esa-sceva/satcom-chunk-collection huggingface dataset:
# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 6 Conclusions  
In this paper, we propose a simple yet effective approach for primary video object segmentation. Based on the complementary relationship of the foreground and the background, the problem of primary object segmentation is turned into an optimization problem of objective function. According to the proposed objective function, a complementary convolutional neural network is designed and trained on massive images from salient object datasets to handle complementary tasks. Then, by the trained models, the foreground and background in a video frame can be effectively predicted from the spatial perspective. After that, such spatial predictions are efficiently propagated via the inter-frame flow that has the characteristic of neighborhood reversibility. In this manner, primary objects in different frames can gradually pop out, while various types of distractors can be well suppressed. Extensive experiments on three video datasets have validated the effectiveness of the proposed approach.  
In the future work, we intend to improve the proposed approach by fusing multiple methods of defining primary video objects such as motion patterns, semantic tags and human visual attention. Moreover, we will try to develop a completely end-to-end spatiotemporal model for primary video object segmentation by incorporating the recursive mechanism.  
Methodology, J.W. and J.L.; Project administration, J.W.; Validation, L.X.; Writing--original draft, J.W.; Writing--review & editing, J.L. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grant number 62132002 and 11790305.  
Not applicable.  
Figure 8: Failure cases of our approach. Rows from top to bottom: video frames, ground-truth masks and our results.
## 3 Data and Methodology
### Data Input for the Urban LULC Modeling  
The affirmation satellite data pre-processing was an important step to extract the LULC in each year of study. To model the urban LULC, however, there were many input data to collect, such as demographic, economic data, and other geo-database of physical and environment related to urban features including topography, transportation network, stream, and administrative boundary. These data were created and stored in different formats and scaled according to the data owner. The details of the input data are shown in Table 1.  
Figure 2: Overall Methodology.  
Demographic data at the sub-district level and economic data at the provincial level were converted through rasterization, performed using ArcGIS 10.3 (ESRI, Redlands, CA, USA), to produce the raster input maps. Some distance maps were generated such as distance to river, transportation network, agriculture, build-up, forest, and miscellaneous to use in the data analysis step. Finally, all raster inputs were resized into 100-meter resolution.
# Hybrid Wavelet-CNN Fault Diagnosis Method for Ships' Power Systems
## 2 Related Works
Early fault detection is extremely crucial in the energy sector, and a combination of wavelets and DL is applied in various power systems. The authors in [19] presented a method based on a CNN in coordination with DWT that could diagnose and locate defects in power system operators for substation equipment. [PERSON] et al. [20] diagnosed defects in the hydraulic piston pump of a transmission system. The authors conducted their analysis on an experimental setup, and besides the normalized CNN in conjunction with the wavelet transform, an interesting addition was the Bayesian algorithm that was implemented to automatically tune the model. Many of the latest studies of 2022 focused on fault event classification in grid-connected photovoltaic systems [21; 22; 23]. [PERSON] et al. [21] tested an intelligent scheme using a wavelet transform and an extreme learning machine, and achieved outstanding accuracy on simulated noisy data. Similarly, [PERSON] et al. [22] introduced an approach based on CNN and CWT for the extraction of scalograms. The grid-connected photovoltaic system was modeled in Simulink, and the detection was achieved within 2 s. [PERSON] et al. [23] presented a method for the visual faults of photovoltaic modules such as discoloration, delamination, and glass breakage. DWT was applied to preprocess captured images, and other signal-processing techniques were evaluated, including the gray-level co-occurrence matrix, FFT, and the gray-level difference method. Afterwards, the preprocessed experimental images were fed into ensemble deep neural networks. In our previous work [8], 1D CNN models were utilized for fuel-oil consumption estimation and failure detection. Moreover, we proposed signal-preprocessing methods in conjunction with DL for condition monitoring cases [6; 7]. Specifically, we generated spectrograms to train 2D CNN models, efficiently detecting defective behavior in the operational state of vessels [6], and oil and gas pipelines [7]. Going one step further in the current study, we investigate wavelet efficiency for preliminary signal analysis instead of spectrograms.
