esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Overviews of Both the 5G AKA Protocol and the 5G-IPAKA Protocol
### The 5G-IPAKA Protocol
If \(RES^{*}\) and \(XRES^{*}\) are equal, then the AUSF considers that the authentication is successful from the home network point of view. Then, the AUSF informs the UDM of the authentication result. 16. The AUSF indicates to the SEAF whether the authentication was successful or not from the home network point of view (i.e., \(Result\)). In step 11, if \(XMAC\) and \(MAC\) are different, then the UE directly discards the first received message of the UE without responding to a \"MAC failure\" indication, so the HN will initiate a new authentication procedure towards the UE when the HN does not receive an authentication response message or a synchronization failure message within a certain period of time. In step 11, if \(SQN\) is not in the correct range, then the USIM computes \(AUTS=SQN_{UE}\oplus AK^{*}||MAC-S\), and then sends \(AUTS\) with a \"Synchronization failure\" indication to the ME, where \(AK^{*}=f_{5}^{*}(BK,RAND)\) and \(MAC-S=f_{1}^{*}(BK,SQN_{UE}||RAND||AMF_{0})\). Then, the ME computes \(MAC_{UE,2}=HMAC(K_{SEAF},RAND_{SN}||Snyf||AUTS)\), and then sends \(AUTS\) and \(MAC_{UE,2}\) with a \"Synchronization failure\" indication to the SEAF, where \(Syncf=\) \"Synchronization failure\". Further, the SEAF verifies \(MAC_{UE,2}\). If the verification fails, then the SEAF aborts, otherwise the SEAF sends \(RAND\) and \(AUTS\) with a \"Synchronization failure\" indication to the AUSF. Finally, the ASUF sends \(RAND\) and \(AUTS\) with a \"Synchronization failure\" indication to the UDM/ARPF. Compared with the latest version of the 5G AKA protocol, the main improvements of the 5G-IPAKA protocol are as follows:  
* Replace the pre-shared key between the UE and the HN with a derivation key of the pre-shared key. In detail, \(K\) is replaced with \(BK=KDF(K,x\cdot y\cdot G||SNN)\) on both the UE and the HN. * Add the challenge-response mechanism for the SN. Firstly, \(RAND_{SN}\) is added to the first sent message of the SEAF as a challenge and is added to the second received message of the SEAF as a response. Then, \(RAND_{SN}\) is added to the second sent message of the SEAF as a challenge and is added to the third received message of the SEAF as a response (i.e., \(RAND_{SN}\) in \(MAC_{UE,2}\)). * Add the mutual authentication and key confirmation between the UE and the SN. Firstly, \(K_{SEAF}\) and \(SUPI\) are moved to the second sent message of the AUSF. Then, the UE and the SN perform a mutual authentication and key confirmation process based on \(MAC_{SN}\) and \(MAC_{UE,2}\), which are generated by using \(K_{SEAF}\). * Replace the MAC failure procedure with the timeout mechanism on the HN. If \(XMAC\) and \(MAC\) are different, then the UE directly discards the first received message of the UE without responding to a \"MAC failure\" indication, so the HN will initiate a new authentication procedure towards the UE when the HN does not receive an authentication response message or a synchronization failure message within a certain period of time.
# Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
## 5 Applications
### SEAM Synthetic Seismic Example
This indicates the robustness of the model, although the dataset is unbalanced. The reason behind this was because we used binary cross entropy as the loss parameter. This loss allocates a higher penalty to minority label samples during the learning process, and it ensures that minority label samples are detected more accurately [47]. The details of each evaluation metrics including precision, recall, and f1-score for training and validation dataset are shown on the Tables 4 and 5 respectively. Also, Figure 16 shows the visualization a few examples of random validation data where the top part is the input, the middle part is the ground truth or the true output, and the bottom part is the prediction. This image shows the model successfully detecting the salt feature both for the boundaries and the body. The result of model prediction differs from the ground truth in the middle column, where the model predicted non-layered seismic features such as salt features, but these features are absent on the ground truth. After training the optimum model, we applied this model to the whole seismic data. We divided the entire seismic into 64 \(\times\) 64 \(\times\) 64 patches to feed into the model, and then concatenated the predicted patches back into the original size. Figure 17 shows the predicted result with a 2D seismic section display. As we can see, the predicted result gives an outstanding detection of salt bodies on the seismic data. The inline sections (Figure 17a) show the accurate salt feature detection. However, there is also a noisy and misclassified both for crossline and time-slice seismic section (Figure 17b,c). Finally, for better visualization, we can also display the predicted result in 3D cube visualization. Figure 18 shows the predicted salt body or salt dome. This predicted result, has the same geomorphology as the true salt body that is shown in Figure 1, but on a smaller scale. Figure 17: The predicted result of salt features on (**a**) inline, (**b**) crossline, and (**c**) time-slice seismic section.
