esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup and Mission Architecture
### RF Payload  
The diagram in Figure 6 shows the architecture of the RF payload. All the hardware components used to integrate the RF payload are Commercial-Off-The-Shelf. The on-board antenna, the pass-band filter and the LNA (Low-Noise Amplifier) are tuned on UHF or S bands, while the power amplifier, the SDR (Software-Defined Radio) device, a Ettus B200 mini [18] and the Raspberry Pi 3 are common to both bands. A GPS OCXO (Oven-Controlled Crystal Oscillator) has been added to flight ISM-HAB #5 in order to increase the frequency stability of the SDR oscillator during the sample recording.  
Figure 5: (**a**) Automatic Packet Reporting System (APRS) transmitter, (**b**) SPOT Trace modem, (**c**) EAGLE flight computer, (**d**) Inertial Measurement Unit (IMU) board.  
The S-band patch antenna is attached to base of the payload while the UHF dipole antenna is installed parallel to the base at a distance of 7 cm using a plastic bracket in order to maximize the antenna gain (Figure 7). Both antennas are connected to the pass-band filter with a short SMA coaxial cable (the antenna feeder link). All other components of the RF payload apart from the RX antennas, the feeder link and the GPS antenna of the GPS OCXO are installed within a Styrofoam box (20 \(\times\) 24 \(\times\) 15 cm) in order to keep the components warm during the flight. The pass-band filter, the LNA, the PA and the SDR device are connected to each other using their SMA connectors. Finally, the SDR device is connected to the Raspberry Pi 3 using a USB cable. The Raspberry Pi 3 stores the collected data within its micro-SD (micro Secure Data card).
# Mathematical Foundations and Implementation of CONIKS Key Transparency
###### Abstract  
This research paper explores the CONIKS key management system's security and efficiency, a system designed to ensure transparency and privacy in cryptographic operations. We conducted a comprehensive analysis of the underlying mathematical principles, focusing on cryptographic hash functions and digital signature schemes, and their implementation in the CONIKS model. Through the use of Merkle trees, we verified the integrity of the system, while zero-knowledge proofs were utilized to ensure the confidentiality of key bindings. We conducted experimental evaluations to measure the performance of cryptographic operations like key generation, signing, and verification with varying key sizes and compared the results against theoretical expectations. Our findings demonstrate that the system performs as predicted by cryptographic theory, with only minor deviations in computational time complexities. The analysis also reveals significant trade-offs between security and efficiency, particularly when larger key sizes are used. These results confirm that the CONIKS system offers a robust framework for secure and efficient key management, highlighting its potential for real-world applications in secure communication systems.  
Merkle tree; CONIKS; key; transparency; security and hash Article
## 3 Materials and Methods
### Detailed Subsystem Feature Extraction Process
#### 3.1.2 SE-Based Feature Importance Adjustment  
The Squeeze-and-Excitation process enhances the feature selection by learning dynamic weight coefficients:  
\[F^{\prime}=\sigma(W_{2}\cdot\text{ReLU}(W_{1}\cdot F)) \tag{5}\]  
where \(F\) is the feature vector before recalibration, \(W_{1}\) and \(W_{2}\) are learnable parameters, \(\text{ReLU}(\cdot)\) applies nonlinearity, and \(\sigma(\cdot)\) applies the sigmoid activation function.  
Each subsystem's feature recalibration ensures that more influential indicators receive higher attention, improving the extracted feature set's robustness.
## 3 Feature extraction and dimension reduction
### Feature subset selection
#### 3.3.3 Simulated annealing (SA)  
Simulated annealing (SA) is another global searching approach motivated by statistical thermodynamics [44]. During an optimization process, SA algorithms replace the current solution with a random neighbor solution based on a probability p which is a function of current \"annealing\" temperature (T). This replacement can help prevent searching processes from falling into local optima. SA has been applied to detect \"representative\" mass spectrometry fragments for an E-nose based food classification [50]. The key for SA is to establish a proper \"annealing\" temperature (T) decrease function. In some cases, it could be challenging to design a proper decreasing process of T and also SA could require prohibitively long periods of time to follow this process.
