esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 3 Secure Dynamic Group Management Scheme  
This section proposes a secure communication scheme that can conduct simultaneously mutual authentication between group sensor nodes and validate the distance between group sensor nodes by using the four kinds of fundamental arithmetic operations, substitution, logical operation, and challenge-response technique. The proposed scheme is made with four protocols that contain cluster header authentication and group authentication. During the authentication, the seed value which is required for generating and confirming the key is securely transmitted with the use of the multiplication, addition, and logistic XOR or \(||\) only.  
Figure 1 is a conceptual diagram of the secure dynamic group management scheme. Once the cluster header is selected, it will obtain a certain value for the group authentication from BS, and the sensor group will conduct the group authentication using that value. It is assumed that base station and secret key were shared when distributing sensor nodes. Also, it is assumed that the filter function was shared for the reduction of bit stream as shown in Figure 2. The process of filter function action is as follows. If a series of bit streams is put in the function, the values of bits located in the specified position would be selected for first filtering. Then, the values of bits, which are located in the specification positions, are kept among those that are filtered in unit of block while others would be removed. Lastly, only one bit is selected which is located as a specified position. The filter function continues to act until it gets the bit steam of the specified size while the dual values are designed not to come out by changing the input values or the position of filtering. For example, assuming that the seed is x in the size of 48 bits, x undergoes the FA and FC filtering functions in the first layer and is then divided into 5-bit values. In addition, one output value is obtained through the FC filtering function in the second layer. In addition, if there is more than 1-bit value, the filtering shall be repeated from first layer to only 1-bit value is left. The used symbols are explained in Table 1.
## 3 Our Proposed MQSOS Algorithm and Its Improvement on the DV-Hop Algorithm
### Our Proposed Algorithm's Application in WSN Localization Based on DV-hop  
This section describes the use of MQSOS positioning in DV-hop-based wireless sensor network node location. We know that the hop count between anchor nodes is obtained by the anchor node transmitting broadcast information, and the distance between the anchor node and the unknown node is multiplied by the average distance per hop of the anchor node by the anchor node to the unknown node. The number of hops between the two nodes is obtained. The location of the unknown node is then estimated by the method of least squares or maximum likelihood estimation of the nodes. However, since the method has an error in estimating the average distance per hop, it presents a decrease in positioning accuracy. The main purpose of the positioning problem is to minimize the estimation error and improve the positioning accuracy. An improved DV-hop algorithm based on swarm intelligence for the unknown node location in WSN is proposed to reduce the estimation error.  
It is known that in network node positioning, the greater the number of hops between nodes, the higher the positioning accuracy. Therefore, the fitness function in the WSN location node positioning algorithm is shown in Equation (30), where \(hop_{ui}\) is the number of hops from anchor node \(u\) to an unknown node, \(\hat{d}_{ui}\) is the estimated distance from the anchor node \(u\) to the unknown node \(i\) by the maximum likelihood estimation method, and \(d_{ui}\) is the actual distance between node \(u\) (\(x_{u},y_{u}\)) and position node \(i\) (\(x_{i},y_{i}\)).  
\[d_{ui}=\sqrt{(x_{u}-x_{i})^{2}+(y_{u}-y_{i})^{2}} \tag{29}\]  
\[f(x,y)=min(\sum_{i=1}^{m}(\frac{1}{hop_{ui}})^{2}(d_{ui}-\hat{d}_{ui})^{2}) \tag{30}\]  
The MQSOS_DV-hop algorithm first calculates the minimum hop count and the distance between the anchor nodes by communication between the anchor nodes, and then calculates the average step size of each anchor node. The hops received by all anchor nodes are then weighted to calculate the hop of the unknown node. Finally, the location of the unknown node is estimated by the proposed MQSOSE algorithm.  
For each unknown node we use the complete MQSOS algorithm for positioning. The steps are as follows.  
Step 1: calculate the average distance per hop of all anchor nodes, the number of hops between nodes, and so on.  
Step 2: calculate the distance between the unknown node and all the anchor nodes.  
Step 3: initialize the parameters used by the MQSOS algorithm (population size, population search space size, population dimension, etc.).  
Step 4: randomly generate the position of the individual, and divide it into \(gs\) subgroups for iteration, with the inter-group communication based on the quantum behavior being performed every \(cs\) times.  
