esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## 2 Related Literature
### Post-Quantum Cryptography (PQC)
#### 2.2.2 The Rise of the 'Quantum' Machines  
The general availability of powerful quantum computers would fundamentally alter the capability to attack existing cryptography standards, and significant progress has been made in theoretical attack vectors and the production of small-scale quantum computers. Although the progress achieved may currently seem limited, technological improvements often occur on a logarithmic scale (see Moore's Law [20]), with seemingly slow initial progress rapidly transforming into significant advancements. Algorithms such as Shor [8] exemplify this threat, reducing what was once a logarithmically difficult factorisation problem for classical devices into one that can be solved relatively easily using sufficiently large quantum computers. Quantum computing has the capacity to perform calculations across many states simultaneously, breaking the logarithmic protection of the techniques commonly deployed including ECC and RSA. Widespread quantum computing availability in the near future poses a particular concern, as adversaries may already be engaging in 'Store Now, Decrypt Later' (SNDL) activities in anticipation. This is clearly a concern for sectors where long-term data confidentiality is crucial, such as the protection of proprietary information in industry and 'top-secret' government activities.
# Biometrics for Internet-of-Things Security: A Review
## 4 Biometric-Based Systems for IoT-Oriented Authentication
### Single-Modal Versus Multi-Modal Biometric Authentication Systems
#### 4.1.1 Single-Modal Biometric Authentication Systems
In this paper, the scale-invariant feature transform is used for feature extraction and an artificial neural network-based classification algorithm is employed for predicting the user's class. In this sub-section, a large number of biometric-based authentication systems involving a single biometric trait are overviewed. Among them, the fingerprint is the most frequently used biometric trait due to its convenience and high accuracy offered by fingerprint recognition, while the ECG is most popular in the IoT healthcare field.
## 5 Conclusions  
In this study, a series of axial model pile load tests were executed to examine the shaft friction resistance of different model piles (different materials) embedded into loose to very dense sand. The model piles were installed as non-displacement (bored) piles. The lateral earth pressure coefficient was back-calculated from the experimental test results. Multiple nonlinear regression analysis was carried out, and a correlation was proposed for estimation of K values. Based on the experimental outcomes and statistical analysis, the following conclusions can be drawn.  
* The shaft friction resistance of all model piles increases with increasing relative density in a nonlinear relationship. From the load-displacement curves, the ultimate shaft friction resistance of the model piles is mobilized when the pile displacement reaches approximately 5-10% of the pile diameter.
* The ultimate shaft friction resistance of the model piles is strongly affected by the surface roughness of the pile materials. This effect can be clearly observed between ultimate shaft resistance of timber and steel model piles. This indicates that the pile with high surface roughness develops the propensity of sand to dilate during axial loading, particularly at high relative density, which leads to an increase in lateral earth pressure against the pile surface.
* The maximum K value was obtained from the PVC model pile at 90% relative density. The reason is that due to the low surface hardness of PVC, sand particles try to scratch the surface of the PVC pile during loading, which results in high friction resistance values.
* From back-calculation of the lateral earth pressure coefficient by using experimental results and statistical analysis, the K value depends on the initial stress condition, the relative density of sand, internal friction angle of sand, the skin friction angle between sand and pile materials, and normalized roughness of the pile materials. A correlation was proposed for the estimation of K value, and good agreement was found between the predicted K values and calculated K values from the experimental results.  
Methodology, M.G. and O.M.E.; Formal analysis, N.R.T.; Writing--original draft, N.R.T.; Writing--review & editing, H.S.A., N.R.T. and A.O. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Firat University Scientific Research Projects Coordination Unit (FUBAP) with the project number MF.18.39 and MF.18.42.  
The study did not require ethical approval.  
Theoretical Consent Statement: Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** \\ \hline
0.0639 & 0.1000 & 0.0324 & \(-1.1846\) & \(11.2511\) & \(10.5987\) & \(-11.9945\) & \(1.5905\) \\ \hline \end{tabular}
\end{table}
Table 9: Coefficients of Equation (3).  
Data Availability StatementAll data, models, and code generated or used during the study appear in the submitted article.  
The authors declare no conflict of interest.
# Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid
## 2 Methodology
### Data Processing: Cumulative Distribution Functions, Histograms, and Percentiles  
Statistical analysis of data in this work is fairly standard although some adaptions are made to deal with the large volumes. We explore and present the statistical distributions of measurements using their (normalized) cumulative distribution distribution functions (CDFs). For some variable \(x\) (for example, THD measurements), the normalized CDF is \(\mathcal{C}(x)=\int_{0}^{x}\rho(x^{\prime})\,\mathrm{d}x^{\prime}/\int_{0}^{ \infty}\rho(x^{\prime})\,\mathrm{d}x^{\prime}\), where \(\rho(x)\) is the probability density function of \(x\). For  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Site** & **Voltage** & **Period** & \(\boldsymbol{V}_{\text{Phases}}^{\text{Harmonics}}\) & **Aggregation** \\ \hline
1 & 22 kV & 2015 to 2018 & \(V_{1,2,3,12,23,31}^{0.8-8}\) & \(1/50\,\mathrm{Hz}\), Mean \\
2 & 66 kV & 2015 to 2018 & \(V_{1,2,3,12,23,31}^{0.8-8}\) & \(1/50\,\mathrm{Hz}\), Mean \\
3 & 300 kV & 2015 to 2018 & \(V_{1,2,3,12,23,31}^{0.8-8}\) & \(1/50\,\mathrm{Hz}\), Mean \\ \hline
1 & 22 kV & January 2017 & \(V_{1,2,3}^{0.9-96}\) & \(1/50\,\mathrm{Hz}\), Mean \\
2 & 66 kV & January 2017 & \(V_{1,2,3}^{0.9-96}\) & \(1/50\,\mathrm{Hz}\), Mean \\
3 & 300 kV & January 2017 & \(V_{1,2,3}^{0.9-96}\) & \(1/50\,\mathrm{Hz}\), Mean \\ \hline \hline \end{tabular}
\end{table}
Table 1: We extract two data packages. The first covers four years, six voltage channels, eight harmonics. The second covers a month, three voltage channels, 96 harmonics. They contain \(3.6\times 10^{11}\) and \(3.9\times 10^{10}\) samples, respectively.  
a finite number of samples, \(\mathcal{C}\) and \(\rho\) can be approximated by computing the (cumulative) histogram of \(x\). In other words, given \(N\) samples, \(\mathcal{C}(x_{t})=N(x<x_{t})/N\) is the fraction of samples with values of \(x\) below some threshold \(x_{t}\).  
Owing to the large amounts of data, we calculate histograms individually for each voltage channel and harmonic order in time-slices of one month. The histograms over the entire time-period are then the sums of the monthly histograms. The cumulative histograms are then computed as their cumulative sum.  
All percentiles in our analysis are approximate. The standard (exact) way of calculating percentiles requires loading (and sorting) all samples in memory, which proved difficult. Instead, we estimate percentiles from the cumulative distribution functions. Numerically, for the desired percentile \(\mathcal{P}\), this amounts to finding the value of \(x_{t}\) at \(\mathcal{P}=\mathcal{C}\). This means that the numerical accuracy of our percentile calculations is limited by the binning used during histogram calculation. We use 256 logarithmically spaced bins in the range 0.01 to 10, corresponding to an upper bound on the numerical accuracy of \(\approx\)10\({}^{-2}\).
# A Co-Design-Based Reliable Low-Latency and Energy-Efficient Transmission Protocol for UWSNs
## 2 Related Works
### Reliable Transmission Protocols  
[PERSON] et al. [9] proposed the FBR protocol for UWSNs. Before a sender has a data packet to be sent to the destination, it will multicast a request-to-send (RTS) packet with initial power \(P\) and location information. After calculation, only the nodes that lie within a cone of angle \(\pm\theta/2\) are considered best hop candidates and will reply to the sender with a clear-to-send (CTS) packet. RACAA [10] creates some reliable end-to-end connected routes from source to destination based on neighborhood information. A node chooses the route with the highest probability of success. However, the fluctuation of the channel is ignored. RECRP [8] further considers the mobility of nodes and exploits cross-layer information to update routing tables. Forwarding nodes are selected based on the routing tables. During the periodic routing update phase, excessive energy will be spent.  
Generally in CSMA/CA-based MAC protocols, RTS/CTS handshake protocols are used to handle the hidden terminal problem. This method can improve the reliability of the transmission, and the size of control packets is also very small. However, the underwater transmission time and limited bandwidth mean this method cannot be ignored. In order to reduce the number of RTS/CTS packets, [11] considered the CTS corresponding to the RTS from the source node as both the positive response to the RTS, and the RTS of the CTS sender to one of its neighbors toward the destination of the packet. Each node in an FDMA-based protocol UW-OFDMAC [12] is assigned a dedicated sub-channel according to the receiver location and motion effects to avoid multi-node interference. A sub-channel is further divided into multiple orthogonal subcarriers. The notification frame is needed when receiver location and motion effects are present, and OFDMA is not suitable for underwater because of the scarcity of available channels.
## 3 Materials
### Satellite Image Pre-Processing
#### 3.3.1 HR Reflectance Data
To rectify GF-1 and HJ-1 satellite data geometrically, Landsat TM data serve as the benchmark, and obvious boundary points such as roads and corners of buildings serve as datum points to implement ground control point (GCP) corrections. Hence, the error in point measurements is within 0.5 pixels.
