esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Quantum Migration of the Tor Application
## 5 Evaluation
### Effectiveness  
The network benchmarking process displays consistent results. Asymmetric latencies have been identified; however, they are not necessarily problematic. The real Tor network relies on a diversity of hardware, resulting in asymmetric bandwidth. Compared to advertised metrics (see Figure 11), the circuit round-trip latencies share common characteristics in their results, with a higher but faster onion latency compared to when directed to a public server. On the other hand, estimating a precise proportion of public key cryptography processes is difficult by solely analysing the timings of cryptographic functions.  
Related Work  
[PERSON] et al. [56] highlight the impact of PQC on Tor but focus mainly on the scheme's performances and less on Tor software. Their work acknowledges the advantage of lattice-based KEM cryptosystems but presents SIKE as an optimal choice; it does not cover the efficiency of DSA schemes. In comparison, this paper benefits from a more advanced status in the NIST standardisation process. [PERSON] [57] gives a brief cryptographic analysis of potential candidates and emphasises hybrid Ring-LWE approaches. Yet, [PERSON]'s paper also promotes SIKE as the appropriate choice to minimise  
Figure 6: Proportion of iterations per relay role for a given function. In this experiment, OS and middle relay are the same devices.  
overhead. In contrast, this work dived into Tor's software performances in a constrained environment, evaluating the potential PQC load in a pragmatic manner.
## 4 Identification of Breathing Cracks in Beams
### Damage Identification Results  
The optimal wavelet basis function needs to be determined before CWT. The rbio2.4 wavelet has the following attractive properties [39]: an appropriate number of vanishing moments, compact support, symmetry, biorthogonality, and similarity to the waveform of the signal, which endows the rbio2.4 wavelet with powerful damage feature extraction capabilities. Therefore, in this paper, rbio2.4 is chosen as the optimal wavelet basis function, and the following analysis is based on rbio2.4 wavelets.  
Figure 11 shows the first four orders \(CS(b,s)\) without the extension boundary under scenario I. From Figure 11, it can be seen that the CWT coefficient of the left boundary is extremely large, and the sudden spikes due to damage are relatively insignificant. In order to reduce the boundary problem, IWLSF is applied to extend the left boundary of each order SSSM curve, and the damage identification is performed using the extended SSSM curve.  
Scenario I: \(a\) is 0.05 m, and the damage severity is 5%. Scenario II: \(a\) is 0.1 m, and the damage severity is 10%. The left boundary of the first four-order SSSM curves under scenarios I and II in Figure 10 is extended. Figures 12 and 14 show the \(CS(b,s)\) after extending the boundary under scenarios I and II, respectively. Figures 13 and 15 show the \(\overline{CS}(b,s)\) and damage index \(SSSM_{\text{DJ}}\) after fusion under scenarios I and II, respectively, with the actual crack locations (\(\zeta=0.35\) and \(\zeta=0.6\)) indicated by the small red circles in the figures. The following conclusions can be drawn from Figures 12-15: 1 After the left boundary extension, the boundary distortion is significantly reduced, and there is almost no great abnormal mutation at the boundary. 2 The \(CS(b,s)\) values of the same order are different at two cracks of the same depth, which means that the sensitivity of \(CS(b,s)\) to damage at different locations is different, whereas after fusing the first four order \(CS(b,s)\) by averaging, the values of \(\overline{CS}(b,s)\) at the two cracks are almost equal. 3 The damage index \(SSSM_{\text{DJ}}\) only mutates at the damage under scenarios I and II, which indicates that the CWT-SSSM feature-based identification method can accurately identify the two crack locations of the cantilever beam and is capable of characterizing slight damage at the same time.  
Figure 10: The first four order SSSM of the cracked cantilever beam: (**a**) scenario I; (**b**) scenario II.  
Figure 11: \(CS(b,s)\) without extension of SSSM boundary under scenario I.  
Figure 12: \(CS(b,s)\) of the extended SSSM boundary under scenario I.  
Figure 14: \(CS(b,s)\) of the extended SSSM boundary under scenario II.  
Figure 13: Damage identification results under scenario I: (**a**) \(\overline{CS}(b,s)\); (**b**) Damage index \(SSSM_{DI}\).
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment  
### Dataset Description  
To verify the effectiveness of our approach, we use a real web service QoS dataset WS-DREAM obtained from the [[https://wsdream.github.io](https://wsdream.github.io)]([https://wsdream.github.io](https://wsdream.github.io)) website for experimental evaluation. The dataset contains two QoS attribute subdatasets, the response time (RT) and the throughput (TP) dataset. Each dataset includes 1,974,675 records of calls made by 339 users to 5825 services. The specific data information is as follows:  
* User Information: User ID, IP Address, Country, IP No., AS (Autonomous System), Latitude, and Longitude.
