esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Comparison
### Effects of the Models with/without FSI  
Figure 9a-c presents the \(C_{L}\) for Risers 1, 2 and 3 with/without FSI while Figure 9d-f illustrates the \(C_{D}\) for Risers 1, 2 and 3 with/without FSI.  
As can be seen in Figure 9, FSI affects all the risers' \(C_{D}\) and \(C_{L}\). In general, the FSI has greater effect on \(C_{L}\) than \(C_{D}\). From the graph of \(C_{D}\), the curve of \(C_{D}\) ascends initially, then descends, after that rises again, and at last tends to be stable. Through the development of vortex shedding, it is found that the reason for the change of the \(C_{D}\) curve is that, due to the impingement of the water flow, the riser is subjected to a large resistance in the forward direction of the \(X\)-axis for 0.4 s (Figure 10a). After 0.4 s, a pair of symmetrical vortices begins to form, and these symmetrical vortices develop steadily until 8 s (Figure 10b). Because of the existence of the vortices behind the riser, the riser is subjected to a negative force along the \(X\)-axis. Hence, the resistance of the riser along the \(X\)-axis is reduced and the \(C_{D}\) curve decreases. After 8 s, the symmetric tail vortices begin to fall off gradually, and the \(C_{D}\) begins to increase until 14 s (Figure 10c). When one of the symmetric vortices completely falls off, the \(C_{D}\) returns to a relatively large value.  
Figure 9: \(C_{L}\) and \(C_{D}\) of the risers with/without coupled fluid–structure interaction. (**a–c**) Presents the \(C_{L}\) for Risers 1, 2 and 3 with/without FSI; (**d–f**) illustrates the \(C_{D}\) for Risers 1, 2 and 3 with/without FSI.
# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 5 Conclusions and Future Directions  
Three-dimensional hand pose estimation is a very popular research field and, thanks to the emergence of deep learning, recent approaches have seen a significant breakthrough. Earlier methods, due to hardware limitations and smaller amount of available training data, required a very careful design, targeted on the specific dataset that they were employed. Recent DNN-based methods are more powerful, efficient, and have better generalization capabilities than their predecessors.
## 5 Construction of Secure and Efficient Private Image Matching (SEPIM)
### Secure and Efficient Private Image Matching  
The implementation of SEPIM utilizes homomorphic encryption to evaluate similarity. The main steps are highlighted in Algorithm 3. To match her private image, _[PERSON]_ goes into two rounds. In the first round, she encrypts her feature vector set and sends them to _[PERSON]_. _[PERSON]_ receives _[PERSON]'s_ encrypted vectors. As an example shown in Figure 3, _[PERSON]_ begins with the top level of the _Treelndex_ when undertaking a search request, subsequently moving to leaf level. At each level, _[PERSON]_ discovers the entry points of the subsequent level as he moves through the levels, through finding the node which has a vector \(VF_{hi}\) of lowest cosine distance, calculated by a secure dot product to the search image vector.  
When he reaches the leaf level, (i.e., level \(h\)-\(n\) (see Figure 3), _[PERSON]_ will acquire the indexes of images which have the best relevances with the image used in the search enquiry. In other words, _[PERSON]_ will get all vectors \(\overrightarrow{\{F_{i}\}}_{1\leq l\leq M}\) associated with the leaf of _Treelndex_. Finally, he employs the secure_dot_product subroutine (as explained in Algorithm 4 ) to return the dot product matrix of the input vector set and the feature vector set \(\overrightarrow{\{F_{i}\}}_{1\leq l\leq M}\) of each image that have top lower distances with the search request. The details of the above subroutine are explained in Section 4.3. Without loss of generality and to make the presentation clearer, we assume that all of _[PERSON]'s_ images have the same number \(p\) of descriptors. In the second round, _[PERSON]_ uses her private key to decrypt the dot product terms and obtains the actual values, which will be employed in assessing the similarity scores as explained in Algorithm 1.  
Figure 3: Image Search Example.
## 1 Introduction
### _Power efficiency_  
The sensor networks should function for as long as possible since it may be inconvenient or impossible to recharge node batteries. Therefore, all aspects of the node, from the hardware to the protocols, must be designed to be extremely energy efficient. In this paper, power efficiency is defined as the mean number of \"one-hop\" sink (sink to sensor node is one-hop) as each sensor node.
# Design and Implementation of an Indoor Localization System Based on RSSI in IEEE 802.11 ax
## 4 Discussion
However, the Maximum Likelihood algorithm requires more nodes to achieve this level of accuracy, which may increase the system complexity and resource usage. On the other hand, algorithms like trilateration and Min-Max, with just three nodes, provide a reasonable approximation of the real position of the unknown node. Although the error inX, Y, and the position is higher compared to that with the Maximum Likelihood algorithm, they still offer a practical and efficient solution for many localization tasks, especially in scenarios with fewer nodes or limited computational resources.