## 2 Materials and Methods
### Building Monitoring  
As part of the preventive conservation strategy, a monitoring system was installed in the church. Six Testo 160 IAQ sensors were installed: three at the bottom of the church main nave walls at 1 m high and three at the top of the gothic arches at 4 m high. The sensors were distributed throughout the church floor, alternately, on both sides of the main nave. These sensors are autonomous and wireless and collect data on relative humidity, indoor temperature, atmospheric pressure, and CO\({}_{2}\) concentration every 15 min. These data have been collected continuously since the installation of the sensors. Calibration of the sensors is carried out periodically by comparing their readings with those taken through a portable Testo 435-2 sensor. The precision of the measuring equipment is shown in Table 1.  
The data were compared with meteorological data (external temperature and relative humidity) of the city of Valencia, which was collected by the Spanish Meteorological Agency at the meteorological station closest to the church [78].  
Because the summer of 2023 was the hottest since records began in the city of Valencia, and because the temperatures and relative humidity reached inside the church have been very high, it was decided to use the monitoring data that covered this period (1 May to 31 October). The monitoring information was transmitted to a processing system and applied to the digital twin of the church. During this period, visitor numbers were counted with the aim of defining patterns and of understanding how visitors affect the behaviour of the building and impact the conditions of the heritage assets. The church has a capacity of approximately 230 people, which is only reached during large celebrations, such as weddings.
## 3 Crack Identification and Measurement System
### Measurement System Based on Camera and Laser Rangefinder
#### 3.1.2 Geometric Transformation Formula of Pixel Length
As a laser point is usually a red circle or ellipse, as shown in Figure 7a, the center is identified using a circular Hough transform, which is widely adopted in much image processing research due to its sub-pixel accuracy and high stability [35; 36]. Since the color of the laser point selected in this study is red, among the multiple circles identified in the circular Hough transform, the reddest one is considered to be the laser point as shown in Figure 7b, and the location in the images is recorded. The proposed measurement model is composed of a laser rangefinder and a camera with a fixed relative position and parallel optical axis. To calculate the target length by the captured image pixel, the target plane is decomposed into horizontal and vertical rotations, and the center of rotation is the direct measuring point by the laser rangefinder. Rotation angles can be calculated from another point measured by the laser rangefinder, which can be used to derive the pixel length conversion formula, and the positional relationship from a random point on the image to the center of rotation can be determined. Therefore, the distance between two random points on the image, such as the edge of a crack, can be obtained by the distances and angles measured by the laser rangefinder. Figure 7: Laser point center recognition: (**a**) original image of laser measuring point; (**b**) identified center by Hough transform.
## 4 Simulation Results
Finally, we compare the computational complexity of the proposed greedy algorithm with that of exhaustive search for various numbers of antennas when a BS serves two users and each subarray in the proposed architecture contains two VPSs. We define computational complexity as the total number of addition and multiplication operations. Table 1 presents the simulation results of computational complexity obtained by varying the number of antennas from 16 to 64 and setting the number of users to two. The results show that the proposed greedy VPS/CPS allocation algorithm is significantly less complexity than the exhaustive search algorithm while achieving nearly the same sum-rate. In the proposed greedy algorithm, complexity depends mainly on the the number of iterations and the computation of (3) in step 9. The total number of iterations is \(N_{C}\times U\times U\times M\), which can be written as \(N_{C}UN_{t}\) because we have \(U\times M=N_{t}\). The computational complexity of (3) is proportional to \(UN_{t}\), and it is computed throughout the iterations. We note that the other steps in the algorithm have lower complexity orders compared to step 9. Therefore, the overall complexity of the proposed algorithm can be expressed as \(\mathcal{O}(N_{C}U^{2}N_{t}^{2})\). Furthermore, if we assume that \(N_{V}\) is fixed to a constant value, as in Table 1, the overall complexity of the proposed algorithm can be rewritten as \(\mathcal{O}(UN_{t}^{3})\). For a fixed value of \(U\), its complexity becomes approximately proportional to \(N_{t}^{3}\), as presented in Table 1.  
Figure 3: Average sum rate versus SNR. \(N_{t}=64\) and \(U=4\).
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 6 Conclusions  
This paper introduces SecFL-IoT, an FL framework designed to improve intrusion detection and malware classification in IoT environments with resource constraints and privacy concerns. The system integrates lightweight yet robust mechanisms, such as gradient clipping, Fisher-based parameter pruning, differential privacy, SMPC, and post-quantum encryption (Dilithium). These components are orchestrated through a three-tier edge-fog-cloud architecture, which enables secure and scalable model training across heterogeneous devices.  
To ensure tamper resistance and traceability, the framework uses a private permissioned blockchain to log model updates and enforce provenance via smart contracts. This setup protects against rollback and poisoning attacks while preserving decentralization and auditability.  
