esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Deep Learning for Road Environment Recognition
## 3 Proposed Method
### Lane Detection Network  
The semantic segmentation network is a neural network and a model based on deep convolutional networks. Its most basic task is to classify different types of pixel points in an image and to aggregate the same type of pixel points to distinguish different target objects in the image [35; 36; 37]. One of the U-Net model structures can increase the local perceptual field and collect multi-scale information without reducing the dimensionality. Deep learning networks usually require many datasets for training. This training method allows more efficient use of datasets and enables the network to perform more accurate segmentation even with a small number of training images, so the U-Net model is often  
Figure 11: The proposed object detection network.  
Figure 10: The parameters of each improved model are compared with the original model.  
used in medical image processing. Continuing this idea of U-Net, this work proposes three lightweight road semantic segmentation models using the MobileNet series as the backbone network, as shown in Figure 12, and the models are divided into four parts.  
The first part is MobileNetv1 as the backbone network is the encode process, i.e., the feature extraction process. It consists of many depthwise separable convolutions iteratively strung together. According to its characteristics mentioned before, we can understand that this can significantly reduce the number of network parameters. In this way, we can use the backbone to obtain five feature layers, whose shapes are (208, 208, 64), (104, 104, 128), (52, 52, 56), (26, 26, 512), and (13, 13, 1024). Since we only need to single extract the safe lane that the driver is driving in, with fewer classification items, the first feature layer (208, 208, 64) and the last layer (13, 13, 1024) are discarded from use. This way we will use the middle three effective feature layers for feature fusion.  
The second part is feature layer fusion, which enhances the diversity of feature extraction. The backbone network outputs feature layers (26, 26, 512), and after the ZCB module (ZeroPadding+Conv2D+BN) the output is combined with the attention mechanism module proposed in this study for upsampling and then superimposed with the feature layer (52, 52, 512) of the backbone network output, and this process is repeated to finally obtain a valid feature layer that fuses all features.  
The third part is the attention mechanism. Adding the LRCA-Net module to the overlay process of the feature fusion layer enables the network to focus its attention on the effective features and ensures the normal convergence during training.  
The fourth part is the prediction part, where we use the final effective feature layer obtained in the second part to classify each feature point by softmax, which is equivalent to classifying each pixel point. The semantic segmentation model thus designed has the advantage of both a lightweight backbone model with a small number of parameters and combines the features of encode-decode structure and skip connection.
# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 3 Results and Discussion
Figure 7 shows the scatter plots of the models. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Logistic Regression** & **Artificial Neural Network** & **Support Vector Regression** \\ \hline R\({}^{2}\) & 0.949 & 0.936 & 0.923 \\ \hline MAE & 0.052 & 0.053 & 0.065 \\ \hline MSE & 0.005 & 0.006 & 0.009 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation of models. Figure 4: R\({}^{2}\) evaluation. Figure 5 shows the scatter plot of the machine learning models used for methane gas prediction. The \(x\)-axisin the figure shows the actual values and the \(y\)-axisshows the predicted values. The scales of the x and y axes were between 0 and 1. In all three models, there was a positive correlation between the methane gas value and the predicted results. Moreover, this link was quite strong. As the value of one of the variables increased, the other increased and the points clustered near the line. Figure 8 shows the line plots of the models. Figure 5: MAE evaluation. Figure 6: MSE evaluation. Figure 7: Scatter plot of methods. Figure 8 shows the relationship between actual and predicted values. The \(x\)-axis in the figure shows the years and the \(y\)-axisshows the actual and predicted values. The \(x\)-a-xisscale covered the period between 1990 and 2020; the \(y\)-axiscovered the period between 0 and 1. The relationship was strong for all three models. When the figures were analyzed in detail, it was seen that the model with the strongest relationship was logistic regression, followed by artificial neural network and support vector regression. The boundaries of the study were contained to countries that have coal mines and continue to produce energy from coal, and support cattle breeding and paddy production. In this respect, the model may need to be modified in order to be applied in continental European countries with serious regulations on coal production and very limited paddy production.
