esa-sceva/satcom-chunk-collection huggingface dataset:
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 1 Introduction
The results that were achieved, especially with the common configurations found in Request For Comments (RFCs) and WSN Operating Systems (OS) implementations, show room from improvement: sending more beacons means that the entire network synchronizes and connects much faster, but consequently has a much higher consumption, while reducing the number of beacons can render the network virtually useless. For this reason, a mechanism based on the trickle timer but independent of RPL has been defined. This allows for a reduction in the joining times in addition to maintaining a reduced duty cycle, which saves energy. The complete protocol stack of the WSN has been designed as follows: the IEEE 802.15.4 standard as a physical level and MAC together with the TSCH medium access method, 6 LowPAN as an intermediate layer to adapt the IPv6 frames so they can be transmitted over IEEE 802.1.5.4, and RPL as a dynamic routing protocol. The rest of the article is structured as follows: in Section 2, a brief summary description is made about the IEEE 802.15.4 standard, the TSCH access method, and the RPL dynamic routing protocol. Section 3 shows related work and references addressing the problem of synchronization in WSN, the process of detection and joining the network, and processes that are related to the scanning phase of signalling beacons in the IEEE 802.15.4 TSCH standard. Section 4 details in depth the network synchronization and joining mechanisms that are tested and the modifications that are implied by its use with the RPL routing protocol. In Section 5, the testbed, experiments, and simulations are presented, while the results are analysed in Section 6. To close the article, Section 7 offers conclusions and introduces future work.
## 3 Application Areas of IoT and UAV in Smart Farming
### Detecting Weed and Infestation  
Another most useful sector of smart farming is weed and infestation detection using UAVs. As per a study conducted in the United States, approximately $33 billion of annual damage is caused by pest infestations and infections [46] in crops. Hence, early diagnosis is essential to minimise this damage. Authors in [47] conducted a study to analyze the vegetation index of grapes by acquiring data from vineyards using UAV mounted multi-spectral cameras. These vegetation indices can be used in various applications such as weed detection, infestation detection, weed mapping, etc. Many researchers have used RGB cameras, hyper spectral camera and multi-spectrum sensors mounted on UAVs to detect weed and infestation in plants and crops [26; 48; 49].
# Effect of Laser Treatment on Intrinsic Mechanical Stresses in Titanium and Some of Its Alloys
## 2 Materials and Methods
### Analysis of Residual Stress  
The nano-indentation method was used to evaluate residual stresses. The tests were performed for Ti, Ti15 Mo, and Ti7 Nb6 Al as base materials (BMs), after laser treatment (LT) and heat treatment, i.e., stress relieving annealing [28]. Based on each series of measurements, the average value of the force acting on the sample was determined for a depth of 300 nm. The penetration value was the same for both the 50 and 200 mN force setpoints. The next step in determining the internal stresses was to calculate the surface area of the Berkovich indentation [29].  
\[h_{c}=\left(\frac{P_{max}}{3\sqrt{3}Htan^{2}\theta}\right)^{\frac{1}{2}} \tag{1}\]  
\[E^{*}=\frac{dP}{dt}\cdot\frac{1}{2}\cdot\frac{\sqrt{\pi}}{\sqrt{A}} \tag{2}\]  
\[A=3\sqrt{3}h_{c}^{2}tan^{2}\theta=24.49l_{c}^{2} \tag{3}\]  
\[h_{c}=\frac{h_{t}+h_{r}}{2} \tag{4}\]  
where:  
\(P\)--indentation load;  
\(H\)--indentation displacement;  
\(\theta\)--the half-apex angle of the conical indenter;  
\(h_{t}\)--maximum depth of the indenter during measurements;  
\(h_{r}\)--depth of indentation for a force equal to 0.  
Internal stresses were determined as the load difference's quotient and the indenter's surface area. The difference was determined as the load before and after the stress relieving of the samples. Formula (5) was used [28]:  
\[\sigma=\frac{\Delta P}{A} \tag{5}\]  
where:  
\(\Delta P\)--load difference causing the same displacement of the indenter (for samples before and after stress relief annealing),  
\(A\)--surface area of the Berkovich indenter impression.  
The forces at set depths and resultant residual stresses were calculated as the means of 25 measurements for any alloy, load value, and the presence or absence of laser treatment. As the standard deviations of arithmetic means were very high and no normal distribution of data could appear, in addition to the means, the medians, i.e., middle numbers of 25 measured values, were also found.
