esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Microstructural Evolution  
#### 3.3.1 Optical Micrographs  
Figure 8 presents the optical micrographs obtained after Fe-30.9 Mn-4.9 Al-4.5 Cr-0.4C was deformed. Deformation temperature and strain rate played a significant role in the evolution of the alloy's microstructure. Figure 8a,c show that at 800 and 900 \({}^{\circ}\)C and a strain rate of 0.1 s\({}^{-1}\), the fully dendritic structure in the as-cast alloy evolved into elongated and globular ferrite grains. At 1000 \({}^{\circ}\)C and 0.1 s\({}^{-1}\) (Figure 8e), the dendritic features were eliminated, and strands of carbide (M\({}_{7}\)C\({}_{3}\), M\({}_{3}\)C, M\({}_{23}\)C\({}_{6}\), or Kappa) phase embedded within the serrated austenite matrix were observed. In addition, the phase fraction of the ferrite phase depleted significantly. The depletion of the ferrite phase under this condition can be  
Figure 6: Extracted peak stresses from flow curves for alloys Fe-30.9 Mn-4.9 Al-4.5 Cr-0.4C (Sample A) and Fe-21.3 Mn-7.6 Al-4.3 Cr-1C (Sample B) deformed at different temperatures and constant strain rates of (a) 0.1 s\({}^{-1}\); (b) 5 s\({}^{-1}\).  
Figure 7: The hardness of Fe-30.9 Mn-4.9 Al-4.5 Cr-0.4C (Sample A) and Fe-21.3 Mn-7.6 Al-4.3 Cr-1C (Sample B) deformed at different temperatures and constant strain rates of (a) 0.1 s\({}^{-1}\); (b) 5 s\({}^{-1}\).  
ascribed to the low strain rate applied at a higher temperature allowing enough time for phase transformation to occur. Figure 8b,d,f also show the elongated and globular austenite grains with no distinct dendritic feature at all deformation temperatures and a constant strain rate of 5 s\({}^{-1}\).  
Similar to the Fe-30.9 Mn-4.9 Al-4.5 Cr-0.4C austenitic-based duplex low-density steel, Fe-21.3 Mn-7.6 Al-4.3 Cr-1C austenitic low-density steel followed the same response to changing deformation parameters in terms of the disappearance of the as-cast dendritic structure in the deformed samples under all conditions except at 1000 \({}^{\circ}\)C at 5 s\({}^{-1}\) (Figure 9f) where dendritic features were still observed. At all strain rates and deformation temperatures below 1000 \({}^{\circ}\)C (Figure 9a-d), serrated austenite grains with a network of intergranular carbides (M\({}_{7}\)C\({}_{3}\), M\({}_{3}\)C, M\({}_{23}\)C\({}_{6}\), or Kappa) were evident. Some of these austenite grains had a near-globular morphology. These serrated grains are signatures of dynamic recovery dominating at the onset of DRX [28; 29; 30]. The near-globular morphology confirmed the occurrence of the small amount of dynamic globalization, as suggested by the continuous flow softening in Figure 6b.
## 2 Formulation of the Model
General equations to determine \"\(P\)\", \"\(M_{xT}\)\", and \"\(M_{yT}\)\" are obtained as follows:  
\[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]2 \[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]3  
\[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]4  
\[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]5  
\[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]6  
\[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]7  
The optimal area, \"\(A_{min}\)\" (objective function), is obtained as follows:  
\[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]8  
Table 1 shows the constraint functions for the resultant force and the resultant moments for each case. Figure 5 shows the flowchart of the procedure for finding the optimal area of a rectangular footing; this procedure is performed for all five cases, and the smallest area is taken. Figure 6 presents the flowchart, obtained using Maple 15 software, for the optimal area of a rectangular footing, considering each case separately. \[\sigma_{1}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}+\frac {6\big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{2}\]9 Flowchart for the optimal area procedure. \[\sigma_{2}=\frac{P}{L_{x}L_{y}}+\frac{6(M_{x}+Py_{c})}{L_{x}L_{y}{}^{2}}-\frac{6 \big{(}M_{y}+Px_{c}\big{)}}{L_{x}{}^{2}L_{y}}, \tag{3}\]0 Flowchart of optimal area, obtained using Maple software. \begin{table}
\begin{tabular}{c c} \hline \hline
**Case** & **Constraint Functions** \\ \hline I & Equations (2)–(5) and \(0\leq\sigma_{1}\), \(\sigma_{2}\), \(\sigma_{3}\), \(\sigma_{4}\leq\sigma_{max}\) \\ II & Equations (10)–(12), \(L_{x}\geq L_{x1}\) and \(L_{y}\geq L_{x1}\) \\ III & Equations (13)–(15), \(L_{x}\leq L_{x1}\) and \(L_{y}\geq L_{y1}\) \\ IV & Equations (16)–(18), \(L_{x}\geq L_{x1}\) and \(L_{y}\leq L_{y1}\) \\ V & Equations (19)–(21), \(L_{x}\leq L_{x1}\) and \(L_{y}\leq L_{y1}\) \\ \hline \end{tabular} \(L_{x}\), \(L_{y}\), \(L_{x1}\), and \(L_{y1}\) are limited to satisfy the conditions of each case.  
