esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Progress on Novel DSP Techniques for Mode Division Multiplexing Systems: A Review
## 4 DSP Techniques on Compensation of MDL Effects in FMF Links
### Maximum-Likelihood (ML) Detector  
A receiver-side ML detection scheme was firstly reported in [53] to compensate MDL in FMFs. The ML detector is designed to maximize the probability \(P(x\mid y)\) for all transmitted hyper-symbols. In an additive white Gaussian noise (AWGN) channel model, the typical form of ML detection principle [54] is given by Equation (11):  
\[\hat{\mathbf{x}}_{ML}=\arg\min_{\mathbf{x}\in\mathbb{A}^{N_{T}}}\|\mathbf{y}- \mathbf{H}\mathbf{x}\|^{2} \tag{11}\]  
where \(\mathbb{A}\) is the set of value of ideal constellation symbols, \(\mathbf{x}\) is the transmitted symbols vector, \(\mathbf{y}\) is the received symbols vector, and \(\hat{\mathbf{x}}_{ML}\) is the estimation of transmitted symbols of _ML_ detector. The _ML_ detection seeks for the transmit symbols vector multiplied by transmission matrix \(\mathbf{H}\) which has the minimum Euclidean distance from the received signal.  
Figure 7: Mean Q\({}^{2}\) factor vs. rotational angular frequency for different algorithm: (**a**) A lower steady-state misalignment with a poorer tracking capability; (**b**) A higher steady-state misalignment with a better tracking capability.  
The search is conducted over all possible symbol combinations from all spatial channels and possible modulation symbols.  
As shown in Figure 8, ML detection is the optimal receiver detector among all the simulation results compared with other linear equalizers such as zero-forcing (ZF) and minimum-mean-square-error (MMSE) equalizer [53]. The advantages of ML detection make it a powerful tool to reduce MDL in combination with strong-coupling fibers or mode scramblers [33; 53].  
However, its complexity grows exponentially with both the number of modes and the order of the modulation constellation with regard to Figure 9, forcing us to consider the compromise between its performance to and the extremely high computational complexity. Thus, for MDL reducing, few DSP methods were proposed to achieve near-ML performance without being impacted by the huge computational complexity, such as the improved reduced-search (IRS) ML detection [55; 56], the sphere decoding (SD) [56] and complex lattice reduction aided detection (CLR) [57]. Each algorithm outperforms the linear equalizer MMSE to improve the tolerance of MDL for MDM system with acceptable computational complexity gain.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Technologies
#### 3.1.2 Bluetooth  
A technical standard called Bluetooth makes it possible for electrical appliances to communicate wirelessly across short distances. Bluetooth uses short-range radio frequencies. Bluetooth is connected to other Bluetooth-enabled devices for short-range communication. The Bluetooth device mostly works around a 10 m distance. It is good for devices within the medical field.
## 3 Methodology
### Discriminative Attention Multi-Network (DAM-Net)  
Our main task is executed using the DAM-Net, designed for fine-grained feature recognition of pests. Figure 2 outlines the composition of the DAM-Net, which includes a base branch, a target branch, and a discriminative branch. The base branch captures global features, providing a holistic image representation. The object focus location module (OFLM) synthesizes feature maps from the base branch to determine the target object's bounding box, enabling cropping of the base image. This cropped image undergoes processing via the target branch to extract structural and fine-grained features. Complementing this, the specific part identification module (SPIM) isolates and crops the most discriminative and least redundant parts of the object. These regions are fed into the discriminative branch, which delves into intricate details across various scales. Equations (1)-(3) represent the loss functions for the three branches, respectively.  
