attention Network for Speech Resampling Detection
###### Abstract  
Speech forgery and tampering, increasingly facilitated by advanced audio editing software, pose significant threats to the integrity and privacy of digital speech avatars. Speech resampling is a post-processing operation of various speech-tampering means, and the forensic detection of speech resampling is of great significance. For speech resampling detection, most of the previous works used traditional methods of feature extraction and classification to distinguish original speech from forged speech. In view of the powerful ability of deep learning to extract features, this paper converts the speech signal into a spectrogram with time-frequency characteristics, and uses the feature pyramid network (FFN) with the Squeeze and Excitation (SE) attention mechanism to learn speech resampling features. The proposed method combines the low-level location information and the high-level semantic information, which dramatically improves the detection performance of speech resampling. Experiments were carried out on a resampling corpus made on the basis of the TIMIT dataset. The results indicate that the proposed method significantly improved the detection accuracy of various resampled speech. For the tampered speech with a resampling factor of 0.9, the detection accuracy is increased by nearly 20%. In addition, the robustness test demonstrates that the proposed model has strong resistance to MP3 compression, and the overall performance is better than the existing methods.  
speech resampling detection; feature pyramid network; SE attention; robustness
# Application of Image Recognition Methods to Determine Land Use Classes
## 4 Results
### Classification Results and Reached Accuracy  
#### 4.3.1 Most Accurate Classifier  
The literature analysis (see Section 2) has demonstrated that the most accurate algorithm, as determined by other scientific studies, is RF. Nevertheless, we have compared the results obtained from RF with those from the other two algorithms (i.e., Support Vector Machine (SVM) and K-Nearest Neighbors (KNN)) to determine which one is the most suitable for the case of the Baltic countries.  
According to [66; 67; 68], the optimal hyperparameter search was performed for each classification algorithm. The optimal hyperparameters obtained for each algorithm are presented in Table 3.  
The classification accuracy averages achieved by each evaluated algorithm are summarized in Table 4.  
As shown in Table 4, the classification results demonstrate significant performance differences among the evaluated algorithms. These differences can be attributable to variations in both model architecture and optimized hyperparameter configurations. The Random Forest (RF) classifier demonstrated the highest level of validation performance, highlighting its reliability and ability to identify complex, non-linear relationships in the feature space through ensemble learning and deep tree structures. The Support Vector Machine (SVM), utilizing an RBF kernel with a soft margin, performed comparably well, highlighting its efficacy in managing high-dimensional data and delineating non-linear class boundaries. Although the K-Nearest Neighbors (KNN) algorithm demonstrated slightly lower performance, its straightforward approach and reliance on local neighborhood structures still yielded competitive results. The findings indicate that ensemble-based methods, particularly RF, are highly suitable for land cover classification tasks using multi-spectral satellite imagery. SVM and KNN offer viable alternatives when considering computational constraints and data complexity.
## 3 Methodology
### [PERSON]'s Tau (\(\tau\))  
The accuracy of different algorithms is subsequently evaluated through the application of [PERSON]'s tau correlation coefficient [32]. [PERSON]'s tau represents a widely adopted statistical measure for quantifying the strength of the correlation between two sequences. When considering two sequences \(X\) and \(Y\) with the same length, two pairs of elements \((x_{i},y_{i})\) and \((x_{j},y_{j})\) are selected from corresponding positions within these sequences. These pairs are classified as concordant if \(x_{i}>x_{j}\) and \(y_{i}>y_{j}\), or if \(x_{i}<x_{j}\) and \(y_{i}<y_{j}\). Conversely, the pairs are considered discordant if \(x_{i}>x_{j}\) and \(y_{i}<y_{j}\), or if \(x_{i}<x_{j}\) and \(y_{i}>y_{j}\). The pairs are neither concordant nor discordant if \(x_{i}=x_{j}\) or \(y_{i}=y_{j}\). Denoting the number of concordant and discordant pairs as \(N_{c}\) and \(N_{d}\), respectively, [PERSON]'s tau between sequences \(X\) and \(Y\) can be expressed as Equation (14).  
\[\tau(X,Y)=\frac{N_{c}-N_{d}}{N(N-1)/2} \tag{14}\]  
where \(\tau\in[-1,1]\). A value of \(\tau=1\) indicates a perfect match between the rankings of sequences \(X\) and \(Y\), whereas \(\tau=-1\) signifies a complete inversion of rankings. When \(\tau=0\), the rankings of the two sequences \(X\) and \(Y\) are completely independent. \(N_{c}\) and \(N_{d}\) denote the number of concordant and discordant pairs, respectively. \(N\) is the number of total nodes in the network.  
