esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Authentication Results  
As the reference hashing performance for authentication, we compared the proposed multi-attack reference hashing (MRH) with original image-based reference hashing (ORH) on four baseline image hashing methods, i.e., wavelet-based image hashing, SVD-based image hashing, RPIVD-based image hashing and QFT-based image hashing, with twelve content-preserving operations. The results are shown in Tables 2 and 3. Note that higher values indicate better performance for all metrics. It was observed that the proposed MRH algorithm outperformed the ORH algorithm by a clear margin, irrespective of the content preserving operation and image datasets (RTD and CASIA). This is particularly evident for illumination correction. For instance, in contrast to original image-based reference hashing, the multi-attack reference hashing increased the AUC of illumination correction by 21.98% on the RTD image dataset when getting the reference hashing by wavelet, as shown in Table 2. For the QFT approach, the multi-attack reference hashing we proposed was more stable and outstanding than other corresponding reference hashings. Since the QFT robust image hashing technique is used to process the three channels of the color image, the chrominance information of the color image can be prevented from being lost and image features are more obvious. Therefore, the robustness of the multi-attack reference hashing is more able to resist geometric attacks and content preserving operations. For instance, the multi-attack reference hashing increased the precision of Gaussian noise by 3.28% on the RTD image. Figure 4: Distribution of hashing distances between hashing pairs with varying thresholds.
# Interference-Aware Opportunistic Dynamic Energy Saving Mechanism for Wi-Fi Enabled IoTs
## 3 Dynamic Bandwidth Operation  
Therefore, heterogeneous standard terminals may be present in the same channels in adjacent locations or in adjacent channels. In such a condition, it is difficult to provide terminals that support a wide bandwidth with a high speed service due to interference from the same or adjacent channels.  
To solve the limit described above, a dynamic bandwidth allocation mechanism as shown in Figure 1 was introduced in the IEEE 802.11 ac standard [1]. In an environment including a total of four sub-channels, channel bonding may be performed with respect to only two sub-channels meeting the condition, instead of all the four sub-channels, to transmit data. That is, when a sub-channel that is to be bonded for data transmission includes another signal, data transmission may be performed using the remaining channels and excluding the sub-channel.  
Figure 2 illustrates the procedures for dynamic bandwidth allocation. The transmitter may transmit the RTS to the receiver through bonded multiple communication sub-channels. The receiver may receive the RTS transmitted from the transmitter. Then, the receiver may select at least one communication sub-channel from the multiple communication sub-channels that received the RTS. After receiving the RTS frames, the receiver may transmit the CTS to the transmitter as a response to the RTS with respect to the selected communication sub-channels. It should be noted that the sub-channel is selected by the receiver. The transmitter may receive the CTS transmitted from the receiver. Then, the transmitter may transmit a data packet to the transmitter through the selected communication sub-channels. It may use all or a subset of the communication channels that successfully received the CTS. The receiver may receive the data packet from the transmitter.  
Figure 1: Dynamic bandwidth allocation mechanism; CTS: clear to send; RTS: request to send; Ack: acknowledgment; PIFS: point coordination function.  
Figure 2: Flowchart for dynamic bandwidth allocation.
## 4 Model Comparison and Validation  
### Test Data of Slip Zone Soil  
In order to verify the applicability of the constitutive models based on various statistical distributions and analyze the optimal model suitable for different softening types of ring shear curves, the slip zone soils of Huangtopo landslide and Shizibao landslide were selected for experimental comparison [39; 40; 41].  
The Huangtopo landslide is one of the major landslides in the Three Gorges Reservoir Area (TGRA), located in Badong County (as shown in Figure 7). The Huangtopo landslide is composed of four different slide masses, including the Riversle Slump I#, the Garden Spot Landslide, the Riversle Slump II#, and the Substation Landslide. The slip zone soil samples were taken from test tunnel TP4, which is located in the Riversle Slump II#. The samples were silty clay with gravels in which the content of particles less than 0.075 mm was 40.2%-62.2%, the content of silt was 27.6%-35.5%, with a plasticity index of 7.84%-10.99%. The natural density was 2.09-2.34 g/cm\({}^{3}\), and the natural water content was 12.07%-15.33%.  
The Shizibao landslide is located on the south bank of the Yangtze River in Guojiaba Town, Zigui County, TGRA [40; 41]. Field investigation shows that this landslide is a rockslide with is bedding plane failure mode. The slip zone soil samples were collected from the slip zone of the Shizibao rockslide, exposed at the left boundary of the landslide site. The samples were of a predominantly sandy texture with 90% of the sand and silt. They have 50% of platy clay minerals that involve illite and montmorillonite, 20% of feldspar and 29% of quartz minerals, with a plasticity index of 13%. The dry density of the samples was 1.77g/cm\({}^{3}\), and the natural water content was 7-8%.  