* Service Information: Service ID, WSDL Address, Service Provider, IP Address, Country, IP No., AS, Latitude, and Longitude.  
Under further analysis, the 339 users are distributed in 137 autonomous regions and 31 countries, and the 5825 services are distributed in 1021 autonomous regions and 74 countries. QoS values include response time (RT), with the attribute ranging from 0-20 s; and throughput (TP), with the attribute ranging from 0-1000 kbps.  
In the comparison experiment, we randomly remove a part of the data to sparse the data, so we set the matrix density to 5%, 10%, 15%, 20%, 25%, and 30%; the randomly removed QoS data are used as the test set and the rest of the QoS data are used as the train set. For example, when the QoS matrix density is 5%, 5% of the data are used as the training dataset and the remaining 95% are used as the test set. We perform multiple experiments, averaging all the experimental results as the final prediction.
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 4 Experimental Demonstration Wireless OAM Multiplexing Technology using 28 GHz
### Experimental OAM Multiplexing Results Using Multiple UCAs  
We conducted wireless OAM multiplexing experiments using five different OAM modes (\(-2\), \(-1\), \(0\), \(1\), and \(2\)) over 28 GHz. Detailed descriptions regarding experimental parameters are given in Table 2.  
We used the single carrier with frequency domain equalization (SC-FDE) to average channel characteristics over a wide signal bandwidth. The signal bandwidth was 2 GHz and 64-QAM modulation was used for all five OAM modes. The transmission rate per single stream was 9 Gbps. Experiments were conducted in a shielded room, as shown in Figure 9. The distance between Tx and Rx antennas was 2.5 m. The propagation loss can be calculated with Equation (2) with UCA sizes and distance between Tx and Rx. Tx signals were generated by arbitrary waveform generators and fed into a Tx antenna. The Rx antenna received signals and fed them into a digital oscilloscope that worked as an analog-to-digital convertor. Since the digital oscilloscope is equipped with four ports, reception was done sequentially using the four ports. This is not significant since the channel environments was static in the shield room. Offline digital signal processing was conducted using all the converted signals.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Center frequency & 28.5 GHz & OAM modes & \(-2\), \(-1\), \(0\), \(1\), \(2\) \\ Signal bandwidth & 2 GHz & Number of streams & \(5\) \\ Number of UCAs & 4 & Signal carrier & Single carrier \\ Number of antenna elements & 16 & Modulation & 64 QAM \({}^{1}\) \\ in a UCA & 65 & Channel coding & LDPC (DVB-S2) 3/4 \\ Number of antenna elements & 65 & Channel coding & Frequency domain \\ Diameter of UCA & 24, 26, 48, 60 cm & Equalization & equalization \\ Tx/Rx distance & 2.5 m & Block size & 256 \\ \hline \hline \end{tabular}  
* \({}^{1}\) T quadrature amplitude modulation, *2 low density parity check, \({}^{3}\) digital video broadcasting satellite second generation.  
\end{table}
Table 2: Experimental parameters (OAM multiplexing).  
Figure 8: Implemented multiple uniform circular arrays (Four UCAs and a center antenna).  
Table 3 shows the experimental results we obtained for two methods. For both antenna selection and receiver diversity, we, respectively, used UCA No. 4, UCA No. 2, UCA No. 0, UCA No. 3, and UCA No. 1 for transmitting OAM modes \(-\)2, \(-\)1, 0, +1, and +2 signals. For the antenna selection method, we selected UCA No. 3, UCA No. 4, UCA No. 0, UCA No. 3, and UCA No. 1. By applying the LDPC channel coding (rate 3/4), we confirmed that error-free transmissions were obtained except for the OAM mode 2 case. For the receiver diversity method, we, respectively, selected UCA No. 4 and UCA No. 2 for OAM modes \(-\)1 and +2. We were able to confirm that error-free transmissions were received in all OAM modes with the same LDPC channel coding in this case. Using 2 GHz of signal bandwidth yielded a 45 Gbps transmission rate.
