esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[PERSON] and [PERSON] have done lots of work in filter sensor calibration and curve fitting to eliminate errors resulting from the conventional method [20; 21]. [PERSON] reached an acceptable accuracy of roll angle measurement within \(5^{\circ}\) but the compensation complicates the system and results in increased power consumption. To overcome the weakness of traditional method based on geomagnetism, some institutes presented methods based on time-frequency domain analysis [22] and frequency-locked loop [23] information obtained from single-axis magnetic sensor rather than multiple outputs or operation of inverse trigonometric function. They managed to measure the rotational speed but no progress in roll angle. Additionally, all these study above did not explain clearly how to address the problem of geomagnetic blind area [22]. [PERSON] proposed the conception of blind area of roll angle, certain range of roll angle in which detection module cannot satisfy the expected accuracy [24], which will be recapped to assist the method proposed in this paper to reduce the effect from blind area. Inspired by the integrated navigation and advantages from both GNSS and magnetoresistive sensors, a real-time estimation for roll angle based on phase-lock loop on signals from single-axis magnetoresistive is proposed. Unlike the conventional measurement by an inverse trigonometric operation based on amplitude of multiple outputs, a second-order frequency-locked loop (FLL)-assisted third-order phase-locked loop (PLL) is designed to obtain the phase information of the output of single-axis magnetometer, which is relevant to rolling attitude. Meanwhile, angle of pitch and yaw obtained from the omnidirectional antenna (technology of 1D-CCF), and declination, inclination obtained from IGRF (International Geomagnetic Reference Field) model [25], the real-time rotational information can be figured out. It is able to avoid the measurement error from conventional ways and reduce effect from blind area, reaching cost-reduction, low-power consumption, as well as miniaturation. This novel method is proposed to obtain rolling attitude of kinds of spin stabilized projectiles launched by howitzer or tank. They have a frequency range from 3 Hz to 300 Hz. Also, it could be applied to measure the roll angle of vehicle in the state of continuous rotation. In the following sections, at first, measurement model is established after the description of geomagnetic field vector in different coordinate systems. Then, the method based on second-order FLL-assisted third-order PLL to detect the rotatory attitude is studied and optimized. After that, error analysis of measurement model is discussed in detail. Solutions are studied to address different errors including that resulted from tracking system, compensation angle (defined in chapter 2), blind area. At last, the results of experiments on rotary table show the system designed is able to work effectively with a good accuracy.
# An Adaptive Neuro-Fuzzy Inference Model to Predict Punching Shear Strength of Flat Concrete Slabs
## 1 Introduction  
Generally, the contact surfaces between columns and slabs are very small in flap slab systems, and therefore high stresses are concentrated in the connections area. A punching shear failure may occur if the stresses exceed the limitations. This failure is brittle and may occur unexpectedly. To avoid this type of failure, various construction methods have been developed [1].  
In the design and analysis of two-way flat slabs without shear reinforcement, the punching shear strength is an important parameter. Much research has been conducted throughout the current century, and the key variables affecting the punching shear strength of slabs have been identified [2; 3; 4; 5]. Most of the research has been concerned with the generation of experimental data and the development of empirical equations in addition to the equations proposed by ACI 318-14 [6], BS-8110-97 [7], Model Code 2010 [8], and Euro-Code 2 [9]. However, the subject still needs further study to understand the complexity of punching shear behavior and to develop better prediction tools.  
Fuzzy logic (FL) and neural network (NN) techniques have been widely used in civil engineering applications over the last two decades. In this study, an alternative model was developed within the framework of an adaptive neuro-fuzzy interface system (ANFIS) to predict the punching shear of two-way slabs without shear reinforcement. This model was developed using a large database (207 experimental results) compiled from 17 scientific studies. The predictions from this model were compared to those from the equations proposed in commonly used codes.