\[L_{base} =-\log(P_{b}(c)) \tag{1}\] \[L_{target} =-\log(P_{a}(c))\] (2) \[L_{discriminative} =-\sum_{n=0}^{N-1}\log\Bigl{(}P_{d(n)}(c)\Bigr{)} \tag{3}\]  
In this model, \(c\) represents the actual category of the input image. The probabilities \(P_{b}\) and \(P_{a}\) are the softmax outputs from the base and target branches, respectively. Meanwhile,\(P_{d(n)}\) is the softmax output for the \(n\)-th image in the discriminative branch, with \(N\) being the total number of such discriminative images. The total loss is computed as  
\[L_{total}=L_{base}+L_{target}+L_{discriminative} \tag{4}\]  
The total loss combines the losses from the three branches, utilizing shared convolutional and classification layers to ensure unified feature extraction across the network. During backpropagation, this cumulative loss function adjusts the entire network by considering the losses from each branch, optimizing both global and local feature detection. To enhance efficiency in practical applications, we exclude the discriminative branch during testing, reducing complexity and accelerating predictions. The final results are achieved by merging the logits from the base and target branches.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 5 Ablation Study
### Effectiveness of Components in the BADE Module  
We conducted in-depth ablation analysis on two components of the BADE module: the blur-aware attention block (BAAB) using sharpness priors and deformable convolution alignment based on optical flow residuals. As shown in Tables 5 and 7, adding BAAB alone yields limited improvement (0.85 dB). However, incorporating BAAB on top of deformable convolution alignment shows more significant improvement (1.81 dB). This reveals important synergistic effects between BAAB and feature alignment operations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{**MSPF Module**} \\ \hline Progressive Fusion & & ✓ & ✓ \\ SFFB & ✓ & & ✓ \\ \hline PSNR & 33.15 & 32.26 & 34.87 \\ SSIM & 0.9621 & 0.9345 & 0.9729 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study for MSPF module design on the GoPro dataset.  
Figure 8: Visualization of ablation on the MSPF module.  
Figure 7: Effectiveness of the proposed module for video deblurring.  
To better understand BAAB's working mechanism, we visualize intermediate feature maps in Figure 9. Without BAAB, the vehicle headlight area suffers more interference from blurred pixels. This leads to decreased detail restoration quality. BAAB adaptively modulates feature responses using sharpness prior information. It enhances feature responses in sharp regions while suppressing them in blurred areas. The combination of BAAB and deformable convolution alignment maximizes their respective strengths. BAAB provides sharpness awareness to help identify and preserve features in clear regions. Meanwhile, deformable convolution ensures effective temporal propagation and fusion of these valuable features. This synergy enables better detail recovery in complex dynamic scenes.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
### Load-Sharing Coefficients
#### 4.3.2 Dynamic LSCs under External Excitation  
When subjected to external excitation, taking the dynamic LSCs on the left side as an example, the variations in the LSCs in the fixed-axes PGT and the differential PGT are shown in Figures 21 and 22, respectively. The comparative analysis of the dynamic LSCs considering the impact of external excitation versus the absence of external excitation is shown in Figure 23.  
From Figures 21 and 22, it can be observed that under the influence of internal excitations, the overall trend of the LSCs was similar to that under the rated condition. However, the LSCs become sharper due to the alternating external loads. Figure 23 indicates that considering the external excitation results in higher LSCs compared to the case without external excitation, indicating an uneven load distribution caused by the external excitation.  
Figure 19: Variations in the dynamic LSCs in the fixed-axes PGT under the rated condition: (**a**) left external meshing pairs; (**b**) left internal meshing pairs.  
Figure 20: Variations in the dynamic LSCs in the differential PGT under the rated condition: (**a**) left external meshing pairs; (**b**) left internal meshing pairs.