Figure 6: Fitting flow chart of shear constitutive model of slip zone soil. (**A**) Construction of statistical damage constitutive model; (**B**) Least square fitting method; (**C**) Iterative method for solving model parameters.  
The ring shear test data of the above two slip zone soil samples represent two types of shear stress-displacement curves with different softening features. The softening characteristic of the slip zone soil of Huangtupo landslide was not obvious and even shows hardening under high normal stress. As shown in Figure 8a, it can represent the shear curve of the weak softening type (Type A curve). The test curve of slip zone soil in the Shizibao landslide has characteristics of large softening degree, as shown in Figure 8b, which can represent the shear curve of strong softening type (Type B curve). These two types of test curves are representative for analyzing the applicability of various constitutive models and softening characteristics of different shear curves.  
Figure 8: Ring shear test curve of slip zone soil. (**a**) Ring shear test curve of slip zone soil from Huangtupo landslide; (**b**) ring shear test curve of slip zone soil from Shizibao landslide.  
Figure 7: Geological map of TGRA showing the locations of landslides and rock avalanches (Geological map from [PERSON] et al. 2019 [42]).
## 5 Performance Evaluation
### Mobility Patterns  
The previous tests required a deterministic configuration that could only be performed using a linear moving trajectory and a chosen topology. Now we shift the focus on a random placement of nodes and trajectory. We utilized an open-source library called  
Figure 14: Simulation scenarios when increasing distance between MN and controller, (**a**) with 3-hop distance, (**b**) with 4-hop distance, and (**c**) with 5-hop distance.  
Figure 13: Simulation results for increasing number of neighbors with SDMob—(**a**) PDR; (**b**) localization error; and (**c**) end-to-end delay.  
Figure 15: Simulation results for increasing distance between MN and controller with SDMob—(**a**) PDR; (**b**) localization error; and (**c**) end-to-end delay.  
pymobility [49] to create some of the most well-known mobility traces in the field. The number of neighbors and hop-distance depends on the mobility pattern and changes in time. There are 25 anchors and one MN in each of the scenarios below.
# A Wideband Circularly Polarized Antenna with a Multiple-Circular-Sector Dielectric Resonator
## 3 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Structure** & **Description** & \(-\)**10 dB Reflection Bandwidth (GHz)** & **3 dB AR Bandwidth (GHz)** & **Dimensions (\(\Delta\))** & **Peak Gain (\#R)** \\ \hline
[11] & CP DRA fed by a lumped positively loaded monodaira-spiral-dot & 1.86-32.32 (53.5\%) & 2.28-272 (18.7\%) & 0.36 \(\times\) 0.36 \(\times\) 0.083 & 2.9-5.0 \\
[12] & CP DRA fed by a modified cross-slot & 2.19-2.92 (28.6\%) & 2.25-2.28 (28.4\%) & 0.43 \(\times\) 0.43 \(\times\) 0.27 & 4.3-6.1 \\
[13] & CP DRA with a Spifoton fractal DR & 4.3-3.40 (30.2\%) & 5.13-5.76 (15.1\%) & 0.91 \(\times\) 0.31 \(\times\) 0.13 & 2.20-316 \\
[14] & CP DRA with a grouped rectangular DR & 1.94-2.92 (43.0\%) & 2.30-29.22 (23.7\%) & 0.35 \(\times\) 0.35 \(\times\) 0.086 & 2.30-243 \\
[15] & CP DRA with a trapezoidal DR & 2.88-4.04 (33.5\%) & 3.11-3.86 (21.5\%) & 1.16 \(\times\) 1.16 \(\times\) 0.44 & 5.28-8.39 \\ Proposed antenna & CP DRA with a multiple-circular-sector DR & 1.88-2.58 (31.9\%) & 2.06-2.50 (93.0\%) & 0.84 \(\times\) 0.84 \(\times\) 0.39 & 4.95-7.65 \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of the proposed antenna with antennas in previous studies. Note that \(\lambda_{0}\) is the wavelength corresponding to the center frequency of the AR band. CP: circularly polarized; DRA: Dielectric resonator antenna.
## 2 Materials and Methods
### Deep Learning Architecture and Parameters  
In this study, we employed a one-dimensional residual neural network (ResNet1D) architecture, as shown in Figure 1, to perform classification tasks on transformed spectral data. The architecture employs residual connections to mitigate the vanishing gradient problem and facilitate the training of deeper models, thereby improving generalization and accuracy on complex datasets. Our model is specifically designed for processing 1D data, making it suitable for applications such as time-series analysis and sequential data classification. The ResNet1D architecture begins with an initial convolutional layer (Conv 1D) that applies a 1D convolution operation to the input data, with a kernel size of 7 and a stride of 2, followed by batch normalization (BatchNorm1D) and a ReLU activation function. This combination of layers serves to extract low-level features from the input data. A max-pooling layer (MaxPool1D) is then applied, reducing the dimensionality of the input and increasing computational efficiency.  