## 3 Analysis of Main Rotor Rotating Effects
### Drag Performance
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Component** & **Rotor-Off \(C_{D}\)** & **Rotor-On \(C_{D}\)** & \(\Delta\) \\ \hline Fuselage & 0.079 & 0.177 & +124.1\% \\ Horizontal tail & 0.024 & 0.038 & +58.3\% \\ Left tail plane & 0.008 & 0.012 & +50.0\% \\ Right tail plane & 0.010 & 0.006 & \(-40.0\%\) \\ Landing skid & 0.038 & 0.022 & \(-42.1\%\) \\ Vertical tail & 0.017 & 0.023 & +35.3\% \\ Whole & 0.176 & 0.278 & +57.9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(C_{D}\) of the rotor-off and rotor-on configurations at \(\alpha=-8^{\lx@math@degree}\). due to the interference of the blade and fuselage when the blade sweeps over, thus the pressure drag is large under this condition. The \(C_{D}\) of the whole fuselage at \(\Psi=25^{\circ}\) decreases significantly compared with that of the fuselage at \(\Psi=0^{\circ}\). At \(\Psi=45^{\circ}\), the \(C_{D}\) of the fuselage and horizontal tail increase. For \(25^{\circ}\) and \(45^{\circ}\), the pressure drop on the upper surface of the forehead can be clearly seen in the figure, leading to a decrease in the fuselage pressure drag. Figure 12: Comparison of the surface pressure distribution and streamlines between the rotor-off and rotor-on configurations. Figure 13: Comparison of the surface pressure distribution in the symmetry plane between the rotor-off and rotor-on configurations. In the case of an azimuth of \(70^{\circ}\), the \(C_{D}\) of the fuselage and horizontal tail rises to \(0.185\) and \(0.041\), respectively. Then, the pressure on the nose of the fuselage increases again, while the pressure on the upper surface of the horizontal tail does not increase significantly. As given in Table 4, the blade azimuth angle is changing constantly, although the \(C_{D}\) of the left and right tail planes, skids and vertical tail of the helicopter do not change much. It can be concluded that the change in the azimuth angle mainly affects the drag of the fuselage and the horizontal tail. Additionally, the pressure distribution indicates that the bottom surface of the fuselage is less interfered with by the unsteady effect, while the bottom surface of the tail boom and the surface of the nose are affected by the rotating flow, which varies slightly with the change in the blade azimuth angle. Due to the counterclockwise rotation of the rotor, the wake generated by the rotor firstly sweeps through the left tail plane, then passes through the horizontal tail, and finally passes through the right tail plane; thus, the total drag increment of the two tail planes is zero in one period. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Fuselage** & **Horizontal Tail** & \ \begin{tabular}{c} **Left** \\ **Tail Plane** \\ \end{tabular}  & \begin{tabular}{c} **Right** \\ **Tail Plane** \\ \end{tabular}  \\ \hline Rotor-off & 0.079 & 0.024 & 0.008 & 0.010 \\ \(\Psi=0^{\circ}\) & 0.177 & 0.038 & 0.012 & 0.006 \\ \(\Psi=25^{\circ}\) & 0.104 & 0.033 & 0.011 & 0.006 \\ \(\Psi=4
## 2 Material and Methods
### Environmental and Economic Cost Considerations  
Based on the operational data of the R.O.P. full-scale sawing plant, the present LCA follows ISO 14040 [26] and 14044 [27] standards. LCA aims to provide a set of information for future decisions on the most adequate GS valorisation pathways concerning environmental burdens. Aiming to integrate the LCA environmental results with costs considerations, an LCC Analysis [20] and cost-benefit analysis were developed along with LCA, harmonising their goals and scope as well as the LCI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Concrete** & **Reference Concrete** \\  & **5\% w/w GS** & **C30/37.S4.D22.Cl0,40** \\ \hline  & \multicolumn{2}{c}{kg/m\({}^{3}\)} \\ \hline Cement & 270 & 270 \\ Binding agent & 320 & 320 \\ Calcarous filler & 50 & 50 \\ GS & 100 & 0 \\ Fine sand & 220 & 320 \\ Medium sand & 560 & 600 \\ Coarse inert materials & 960 & 960 \\ Tap water & 180 & 165 \\ Plasticizer & 2.88 & 2.88 \\ Superplasticizer & 1.6 & 1.6 \\ Water-Cement ratio & 0.56 & 0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete formulations incorporating 5% \(w\)/\(w\) GS (Concrete 5% \(w\)/\(w\) GS) and reference concrete (C30/37.S4.D22.Cl0,40).