\begin{tabular}{c} \hline \hline Start with the known parameters \(x_{n},y_{n}\), \(P\), \(M_{n}\), \(\sigma_{max}\) \\ \hline Determine the objective function for the optimal area using Equation (22) \\ \hline Calculate the constraint functions for the five cases separately \\ \hline \end{tabular}
\end{table} 
Table 1: Biaxial bending.
# A Microfluidic Approach for Biosensing DNA within Forensics
## 3 Biosensing of DNA
### Next Generation Sequencing
#### 3.2.1 MiSeq/HiSeq  
Solexa, later on acquired by Illumina, made a sequencing system based on sequencing-by-synthesis in combination with reversible dye terminators and a planar solid glass support. The adapters are placed at the ends of the DNA sequence. [23; 24; 25; 26]. Sequences complementary to the adapters 'A' and 'B' are present on the complete inside of these flow cell lanes [27]. Once the other end of the target DNA hybridizes to the complementary sequence present on the support, a bridge structure arises [24]. Each one of those bridge amplified clusters contains a unique DNA template, which is subsequently primed and sequenced [26]. There are different fluorescent labels present on each of the ddNtPs and a removable blocking group. The DNA sequence can be determined by completing the template one base at a time and recording of the fluorescent signal with a CCD camera. The HiSeq series, and later the MiSeq series, succeeded the Solexa Genome Analyzer [28].
## 4 Experimental Setup and Result Analysis
### System Testing
#### 4.6.3 Wide-Angle Video Stitching Implementation  
The real-time wide-angle image stitching system was deployed on the Jetson TX2 NX edge computing platform. Utilizing the DeepStream framework, dual wide-angle video streams were captured from two cameras. The first frame from each stream was first processed by the deployed correction network for distortion rectification. The precomputed homography matrices and optimal stitching seams (i.e., image registration and alignment parameters) were then stored in GPU memory to initiate the stitching process. The final stitched video output was streamed to a designated URL, as illustrated in Figures 24 and 25.  
Figure 23: Wide-angle distorted image (**left**) and rectified image (**right**), captured by camera unit 2.  
Figure 22: Wide-angle distorted image (**left**) and rectified image (**right**), captured by camera unit 1.  
In the tested configuration--featuring two wide-angle cameras each capturing images at a resolution of 1024 \(\times\) 768 and producing a stitched output of 1400 \(\times\) 1400 pixels--the system achieved a stable frame rate of 15 FPS on the Jetson TX2 NX platform. The correction network performed inference at 56 ms per frame, while the stitching operation required an additional 15 ms. The six-core CPU exhibited an average utilization exceeding 90%, and the GPU maintained an average utilization of 75%, with memory usage around 2031 MB.  
Experimental results demonstrate that the proposed system is capable of delivering high-quality, low-latency wide-angle image stitching even under constrained embedded computing resources, effectively meeting real-time application requirements.  
Figure 24: Image registration result after rectification.  
Figure 25: Real-time output of stitched and rectified images.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 4 Preliminaries
### Overview of Seagull Optimization Algorithm
#### 4.4.1 Migration  
In process of migration, the seagull needs to recompense the following multiple scenarios.  
