esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Comparative Feature-Guided Network
#### 2.4.3 Multi-Channel Spatial Information Fusion Mechanism  
Due to the spatial differences between the 6-channel feature maps, it is necessary to independently process the features of each channel. Traditional methods typically use 3D convolution with a 3 \(\times\) 3 \(\times\) 6 kernel to achieve this, but this approach introduces significant computational overhead and weight expansion, which not only increases computational complexity but may also lead to higher data requirements and affect network convergence. The core difference between DepthwiseConv2d [39] and traditional convolution lies in the way they handle cross-channel information interaction and computational efficiency. DepthwiseConv2d completely decouples the inter-channel dependencies by assigning each input channel an independent filter, ensuring that the number of filters strictly matches the number of input channels. This design entirely eliminates cross-channel computation, making the number of parameters dependent only on the input channels and kernel size. However, the drawback is that the output channels cannot directly integrate multi-channel features. As shown in Figure 16, to address this issue, this study innovatively adopts the grouped convolution strategy of DepthwiseConv2d for feature extraction. Specifically, in the depthwise separable convolution layer, we use a 3 \(\times\) 3 2D convolution layer with group = 6, implementing independent calculation of 6-channel features through grouped convolution. Based on this strategy, we improved the residual structure of ResNet [8] by replacing the original convolution kernels with DepthwiseConv2d, establishing the Residual-6 block.  
DepthwiseConv2d decomposes the standard convolution into depthwise convolution and pointwise convolution, significantly reducing the number of parameters and computational cost. The formula is as follows:  
\[DepthwiseConv(x)=\sum_{i=1}^{C}W_{i}*x_{i} \tag{5}\]  
where \(W_{i}\) represents the convolution kernel of the \(i\)-th channel, and \(x_{i}\) represents the input feature of the \(i\)-th channel.  
During the feature extraction process, we first use the Residual-6 block to extract features for each channel and then introduce the self-attention mechanism of the Transformer layer to adaptively fuse the 6-channel features. This strategy, combining grouped convolution and attention mechanisms, not only effectively reduces computational complexity but also enables independent extraction and optimized fusion of spatial features from each channel, significantly improving the recognition performance of the ResNet [8] architecture in handling 6-channel feature tasks. Through the self-attention mechanism of the Transformer layers, the model can adaptively fuse multi-channel features, enhancing its ability to recognize refractive errors.  
Figure 15: Image contrast module \(\partial(\cdot)\).
###### Abstract  
A multiple-input and multiple-output ultra-wideband (MIMO-UWB) system provides a higher data rate. However, the multipath effect of the intersymbol interference (ISI) increases the bit error rate (BER) and outage probability of the MIMO-UWB system. For this paper, the authors applied the real orthogonal design (ROD) to an MIMO-UWB system to improve the efficiency of that system. A ray-tracing technique and an inverse fast Fourier transform were used to get the impulse response of the indoor environment. In addition, a rake receiver was used to increase the strength of the received signal to minimize the multipath effect. For this paper, two cases of an indoor wireless MIMO-UWB system were studied: case (A) used different antenna arrays, whereas case (B) placed antenna arrays in different locations to find the best position of the transmitter. In case (A), three different shapes of antenna arrays, namely L-shape, circular-shape, and Y-shape, were used for the transmitter and receiver. The BER performance for these arrays in the UWB frequency of 3.1-10.6 GHz was examined. Numerical results showed that the outage probability of the circular array was better than that of the other two arrays. In case (B), the transmitter used was an array with two antenna elements. The optimal location for the transmitter was found by using both asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE). The numerical results indicated that the performance of APSO was better than that of SADDE.  
multiple-input and multiple-output (MIMO); ultra-wideband (UWB); real orthogonal design (ROD); self-adaptive dynamic differential evolution (SADDE); asynchronous particle swarm optimization (APSO)
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 3 Satellite Measurement of Precipitation
### Science and Technology Advances
#### 3.1.3 Smallsat Sensor Constellations  
The debate on how to qualitatively and quantitatively improve global precipitation monitoring is not confined to merely maintaining the present constellation in orbit. The number of PMW sensors is obviously a prerequisite for ensuring the necessary continuity of products in the future. However, costs are high, and the spatio-temporal coverage is hindered by the continual loss of spacecrafts due to aging. Thus, the community is at work to look beyond the GPM constellation which is now in orbit. In particular, the increasing miniaturization of sensors makes it possible to design satellite constellations at a fraction of the current cost of large satellites, and where any single member can easily be replaced when necessary. Moreover, the frequent sampling of the new sensor constellations is fundamental for better dynamic and thermodynamic characterization of precipitating clouds [132]. Smallsats and CubeSats are among the alternatives being explored with different payloads.  
The Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS, [133]) mission was recently selected by the National Aeronautics and Space Administration (NASA), as part of the Earth Venture-Instrument (EVI-3) program. All-weather observations of 3D temperature and humidity, as well as cloud ice and precipitation at high temporal resolution (median refresh time better than 60 min) for investigating tropical cyclones, are the goals of the mission. The proposed Temporal Experiment for Storms and Tropical Systems (TEMPEST, [134]) mission consists of five 6U-Class nanosatellites observing at five millimeter-wave frequencies with a 5-min temporal sampling to observe the time evolution of clouds and the formation of precipitation.  
The smallsats concept for precipitation monitoring is not only limited to PMW radiometers; the excellent performance of clouds and precipitation radars in space has initiated studies on low-cost, quick-turnaround platforms. The Jet Propulsion Laboratory (JPL) Ka-band RainCube [135] smallsat now in orbit is based on a half-meter parabolic antenna, and has already met all requirements through repeated observations of precipitation.
# Harnessing Spatial-Frequency Information for Enhanced Image Restoration
## 4 Experiments
### Single-Image Defocus Deblurring Results  
Following the training strategy of SFNet [10], our model was trained on LFDOF [38] and fine-tuned on DPDD [37]. Table 2 presents the quantitative results of single-image defocus deblurring on the DPDD. As can be seen in Table 2, our model improves performance on all metrics except SSIM. Compared to IRNeXt [18], the proposed FreANet, with 13.63% fewer parameters, achieves gains of 0.1 dB PNSR and 0.02 SSIM in the indoor scenes. FreANet outperforms the frequency-based SFNet [10] in the outdoor scenes, achieving improved scores of 0.1 dB in PSNR and 0.01 in SSIM. Figure 2 demonstrates that our model restores visibility-degraded parts more effectively than previous algorithms.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Task** & **Dataset** & \begin{tabular}{c} **Test** \\ **Subname** \\ \end{tabular} & **\#Train** & **\#Test** \\ \hline Defocus Deblurring & DPDD [37] & - & 350 & 76 \\  & LFDOF [38] & - & 11,261 & 725 \\ \hline Motion Deblurring & GoPro [39] & - & 2103 & 1111 \\  & HIDE [40] & - & 0 & 2025 \\ \hline  & RESIDE-ITS [41] & \
\begin{tabular}{c} SOTS- \\ Indoor \\ \end{tabular} & 13,990 & 500 \\ Dehazing & RESIDE-OTS [41] &
\begin{tabular}{c} SOTS- \\ Outdoor \\ \end{tabular} & 313,950 & 500 \\  & Dense-Haze [42] & - & 45 & 5 \\  & NH-HAZE [43] & - & 45 & 5 \\  & NHR [44] & - & 16,146 & 1794 \\ \hline Enhancement & LOLv1 [45] & - & 485 & 15 \\  & LOLv2-real [46] & - & 689 & 100 \\  & LOLv2-syn [46] & - & 900 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset summary for four restoration tasks.
## 3 Problem Optimization
### Optimization of Transmit Power  
In this subsection, we find the value of \(n\) (\(n\leq N\)) to maximize the covert rate under the constraint as follows:  
\[\mathcal{D}(P_{F},P_{M})\leq 2\epsilon^{2}, \tag{22}\]  
Assuming that the UAV communicates with U2, the KL scatter \(\hat{\mathcal{D}}(P_{F},P_{M})\) can be expressed as  
\[\hat{\mathcal{D}}(P_{F},P_{M})=n\left[\ln\left(\frac{P+\sigma_{w}^{2}}{\sigma_ {w}^{2}}\right)-\frac{P}{P+\sigma_{w}^{2}}\right]. \tag{23}\]  
According to the limitation of the covert channel numbers, the optimal \(\mathcal{D}(P_{F},P_{M})\) and \(n\) can be assumed as  
\[n^{*}=N, \tag{24}\]  
and  
\[\hat{\mathcal{D}}(P_{F},P_{M})=n^{*}\left[\ln\left(\frac{P+\sigma_{w}^{2}}{ \sigma_{w}^{2}}\right)-\frac{P}{P+\sigma_{w}^{2}}\right]=2\epsilon^{2}. \tag{25}\]  
where \(n^{*}\) is the optimal value.  