# Heat Loss Coefficient Estimation Applied to Existing Buildings through Machine Learning Models
## Materials and Methods
### Machine Learning Models
#### 2.2.2 Support Vector Regression  
SVR is a nonlinear kernel based regression model that focuses on finding the best regression hyperplane with the least structural risk in a high-dimensional feature space [25; 26; 27]. The SVR function is represented in Equation (6):  
\[g(\mathbf{x})=\mathbf{w}^{T}\delta(\mathbf{x})+\mathbf{b} \tag{6}\]  
\(\delta(x)\) being a nonlinear mapping that connects the input space to the feature space, \(b\) a bias term and \(w\) the weight coefficient vector. In this case, both \(w\) and \(b\) are estimated by resolving the following optimization problem [26; 28]:  
\[\begin{split}\underset{\mathbf{w},\mathbf{b}}{\text{minimize}}& \frac{1}{2}\parallel\mathbf{w}\parallel^{2}+C\sum_{j=1}^{l}( \tau_{j}+\tau_{j}^{*})\\ \text{subject to}&\mathbf{y}_{j}-g(\mathbf{x}_{j}) \leq\epsilon+\tau_{j}\\ & g(\mathbf{x}_{j})-\mathbf{y}_{j}\leq\epsilon+\tau_{j}^{*}\\ &\tau_{j},\tau_{j}^{*}\geq 0\end{split} \tag{7}\]  
where the constant \(C>0\) represents the trade-off between training error and model complexity, \(\epsilon\) corresponds to a threshold value and \(l\) is the number of training patterns. As [PERSON] et al. [28] and [PERSON] et al. [29] showed, once having resolved the optimization problem (Equation (7)) and taking the Lagrangian, the model solution can be reached with its dual representation (see Equation (8)):  
\[g(\mathbf{x})=\sum_{j=1}^{l}(\alpha_{j}^{*}-\alpha_{j})K(\mathbf{x},\mathbf{x }_{j})+\mathbf{b} \tag{8}\]  
where \(\alpha_{j},\alpha_{j}^{*}\) are the Lagrangian multipliers (\(\
eq\)0) and the solution for the dual problem, \(b\) the bias term and \(K(x_{j},x)\) the kernel function based on the inner product \(\langle\delta(x_{j}),\delta(x)\rangle\).  
Specifically, the model optimization for each of the validation samples studied is carried out through the tuning of certain parameters. In this case, the selected parameters were penalty \(C\) and term \(\epsilon\)[24; 29]. With a cross-validation process (\(k\)-fold = 10) [49], different values of these parameters were evaluated, and the best adjustment was obtained depending on the validation sample considered.
# Distinct Signatures of Host Defense Suppression by Plant-Feeding Mites
## 4 Material and Methods
### Real-Time Quantitative Reverse-Transcriptase Polymerase Chain Reaction (qPCR)  
The RNA samples that were used for microarray hybridizations were also used for cDNA synthesis and subsequently for gene expression analysis by means of qPCR. We followed the same experimental setup as for the microarray hybridizations, meaning that equal amounts of RNA, isolated from each of the six plants per treatment, were pooled, resulting in a total of 24 samples (6 treatments \(\times\) 4 experimental replicates). DNAse treatment, cDNA synthesis and qPCR were performed as described previously [41]. qPCR data was analyzed with PASW Statistics 18 software (SPSS Ltd.) with a GLM, using a \"\(\gamma\)\" probability distribution and \"log\" link function. The model contained the following factors: \"treatment\" and \"technical replicate\" (i.e., two for each cDNA sample), the latter was nested within \"biological replicate\" [33]. When significant differences were found, means of each group were compared using [PERSON]'s LSD post-hoc test. Gene identifiers, primer sequences and references [41; 61; 88; 107; 113; 114; 147; 148] are listed in Table S10.  
Supplementary materials can be found at [[http://www.mdpi.com/1422-0067/19/10/3265/s1](http://www.mdpi.com/1422-0067/19/10/3265/s1)]([http://www.mdpi.com/1422-0067/19/10/3265/s1](http://www.mdpi.com/1422-0067/19/10/3265/s1)).  
**Author Contributions:** Conceptualization B.C.J.S., J.M.A., J.G. and M.R.K.; Data curation B.C.J.S. and N.W.; Formal analysis B.C.J.S., J.M.A., and N.W.; Funding acquisition N.W. and M.R.K.; Investigation B.C.J.S., J.M.A., J.G. and T.T.M.; Project administration B.C.J.S. and M.R.K.; Resources R.C.S. and M.R.K.; Software N.W.; Supervision B.C.J.S., J.M.A. and M.R.K.; Validation B.C.J.S. and J.M.A.; Visualization B.C.J.S. and N.W.; Writing--original draft B.C.J.S.; Writing--review and editing B.C.J.S., J.M.A., N.W., J.G., T.T.M., R.C.S. and M.R.K.  