## 2 Proposed Approach
### Model Architecture
#### 2.3.3 Overall Procedure
Each of the three channels has a 256-dimensional IV concatenated with a 23-dimensional zero vector to match the dimension by zero padding per frame. The SED and DOAE streams have the same encoder architecture, which consists of four convolutional blocks, and a layer of CNN, BN, and ReLU activation is repeated twice in each block. After an embedding passes a block followed by average pooling in each stream, the resulting embeddings in both the streams are combined by soft parameter sharing to provide embeddings input to the next block, which are expressed as  
\[\overline{\mathbf{D}}_{c}^{(i+1)} = \alpha_{c}^{(i)}\mathbf{D}_{c}^{(i)}+\beta_{c}^{(i)}\mathbf{S}_{c} ^{(i)}, \tag{11}\] \[\overline{\mathbf{S}}_{c}^{(i+1)} = \gamma_{c}^{(i)}\mathbf{D}_{c}^{(i)}+\delta_{c}^{(i)}\mathbf{S}_{c }^{(i)}, \tag{12}\]  
where \(\overline{\mathbf{D}}_{c}^{(i)}\) and \(\overline{\mathbf{S}}_{c}^{(i)}\) denote the input embeddings for the \(i\)-th convolutional block at the \(c\)-th channel in the DOAE and SED streams, respectively, while \(\mathbf{D}_{c}^{(i)}\) and \(\mathbf{S}_{c}^{(i)}\) denote the corresponding output embeddings from a block followed by average pooling, respectively. \(\mathbf{\alpha}^{(i)}=[\alpha_{1}^{(i)},\alpha_{2}^{(i)},...,\alpha_{c}^{(i)}]\), \(\mathbf{\beta}^{(i)}=[\beta_{1}^{(i)},\beta_{2}^{(i)},...,\delta_{C}^{(i)}]\), \(\mathbf{\gamma}^{(i)}=[\gamma_{1}^{(i)},\gamma_{2}^{(i)},...,\gamma_{C}^{(i)}]\), and \(\mathbf{\delta}^{(i)}=[\delta_{1}^{(i)},\delta_{2}^{(i)},...,\delta_{C}^{(i)}]\) are learnable weights applied to the output embeddings for the \(i\)-th convolutional block in the soft parameter sharing, where \(C\) is the number of channels. In each stream, the encoder output is input to the decoder based on the decoder part of the transformer, as mentioned above. The decoder in each stream is composed of a stack of two identical layers, and each layer has a masked multi-head self attention block and a multi-head attention based on the DCMA followed by an FC network. In particular, the second attention block utilizes embedded features for SED and DOAE used as the queries and keys/values to enhance the related information between the features. The decoder is followed by an FC layer to predict SED outputs. Different from the class-wise output format, the track-wise output format can detect sound events of the identical class from different directions, which is called the case of homogeneous overlap [13]. Since the DCASE2021 Task3 may have up to three overlapping events, we adopt the track-wise output format [13] where three SED outputs and three DOAE outputs should be given by the FC layer. In either the SED or DOAE stream, three FC blocks in parallel simply pass a decoder output to predict three SED or DOAE outputs. Our system adopting the track-wise output format is trained by the permutation-invariant training for each frame [13].
## 2 Materials and Methods
### Governing Equations  
#### 2.2.1 Governing Equations  
The electric field distribution in the microwave heating cavity can be obtained by solving [PERSON]'s equation [21]:  
\[\
abla\times\mu_{r}^{-1}\times(\
abla\times\mathbf{E})-k_{0}^{2}\bigg{(}\varepsilon- \frac{j\sigma}{\omega\varepsilon_{0}}\bigg{)}\mathbf{E}=0 \tag{1}\]  
where \(\mathbf{E}\) is the electric field, \(\varepsilon_{0}\) is the dielectric constant of vacuum, \(\sigma\) is electrical conductivity, \(\mu_{r}\) is relative permeability, \(\varepsilon_{r}\) is relative permittivity and \(k_{0}\) is the wave number in free space.  
In order to realize the coupling of the electric field and heat transfer in the microwave heating process, the microwave dissipative power should be analyzed after computing the electric field. The microwave loss power \(Q_{e}(t)\) in the sample as the heat source can be obtained by the following formula [17]:  
\[Q_{e}=\frac{1}{2}\omega\varepsilon_{0}\varepsilon^{\prime\prime}\left|\mathbf{ E}\right|^{2} \tag{2}\]  
where \(\omega\) is the angular frequency microwave, \(\varepsilon^{\prime\prime}\) is the sample's imaginary part of dielectric relative permittivity and \(\left|\mathbf{E}\right|\) is the magnitude of the electric field.  