According to [32], we can obtain the optimal solution of transmit power:  
\[P^{*}=\left(\sigma_{w}^{2}+P^{*}\right)\left[\ln\left(\frac{P^{*}}{\sigma_{w}^ {2}}+1\right)-2\epsilon^{2}N\right]. \tag{26}\]In Equation (26), as \(n=N\), the covert rate achieves the maximum value. From Equation (26), we can see that the \(P^{*}\) decreases as \(n\) increases. Traditionally, to ensure the covertness, we select PU1 and PU2, which are smaller than \(P_{\text{max}}\), rather than \(P_{\text{max}}\) as adopted in traditional systems. With the over-KL scatter \(\mathcal{D}(P_{F},P_{M})\) can be rewritten as  
\[\mathcal{D}(P_{F},P_{M})=N\Bigg{[}\ln\Bigg{(}1+\frac{P_{u_{2}}^{*}h_{w}^{2}}{P_ {u_{1}}h_{w}^{2}+\sigma_{w}^{2}}\Bigg{)}-\frac{P_{u_{2}}^{*}h_{w}^{2}}{\big{(} P_{u_{2}}^{*}+P_{u_{1}}\big{)}h_{w}^{2}+\sigma_{w}^{2}}\Bigg{]}. \tag{27}\]  
\(P_{u_{2}}^{*}\) is analogous to \(P^{*}\), and the same level of covert performance can be achieved by optimizing \(P_{u_{2}}^{*}\) when \(n^{*}=N\) is optimal.
## 3 Experimental Results and Analysis
### Energy Consumption Analysis  
In this study, the seismic performance of five specimens was mainly evaluated using the energy dissipation coefficient. The specific results are listed in Table 4. The calculation diagram is shown in Figure 10. The sketch of the calculation of the energy dissipation coefficient and Equation (2) is as follows [29].  
\[\psi=S_{ABCDA}/(S_{OBE}+S_{ODF}) \tag{2}\]  
A W-1 specimen was used as the control group. The energy dissipation coefficients of the five specimens were compared, as shown in Table 5. The observations in Table 5 show that the energy dissipation coefficient of the W-2 specimen was the largest and that of W-3 was the smallest. With the increase of the fiber doping, the energy dissipation capacity of the wall panel roughly showed the trend of first increasing and then decreasing. The incremental results of W-1, W-4, and W-5 indicated that the sandwich wall panels had a relatively exceptional heat dissipation capability.
# Exploiting Concurrent Wake-Up Transmissions Using Beat Frequencies
## 5 Experimental Results  
In this section, we present the results of the experiments that we performed to verify the assumptions made above. For the experiments, we utilized wireless sensor nodes as visualized in Figure 17a. Figure 17b illustrates the block diagram of the sensor node schematically. The antenna switch connects in an ultra-low-power listening state the antenna to the low-frequency wake-up receiver AS3932. After detecting a valid wake-up message, the AS3932 awakens the EFM32 microcontroller from the deep sleep state. The microcontroller controls the antenna switch and  
Figure 16: Overview of the concurrent wake-up flooding protocol. Here, we use the abbreviation WUC for a wake-up call, SYNC for the synchronization message sent by the initiator and CWUC for a concurrent wake-up call.  
connects the CC1101 communication transceivers to the antenna to be ready to transmit or receive communication messages.  
In combination with the passive demodulator and the 125-kHz wake-up receiver AS3932, the wireless sensor node has a wake-up sensitivity of around \(-51\,\mathrm{dBm}\). Supplied at \(3\,\mathrm{V}\), it has a power consumption of less than \(10\,\mathrm{\SIUnitSymbolMicro W}\) in ultra-low-power listening state. The AS3932 supports several data rates in the range from 1024 to 8192 bits per second. In the communication mode, the node has a sensitivity of around \(-100\,\mathrm{dBm}\), depending on the data rate.
