esa-sceva/satcom-chunk-collection huggingface dataset:
# Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
## 3 Analyses and Discussion
### SAR Results  
The results from the SAR data related to the monthly changes are presented in Figures 7-9. While the backscatter values of the water class, as expected, do not change much over the months, the other classes' values vary depending on the vegetation growth. Thus, the values of the swamp class starts to rise up in March at the beginning of the leaf-on season and then drastically falls in October at the beginning of the leaf-off season. In the leaf-off season, the swamps are generally open water bodies, which explain the similar backscatter values with the water class. The other class values are similar to each other. However, the land class has the lowest backscatter values through the year except for August and September, when the bog/sediment class has the lowest values. The detailed comparison between the classes can be seen in Figures 11-14.  
Figure 8: Sentinel-1 backscatter average VV values.  
Figure 6: Statistical results of observed LST of the six different wetland classes in the leaf-on season.  
Figure 7: Sentinel-1 backscatter average VH values.
# FF-YOLO: An Improved YOLO11-Based Fatigue Detection Algorithm for Air Traffic Controllers
## 3 Materials and Methods
### FF-YOLO Network
#### 3.3.3 MPDIoU Loss Function  
In facial fatigue detection tasks, bounding boxes need to cover dynamically changing facial regions. When the head moves closer to or farther from the camera, detection targets with identical aspect ratios but varying scales are formed. YOLO11 employs the CIoU regression loss function, which optimizes bounding box regression by introducing a center point offset penalty and aspect ratio constraints [36]. However, its aspect ratio penalty term becomes ineffective when predicted and ground-truth boxes share identical aspect ratios, leading to reduced regression efficiency for targets with significant scale differences [37]. Given this, FF-YOLO replaces the CIoU function in YOLO11 with the MPDIoU loss function.  
Minimum Point Distance Intersection over Union (MPDIoU) is a loss function designed for bounding box regression tasks [38]. It optimizes geometric similarity measurement by directly minimizing the distances between the top-left and bottom-right corner points of predicted boxes and ground-truth boxes. As shown in Figure 11, the blue box A represents the true box, while the yellow box B represents the predicted box. The MPDIoU loss function calculates the distance \(d_{1}\) between the top-left corners of boxes A and B, using the squared distance \(d_{1}^{2}\) for computational simplicity as specified in Equation (13) [38]. Similarly, it calculates the distance \(d_{2}\) between the bottom-right corners of boxes A and B, using the squared distance \(d_{2}^{2}\) as defined in Equation (14) [38]. The MPDIoU metric is computed based on Equation (15) [38], where w and h represent the width and height of the input image, respectively. The MPDIoU-based loss function is given by Equation (16) [38].  
\[d_{1}^{2}=\left(x_{1}^{B}-x_{1}^{A}\right)^{2}+\left(y_{1}^{B}-y_{1}^{A} \right)^{2} \tag{13}\]  
\[d_{2}^{2}=\left(x_{2}^{B}-x_{2}^{A}\right)^{2}+\left(y_{2}^{B}-y_{2}^{A} \right)^{2} \tag{14}\]  
\[MPDIoU=\frac{A\cap B}{A\cup B}-\frac{d_{1}^{2}}{w^{2}+h^{2}}-\frac{d_{2}^{2}}{ w^{2}+h^{2}} \tag{15}\]  
\[L_{MPDIoU}=1-MPDIoU \tag{16}\]
## 2 Materials and Methods
### Methods
#### 2.1.4 Calculation of Costs  
The total costs comprise investment costs for equipment, operating costs, and maintenance expenses. The present value of total costs is determined using the following equation:  
\[C_{\text{t}}\left(\tau\right)=C_{\text{I}}+\text{SUM}\left[C_{\text{a,i}}\cdot R \text{d}-V_{\text{f,\tau}}\right] \tag{12}\]  
where \(\tau\) represents the accounting period, \(C_{\text{I}}\) is the initial investment cost, \(C_{\text{a}}\) is the annual operating cost, \(R_{\text{d}}\) is the discount factor for each year, and \(V_{\text{f,\tau}}\) is the residual value at the end of the evaluation period. The calculation is repeated annually over the accounting period.  
The discount factor \(Rd(p)\) is defined by the following:  
\[Rd(p)=\left[1/(1+r/100)\right]p \tag{13}\]  
\(p\) represents the year, and \(r\) is the discount rate.  