The temperature field of the sample is computed by the heat transfer formula [17]:  
\[\rho C_{p}\frac{\partial T}{\partial t}-\
abla\cdot(k\
abla T)=Q_{e} \tag{3}\]  
where \(\rho\) is the density of the sample, \(C_{p}\) is the heat capacity and \(k\) is the thermal conductivity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Applied Domain** & **Value** & **Source** \\ \hline \multirow{2}{*}{Dielectric loss (\(\varepsilon^{\prime\prime}\))} & Potato & \(-10^{-4}\) T\({}^{2}\) - \(1.08\times 10^{-1}\) T + 16.1 & [13] \\ \cline{2-4}  & Others & 0 & \multicolumn{1}{c}{COMSOL build-in} \\ \hline Density (kg/m\({}^{3}\)) & Potato & 1050 & [13] \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Thermal conductivity \\ (W/(m\({}^{3}\cdot\)K)) \\ \end{tabular} } & Potato & 0.64 & [13] \\ \cline{2-4}  & Turntable & 1.4 & \multicolumn{1}{c}{COMSOL build-in} \\ \cline{2-4}  & Support rod & 0.38 & \multicolumn{1}{c}{COMSOL build-in} \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Specific heat capacity \\ (J/(kg\(\cdot\)K)) \\ \end{tabular} } & Potato & 4180 & [13] \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Structure diagram of computation 3D model (unit: mm).
## 3 The Model Description and Data Preprocessing
### RNN Models
#### 3.1.3 Our Proposed RNN Model  
In the conventional method, there are various problems such as an increase in the amount of calculation, difficulty in the analysis of the gate structure, and dependence on data due to batch normalization. Therefore, in order to solve these problems in the conventional RNNs, we constructed the new structure using the sech function as the gate structure [10]. In the conventional gate structure, if the gate structure has weight parameters, the calculation cost is very high, but if it does not have weight parameters, scaling for negative and positive values does not work properly, because the sigmoid  
Figure 1: A layer of typical LSTM. LSTM has three gates with weight parameters to control the information that should be retained. The biases are omitted here.  
function is not an even function. Hence, instead of the sigmoid function, we used the sech function that is an even function and has an output range from 0 to 1 for the gate structure [10].  
The sech function is defined by Equation (11) as follows:  
\[\text{sech}(x)=2/\left(e^{x}+e^{-x}\right) \tag{11}\]  
Additionally, differentiating (11) is defined by Equation (12) as follows  
\[\frac{d}{dx}(\text{sech}(x))=-\text{sech}(x)\cdot\text{tanh}(x) \tag{12}\]  
Figure 3 shows a graph of the sech function and sech's differentiated function.  
As is shown in Figure 4, a layer of the RNN with sech gate structure with the highest performance in the paper [10] is defined by Equations (13)-(15) as follows:  
\[\widetilde{h_{t}}=\text{sech}\big{(}ah_{t-1}\big{)}\circ h_{t-1} \tag{13}\]  
\[h_{t}=\left(W_{x}x_{t}+b_{x}\right)+\widetilde{h_{t}} \tag{14}\]  
Figure 3: A graph of the sech function and sech’s differentiated function (cited from [10]). Sech function is even function and has output range from 0 to 1.  
Figure 2: A layer of GRU. GRU controls how much information is retained by two gates, reset gate and update gate. The biases are omitted here.  
\[o_{t}=\text{act}(h_{t}) \tag{15}\]  
where \(a\) is a scalar value, which is introduced as a parameter for controlling the degree of vanishing gradient. Additionally, the output to the lower layer is represented by \(o_{t}\), and act is activation function. Hereinafter, for the sake of simplicity, the RNN with sech gate structure is referred to as \"RNN-SH\".
## 5 Experiment and Result Analysis of Shaft Frequency Extraction
### Experiment on Real Data
In addition, the extraction performance of the SSDAE-DRN model will be better improved by using real data and hard negative samples for training and fine-tuning. The proposed method realizes end-to-end shaft frequency extraction. The computational cost is mainly concentrated in the training and fine-tuning of the model, and only a simple weighted summation is required to extract the shaft frequency. Therefore, it has faster extraction efficiency and has more advantages than traditional methods in terms of real-time performance.
