esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### ML Classifiers for Intrusion Detection
#### 3.5.2 Recurrent Neural Network  
RNNs are improved feed-forward neural network models that have the capacity to memorize data at each step for subsequent outputs. In an RNN, neurons' output is linked to their own and other neurons' input. RNNs can therefore use their internal memory to represent data sequences and time series [29]. The following is a formalization of the standard RNN at time \(t\):  
\[\left\{\begin{aligned} h_{t}&=g\Big{(}x_{t}W^{(in, hi)}+h_{t-1}W^{(hi,hi)}+b^{(h)}\Big{)}\\ y_{t}&=g\Big{(}h_{t}W^{(hi,ou)}+b^{(y)})\end{aligned}\right. \tag{4}\]  
where \(x_{t}\) is the input sequence. The weight matrices for the input layer to the hidden layer, the hidden layer to the hidden layer, and the hidden layer to the output layer are denoted by \(W^{(\text{in},\text{in})}\), \(W^{(hi,hi)}\), and \(W^{(hi,ou)}\), respectively. In addition, \(g(\cdot)\) denotes the activation function, and \(b\) stands for the bias.  
The long short-term memory (LSTM) is a special type of RNN model for our model evaluation. The addition of the cell state to the LSTM network makes a difference compared to the regular RNN. The cell state will be sent to the subsequent cell with time \(t\), which stands for long-term memory. The cell individually chooses whether or not to forget throughout the calculation of the cell sequence, allowing it to retain past information for a considerable amount of time [30]. The LSTM hidden layer is developed using the following formula (Equation (5)) at time step \(t\):  
\[\left\{\begin{aligned} i_{t}&=\sigma\Big{(}x_{t}W^{(x, j)}+h_{t-1}W^{(h,j)}+c_{t-1}W^{(c,j)}+b^{(i)}\Big{)}\\ f_{t}&=\sigma\Big{(}x_{t}W^{(x,f)}+h_{t-1}W^{(h,f)}+c_{t-1}W ^{(c,f)}+b^{(f)}\Big{)}\\ c_{t}&=f_{t}*c_{t-1}+i_{t}*\tanh\Big{(}x_{t}W^{(x,c)}+h_{t- 1}W^{(h,c)}+b^{(c)}\Big{)}\\ o_{t}&=\sigma\Big{(}x_{t}W^{(x,\rho)}+h_{t-1}W^{(h, \rho)}+c_{t}W^{(\rho,\rho)}+b^{(o)}\Big{)}\\ h_{t}&=o_{t}*\tanh(c_{t})\\ y_{t}&=\text{SoftMax}(Wh_{t}+b)\end{aligned}\right. \tag{5}\]  
where at time step \(t\), \(i_{t}\) stands for the input gates, \(f_{t}\) stands for the forget gates, \(c_{t}\) stands for the memory cells, \(o_{t}\) stands for the hidden output, and \(y_{t}\) stands for the output layer.
# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 3 Experiments
### Comparison Experiments  
#### 3.2.1 Results on the Vaihingen Dataset  
The Vaihingen dataset is one of the most widely used datasets for the semantic segmentation of remote sensing imagery, and many advanced models have achieved good results on this dataset. As shown in Table 1, our method obtained the best F1 score (90.7%) and OA (91.3%), outperforming other lightweight networks. Moreover, our model obtained the highest F1 score on three categories (low vegetation, tree, and impervious surface); specifically, the category impervious surface was at least 1.1% higher than with other models. In addition, as shown in Figure 7, we visualized the comparison experiment results, and the areas that need to be focused on are shown in the red box, which further demonstrates the superiority of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Models** & **Backbone** & **Lowveg.** & **Tree** & **Car** & **Building** & **Impsurf.** & **MeanF1** & **OA** \\ \hline Fast-SCNN & – & 80.1 & 82.6 & 62.1 & 87.2 & 90.4 & 79.3 & 84.5 \\ ABCNet & ResNet18 & 84.5 & 89.7 & 85.3 & 95.2 & 92.7 & 88.5 & 90.3 \\ DANet & – & 82.3 & 87.4 & 64.5 & 93.6 & 90.1 & 82.1 & 87.2 \\ MANet & ResNet50 & 84.7 & 90.2 & **88.6** & 95.4 & 93.0 & 90.4 & 91.0 \\ A2 FPN & ResNet18 & 80.7 & 87.2 & 71.5 & 93.3 & 93.2 & 86.3 & 89.7 \\ EANet & ResNet18 & 83.2 & 89.1 & 80.2 & 94.5 & 91.6 & 87.6 & 90.1 \\ DC-Swin & Swin-Tiny & 85.3 & 90.3 & 87.8 & **96.2** & 93.4 & 90.5 & 91.2 \\ BANet & ResT-Lite & 83.7 & 89.9 & 86.8 & 95.4 & 92.3 & 89.6 & 90.6 \\ SwinUNet & Swin-Tiny & 84.3 & 88.7 & 85.3 & 95.1 & 93.3 & 89.1 & 90.4 \\ VPANet & ResNet50 & 80.2 & 85.6 & 72.4 & 93.1 & 92.5 & 85.7 & 89.2 \\ SegFormer & MiT-B1 & 80.7 & 87.2 & 71.5 & 93.3 & 93.2 & 86.9 & 90.3 \\ UNetFormer & ResNet18 & 84.9 & 90.1 & **88.3** & 95.3 & 92.7 & 90.4 & 90.9 \\ Our model & ResNet18 & **85.6** & **90.6** & 87.9 & 95.8 & **94.5** & **90.7** & **91.3** \\ \hline \end{tabular}
\end{table}
Table 1: Quantitative evaluation results on the Vaihingen test set. The highest values are marked in bold.  