# Effective Blind Frequency Offset Estimation Scheme for BST-OFDM Based HDTV Broadcast Systems
## 4 Proposed Estimation Scheme
### MSE Analysis
In a similar way, neglecting these terms leads to  
\[E\left\{\frac{\sum_{l=1}^{N_{a}}|\hat{Z}_{l}^{Q}(k_{i})|^{2}}{( \sum_{l=1}^{N_{a}}|H_{l}(k_{i})|^{2}|H_{l}(k_{i+1})|^{2})^{2}}\right\} =E_{X}^{2}E\left\{\frac{\sum_{l=1}^{N_{a}}|H_{l}(k_{i})|^{4}|\hat{ Z}_{l}^{Q}(k_{i+1})|^{2}}{(\sum_{l=1}^{N_{a}}|H_{l}(k_{i})|^{2}|H_{l}(k_{i+1})|^{ 2})^{2}}\right\} \tag{31}\] \[+E_{X}^{2}E\left\{\frac{\sum_{l=1}^{N_{a}}|H_{l}(k_{i+1})|^{4}| \hat{Z}_{l}^{Q}(k_{i})|^{2}}{(\sum_{l=1}^{N_{a}}|H_{l}(k_{i})|^{2}|H_{l}(k_{i+ 1})|^{2})^{2}}\right\},\]  
which is further derived as  
\[E\left\{\frac{\sum_{l=1}^{N_{a}}|\hat{Z}_{l}^{Q}(k_{i})|^{2}}{( \sum_{l=1}^{N_{a}}|H_{l}(k_{i})|^{2}|H_{l}(k_{i+1})|^{2})^{2}}\right\} =\frac{E_{X}^{3}\sigma_{Z}^{2}}{N_{a}}\sum_{m=0}^{1}E\left\{\frac{ 1}{|H_{l}(k_{i+m})|^{2}}\right\} \tag{32}\] \[+\frac{E_{X}^{2}}{2N_{a}}(2\sigma_{\mathbb{C}}^{2}\sigma_{Z}^{2}+ \sigma_{Z}^{4})\sum_{m=0}^{1}E\left\{\frac{1}{|H_{l}(k_{i+m})|^{4}}\right\}.\]  
Since the probability density function of \(x=|H_{l}(k)|^{2}\) in (32) is a central chi-square distribution with two degrees of freedom denoted by \(f_{H}(x)=\sigma_{H}^{-2}e^{-x/\sigma_{H}^{2}}\), it is effortlessly obtained that for any positive integer \(n\)  
\[E\left\{\frac{1}{|H_{l}(k_{i+m})|^{2n}}\right\} =\int_{p_{min}}^{\infty}\frac{1}{x^{n}\sigma_{H}^{2}}e^{-x/\sigma _{H}^{2}}dx \tag{33}\] \[=(-1)^{n}\frac{1}{(n-1)!(\sigma_{H}^{2})^{n}}E_{i}(-p_{min}/ \sigma_{H}^{2})\] \[+\frac{e^{-p_{min}/\sigma_{H}^{2}}}{p_{min}^{n-1}\sigma_{H}^{2}} \sum_{k=0}^{n-2}\frac{(-1)^{k}(p_{min}/\sigma_{H}^{2})^{k}}{n(n-1)\cdots(n-k)},\;m=0,1,\]  
where \(E_{i}(-x)=-\int_{x}^{\infty}e^{-t}/tdt\). From (30) and (32), one can see that they are not indepedent of index \(i\).
## 3 Results and Discussion
### Chlorophyll  
Results from the Kolmogorov-Smirnov and Student's \(t\)-test suggest that chlorophyll \(a\) concentrations were reduced by the CyanoSTUN UV-C treatment at a statistically significant level (Figure 3; Table 1, Table 2, and Table S1), but this determination is not definitive, because the statistical significance of treatment conditions varied across the regrowth period. For example, for Day 1 of the regrowth period (Table 2), chlorophyll \(a\) data at the 15 mJ/cm\({}^{2}\) dose exhibited statistical significance (_p_ < 0.05) from control data for KS and T tests applied using both pooled and unpooled analysis methods. However, none of the other doses exhibited this level of statistical significance on Day 1 of the regrowth period. Importantly, for the control dose (0 mJ/cm\({}^{2}\)), the front data were not statistically different from the rear measurements (Table 1), indicating that measurements were reliable despite the few replicates provided. Across Days 2-9 of the regrowth period, chlorophyll \(a\) concentrations affected by UV-C treatment demonstrated significant differences between treatment doses and control demonstrated on some days but not others. The authors conclude that it is likely that, at least in the earlier days of the 9-day regrowth period, chlorophyll \(a\) is affected by the UV-C treatment.  
Figure 3: The percent reduction in chlorophyll \(a\) over a 9-day period post-field trial is shown at different concentrations (**A**) 0 mJ/cm\({}^{2}\), (**B**) 15 mJ/cm\({}^{2}\), (**C**) 30 mJ/cm\({}^{2}\), and (**D**) 30 mJ/cm\({}^{2}\).