# Design and Additive Manufacturing of TPMS Heat Exchangers
## 2 Design and Modeling of TPMS HXs
### Design Procedures of TPMS HXs
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Lattice** & **Lattice Unit** & **Surface** & \multirow{2}{*}{**Flow Rate**} & **Heat Transfer** & **Pressure** & **HTC\(\times\)A** & **HTC** \\  & **Type** & **Length** & **Area** & **Power** & **Drop** & **W/K** & **W/(K\({}^{\star}\)m\({}^{2}\))** \\ \hline
1 & Gyroid & \(5\times 5\times 5\) & 0.037 & 0.1 & 7.15 & 1768 & 167 & 4523 \\ \hline
2 & Gyroid & \(5\times 5\times 5\) & 0.037 & 0.2 & 9.96 & 5750 & 208 & 5629 \\ \hline
3 & Gyroid & \(5\times 5\times 5\) & 0.037 & 0.5 & 17.35 & 30,253 & 338 & 9122 \\ \hline
4 & Gyroid & \(5\times 5\times 5\) & 0.037 & 2 & 44.58 & 435,583 & 820 & 22,169 \\ \hline
1 & Diamond & \(6\times 6\times 6\) & 0.039 & 0.1 & 7.5 & 1707 & 177 & 4545 \\ \hline
2 & Diamond & \(6\times 6\times 6\) & 0.039 & 0.2 & 9.8 & 5860 & 204 & 5231 \\ \hline
3 & Diamond & \(6\times 6\times 6\) & 0.039 & 0.4 & 14.3 & 21,520 & 278 & 7125 \\ \hline
4 & Diamond & \(6\times 6\times 6\) & 0.039 & 2.0 & 46.0 & 499,229 & 844 & 21,648 \\ \hline
1 & SplitP & \(7.5\times 7.5\times 7.5\) & 0.041 & 0.1 & 7.2 & 2365 & 181 & 4406 \\ \hline
2 & SplitP & \(7.5\times 7.5\times 7.5\) & 0.041 & 0.2 & 10.5 & 7922 & 233 & 5693 \\ \hline
3 & SplitP & \(7.5\times 7.5\times 7.5\) & 0.041 & 0.4 & 15.2 & 27,843 & 313 & 7625 \\ \hline
4 & SplitP & \(7.5\times 7.5\times 7.5\) & 0.041 & 2.0 & 51.3 & 620,486 & 964 & 23,508 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results for different lattices. Figure 4: HX-40 core infilled with different lattices. Figure 5: HTC\(\times\)A of the HX under different input pressure drops. Figure 6: Definition of gyroid lattice parameters. Definition of the gyroid unit length and lattice wall thickness (**Left**) and surface to volume ratio for gyroid lattice with different unit length (**Right**). Figure 7: Modeling and design flowchart for TPMS HX.
## 4 Conclusions  
The RAP mortars containing six RAP sources blending one soft binder at three aging states were investigated with respect to their stiffness and m-values at three minimum test temperatures of \(-6\)\({}^{\circ}\)C, \(-12\)\({}^{\circ}\)C, and \(-18\)\({}^{\circ}\)C. The following conclusions can be drawn:  
1. For the sieved RAP containing a high percentage of aged binder over 15% it was generally not easy to conduct the BBR test, and thus it was recommended to use a low-aged binder of less than 15%.
2. The conducted BBR tests for RAP mortars were effective and no modifications were needed to test RAP mortars, which were combined with sieved fine RAP and asphalt binder.
3. The stiffness values and m-values at 60 s from BBR tests could be utilized to explore the minimum low temperatures based on the stiffness value of 300 MPa and m-value of 0.300.
4. RAP mortar with a higher aged binder content had a higher minimum low temperature regardless of RAP source. RAP mortars with virgin soft binder had the best low temperature resistance followed by the RAP mortars with RTFO and PAV binders.
5. The source of RAP did not play a crucial role in determining the low temperature performance characteristics of RAP mortars.  
Figure 10: Minimum low temperatures of RAP mortars in terms of aged binder percentage and binder aged states, (**a**) 5% aged binder; (**b**) 10% aged binder; (**c**) 15% aged binder.  
