esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
Common downsampling methods face certain challenges, such as the high number of parameters used in Strided Convolution, the inability of patch merging to extract spatial and channel information, and the loss of detailed information via the pooling operation. To address these issues, we propose exploring a hybrid method which combines slice sampling with depthwise separable convolutions (DSConvs). In this section, we describe the algorithmic concepts and architectures of the proposed downsampling and upsampling components. The downsampling component, SliceSamp, uses a combination of slicing and DSConv to reduce the spatial resolution of input feature maps while retaining all their features. The corresponding upsampling component, SliceUpsamp, uses the inverse operation of slicing to reconstruct high-resolution features; this is a process of reorganizing high-dimensional features rather than simply using interpolation to fill in the missing values.
# Linked Coupling Faults Detection by Multirun March Tests
###### Abstract  
This paper addresses the problem of describing the complex linked coupling faults of memory devices and formulating the necessary and sufficient conditions for their detection. The main contribution of the proposed approach is based on using a new formal model of such faults, the critical element of which is the introduction of roles and scenarios performed by the cells involved in the fault. Three roles are defined such that the cells of the complex linked coupling faults perform, namely, the roles of the aggressor (\(A\)), the victim (V), and both (B), performed by two cells simultaneously in relation to each other. The memory march test and applied address sequence and background determine the scenario for implementing the roles of memory faulty cells. The necessary and sufficient conditions for detecting linked coupling faults are given based on a new formal model. Formally, the undetectable linked coupling faults are defined, and the conditions for their detection are formulated using multirun memory march tests. The experimental investigation confirmed the validity of the proposed formulated statements. Based on the example of a linked coupling fault, this study demonstrates the fulfillment of the necessary and sufficient conditions for its detection using a single march test. As demonstrated in this article, employing the approach proposed by the authors, a two-pass march C test, for instance, enables the attainment of 55.42% fault coverage for linked coupling faults, inclusive of undetectable faults identified by the single-pass march test.  
memory tests; march tests; coupling faults; linked faults Article
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 3 Methodology
### Jamming-Aware Routing Protocol (JRP)  
#### 3.2.1 Routing Factors Design  
WSN deployment is mostly conducted in unattended environments. This makes the data transmission process of sensor nodes vulnerable to malicious factors. Therefore, the routing protocol should avoid information transmission paths through jammed areas to reduce the impact of the jammer on the routing performance. This routing idea has already been applied to robot navigation, where the robot needs to avoid high-risk areas (e.g., areas with jammers). Regarding sensor nodes, they can collect electrical signals of their location through antennas, and also obtain jamming data of their adjacent areas through message exchange between adjacent nodes.  
As we know, the intensity of signals decreases with an increase in transmission distance. This is applicable to both communication transmission and communication interference. Thus, focusing on the jammer, an interference intensity (we call it jamming) is formed. The intensity that decreases gradually from the inside to the outside is a high-risk area for sensor communication. Therefore, routing protocols should avoid information transmission paths that pass through such areas.  
Of course, in order to provide the most fundamental routing function, each sensor node should also possess the depth information of itself and its surrounding sensors, which refers to the number of hops along the feasible shortest path to reach the base station. Hence, the base station is designated with a depth of 0, and the depth of a node increases as its distance from the base station increases. By selecting the node with the smaller depth as the successor node each time, it can be ensured that the data will eventually reach the base station.  
The JRP uses the sensor node's depth and jamming values as input, which can be obtained through environment sensing, and message exchange with neighboring nodes. Since JRP follows the distributed routing paradigm, its output provides the ID of the next-hop node of the message to guide it to the base station through multiple hops.
## 3 Transformation from 3 SAT-Instances to String Puzzles
### 3 SAT Problem  
The definition of 3 SAT is mostly from [18]. Let \(U=\{x_{1},x_{2},\ldots,x_{n}\}\) be a set of Boolean _variables_. Boolean variables take on values \(0\) (false) and \(1\) (true). If \(x\) is a variable in \(U\), then \(x\) and \(\overline{x}\) are _literals_ over \(U\). The value of \(\overline{x}\) is \(1\) (true) if and only if \(x\) is \(0\) (false). A _clause_ over \(U\) is a set of literals over \(U\), such as \(\{\overline{x_{1}},x_{3},x_{4}\}\). It represents the disjunction of those literals and is _satisfied_ by a truth assignment if and only if at least one of its members is true under that assignment.  