## 3 Methods
### Stereo Processing  
By default, all images of the aligned image block are orthorectified (_mapproject_) before stereo reconstruction using the optimized RFMs and the approximation DEM. Orthorectifying the PSS images in advance results in more similar perspectives, which likely improves both the number and quality of stereo matches, even at the cost of slightly higher computing times during the initial image matching step, running _parallel_stereo_. The disparity remains as residual information because the DEM does not perfectly model the actual surface (e.g., due to its coarser resolution, different acquisition times, or inherent inaccuracies). ASP uses these residual disparities, together with the optimized camera models, to reconstruct the actual 3D surface by updating the approximation DEM. In the absence of orthorectified images and approximation DEM (e.g., when using the Expert setting _no_ortho_), image rectification is carried out using an affine epipolar transformation. This step is mandatory for subsequent image matching that requires rectified images. The information from the camera models is then used to perform traditional triangulation, transferring the disparity information into object space. Disparity is measured using an adaptation of the SGM algorithm, called more global matching (MGM) [33]. Compared to SGM, MGM offers significant advantages in low-texture scenarios: by aggregating matching costs over a larger spatial context, striping artefacts and false matches in low-texture areas are significantly reduced. This improves the robustness of the disparity computation despite higher computational cost. Subpixel refinement is applied using Bayes Expectation-Maximization (Bayes EM), as recommended by ASP, which provides the highest-quality results. The kernel sizes for correlation and subpixel refinement were selected based on empirical evidence and align with the default values. These values are optimal for mountainous and glaciated regions. In areas with limited depth variation and minimal contrast (i.e., texture) in the PSS, it is recommended to slightly increase the kernel sizes in the _Expert Settings_ to reduce ambiguities in the correlation. However, excessive kernel sizes may lead to a loss of sharp edges in the resulting disparity map. The pairwise-generated 3D point clouds undergo a filtering process, where isolated blobs with fewer than nine pixels are identified and removed.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 1 Introduction
Similarly, [PERSON], [PERSON], [PERSON], and [PERSON] [16] introduced a latent variable model with change points to capture shifts in behavior under time pressure, demonstrating the psychological relevance of response timing. In the realm of AI-based adaptivity, the work of researchers published in Nature Scientific Reports--namely, [PERSON], [PERSON], and [PERSON] [17]--demonstrated how machine learning techniques can be leveraged to optimize adaptive testing, including the modeling of time-based variables in real-time test environments. In terms of practical implementation, [PERSON] et al. [18] presented an IRT-based computer adaptive testing system that emphasizes both digital scalability and test precision. Additionally, [PERSON] and [PERSON] [19] contributed a recent methodological tutorial on sample-size planning in IRT models, offering updated guidelines that are particularly relevant when incorporating multidimensional elements such as response time. Despite these advancements, the literature lacks a systematic comparison of multiple response-time-integrated IRT models under the same testing conditions using real-world data. This study aims to address this gap by evaluating six distinct time-sensitive IRT models alongside the traditional 1 PL model,using empirical data collected from the authors' adaptive testing platform, TestYourself. By comparing these models across multiple performance indicators (AIC, BIC, AUC), this research contributes a comprehensive, up-to-date investigation into how response time can be effectively incorporated into modern educational measurement systems. A recent empirical study utilizing the TestYourself platform investigated the effectiveness of these models using data from 150 students completing 30 mathematics items. Model performance was evaluated using key statistical measures, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Area Under the Curve (AUC), and Item-Test Correlation [20]. The results indicated that the DTA-IRT model exhibited greater stability, highlighting a trade-off between responsiveness and predictive reliability. These findings suggest that integrating response time into IRT-based adaptive testing can significantly enhance student ability estimation while ensuring fairness. As the field progresses, further research is needed to explore large-scale implementations of response-time-aware IRT models across diverse student populations. Additionally, integrating Artificial Intelligence (AI) into adaptive testing frameworks may further refine real-time decision-making and test personalization [21; 22].