Figure 7: The test results on the Vaihingen dataset.
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 2 Preliminaries
### Privacy Amplification  
Generally speaking, we will use two-universal hash function for privacy amplification. The definition of two-universal hash function is as follows:  
**Definition 2** (Two-universal hash function).: _Let \(\mathcal{F}\) be a cluster of functions \(f:\{0,1\}^{n}\rightarrow\{0,1\}^{l}\) (\(l\leq n\)). If for all \(x\
eq y\in_{R}\{0,1\}^{n}\), we have:_  
\[Pr_{f\in_{R}\mathcal{F}}[f(x)=f(y)]\leq 2^{-l}.\]  
_Then we say that \(\mathcal{F}\) is two-universal._Using two-universal hash function for privacy amplification, we also have privacy amplification theorem [2].  
Firstly, we know the security of a key is defined with respect to its L1-distance from a perfect key which is uniformly distributed and independent of the adversary's state. Then the L1-distance from uniform of \(\rho_{XQ}\) given Q is :  
\[\mathrm{d}(\rho_{XQ}|Q):=||\rho_{XQ}-\rho_{U}\otimes\rho_{Q}||\]  
where \(\rho_{U}\) is the fully mixed state.  
**Theorem 2** (Privacy amplification [25]).: _Given a set of two-universal hash functions \(\mathcal{F}:\{0,1\}^{n}\otimes\mathcal{R}\rightarrow\{0,1\}^{l}\), and a hash function \(F\in_{R}\mathcal{F}\), let \(\rho_{XQ}\) be a classical-quantum state, then for any \(\varepsilon\geq 0\). we have:_  
\[\mathrm{d}[F(X)|F,Q]\leq 2^{-\frac{1}{2}\left[H_{\min}^{\varepsilon}(X|Q)-l \right]-1}+\varepsilon.\]
## 3 Experimental Setup and Measured Results in the Salt Mine
### Node Deployment Analysis in the Mine  
#### 3.2.1 Node Position  
The propagation model presented in this study mainly focuses on node location and node separation in underground mines. To determine these metrics, it was important that the Transverse Electric (TE) and Transverse Magnetic (TM) modes of propagating waves were controlled. The sensor nodes were placed at the 1/10 th distance from the mine roof ceiling and at the side walls in order to find a suitable node position. The following four scenarios of node position were used during the experiments: P(1,1), when both Tx and Rx were placed at the roof ceiling; P(1,2), when Tx was placed at the roof ceiling and Rx was placed at the side wall; P(2,1), when Tx was placed at the side wall and Rx was placed at the roof ceiling; and P(2,2), when both Tx and Rx were placed at the side wall. Figure 6c,d shows the trend for RSSI and the modes of propagation versus the node separation derived from Equation (12). There was only a 6% mean variation in RSSI values for the node position matrix when monopole omnidirectional antennas were used. A separation distance of 110 m was achieved between the sensor nodes for single-hop communication after continuous experimentation over 3 weeks. The higher modes of propagation attenuated after a 50 m separation distance between the sensor nodes was established, which reduced the impact of reflected waves on the overall propagation link performance and provided the chance to establish a link for larger node separations (see Figure 6d).  
Figure 5: Multi-hop links between stationary nodes in (**a**) tunnel and (**b**) room and pillar mining methods.