An instance of 3 SAT is a collection \(f=\{c_{1},c_{2},\ldots,c_{m}\}\) of clauses over \(U\) such that \(|c_{j}|=3\) for \(1\leq j\leq m\). The 3 SAT problem asks whether there exists some truth assignment for \(U\) that simultaneously satisfies all the clauses in \(f\). For example, \(U=\{x_{1},x_{2},\ldots,x_{5}\}\), \(f=\{c_{1},c_{2},c_{3}\}\), and \(c_{1}=\{x_{1},x_{2},x_{3}\}\), \(c_{2}=\{\overline{x_{1}},\overline{x_{3}},x_{4}\}\), \(c_{3}=\{\overline{x_{2}},x_{4},x_{5}\}\) provide an instance of 3 SAT. For this instance, the answer is \"yes\", since there is a truth assignment \((x_{1},x_{2},x_{3},x_{4},x_{5})=(1,1,0,0,1)\) satisfying all clauses.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 4 Optimal Resource Allocation of Passive Tags
Commun._**2016**, _11_, 7547-7560. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The tradeoff analysis in RF-powered backscatter cognitive radio networks. _arXiv_**2016**, arXiv:1608.01789. Available online: [[https://arxiv.org/abs/1608.01789](https://arxiv.org/abs/1608.01789)]([https://arxiv.org/abs/1608.01789](https://arxiv.org/abs/1608.01789)) (accessed on 5 August 2016). * [PERSON] and [PERSON] (2010) [PERSON]; [PERSON] A new GA-based resource allocation scheme for a reader-to-reader interference problem in RFID systems. In Proceedings of the IEEE International Conference on Communications (ICC), Cape Town, South Africa, 23-27 May 2010. * [PERSON] et al. (2014) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Resource Allocation in Spectrum-Sharing OFDMA Femtocells With Heterogeneous Services. _IEEE Trans. Commun._**2014**, _62_, 2366-2377. * [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Resource allocation for cognitive small cell networks: A cooperative bargaining game theoretic approach. _IEEE Trans. Wirel. Commun._**2015**, _14_, 3481-3493. * [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON]; [PERSON] Goodput maximization for backscatter communication systems. _IEEE Commun. Lett._**2016**, in press. * [PERSON] (2001) [PERSON] Convergence of a block coordinate descent method for nondifferentiable minimization. _J. Optim. Theory Appl._**2001**, _109_, 475-494. * [PERSON] (2003) [PERSON]; [PERSON] Fully integrated passive UHF RFID transponder IC with 16.7-\(\upmu\)W minimum RF input power. _IEEE J. Solid State Circuits_**2003**, _38_, 1602-1608. * [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON] Improving backscatter radio tag efficiency. _IEEE Trans. Microw. Theory Tech._**2010**, _58_, 1502-1509. * [PERSON] (2004) [PERSON]; [PERSON] _Convex Optimization_; Cambridge University Press: New York, NY, USA, 2004. * [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON] Biconvex sets and optimization with biconvex functions: A survey and extensions. _Math. Methods Oper. Res._**2007**, _66_, 373-407.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 4 Adaptive Cascade Observer with Machine Learning for Fault Identification  
After finding the state-space equation for the normal signal function approximation part, the adaptive cascade observer for unknown signal estimation is developed. This Section has two main sub-sections: (a) development of the adaptive cascade observer for unknown signal estimation, and (b)  
Figure 2: The error of normal signal approximation for the CWRU (Case Western Reserve University) dataset using the R-approach, the orthonormal regressive (OR) technique, and the fuzzy orthonormal regressive (FOR) procedure.  
generating the residual signals, extracting the energy features from those signals to evaluate the highly accurate technique for fault identification using the SVM technique.