## 2 Study Area  
The Maharlou Lake basin (Figure 1), with a catchment area of 4720 km\({}^{2}\), is located in southwestern Iran in the area circumscribed by a rectangle at 29.32\({}^{\circ}\)-29.55\({}^{\circ}\) N and 52.69\({}^{\circ}\)-52.90\({}^{\circ}\) E. While the basin normally (based on data from 1987 to 2016) experiences mild variations in rainfall and temperature (Figure 2), the southern part of the watershed receives an average of approximately 250 mm of precipitation annually, and the northern and central parts of the basin receive as much as 480 mm. The basin's average annual rainfall is approximately 390 mm. Average annual temperatures range from 18 to 19 \({}^{\circ}\)C, with a regional average of 19 \({}^{\circ}\)C [26]. Due to the region's semi-arid climate, the streamnetwork of the region is ephemeral; in most cases, streams and other water bodies only appear during wet seasons [26].  
Maharlou Lake is a local landmark, a recreational site for locals, and a considerable environmental and economic asset for the basin. At an elevation of 1460 m.a.s.l., the lake lies in a vast endorheic basin surrounded by mountains that reach 2800 m.a.s.l. It is worth noting that the lake is mostly recharged via surface water, with no recharge points from the region's groundwater system. Despite observed historical patterns, the lake's water volume was, until recently, relatively consistent in both the wet (winter and spring) and dry (summer) seasons. Since the early 2000s, however, the lake's reservoir has seemingly experienced more pronounced fluctuations. The lake's depth varies seasonally from 0 m in the dry season to about 3.5 m in the wet seasons, and the lake's ponded area spreads to 275 km\({}^{2}\)[25]. This begs the question: are the recently observed fluctuating patterns in line with the historic hydro-climatic behavior of the lake? If not, at what point did such changes start revealing themselves?
# Fatigue Analysis of Greenhouse Structure under Wind Load and Self-Weight
## 4 Conclusions  
In this study, fluid dynamics analysis, structural analysis, and fatigue analysis were conducted to investigate the structural safety of a Venlo-peach-type greenhouse under dynamic wind load. The reliability of the fluid dynamics analysis results was proven by comparing them with experimental results, and the validity of the analytical model was verified with a correlation coefficient of \(R^{2}=0.99\). The following conclusions can be drawn based on the stress-normalized model for fatigue analysis and the method of considering the influence of self-weight: In the case of the greenhouse fabricated from zinc-plated SPSR 400, a maximum stress of 85.9 MPa was observed under self-weight. This stress cannot be neglected. The fatigue life cycle increased by 21% under self-weight, based on the wind speed distribution in the island region of Korea. In addition, the effect of self-weight decreased at high wind speeds when the fatigue model was used without stress compensation while self-weight and wind load were applied simultaneously. Furthermore, the changes in stress with wind speed were proportional to the square of wind speed. Thus, fatigue analysis can be simplified if the stress-based model is normalized by the square of wind speed and fatigue loading data are generated using the square of wind speed. Therefore, the life of greenhouses can be predicted more accurately and easily by correcting the static stress generated by self-weight to fatigue estimation stress, normalizing the stress model by the square of wind speed, and using the time series data for the square of wind speed.  
This research was supported by export promotion technology development program (114083-03), Ministry of Agriculture, Food, and Rural Affairs.  
All authors discussed and agreed on the idea and scientific contribution and contributed to the writing. [PERSON] did the mathematical modeling, performed the composition and organization of the paper, [PERSON] contribution to the computational simulation and discussion of results.  
The authors declare no conflict of interest.
## 4 Solution Methodology
### Fixed-Step Gradient Algorithm  
Figure 2 shows the flowchart of the fixed-step gradient (FG) algorithm. The pseudocode is given in Algorithm 1, using the following notations:  
* \(\mathbf{b}=(b_{1},\dots,b_{K-1})\): the current solution;
* \(N\): the specified total buffer capacity;
* \(R\): the remaining capacity to be allocated;
* \(\mathbf{g}=(g_{1},\dots,g_{K-1})\): the gradient of \(f(\mathbf{b})\) with respect to \(b_{1},\dots,b_{K-1}\), i.e., \(g_{i}=\frac{\partial f(\mathbf{b})}{\partial b_{i}}\).  
Figure 2: Flowchart for the FG algorithm.  