Step 5: repeat step 4 until the maximum number of iterations is reached and then output the position of the optimal node.
## 3 Results
### Models with Topography
#### 3.3.1 Canyon Model  
For this model, as shown in Figure 6, the canyon is treated as part of the air layer. The background conductivity is \(10^{-1}\) S/m. After tests, frequencies considered are \(5\times 10^{0}\), \(10^{2}\), and \(5\times 10^{2}\) Hz. Solutions for cases based on the quasi-static approximation are compared with those using the complete wavenumber along one survey line.  
Figure 7 shows the comparison of modeling results at different frequencies for the canyon model by treating the air conductivity differently, along with relative errors for the solution based on the quasi-static approximation. As indicated in the figure, as the frequency increases, the deviation between the two solutions becomes larger, leading to an increase in the relative errors. At a frequency of \(5\times 10^{0}\) Hz, the modeling results from cases in which the air conductivity is treated differently are in good agreement, as indicated in Figure 7 a-d, for both real and imaginary parts of the \(x\)-component electrical field, with biggest relative errors of less than 1%. At a frequency of \(10^{2}\) Hz, the comparison is shown in Figure 7 e-h. Most relative errors are larger than 1% for both the real and imaginary parts, with the respective biggest relative errors of 2% and 5%. At \(5\times 10^{2}\) Hz, significant mismatches are shown in Figure 7 i-l for both the real and imaginary parts. The largest relative error reaches 11% for the real part and 21% for the imaginary part. For all frequencies, larger errors are shown for the imaginary part relative to the real part. The comparison for apparent resistivity and phase is shown in Figure 8, indicating that the quasi-static approximation generally works well for apparent resistivity and phase calculations (similar results for other high frequencies).  
It is worthy to note that due to the canyon, large relative errors tend to happen at substantially lower frequencies than the corresponding flat model with the quasi-static approximation. This indicates that the existence of the canyon makes the upper limit of frequency, below which the quasi-static  
Figure 6: Sketch of the canyon model. (**a**) \(x\)-\(z\) section view along \(y=0\) km, (**b**) \(x\)-\(y\) plan view at \(z=0\) km.  
approximation is valid, lower than expected. In this case, the quasi-static approximation can affect the EM modeling results significantly at frequencies as low as several hundred Hz.  
Figure 7: Comparison of the \(E_{x}\) numerical solutions from the case based on the quasi-static approximation (upward-pointing triangle lines)) with \(10^{-6}\) S/m for air conductivity, and the one with the complete wavenumber (circle lines) at frequencies of (**a–d**) \(5\times 10^{0}\), (**e–h**) \(10^{2}\), and (**i–l**) \(5\times 10^{2}\) Hz for the canyon model. The relative errors are referenced to the results based on the use of the complete wavenumber.
## 4 Experiments and Performance Evaluation
### Real-Device Deployment and System Performance Analysis  
In this experiment, the optimized hearing aid was connected to a Raspberry Pi 4B via a USB sound card to simulate a real-device operating environment. A real-time noise reduction system was implemented in Python (3.8.0) based on an ONNX model converted from the proposed TSDCA-BA architecture. The system combines neural network-based mask estimation with dynamic spectral subtraction to continuously enhance the microphone input signals. Table 4 presents the model's performance under actual deployment. We tested the trained model using data collected from the hearing aid; the PESQ score improved from 1.2897 before noise reduction to 2.0964 after, representing an approximate performance increase of 62.55%. The system achieves an overall latency of less than 3 ms, with memory usage around 200 MB. Considering the relatively high memory consumption of the Python runtime, future plans include encapsulating the model in C++ and running it via TensorFlow Lite, where memory usage is expected to be reduced to approximately 5-10 MB. The results demonstrate that the proposed model performs effectively on low-power hearing aid devices, with its extremely low computational load providing a viable solution for achieving low-latency, real-time noise reduction in resource-constrained environments. Moreover, the model features a lightweight architecture with low training costs and can be trained on large-scale datasets using only a CPU. The hearing aid's circuit design is simple, cost-efficient, and delivers satisfactory performance, making it suitable for mass production and wide-scale deployment.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Layer** & \multicolumn{2}{c}{**Module**} & \multicolumn{2}{c}{**PESQ**} & \multicolumn{1}{c}{**STOI**} & \multicolumn{1}{c}{**Params.**} \\ \hline Bi-gru-convX1 & — & — & — & 2.44 & 0.8904 & **24 k** \\ Bi-gru-convX2 & — & — & — & 2.60 & 0.9026 & 29.5 k \\ Bi-gru-convX3 & SDCA & — & — & 2.68 & 0.9030 & 110.9 k \\ Bi-gru-convX3 & SDCA & SE & — & 2.79 & 0.8894 & 111.5 k \\ Bi-gru-convX3 & SDCA & SE & SE & 2.85 & 0.8745 & 111.5 k \\ Bi-gru-convX3 & TSDCA & SE & SE & 2.62 & **0.9116** & 112.3 k \\ Bi-gru-convX3 & SDCA-BA & SE & SE & 2.64 & 0.9111 & 111.5 k \\
**Bi-gru-convX3** & TSDCA-BA & SE & SE & **2.92** & 0.9045 & 112.5 k \\ \hline \hline \end{tabular} The bold text indicates the best performance.  