## 3 Results and Discussion  
### Color Analysis (L*a*b*) and Browning Index  
Color is an important quality parameter of apples and influences consumers [13], stimulating appetite and affecting the desirability of the product [10]. The apple pulp color varies among different varieties and undergoes immediate changes when cut and exposed to air, primarily due to enzymatic browning [24]. During the drying process, enzymatic browning, Maillard reactions, ascorbic acid oxidation and pigment degradation can cause color alteration in the apple [25], which can be temperature-dependent. Surface browning is one of the most visible alterations in color and is linked to a decrease in lightness \(L^{\star}\) and an increase in the values of \(a^{\star}\) (greenness) and \(b^{\star}\) (yellowness) [26]. These color changes can be quantitatively measured using indicators such as \(\Delta\)E and BI (Table 1).  
The drying process reduces lightness (\(L^{\star}\)), with the highest loss observed in OD85\({}^{\circ}\), in the samples dried by us. This is expected due to browning reactions (e.g., Maillard reactions) and pigment degradation at higher temperatures. In relation to the \(a^{\star}\) parameter, fresh samples are particularly greenish, but this decreases with the drying process. OD85\({}^{\circ}\) shifts to a slightly red tone, likely due to chemical changes caused by heat. The yellow tone (\(b^{\star}\)) increases in all dried samples, with OD65\({}^{\circ}\) and OD85\({}^{\circ}\) showing similar intense yellow. This might be related to caramelization or other heat-induced reactions (Tables S1-S6).  
The absolute color difference \(\Delta\)E is quite similar in SD and OD65\({}^{\circ}\) samples, which is probably related to the utilization of the same drying temperature. The greatest deviation is observed in OD85\({}^{\circ}\), showing that higher drying temperatures lead to more pronounced visual changes (Tables S7 and S8).  
The browning index is used to characterize the overall changes in browning color and is one of the most common indicators in food products containing sugar [27]. Browning of apples results from both enzymatic and non-enzymatic reactions and is related with cultivar [28]. In our work BI increases the same way as \(\Delta\)E, fresh \(<\) SD \(<\) OD65\({}^{\circ}\)\(<\) OD85\({}^{\circ}\). A big difference can be observed between the commercial and the other dried samples in the BI values. Again, SD and OD65\({}^{\circ}\) have similar results and all the other samples are significantly different, which is also represented in BI/BI\({}_{0}\), compared with fresh apples (Tables S9-S12).
## 4 Experiments and Results
### Results for Redundancy, Overhead, and Delay
#### 4.5.1 Redundancy Analysis (Duplicate Packets)  
The increased Redundancy observed with node density in baseline methods (RL-PT, NFRL, and D-FL+RL) results primarily from the aggressive or indiscriminate broadcasting decisions triggered by static or less-adaptive decision mechanisms. Specifically, the Redundancy spike for RL-PT occurs due to frequent shifts in parameter weighting, causing nodes to make broadcast decisions independently without considering global redundancy states, leading to overlapping broadcast zones and repeated coverage.  
NFRL's high Redundancy under dense scenarios stems from the neural-fuzzy approximation's slow convergence and occasional overshooting of optimal broadcast thresholds. The neural network, despite effective training, sometimes misinterprets sudden density increases, causing excessive transmissions until parameters are retrained and stabilized. Similarly, D-FL+RL faces Redundancy challenges because its single-profile fuzzy inference approach, while adaptable, still lacks the granularity needed to differentiate effectively among diverse node roles under highly congested environments.  
Conversely, the superior Redundancy performance of our Proposed Method B is attributed to its adaptive multi-profile structure. Each specialized profile (P1, P2, and P3) explicitly tailors broadcast decisions based on unique subsets of network metrics (e.g., Residual Energy, Number of Neighbors, and Mobility). For example, Profile P2 dramatically reduces Redundancy by prioritizing parameters such as Residual Energy and neighbor count during periods of congestion, strategically limiting node participation based on energy constraints, and reducing the number of unnecessary broadcasts. Thus, Redundancy remains systematically low due to the dynamic selection of optimal decision metrics rather than simple broadcast probability adjustments.
## 3 Detection in Receiver
### SVM Detector
#### 3.2.3 The Detection Procedure of SVM  
Based on above elaborations, we have taken full advantage of the EBPSK waveform and established a quantitative feature set \(\text{Fl}=[f_{1},f_{2},f_{3},f_{4}]\) for long bit duration and \(\text{Fs}=[f_{1},f_{2},f_{3}^{\prime},f_{4}]\) for short bit duration, respectively, which is dedicated to separate the symbols \"0\" and \"1\". Then, the detection of received signals can be formulated as determining a separating hyper-plane which divides the two-group pattern objects in a multidimensional features space, under the minimum classification errors. The SVM technique for EBPSK signal detection can be depicted as below:  
Step 1: Features selection.  
