esa-sceva/satcom-chunk-collection huggingface dataset:
# Strength Prediction Model for Cohesive Soil-Rock Mixture with Rock Content
## 2 Test Materials
### Analysis of Material Proportioning Results  
The results indicated that as water content increased, the matrix strength of the bentonite-cement pastes initially rose and subsequently declined, which suggested the existence of an optimal water content that maximized matrix strength. This phenomenon could be attributed to two factors: when the water content was too low, the cement paste was inadequately hydrated, resulting in the insufficient cementation of the soil; when the water content was too high, the strength of the cement paste decreased, weakening the soil's cementing effect. With the increase in bentonite dosage, the matrix strength of the bentonite-cement pastes showed a tendency of first increasing and then decreasing, thus indicating the presence of an optimal bentonite dosage for maximizing matrix strength.  
The effects of different water-to-bentonite (W/B) ratios on the fluidity and stability of the matrix are depicted in Figure 3. A W/B ratio of 1:0.1 required the matrix to cure for five days before achieving a gel suitable for holding the rock blocks, which indicated that the fluidity of the matrix was too low for use. A W/B ratio of 1:0.15 resulted in excessive fluidity, as the matrix failed to adequately hold rock blocks even after one-day curing, which indicated that the stability of the matrix was too poor for use. At a W/B ratio of 1:0.2, the matrix exhibited excellent fluidity and stability after one-day curing, while rock blocks could be held efficiently, which indicated that the performance of the matrix at this W/B ratio was more suitable for testing. When the W/B ratio was 1:0.33, the matrix displayed  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Water** & **Bentonite** & **Cement** & **Shear Strength** \\  & **(kg)** & **(kg)** & **(kg)** & **(kPa)** \\ \hline a & & & 2 & \textless{}10 \\ b & & 1 & 3 & 38.65 \\ c & & & 4 & 40.35 \\ d & & & 2 & 33.52 \\ e & 10 & 1.5 & 3 & 51.35 \\ f & & & 4 & 65.72 \\ g & & & 2 & 42.74 \\ h & & 2 & 3 & 61.84 \\ i & & & 4 & 84.82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Undrained shear strength of cohesive fault mud with different ratios.  
Figure 2: Undrained shear strength of cohesive fault mud with different ratios.  
extremely high viscosity during mixing, while rock blocks could be placed directly, which indicated that the matrix was very expansive but too viscous for use.
# Characterization of Tiled Architecture for C-Band 1-Bit Beam-Steering Transmitarray
## 3 Unit Cell Design and Characterization
Prototype unit cells emulating the structure of the tiled (1 mm gap width) and single-panel (0 mm gap width) unit cells were fabricated and measured inside the waveguide, see Figure 3a,b respectively. The results in both cases demonstrate noticeable downshift of the central frequency with respect to the design value, c.f., Figure 2b,c, as well as expected degradation of the differential phase shift for the tiled structure, c.f., Figure 2d. The observed shift of the central frequency has been attributed primarily to the specifics of the measurement setup, i.e., different boundary conditions for the unit cell in the waveguide, as compared with the FP \begin{table}
\begin{tabular}{c c c c c c} \hline
**Gap Width (mm)** & \(R_{p}\) (\(\Omega\)) & \(L_{p}\)(nH) & \(C_{p}\)(pF) & \(Y_{0}\)(\(\Omega^{-1}\)) & \(Y_{g}\)(\(\Omega^{-1}\)) \\ \hline
0 & 300 & 0.038 & 19.84 & \(\pm\)j0.021 & N/A \\
1 & 300 & 0.038 & 19.86 & \(\pm\)j0.021 & \(-\)j0.23 \\
2 & 300 & 0.038 & 19.89 & \(\pm\)j0.021 & \(-\)j0.18 \\ \hline \end{tabular}
\end{table} 
Table 1: Parameters of the compact circuit model of the transmitarray unit cell extracted by fitting to the full-wave simulations (see Figure 2b,c). Concluding on the results of characterization of the single-panel and tiled unit cells, it can be noticed that both structures demonstrate similar performance within the operating band. Moreover, two orthogonal polarizations demonstrated close performance, according to the full-wave simulations with FPBCs in [10]. The effects of the gap width can be modelled with a reasonably good accuracy using the compact circuit model in Figure 2a. The beam collimating and steering performances of the transmitarray with specific binary phase distributions are discussed in the next section. Figure 3: Simulated and measured transmission and differential phase shift of the single-panel without gap (**a**) and tiled with 1 mm gap (**b**) transmitarray unit cells. The results were obtained inside a rectangular waveguide (**c**).