\end{table}
Table 3: The ablation results of the TSDCA-BA model.
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 4 Results and Discussion
### Prediction Performance of EE-DL Model
The longest input of 56 dates from 2015-12-02 to 2017-10-04 was for Run-D.  
As expected, the RMSE of all models gradually increased away from the 3 mm plateau near the failure date in March 2018, as illustrated in Figure 7. This supports the hypothesis that an increasing amount of anomalous behaviour was present before failure. There was a sudden jump in the RMSE on 2017-10-16 for all models. This was likely due to inaccuracies in the InSAR processing, such as in the separation of the atmospheric phase component. Moreover, Run-B operated at a one-step-ahead (\(n+1\)) and Run-C at a four-step-ahead (\(n+4\)) prediction horizon. The variability in the RMSE of the four-step-ahead prediction horizon was larger at the 3 mm plateau, but this difference disappeared as the RMSE gradually increased for all experiments from 2017-10-28 to 2018-02-25. Finally, these results showed that the EE-DL model performed well at both one-step and four-step-ahead predictions. Therefore, the four-step-ahead prediction horizon was adopted for the purposes of comparing the performance of EE-DL with the GPR and RF models. The predictions of the last four temporal data points were used for comparing the performance of the models in detecting anomalous deformation behaviour.
## 3 Discussion  
Surgical guides are usually fabricated as a reference for crown lengthening procedures, especially when anterior teeth are involved. When using Vacuform shells or an acrylic mock-up, diagnostic waxing is required to anticipate the future crown length and to act as a guide for soft tissue incision and freehand osteeOCTomy. In contrast, the technique presented provides guides for both gingival and bone resection; thus, it facilitates the surgical procedure and provides predictable outcomes. This is important for avoiding unnecessary bone removal, which may result in root exposure and tooth sensitivity.  
The use of a digital workflow to fabricate a surgical guide has been reported recently [10], where the surgical guide was designed based on a digital wax-up. In this report, the CBCT scan was used to locate the cementoenamel junction and the surgical guide was designed and printed accordingly; this technique eliminates the need for diagnostic waxing when restorative treatment is not indicated. In some patients, when maxillary anterior teeth have incisal wear followed by compensatory eruption, the cementoenamel junction is more coronal than expected. Performing esthetic crown lengthening in such patients based on a diagnostic wax-up can cause excessive removal of soft tissues, which may in turn result in root exposure, mandating a restorative treatment that was otherwise not planned. Using a precise outline of the anatomical cementoenamel junction location of the patient can compensate for the variability among patients and reduce the chance of under- or over-contouring of hard and soft tissues. However, additional clinical studies are needed to confirm the reliability and repeatability of this technique.
## 4 Experiments and Results
### Objective Evaluation  
The objective evaluation measured the distortion between the original and generated \(F_{0}\) contours in terms of RMSE and CORR. The values for the proposed model named sampling (SAMP)-based model and baselines are shown in Table 7. The generation time of each model used for generating \(F_{0}\) contour for all utterances in the test set is also reported. The DCT-based model gave the poorest performance in terms of both RMSE and CORR. The SAMP-based model outperformed the frame-based model in terms of RMSE while their CORR values were similar. It can be found that the generation time of the SAMP-based model is lower than the Frame-based model although the SAMP-based model have a larger number of parameters than the Frame-based model. The possible reason is the Frame-based model generates \(F_{0}\) values frame by frame while the SAMP-based model generates \(F_{0}\) syllable by syllable. The generation time of the DCT-based model is comparable to the Frame-based model because it requires the additional time for converting DCT-coefficients to \(F_{0}\) contour.  