# A Vision Detection Scheme Based on Deep Learning in a Waste Plastics Sorting System
## 3 Materials and Methods
### Multiple Object Tracking
#### 3.3.2 Deep Cosine Metric Learning and Optimization  
The cosine distance, also known as cosine similarity, is measured by calculating the cosine of the angle between two vectors. The higher the consistency of the pointing direction of two vectors, the higher the cosine similarity. When DeepSORT adopts cosine distance to associate appearance information, it uses CNN to extract features of tracking objects. The appearance feature set is established, and the relationship between the feature vectors is used to determine whether the detection results match the tracking results.  
The feature extractor employed by DeepSORT is a wide residual network [40]. It is trained on a pedestrian dataset and suitable for the pedestrian tracking task. To solve the plastics tracking problem and to keep the running cost as low as possible while ensuring tracking accuracy, ShuffleNetV2 [41] is used as the feature extractor in this paper. The network is trained offline on the plastics dataset so that it can better extract plastic features and thus optimize the cosine metric of the tracker.  
ShuffleNetV2 is a lightweight CNN with strong performance and applicability. The building blocks of this model are shown in Figure 5. Figure 5a shows the basic unit; the input of the feature channel is split into two branches by channel split. One branch is mapped equally, and the other branch performs two 1 \(\times\) 1 convolutions and one 3 \(\times\) 3 depthwise convolution. The two branches are concatenated after completing their respective operations, and then the channel shuffle operation is carried out. In this case, the number of input and output channels is the same, which can effectively reduce the memory access consumption time. The purpose of channel shuffle is to ensure feature information communication between different branches and improve the generalization of the model. Figure 5b shows the downsampling unit. All features are input into two branches, and a series of convolution operations are performed respectively. Features are output after concatenation and channel shuffle.  
The architecture of ShuffleNetV2 is a stack of the previously mentioned building blocks, since this paper does not perform classification but uses the network for object appearance feature extraction in the tracking task. Therefore, the output of the global pool is used as the feature mapping for the final output.  
Figure 5: Building blocks of ShuffleNetV2: (**a**) basic unit; (**b**) downsampling unit.
## 2 Materials and Methods
### Bread Preparation  
The bread production process goes through several stages: weighing and mixing the ingredients, molding, fermentation, and baking. All the ingredients, including the water, were weighed in this work. To make 2.5 kg of dough, 2.250 kg of Type 45 flour, 250 g of Type 70 flour, 125 g of Bavaria, 25 g of special Grampan, 2 kg of water, 45 g of salt (NaCl), 75 g of yeast (_S. cerevisiae_), and 75 g of olive oil were weighed. First, in a spiral mixer (model AEF012, Ferneto), the salt and flours were mixed for 3 min (min) on rotation I. After this time, 1.5 kg of drinking water (DW), bottled mineral water (BMW), or thermal water (TW) was added for another 3 min. At 6 min, the rotation was changed to position II. Two minutes after switching to rotation II, the olive oil was slowly added, followed by the remaining water, leaving it to beat until 6 min had passed. At 12 min, the yeast was added, and the dough was left to beat for another 6 min on rotation II. After this time, the dough rose for approximately 40 min. Once this step was completed, the dough was divided into two parts of roughly 2.750 kg, each of which resulted in 30 breads of approximately 90 g. After this step, the doughs were modulated and left to rest at a temperature of \(\pm\)30 \({}^{\circ}\)C for 30 min. At the end of this time, the breads were placed in an oven for 10 min at 41 \({}^{\circ}\)C and 77% humidity. Finally, the bread was baked for 13 min at a top temperature of \(\pm\)233 \({}^{\circ}\)C and a bottom temperature of \(\pm\)212 \({}^{\circ}\)C. Therefore, three bread formulations were developed: (1) biju bread with drinking water (B_DW), (2) biju bread with thermal water (B_TW), and (3) biju bread with bottled mineral water (B_BMW).  
Given the high sodium content observed in both BMW and TW (Table 1), which caused an increment in the levels of sodium in these samples, bread incorporated with these types of water was also prepared with no added salt, yielding two more samples, namely (4) B_TWns and (5) B_BMWns.