**Author Contributions:** [PERSON] and [PERSON] set up the experimental designs and wrote the paper; [PERSON] summarized tested data and contributed the data analysis; [PERSON] performed the tests.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Effects of Temperature on the Variations in Carbon Assimilation and Fluorescence Emission  
For the temperature response of CO\({}_{2}\) assimilation, soybean \(A_{\rm G}\) and g\({}_{\rm sw}\) showed a bell-shaped response to leaf temperatures (\(T_{\rm leaf}\)), with an optimum temperature (\(T_{\rm opt}\)) at around 25 \({}^{\circ}\)C (Figure 4a,b). The temperature response curves of soybean \(\Phi_{\rm PSII}\) were also symmetrical but more shallow, showing that the potential range of \(T_{\rm opt}\) for soybean \(\Phi_{\rm PSII}\) was likely broader (Figure 4e). Soybean NPQ changed with changing \(T_{\rm leaf}\) in an opposite direction to \(A_{\rm G}\) (Figure 4f), exhibiting a bowl-like shape, and the net effect of \(A_{\rm G}\) and NPQ on fluorescence flux tended to diminish. As a result, TChIF\({}_{760}\) and F\({}_{\rm s}\) of soybean appeared to be sensitive to varying leaf temperatures (\(T_{\rm leaf}\) < 30 \({}^{\circ}\)C) when the light intensity was fixed (Figure 4i,j). Maize showed higher \(T_{\rm opt}\) and CO\({}_{2}\) fixation rates at \(T_{\rm opt}\): \(A_{\rm G}\) and \(\Phi_{\rm PSII}\) of maize increased rapidly with increasing \(T_{\rm leaf}\) below \(T_{\rm opt}\), and they tended to stabilize or slightly decrease at high \(T_{\rm leaf}\) (Figure 4c,g). The temperature dependency of maize NPQ also followed a bowl-shaped optimum curve, but with a more asymmetrical pattern than observed for soybean (Figure 4h). At variable \(T_{\rm leaf}\) and a fixed light intensity, TChIF\({}_{760}\) accounted for more than 92% of the variability in \(A_{\rm G}\) (Table 2). By contrast, F\({}_{\rm s}\) showed an unstable predictive power, accounting for 89% of the variability in soybean \(A_{\rm G}\), compared with 71% for maize \(A_{\rm G}\) (Table 2). This variation was possibly due to the fluorescence measurements probing a small leaf area, while gas exchange techniques provide an integrated measurement over an entire leaf.  
Figure 4: Variation in gross carbon assimilation (\(A_{\rm G}\), \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\), (**a**,**c**)), stomatal conductance to water vapor (\(g_{\rm sw}\), mol m\({}^{-2}\) s\({}^{-1}\), (**b**,**d**)), quantum efficiency of photosystem II (\(\Phi_{\rm PSII}\), (**e**,**g**)), nonphotochemical quenching (NPQ, (**f**,**h**)), total fluorescence radiance at 760 nm measured from both sides of leaves (TChIF\({}_{760}\), mW m\({}^{-2}\) nm\({}^{-1}\) sr\({}^{-1}\), (**i**,**k**)), and light-adapted steady-state PAM fluorescence (F\({}_{\rm s}\), (**j**,**l**)) with leaf temperature (\(T\), \({}^{\circ}\)C) measured on three leaves of soybean (left two columns) and maize (right two columns) under irradiance of 800 (soybean) and 1200 (maize) \(\mu\)mol m\({}^{-2}\) s\({}^{-1}\). The three different colored lines represent three individual samples. \(\eta_{T}\) = 6 for each leaf.
# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results  
### Sat_SD2023 Dataset  
The dataset Sat_SD2023 is a satellite signal detection dataset proposed by mapping the simulated environment of the actual background based on the recently received satellite data in a certain location; it comprises 2100 simulated signal spectrograms. The distribution range of the SNR is \(-15\) dB to \(15\) dB, with an interval of \(5\) dB. The simulation experiments in the MATLAB environment utilized parameters consistent with those observed in the real environment. The constant presence of wireless 3G and 4G signals in the background environment was simulated using the MATLAB Communications Toolbox. The channel environment considered Gaussian noise and Rayleigh fading. The parameters for the dataset simulation and experiments are detailed in Table 1.  