# Detection of Exceptional Malware Variants Using Deep Boosted Feature Spaces and Machine Learning
## 3 Methodology
### Classification Schemes
#### 3.2.2 The Proposed Deep Feature Space-Based Malware Classification (DFS-MC)  
The malware classification challenge is addressed by a hybrid learning scheme and named as Deep Feature Space-based Malware Classification (DFS-MC). The classification ability of the proposed framework is enhanced by combining the benefits of both empirical and structural risk minimization [29]. CNNs are high-capacity learning models and follow the principles of empirical risk minimization learning theory, which focuses on minimizing training loss. Sometimes, this training may lead to overfitting. Contrary to this, a classical ML classifier like SVM is based on a structural risk minimization principle. This principle improves the SVM's generalization ability by looking at a test error [30]. Therefore, features are extracted from the second last fully connected layer (FC) of customized CNNs and individually provided to SVM for classification. The feature extraction layer and its respective dimensions are given in Table 3.  
\[\sigma(\mathrm{v})_{i}=\frac{e^{V_{i}}}{\sum_{j=1}^{K}e^{V_{j}}} \tag{1}\]  
\[w^{T}\mathrm{x}+\mathrm{b}=0 \tag{2}\]  
In Equation (1), \(\sigma\) represents the softmax activation function, whereas \(\mathrm{v}\) are features from the last layer of customized CNNs and input to softmax. The exponential function for input and normalization vectors are represented by \(e^{V_{i}}\) and \(e^{V_{j}}\). K represents the number of classes. In Equation (2), \(\mathrm{x}\) is a feature space, whereas \(w^{T}\) and \(\mathrm{b}\) are weight and bias, respectively.
# An In-Depth Analysis of Domain Adaptation in Computer and Robotic Vision
## 3 Applications and Real-World Scenarios
### Domain Adaptation in Computer Vision: Real-World Applications  
#### 3.1.1 Autonomous Driving Systems  
One noteworthy arena where domain adaptation proves invaluable is in the development of autonomous driving systems. Here, vision-based perception is a linchpin for safe navigation in dynamic and unpredictable environments. Domain adaptation methodologies empower autonomous vehicles to maintain exceptional accuracy in tasks such as object identification, lane segmentation, and pedestrian recognition, even amidst diverse weather conditions, fluctuations in illumination, and changing road geometries. For instance, deep learning-based domain adaptation models facilitate seamless adaptation to shifting weather conditions, enabling autonomous vehicles to effectively detect and respond to critical elements such as pedestrians and obstacles, even in challenging weather conditions like rain or fog, by training on comprehensive datasets encompassing a spectrum of weather scenarios [313]. Figure 8 shows how the domain gap is visible in the acquired dataset due to changes in weather.
# A Review of Multifunctional Antenna Designs for Internet of Things
## 2 Materials and Methods
### Biomedical Devices
#### 2.1.4 Various Antenna Types  
In the realm of antenna design, especially for applications requiring operation within or around the human body, a variety of innovative electrical structures have been explored and documented in the literature. Some indicative examples include planar inverted-F antennas (PIFAs) [38; 39], dielectric resonator antennas (DRAs) [40], metamaterial-based antennas [41], and Vivaldi antennas [42]. Each of these designs offers unique advantages for specific applications, addressing different challenges related to size, efficiency, and bandwidth.  
Figure 2: Differentially fed antenna enclosed in a capsule shell for ingestible wireless capsules.  
In particular, the Vivaldi antenna [42], when embedded vertically against the dura mater within a skull phantom, demonstrates particularly noteworthy performance characteristics. This configuration results in increased radiation gain directed toward the end-fire orientation of about 30% with respect to the conventional antenna, namely, with an average gain of \(-15.7\) dBi across a wide bandwidth range from 3 to 5 GHz. This extensive bandwidth may go underutilized because even the most bandwidth-intensive modulation schemes used in such applications will not exceed 100 MHz. However, this antenna can offer versatility in its frequency of operation, particularly if certain frequencies experience high attenuation. Furthermore, a link budget analysis of this skull-embedded Vivaldi antenna underscores its potential for reliable wireless communication within the challenging harsh environment of the human body. The analysis indicates that effective communication can be maintained over a distance of 10.8 cm between the two antennas (receiver and transmitter), even with a transmitted power as low as \(-25\) dBm [42]. Another advantage of the antenna is the low SAR, which permits higher power input in comparison with previously mentioned antennas (5 mW).