## 4 Conclusions  
The resulting MOF based on Cu-containing terephthalatic acid was characterized using XRD, SEM, and IR spectroscopy. In Cu-MOF, the existence of prismatic crystals 30 to 100 \(\upmu\)m long and 10 \(\upmu\)m thick was found. Copper terephthalate was used for the adsorptive removal of azo dyes from aqueous solutions (SY, TAR, Orange II, and Methyl Orange), as well as for the separation of food additives in real systems. The kinetic data are consistent with both first- and second-order pseudokinetic models. The Langmuir and Freundlich isotherm models were used to describe the adsorption isotherms. According to the correlation coefficients (R\({}^{2}\)), the Freundlich model fits the data better than the Langmuir model for SY and TAR dyes. The adsorption of the Orange II dye is better described by the Langmuir model. For Methyl Orange, both models are acceptable. Copper terephthalalate showed a satisfactory ability to adsorb azo dyes. The values of thermodynamic parameters (\(\Delta\)G\({}^{0}\), \(\Delta\)H\({}^{0}\), and \(\Delta\)S\({}^{0}\)) show that the dye adsorption process is spontaneous and exothermic; in the process of sorption, the number of degrees of freedom increases, which characterizes the good affinity of the sorbate with the sorbent. When studying real objects, it was determined that the sorbent under study makes it possible to separate food additives that can be used to detect these dyes and determine their concentrations.  
Conceptualization, V.A.Z. and I.E.U.; investigation, M.A.C. and N.S.K.; methodology, M.A.C.; validation, M.A.C.; visualization, N.S.K.; writing--original draft, V.A.Z. and I.E.U.; writing--review and editing, V.A.Z. and I.E.U. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### Ugr'16  
This dataset was created from several NetFlow v9 collectors located in the network of a Spanish ISP [10]. It is composed of two different types of datasets that are split in weeks. First, the calibration set contains real background traffic data, and second, the test data contain real background traffic and synthetically generated traffic data with well-known types of attacks. Due to the nature of the NetFlow data, payloads from the network traffic were not included. The types of attacks implemented in this dataset are Low-rate DoS, Port scanning, and Botnet traffic. Between 2017 and 2021, we found mixed methods from several published papers, such as [49; 50], [PERSON] et al. [49], who argued that conventional machine learning methods were ineffective and instead used stacking ensembles to improve performance and reliable predictions, while [50] proposed hybridized multi-model system to improve the accuracy of detecting the intrusion. [PERSON]. [51] addressed imbalanced data problems by producing synthetic data with the Generative Adversarial Network (GAN).
# A Transfer Learning Method for Pneumonia Classification and Visualization
## 3 Materials and Methods
### Performance Measures  
In a classification task, the results can be expressed in an especial matrix called Confusion Matrix (CM). In a binary classification task, a CM contains the following information: class of the instances, and the number of instances classified as true positives (tp) that are the ones correctly recognized of the class of interest; true negatives (tn) that are the number of instances correctly recognized that belong to the other class; false positives (fp) are instances that were assigned to the class of interest but do not belong to it; and false negatives (fn) that represent instances that were assigned to the class of interest but belong to the complementary or negative class [46]. Figure 5 represents the convention of CM used in this research.  
Figure 4: Xception model. (**a**) Shows the general structure of Xception model; (**b**) shows the detailed architecture of the original Xception model. After each convolution and separable convolution block, a batch normalization operation is performed. In a Conv block, for example, the first number indicates the size of the kernel, the second number indicates the size of the stride, and finally, the last number indicates the number of filters used in each specific block.  
Figure 5: Confusion matrix convention.  
A common method to evaluate the performance of a classification model is to use specific metrics measures based on the values contained in a CM. Accuracy is the most common measure used for binary classification and evaluates the overall effectiveness of a classifier. Accuracy is defined as follows:  
\[Accuracy=\frac{tp+tn}{tp+fn+fp+tn}. \tag{3}\]  
However, when working with an imbalanced dataset is common to use different performance measures in addition to the Accuracy. The most common are precision, recall (formally named sensibility), and the F1-Score. A graphic such as the reception operating characteristic curve (ROC curve) and the measure associated with it, the area under the curve (AUC) is also useful. These measures are defined below.  
\[Precision=\frac{tp}{tp+fp} \tag{4}\]  
\[Recall=\frac{tp}{tp+fn} \tag{5}\]  
\[F1-Score=\frac{2\,tp}{2\,tp+fp+tn}=2\frac{precision\times recall}{precision+ recall}. \tag{6}\]