# Correlation Analysis of Microbiota and Volatile Flavor Compounds of Caishiji Soybean Paste
## 3 Results
### Correlation Analysis of Microbiota and Characteristic VFCs  
In this study, \(R\) values and Pearson correlation coefficients were calculated to analyze the connections between microbiota and characteristic VFCs in CSP. The bacterial and fungal genera with the 10 highest abundances were included in the correlation analysis. Redundancy analysis (RDA) was conducted to evaluate the complex relationships among the microbiota, VFCs, and CSP fermentation stages. On the basis of the PCA results, isovaleraldehyde, 2-methylbutyratealdehyde, and 3-methylhiopropionaldehyde were selected for the RDA. The results revealed that compared with isovaleraldehyde and 3-methylhiopropionaldehyde, the microorganisms exerted a greater effect on 2-methylbutyraldehyde in CG3, CG6, and CG8. These three characteristic VFCs were positively correlated (Figure 4c,d).  
Figure 3: Circos of bacteria (**a**) and fungi (**b**) of Caishiji soybean paste in different fermentation stages. The right half circle represents the distribution of the species in different samples at the taxonomic level, the outer ribbon represents the species, the inner ribbon color represents the different groups, and the length represents the distribution of the sample in a species.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 4 Simulation Results
In addition, we can notice that the secrecy capacity also increases when the value of Eb/N0 (dB) increases. Finally, we compare the secrecy capacity of the proposed scheme with that of the benchmark scheme (STBC-DCSK) under two kinds of channels when the eavesdroppers steal the initial value of chaotic sequences, and the results are shown in Figure 8. As can be seen from the figure, even if the eavesdroppers steal the initial value of chaotic sequences, by adding the FH module, the scheme proposed in this paper can achieve higher secrecy capacity. In addition, we can see from Tables 3 and 4 that, if the eavesdroppers steal the key information of STBC-DCSK scheme and chaotic reference sequences, the user data \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Secrecy Capacity**} \\ \hline  & \multicolumn{4}{c}{**MIMO-FH-OFDM-DCSK**} & **Alimouti** \\ \hline
**Eb/N0 (dB)** & \(\beta=N=\textbf{32}\) & \(\beta=N=\textbf{64}\) & \(\beta=N=\textbf{128}\) & \\ \hline
2 & 0.332 & 0.217 & 0.117 & 0.000 \\
3 & 0.501 & 0.311 & 0.190 & 0.000 \\
4 & 0.620 & 0.448 & 0.271 & 0.000 \\
5 & 0.756 & 0.553 & 0.399 & 0.000 \\
6 & 0.870 & 0.718 & 0.537 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Secrecy capacity comparison between the proposed system and the benchmark scheme (STBC) in the AWGN channel. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Secrecy Capacity**} \\ \hline  & \multicolumn{4}{c}{**MIMO-FH-OFDM-DCSK**} & **Alimouti** \\ \hline
**Eb/N0 (dB)** & \(\beta=N=\textbf{32}\) & \(\beta=N=\textbf{64}\) & \(\beta=N=\textbf{128}\) & \\ \hline
8 & 0.453 & 0.353 & 0.188 & 0.000 \\
9 & 0.561 & 0.496 & 0.252 & 0.000 \\
10 & 0.769 & 0.673 & 0.380 & 0.000 \\
11 & 0.873 & 0.800 & 0.543 & 0.000 \\
12 & 0.946 & 0.919 & 0.690 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Secrecy capacity comparison between the proposed system and the benchmark scheme (STBC) in the Rayleigh channel. Figure 7: The secrecy capacity comparison between the proposed system and the benchmark scheme (STBC). (**a**) AWGN channel; (**b**) Rayleigh channel. the effectiveness of the proposed scheme. By comparing the BER of legitimate users and eavesdroppers and the secrecy capacity of the proposed scheme and the benchmark scheme, it can be seen that the proposed scheme can effectively improve the security of the MIMO communication system. The potential future work can be extended to reduce the complexity. Writing--review and editing, W.Q., Y.Y., Y.F., L.Z. and Z.W. All authors have read and agreed to the published version of the manuscript. This work was supported by the Project of National Natural Science Foundation of China under Grant No. U20A20162, in part by Key Research & Development and Transformation Plan of Science and Technology Program for Tibet Autonomous Region under Grant No. XZ201901-GB-16. The authors declare no conflict of interest.
# A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models
## 4 Empirical Evaluation
### Main Results  
To fine-tune parameters for lung delineation, lesion identification, and three-dimensional reconstruction methods, we employed stratified partitioning validation. This highly effective method ensures the resilience and transferability of our approach. This process involved dividing the dataset into equal parts and iterating the training cycle for each division, using one as the test set and the others for training. We conducted systematic experiments to isolate the impact of individual parameters by varying one at a time while keeping others constant. This approach allowed us to identify which parameters most significantly influenced the effectiveness of our methods.  