## 3 Methodology  
We propose HyMePre, a two-stage framework for multi-step weather forecasting. The model consists of a self-supervised pretraining module and a hypergraph-based forecasting module. In the pretraining stage, we apply spatial and temporal masking separately to learn disentangled representations from historical meteorological time series. In the forecasting stage, we construct hypergraphs to capture higher-order spatial dependencies among stations and use hypergraph convolutions to generate future predictions.
## 3 Results and Discussion  
The developed MLPW-SR spectral band selection and other existing feature selection algorithms were tested on three different hyperspectral imaging datasets. In this research work, the analysis has shown only 4 and 10 spectra sets. Four spectra were targeted to use for in-line application and 10 spectra were set for quality control application to meet industrial requirements. The performance was evaluated using classification accuracy and F1-score. Classification accuracy reflects the overall proportion of correct predictions by the model, whereas the F1-score balances precision and recall, making it particularly valuable for evaluating performance on imbalanced datasets.
## 6 Discussion
### Accuracy Evaluation of the DSMs
Secondly, the CHMs derived from remote sensing data give a mixture of canopy objects and gaps. Therefore not all of the pixels in the CHM are indicating tree height. However, typical \"heights\" associated to management relevant development stages are 0-10 (-12) m (stand established), 10-12 m to 20-24 m (qualification, stabilizing measures) and above 20-24 m (dimensioning, up to harvesting, establishing of a new stand by successive extraction of single trees, etc.) [45]. Such categories and additionally gaps and disturbances are interesting for the practitioner. When using a region-based height analyses by taking the average or maximum values of each region, the mentioned problems regarding distinct heights and boundaries can be ignored. Thirdly, the data used in this paper have been captured for various purposes over a 13-year timespan. Besides tree growth, the shape of the tree canopies can change in different years and seasons. Two LiDAR data from 2001 and 2010 were used as reference data. Thus, the longest distance between LiDAR measurement and another remote sensing data acquisition is three years. In this study, almost all photogrammetric data were captured in leaf-on season, which enables a better accuracy for the image matching. Unfortunately the LiDAR data were captured in early spring, which is leaf-off season. However, the main tree species in the test site are coniferous trees, which are less influenced by seasonal differences. More detailed research is required to analyze the influences of leaf-on/leaf-off status to the forest heights obtained from remote sensing sensors. Furthermore, the statistical accuracy can certainly be improved if the data are captured in the same year and month. However, a time difference does allow change monitoring.
## 3 Results  
### Results of the Study  
#### 3.1.1 Profiles of Condominiums in Bangkok  
The profiles of 485 condominiums in this study (namely locations, heights, sizes of room, numbers of residents, numbers of occupants, and distribution transformers) could be graphically summarized in percentages as seen in Figure 3.  