1. Collision avoidance  
In the SOA, in order to avoid impact between neighbors, extra constraints are in place for the calculation of the ideal exploration agent location as expressed in Equation (3).  
\[Cs=A\times Ps(X) \tag{3}\]  
where the drive features of the search agent are characterized as \(A\), present iteration is stated as \(X\), search agent's present location is specified as \(Ps\), and \(Cs\) is not impacted by residual agents. The search agent's movement patterns are obtainable in Equation (4),  
\[A=Fc-(X\times\left(\frac{Fc}{maxiteration}\right) \tag{4}\]where \(X=0,1,2,\ldots max\)_iteration_.  
\(Fc\) is designated as 2; \(A\) is labeled linearly and condensed from \(Fc\) to 0; \(Fc\) is used to regulate the constraint's frequency.  
2. Movement in the direction of optimal neighbors  
As soon as collision between neighbors is eliminated, the exploration agents are detected in the direction of optimal neighbor movements, which is formulated as (5).  
\[Ms=B\times(Pb(X)-PS(X)) \tag{5}\]  
where the search agent and its position is specified as \(PS(X)\) & \(Ms\); \(B\) is labeled as a random agent, which is accountable for effectual assessment between examination and manipulation; the exploration agent with optimal fitness is stated as \(Pb(X)\); and the random value calculation is expressed in Equation (6)  
\[B=2\times S=A^{2}\times RD \tag{6}\]  
where \(RD\) is designated as a random quantity accessible in diversity between [0,1].  
3. Remain close to the finest search agent  
At last, the location of the updated search agent is connected to the ideal search agent, which is expressed in Equation (7),  
\[DS=|Cs+Ms| \tag{7}\]  
where \(DS\) is defined as the distance between the actual search agent and the best-fit search agent.
## 3 Proposed Methodology
### Feature Extraction  
In the sake of speech emotion classification feature extraction can help to diminish computational errors, computational time, and model complexity. However, it is necessary to extract such acoustic feature who provide valid information about the emotion. For the proposed model, Mel-spectrogram, Chromagram, MFCCs, delta MFCCs, delta-delta MFCCs and Tonnetz representation and Spectral Contrast features were extracted from BAVED, EMO-DB, SAVEE and EMOVO datasets. The following list provide some details about extracted features.  
* _Mel-spectrogram features:_ to extract these features audio signals broken down into frames and for each frame Fast Fourier transform is applied. The frequency spectrum is separated by the frequency of equal space to get the Mel-scale of signal frame.
* _Chromagram features:_ 12 distinct pitch classes used to extract Chromagram features using STFT and binning method. These features help to differentiate harmony and pitch classes.
* _MFCCs, delta MFCCs, delta-delta MFCCs based features:_ MFCCs features represent short-term power spectrum while Mel-frequency Cepstrum have equally spaced frequency band and provide insights for better classification by measuring the capacity of human ear to bear the audio signal. There are 40 sets of MFCCs, delta MFCCs, delta-delta MFCCs in the three Mel-cepstrum extracted features. All audio signals were divided into equal length frames to extract MFCC features. These frames revised to remove silence portion for both the start and end of the frame by applying windowing operations. Furthermore, the time domain signal converted to the frequency domain using Fast Fourier Transform (FFT). A mean scale filter bank applied to compute values of FFT computed frequencies. Equation of Mean scale filter bank is \[\text{Mel(f)}=2595\times\text{log10}\left(1+\text{f}/700\right)\] (2) After the computation of frequency values power logs are further computed for each Mel-frequency. At the end Discrete Cosine Transform (DCT) used to transform all log-Mel-spectrums into time domain and the extracted amplitudes known as MFCCs.
* _Tonnetz based features:_ the relation in between fall and rise of speech signal of harmonic network is described by pitch space of six-dimensions. To distinguish Q1 environmental sounds the tonal features of audio frames play an important role.
* _Spectral contrast-based features:_ the root mean square difference between spectral proof and the peak of signal frames computed to attain Spectral features.  
The total number of features extracted for this study is 273 which includes 128 + 12 + 40 + 40 + 40 + 6 + 7 features of Mel-spectrogram, chromagram, MFCC, delta-MFCC, delta-delta MFCC, tonnetz, and spectral respectively. These extracted features are further used for the training of the proposed model.  
Figure 1: Audio features extraction after data augmentation in the form of spectrogram (**a**) MFCC (**b**) Chromagram (**c**) Spectral Contrast (**d**) Mel spectrogram (**e**) Tonnetz.