## 5 Future Advancements, Clinical Impact, and Comparison of PET Systems with Standard Imaging Methods  
Recent advancements in PET scanner design are driving a new era of molecular imaging with sub-2 mm, and even 1 mm\({}^{3}\), isotropic spatial resolution. These improvements  
Figure 9: The top figure displays a micro-Derenzo hot-rod phantom (**top**) and its placement in two positions within the FOV for imaging (**middle**). Two separate scans were performed, with the reconstructed images of these scans shown in the middle. At the **bottom**, a line profile was generated along the X-axis through the 1.2 mm diameter rods in the reconstructed images (**middle**). The dashed lines represent Gaussian fits applied to the peaks corresponding to each rod, illustrating the system’s ability to resolve the 1.2 mm rods’ ultra-high spatial resolution clinical positron emission tomography (PET) systems [50].  
are motivated by the need for better early-stage disease detection, more accurate lesion localization, and personalized treatment planning. A key trend in this direction is the development of organ-targeted, or loco-regional, long axial FOV PET systems that optimize photon detection efficiency (PDE) while minimizing signal-to-noise ratio (SNR) degradation. Innovations include anatomically optimized geometries (e.g., for the head/neck and breast), high-performance scintillators, fast readout electronics, and image reconstruction algorithms that account for complex physical effects in the scanner.  
These ultra-high-resolution PET systems complement traditional anatomical imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI) [78]. While CT and MRI detect changes in structure or density, PET provides molecular information by visualizing biological processes at the molecular level. This enables earlier disease detection, often before anatomical changes become visible. The latest PET technologies, including TOF and DOI configurations, further enhance contrast, reduce image noise, and improve the detection of small or low-contrast lesions.  
For example, the 1 mm\({}^{3}\) resolution PET scanner developed in our laboratory for head-and-neck or breast imaging demonstrates the clinical potential of these advances. With the ability to target specific molecular markers using radiotracers, PET offers unmatched sensitivity and versatility for applications in oncology, neurology, and cardiology. These advantages make next-generation PET systems a valuable addition to standard imaging, improving diagnostic precision and patient management across a range of clinical practices.
## 2 Materials and Methods
### 1-D Convolutional Neural Network  
As shown in Figure 5, a standard CNN model usually includes the input layers, convolution layers, a fully connected layer and an output layer. The input data were transferred through a series of layers (two convolution layers, activation function, loss function). Finally, the mapping calculation obtained the amplification factors. Specifically, the input of the 1-D CNN was either a \(1\times\) N or an N \(\times\) 1 array. As shown in Figure 5, an N \(\times\) 1 array was passed through a series of convolution layers and the fully connected layer. Then, the amplification factors for the local ground motion were obtained in the output layer.  
As shown in Figure 6, the convolution process was to multiply each element in the convolution kernel with the corresponding element in a sub-region (e.g., green box or blue dotted box) of the input data of the convolution layer and to sum the products to obtain an element in the feature map. Each time, the sub-region moved down by one step, and the process was repeated until all elements of the input data were involved. Finally, the convolution operation formed a new array (i.e., the feature map).  
The activation function has a non-linear ability that gives the neural network better learning capabilities. Commonly used activation functions include the sigmoid, tanh, ReLU (rectified linear unit) and LeakyReLU functions, as shown in Figure 7. The LeakyReLU activation function was used in this study, as it is much faster than the sigmoid function and the tanh function.  
Figure 5: One-dimensional CNN model for predicting site amplification factors.  
The loss function was employed to evaluate the convergence of the trained CNN. It was embedded in the output layers. In this paper, the convergence of the network training process was evaluated by the validation samples. The loss function (RMSE, root mean squared error) was as given in Equation (4).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}{(predicted_{t}-label)^{2}}} \tag{4}\]  
Figure 6: Based on comparisons of the three activation functions, the ReLU activation function was used in our model as it was much faster than the sigmoid and tanh functions.  