**Funding:** This research was funded by: Netherlands Organization for Scientific Research (NWO) Earth and Life Sciences (ALW) TTI Green Genetics, grant number 828.08.001 (B.C.J.S.); NWO ALW TOP, grant number 854.11.005 (J.M.A.); European Union Horizon 2020 Marie Sklodowska-Curie Action (MSCA) Individual Fellowship, grant number 658795 (DNGMITE) and Fianders Research Foundation (FWO) fellowship, grant number 12T9818N (N.W.); NWO ALW \"More with Less\", grant number 847.13.005 (J.G.); NWO Technology Foundation (STW), grant number VIDI-13492 (T.T.M., M.R.K.).  
**Acknowledgments:** The authors wish to thank [PERSON], [PERSON] and [PERSON] for handling the plants and [PERSON] for technical assistance.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Data Availability:** Tomato transcriptomic data has been uploaded to the Gene Expression Omnibus repository (accession number GSE116827). Tomato phytohormone and qPCR data has been uploaded to FigShare (10.6084/m9.figshare.7195097). All raw data is publicly available.
# Smart Roads for Autonomous Accident Detection and Warnings
## 2 Related Works
### Analysis of Previous Approaches  
All the previous accident-related techniques are based on some sort of continuous monitoring in the vehicle of its surroundings through various sensors with the help of a microcontroller-based processing unit. Calibration of these devices from time to time is necessary for proper function, which becomes costly. Communication between vehicles is carried out by wireless technology. Although a GPS offers easy and accessible localization, the precision of the GPS still has room for further improvement in providing accuracy. To be more specific, a GPS suffers influences from several factors (e.g., receiver noise; a multipath effect), such that the received GPS coordinates have large errors in the actual coordinates of the vehicle, thereby posing a threat to the safety of Avs or the precision of ITS applications [21]. Another problem with GPS technology is that not all driving surfaces have satellite visibility [32]. The received GPS data can be influenced in urban areas by building occlusions, making the data less accurate [13]. On the other hand, post-accident techniques use a GPS to detect and find the location of the accident, with GSM and 5G technology for messaging to emergency service centers [51; 52]. These techniques require internet connection. Sometimes, owners do not want to make changes in their vehicles, such as installing vehicular sensors. Another issue is the calibration of these sensors from time to time to avoid false readings. Sensor and communication-link checks for proper function are not always easy. Most of the previous techniques for accident detection apply to Evs only. Our work is also different from other studies [44; 45; 46; 47; 48; 49; 50] where the major focus was to provide information about road conditions to drivers, reduce traffic congestion by controlling traffic lights, and manage communication resources to improve system capacity. A summary on the analysis of previous approaches is provided in Appendix A.
## 5 Strength Check and Fatigue Properties of Seven-Wire Strands in Hangers under Lateral Bending
### Advice to Mitigate the Adverse Influence of Lateral Bending on the Seven-Wire Strands in Hangers  
The strength of the seven-wire strands in the hangers can be checked by Equation (20), above, for the constructed HTABs and TABs with a floating deck structure. For HTABs and TABs that will be constructed in the future, it is necessary to take corresponding measures during design to decrease the adverse influence of lateral bending on the seven-wire strands in the hangers. The lateral bending that is caused by the different displacements at both ends of the hangers is the main bending form of hangers based on the conclusions in this paper. Therefore, the most desirable method is to eliminate the relative displacement between the arch ribs and bridge decks. Based on the above analysis, some measures could be taken to mitigate the adverse influence of lateral bending on hangers.  
1. The tied arch structure can be used for TABs to mitigate the adverse influence of lateral bending of hangers effectively when the tied arch structure meets the requirements of the topography. The bridge deck of the tie arch structure is straightly connected to the arch rib at both ends of  
Figure 20: The fatigue properties of the seven-wire strands versus the lateral bending angle.  
the arch rib; therefore, the arch rib can restrict the longitudinal deformation of the bridge deck. The extent of lateral bending could be significantly decreased in this way.
2. Hinged connections could be used for short hangers that are closed to the abutment instead of fixed connections. The shorter length and larger longitudinal deformation of the lower end of the short hangers when compared with those of the long hangers makes lateral bending more apparent with short hanger. Therefore, when fixed connections are used in the hangers for HTABs and TABs with floating deck structures, fixed connections could be replaced by hinged connections for short hangers. This method might make the hangers require more devices for their connections and slightly increase the cost of constructing bridges.