# A Review of Practice and Implementation of the Internet of Things (IoT) for Smallholder Agriculture
## 3 Results and Discussion
### Discussion of Challenges and Recommendations
#### 3.3.2 Data Transmission Challenges:  
Poor connectivity: Due to the remote location of some farming communities, poor mobile network connectivity and reliability is a common challenge.  
_Technologists_: As a last resort, data can be collected manually, i.e., from a central hub connected to individual devices by a local wireless network.  
_Project managers_: Check mobile network coverage in your implementation area using GSMA maps and cross-check with non-industry sources, for example, a compilation of user contributed Nperf data.  
_Transmission cost_: While data costs have decreased significantly, the recurring cost of providing IoT services was frequently identified as a challenge for commercial applications.  
_Technologists_: For some applications, satellite and LPWAN-based service providers are increasingly cost-competitive with conventional mobile data.
## 5 Computational Experiments  
In this section, extensive numerical experiments were conducted to evaluate the algorithm's performance. The experiments were performed on a desktop computer equipped with a 4-core Intel Xeon Gold 5218 2.3 GHz processor and 31 GB RAM, with the heuristic algorithm implemented in MATLAB R2019a. To comprehensively evaluate the computational efficiency and practical viability of the proposed HACO algorithm, we designed a series of systematic experiments.  
Comparative studies with exact and heuristic approaches validate EVRP-HERI-CC's NP-hard property while highlighting HACO's exceptional solution quality through problem-specific adaptations. Capacity-dependent analyses reveal HACO's robust HERI integration advantages over baseline algorithms, quantifying the substantial impact of CI capacity constraints on algorithmic performance. Furthermore, partial charging strategies significantly reduce operational costs, and sensitivity analyses on large-scale instances provide practical guidelines for optimal CI capacity allocation and route planning optimization.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 3 Results and Discussion in Axle Recognition
### Training Process
#### 3.1.4 Training Optimization  
To facilitate model training and testing, Pytorch was selected to construct sequence-to-sequence models based on Transformer-GRU.  
Considering that there were only two classes, class 0 and class 1, in the target output sequence, the sigmoid activation function was added after the fully connected output layer to map the output value between 0 and 1, thereby accelerating the model's convergence.  
At the same time, since the number of class 0 in the label sequence was generally much more than the number of class 1, the focal_loss function was chosen to prevent the model from neglecting the learning of class 1, with an increased weight assigned to the class 1 and hard-to-classify samples. We do not want to lose class 1, so focal_loss helps the model avoid false negatives.  
The Adam optimization algorithm was used during model training, gradient clipping was used to prevent gradient explosion, and L2 regularization was used to prevent overfitting.  
The Transformer-GRU model was trained with the above conditions. The trend of training and validation loss is shown in Figure 5. Since focal_loss dynamically adjusted the weight of hard-to-classify samples, validation loss fluctuated slightly, but both training loss and validation loss converged and the gap was small. The model was well trained and had high accuracy in testing.  
Finally, the model was verified on the test set, and it was deployed at the Epang Palace highway toll station for field tests.
# Advanced Unified Earthquake Catalog for North East India
## 4 Regression Analysis for Magnitude Conversion
### Intensity Conversion Relation  
Considering 29 earthquakes in India and adjacent areas from 1897 to 2016, a magnitude-intensity GOR1 relationship has been developed, with independent MMI (I\({}_{0}\)) in the range 5-12 and seismic moment magnitude (M\({}_{\rm wg}\)) determined from several sources, as follows:  
\[\rm{M}_{\rm wg,GCMT}=0.48\;I_{\rm max}+3.07(0.1) \tag{7}\]  
Figure 15 depicts the plot of the Intensity relationship.  
Ref. [5] derived the GOR1 relationships between M\({}_{\rm w}\) and MMI for the same datasets and found a lower slope (0.389) than the present study (0.48). The maximum difference between M\({}_{\rm w}\) and M\({}_{\rm wg}\) estimations using MMI is found to be 0.5, which may lead to serious biased in the preparation of seismicity parameters. It is found that the GOR1 relationship derived between MMI and M\({}_{\rm wg}\) has the highest accuracy when compared to SLR and GOR2 (Tables 2 and 3).  