The core of the network consists of two residual blocks, each composed of two convolutional layers interspersed with batch normalisation and ReLU activations. Each residual block is equipped with a shortcut (skip connection) that bypasses the convolutional layers, directly adding the input to the block's output. This allows the network to preserve gradient flow during backpropagation, enabling deeper models to be trained effectively without suffering from performance degradation. The first residual block maintains the same number of channels, while the second block doubles the number of channels and reduces the spatial resolution through downsampling. This structure allows the network to capture both local and global patterns in the data. After passing through the residual blocks, the feature map is further reduced by an adaptive average pooling layer (Adaptive AvgPool 1D), which pools the feature map to a fixed length of one, irrespective of the original input length. This ensures compatibility with fully connected layers, regardless of the input sequence's length. The resulting feature vector is passed to a fully connected layer for final classification. The output of the fully connected layer represents the class probabilities, which are computed using the softmax function for multi-class classification. To stabilise training and prevent overfitting, batch normalization is applied throughout the network. The network was trained using the Adam optimiser with a learning rate of 0.001, and cross-entropy loss was employed as the loss function. During training, the model's parameters were updated using gradient-based optimisation to minimize the classification error over a series of epochs. This ResNet1D architecture incorporates residual connections, batch normalisation, and adaptive pooling, making it a robust and efficient solution for 1D data classification tasks. Its ability to handle long sequences and complex patterns makes it well-suited for a variety of applications, including time-series analysis and medical signal classification.
## 2 Materials and Methods  
### Patient Selection  
The sample of this study consisted of 102 consecutive pathological subjects (23 men/79 women) aged between 16 and 77 years (44.4 \(\pm\) 6.9 years on the average) who had been referred to the Department of Innovative Technologies in Medicine and Dentistry, University of Chieti-Pescara, Chieti, Italy, for diagnosis and treatment of TMD-related facial pain during the time interval from February 2017 to February 2020. The study was conducted in observance of the Helsinki Declaration (revised version of Tokyo in 2004) and Good Clinical Practice Guidelines. All patients were informed about the purpose of the study and were required to give written, informed consent before enrolment.  
The following inclusion criteria were used for the enrolment: (i) patients with clinical features fulfilling the DC/TMD [10] of local myalgia, myofasical pain with spreading and myofasical pain with referral; (ii) absence of long-term illnesses that may affect TMJs or the masticatory muscles (i.e., rheumatoid arthritis); (iii) absence of previous treatment for TMD; (iv) no vestibular apparatus alteration or history of vertigo caused by central nervous disease; (v) absence of neuropathic pain, generalized pain (e.g., fibromyalgiaalgia), neurovascular headache (e.g., cluster headache or migraine), or any psychiatric disorder. Moreover, the participants did not undergo any dental restoration during the whole period of the study, hence preventing any alteration of occlusal surfaces of the teeth. All patients underwent a clinical pathological visit to assess the DC/TMD [10]. After collecting a complete medical and dental patient history including a description of the pain in head and neck area (type, frequency, and intensity), a clinical evaluation was performed. The visit included maximum comfortable jaw opening measurement, assessment of clicking, tenderness at rest and during jaw eccentric movements, deviation during opening and closing mouth movements. Tenderness of the extraoral and intraoral masticatory muscles and neck muscles was evaluated by manual palpation. Eighty-sixsubjects (15 men and 71 women) of the initially 102 fulfilled our inclusion criteria and were randomly divided in two groups: 42 (10 men and 32 women) constituted the test group wearing mandibular repositioning splints, while the other 44 (5 men and 39 women) constituted the non-treatment control group.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 4 Methodology
### Data Preprocessing  
The input data play an important role in the CAE architecture for data reconstruction. To efficiently use the CAE, we propose to format the input data as a combination of data from multiple sensors in the WSN system. We thereby utilize a temporal data structure that combines a historical record and the relationship between neighboring sensors. At each sample time, the sensor measures the data from the environment and transmits it to the central sensor via a wireless channel.  
We set \(N\) as the number of samples used to build the data for the training process. By employing sequential data, we can utilize historical records to explore the relationship between data in contiguous time samples in the same sample interval for the training process. Thus, the data of the \(i\)-th sensor \(s_{i}\) can be expressed as follows:  
\[S_{i}=\{s_{i,1},s_{i,2},\ldots,s_{i,N}\}, \tag{22}\]  
where \(s_{i,n}\) denotes the received data of sensor \(S_{i}\) at the \(n\)-th sample time. We set \(M\) as the number of sensors required to obtain data from the environment.  