An appropriate selection of discriminant features is carried out in order to determine the best performing features for the signal detection as Fl and Fs. By using the method, the original higher-dimensional inputs (the number of sampling points is M) will be transformed into lower-dimensional features. In our scheme, four remarkable features are chosen to separate the symbols \"0\" and \"1\".  
Step 2: Scaling.  
Scaling before applying SVM is very important. The main advantage of scaling is to avoid attributes in greater numeric ranges dominating those in smaller numeric ranges. Another advantage is to avoid numerical difficulties during the calculation. Because kernel values usually depend on the  
Figure 5: The SIF output of symbol “1” and “0”, respectively, in (**a**) and (**b**) for \(\text{SNR}=0\) dB.  
inner products of feature vectors, e.g., the linear kernel and the polynomial kernel, large attribute values might cause numerical problems. We use linearly scaling each attribute to the range [0,1].  
Step 3: The training phase.  
The initial training stage only needs to be performed once unless the channel condition has varied significantly. Some training examples are given to the machine to create certain decision functions in order to differentiate the different types of objects, or so-called classes.  
Step 4: The testing phase.  
During the testing stage, the SVM detector is ready for estimating the source bit based on classifying an unforeseen object, which is a new noisy data stream, and then classified by those decision rules. The detection task then becomes a pattern classification problem. The transmitted message bit is estimated by making a hard-decision from the decision function formed earlier in (7). The complexity of the SVM on the testing stage is independent on the number of symbols, but rather on the number of features per SV.
## 3 Results and Discussion
### Carbon Dioxide Emissions during Transporting of the Building Materials  
Carbon dioxide emissions associated with transporting building materials from the manufacturer's location to the construction site were calculated based on the fuel efficiency of each vehicle, and the distance between the manufacturing place and the construction site. The total carbon dioxide emissions from the transporting building materials were 24,381, 23,428, and 22,657 kg CO\({}_{2}\) eq. for the ordinary reinforced concrete slab, flat plate slab, and voided slab systems, respectively. As to the lower total CO\({}_{2}\) emissions in the voided slab system, which involves two more types of transportation than the other slab systems, the emissions of CO\({}_{2}\) from transporting building materials were less than those of the ordinary reinforced concrete slab and flat plate slab systems. In this study, it was interesting that the carbon dioxide emissions for transporting the void formers were larger than for the forms, even though the unit weight of void former (i.e., approximately 1.6 kg/void former) was relatively lighter than that of the other building materials. This was attributed to the height of void formers being 0.9 m, which required more vehicles to convey them to the construction site. Due to the road traffic act in South Korea, the maximum clearance of each vehicle was 4.5 m, so the number of lorries for the void formers was larger than for the other materials.  
In this study, the emissions of carbon dioxide from the transporting of building materials were calculated by two different approaches. The first method was to compute the CO\({}_{2}\) emissions based on the fuel efficiency of vehicle type, and the distance of location between the manufacturer and the construction site. The other approach was to determine the carbon dioxide emissions for transportation based on the operating time of vehicles by the vehicle logs of operation. Table 9 shows that the emissions of carbon dioxide had slightly different value, depending upon the calculation methods. For ready-mixed concrete, the CO\({}_{2}\) emissions from calculation by vehicle operation time indicate more than the former method. The distance from ready-mixed concrete supplier to the construction site was 25 km, which was relatively shorter than that of the other building materials. However, the transporting time for the ready-mixed concrete was influenced by traversing the central district area, and it would  
Figure 6: Carbon dioxide emissions of building materials.  
waste more time to convey the building materials, compared to the others in this study. Moreover, the transporting quantity of the ready-mixed concrete was unable to be altered, since it was delivered by the standardised size of lorries (i.e., the Ready-mixed concrete mixer capacity is 5 m\({}^{3}\) in standardised size). On the other hand, different sized vehicles were able to be used flexibly for other building materials, depending upon the situation at the construction site.
# Generating Bessel-Gaussian Beams with Controlled Axial Intensity Distribution
## 2 Modeling
theory correlate well with the ray model. It is important to note that the length and position of the Bessel zone directly depend on the parameters of the amplitude mask: the left boundary along the propagation direction is determined by the size of the inner aperture of the mask, and the right boundary is determined by the outer aperture of the mask. The effective length of the Bessel zone is defined as the difference between the positions corresponding to the outer and inner boundaries of the amplitude mask.