## 5 Conclusions  
To reduce the complexity of graph structure construction, superpixel segmentation is often performed on an HSI first; however, superpixel segmentation processing leads to similar features within each superpixel node, resulting in a lack of local details in the classification map. To solve the above problems, a new hyperspectral image classification algorithm, the FCGN, was proposed in this paper, in which a graph convolutional network based on superpixel segmentation was fused with an attentional convolutional network for feature fusion, a GCN based on superpixel segmentation was used to extract superpixel-level features, an attentional convolutional network was used to extract local detail features, and, finally, the obtained complementary features were used to improve the classification results. In order to verify the effectiveness of the algorithm, experiments were conducted on three datasets and compared with some excellent algorithms. The experimental results show that the FCGN achieved a better classification performance. Although the FCGN achieved better classification results, there are still some shortcomings. In particular, this paper did not consider the variability of different neighbor nodes during the construction of the graph structure which may limit the ability of the model. In addition, only a simple feature splicing fusion method was used in this paper, so the construction of the graph structure and new fusion mechanism will be further explored in subsequent research.  
Conceptualization, L.G.; methodology, L.G.; software, L.G.; validation, L.G., S.X. and C.H.; formal analysis, L.G.; investigation, L.G.; resources, L.G., S.X. and C.H.; data curation, L.G.; writing--original draft preparation, L.G.; writing--review and editing, L.G. and Y.Y.; visualization, L.G. and S.X.; supervision, C.H. and Y.Y.; project administration, C.H.; funding acquisition, C.H. and Y.Y.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Advanced technology industrialization of science and technology cooperation between Jilin Province and Chinese Academy of Sciences (grant number 2020 SYHZ0028).  
Indian Pines dataset, Pavia University dataset, Salinas dataset ([[https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)]([https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes](https://www.ehu.eus/ccwintco/index.php/Hyperspectral_Remote_Sensing_Scenes)), accessed on 11 June 2022).  
The authors declare no conflict of interest.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 4 CLOR Protocol
### Process of CLOR  
#### 4.2.1 Network Initialization and Maintenance  
In the initialization phase, the sink node initializes its hop count information with 0, while the sensor nodes update their hop count information as infinity. Then, the sink establishes and maintains a tree topology by periodically flooding HELLO packets throughout the network [5; 22; 23]. A HELLO packet contains the unique identifier (ID) of the sink, the hop count (HC) information of the sink, and the stamp time (ST) of this packet. The node that has received a HELLO packet checks whether its own HC is larger than the HC carried by the packet plus 1. If not satisfying the condition, the node discards the packet after updating the neighbor table according to this packet. Otherwise, the node updates its HC by plus 1 to the HC carried in the packet and then rebroadcasts it by inserting its own information into the HELLO packet. This procedure continues until all the remote nodes receives a HELLO packet. After finishing network initialization, every node knows the hop distance from the sink and the topological information of its one-hop neighbors.  
The neighbor table in a node includes the ID, the topological information, and the bidirectional link quality of the neighboring nodes. Node degree \(D_{x}\) is used to record the number of neighboring nodes of the node \(x\). We add them to both the control packet and data packet to inform other nodes of the variation of topological information and link information.
## 2 System Architecture
### Automatic Frequency Control (AFC)  
The AFC loop is composed of a counter, differentiator, comparator, and binary search engine. The rising edges of the digitally controlled oscillator (DCO) are counted, and the differentiator calculates the number of edges within one period. This count is compared with the target frequency command word (FCW) and passed to the binary search engine. Designed based on successive approximation register (SAR) logic, the binary search engine detects frequency deviations using the comparator and sequentially updates the frequency adjustment values in a binary search manner. Starting from an initial value, it halves the frequency difference iteratively to find the appropriate correction value, which is then sent to the DCO to adjust the frequency. The AFC loop manages the initial frequency locking of  
Figure 1: Block diagram of multi-loop configuration phase-locked loop.  
Figure 2: Block diagram of triple-loop wideband fractional-N all-digital phase-locked loop.  
the PLL and coarsely adjusts the output frequency of the DCO using 6 bits out of the total 15 control bits.
# Predicting Online Item-Choice Behavior: A Shape-Restricted Regression Approach
## Appendix B Pseudocodes of Our Algorithms  
### Constructive Algorithm for \((\Gamma,E^{*}_{\textsc{UM}})\)  
The nodes and directed edges of graph \((\Gamma,E^{*}_{\textsc{UM}})\) are enumerated in a breadth-first search and are stored in two lists \(L\) and \(E\), respectively. We use \(\textsc{append}(L,\mathbf{v})\), which appends a vertex \(\mathbf{v}\) to the end of \(L\). We similarly use \(\textsc{append}(E,(\mathbf{u},\mathbf{v}))\).  