Obviously, in addition to considering the relationship between data on the same sensor, the relationship between sensors also needs to be considered. Therefore, it motivates us to combine the data of \(M\) sensors by formatting them as a two-dimensional (2D) array for the input data. Therefore, input data \(A\), formatted for the CAE networks, is illustrated in Figure 4 and expressed as  
\[Y=\{S_{1},S_{2},\ldots,S_{M}\}. \tag{23}\]  
Therefore, a CAE architecture was utilized to exploit the input data as a 2D array for the training process. The CAE architecture for reconstruction is presented in detail in the following subsection.  
Figure 3: Overview of the proposed architecture.
## 2 Experiment
### Experimental Facility  
Figure 2 shows the schematic diagram of the experiments. The LIB charging and discharging instrument (provided by Neware Electronics Co., Ltd., Shenzhen, China) is provided for the LIB normal charging and discharging cycles, and the accuracy and stability of the whole range can reach 0.1%. The LIB temperature was measured by T-type thermocouples (provided by OMEGA), and recorded by the temperature data acquisition unit (provided by National Instruments) as shown in Figure 2. According to the factory temperature check, the thermocouple temperature measurement accuracy is controlled within 0.5 \({}^{\circ}\)C. The thermocouple is patch type, which is attached to the middle of the battery surface. The PCM is wrapped around the battery, and the thermocouple is embedded in the PCM. The thermocouple was calibrated before the experiments. The temperature of the LIB by thermocouple was measured only to analyze the cooling effect of PCM on the LIB, not to study the internal reaction of the LIB in this study. Hence, the T-type thermocouple is attached to the center position of the batteries surface to measure the optimum temperature.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Thermal Physical Characteristic** & **Parameters** \\ \hline Phase change temperature (\({}^{\circ}\)C) & 52\(\sim\)55 \\ Thermal conductivity (W/m\(\cdot\)K) & 3 \\ Latent heat of phase change (J/g) & 170 \\ Specific heat capacity (J/kg\(\cdot\)C) & 1.8 \\ Density (g/cm\({}^{3}\)) & 0.2 \\ Materials & Phase change wax, Expanded graphite \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal physical properties of PCM.  
Figure 1: Experimental conditionsBefore the experiment, the LIBs were discharged to the cut-off voltage, charged to maximum voltage, and then placed 24 h to keep the LIB stable. The temperature in the carton is 25 \({}^{\circ}\)C, and its environmental temperature error is controlled within the normal range of 0.5 \({}^{\circ}\)C. The charge and discharge process of the two batteries are shown in Table 3. The cycle of Sanyo LIB can be divided into four stages: (1) discharge stage, with 2 C constant current discharge to 2.5 V. (2) shelving stage for 5 min. (3) charging stage with 1 C constant current and voltage to 4.2 V. (4) shelving stage with 5 min. The Sony LIB is tested in the same charge-discharge cycle as the Sanyo LIB. The only differences are the cutoff discharging voltage of 2.0 V and cutoff charging voltage of 3.6 V because of its relatively small capacity and voltage. Besides, two kinds of cells are placed in the PCM to discharge at a discharge rate of 1 C and 2 C.
# Extensive Benchmarking of DFT+\(U\) Calculations for Predicting Band Gaps
## Appendix B Computational Cost Comparison of HSE06 and Hubbard Parameter Calculations
Moreover, it is important to note that not only the computational cost matters when comparing HSE06 with DFT+\(U\) calculations, but also the accuracy of the final results. In fact, we find that the band gap value predicted for TiO\({}_{2}\) using HSE06 (see Table 10 is in slightly better agreement with the experimental value of 3.0 eV than the best band gap value predicted using DFT+\(U\) of 2.60 eV (see Table 3), however the band gap value predicted for NbNO using HSE06 is in much worse agreement with the experimental value of 2.1 eV than the best band gap value predicted using DFT+\(U\) of 2.05 eV (see Table 3). This observation can be explained by the fact that in the fully first-principles DFT+\(U\) approach, \(U\) is a material-specific parameter and it is computed _ab initio_ as a response property of the material. In contrast, in HSE06 (and other popular hybrids) the amount of the exact-exchange (the screening parameter) is fixed (i.e., it is not material-specific) and in solids this often leads to unsatisfactory results. Thus, frequently this screening parameter is tuned until the experimental value of some property (e.g., the band gap) is reproduced, which makes this approach not fully first-principles. However, it is important to mention that this screening parameter in hybrids can be computed _ab initio_ (see, e.g., Refs. [13; 15; 18]), which is not always an easy task. In this case, an extra computational cost for HSE06 must be added in Table 10 which will make the ratio \(t_{\rm HSE06}/t_{U}\) even larger. Therefore, the overall observations in this appendix further confirm that DFT+\(U\) can be considered as a reliable and efficient method to calculate band gaps at a fraction of the computational cost of hybrids functionals such as HSE06 (especially for large systems).