# The Use of Construction Waste to Remediate a Thermally Active Spoil Heap
###### Abstract  
This article presents the results of experimental research on the possible use of construction and demolition waste (CDW) to improve the properties of unburnt tailings originating from the thermally active spoil heap in Hermanice (Ostrava, Czech Republic). Mining activity anywhere in the world generally entails a lot of negative impacts on the environment, which are of a long-term nature. One of the most pressing challenges in the remediation of the consequences of mining activity is the thermal activity of spoil heaps associated with the high acidity of the tailings. Active acidity (pH/H\({}_{2}\)O), exchangeable acidity (pH/CaCl\({}_{2}\)), hydrolytic acidity (H\({}_{4}\)), and elemental composition of tallings and CDW have been monitored. Based on an acidity study, it has been proven that compared to burnt tailings (pH/H\({}_{2}\)O = 8.4, pH/CaCl\({}_{2}\) = 8.9 and H\({}_{4}\) = 1.4 mmol kg\({}^{-1}\)), unburnt tailings show acidic properties (pH/H\({}_{2}\)O = 3.7, pH/CaCl\({}_{2}\) = 3.6 and H\({}_{4}\) = 205 mmol kg\({}^{-1}\)). The bioavailability of two selected potentially toxic elements (PTEs), namely Al and Fe, was examined based on the elemental composition. BCR sequential extraction analysis was used to determine their bioavailability. It has been proven that mixing CDW with tailings has a positive effect on the pH value, which has a positive effect on the further development of the entire site. The increase in the pH value is provably dependent on the amount of construction waste added, so it can be said that the increasing amount of construction waste will result in improved parameters of the burnt tailings. The results of the BCR analysis show that aluminum from the tailings will be released both from the reducible and oxidisable fractions, where it will be mainly bound to sulphides. The relatively high concentration of Fe in the oxidisable fraction (2002 mg Fe kg\({}^{-1}\)) suggests that Fe is bound to sulphides in the tailings, and it is due to the high residual pyrite and sulphide content in the dumped material, as expected. This work has found no limits where CDW no longer positively affects the acidity of unburnt tailings. For practical application, however, it is important that the mixture of CDW and tailings is properly mixed and then used for remediation.  
Keywords:thermally active spoil heap; mine waste; construction demolition waste; remediation; mining activity; sequential extraction analysis +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### An Enhanced Approach to Wind Power Curve Optimization via the Improved PCF Algorithm
#### 2.1.3 Refinement of the PCF Algorithm
In the formula, \(\frac{\lambda}{2}||\omega||^{2}\) represents the regularization term, \(||\omega||^{2}=\omega^{T}\omega=\omega^{2}_{0}+\omega^{2}_{1}+\ldots+\omega^{2} _{M}\) associated with ridge regression, and \(\lambda\) is the smoothing parameter that dictates the level of penalty applied to the roughness of the data, with larger \(\lambda\) values leading to greater losses. The regularization term consists of the sum of the decay constant \(\lambda\) multiplied by the ridge regression. In cases of limited sample data, the modeling curve attempts to fit all data points, including noise, resulting in a large number of coefficients that tend to be oversized. The regularization term in Equation (15) aims to minimize the influence of noise points by reducing the sum of the squares of \(\omega\) to smaller absolute values, thus penalizing larger coefficients. Introducing the regularization term helps to prevent overfitting and enhances the model's generalization capability. In wind power curve modeling using the improved PCF algorithm, selecting the optimal \(\lambda\) parameter is crucial. The goal of parameter selection is to balance between improving data fit and minimizing model complexity, favoring the simplest model that adequately fits the wind speed-power data. An excessively large \(\lambda\) can overly suppress the model, making the choice of an appropriate \(\lambda\) critical for the final fitting result of the model. This choice should be made based on the complexity of the model, with cross-validation (CV) used to determine the optimal value. \[C_{V}(\lambda)=min\Bigg{\{}\frac{1}{N}\sum_{i=1}^{N}(y(\mathbf{x}_{i},W)-\mathbf{t}_{i })^{2}\Bigg{\}} \tag{15}\]  
In this equation, N represents the number of data points. The \(\lambda\) value that minimizes the generalization error \(C_{V}(\lambda)\) is considered the optimal parameter. In the wind power curve modeling process using the improved PCF algorithm, data preprocessing is a key step that significantly contributes to the accuracy of the modeling. Although it is impossible to eliminate all outliers during the preprocessing phase, there will still be a few noise points that affect the smoothness and precision of the wind power curve. By employing the aforementioned methods, the impact of these noise points can be maximally reduced, thereby enhancing the accuracy and reliability of the modeling.
# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 1 Introduction
However, relying exclusively on synthetic data for training and deploying the model in real-world object detection scenarios leads to a noticeable degradation in performance. This is primarily attributed to substantial disparities in terms of texture, viewpoint, imaging quality, and color between synthetic and real images. Such dissimilarities between the synthetic domain and the real domain are commonly known as domain shift. In the context of cross-domain object detection [13; 14; 15; 16; 17; 18; 19] tasks, the synthetic domain is commonly referred to as the source domain, whereas the real domain is referred to as the target domain. To mitigate the domain shift between the source and target domains, we propose an algorithmic framework for domain adaptation that aims to adapt to various source and target domains. The structure of this paper is as follows:  
* The Introduction section describes the problem of aerial object detection and our solution using synthetic images, and it also discusses the problem of the interaction of the synthetic domain with the real domain, which needs to be solved by domain-adaptive algorithms to apply synthetic image datasets for object detection. * The Related Works section describes existing real-world image datasets, synthetic image datasets, and domain-adaptation algorithms for object detection. * The Methods section describes how we built the synthetic image datasets and knowledge about the domain-adaptive algorithms. * The Experiments section describes the synthetic-to-real domain, adaptive object detection experiments, including the implements, real datasets, detailed data, and experimental conclusions. * The Conclusion section describes the contributions of the full paper and the shortcomings of this study, and it also points out directions for future improvements.
## 3 Materials and Methods
### Performance Analyses  
For the training, validation, and testing cohorts, the NLP algorithm was applied to patients lacking structured ECOG PS scores in the extraction time window and compared to manually-abstracted ECOG PS scores for the equivalent time window. We evaluated algorithm performance for individual ECOG PS strata as well as for binary ECOG PS categories (e.g., ECOG 0-1 vs. ECOG > 1, a cutoff commonly used for stratified analyses in clinical research).  
We evaluated performance via the following metrics calculated in the testing cohort: overall accuracy (the number of assignments matching between the algorithm and the manually-abstracted information, relative to the total number of possible assignments);sensitivity (the number of manually-abstracted known ECOG PS scores for which the algorithm identifies the same score, relative to the total number of manually-abstracted known ECOG PS scores); PPV (the number of model-extracted known ECOG PS scores that match the manually-abstracted score, relative to the total number of model-extracted known ECOG PS scores); and F1-score (the harmonized mean of sensitivity and PPV). Accuracy, sensitivity, PPV, and F1-score were analyzed on a held-out test cohort of 7709 treatment lines from 5341 patients present in the database as of July 2021. These performance metrics were also evaluated by specific strata (e.g., ECOG 0-3, ECOG \(<\) 2).  
The performance of the NLP algorithm was further characterized by applying it to a cohort of patients with aNSCLC who were missing a structured ECOG PS score in their 1L treatment. We quantified the proportion of patients for whom the algorithm extracted an ECOG PS score, shifting their standing from PS-unknown to PS-known. We then investigated the prognostic value of the composite ECOG PS scores that resulted from combining patient-lines with structured scores with patient-lines with extracted scores. We first compared rwOS via KM estimates by the source of the ECOG PS score (extracted vs. structured), stratified by ECOG PS Score (0, 1, 2, 3). We additionally compared median rwOS by data source (extracted vs. structured), stratified by ECOG PS Score (0, 1, 2, 3).
## 5 Simulation Framework and Experimental Scheme
### Simulation Framework  
The decision to employ SUMO and Artery-C was based on their complementary strengths in simulating both the physical and network layers. SUMO provides detailed traffic flow modeling, ensuring a realistic representation of vehicular movements, while Artery-C is specifically designed to simulate vehicular communications in 5G environments, supporting V2N, V2V, and V2I communications. This dual-layer approach enables the simulation to capture both vehicular mobility and network interactions, which are critical for accurate modeling.  
By combining real-time traffic data with network communication simulations, the framework provides a realistic environment for analyzing network handovers and congestion during periods of high traffic density. Additionally, this integration effectively captures the influence of human uncertainty, such as deviations from optimal routes, on both vehicular mobility and 5G network performance.  