The GOR1 relationships derived above for the study region are compared to the similar GOR1 relationships derived by Ref. [5] for NE India and its bordering region. It is found that the regression relationships derived in this work for the NE India area are not the same as those defined by Ref. [5], as in this paper, the regression relationship is for the seismic moment magnitude (M\({}_{\rm wg}\)) while Ref. [5] is for the moment magnitude (M\({}_{\rm w}\)). For easy reference to the reader, we are reproducing the GOR1 relationships of Ref. [5] as below:  
\[\rm{M}_{\rm w,GCMT}=0.615\;M_{\rm s,ISC}+2.32,\,4.1\leq M_{\rm s,ISC}\leq 6.1 \tag{8}\]  
\[\rm{M}_{\rm w,GCMT}=0.699\;M_{\rm s,NEIC}+1.878,\,4.2\leq M_{\rm s,NEIC}\leq 6.1 \tag{9}\]  
\[\rm{M}_{\rm w,GCMT}=1.084\;m_{\rm b,ISC}-0.3106,\,4.8\leq m_{\rm b,ISC}\leq 6 \tag{10}\]  
\[\rm{M}_{\rm w,GCMT}=1.104m_{\rm b,NEIC}-0.495,\,4.8\leq m_{\rm b,NEIC}\leq 6.1 \tag{11}\]  
\[\rm{M}_{\rm w,GCMT}=1.193M_{\rm L}-0.943,\,5.0\leq M_{\rm L}\leq 6.6 \tag{12}\]  
\[\rm{M}_{\rm w,GCMT}=0.742M_{\rm D}+1.565,\,4.2\leq M_{\rm D}\leq 6.8 \tag{13}\]  
Appendix B contains abbreviations for various magnitude scales. A procedure for conversion of different magnitudes towards seismic moment scale M\({}_{\rm wg}\) has been suggested in Appendix C. Homogeneous earthquake catalog in terms of M\({}_{\rm wg}\) has been reported in Appendix A. A full data catalog can be obtained from the corresponding author.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 2 Background
### Current Frequency Bands Operating in Colombia
In this latest auction, there were no bids for the 700 MHz and 1900 MHz bands, possibly because the spectrum blocks offered were small (a maximum of 10 MHz), or because some operators had already reached the maximum allowed cap in those bands. Table 6 presents the maximum spectrum caps that a mobile operator can hold in Colombia. \begin{table}
\begin{tabular}{c c c} \hline
**Year** & **Frequency (MHz)** & **Amount of Spectrum Assigned** \\ \hline  & 703–713 & 20 MHz \\  & 758–768 & 20 MHz \\ \hline  & 713–723 & 20 MHz \\  & 768–778 & 20 MHz \\ \hline  & 723–733 & 20 MHz \\  & 778–788 & 20 MHz \\ \hline
2019 & 2515–2520 & 10 MHz \\  & 2635–2640 & 10 MHz \\ \hline  & 2520–2525 & 10 MHz \\  & 2640–2645 & 10 MHz \\ \hline  & 2540–2545 & 10 MHz \\  & 2660–2665 & 10 MHz \\ \hline  & 2545–2550 & 10 MHz \\  & 2665–2670 & 10 MHz \\ \hline  & 2550–2555 & 10 MHz \\  & 2670–2675 & 10 MHz \\ \hline  & 2555–2560 & 10 MHz \\  & 2675–2680 & 10 MHz \\ \hline  & 3300–3380 & 80 MHz \\ \hline
2023 & 3380–3460 & 80 MHz \\ \hline  & 3460–3540 & 80 MHz \\ \hline  & 3540–3620 & 80 MHz \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._Despite this situation, the interest of operators in acquiring the blocks allocated for 5G (3.500 MHz) stands out, reaching an approximate total of USD 334 million and requiring the winners to invest around USD 7.1 billion in infrastructure and coverage expansion over a ten-year period [51].
# Photogrammetry-Based 3D Textured Point Cloud Models Building and Rock Structure Estimation
## 4 The Accuracy Verification of the 3D Point Cloud Model
The real accuracy of a textured 3D point cloud model is controlled by the following factors: the accuracy of the total station, the number and the quality of the images of the rock mass, the grid density of the point cloud, and the fineness of the texture of the rock mass.