However, we noticed that the RMSE and CORR of the models were slightly different. In this case, the perceptual evaluation is required to further measure the performance of the models because the objective result might not always be well correlated with perception of the listener [58].
# Underwater Target Recognition Based on Multi-Decision
## 3 LOFAR Spectral Line Enhancement Based on Multi-Step Decision
### Structure LOFAR Spectrum  
The LOFAR spectrum is calculated by short-time Fourier transform (STFT). Unlike the traditional Fourier transform, which requires signal stability, STFT is suitable for non-stationary signals. It takes advantage of the short-term stationary characteristics of the signal. After windowing and framing the signal, the Fourier transform is performed to obtain the signal at time-frequency. Then it is more accurately characterize the distribution of signal frequency components and time nodes. The calculation formula is as follows:  
\[STFT\{s(t)\}=\int_{-\infty}^{\infty}s(t)w(t-\tau)e^{-jwt}dt, \tag{16}\]  
where \(STFT\{\cdot\}\) is short-time Fourier transform, \(s(t)\) is the signal to be transformed and \(w(t)\) is the window function (truncating function). The process of calculating the LOFAR spectrum can be compared with the \"LOFAR Spectrum\" in the Feature Processing stage in Figure 1. The specific calculation steps are as follows:  
1. Framing and windowing. The sound signal is unstable globally, but can be regarded as stable locally. In the subsequent speech processing, a stable signal needs to be input. Therefore, it is necessary to frame the entire speech signal, i.e., to divide it into multiple segments. We divide the sampling sequence of the signal into \(K\) frames and each frame contains \(N\) sampling points. The larger the N and K, the larger the amount of data, and the closer the final result is to the true value. Due to the correlation between the frames, there are usually some points overlap between the two frames. Framing is equivalent to truncating the signal, which will cause distortion of its spectrum and leakage of its spectral energy. To reduce spectral energy leakage, different truncation functions which are called window function can be used to truncate the signal. The practical application window functions include Hamming window, rectangular window and Hanning window, etc.
2. Normalization and decentralization. The signal of each frame needs to be normalized and decentralized, which can be calculated by the following formula: \[s^{\prime\prime}(t)=\frac{s(t)-E[s(t)]}{\max(|s^{\prime}(t)|)}.\] (17) Here, \(s^{{}^{\prime}}(t)\) is the normalization of \(s(t)\), which makes the power of the signal uniform in time. \(s^{{}^{\prime\prime}}(t)\) is the decentralization of \(s(t)\), which makes the mean of the samples zero.
3. Perform Fourier transform on each frame signal and arrange the transformed spectrum in the time domain to obtain the LOFAR spectrum.
## 5 Conclusions  
Experimental and analytical investigations of the seismic performance of four assembled beam-column connections using MRPC with different steel fiber types in critical cast-in-place regions and a monolithic concrete beam-column connection were conducted. The following main conclusions were drawn:  
1. The failure mode of the monolithic specimen and assembled specimens was beam-end bending failure. The monolithic specimen's crack width was large, and concrete peeled off a large area of its main crack at the beam end. However, there was no concrete spalling on the MRPC's surface, and the core area of connections was mostly fine inclined cracks, which had good structural integrity. The application of MRPC in assembled beam-column connections significantly improved the structure's damage tolerance capacity and avoided brittle failure;
2. Straight steel fibers were easily pulled out and had the least inhibitory effect on cracks; SFZP-1's comprehensive seismic performance was the worst. The seismic performances of specimens with end-bent steel fibers were better. Higher ductility was achieved using end-bent long fibers, which increased the monolithic concrete specimens' ductility by 102%. Wavy steel fibers had the lowest initial stiffness and shear capacity but the highest energy dissipation capacity and the fullest hysteretic curve. Comprehensively considering various seismic performance indicators, using 30 mm end-bent steel fibers is recommended;
3. End-bent short steel fibers had the greatest impact on crack-bearing capacity. End-bent long steel fibers had the greatest impact on shear capacity. The Chinese code overestimated MRPC's shear contribution. Considering the influence coefficient, formulas for calculating the crack capacity and shear capacity of MRPC-assembled beam-column connections were established.  