Figure 3 illustrates the integration between SUMO and Artery-C, where real-time traffic data from SUMO are processed to simulate vehicle communications with 5G infrastructure. The integration of these tools, along with frameworks like OMNeT++ and INET, creates a comprehensive simulation environment that accurately models vehicular mobility and network performance. The use of TraC14 Matlab enables real-time communication between MATLAB models and the simulation environment, ensuring dynamic evaluation of model parameters.  
Framework Integration Description  
Artery-C is integrated with OMNeT++ to provide a modular, component-based simulation environment. This design facilitates the development of complex models using OMNeT++'s NED language, enhancing reusability and simplifying the integration of core simulation components. Leveraging the INET framework, Artery-C simulates network devices such as hosts, routers, and switches via modular, message-based interactions.  
A key aspect of this research is the integration with SUMO, which is essential for synchronizing detailed traffic data (e.g., vehicle counts and road configurations) with network simulations, providing a holistic system view. The MMQHU model, implemented in MATLAB, is integrated using the TraC14 Matlab protocol, which bridges MATLAB and SUMO, enabling continuous injection and evaluation of model parameters throughout the simulation. This seamless connection between MATLAB, SUMO, and Artery-C is crucial for effectively applying research models in complex environments.  
Figure 3: Framework integration for traffic and network system simulation with the MMQHU Model in the realistic InTAS scenario (adapted from [16]).  
Alternative platforms, such as Cooja and VISSIM, were considered but ultimately dismissed due to their limitations in simulating detailed real-time V2X communications and their lack of compatibility with SUMO.
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
## 3 Covert Channels in Hash Chains
### Channel Characteristics  
Our channel has certain features that are traditionally used to categorize and describe covert channels:  
1. Our channel variants allow _plausible deniability_ as they replace selected bits of hash values in a pseudo-random manner so that the probability distribution of original and modified hash values are similar.
2. If CR is not just a passive observer but a hop on the path to B, CR is able to reconstruct the original hash value and can thus forward the illicit original message to B, rendering our approach _reversible_. This is feasible if, for example, B is a routing hop on a program in an IPC-chain between A and B. Due to the fact that we achieve full reversibility and do not exploit implicit or explicit reversibility methods, our technique is an _intrinsic_ reversibility method.
3. Finally, our channel does not rely on indirect signaling methods, which makes it a _direct_ covert channel. However, if CS applied a method to indirectly influence the transferred hash value, our channel would be a semi-passive one, while it would also be passive (indirect) if CR indirectly obtained the sent hash value (e.g., using a side channel) [38].
4. It does not matter for our covert channel whether the hash value is transmitted over a network or between local processes. The hash value can also be stored in a file at some point in time and then read later by another process, i.e., sender and receiver processes must not necessarily be active at the same time. However, in practice, most authentication scenarios would require \(A\) and \(B\) to be active simultaneously.
###### Abstract  
Three factors that complicate the position and orientation control of unmanned autonomous vehicles are the hardware-induced delays, delays caused by teleoperated modes of operation, and persistent disturbances such as wind. In this article, we study how to overcome these challenges by controlling a twin rotor multi-input, multi-output system. This nonlinear system has two degrees of freedom and significant cross-coupling, making its control particularly challenging. Time delay and external disturbances contribute to system instability and complicate flight control. The effects of time delay can be mitigated using the standard Smith predictor controller. However, this approach performs poorly when persistent disturbances are present. Our contribution is a modified Smith predictor that effectively mitigates disturbances. Additionally, a decoupler is employed to transform the system into two single-input, single-output models, enabling precise control of both the horizontal and vertical planes. The proposed method is implemented on a prototype and compared with the original Smith predictor-based control scheme. Experimental results validate the effectiveness of the proposed approach, showing that our approach reduces the steady-state tracking error to less than 2%, decreases settling time from 30 s to 20 s, and improves disturbance rejection under wind disturbance conditions by 35% compared to the standard Smith predictor. These results demonstrate the superior set-point tracking performance and enhanced disturbance rejection of the proposed approach.  