# High-Efficiency Navigation of Nonholonomic Mobile Robots Based on Improved Hybrid A* Algorithm
## 2 Path Planning for Nonholonomic Robots
### Robot Positioning  
The positions of a robot can be determined via the SLAM algorithm which utilizes the probability distribution approach; particles are used to represent an estimation of the robot's position based on sensor measurements. However, the algorithm could generate a very large number of particles, leading to high computational load and time delay. Furthermore, the SLAM algorithm is less effective in addressing the robot kidnapping problem, i.e., when the robot is suddenly moved to a different location or experiences tire skidding. As a result, the positioning of robot was implemented via the AMCL algorithm, which utilizes a particle filter to calculate the robot's position according to the data from both the odometer and LiDAR sensors. Moreover, AMCL can be applied in solving both global and local localization problems to reduce the workload required for subsequent navigation algorithm. With regard to robot kidnapping, AMCL incorporates weights determined by the information from short-term observation and long-term observation. These weights enable more reasonable particle number and time allocation, as expressed in Equations (2) and (3), respectively:  
\[\mathrm{w_{long\_t+1}}=\mathrm{w_{long\_t}}+a_{long}\Big{(}\mathrm{w_{avg}}- \mathrm{w_{long\_t}}\Big{)} \tag{2}\]  
\[\mathrm{w_{short\_t+1}}=\mathrm{w_{short\_t}}+a_{short}\big{(}\mathrm{w_{avg}}- \mathrm{w_{short\_t}}\big{)} \tag{3}\]  
where \(w_{i}\) is the observation average function, \(a_{i}\) is the average weight coefficient determined by the observed information, \(i\) has three states: long-term, short-term, and average. In the AMCL algorithm, the probability of increasing particle number is related to the two weight coefficients. The weights were updated by iteration, and the values were typically within the range of 0.0010-0.01. The probability of increasing the particle number can be calculated using the following equation:  
\[\mathrm{P}=\mathrm{max}\Big{\{}0.0,1.0-\frac{\mathrm{w_{long}}}{\mathrm{w_{ short}}}\Big{\}} \tag{4}\]It can be seen from Equation (4) that the increase in the particle number is related to the value of \(\frac{w_{\text{long}}}{w_{\text{short}}}\). When the weight of short-term information is less than that of long-term information, which indicates that the robot has deviated from the predetermined trajectory, the system will increase the number of random particles. At this stage, the robot's self-positioning capability can be achieved more quickly and accurately by matching a larger number of particles. The visualized process of the robot's self-positioning process via AMCL algorithm is illustrated in Figure 2. Through multiple updates and resampling of the particle set, the particle filter can adjust the weight and position of the particles to reflect the probability distribution of the robot's position. The location where particles appear more frequently is where the robot is more likely to be located.
# Energy Optimization in Cluster-Based Routing Protocols for Large-Area Wireless Sensor Networks
## 3 Energy Consumption Analysis
### Energy Dissipation by Non-CH Nodes  
In this subsection, we analyze the expected amount of energy consumed by non-CH nodes in a round. First, consider sending Join-REQ messages with transmission radius of \(R\). Since we have \(R>L\) and assume  
\[L\gg\delta,\]  
the expectation of energy dissipation \(E_{T-Join}\) by a non-CH node sending a Join-REQ message is given by  
\[E_{T-Join}=(1-P_{k,R}^{(0)})l_{J}(E_{e}+\epsilon_{m}R^{4}). \tag{9}\]  
Modeling each of \(k\) clusters as a circle with the expectation of radius, \(r_{CH}\), we calculate the area of each cluster as  
\[\pi r_{CH}^{2}=\frac{\pi L^{2}}{k},\]  
from which  
\[r_{CH}=\frac{L}{\sqrt{k}}. \tag{10}\]  
If we have \(k<(L/\delta)^{2}\) then \(r_{CH}>\delta\) and vice versa. Let \(d_{CH}\) denote the distance from a member node to its CH. The expected amount of energy \(E_{T-toCH}\) consumed by a member node while sending a DATA message to its CH is calculated by  
\[E_{T-toCH}=(1-P_{k,R}^{(0)})l_{D}\left(E_{e}+\epsilon_{f}\overline{d_{CH}^{2} }+\epsilon_{m}\overline{d_{CH}^{4}}\right), \tag{11}\]  
where, using Equation (10),  
\[\overline{d_{CH}^{2}} =\frac{2\pi}{\pi r_{CH}^{2}}\int_{0}^{\min(r_{CH},\ \delta)}r^{3}\,\mathrm{d}r\] \[=\min\left(\frac{L^{2}}{2k},\ \frac{\delta^{2}}{2}\right),\]  
and  
\[\overline{d_{CH}^{4}} =\frac{2\pi}{\pi r_{CH}^{2}}\int_{\delta}^{\max(\delta,\ r_{CH}) }r^{5}\,\mathrm{d}r\] \[=\max\left(\frac{L^{6}-k^{3}\delta^{6}}{3k^{2}L^{2}},\ 0\right).\]  
We assume that the base station (BS) resides at coordinate \((S,0)\) where \(S\geq L\). A non-CH node, failing to join a cluster, sends a DATA message to the BS directly, consuming the amount of energy \(E_{T-toBS}\). Let \(d_{BS}\) denote the distance from a node to the BS. With large fields, \(E_{T-toBS}\) will be dominated by nodes with \(d_{toBS}>\delta\), as follows.  