Figure 7: Activation function: (a) sigmoid; (b) tanh; (c) ReLU; (d) LeakyReLU.  
where \(N\) is the number of validation samples.
# Mechanical Properties of Sandstone Cement-Stabilized Macadam
## 3 Test Results
### Unconfined Compressive Strength
#### 3.1.3 Influence of Curing Time  
In addition, a significant factor influencing the UCS of CSM is the curing period of specimens. Numerous studies have reported the curing time's influence on the UCS. The UCS development with the curing time is shown in Figure 5. It is observed from this figure that the effects of the curing period on the strengths of the SCSM blend and the CSM blend are similar in the case of the identical cement content. The longer the curing time, the greater the strength. [PERSON] carried out the same performance test on CSM with asphalt emulsion, and the growth trend of strength was similar to the test results in this paper with the increase of curing age [28]. The reason for this phenomenon is that the hydration reaction is the time-dependent action. The developing velocity of the UCS is usually proportional to the cement content, which is since the more cement is added in the blends, the more products of hydration reaction [29] and the better the strength enhancement and bonding effects. As shown in Table 5, the compressive strength increases rapidly in the first 28 days, but the increase slows down at 60 days and 180 days. In particular, from 7 to 28 days, the strength of the SCSM increased by 30%, while from 28 to 60 days, the SCSM strength only increased by 7%. This result is because the cement granules without hydration reaction are surrounded by formed cement slurry, making it difficult for water to enter the surface of the un-hydrated cement particles. This phenomenon impedes the hydration of the cement granules, thus leading to a slower increase in the UCS at late period. According to JTG D50-2017 [30], the UCS of CSM mixture bases used in medium or light traffic roads at an age of 7 days should be between 3.0 and 5.0 MPa. From the experimental results, the UCS of the 7-day SCSM successfully met the requirements.  
Figure 4: Relationship between the UCS and aggregate type.
# Marble and Glass Waste Powder in Cement Mortar
## 3 Results and Discussion
### Microstructure Properties of Hardened Mortar  
#### 3.4.1 SEM  
The pozzolanic reaction between pozzolanic materials and calcium hydroxide, as reported by [PERSON] and [PERSON] [60], leads to the formation of additional cementitious compounds such as C-S-H. This reaction densifies the mortar matrix, resulting in a more compact morphology. [PERSON], [PERSON] [65] observe that the C-S-H gel exhibits a fiber-like structure, while calcium hydroxide (CH) crystals are visible in variable shapes and sizes such as flat, prism-shaped, and long crystals, or showed as a plate-like and hexagonal structure. Additionally, Ettringite typically manifests as a needle-like structure during cement hydration.  
Figure 12 presents an SEM image of mortar samples at 28 days of curing, following compressive strength for samples with 0%, 10%, 15%, and 30% replacement of cement with MGWP. The SEM analysis reveals several key structural features, including the amorphous phase of C-S-H, CH, pores, and cracks due to the sampling process. The mortar containing 10% MGWP demonstrates a notably denser and more compact structure, reflecting the enhanced cementitious properties due to the pozzolanic activity of the glass waste powder. The reduction in CH content is attributed to the chemical reaction between the reactive silica present in GWP and calcium hydroxide. Additionally, the SEM results are consistent with the highest compressive strength observed at 10% MGWP, as this mixture exhibits less porosity compared to the control mix, which is further corroborated by the results of the TGA-DTA analysis reported in the following subsection.  
Figure 12: SEM image for MGWP-containing mortar at curing age of 28 days.  
In contrast, the SEM for the 0% MGWP mix shows a greater presence of pores and CH, indicating a less compact structure. The 30% MGWP mix exhibits a high number of pores, suggesting that this higher replacement level may lead to reduced packing efficiency. Meanwhile, mortar with 15% MGWP shows a less porous and more compact morphology, suggesting an improvement in the cement matrix due to the inclusion of MGWP. These SEM observations align with the compressive strength, UPV, water absorption, and porosity tests, which demonstrate that incorporating MGWP into mortar significantly refines the microstructure and enhances the material properties.