Figure 3 illustrates proportions of locations, heights, room sizes, numbers of residents, numbers of occupants, and distribution transformers of the condominiums in this study. On location, the majority of condominiums (85%) were located adjacent to the main road (29%), in alleys (29%), near the main road (29%), and near BTS line (27.6%) respectively. The minority of condominiums (12.7%) are located near the MRT line. On height, the majority of condominiums (85.5%) were of 23-50 m high (30.5%), more than 51-100 m high (27.5%) and more than 100 m (27.5%), respectively. The minority of condominiums were less than or equal to 23 m high (14.4%). On sizes of room, the sizes were arranged from the largest to the smallest as follows: 31-40 square metres (38.1%), more than 50 square metres (25.6%), 20-30 square metres (20.8%), and 41-50 square metres (15.5%), respectively. On numbers of residents, the majority of rooms (74.9%) had 2 residents, while the minority (18.6%) had 3 residents and the rest (6.5%) had either more than 3 residents or just one. On numbers of occupants, the proportion of occupants can be arranged as follows: 61-80% (48.2%), 41-60% (25.6%), 81-100% (18.5%), and 20-40% (7.7%), respectively. Lastly, on distributiontransformers, the transformer in conjunction with the room accounted for 82.6% while the central distribution transformer accounted for 17.4%.
## 3 Results and Discussion
### Accuracy and Uncertainty of the Models  
Figure 12 illustrates a comparison of combo graphs between scenarios and machine learning models based on uncertainty criteria (U\({}_{95}\)) and accuracy. The analysis reveals significant differentiation in the values of accuracy and U95. The uncertainty of scenarios used in modelling was assessed based on the limits of the 95% confidence interval, signifying the likelihood of obtaining results close to the expected value within the defined uncertainty range. It was observed that the highest U95 was associated with Sc3 scenarios, considering temperature, wind speed, and crop factor, leading to the lowest accuracy. RF-SVR for Sc3 showed the highest U\({}_{95}\) at 53.56, while XGB-SVR for Sc2 had the lowest at 9.10, with uncertainty values ranging from 9.10 to 14.30 for all scenarios except Sc3. Notably, the lowest uncertainty values were recorded for Sc1 across all models except XGB-SVR, where Sc2 surpassed it. The accuracy analysis indicated the highest accuracy levels for Sc1 and Sc4 scenarios in SVR and XGB models, respectively, while the lowest accuracy values were found under Sc3, particularly under RF. The accuracy values for Sc1, Sc2, Sc4, and Sc5 were above or equal to 0.95, displaying excellent performance in predicting GWFP.  
On the contrary, the analysis of accuracy indicated that the Sc1 and Sc4 scenarios achieved the highest accuracy levels of 1.08 in the SVR and XGB models, respectively. Conversely, the lowest accuracy values were observed in Sc3, with RF performing the worst by \(-\)0.21. It was challenging to differentiate between scenarios Sc1, Sc2, Sc4, and Sc5, as their accuracy values were all above or equal to 0.95, with differences not exceeding 1-2%. Despite this, their predictive performance for GWFP was exceptional (Figure 12).  
Figure 10: Bar charts to compare the models in each scenario separately, based on the CC, NSE, MAE, and T\({}_{\text{stat}}\).  
Figure 11: Radar charts to compare the models in each scenario separately, based on the RMSE criterion.
# Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
## 2 Fundamentals of Fourier Basis Pursuit
### Windowing  
Due to the finite length of time series, it is often related to spectral leakage, where energy from one frequency component \"leaks\" into adjacent frequencies. Window functions are thus applied in harmonic analysis to minimize the undesirable impact of spectral leakage. The windowed DFT is expressed as:  
\[X_{w}[k]=\sum_{n=0}^{N-1}x[n].w[n].e^{-j\frac{\pi}{N}kn} \tag{3}\]  
where \(X_{w}[k]\) is the DFT of the windowed signal, and \(w[n]\) is the window function. Equation (1) employs a rectangular window for the time series \(x[n]\). Hanning and Blackman windows are also used in this study. The definitions of the rectangular, Hanning and Blackman windows are provided in Equations (4), (5) and (6), respectively [17; 18].  
\[w_{rectangular}[n]=1 \tag{4}\]  
\[w_{Hanning}[n]=0.5-0.5 cos\bigg{(}\frac{2\pi n}{N-1}\bigg{)} \tag{5}\]  
\[w_{Blackman}[n]=0.42-0.5 cos\bigg{(}\frac{2\pi n}{N-1}\bigg{)}+0.08 cos\bigg{(} \frac{4\pi n}{N-1}\bigg{)} \tag{6}\]  
where \(n=0,\ 1,\dots,\ N-1\), and \(N\) is the length of the time series to be analyzed.  
