esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Mapping Traditional PKI Roles to the Proposed System and Key Allocation and Management Process  
Public Key Infrastructure (PKI) typically relies on the Certification Authority (CA), Registration Authority (RA), and Verification Authority (VA) for managing trust. In the proposed IOTA-based framework:  
* Certification Authority (CA): The IOTA Tangle serves as a decentralized equivalent to a CA. The distributed nature of the Tangle eliminates the need for a centralized entity to issue certificates. Instead, trust is established through cryptographic proofs and consensus mechanisms inherent in the Tangle's architecture.
* Registration Authority (RA): IoT devices independently generate their Decentralized Identifiers (DIDs) and register them on the Tangle. This eliminates reliance on third-party registration authorities, decentralizing identity creation and ownership.
* Verification Authority (VA): The Tangle allows any entity within the network to verify identity claims using cryptographic signatures, publicly stored DIDs, and Verifiable Credentials (VCs). This decentralized verification process ensures transparency and trust across the network.  
Private keys are generated locally on each IoT device using secure hardware or cryptographic libraries. To protect these keys, the proposed system integrates IOTA Stronghold, a secure key management solution that encrypts and stores cryptographic material in a tamper-proof environment. The key assignment process includes the following steps:  
1. Key Generation: Each IoT device generates a unique cryptographic key pair (private and public keys).
2. DID Creation: The device uses its public key to create a DID and stores it securely on the Tangle.  
3. Certificate Issuance: A trusted entity issues a VC, which includes identity attributes of the device (e.g., serial number, model). The VC is digitally signed and recorded on the Tangle for public verification.
4. Verification: During interactions, entities validate the authenticity of the VC and DID using cryptographic proofs available on the Tangle, ensuring trustworthiness without intermediaries.
## 3 Methodologies
### Distance Minimization  
The objective of this method is to reduce the disparity between sentence embeddings in different vector spaces. To achieve this, a transformer encoder architecture is being used. The generated sentence vectors are then minimized using regression. We explore the combination of two types of distance metrics as loss functions: Euclidean distance and cosine similarity. The combined loss function will help to capture the important information which the other distance metric misses.  
In this approach, the _FastText_ word embeddings are updated by using the outcome vectors of the _sBert_ model for corresponding sentences. This enables us to augment the informational value in _FastText_ embeddings, resulting in a closer resemblance to the _sBert_ embeddings within the desired space.  
The proposed model architecture (Figure 2) consists of a word-embedding layer and a transformer encoder structure. The encoder structure generates embeddings for all words in a sentence. For each batch, the encoder produces a tensor of shape \(DxLxB\), where \(D\) represents dimensionality, \(L\) indicates sentence length, and \(B\) denotes the batch size. The average of the tensors with shape \(DxLxB\), resulting in an output tensor of shape \(DxB\). Subsequently, the loss is calculated by using the Euclidean and the cosine distances, each with equal weights.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 3 Taxonomy of Cyber-Physical Attacks
### Network Layer  
The network layer is responsible for exchanging a variety of information between the physical system layer and the application layer with high-level reliability and real-time characteristics. In the network layer, the attacker can disturb data transmission, which violates the integrity of the data and real-time constraints on the CPS, resulting in the destabilization of the physical system.  
Figure 4 illustrates three representative cyber-physical attacks in the network layer. First, the denial of service (DoS) attack forces dropped packets by exploiting physical characteristics or vulnerabilities in network protocols. Second, the flooding attack induces packet transmission delays in order to cause abnormal behavior in the physical systems. Third, the packet manipulation attack violates the data integrity on the network via packet modification. Although network layer attacks do not seem to have a relationship with physical dynamics (1) directly, these attacks can significantly affect the stability of the physical system.