# Influence of Recycled Precast Concrete Aggregate on Durability of Concrete's Physical Processes
## Materials and Methods
### Experimental Programme  
The SCC is mixed in a vertical shaft concrete mixer, following the procedure described in ASTM C192 [30]. The mix procedure is as follows: first, the mixer is wetted, the coarse aggregates are added and half the water of the mixture is poured in. Next, the finest materials, cement and filler, and the other half of the water are introduced. A first mixing of 3 min is carried out and the mixture is left to rest for 3 min. Finally, a mixing of 2 more minutes is carried out. After this time, the mixing process is finished.  
The specimens were manufactured according to the specifications of the UNE-EN 12350-1 standard [31]. The dimensions of the specimens are cubic specimens of \(10\times 10\times 10\) cm\({}^{3}\) and prismatic specimens of \(7.5\times 7.5\times 27.5\) cm\({}^{3}\). After demoulding, all the specimens were transferred to a humidity chamber where they were cured at a temperature of \(20\pm 2\)\({}^{\circ}\)C and a relative humidity of 95% for 28 days, following the recommendations of the UNE-EN 12390-2 standard [32].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Material** & **HR-30-0\% Control** & **HR-30-20\%** & **HR-30-50\%** & **HR-30-100\%** &
\begin{tabular}{c} **HR-45 0\%** \\ **Control** \\ \end{tabular} & **HR-45-20\%** & **HR-45-50\%** & **HR-45-100\%** \\ \hline RA 4/12.5 & 0 & 250 & 540 & 1040 & 0 & 250 & 540 & 1040 \\ Sand 2/12.5 & 1150 & 920 & 540 & 0 & 1150 & 920 & 540 & 0 \\ Sand 0/2 & 650 & 650 & 670 & 720 & 650 & 650 & 670 & 720 \\ Limestone filler & 320 & 320 & 320 & 320 & 280 & 280 & 280 & 280 \\ CEM I-52.5 R & 250 & 250 & 250 & 250 & 320 & 320 & 320 & 320 \\ Water & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 & 112 \\ Sika 20 HE & 0.50 & 0.50 & 0.65 & 0.85 & 0.50 & 0.50 & 0.65 & 0.85 \\ Sika 5920 & 1.30 & 1.30 & 1.60 & 2.00 & 1.50 & 1.80 & 2.20 \\ m/\({}_{\rm eff}\) & **0.40** & **0.39** & **0.35** & **0.32** & **0.31** & **0.30** & **0.28** & **0.25** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mix proportions of self-compacting concrete (SCC) (kg/m\({}^{3}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**SCC HK-50** & **[MP]** & **[MP]** & **[MP]** & **[MP]** & **[MP]** \\ \hline HR-30-0\% & 49.09 & 12.00 & HR-45-0\% & 63.36 & 5.28 \\ HR-30-20\% & 49.98 & 7.50 & HR-45-20\% & 64.13 & 4.47 \\ HR-30-50\% & 55.34 & 10.35 & HR-45-50\% & 66.82 & 7.04 \\ HR-30-100\% & 56.75 & 3.27 & HR-45-100\% & 72.81 & 8.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of compressive strength and standard deviation \(\sigma\).
###### Abstract  
The leakage problem of supercritical carbon dioxide (SCO2) axial-inflow turbine brings great challenges to the efficiency and security of the power system. Labyrinth seals are usually utilized to improve the leakage characteristics of the blade tip. In this paper, a 1.5-stage SCO2 axial-inflow turbine is established and labyrinth seals are arranged on the top of the first stage stator and rotor blades. The effects of seal clearance, groove on seal cavity surface and circle groove shape on flow characteristics and aerodynamic performance under different pressure ratio are investigated. Increasing seal clearance can significantly weaken the turbine performance. Arranging rectangle, circle and V-shaped grooves on the seal cavity surface near the outlet of the seal gap can enhance the energy dissipation, reduce the relative leakage and improve the power and efficiency. Increasing the groove width can improve the aerodynamic performance while the effect of the groove depth is weak. The configuration where the circle groove width is 50% of the pitch of seal tooth achieves the best performance with the relative leakage of stator1 and rotor, power and efficiency of \(6.04\times 10^{-3}\), \(8.09\times 10^{-3}\), \(3.467\) MW and \(86.86\%\) respectively. With an increase in pressure ratio, the relative leakage increases firstly and then remains almost constant. The power increases while the efficiency increases firstly and then decreases, reaching the peak value under the design condition.  
supercritical carbon dioxide; axial-inflow turbine; labyrinth seal; flow characteristics; aerodynamic performance +
Footnote †: journal: Applied Sciences