A queue \(Q\) is used to store nodes of \(L\) whose successors are under investigation (the \"frontier\" of \(L\)). The nodes in \(Q\) are listed in ascending order of depth, where the depth of \(\mathbf{v}\) is the shortest-path length from \((0,0,\ldots,0)\) to \(\mathbf{v}\). We use \(\textsc{dequeue}(Q)\), which returns and deletes the first element in \(Q\), and \(\textsc{enqueue}(Q,\mathbf{v})\), which appends \(\mathbf{v}\) to the end of \(Q\).  
Algorithm 1 summarizes our constructive algorithm for computing the transitive reduction \((\Gamma,E^{*}_{\textsc{UM}})\). For a given node \(\mathbf{u}\) in line 6, we find all nodes \(\mathbf{v}\) satisfying condition (UM1) in lines 7-10 and those satisfying condition (UM2) in lines 11-15.  
```
1:Input a pair \((n,m)\) of positive integers
2:Output transitive reduction \((\Gamma,E^{*}_{\textsc{UM}})\)
3:procedure
4:\(L\leftarrow\) list consisting of \((0,0,\ldots,0)\)\(\triangleright\) returns \(\Gamma\)
5:\(E\leftarrow\) empty list
6:\(Q\leftarrow\) queue consisting of \((0,0,\ldots,0)\)
7:while\(Q\) is not empty do
8:\(\mathbf{u}\leftarrow\textsc{dequeue}(Q)\)
9:if\((\mathbf{u},n)\in\mathcal{D}_{\textsc{U}}\)then\(\triangleright\) for (UM1)
10:\(\mathbf{v}\leftarrow\textsc{Up}(\mathbf{u},n)\)\(\textsc{append}(L,\mathbf{v})\), \(\textsc{append}(E,(\mathbf{u},\mathbf{v}))\)
11:\(\textsc{enqueue}(Q,\mathbf{v})\)
12:for\(s\in[1,n-1]\)do\(\triangleright\) for (UM2)
13:if\((\mathbf{u},s,s+1)\in\mathcal{D}_{\textsc{M}}\)then
14:\(\mathbf{v}\leftarrow\textsc{Move}(\mathbf{u},s,s+1)\)
15:\(\textsc{append}(L,\mathbf{v})\), \(\textsc{append}(E,(\mathbf{u},\mathbf{v}))\)
16:\(\textsc{enqueue}(Q,\mathbf{v})\)
```  
**Algorithm 1** Constructive algorithm for \((\Gamma,E^{*}_{\textsc{UM}})\)  
By definition, the membership test for \(\mathcal{D}_{\textsc{U}}\) and \(\mathcal{D}_{\textsc{M}}\) can be performed in \(\mathcal{O}(1)\) time. Recall that \(\textsc{dequeue}\), \(\textsc{enqueue}\), and \(\textsc{append}\) can be performed in \(\mathcal{O}(1)\) time. The FOR loop in lines 11-15 executes in \(\mathcal{O}(n)\) time. Therefore, recalling that \(|\Gamma|=(m+1)^{n}\), we see that Algorithm 1 runs in \(\mathcal{O}(n(m+1)^{n})\) time.
## 2 Materials and Methods
### Experimental Program
#### 2.2.2 Oedometer Method  
The oedometer device used in this study consisted of a specimen cell (d = 150 mm; h = 80 mm), top platen, sliding rail, displacement transducers (two calipers and one LVDT), weights, and a four-part steel rod. The cross-section of the used oedometer device is shown in Figure 2. The vertical stress was applied as a \"dead\" load using squared metal weights. The purpose of the steel rod was to keep the weights in the center of mass.  
The sitting pressure caused by the top platen was 5.39 kPa. To reduce the friction between the top platen and the oedometer cell, talc powder and silicone spray were applied. The differential settlements and tilting of the top plate were prevented with a sliding rail. The absence of tilting was verified with three displacement transducers positioned in a triangular shape.  