Conceptualization, X.W. and D.H.; funding acquisition, X.W.; investigation, X.W., D.H., and Q.G.; methodology, X.W. and D.H.; software, D.H. and Q.C.; supervision, D.H.; validation, Q.G.; visualization, D.H.; writing--original draft, X.W.; writing--review and editing, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Shandong Province under Grant ZR2022 ME164, Major Scientific & Technological Innovation Projects of Shandong Province under Grant 2021 CXGC011204, and Engineering Research Institute of Appraisal and Strengthening of Shandong Jianzhu University CO., LTD under Grant, H19230Z.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(V_{\rm jfk}\) (kN) & \(V_{\rm j}\) (kN) & \(V_{\rm jfk}/V_{\rm j}\) \\ \hline SFZP-1 & \(+\) & 436.7 & & 0.93 \\  & \(-\) & 443.2 & & 0.94 \\ SFZP-2 & \(+\) & 430.9 & & 0.96 \\  & \(-\) & 455.8 & & 1.01 \\ SFZP-3 & \(+\) & 409.0 & & 0.85 \\  & \(-\) & 431.4 & & 0.90 \\  & \(+\) & 368.7 & & 0.95 \\ SFZP-4 & \(-\) & 393.6 & & 1.01 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Test and theoretical value of shear capacity.
The metabolic precursors of terpen, isopentenyl diphosphate (IPP) and its isomer, dimethylallyl diphosphate (DMAPP), are both produced by either the plastid-localized MEP pathway or the cytosolic MVA pathway. The pathways utilize glyceraldehyde-3-phosphate (G3P) and pyruvate as carbon feedstocks that are generated via plastid-localized glycolytic steps [19; 20]. Following this, the condensation of single molecules of five-carbon IPP and DMAPP leads to the formation of a 10-carbon GPP molecule (which is also used for cannabinoid production). A similar process between GPP and IPP then results in a 15-carbon famesyl diphosphate (FPP) moiety, which further condenses with IPP to form a 20-carbon geranyldigmosphate (GGPP) molecule. GPP, FFP, and GGPP are then acted upon by various terpene synthases (TPSs) to form monoterpenes, sesquiterpenes, and diterpenes, respectively. In this regard, monoterpenes and diterpenes are synthesized via the MEP pathway, whereas sesquiterpenes are made via the MVA pathway. Depending on the isoforms, GPP synthase (GPPS) and GGPP synthase (GGPPS) can be found in the chloroplasts, mitochondria, and endoplasmic reticulum [21; 22], while FFP synthases (FPPS) are mostly cytosolic, although some are found in the peroxisome or endoplasmic reticulum [20]. As the MEP and MVA pathways are also used to provide precursors for primary metabolism and hormone biosynthesis [23], depending on developmental, environmental, and genetic factors, it is imperative to characterize them alongside cannabinoid and terpene biosynthetic pathways in Cannabis plants for the selection and development of cultivars. Although it is generally accepted that genetic regulatory adaptations exist in the trichomes to facilitate secondary metabolite production across diverse plant species, most studies in Cannabis to date have focused on secondary metabolism. However, we hypothesized a more holistic and coordinated adjustment of the metabolism, including the primary pathways, that are also reflected at the transcriptome level. To test the hypothesis, the transcriptome of female infloresence trichomes from five different Cannabis cultivars were profiled and then compared to a non-trichome tissue, namely, stem tissue (stripped of any existing trichomes), to identify differentially expressed genes (DEGs) or those genes specific to trichomes that are broadly conserved among the different cultivars. The expression of biosynthetic enzymes in the central and secondary metabolic pathways in the trichomes was then examined and interpreted in terms of functional coordination to enhance cannabinoid and terpene production. Furthermore, their levels were compared among the cultivars to provide the molecular basis for characterizing their yield variations. Additionally, three TPSs were functionally characterized using in vitro and in vivo assays. Altogether, these findings offer opportunities for the selection and improvement of yield profiles in Cannabis cultivars.