## 3 Experiments and Analysis
### Quantitative Comparison
#### 3.5.6 Proposed STFD Method  
Analyzing Figure 9 and Table 3, our proposed method demonstrates superior performance, with its ROC curve closest to the top-left corner and all metrics ranking as either the best or second-best. This superiority originates from the TRC and RIA techniques, which effectively discriminate target-background intensity temporal profiles: despite significant background fluctuations in Seq.3, TRC effectively captures subtle temporal anomalies and synergizes with the GELCM-RIA fusion mechanism to eliminate interference. Notably, in Seq.2, Seq.3, and Seq.6, IMNNLWC (Seq.2-3) and IPI (Seq.6) as representative single-frame methods fail to detect any targets in certain frames, resulting in abnormally high BSF values that exceed those of our method. However, this does not indicate superior detection performance because their collapsed SCRG scores (0.00), near-random AUC values, and ROC curves hugging the x-axis in Figure 9 demonstrate conclusive evidence of complete detection failure. In Seq.2, the SCRG metric for IPI surpasses that of our method due to its sparse target assumption, which yields cleaner detection in the target neighborhood. Nevertheless, when considering the comprehensive AUC metric and the global background suppression ratio, IPI's performance in Seq.2 is inferior to the proposed STFD.  
Through quantitative experimental analysis, the STFD method demonstrates significant advantages in both cluttered and simple background scenarios. The background complexity quantified by the signal-to-clutter ratio (SCR) (as shown in Table 1) indicates that Seq.1-Seq.3 and Seq.6 correspond to low-SCR scenarios (SCR < 10, high background complexity), whereas Seq.4-Seq.5 represent high-SCR scenarios (SCR > 10, low background complexity). Under this classification, STFD exhibits superior performance across key metrics. In low-SCR environments (Seq.1-Seq.3 and Seq.6), the Background Suppression Factor (BSF) achieves 21.39-80.34, with AUC-ROC values exceeding 0.95, demonstrating clear advantages over baseline methods. In high-SCR scenarios (Seq.4-Seq.5), the Signal-to-Noise Gain (SRG) improves to 71.34-91.46, accompanied by statistically significant superiority in the D-statistic compared with competing approaches. These results substantiate that STFD effectively mitigates the impact of background complexity variations on detection performance through its temporal profile characterization and spatiotemporal fusion mechanisms.
## 1 Introduction  
### Background and Purpose of Research  
Historically, the productivity of the construction industry has been lower than that of other industries, such as manufacturing. However, a newly emerging argument suggests that embracing digital transformation may lead to productivity improvements similar to those observed in other industries [1; 2; 3]. Achieving such a digital transformation within the construction industry requires a method for systematizing construction information and converting it into digital data [4; 5]. Furthermore, establishing approaches for its production, storage, delivery, and utilization is equally important. The extent and diversity of construction information make it challenging to precisely specify its scope. Hence, an appropriate construction information classification system must be developed to effectively store essential information in a digital format. However, the existing research on construction information classification systems primarily focuses on completed facilities, concentrating solely on storing and retrieving facility-related information. These existing systems are inadequate for comprehensively managing task-based information throughout the construction project lifecycle.  
In particular, infrastructure construction projects involve long-term endeavors that span planning through operation. Therefore, they require the continuous securing of digitally transformed construction information throughout the project lifecycle. Ensuring the continuity of construction information throughout the infrastructure construction process facilitates the easier acquisition, analysis, and sharing of the required data at each stage. Thus, it enables prompt decision making and optimizes project outcomes.  
This study proposes a construction information classification system that ensures the integration of information throughout the lifecycle of road construction projects. The primary aim is to generate construction-related information as digital data during the construction process and utilize various software tools for their analysis, transmission, and application. Thus, the proposed system aims to accelerate information dissemination in construction projects, optimizing resource allocation throughout the construction project's lifecycle.
## 4 Results and Discussion
### Verification of the Algorithm's Noise Resistance  
In practical time-of-flight (TOF) measurements, sampling errors are likely, so we introduce 1% Gaussian noise into the measured TOF data. The reconstructed temperature field at a height of 1 m and the error distribution after the addition of noise are shown in Figure 12a,b. From Figure 12a, it is evident that significant errors occur in the areas with the steepest temperature gradients, as these regions are more sensitive to noise. Additionally, the TOF-based temperature field reconstruction exhibits limitations in high-temperature areas. However, the number of errors in other areas is relatively small, and the reconstructed temperature field maintains the primary trends seen in the original field, accurately reflecting both the temperature gradients and regions. As shown in Figure 12c, the relative error rates are mostly within 10%, with only a few areas experiencing larger numbers of errors. Compared to the temperature field reconstructed via interpolation from two-dimensional acoustic temperature measurements using 8 measurement points and 24 channels, the one reconstructed using our algorithm still shows good results. This indicates that the algorithm possesses strong resistance to noise.  
Figure 11: Multi-layer temperature field reconstruction based on point measurements of the Tucker decomposition (Experimental Condition 2).