Employing a narrow main-lobe window function, such as the rectangular window, yields high spectral resolution, thereby enhancing the ability to distinguish closely spaced frequency components. However, the rectangular window has significant sidelobes, which may lead to substantial spectral leakage. In contrast, window functions with fast sidelobe attenuation, such as the Blackman window, significantly reduce spectral leakage, minimizing interference from sidelobes. This reduction, however, comes at the cost of some spectral resolution.
## Materials and Methods
### SIF Retrievals  
Algorithms based on the Fraunhofer line depth (FLD) principle [44; 45] are most commonly used for SIF retrievals at the canopy level. The FLD method needs spectral measurements at two channels, one inside and one outside a Fraunhofer line [46]. It assumes that reflectance (r) and SIF are constant at the two channels. However, in fact, these two variables have significant differences at the two channels, especially for r at 687 nm and SIF at 760 nm [37; 47]. One of the FLD-based algorithms, the three-band FLD (3 FLD) method, has been proposed and proved to be robust and simple for SIF retrieval [48]. 3 FLD are based on the assumption that the r and SIF vary linearly in the spectral domain considered, which overcomes the limitations given by FLD assumptions. In this way, the 3 FLD-based SIF can be derived as Equation (1):  
\[SIF_{in}=\frac{\left(E_{left}\times w_{left}+E_{right}\times w_{right}\right) \times L_{in}-\left(L_{left}\times w_{left}+L_{right}\times E_{right}\right) \times E_{in}}{\left(E_{left}\times w_{left}+E_{right}\times w_{right}\right) -E_{in}} \tag{1}\]  
where \(w_{left}\) and \(w_{right}\) are the weight of the channel, which is proportional to the length between the longer/shorter channel and the inner channel. E and L are the downwelling irradiance and the upwelling radiance arriving at the top of the canopy, respectively. The subscripts \"in\", \"left\" and \"right\" represent the channels inside, at the left of and at the right of the absorption domain (758 nm, 760 nm, and 770 nm for O\({}_{2}\)A absorption domain; 680 nm, 684 nm, and 690 nm for O\({}_{2}\)B absorption domain), respectively.
###### Abstract  
To assess the impact of solid waste phosphogypsum on the road performance of lime-fly ash-stabilized red clay, we conducted comprehensive tests on the road performance, swelling and shrinkage characteristics, and mechanical properties of lime-fly ash soil with varying phosphogypsum content and curing age. Additionally, we analyzed the microstructure and composition changes using scanning electron microscopy and X-ray diffraction tests. The results revealed that phosphogypsum significantly enhances the early strength and moisture stability of lime-fly ash soil. The mechanical properties of lime-fly ash soil continue to improve with increased curing age, with performance improvements tapering off after 60 days and eventually stabilizing. Moreover, as the phosphogypsum content increases, the unconfined compressive strength (UCS), splitting strength, and CBR value of the lime-fly ash soil initially increase and then decrease. The optimal mixing ratio was determined to be 4% phosphogypsum, resulting in a 7-day UCS increase of 67.2%, a 28-day UCS increase of 3 times, and a 28-day splitting strength increase of 4.3 times. The moisture stability coefficient also exhibited a 43% increase after 7 days, and its anti-disintegration ability was enhanced, reaching 0.91 after 28 days, which meets the specified standards. Microscopic analysis revealed that the addition of phosphogypsum improved the overall integrity of the lime-fly ash soil, and the formation of etringite effectively filled the soil's pores. However, excessive ettrinigite caused increased expansion and deformation. To optimize the use of phosphogypsum-modified lime-fly ash-stabilized red clay as subgrade filler, it is advisable to incorporate additives to further reduce swelling deformation.  