For Sat_SD2023 datasets, we manually drew the ground truth bounding boxes using Labelme v5.1.0 (available at [[https://gitcode.com/wkentaro/labelme/overview](https://gitcode.com/wkentaro/labelme/overview)]([https://gitcode.com/wkentaro/labelme/overview](https://gitcode.com/wkentaro/labelme/overview)), accessed on 15 November 2022). The annotated files were then converted into the PASCAL VOC format [49] for ease of validation. Our experiments divided the entire dataset into three parts, allocating 60% for the training dataset, 20% for the testing dataset, and 20% for the validation set used during inference detection. The training and testing sets were used to train the model, while the validation set was used to evaluate the performance of the model. As for the validation set, the spectrograms were not labeled but manually inspected in order to evaluate the effectiveness of our method. During training, the annotated spectrogram was input into the TATR network, and the network parameters were updated through backpropagation until the training round was reached. The experiments were conducted in the Windows environment, utilizing the PyTorch open-source framework for both training and testing. The GPU was RTX3090 (24 G) from NVIDIA Corporation in Santa Clara, California.
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 2 System Model
### Downlink Data Transmission  
In the downlink, the APs transmit the signals to the users. The downlink signal received at user \(u\) is  
\[y_{u}^{Dl}=~{}~{}\sum_{a=1}^{A}h_{ua}^{H}x_{a}+n_{u} \tag{7}\]  
where \(n_{u}\sim\mathcal{N}\big{(}0,\sigma^{2}\big{)}\) is the receiver noise and \(x_{u}\) is the signal transmitted by AP \(a\), given as  
\[x_{u}=~{}~{}\sum_{i=1}^{U}G_{ia}w_{ia}\psi_{i} \tag{8}\]Here, \(G_{ia}w_{ia}\) is the effective transmit precoding; it is non-zero if AP \(a\) transmits to user \(i\). Otherwise, it will be zero.  
\[G_{ia}w_{ia}=\left\{\begin{array}{cc}w_{ia}&aeA_{i}\\ 0_{N}&a\
otin A_{i}\end{array}\right\} \tag{9}\]  
where \(A_{i}\) is the subset of APs that serve a user \(i\). Equation (4) can be written equivalently as follows, by taking the summation over the user set \(U_{a}\) served by AP \(a\):  
\[x_{u}=\sum_{i\in U_{a}}w_{ia}\psi_{i} \tag{10}\]  
Thus, the received signal becomes  
\[y_{u}^{DI}=\sum_{i=1}^{U}h_{i}^{H}G_{i}w_{i}\psi_{i}+n_{u} \tag{11}\]  
where \(h_{u}=\left[h_{ul}^{T}\ldots\ldots h_{uA}^{T}\right]^{T}\) is the collective channel from all APs to a user \(u\). \(G_{i}=diag(G_{i1}\ldots\ldots G_{iA})\) is the diagonal matrix which identifies which APs transmit to which user and \(w_{i}=\left[w_{1}^{T}\ldots\ldots w_{iA}^{T}\right]^{T}\) is the collective precoding vector assigned to user \(i\).  
The achievable sum spectral efficiency is given by  
\[SE_{u}=\frac{l_{d}}{l_{c}}log_{2}(1+\ SINR_{u}) \tag{12}\]  
where \(SINR_{k}\) is the signal-to-interference-plus-noise ratio achieved by the \(k\)th user, given by  
\[SINR_{u}=\frac{|E\{h_{i}^{H}G_{u}w_{u}\}|^{2}}{\sum_{i=1}^{K}E|h_{i}^{H}G_{u}w _{u}|^{2}-|E\{h_{i}^{H}G_{u}w_{u}\}|^{2}+\sigma^{2}} \tag{13}\]
## 3 Results
### Environmental Impact Assessment  
The environmental impact indicators were determined using energy consumption data from the 2023/2024 academic year (103.42 MWh of electricity and 296.77 MWh of natural gas), focusing exclusively on the operational phase.  
The simulation, conducted using the OneClick LCA software ([[https://oneclicklca.com/](https://oneclicklca.com/)]([https://oneclicklca.com/](https://oneclicklca.com/))), yielded a global warming potential of 120,199.82 kg CO\({}_{2}\)e/year and 27.09 kg CO\({}_{2}\)e/m\({}^{2}\)/year for the facility. Of this, 46,007.57 kg CO\({}_{2}\)e was attributed to electricity consumption, while 74,192.25 kg CO\({}_{2}\)e was attributed to natural gas consumption. As  
Figure 8: Electricity consumption.  
illustrated in Figure 9, most operational energy emissions are related to the consumption of gas to heat the building, accounting for 61.72% of the total. In contrast, the emissions from electricity consumption represent only 38.28% of the total operational energy emissions.  
A comprehensive assessment of the environmental impact indicators (Figure 10) reveals that emissions resulting from the consumption of natural gas are the primary contributor to the indicators GWP, ODP, ADPE, ADPF, and PENRT. Conversely, the consumption of electricity has a high impact on the indicators AP, EP, POCP, PERE, PERT, and FW.