Levelized cost of heating \(C_{\text{LH}}\) is calculated for all scenarios and system variants considered in this study. It is defined as the ratio of the total system costs to the total thermal energy produced over the evaluation period, as expressed by the following equation:  
\[C_{\text{LH}}=C_{\text{t}}\left(\tau\right)/E_{\text{prod,t}}\left(\tau\right) \tag{14}\]
## 5 Aerodynamic Loads and Safety Analysis
### Flow Structure and Aerodynamic Loads
For the aerodynamic moments, the variation characteristic of the rolling moment was similar to that of the yawing moment. Once the corresponding carriages entered the tunnel, they all had a positive peak (see Figure 16c,e). The reason for the overshoot of the yawing moment was the pressure difference on both sides of the train changes, significantly along the longitudinal side, as that part of the carriage was shielded from the crosswind by the tunnel, while the overshoot of the rolling moment may have also attributed to the jet flow. The double-track tunnel adopted in the simulation caused the center of the train not to coincide with that of the tunnel. The aerodynamic impact pressure caused by jet flow in the near-wall side was larger than that in the tunnel center side. In addition, the flow structure of jet flow presented a laminar flow on the tunnel wall side and a turbulent flow on the tunnel center side. The pitching moment of the head and middle carriages presented a downward pulse successively when entering the tunnel (see Figure 16e). While in the corresponding process, the pitching moment of the rear carriage appeared as upward and downward pulses successively in the time interval of about 0.3 s.  
Figure 16: Cont. To sum up, except for the side and lift forces of the head carriage, the remaining aerodynamic loads all showed upward or downward impulses when entering the tunnel, which may have been a key factor threatening the safety of the train traffic. However, the variation amplitudes of aerodynamic side force, lift force, and the yawing moment of the head carriage were greater than those of the middle and rear carriages. The safety risks of the three carriages still need further discussion in the next section.
# Relationships between Anthropometric and Strength Profiles of Streetlifting Athletes
## 3 Results
### Relationships between Anthropometrics, Body Composition and Streetlifting Performance  
Significant positive correlations were detected between the chin-up performance and flexed and tensed arm girth (r = 0.470; \(p<0.001\)), forearm girth (r = 0.35; \(p=0.002\)) chest girth (r = 0.40; \(p<0.001\)), fat-free mass (r = 0.42; \(p<0.001\)), AMA (r = 0.52; \(p<0.001\)) and Brugsch's index (r = 0.25; \(p=0.029\)). Negative correlations were found between the chin-up performance and calf girth (r = \(-0.23\); \(p=0.039\)), %bodyfat (r = \(-0.42\); \(p<0.001\)) and endomorphy (r = \(-0.32\); \(p=0.004\)). The dip performance was correlated with flexed and tensed arm (r = 0.60; \(p<0.001\)), forearm (r = 0.54; \(p<0.001\)), chest girth (r = 0.41; \(p<0.001\)), fat-free mass (r = 0.37; \(p<0.001\)), AMA (r = 0.64; \(p<0.001\)), mesomorphy (r = 0.26; \(p=0.022\)) and Brugsch's index (r = 0.38; \(p<0.001\)). Low and moderate negative correlations were found between dip performance and thigh girth (r = \(-0.28\); \(p\) = 0.046), calf girth (r = \(-0.33\); \(p\) = 0.003), femur length (r = \(-0.34\); \(p\) = 0.002), tibial length (r = \(-0.31\); \(p\) = 0.006), %body fat (r = \(-0.42\); \(p\) < 0.001) and endomorphy (r = \(-0.28\); \(p\) = 0.013). Muscle-up performance correlated with body mass (r = 0.29; \(p\) = 0.027), flexed and tensed arm (r = 0.43; \(p\) < 0.001), chest girth (r = 0.44; \(p\) < 0.001), fat-free mass (r = 0.30; \(p\) = 0.020), AMA (r = 0.39; \(p\) = 0.002) and [PERSON]'s index (r = 0.29; \(p\) = 0.024). Finally, positive correlations between squat and thigh girth (r = 0.44; \(p\) < 0.001), fat-free mass (r = 0.38; \(p\) < 0.001), CSA (r = 0.59; \(p\) < 0.001) and [PERSON]'s index (r = 0.45; \(p\) < 0.001) were found. On the contrary, squat performance was negatively correlated with height (r = 0.28; \(p\) = 0.012), femur length (r = \(-0.23\); \(p\) = 0.036), tibial length (r = \(-0.32\); \(p\) = 0.005) and ectomorphy (r = \(-0.47\); \(p\) < 0.001).