Keywords:phosphopysum; lime-ash stabilized red clay; road performance; moisture stability; microscopic mechanism +
Footnote †: journal:
## 3 Analysis Plan
### Performance Metrics for Binary Classifiers  
A confusion matrix is the cross-tabulation between the true labels from the labeled dataset and the predicted labels from a classifier. Depending on the values of true and predicted labels, the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) can be defined, and these four cases are used to define sensitivity (or recall, TP/(TP + FN)), specificity (TN/(FP + TN)), accuracy ((TP + TN)/(TP + FN + FP + TN)), negative predictive value (TN/(FN + TN)), and positive predictive value (or precision, TP/(TP + FP)).  
The area under the curve (AUC) is another popular metric. The AUC is the area under the receiver operating characteristic (ROC) curve [31]. The ROC curve is the curve in the two-dimensional ROC space whose \(y\)-axis represents a classifier's true positive rate (or sensitivity) and \(x\)-axis represents a classifier's false positive rates (or \(1-\) specificity). Each point in the ROC space represents a classifier's (false positive rate, true positive rate) pair. If the classifier is the discrete classifier that produces only a class label (e.g., dropout or non-dropout), then the performance of the discrete classifier can be represented as a point in the ROC space. In the ROC space, the upper left point (0, 1) represents a perfect classifier that never predicts the true negative labels as positive (i.e., false positive rate = 0) and predicts all the true positive labels as positive (i.e., true positive rate = 1). Some classifiersproduce the probability of the instances being a member of a class, instead of the discrete class label. Such classifiers can be converted into the discrete classifiers by introducing a threshold. The ROC curve for such classifier is the set of (false positive rate, true positive rate) pairs for all possible thresholds. The ROC curve allows us to visualize the trade-off between the true positive rate (or sensitivity) and false positive rates (or \(1-\) specificity) of a classifier. That is, it shows that a classifier cannot increase the true positive rate without increasing the false positive rate [9].  
Although the ROC curve is a popular evaluation metric for binary classifiers, the precision-recall (PR) curve is recommended when evaluating binary classifiers trained on imbalanced datasets [10]. When the number of negative instances is very large, the true negative (TN) is also likely to be very large. Then, the large true negative (TN) makes the specificity less sensitive. Therefore, the precision (or positive predictive value) could be a more sensitive measure for the imbalanced data because the precision is not affected by the large true negative (TN). Notice that recall is just another name for the sensitivity, and therefore the PR curve only replaces the false positive rate in the ROC curve with the precision. In PR curves, good classifiers aim for the upper right corner. In the present study, we used both the ROC and PR curves for model-wide threshold-free evaluation of binary classifiers.
# Measurements of High-Froude Number Boat Wakes near a Seawall
## 1 Introduction
Additionally, most previous publications examining measurements of boat wakes used a single sensor, from which only amplitude and frequency could be computed, or a group of linearly arranged sensors, from which propagation along the array line could also be found. Directional wave information would aid in determining wave origins, attribution to their impacts, and help the development of any management response [37; 38], as well as wave-infrastructure interactions, including reflection. Here, a geometric (square) array of pressure sensors was used, allowing for the calculation of directional spectra, differentiation between incoming and reflected wakes, and improved understanding of the wake dynamics and behaviors. Geometric arrays of sensors have been used to examine open ocean and coastal directional spectra e.g., [39; 40], but have not been widely applied to wakes. This manuscript presents one of the first studies of wake reflection, though the presence of reflected wakes have been inferred in previous studies [41]. The new approaches presented can also support the design and implementation of living shorelines [42; 43; 44; 45], which are of increasing interest as alternatives to seawalls and other armored coastal structures built to protect shorelines from erosion due to wave action. The design of most living shorelines are often guided by numerical models that may not fully represent the range of conditions in a specific location [46; 47; 48]. Directional wave gauges can provide additional information on the wave environment useful for planning such projects and for measuring their effectiveness at wave absorption post-installation by measuring changes in shoreline reflectivity. This study was part of the pre-installation phase of living shoreline planned for installation at the study site. The following section describes the study site, the design of the wave gauge, and the methodologies used in the analysis, particularly the spectral methods employed, including directional and wavelet methods, and the algorithm for objective identification of wakes. Section 3 details the types of wave signatures found, the effectiveness of the identification algorithm, and the presence of wake reflections. Section 4 presents a Discussion of the results.