3. A jointless bridge structure should be used [46]. A jointless bridge does not have expansion joints, and the abutment is directly connected to the superstructure of the bridge, which could decrease the deformation of the bridge decks [47; 48]. When compared with a tied arch structure, the abutments of jointless bridges would suffer lateral forces from the decks, which require a stronger design for lateral abutment loads. Therefore, this method is suitable when the bridges have good rigid foundations.  
Tied arch structures can effectively mitigate lateral bending and may produce fewer side effects than the other two methods. From the result of the analysis, using tied arch structures is more strongly recommended than other methods.
## 3 Experimental Set-Up and Electronic Equipment
### Electronic Instrument Design for SHM Evaluation
Figure 4: The block-scheme of the new design of the receiver board. Figure 3: Peak voltage from impacts on the aluminum laminate generated by steel balls with mass ranging from 2 to 8.5 g in free-fall. The sensitivity of the two different sensors (see Figure 2) can be compared by the output voltages: the PVDF sensor amplitudes were multiplied by a constant factor, 20, to allow for comparison with the trend of the PZT amplitude. We can observe the good correlation between impact energy and output voltage. These values are adequate considering the low-voltage analog-to-digital converter (ADC) has 12-bit resolution and an input dynamic of 3.3 V present on the electronic platform. Moreover, we observed that the programmable attenuator is necessary to limit the input signals in the AFE chain to avoid saturation and to protect the first amplifier from large signals exceeding the allowable common mode range at the operating frequency. The main difference, with respect to the previous version [21], is the versatility of the receiver, which can be programmed to receive ultrasonic impact signals that in general include S\({}_{0}\) and A\({}_{0}\) modes. This makes it possible to receive higher frequencies, up to 270 kHz, with high signal-to-noise ratio (10 dB), to analyze S\({}_{0}\) Lamb modes that propagate at higher velocities, with respect to A\({}_{0}\) modes, and with lower amplitude. An example of the output signal after the analog processing stage is shown in Figure 5. The fast S\({}_{0}\) mode and the high-frequency component of the A\({}_{0}\) mode are now present in the signal. The early S\({}_{0}\) mode signal is used later for the calculation of DToAs for the impact localization by triangulation algorithm.
# Difference DV_Distance Localization Algorithm Using Correction Coefficients of Unknown Nodes
## 5 Simulations and Performance Comparison
### Simulation Environment Requirement and Parameter Settings  
#### 5.1.1 Simulation Environment Requirement  
In order to objectively evaluate the performance of the improved algorithm and compare it with the existing algorithms, the simulation environment is set as follows:  
Figure 4: Simulation flow chart of the proposed algorithm.  
1. The network environment parameters are the same at each simulation running, such as the monitoring area of WSN, the total number of sensor nodes, the number of anchor nodes, and the communication radius of sensor nodes and so on;
2. In the monitoring area of simulation, we randomly generate and deploy all the sensor nodes, and some sensor nodes randomly selected from sensor nodes are regarded as anchor nodes;
3. The average error and the mean square deviation are respectively used to measure the accuracy and stability of localization, and the statistical result is based on independent simulation results of 100 times; The equation for measuring localization accuracy is expressed as follows: \[\gamma=\frac{\sum\limits_{i=1}^{Num}{\sqrt{{{\left({{x_{i}^{true}}-{x_{i}^{ est}}\right)}^{2}}+{\left({{y_{i}^{true}}-{y_{i}^{est}}}\right)}^{2}}}}}{{Num \cdot R}}\] (22) where \(({x_{i}^{true}},{y_{i}^{true}})\) is the actual position of unknown node \(i\), and \(({x_{i}^{est}},{y_{i}^{est}})\) represents the estimated coordinate of unknown node \(i\) calculated by localization algorithms. \(Num\) is the total number of unknown nodes, and \(R\) is the maximum communication radius of sensor nodes.  
The distance of adjacent nodes is determined according to the power of the received signal, and the influence of environment interference factors will cause some ranging error in the actual environment. In order to simulate this error, we use the product of the actual communication distance and Gauss white noise which standard deviation is \(\sigma\).