cross-coupling; twin rotor \(MIMO\) system (\(TRMS\)); Smith predictor; disturbance rejection; single-input single-output (\(SISO\)); time delay compensation Article
## 4 Field Test Results and Discussion
### Data Sharing  
In order to satisfy remote monitoring and control requests, as seen in Figure 15, a GUI manager interface platform based on LabVIEW was developed as a web application server (WAS). The received packaged data were separated and displayed in the corresponding monitoring windows. From the blue dotted circle, the curve number corresponds to the sensor node number, which allows managers to monitor either the single node or multiple nodes. At the same time, the separated data were recorded in a database, Access 2007, linked and programmed by the Structured Query Language (SQL), which was also visible and shown in a data table in the front panel. In the communication process, when the received and sent bits recorded in real-time were over the deadline, a new database based on access would be established to avoid the continued occupancy of the local memory resources. Some optional configurations were supplied in the middle bottom of the plant, such as node sleep and database directions. Besides remote monitoring, there were also some related supports for remote control, which required Secure Sockets Layer (SLL) verification for network safety.  
In addition to web publishing, PDA terminals like IOS iPad and Android iPad are also able to realize remote monitoring and control functions based on a Dashboard, which is a terminal software application on LabVIEW, as shown in Figure 16b. The key point that should be paid attention to is the accurate matching between the virtual IP of the PC terminal and the actual IP of the mobile terminal, whose hardware circuit is shown in Figure 16a.  
Figure 14: (**a**) The measured RSSI with increasing communication distance in a greenhouse; (**b**) the detection error of measured RSSI with the increasing communication distance in a greenhouse.
## 4 Proposed Method: XQIFFT
### Invariance Properties of QIFFT Methods  
In this section, we show that for the XQIFFT along with the MQIFFT and LQIFFT, the estimate \(\hat{\mathcal{K}}\) is insensitive to amplitude and that the amplitude estimate \(\hat{\mathcal{X}}\) scales linearly as a function of the true amplitude \(\mathcal{A}\). This property shows that the XQIFFT and its cousins are suitable for estimating sinusoids of any amplitude (other nonlinear scaling functions may not have these properties).  
All the scalings discussed so far verify that the estimate in fractional bin index is insensitive to the true amplitude \(\mathcal{A}\), while the estimate \(\hat{\mathcal{X}}\) scales linearly as a function of the true amplitude \(\mathcal{A}\). Indeed, if we have another sinusoid of fractional bin number \(\mathcal{K}\) and amplitude \(\mathcal{A}^{\prime}=\lambda\mathcal{A}\), the QIFFT estimates (11) and (12) yield:  
\[\hat{\mathcal{K}}^{\prime} =k_{m}^{\prime}+\frac{1}{2}\frac{\alpha^{\prime}-\gamma^{\prime} }{\alpha^{\prime}-2\beta^{\prime}+\gamma^{\prime}}=k_{m}+\frac{1}{2}\frac{ \lambda\alpha-\lambda\gamma}{\lambda\alpha-2\lambda\beta+\lambda\gamma} \Rightarrow \hat{\mathcal{K}}^{\prime}=\hat{\mathcal{K}} \tag{19}\] \[\hat{\mathcal{X}}^{\prime} =\beta^{\prime}-\frac{1}{8}\frac{(\alpha^{\prime}-\gamma^{\prime })^{2}}{\alpha^{\prime}-2\beta^{\prime}+\gamma^{\prime}}=\lambda\beta-\frac{1 }{8}\frac{(\lambda\alpha-\lambda\gamma)^{2}}{\lambda\alpha-2\lambda\beta+ \lambda\gamma} \Rightarrow \hat{\mathcal{X}}^{\prime}=\lambda\hat{\mathcal{X}} \tag{20}\]  
These errors have _means_\(\mathrm{E}[|e_{\mathcal{K}}(\mathcal{K})|]\) and \(\mathrm{E}[|e_{\mathcal{K}}(\mathcal{K})|]\):  
\[\mathrm{E}[|e_{\mathcal{K}}(\kappa)|] =2\int_{0}^{1/2}|e_{\mathcal{K}}(\Delta_{\kappa})|\mathrm{d}\Delta_{\kappa} \tag{29}\] \[\mathrm{E}[|e_{\mathcal{X}}(\kappa)|] =2\int_{0}^{1/2}|e_{\mathcal{X}}(\Delta_{\kappa})|\mathrm{d}\Delta_{\kappa} \tag{30}\]  
and _variances_\(\mathrm{Var}\left(e_{\mathcal{K}}(\mathcal{K})\right)\) and \(\mathrm{Var}\left(e_{\mathcal{X}}(\mathcal{K})\right)\).