# Buckling Resistance of Tapered Steel Columns
## 3 Parametric Analysis
### Limitations, Varied Parameters, and Methods of Displaying Results
The observed slenderness area was expanded with the increase in the tapering ratio because the resistance drop trend with the increasing tapering ratio translated the resistance curve along the slenderness axis. Using the Autodesk Robot Structural Analysis, 291 numerical buckling simulations were carried out according to the method by [PERSON] et al. [26] to determine the critical load multiplier \(\alpha_{cr}\) for the varied member cases. Steel was modeled as elastic with a yield strength of 235 N/mm\({}^{2}\) and a modulus of elasticity of 210.000 N/mm\({}^{2}\). An additional buckling analysis using shell finite elements confirmed minor differences in the critical load multipliers. A numerical analysis was also conducted using the ANSYS 2019 software to gain insight into the accuracy of the considered methods for calculating the buckling resistance of the tapered columns. The numerical analysis consisted of two steps. In the first step, an eigenvalue buckling analysis was performed to determine the critical load and to obtain the deformed geometry (initial imperfection) of the tapered member used in the second step, which involved a nonlinear calculation with a gradual increase in the load until failure (Figure 9a). The tapered columns with various slenderness and tapering ratios were modeled using four-node shell elements with six degrees of freedom at each node (SHELL181). At one end of the column, a pinned support was modeled, while on the other side, where the load was applied, a sliding support was modeled, allowing for translation along the member's longitudinal axis. In the buckling analysis, the steel material was modeled as linear elastic with a modulus of elasticity of 210.000 N/mm\({}^{2}\). In the nonlinear \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(\overline{\lambda}_{0,min}\) & 0.2 & 0.4 & 0.6 & 0.8 & 1.0 & 1.2 & 1.4 & 1.6 & 1.8 & 2.0 & 2.2 & 2.4 \\ \hline \(L\) [m] & 2.46 & 4.92 & 7.38 & 9.84 & 12.30 & 14.76 & 17.22 & 19.68 & 22.14 & 24.60 & 27.07 & 29.53 \\ \hline \(\overline{\lambda}_{0,min}\) & 2.6 & 2.8 & 3.0 & 3.2 & 3.4 & 3.6 & 3.8 & 4.0 & 4.5 & 5.0 & 5.5 & 6.0 \\ \hline \(L\) [m] & 32.0 & 34.4 & 36.9 & 39.4 & 41.8 & 44.3 & 46.7 & 49.2 & 55.4 & 61.5 & 67.7 & 73.8 \\ \hline \(\overline{\lambda}_{0,min}\) & 6.5 & 7.0 & 7.5 & 8.0 & 8.5 & 9.0 & 9.5 & 10.0 & 10.5 & 11.0 & & \\ \hline \(L\) [m] & 80.0 & 86.1 & 92.3 & 98.4 & 104.6 & 110.7 & 116.9 & 123.0 & 129.2 & 135.3 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Buckling lengths depending on the non-dimensional slenderness based on the smallest cross-section—HEB 300; S235. analysis, the steel was modeled bilinearly with a yield strength of 235 N/mm\({}^{2}\) and a tangent modulus of 2.100 N/mm\({}^{2}\). To ensure the accuracy and reliability of the numerical model, we drew upon the meticulously documented, verified, and validated numerical model for tapered steel columns by [PERSON] et al. (2016).
## 3 Proposed Method: Adaptive Unscented Kalman Filter Using Sparse Measurements of Accelerations
### Dual Adaptive Filtering for Measurement Noise Estimation
The goal of SF in MS-KF\({}_{\rm ME}\) is to make the innovations, which is employed to provide a measure of optimality of the filter, compatible with their expected covariance matrix. For this purpose, the covariance-matching technique is conducted, i.e.,  
\[\mathbf{z}_{k+1}^{\rm g}=diag\Big{(}\mathbb{E}\Big{[}\mathbf{v}_{k+1}\mathbf{v}_{k+1} \mathbb{T}\Big{]}\Big{)} \tag{13}\]  
\[\mathbf{I}_{k+1}=diag\Big{(}\sum_{i=0}^{2n_{\rm s}}W_{c}^{i}\Big{[}\mathbf{c}_{k+ 1|k}^{i}-\hat{\mathbf{z}}_{k+1|k}\Big{]}\Big{[}\mathbf{c}_{k+1|k}^{i}-\hat{\mathbf{z}}_{k+1| k}\Big{]}^{\rm T}\Big{)} \tag{14}\]  