In Step 1, each buffer capacity \(b_{i}\) is set to 0, and the remaining capacity \(R\) is set to \(N\). Then, the gradient of the throughput function is evaluated by simulation. Moreover, the infinitesimal perturbation analysis (IPA) technique is used to calculate the gradient in a single simulation run [44]. In our implementation, the simulation program is based on the generalized semi-Markov process framework in Suri and Fu [44], and the IPA code is taken from Gurkan [46]. In Step 3, a single buffer unit is allocated to the buffer with the largest gradient component, i.e., the step size is 1 buffer unit. The algorithm repeats the above steps until the remaining buffer capacity \(R\) is 0 and returns \((b_{1},\ldots,b_{K-1})\) as the proposed solution.  
The correctness of the algorithm is based on the concavity property described in the previous section. The algorithm always allocates a buffer unit to \(b_{j}\) that has the largest gradient component \(g_{j}\). Due to the concavity, \(g_{j}\) will decrease as \(b_{j}\) increases. Therefore, by repeating the allocation procedure, the gradient components \(g_{1},\ldots,g_{K-1}\) will be approximately equal, which guarantees the near-optimality of the solution.  
Clearly, the computational complexity of the FG algorithm is \(N\), i.e., the total number of simulation evaluations is \(N\). When it is applied to BAPs in large production lines, the solution process will be time-consuming.
###### Abstract
Moreover, this improvement enhances system scalability and reduces CPU utilization. Secondly, we design and implement an efficient multi-task shared QP transmission strategy, which achieves fine-grained control over memory blocks through status byte transmission. This mechanism provides more stable throughput compared to the sliding window method in multi-task real-time video streaming scenarios. Thirdly, in conjunction with our data transmission mechanism, we use containers as the smallest processing units to handle data and enable collaborative resource scheduling and allocation among edge servers, thereby enhancing the multitask processing and system scalability of edge computing. The rest of the paper is organized as follows. Section 2 provides the background and motivation for our work. In Section 3, we detail the design and implementation of the status-byte-assisted RDMA transmission mechanism. Section 4 presents our experimental evaluation results to illustrate the effectiveness of the proposed status-byte-assisted RDMA transmission mechanism. Finally, Section 5 concludes the paper with a summary.
# Influence of Stray Current on Fastening System Components in Urban Railway Tracks
## 4 Results
### Analysis of the Voltage in Fastening System Components
#### 4.1.2 Voltage in Anchor Bolts in Zagreb 21-CTT Fastening System  
In the Zagreb 21-CTT fastening system, the underside of the steel plate and bores for the anchor bolts at the plate were vulcanized (Figure 10a) to prevent current from leaking from the steel plate to the anchor bolts and lower parts of the track. Because the steel plate of the Zagreb 21-CTT fastening system is vulcanized, the anchor bolts should be isolated from the plate. However, a high voltage was detected in anchor bolt 2. All elastomer elements of the Zagreb 21-CTT fastening system were made of the same material during vulcanization. To determine the electrical resistivity of the material, measurements were performed using a GWInstek GDM9061 multimeter and a direct-current source on the rail pad. The rail pad was placed between the steel plate and rail. The rail was connected to the positive terminal of the current source, and the steel plate was connected to the negative terminal (Figure 10b). The current and voltage were measured using a multimeter, and the calculated electrical resistivity of the rail pad was 1.14 k\(\Omega\)m. The low electrical resistivity of the elastomer elements in the Zagreb 21-CTT fastening system resulted in a current flow from the steel plate to the anchor bolts, which led to a high voltage detected in the anchor bolts.  
Because electrical resistance is not one of the defined properties that elastomer elements used in these fastening systems must satisfy, the electrical resistance between the steel plate and anchor bolt depends on the materials and fillers used to vulcanize the steel plate and produce an elastic pad for the anchor bolt. Currently, the most used filler is soot, whose electrical resistivity varies from 0.0044 to 1.50 \(\Omega\)m. To prevent current from flowing from the steel plate to the anchor bolts, the electrical resistance of the elastomer elements used should be high.  
Figure 8: DEPP fastening system: (**a**) Case 1: elastic pad and nut at the anchor bolt. (**b**) Case 2: elastic pad and nut removed from the anchor bolts.  