With the described oedometer device, a single long-term oedometer test was conducted. The tested waste specimen was installed in the oedometer cell in the material's  
Figure 1: Used air pycnometer setup.  
Figure 2: Used oedometer device.  
dry state. The initial dry mass of the installed specimen was 0.42 kg and the final specimen height was 0.0629 m. Based on the specimen size and its dry mass, the determined dry density of the installed specimen was 380 kg/m\({}^{3}\). The mean solid particle density of the installed specimen, as determined by the air pycnometer, was 1820 kg/m\({}^{3}\), with a standard deviation (SD) of \(\pm\)190 kg/m\({}^{3}\). The initial void ratio of the installed specimen, as established with Equation (3), was 3.79.  
The compression procedure consisted of four consecutive vertical stress increments in the following order: 11.78 kPa, 11.21 kPa, 22.94 kPa, and 22.88 kPa. Each stress increment was kept for 24 h. Before the first vertical stress increment was applied, the specimen was submitted to a sitting pressure of 5.39 kPa and the corresponding initial settlements were recorded. The maximum vertical stress applied on the specimen was 74.2 kPa.  
At a vertical stress of 74.2 kPa, the specimen was submitted to the wetting procedure. Initially, 2 \(\times\) 10\({}^{-4}\) m\({}^{3}\) of water was added through the bottom of the specimen (Figure 2). After the completion of the wetting process, the specimen was left under the same vertical stress for the next 354 days. In the following days, the settlements caused by wetting, mechanical creep, and the decomposition process were continuously monitored. The landfill gas (i.e., CH\({}_{4}\)) was measured once per week with a GA 5000 gas analyzer (Figure 2). In order to support the microbiological activities, additional amounts of water (1 \(\times\) 10\({}^{-4}\) m\({}^{3}\)) were occasionally added to the specimen through the oedometer's wetting channel (Figure 2).  
Based on the measured data, the dry density and corresponding void ratio were calculated after each stress increment. In addition, after the completion of the test, the final solid particle density and final dry mass of the tested specimen were also established.
## 3 Methods
### Task 2--Data Stack of Raster Images  
To efficiently manage and process the data, we created an array structure, in which to store the four bands and other useful information such as the acquisition date and the georeferencing of each raster image for both the satellites. This structure allows for easy access to the data.  
The first check we carried out was to verify whether raster images were missing for any date. In this case, a new raster layer, filled with NaN values, was created. This approach, maintaining data integrity, ensured to appropriately handle missing data for further processing.
## 5 Discussion
### Closeness of Fit and Potential Error Sources
The slight error in normalizing the EMG signal would lead to slightly liberal estimates of these muscles throughout the gait cycle. Knee muscles are typically eccentrically contracted, then concentrically contracted, during gait. The tendons have some compliance, and the in-series tendons can store--and later release--elastic energy to change the rate of muscular work [51]. Energy can also be stored in the cross-bridge heads during eccentric contractions, later released to augment muscle force during the concentric contraction that immediately follows [4]. The muscle contraction history was accounted for in the EFP model [39]. Muscle contraction velocity can affect power estimates. While normal gait, examined here, is notable for relatively slow muscle contractions and thus minimal error in predicting individual muscle power, one should exercise caution when extrapolating this approach to more dynamic movements. Intramuscular (\"wire\") electrodes were required to minimize crosstalk and to record activity from deep muscles. A concern has been extrapolating the results are extrapolated from the smaller muscle volume examined (as compared to surface EMG recordings). This is a reasonable concern, yet generalizing the wire EMG data to the entire muscle (in neurologically intact individuals) is well-supported in the literature [39; 41; 79; 80; 81; 82; 83; 84]. Previous work has demonstrated that the normalized EMG amplitude is not significantly different for wire versus surface electrodes [85]. Surface electrodes could have been used to record data from superficial muscles but could not have been used to record data from deep muscles (e.g., Vastus Intermedius). Thus, a single electrode type--intramuscular (wire) electrodes--was used to examine all tested knee muscles to remove a potential confound.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 4 Results and Discussion  
In order to prove the validity and practicability of the proposed method, the SLI-ESN model was applied to predict the actual observed PM\({}_{2.5}\) time series of Beijing. At the same time, this paper also carried out comparative experiments of ESN, LI-ESN [26], extreme learning machine (ELM) [30], hierarchical ELM (H-ELM) [31] and stacked auto-encoder (SAE) [32]. The experimental environment is Windows 7 system, MATLAB 2016a programming software. The computer memory is 6 GB, clocked at 3.50 GHz, and Intel-i3 CPU.  