where Equation (13) represents the actual covariance based on innovations, and the Equation (14) is the predicted covariance of innovations from MF. If two matrices from Equation (13) and (14) are similar to each other, the current covariance of artificial measurement noise of MF is considered appropriate. If the calculated value of Equation (13) is larger than that of Equation (14), \(\mathbf{R}_{k+1}\) needs to be increased to avoid divergence of MF, and consequently this procedure increases the covariance of states, \(\mathbf{P}_{k+1}^{\mathbf{xx}}\). There are three advantages in using MS-KFME: First, no additional parameter is introduced for the algorithm (as for MS-KFPE, the time-window is required). Second, while the other two adaptive techniques shift the solution space rapidly by increasing \(\mathbf{Q}_{k}\), MS-KFME widens the solution space by increasing \(\mathbf{P}_{k+1}^{\mathbf{xx}}\). Indeed, the biased estimation may be avoided by increasing \(\mathbf{P}_{k+1}^{\mathbf{xx}}\) instead of increasing the size of \(\mathbf{Q}_{k}\) rapidly, and stable estimation will be achieved. Lastly, this correction process for artificial measurements noise is implemented in measurement-update of MF before calculating the Kalman gain. Therefore, the corrected covariance of artificial measurements noise by SF can be utilized immediately. These advantages enable MS-KFME to track the sudden decrease of system parameter in a stable manner during the seismic excitations. The algorithm of MS-KFME with the newly proposed state-space model is summarized in Figure 3. In the time-update part, calculations of SPs are not needed because of the linear system-process model in Equation (8). The part corresponding to the slave LKF is represented in gray background along with detailed descriptions. It is noteworthy that the regularization term in Equation (12) is considered in the form of dummy variables [34] when the auto- and cross- covariance matrices are calculated in the measurement-update part of MF. In these calculations for two covariance matrices, the prior information of regularization is required. When the time-step is shorter than the selected time-window, the values of initial state are used for this prior information. After that time point, this prior is updated in the state-update part. Figure 2: General block diagram illustration of Master-Slave Kalman Filters.
# Multimodal Framework for Long-Tailed Recognition
## 3 Experiment Details  
### Datasets  
This paper presents extensive experiments conducted on three mainstream long-tailed datasets, which are introduced below.  
CIFAR-10-LT and CIFAR-100-LT are long-tailed versions derived from the CIFAR-10 and CIFAR-100 datasets, primarily used to evaluate the model's performance in handling imbalanced data. The original CIFAR-10 and CIFAR-100 datasets contain 10 and 100 classes, respectively, with a balanced number of images per class. In the long-tailed versions, the distribution is modified to follow a long-tailed distribution by downsampling the minority classes, simulating the common long-tail problem in real-world applications. Specifically, the long-tail factor determines the ratio between the number of samples in the head and tail classes. For instance, with an imbalance factor of 200, the class with the most samples has 200 times more samples than the class with the fewest. These long-tailed datasets are used to assess the model's performance in imbalanced classification tasks.  
ImageNet-LT is a long-tailed dataset sampled from the classic ImageNet-2012 dataset. To construct this dataset, researchers downsampled the original ImageNet data based on a Pareto distribution, retaining 1000 classes, with the number of samples per class ranging from hundreds to thousands. ImageNet-LT is designed to simulate real-world long-tailed distributions, where there is a significant imbalance between head classes (with many samples) and tail classes (with very few samples). This dataset is commonly used to evaluate model performance in large-scale, long-tailed image classification tasks.  
iNaturalist2018 is a long-tailed dataset mainly used for studying fine-grained image classification problems. This dataset contains over 8000 species categories and approximately 437,000 images in total, but many categories have very few samples, exhibiting a typical long-tailed distribution. The iNaturalist2018 dataset is particularly challenging because it has not only a large number of categories but also a significant long-tail effect, where many categories have very few samples, while a few head categories have a large number of samples. It is widely used to evaluate model performance in fine-grained classification and long-tailed distribution problems, particularly in real-world applications like species recognition.