# Modeling and Optimizing the System Reliability Using Bounded Geometric Programming Approach
## 1 Introduction
The computational study is presented with a particular focus on system reliability optimization in Section 4. Analyses of the computational complexity are also performed with other existing approaches. Finally, conclusions and the future scope are discussed based on the present work in Section 5.
## 3 Results and Discussion
### Effect on Heat Transfer  
Figure 21 shows the total amount of heat dissipated by the device as a function of the flow rate. The relation between the geometry and heat transfer was confirmed, particularly in the FDU context. It should be emphasized that the geometries that present a greater uniformity also dissipate more heat.  
The Figure 22 clearly shows that the temperature is not uniform in all channels, it also being possible to note that in the exit region of the collector, the average temperature is higher for the heat sink configurations with lower flow distribution uniformity. This was also noted by [32].  
Figure 21: Heat transfer for three different configurations.  
Figure 20: FDU study for different numbers of channels for a 100 mL/min fluid flow at a scale of 5. (**a**) Velocity profile for four-channel configuration. (**b**) Velocity profile for eight-channel configuration.  
As the horizontal parallel geometry has the least uniform flow, it is suitable for the further analysis of the influence of flow distribution on heat dissipation. Figure 23 shows the heat transfer and the flow distribution in each channel. The heat transfer was maximized in the edge channels and was approximately uniform in the central region. The velocity of the flow and the temperature difference vary inversely, the heat transfer being maximized at the edge with greater velocity and at the one with a smaller velocity (by the greater temperature difference), with those effects being mitigated in the central channels, originating an intermedium heat transfer. The heat transfer increased globally in the vertical heat sink, especially in the central channels that present the greatest comparative gain, whichis in agreement with the conclusions reported in [33]. It is in this regard that it was inferred that the development of a uniformly distributed flow will be advantageous, since it will increase the global heat transfer of the heat sink and will likely cover a greater effective area.
# Towards Automatic License Plate Detection
## 3 Proposed Method
### The License Plate Localization Module (LPLM)
#### 3.2.2 Morphological Operations  
In order to boost the license plates blobs in the sample space (\(z\)), the below-mentioned morphological operations are applied to the obtained blob image (\(A\)).  
**Dilation** (\(\oplus\)) is performed using Equation (4) that expands the features by inserting a layer of pixels around the regions of the linked components. As a result, the obtained license plate blob is expanded:  
\[A\oplus B=\{z|(B^{\wedge})z\cap A\
eq\phi\} \tag{4}\]  
where \(B\) denotes the structuring element by which A is dilated. Dilation is followed by the Closing operation as discussed below.  
**Closing** (\(\cdot\)) is performed using Equation (5). The blob image \(A\) is initially processed by dilation operation and later eroded by a structuring element \(B\), which results in the contour smoothing and holes filling in the license plate blob:  
\[A\cdot B=(A\oplus B)\ominus B \tag{5}\]  
When the luminance is too low or high, as is the case in the PKU-G5 category with extreme reflective glare, we propose another methodology, as shown in Algorithm 2. To handle the aforementioned scenario, we apply the PCA [28] from lines (1)-(8) in Algorithm 2 on the detected input vehicle image. The application of the PCA results in the extraction of the luminance and chrominance channels. In our method, to reduce our algorithm's computational cost, we only process the luminance channel in later steps. After the PCA is applied, in next step, we calculate the mean of the luminance vector as shown in line (9) of the Algorithm 2. Meanwhile, we empirically estimate the low and upper limits of threshold as shown in line (10) in Algorithm 2. In our simulations, we set the value of threshold_low_ to 0.25 and threshold_high_ to 0.95. From lines (11)-(13), the luminance is adjusted to obtain the final neat and clean enhanced output image (X'). The enhanced output image with much better and stable luminance is later processed by the License Plate Localization Module (LPLM).  
```
Input :Image (\(X\)) with detected vehicles.
1 Apply the PCA to extract the Luminance channel from (\(X\)). begin
2 Separate RGB components of image and form feature vectors.
3 Compute the covariance matrix.
4 Calculate eigenvalues of covariance matrix.
5 Calculate the eigenvectors.
6 Luminance and chrominance channels calculated.
7
8 end for
9 Calculate the mean of luminance vector.
10 Estimate \(threshold_{high}\) and \(threshold_{low}\)
11 If \(mean>threshold_{high}\rightarrow\) decrease the luminance,
12 else \(mean<threshold_{low}\rightarrow\) increase the luminance,
13 else \(\rightarrow\) retain the luminance.
14 Output:Image (\(X^{\prime}\)) with improved luminance conditions.
```  
**Algorithm 2**Pseudo code to handle the reflective glare scenario.