Firstly, the AQI dataset and evaluation indicators are explained. Secondly, the feature selection experiment based on mRMR is introduced in detail, and the optimal subset is obtained. Finally, the  
Figure 2: Schematic diagram of time series prediction based on supplementary leaky integrator echo state network (SLI-ESN). mRMR: minimum redundancy maximum relevance.  
reconstructed features of optimal subset is used for training the SLI-ESN model, and the validity of the proposed method in PM\({}_{2.5}\) time series prediction is verified by predictive indicators.
## 4 SupMPN: Supervised Multiple Positives and Negatives Contrastive Learning Model
### Desirable Properties of SupMPN Model  
SupMPN extends self-supervised contrastive learning to supervised contrastive learning by adding multiple hard positives and multiple hard negatives. We summarize the desirable properties of SupMPN which boost performance of contrastive learning in fine-tuning BERT, and consequently lead to better sentence representation space.  
**Using multiple hard positives:** Hard positives are true positives which are farthest from the anchor sentence. Hard positives play an important role in contrastive learning and can improve performance greatly. Using multiple hard positives generalizes simple triplet loss to an arbitrary number of positives which enables the model to align similar sentences closely in the representation space [32].  
**Using multiple hard negatives:** Hard negatives are similar (nearest) to the correct answer and are hard to differentiate from the positives. Therefore, using multiple hard negatives generalizes the triplet loss by allowing joint comparison among more hard-negative examples and enables the model to improve discrimination among positive and negative sentences [32; 40].  
**No need for hard-positive mining and hard-negative mining:** Unlike the BatchHard loss [39], due to using multiple hard positives and multiple hard negatives for each anchor, hard positive and hard-negative mining (hard positive and hard-negative selection) are no longer required in SupMPN.  
**Using a batch-wise approach (Online triplet mining):** Using other sentences' positives and negatives in a batch as negatives (technique of in-batch negative) for each anchor, increases contrastive power with more negatives [32].
## 3 Experimental Setup
### Implementation Details  
Anaconda, or more specifically, Spyder software, was used to conduct the experiments. The network models were implemented using Keras (2016) with Tensorflow backend (Keras, 2016). The experiments were conducted on a GPU laptop with NVIDIA GeForce RTX 2060.  
The general working principle of any semantic segmentation algorithm is to investigate each pixel and anticipate whether it represents a point of interest or merely a part of the background. Alternatively, this principle can also be treated as a pixel-wise binary classification problem with the objective of the segmentation algorithm to minimize the binary cross-entropy loss function.  
For image \(A\), let the corresponding truth mask (TM) be \(B\), and the predicted segmentation output be \(B^{\prime}\). For a pixel \(ma\), the TM value is \(b_{ma}\) and the network predicted output is \(b^{\prime}_{ma}\). The binary cross-entropy loss for that image is defined as:  
\[Cross\ Entropy\left(A,B,B^{\prime}\right)=\sum_{maCA}\left(-(b_{ma})\log(b^{ \prime}_{ma})+(1-b_{ma})\log(1-b^{\prime}_{ma})\right) \tag{5}\]  
Figure 10: (**a–d**) Four image sets each having a raw image plus a truth mask.  
For a batch containing \(p\) images, the loss function \(L\) becomes,  
\[L=\frac{1}{p}\sum_{i=1}^{p}Cross\ Entropy\ \big{(}A_{i},B_{i},B_{i}^{\prime}\big{)} \tag{6}\]  
The goal of the model is to minimize the binary cross-entropy loss and the model is trained using the Adam optimizer [61]. The Adam optimizer adaptively computes different learning rates for different parameters from estimates of the first and second moments of the gradients. All the models, used in this research, were trained up to 120 epochs since 120 epochs were found to be the saturation point for model accuracy, and no further progress was observed beyond this point. Finally, 3-fold cross-validation of the dataset was used to validate the consistency of the model.