## 3 Methodology
### Building Detection  
Shadows could provide location and height information for building detection. With the assistance of shadow information, buildings are detected and classified into three layers: low-rise buildings, medium-rise buildings, and high-rise buildings. The processes contain seed-block generation, reliable areas setting, building detection, and building height-classification. Reliable areas refer to the areas that have a high possibility of containing buildings; and the fractal net evolution approach (FNEA) [38], widely used in object-oriented image analysis, is employed for segmentation.
## 6 Conclusions  
This study focused on the FlexRay static segment scheduling, and analyzed transmission characteristics of static segment message, then, proposed the network evaluation criteria. Using NFD algorithm, the signal encapsulation problem is converted to a packing problem. At the same time, a scheduling strategy for message _FID_ multiplexing is established to optimize the transmission of message. The simulation system was established by Network Designer. FlexRay software and CANoe. FlexRay software. The simulation results show that the proposed algorithm could reduce the bus load by 16.3%, the bandwidth utilization by 32.8%, as well as decrease the number of FIDs by 53.8%. The FlexRay static segment message improves the transmission efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Message** & **Signal** & **ECU** & **Repetition** & **Size (bit)** \\ \hline
1 & 1, 14, 4 & 1 & 1 & 40 \\
2 & 9, 15, 2 & & 1 & 48 \\
3 & 5, 10, 16, 3 & & 2 & 48 \\
4 & 6, 11, 7, 8, 12 & & 2 & 46 \\
5 & 13, 17 & & 4 & 24 \\
6 & 21, 26, 22 & 2 & 2 & 40 \\
7 & 27, 18, 19 & & 1 & 44 \\
8 & 23, 24, 28, 29, 20, 25 & & 2 & 48 \\
9 & 30, 34, 38, 35 & 3 & 2 & 48 \\
10 & 31, 36, 32 & & 2 & 49 \\
11 & 33, 39, 37, 40, 41 & & 2 & 42 \\
12 & 42, 43, 44, 47, 45 & 4 & 2 & 48 \\
13 & 46, 48, 49, 50 & & 2 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Signal packing for BCBFD (Bandwidth Consumption and Best Fit Decreasing) scheduling.  
Figure 10: The node set of FlexRay simulation system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Network Performance** & **NFD Algorithm** & **BCBFD Algorithm** \\ \hline Total frames & 1025 & 1225 \\ Null frames & 0 & 0 \\ Busload & 7.23\% & 8.75\% \\ Bandwidth utilization & 83\% & 62.5\% \\ FID number & 6 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Network performance comparison.  
**Author Contributions:** Y.X. ([PERSON]): conceptualization and supervision; [PERSON]: methodology and writing of original draft; [PERSON] and [PERSON]: software and formal analysis; [PERSON] and [PERSON]. ([PERSON]): validation and review & editing of original draft.  
**Funding:** This research was funded by National Natural Science Foundation of China Grant Number 61763047.  
**Acknowledgments:** This research was supported by National Natural Science Foundation of China Grant Number 61763047.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
IoT (Internet of Things)-based remote monitoring and controlling applications are increasing in dimensions and domains day by day. Sensor-based remote monitoring using a Wireless Sensor Network (WSN) becomes challenging for applications when both temporal and spatial data from widely spread sources are acquired in real time. In applications such as environmental, agricultural, and water quality monitoring, the data sources are geographically distributed, and have little or no cellular connectivity. These applications require long-distance wireless or satellite connections for IoT connectivity. Present WSNs are better suited for densely populated applications and require a large number of sensor nodes and base stations for wider coverage but at the cost of added complexity in routing and network organization. As a result, real time data acquisition using an IoT connected WSN is a challenge in terms of coverage, network lifetime, and wireless connectivity. This paper proposes a lightweight, dynamic, and auto-reconfigurable communication protocol (LDAP) for Wide-Area Remote Monitoring (WARM) applications. It has a mobile data sink for wider WSN coverage, and auto-reconfiguration capability to cope with the dynamic network topology required for device mobility. The WSN coverage and lifetime are further improved by using a Long-Range (LoRa) wireless interface. We evaluated the performance of the proposed LDAP in the field in terms of the data delivery rate, Received Signal Strength (RSS), and Signal to Noise Ratio (SNR). All experiments were conducted in a field trial for a water quality monitoring application as a case study. We have used both static and mobile data sinks with static sensor nodes in an IoT-connected environment. The experimental results show a significant reduction (up to 80%) of the number of data sinks while using the proposed LDAP. We also evaluated the energy consumption to determine the lifetime of the WSN using the LDAP algorithm.  