Stratified partitioning validation ensured that each dataset split faithfully, and reflected the entire dataset's overall variety and class proportions. This meticulous approach to dataset handling enhanced the model's robustness by training it across a wide range of cases and minimized the potential for overfitting. By exposing the model to diverse yet statistically similar data subsets, we could consistently evaluate its performance across various scenarios. This method of repeated cycles of training and validation allowed us to incrementally refine our model, boosting its accuracy and dependability while preserving its generalization capabilities. Additionally, the stratified approach was crucial for detecting anomalies or biases in the dataset, enabling timely adjustments before the model'sfinalization. Thus, this comprehensive validation strategy was pivotal in ensuring that our model delivered a strong performance on training and unseen data, effectively reducing the likelihood of overfitting.  
The results of these parameter tuning exercises are summarized in Table 1. This table provides a detailed overview of the optimized parameters, outlining the ranges and specific values that achieved the highest efficacy during the testing phase.  
During the training of our GANs and LSTM network, addressing the challenge of local minima was crucial for robust performance. We implemented several strategies to mitigate this risk. Initially, we initialized the GAN models with weights from a truncated normal distribution to stabilize the early training phase and prevent skewed feature influence. We used the Adam optimizer, known for its ability to handle the decay of past gradients and navigate complex parameter spaces effectively, thus avoiding local minima. For the LSTM network, dropout layers were strategically included to prevent overfitting. These approaches, alongside continuous validation checks, ensured our models avoided local minima and maintained excellent generalization across new datasets.
# Shot Classification of Field Sports Videos Using AlexNet Convolutional Neural Network
## 3 Results and Discussion
### Performance Evaluation  
In this experiment, we computed the precision, recall, accuracy, error rate, and F-1 score against each shot category of 10 different sports videos (soccer and cricket). The results obtained for each class of shot are presented in Table 1. The proposed method achieves an average precision of 94.8%, recall of 96.24 %, F1-score of 96.49%, accuracy of 94.07% and error rate of 5.93% on these videos. These results indicate the effectiveness of our proposed AlexNet CNN framework for shot classification of sports videos.
## 2 Materials and Methods
### Specim Molding and Preparation  
All specimens included in this study were prepared and cured under controlled laboratory conditions: a constant temperature of 23 \(\pm\) 2 \({}^{\circ}\)C and relative humidity of 95%, following a curing period of 28 days for all mixtures, as summarized in Table 4. The preparation procedures were categorized into two groups according to the type of stabilizing agent used: construction residues with Portland cement (CLW, GG, GY) and biopolymer-based mixtures (XG and XG-PPF). While both followed similar curing conditions and specimen geometries, the material handling and compaction processes differed slightly depending on the type of binder.  
The preparation began with air-dried and sieved soil. Stabilizing agents (cement and residue) were dozed by weight relative to the dry mass of the soil. Cement content was fixed at 3% and 6%, while residues were added at 15% and 30% for CLW, and 10% and 20% for GG and GY, respectively. The mixing process was carried out manually to ensure uniformity, and the optimum moisture content was set at 18%, based on a standard Proctor compaction test (yd, max = 17.6 kN/m3) [12].  
Figure 7: (a) Cement Portland Type III [6] (b) PPF [25].  
Each batch was subdivided into three portions to prepare one specimen per dry unit weight level: 17.0, 17.6, and 18.0 kN/m\({}^{3}\).  
For mixtures stabilized with XG, the biopolymer was first dissolved in distilled water and then gradually incorporated into the soil. Dosages of 1, 3, and 5% (by dry soil weight) were selected based on literature recommendations [26; 33; 34]. The amount of water was adjusted to achieve maximum compaction close to \(\gamma\)d = 16.5, 17.0, and 17.6 kN/m\({}^{3}\). Specimens with XG-PPF included 0.5% polypropylene fibers by weight, added during the mixing stage [25].  
The mixtures were compacted in three layers, each with equal mass and carefully scarified between layers to ensure homogeneity. Cylindrical specimens were molded using standard molds (5 cm diameter \(\times\) 10 cm height; volume \(\approx\) 196.35 cm\({}^{3}\)). After compaction, samples were wrapped in plastic, sealed, and stored in a humid chamber for the full 28-day curing period.  
Before testing, specimens were submerged in distilled water for 24 h to minimize matric suction. Acceptance criteria required the dry density to remain within \(\pm\)1% of the target value and moisture content within \(\pm\)0.5% of the intended optimum. This immersion step was adopted based on established methodologies [35; 36], aiming to reduce matric suction effects that could interfere with mechanical performance.