Comprehensive evaluations were conducted using the CICIDS2017 and TON_IoT benchmark datasets, which simulated a distributed FL scenario with non-IID data partitioning and local model training. The results demonstrated over 90% detection accuracy, less than 5% privacy loss, and a 23% reduction in communication overhead. All experiments used a consistent MLP architecture with fixed hyperparameters and local training over ten epochs to ensure comparability with baseline centralized models.  
The smart healthcare case study, which was conducted in a simulated environment, shows how the proposed framework can be used in regulated settings, such as hospitals, where data locality and low latency are important. While not deployed in a clinical setting, the scenario shows how the system's components can align with healthcare-specific privacy and performance requirements. This architecture can be easily adapted for use in other sectors, including smart cities, industrial IoT, and connected vehicles.  
Future work will address open challenges related to client heterogeneity, intermittent connectivity, and adversarial node detection. We also plan to integrate explainable AI and federated meta-learning to improve model interpretability and personalization. Overall, SecFL-IoT establishes the foundation for secure, adaptive, and regulation-compliant cybersecurity infrastructures in next-generation IoT ecosystems.  
The proposed framework was validated through quantitative evaluation and a domain-specific case study in smart healthcare, confirming its relevance for mission-critical, privacy-constrained contexts. By unifying theoretical modeling with practical implementation scenarios, this research bridges the gap between academic innovation and real-world deployment. Our design choices and results contribute to the ongoing discourse on secure federated intelligence by offering a replicable foundation for future enhancements in regulated and heterogeneous IoT environments.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
###### Abstract  
Assessing the visual quality of rural landscapes is pivotal for quantifying ecological services and preserving cultural heritage; however, conventional ecological indicators neglect emotional and cognitive dimensions. To address this gap, the present study proposes a novel visual quality assessment method for rural landscapes that integrates multimodal sentiment classification models to strengthen sustainability metrics. Four landscape types were selected from three representative villages in Dalian City, China, and the physiological signals (EEG, EOG) and subjective evaluations (Beauty Assessment and SAM Scales) of students and teachers were recorded. Binary, ternary, and five-category emotion classification models were then developed. Results indicate that the binary and ternary models achieve superior accuracy in emotional valence and arousal, whereas the five-category model performs least effectively. Furthermore, an ensemble learning approach outperforms individual classifiers in both binary and ternary tasks, yielding a 16.54% increase in mean accuracy. Integrating subjective and objective data further enhances ternary classification accuracy by 7.7% compared to existing studies, confirming the value of multi-source features. These findings demonstrate that a multi-source sentiment computing framework can serve as a robust quantitative tool for evaluating emotional quality in rural landscapes and promoting their sustainable development.  
multi-source data experiment; affective computing; rural landscape; visual quality assessment Article
cognitive experience influencing community identity and ecological behavior [2]. This issue arises from the methodological limitations of a one-dimensional approach, where the \"hard-data\" of ecosystems and the \"soft-value\" of human emotions have long existed in a disciplinary divide [3]. As an emerging interdisciplinary innovation, emotional computing technology, through its (1) theoretical evolution, (2) technological breakthroughs, and (3) paradigm shifts, constructs a quantitative sustainability framework that integrates multi-source emotional computing with rural visual landscapes, unveiling the synergistic potential between the two.
# Weak and Dynamic GNSS Signal Tracking Strategies for Flight Missions in the Space Service Volume
###### Abstract  
Weak-signal and high-dynamics are of two primary concerns of space navigation using GNSS (Global Navigation Satellite System) in the space service volume (SSV). The paper firstly defines a reference assumption third-order phase-locked loop (PLL) as the baseline of an onboard GNSS receiver, and proves the incompetence of this conventional architecture. Then an adaptive four-state Kalman filter (KF)-based algorithm is introduced to realize the optimization of loop noise bandwidth, which can adaptively regulate its filter gain according to the received signal power and line-of-sight (LOS) dynamics. To overcome the matter of losing lock in weak-signal and high-dynamic environments, an open loop tracking strategy aided by an inertial navigation system (INS) is recommended, and the traditional maximum likelihood estimation (MLE) method is modified in a non-coherent way by reconstructing the likelihood cost function. Furthermore, a typical mission with combined orbital maneuvering and non-maneuvering arcs is taken as a destination object to test the two proposed strategies. Finally, the experiment based on computer simulation identifies the effectiveness of an adaptive four-state KF-based strategy under non-maneuvering conditions and the virtue of INS-assisted methods under maneuvering conditions.  
GNSS; adaptive Kalman filter; INS-assisted navigation; maximum likelihood estimation; space service volume; Doppler frequency estimation +
Footnote †: journal: Sensors