distributed wireless sensor network; wide-area remote monitoring; lightweight protocol; internet of things; dynamic protocol; water quality monitoring 2020 IEEEexample:BSTcontrol
# Insights on Osmotic Tolerance Mechanisms in _Escherichia coli_ Gained from an _rpoC_ Mutation
## 2 Materials and Methods
### Transcriptional Analysis  
Perturbations to transcriptional regulation in the presence of 0.6 M NaCl were determined using microarray analysis using strains BW25113, EJW3, MG1655, and EYG1 with two biological replicates each. Samples were cultivated using the same condition as in metabolite analysis with 25 mL cultures and an initial OD\({}_{600}\) of \(\sim\)0.05. When OD\({}_{600}\) reached \(\sim\)0.5, cells were quickly chilled to \(\leq\)4 \({}^{\circ}\)C on dry ice/isopropanol bath, then harvested by centrifugation (4470\(\times\)\(g\)) at 4 \({}^{\circ}\)C followed by immediate re-suspension in 5 mL of RNAlater (Sigma-Aldrich, St. Louis, MO, USA). Total RNA was extracted by using the ZR Fungal/Bacterial RNA MicroPrep(tm) (Zymo Research, Irvine, CA, USA) kit. A total of 10 \(\upmu\)g of the isolated total RNA was mixed with 1.5 \(\upmu\)g random primers (Promega, Madison, WI, USA), incubated at 70 \({}^{\circ}\)C for 10 min and then cooled on ice (4 \({}^{\circ}\)C). The cDNA was synthesized by combining the total RNA mixture with 10 U SuperScript@III reverse transcriptase, 1\(\times\) first strand buffer, 0.01 M DTT (Invitrogen, Carlsbad, CA, USA), and nucleotides (0.5 mM dATP, 0.5 mM dCTP, 0.2 mM dTTP (Promega, Madison, WI, USA) and 0.3 mM amino-allyl dUTP (Thermo Fisher Scientific, Waltham, MA, USA)), and incubated at 42 \({}^{\circ}\)C for 3 h. The cDNA was recovered with ice-cold ethanol precipitation, labeled with either Cy3- or Cy5- mono-reactive dye (GE Healthcare, Little Chalfont, Buckinghamshire, UK), and then hybridized to the _E. coli_ Gene Expression Microarray (Agilent Technologies). The arrays were scanned using the GenePix 4100A Microarray Scanner and the images were analyzed using GenePix Pro 6.0 software (Molecular Devices, Sunnyvale, CA, USA). The MIDAS software package (TM4) [31] was used to normalize the data using LOWESS based normalization algorithm [32]. The rank product method with a critical _p_-value < 0.01 was used to identify the differentially expressed genes using MeV (TM4) microarray analysis software [31]. Gene ontology analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) [33; 34].
## 5 Conclusions  
This study investigates a practical maritime TWRN scenario and proposes an efficient and reliable data transmission scheme that combines BMST codes with PNC. By leveraging superposition operations in both the time and spatial domains, the proposed design simultaneously enhances reliability and spectral efficiency, addressing a key challenge in maritime communications. To mitigate information loss at the relay node, we introduce a novel DC algorithm that performs network coding operations after iterative decoding. The numerical results demonstrate that, under identical parameter configurations, the BMST-RSPC-PNC system employing the DC algorithm achieves a remarkable 2.9 dB coding gain over the CD algorithm at a BER of \(10^{-5}\). Capitalizing on the flexible dual-layer decoding architecture of the DC algorithm, we further propose two enhanced schemes, i.e., the SM-DR scheme and the DM-SR scheme, to optimize relay node performance. The theoretical and empirical analyses reveal that the SM-DR scheme exhibits superior waterfall performance, while the DM-SR scheme attains a lower error floor, rendering them adaptable to diverse operational requirements in complex maritime environments.  
The findings of this work provide valuable insights for developing ultra-reliable and high-efficiency maritime communication systems. Within the framework of the SGIN, the proposed scheme presents a promising solution for long-distance maritime transmission, with potential applications in next-generation wireless networks.  
Conceptualization, Y.S. and Y.T.; methodology, Y.S. and Y.T.; software, Y.S.; validation, Y.S.; formal analysis, Y.S. and Y.T.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S. and Y.T.; visualization, Y.S.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Hainan Provincial Natural Science Foundation of China under Grant 622 QN286, 624 RC485, and the Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) under Grant SKLNST-2023-1-07.  
Some or all data or codes that support the findings of this research are available from the author upon reasonable request.  
The authors declare no conflicts of interest.