For evaluating the accuracy of various measures, the spreading capability of nodes is initially determined through the implementation of the SIR model. Subsequently, a ranking sequence is generated by ordering node influence as determined through algorithmic simulation. The final step involves the computation of [PERSON]'s tau \((\tau)\) between these two sequences. Measures with \(\tau\) values close to 1 demonstrate the highest performance in accurately identifying influential nodes within the network.  
A critical advantage of SIR models in transportation network analysis is their natural compatibility with network topology considerations. The adjacency matrix of transportation networks, such as road networks, directly influences the diffusion dynamics, as the spread of any phenomenon is constrained by the physical connectivity of road segments. This topological awareness allows SIR models to capture realistic diffusion patterns that respect the geometric constraints of road infrastructure.
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 3 Synthesis Methods and Characterization  
As already mentioned, one of the main advantages in using ZnO is the availability of a plethora of morphologies which can be easily prepared by various methods [11]. Figure 3 shows the electron microscopy images of microparticles with a very high specific area and various morphologies, such as a flower-like shape [69; 145], multipods [146; 147], nanorods [148], films with a nanostructured surface [77] and nanoparticles [76] of different shapes and dimensions, all of them well established and reported in the literature. Moreover, the methods through which these particles are obtained are even larger in number than the morphologies themselves. Therefore, it is difficult to make a comprehensive overview of all the possibilities that are already present. By considering the specific subject of this review, the most rapid low-cost and high-yield methods for preparing doped ZnO nanoparticles are discussed.  
In the following, wet chemical methods are first presented. This category is probably the most diffuse one, due to the excellent trade-off between the quality of produced materials, versatility and poor requirement in terms of instrumentation, and, in particular, for doping ZnO with rare earth elements. Then, other methods, such as flame spray pyrolysis [128], sputtering [153] or pulsed laser deposition [154], are described as being widely used for the preparation of both doped ZnO films and nanoparticles.
# Ionospheric Remote Sensing with GNSS
## 3 Ionospheric Characteristics and Phenomena  
The ionosphere is an ionized layer of the Earth's upper atmosphere. Due to the solar radiation, the neutral particles in the Earth's atmosphere are converted to electrons and ions. Overall, these electrons are concentrated in the altitude range from \(\sim\)60 km to 1000 km but their characteristics are spatially and temporally dynamic [11]. In equatorial and low-latitude regions (latitude \(<\) 30 degrees North/South), the magnetic field is nearly horizontal and ionospheric electron density is typically higher than other regions due to the high solar angle effect. Equatorial Spread F (ESF) or Equatorial plasma bubbles (EPB) form density structures in the ionosphere and have a typical scale size between 1\({}^{\circ}\) (\(\sim\)115 km) and 4\({}^{\circ}\) (\(\sim\)460 km) [12]. Additionally, plasma irregularities driven by plasma instabilities can be detected to smaller scale (\(\sim\)10\({}^{-1}\) to 10\({}^{3}\) m) as well. The high-latitude (or polar) ionosphere is the region above 60\({}^{\circ}\) magnetic latitude (e.g., auroral zone, polar cap), where plasma instabilities and other dynamic processes (e.g., coupling physics between the magnetosphere, ionosphere and thermosphere) cause ionospheric structures and irregularities [11]. Under the influence of the nearly vertical geomagnetic field as well as the horizontal variation of plasma density and electric fields driven by plasma instabilities, various multi-scale (\(\sim\)10\({}^{-2}\) to 10\({}^{5}\) m) ionospheric structures lead to phenomena such as aurora (and the associated arcs), sub-auroral polarization streams (SAPS), as well as polar tongues of ionization (TOI). A broad range of observation techniques must be used to study these multi-scale space weather phenomena which span seven orders of magnitude or more in space and time scales.  
Aurora, one of the most famous and important space weather phenomena, is typically seen as a visual phenomenon caused by charged particle precipitation along polar geomagnetic field lines and subsequent interaction with the neutral particles in the upper atmosphere. The energetic charged particles are often driven by the solar wind [11]. The length of auroral arcs can range from 100 to 1000 km, the width can range from 50 m to 10 km, and the altitude (maximum energy of particles in the primary beam) is typically from 80m to 400 km [13]. SAPS often refers to a sunward plasma drift/convection in the sub-auroral region with an approximate spatial span of \(\sim\)3\({}^{\circ}\)-5\({}^{\circ}\) latitudinally and temporal duration of several hours in the evening sector [14]. TOI is a continuous stream of cold plasma enhancement with an entrainment pattern of high-latitude convection. The spatial scale of TOI can range from about 100 to 1000 km [15]. A hardware-in-the-loop simulation of TOI and its impact on GNSS was reported by [16].