\[E_{T-toBS} =P_{k,R}^{(0)}l_{D}\left(E_{e}+\epsilon_{f}\overline{d_{BS}^{2}}+ \epsilon_{m}\overline{d_{BS}^{4}}\right)\] \[\simeq P_{k,R}^{(0)}l_{D}\left(E_{e}+\epsilon_{m}\overline{d_{BS}^{4}} \right), \tag{12}\]  
where  
\[\overline{d_{BS}^{4}} =\frac{1}{\pi L^{2}}\int_{S-L}^{S+L}2x^{5}\cos^{-1}\left(\frac{x ^{2}+S^{2}-L^{2}}{2 xS}\right)\mathrm{d}x\] \[=S^{4}+2S^{2}L^{2}+\frac{L^{4}}{3},\ \ (S\geq L). \tag{13}\]  
The expectation of the number of CHs seen by a non-CH node is  
\[\frac{k}{\pi L^{2}}\overline{A}(R).\]  
The energy \(E_{R-ADV}\) consumed by a non-CH node while receiving ADV messages is  
\[E_{R-ADV}=\frac{k}{\pi L^{2}}\overline{A}(R)l_{A}E_{e}. \tag{14}\]  
A non-CH node receives a TDMA scheduling message from its CH by expending energy \(E_{R-TDMA}\):  
\[E_{R-TDMA}=l_{T}^{(k,R)}E_{e}, \tag{15}\]  
where \(l_{T}^{(k,R)}\) is given in Equation (8).  
Consequently, the expectation of total energy expended by all non-CH nodes in a round is given by  
\[E_{non-CH}(k,R)=(N-k)\left(E_{T-Join}+E_{T-toCH}+E_{T-toBS}+E_{R-ADV}+E_{R- TDMA}\right). \tag{16}\]
# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 3 Problem Formulation and Preliminaries
### Information Loss in the Object Detection Model
#### 3.1.1 Sampling Information Loss  
The design and structure choices of neural network models may restrict their expressive power. For instance, using a smaller network depth, fewer convolutional or fully connected layers, etc., might fail to adequately capture the details and complex relationships within the input data, leading to information loss. Additionally, downsampling is often performed in neural network models to reduce computational complexity and memory consumption [24]. These operations result in a decrease in the dimensionality of input features, leading to the loss of some detailed information. Nonlinear activation functions (such as ReLU, sigmoid, etc.) are employed to perform nonlinear transformations on features; while these activation functions introduce nonlinear factors that aid the model in learning more complex feature representations, they may also introduce information loss, especially when features are compressed into a minimal range. As shown in Figure 2,as the depth of the neural network increases, the loss of information in the model becomes more apparent.  
In real-time object detection tasks, we often opt to enhance the model's inference speed by reducing its parameter count and computational complexity [25]. However, due to the decrease in model complexity, the information loss during feature extraction tends to increase further.  
In single-stage object detection models, features are first extracted using convolutional neural networks. Subsequently, multi-scale feature fusion operations are applied to the extracted features, followed by the prediction of object positions and categories using a series of convolutional and fully connected layers. Feature fusion involves utilizing techniques such as feature pyramid networks (FPNs) and path aggregation networks (PANs) to obtain both global and local information, thereby acquiring features with broader receptive fields at different scales.  
However, during the feature fusion process in object detection, models using feature pyramid network (FPN) upsampling and fusion operations are performed on features from different levels. Due to the upsampling operation, the spatial resolution of lower-level features increases, but this may result in the loss of some fine-grained details. Upsampling typically employs interpolation methods (such as nearest neighbor or bi-linear interpolation), which can lead to the loss or blurring of fine details. Additionally, feature fusion operations may also contribute to information blurring. The fusion of lower-level and higher-level features might mix some semantic information, causing the details of lower-level features to be affected by blurring.  
In the path aggregation network (PAN) structure, similar issues arise during the downsampling process of feature information in object detection models.