Figure 9: Measured voltages in anchor bolts for cases 1 and 2 for the DEPP fastening system.
# Roco Proteins: GTPases with a Baroque Structure and Mechanism
## 4 Physiological Functions of Roco Proteins
### Roco Proteins from Dictyostelium Discoideum  
Eleven different Roco proteins have been discovered in _D. discoideum_[26; 27]. Using an axenic mutant [35], it was shown that the expression patterns of several Roco genes in this organism are elevated upon starvation, playing a role in the formation of multicellular aggregates [36]. So far, the function of four of these _D. discoideum_ Roco proteins has been investigated in more detail. GbpC is a key player in cell aggregation by regulating chemotaxis via myosin II phosphorylation [27; 37; 38]. Phosphorylated myosin II locates at the rear of polarized cells, where it suppresses the formation of pseudopodia and stimulates the retraction of the rear of the cell [37]. Protein associated with the  
Figure 1: Classification of Roco proteins based on domain topology. Roco proteins are characterized by the presence of a Roc domain (red) and COR domain (blue). Based on their domain topology, Roco proteins can be divided into three groups [29]. Here some representatives of each group are shown. The first group only contains an LRR domain (yellow) preceding the RocCOR. Representatives of this group are found in metazoo, plants, archaea, and bacteria. The second group contains, in addition to this LRR-Roc-COR domain topology, a C-terminal kinase domain (green). Moreover, members of this group contain several protein–protein interaction and regulatory domains like an Ankyrin repeat (ANK, pink), armadillo repeats (ARM, light blue), a N-terminal motif of RasGEF (N-GEF, light blue), a cyclic nucleotide binding domain (cNB, light grey), a Rab-like GTPase activators and myotubularins domain (GRAM, blue), a Ras Guanine Exchange Factor domain (RasGEF, beige), a N-terminal myotubularin-related domain (myotub, orange), a protein tyrosine phosphatase domain (PTP, light green), a Dishevelled domain, and an Egl-10 domain and Pleckstrin domain (DEP, grey). The last group has one human family member, DAPK1. DAPK1 lacks an LRR domain and is characterized by its C-terminal death domain (DD, brown).  
transduction of signal 1 (Pats1) on the other hand, plays an important role in the recruitment of myosin II to the cleavage furrow of cells during cytokinesis [39; 40]. Quick growth protein A (Qkga) has a negative regulatory effect on cell proliferation. _qkgA_-null cells have a faster cell proliferation and slower cell aggregation resulting in the formation of very large slugs [27; 36; 41]. Finally, Roco4 is another well-studied _D. discoideum_ Roco protein. Although wild-type and _roco4_-null cells initially show similar multicellular aggregation upon starvation, _roco4_-null cells form atypical fruiting bodies with a spore head unable to lift into the air due to the absence of cellulose [36]. Together these data show that the Roco proteins in _D. discoideum_ have a wide variety of cellular functions despite their similar domain architecture.
## 2 Background
### Code-Based GNSS Inter-User Ranging Techniques
#### 2.1.3 Single Point Position (SPP) Estimation  
To proceed to the various DGNSS ranging techniques, an approximation of the involved users' position \(\hat{\mathbf{x}}_{*,k}\) and local clock offset \(\hat{\sigma}_{*,k}^{l}=\hat{b}_{*,k}/c\) is needed. A GNSS receiver can solve both for its own position and for the _aiding user_'s position by using the external pseudorange measurements after extrapolation to its own local timestamps. Through this approach, the exchange of the estimated position is not necessary since it can be computed locally at the aided receiver. The SPP estimate of each user is obtained thanks to an iterative LS estimator, where at each iteration \(l\), the user assumed position \(\hat{\mathbf{x}}_{*,k}\) is updated based on the linearization of the pseudorange equation with a Taylor expansion truncated at the first order:  
\[\hat{\mathbf{x}}_{*,k}=\hat{\mathbf{x}}_{*,k}^{l-1}+\Delta\hat{\mathbf{x}}_{*,k} ^{l} \tag{5}\]  