## 4 Descriptions of Different Topologies of WSNs  
This section identifies and studies various types of topologies of WSNs. First of all, different topologies of WSNs are identified. In doing so, different application protocols proposed by various researchers, such as protocols for data gathering/collection, target tracking, routing, data aggregation, data dissemination, _etc._, are studied. These protocols use various types of logical topologies. From each of the protocols, the topology is identified and listed in Table 1. The identified topologies are (i) flat topology, (ii) cluster-based topology, (iii) chain-based topology and (iv) tree-based topology.  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline Protocol for & References & Topology used in the protocol \\ \hline \multirow{4}{*}{Data gathering} & [30] & Flat \\ \cline{2-3}  & [31] & Cluster-based \\ \cline{2-3}  & [32; 33; 34] & Tree-based \\ \cline{2-3}  & [35; 36] & Chain-based \\ \hline \multirow{4}{*}{Target Tracking} & [37] & Flat \\ \cline{2-3}  & [38] & Cluster-based \\ \cline{2-3}  & [39; 40; 41] & Tree-based \\ \cline{2-3}  & [42] & Chain-based \\ \hline \multirow{4}{*}{Routing} & [43; 44] & Flat \\ \cline{2-3}  & [26; 45; 46; 47] & Cluster-based \\ \cline{2-3}  & [38; 48; 49; 50] & Tree-based \\ \cline{2-3}  & [51; 52] & Chain-based \\ \hline \multirow{4}{*}{Data aggregation} & [53] & Flat \\ \cline{2-3}  & [54; 55] & Cluster-based \\ \cline{2-3}  & [56; 57] & Tree-based \\ \cline{2-3}  & [58] & Chain-based \\ \hline \multirow{4}{*}{Data dissemination} & [59] & Flat \\ \cline{2-3}  & [60] & Cluster-based \\ \cline{2-3}  & [61] & Tree-based \\ \cline{2-3}  & [62] & Chain-based \\ \hline \multirow{4}{*}{Synchronization} & [63] & Flat \\ \cline{2-3}  & [64] & Cluster-based \\ \cline{1-1} \cline{2-3}  & [34; 65] & Tree-based \\ \hline \end{tabular}
\end{table}
Table 1: Different protocols and their corresponding topology.  
The following subsections describe each of the topologies with their advantages and disadvantages in detail.
## 2 Materials and Methods
### Data Collection and Processing
In multi-sensor monitoring projects, where precision and consistency are crucial, regular calibration is vital [39]. Therefore, prior to deployment, all sensors were calibrated. Sensor placement was optimized to avoid external disturbances considering solar radiation, airflows, or thermal imbalances due to the building envelope [40]. In some cases, psychometric analysis was conducted to assess horizontal humidity gradients (assessing both relative humidity (RH%) and specific humidity (SH; g/kg)), identifying anomalies and guiding optimal data logger positioning. Data was recorded hourly, balancing resolution with the slow moisture response of masonry, which was stored until further processing. As stated by EN 15757 [41], a long-term monitoring campaign of at least one year with a recording frequency of 1 h or less is necessary to diagnose climate-vulnerable materials. Prior to applying the data analysis, the collected microclimatic data was inspected for dataset quality and anomaly detection. Given the sensitivity of built heritage environments, ensuring data reliability is critical to avoid misleading interpretations. Outliers, such as sudden spikes caused by sensor malfunctions, temporary obstructions, or electromagnetic interference, were identified and removed using statistical and filtering techniques. The moving average filter with a 4 h interval was applied to smooth transient noise while preserving trend integrity. To distinguish between sensor noise and genuine microclimatic events (e.g., visitor-induced humidity spikes or imbalances due to use), the data was cross-referenced with external meteorological data and the installed external probe for each case study. The choice to use the installed external probe for the purpose of microclimatic monitoring was due to the distance of meteorological stations from the selected built heritage sites in order to capture localized climatic conditions (e.g., urban heat islands, humidity variations near walls, or wind patterns around historical structures). An example of the comparison between ARPA Lombardy--Regional Agency for Environmental Protection of Lombardy [42]--and the installed external probe in San Giacomo Church in Como is illustrated in Figure 1. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & **P-VEN** & **P-BES** & \
\begin{tabular}{c} **S-** \\ **VITT** \\ \end{tabular} & **P-SEM** & **S-GIA** & **P-PIE** & **MU-TI** &
\begin{tabular}{c} **C-** \\ **MAS** \\ \end{tabular} & **S-PER** & **S-FED** & **S-ROM** & **S-GION** & **S-IGN** \\ \hline Name & S3 & S4 & S03 & 2 & AbsIDE & S2 & S04 & S03 & ALTARE & IV’-Navata & ABSIDE & ABSIDE & ALTARE & T \\ Orientation & SE & N & S & S & E & SE & SE & W & W & S & NE & E & E \\ Floor & 1 st & GR & GR & 1 st & GR & GR & GR & GR & GR & Rave & alar & Apse & alar \\ H [m] & 6 & 0.6 & 1.5 & 5 & 1.5 & 2.7 & 1 & 1 & 0.5 & 6 & 1.5 & 1.5 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The selected probes and their location within the case studies.