## 2 Motivation
### Pitfall Trap Monitoring  
Pitfall trap arrays at James Reserve are used by biologists to sample the local population of lizards and amphibians. Biologists deploy arrays of traps in clusters, and when a lizard is caught in a trap, it is tagged and freed. Over time, the count of trapped lizards can be used to estimate the overall population of lizards. To avoid the mortality of lizards and amphibians or any incidentally-captured animals (e.g., small mammals) from a variety of causes while in the trap, the pitfall traps are visited by biologists on a regular basis (on the order of hours) to remove captured animals. Often, however, the visits results in the biologist finding the trap empty. When traps are deployed over a large area (e.g., several square kilometers), the checking of traps represents a considerable investment of time. A wireless imaging system based on a wireless sensor network using low-power cameras can reduce the amount of time that a biologist needs to spend checking traps. Using a low-power wireless imaging system, the biologists can visually inspect images from every trap remotely and only visit a trap when an animal is caught, saving their time and effort.
## 3 Results
### Results Considering PV Surplus Energy Exportation to the Grid  
Figure 16 illustrates the comparisons of the simulations considering the exportation of the PV energy surplus with those results obtained in the scenario of Action 3. The energy consumption from the grid and the LCC decreased with the installation of more PV panels, and the optimal values of the LCC were obtained before the energy consumption from the grid reached the limits of the maximum solar fraction (around 35%). Additional investments can bring about significant reductions in the LCC with payback periods similar to the scenarios where no energy surplus was exported to the grid.  
When considering the energy exportation to the grid, supposing the net metering configuration (exported energy price considered as 100% of the retail price), the optimal number of PV panels to be installed for each scenario would increase from 630 to 1617, from 497 to 1533, and from 434 to 1512, respectively. The exportation of energy allows the LCC savings to move from 21% to 39% in the intermediate configuration, Scenario B.  
In addition, based on Action 3, and considering the energy exportation to the grid, in a net billing configuration, the optimal number of PV panels that would produce the lowest LCC values are shown in Figure 17.  
In order to verify the influence of the exported energy price, for each of the three scenarios mentioned before based on the prices of the different PV panels (0.68, 1.18, and 1.48 \(\upxi\)/Wp), three values were adopted in the simulations, that is, 30%, 60%, and 90% of the retail price of the energy, representing three hypothetical wholesale prices.  
Figure 16: Effects on the LCC and grid energy consumption when not exporting and exporting the PV surplus energy to the grid (net metering, retail price).
# Localization of LHD Machines in Underground Conditions Using IMU Sensors and DTW Algorithm
## 3 Materials and Methods
There are also extensions to the classic string edit distance methods to time series, for example, Edit Distance for Real sequences (EDR) [52], Edit distance with Real Penalty (ERP) [53], or Time-Warp Edit Distance (TWED) [54]. These methods assume the comparison of two time series as the minimum number of transformations needed to transform one series into another. Therefore, for continuous values, it is necessary to establish a threshold that will indicate whether the two values can be considered the same or not. The disadvantages of the above methods include the need to establish a fixed threshold value. The summary of all the considered methods, along with their advantages and disadvantages, is described in Table 1. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Euclidean Distance** & **Cons-correlation** & **Longest Common** & **Edit Distance for Real** & **Edit Distance for Real** & **Time Wave Edit Distance** & **Dynamic Time Waveging** \\  & & & & **Subsequence** & **Sequence** & **Penalty** & **Time Wave Edit Distance** & **Dynamic Time Waveging** \\ \hline Applying to signals of different length, & & & & & & & \\ \hline Applications to signals of different length, & & & & & & & \\ \hline Applications to signals, identifying/graphgraphing pattern & & & & & & & \\ \hline fragments (different voltage vector) & & & & & & & \\ \hline Relations to incoming long fragments of & & & & & & \\ \hline same pattern (vehicle backgrounds) & & & & & & & \\ \hline Noise immunity & & & & & & & \\ \hline No need for arbitrary selection of parameters & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different pattern recognition algorithms. Red dot—considered method does not meet the given criteria, yellow dot—criteria are met only partially, green dot—criteria fully met. As can be seen in the table above, the DTW algorithm is also best suited for solving the problem presented in this case. The main reason here is the fact that, among the other methods, it performs the best in the case of stretching or shortening of some fragments of the signal, which would correspond to the vehicle operator covering the given route fragments more slowly or quickly, respectively. Similarly, it is also best when a given pattern is disturbed, e.g., by a longer fragment of the signal corresponding to the machine's stoppage.