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 1 Introduction
Combined with Kalman filter, this method improves tracking performance during occlusion, but lacks adaptability to an occlusion target model. [PERSON] et al. integrated the occlusion detection method, adaptive model update and prediction system into the KCF tracker [11], where the occlusion detecting method determines the occluding type by the peak sidelobe ratio (PSLR) of the confidence map. An adaptive transition state equation is used to estimate the target's acceleration and speed required in the extend Kalman filter (EKF) to predict the target location. [PERSON] et al. proposed a KCF tracking method based on embedded multi-feature fusion and motion trajectory compensation [12] to which an adaptive Kalman filter is introduced to revise the result of KCF tracker, working out the problem about motion target occluded. Although the average tracking accuracy of partial occluding targets is 89%, its average tracking speed is 18 fps, which is a little bit slow. [PERSON] et al. exploited migrate learning techniques to extract depth characteristics from target images [13]. The algorithm whose average tracking speed is 36 fps effectively increases the tracking ability of sports targets by combining the advanced depth learning and the HOG characteristics. To summarize, the tracking algorithm based on the KCF has a high tracking accuracy and a high tracking speed. But, in the case of occlusion, the tracking accuracy will be greatly reduced due to the small amount of target feature information obtained. When the target is completely occluded, relevant scholars use the Kalman filtering tracking algorithm to predict the trajectory of the target [14; 15]. The tracking accuracy of occluded target can be enhanced by combining the two tracking results effectively. The article mainly has the following contributions:In order to overcome the defect that KCF algorithm cannot fully distinguish target from other similar objects using only one single feature, this method fuses the HOG and Fourier descriptor features of the target for the GM-APD LiDAR intensity image, and combines the frequency-domain information and spatial information to describe the target more comprehensively, sufficiently distinguishing the target from other similar objects and improving the tracking performance of the KCF algorithm. 2. Aiming at the declining of tracking accuracy or even tracking failure when the target is in occlusion, this method uses the peak sidelobe ratio (PSLR) and intrinsic shape signature (ISS) to effectively judge the occlusion state of the target. Then, an adaptive factor is proposed to fuse the tracking results of kernel correlation filter and Kalman filter, according to the occlusion state of the target, improving the tracking accuracy when the target is in occlusion.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### Linear Memory DTW Algorithm
#### 3.2.4. Diagonal Computation with Linear Memory Complexity  
[PERSON] and [PERSON] proposed to compute \(\mathbf{d^{k}}\) recursively, noticing that Equation (6) can be computed recursively diagonal-wise, instead of row- and column-wise. The permitted transitions depicted on Figure 3 are demonstrated for the diagonal-wise computation on Figure 5. It is straightforward to observe that \(\mathbf{d^{k}}\) can be computed knowing \(\mathbf{d^{k-1}}\) and \(\mathbf{d^{k-2}}\). The recurrence rule in Equation (6) can be rewritten as:  
\[\alpha(i,j)=\mathcal{S}(a_{i},b_{j})+\max\begin{cases}\alpha(i-1,j-1)\\ \alpha(i,j-1)\\ \alpha(i-1,j)\end{cases}\qquad\forall(i,j)\in\mathbf{d^{k}},\forall k\in\{3, \dots,K\} \tag{10}\]The initialisation step becomes:  
\[\begin{cases}\mathbf{d_{1}}=[\mathcal{S}(a_{1},b_{1})]\\ \mathbf{d_{2}}=[\mathcal{S}(a_{2},b_{1})+d_{1}[0],\mathcal{S}(a_{1},b_{2})+d_{1} [0]]\end{cases} \tag{11}\]  
The key idea is that once \(\mathbf{d^{k}}\) as been computed, only the values of \(\mathbf{d^{k}}\) and \(\mathbf{d^{k-1}}\) are needed to compute the next diagonal \(\mathbf{d_{k+1}}\), and \(\mathbf{d^{k-2}}\) can be dropped. From an implementation perspective, each diagonal memory buffer pointer is shifted circularly at each step (\(\mathbf{d^{k-2}}\leftarrow\mathbf{d^{k-1}}\), \(\mathbf{d^{k-1}}\leftarrow\mathbf{d^{k}}\), \(\mathbf{d^{k}}\leftarrow\mathbf{d^{k-2}}\)). Only three diagonal memory buffers are thus necessary to compute all diagonals. As a diagonal has a maximal length of \(\min(|\mathbf{a}|,|\mathbf{b}|)\), the linear memory requirement of this recursion scales linearly in \(\mathcal{O}(\min(|\mathbf{a}|,|\mathbf{b}|))\).  
The same reasoning applies to compute \(\mathbf{d_{R}^{k}}\) recursively diagonal-wise.