where \(\hat{\mathbf{x}}_{*,k}^{l-1}\) is the position estimated at the previous iteration for user \(*\) (\(i\) or \(j\), interchangeably) and \(\Delta\hat{\mathbf{x}}_{*,k}^{l}\) is the increment in position for the current iteration \(l\) and is obtained together with the user clock bias increment \(\Delta\hat{b}_{*,k}^{l}\) with:  
\[\begin{bmatrix}\Delta\hat{\mathbf{x}}_{*,k}^{l}\\ \Delta\hat{b}_{*,k}^{l}\end{bmatrix}=\left(\mathbf{H}^{l\top}\mathbf{H}^{l} \right)^{-1}\mathbf{H}^{l\top}\boldsymbol{\Delta}\boldsymbol{\rho}_{*,k}^{l}, \tag{6}\]  
where \(\mathbf{H}^{l}\) is the \(N\times 4\) direction cosine matrix at iteration \(l\) estimated from the most recent estimation of the user * position. It is made of the _unit steering vectors_ to the satellite \(s\) at epoch \(k\), \(\mathbf{h}_{*,k}^{s}\)[14]:  
\[\mathbf{H}^{l}=\begin{bmatrix}\mathbf{h}_{*,k}^{1}&1\\ \mathbf{h}_{*,k}^{2}&1\\ \vdots&\vdots\\ \mathbf{h}_{*,k}^{N}&1\end{bmatrix} \tag{7}\]  
\(\Delta\boldsymbol{\rho}_{*,k}^{l}\) is the pseudorange increment vector \(N\times 1\) which has been corrected for the last estimate of the user clock bias. \(N\) represents the number of GNSS satellites involved in the computation and must be at least equal to 4 for an unbiased estimation of the 3D user position and its clock bias.
## 2 Basic Principles and Methodologies
### The Coefficient of Heating Effect
#### 2.3.3. AGES-AHK Method  
In this research, a novel temperature reconstruction method based on the adaptive hybrid kernel and the adaptive grid evolution strategy is proposed and demonstrated. The proposed AGES-AHK method implements adaptive kernel adjustments on AGES-optimized nonuniform grids, integrating the optimization of AHK and AGES.  
The flow chart of the AGES-AHK method and the complete reconstruction process are shown in Figure 5. Firstly, the acoustic path curve within the ROI is obtained by utilizing the known preset temperature phantom. The reciprocal of sound velocity matrix \(x\) is obtained from Equation (1) and the TOF matrix \(b\) can be derived from simulations or measurements.  
Secondly, the adaptive nonuniform grid collocation is determined by AGES. By implementing adaptive nonuniform meshing without increasing the total grid number, AGES pre-optimizes grid density distribution to match the temperature field topography, thereby facilitating improvement in the reconstruction performance.  
Thirdly, the RBF approximation with hybrid kernels is introduced to expand the reciprocal of sound velocity \(f\) in Equation (3). The finalized hybrid kernel function from the AHK method combines the sharp spatial localization characteristics of G with the global characterization of MQ, recovering the null-space component and reducing the ill-conditioned nature of the problem.  
Next, the ill-conditioned linear equations \(\mathbf{B}\lambda=\mathbf{b}\) in Equation (10) are solved by the reconstruction algorithm, thereby reconstructing the average grid temperature within the ROI and subsequently reproducing the continuous temperature profiles. Finally, the reconstruction errors are calculated to analyze the overall reconstruction performance and the hotspot tracking performance.  
To assess the real-time applicability of the AGES-AHK method, it is important to evaluate its computational complexity. The overall computation of the AGES-AHK method is primarily influenced by the iterative nature of the adaptive hybrid kernel and grid evolution strategy. For a typical problem with n \(\times\) n grids, the method requires several iterations for kernel adaptation and mesh refinement, resulting in increased complexity for grid-based reconstructions [28; 29; 30]. However, the adaptive nature of the algorithm reduces the need for exhaustive calculations across all regions, leading to enhanced efficiency compared to standard approaches. Additionally, optimization of the grid evolution strategy allows the method to focus computational resources on hotspot regions, thereby enhancing real-time applicability. Compared to traditional methods [11; 25; 26; 27] with high  
Figure 5: Flow chart of the AGES-AHK method and the full reconstruction process.  
computational costs, the AGES-AHK method strikes a balance between reconstruction accuracy and computational efficiency, making it suitable for real-time applications in industrial settings.