# Space-Based Displacement Monitoring of Coastal Urban Areas: The Case of Limassol's Coastal Front
## 2 Materials and Methods
### Data Processing  
Monitoring land subsidence in the coastal front of Limassol city has been carried out using several techniques ranging from traditional leveling to PSI, as shown in Figure 2.
###### Abstract  
In this paper, a low-power and low-noise readout circuit for resistive-bridge microsensors is presented. The chopper-stabilized, recycling folded cascode current-feedback instrumentation amplifier (IA) is proposed to achieve the low-power, low-noise, and high-input impedance. The chopper-stabilized, recycling folded cascode topology (with a Monticelli-style, class-AB output stage) can enhance the overall noise characteristic, gain, and slew rate. The readout circuit consists of a chopper-stabilized, recycling folded cascode IA, low-pass filter (LPF), ADC driving buffer, and 12-bit successive-approximation-register (SAR) analog-to-digital converter (ADC). The prototype readout circuit is implemented in a standard 0.18 \(\upmu\)m CMOS process, with an active area of 12.5 mm\({}^{2}\). The measured input-referred noise at 1 Hz is 86.6 nV/\(\upmu\)Hz and the noise efficiency factor (NEF) is 4.94, respectively. The total current consumption is 2.23 \(\upmu\)A, with a 1.8 V power supply.  
low-noise; resistive microsensor; sensor readout circuit; chopper stabilization; recycling folded cascode instrumentation amplifier
# Improvements to Supercomputing Service Availability Based on Data Analysis
## 5 Result
### Analysis of Waiting Time for Main Queues  
As described in the previous section, adding K-hooks may increase users' waiting time. We modified the queue policy to reduce the waiting time of users. In terms of service availability, we reduced the maximum number of submitted jobs and executed jobs to increase job success rate without affecting waiting time. Figure 6 shows the results of analyzing the waiting time for main queues by modifying the policy at the time of adding K-hooks. The main queues to which many computing resources were allocated are the normal, exclusive, norm_skl, and commercial queues. We analyzed the waiting times of these four main queues monthly to confirm that our method does not affect waiting times. The left \(y\)-axis shows the number of jobs and the right \(y\)-axis represents the waiting time (second) by month. The bar graph shows the number of jobs, the jagged line indicates the waiting time, the blue line shows the average waiting time before application, and the red line shows the average waiting time after application. In the normal queue shown in Figure 6a, the average waiting time after application was 1376.9 s, which is 34% lower than  
Figure 5: Comparison of job success rate before and after applying K-hook with scheduling.  
before K-hook was applied. In the exclusive queue shown in Figure 6b, the average waiting time after application was 14,412.1 s, a reduction of 48.9%. In the norm_skl queue shown in Figure 6c, the average waiting time after application was 1582.9 s, a reduction of 80.1%. In the commercial queue shown in Figure 6d, the average waiting time after application was 5416 s, a reduction of 10.5%.  
These results show that from the user's perspective, the waiting time does not increase because of our method being applied to each queue. Although it cannot be generalized due to the unique characteristics of the user's jobs, this is an attempt to satisfy the job success rate from the operator's perspective and ensure the user's convenience. As this is a real service supercomputing environment, we could not compare the before and after scenarios perfectly. Because the job submitted status of users may be different every month, some months may have numerous jobs, and some may have fewer. Therefore, we analyzed the waiting time as a rough trend by displaying the monthly average for one year on a graph.