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 6 Performance Evaluation
### Simulation Results
#### 6.2.1 Impact of Various Parameters  
Impact of Source Level  
Source level influences the network connectivity and the spatial reuse in the network. A lower source level reduces the transmission range, which in turn decreases the size of the candidate set and then degrades the transmission reliability. In contrast, each node requires transmitting a packet with more hops to the sink node with a low source level.  
We set \(\lambda_{T}\) = 2.0 packet/min, \(N_{mBT}\) = 1, and \(N_{MBT}\) = 8. Then, we simulate and analyze the impacts of source level on the network performance of CLOR protocol.  
The network topology changes slightly when SL increases from 154 dB re uPa @ 1m source to 160 dB re uPa @ 1m source. The average hop count decreases from 2.34 to 2.30 (the average of the maximum hop count decreases from 4.52 to 4.47), and the average node degree increases from 4.78 to 4.91. However, it heavily influences performance improvement of CLOR at large packet sizes. Figure 9 shows network performances versus SL for different payload sizes. As expected, the PDR of the scenarios with low SL is worse than that with high SL. Large payload size results in a large value of PER in the same channel condition, which degrades the transmission reliability. Thus, the PDR of the scenarios with a large payload size is significantly smaller than that with a small payload size for different values of SL. As shown in Figure 9, the delay decreases significantly as SL increases, especially in the scenarios with large payload size. Higher SL results in a higher probability of network connectivity and a shorter average hop counts to the sink; this decreases the average end-to-end delay. As shown in Figure 9, when the payload size is large, the Goodput of CLOR can reach an optimal value by setting a relatively high value of SL. The Goodput of payload size = 256 B is nearly two times that of that payload  
comparing Figure 10a with Figure 11a, we can find that \(N_{MBT}\) influences the lower bound of PDR, whereas \(N_{mBT}\) influences the upper bound of PDR. As shown in Figure 11b, the delay of the networks with different payload increases as \(N_{mBT}\) increases. Since each nodes require queuing more packets to start a new handshake, the nodes with the number of packets in queues smaller than \(N_{mBT}\) should wait extra time to execute transmission. This seriously influences the delay performance, especially with a large value of \(N_{mBT}\). As shown in in Figures 10c and 11c, the network can achieve high Goodput with relatively high values of \(N_{mBT}\) and \(N_{MBT}\) in case of large payload size.
# High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks
## 3 Soil Moisture Retrieval Methodology
### ANN Model Architecture
The network uses the training data and back-propagates the error information by updating the weights and bias in each layer [51] to minimize the defined loss function with the help of a stochastic gradient descent algorithm. The entire process makes one iteration of forward and backward propagation. Such iterations are made until the loss function reaches a threshold minimum value or a maximum number of iterations are performed. After the described learning process is finished, the final set of node weights and biases for each layer builds up the trained ANN model for SM predictions. This learned network will produce SM estimates from any new input data parameters through a single forward propagation. We have tested several ANN structures, and the ANN parameters that give the best performance out of our investigations are as follows: The input layer has the same number of nodes as the number of used features, which is 8. The output layer has a single node which is the predicted SM values. ANN has two hidden layers in addition to the input and output layers, as shown in Figure 2. The nonlinear activation function at each layer is chosen to be the Rectified Linear Unit (ReLU) function [53] as it gives the best overall results compared to logistic or tanh activation functions [54]. The last layer is only a regression layer with no activation function. The Adam solver, which is a first-order gradient-based optimizer for stochastic objective functions [55], is employed for solving optimal weights through loss-function minimization with a learning rate of 0.0001.
# Centralized Mission Planning for Multiple Robots Minimizing Total Mission Completion Time
## 2 Preliminaries  
The mission planning problem for minimizing total mission completion time is to find a sequence of missions for each robot that has the minimum total mission completion time as seen below:  
\[\text{Min}\big{(}\text{Max}\big{(}t_{e}(j,\ p_{i})x_{ij}\big{)}\big{)} \tag{1}\]  
subject to  
\[\sum_{i=1}^{N_{r}}x_{ij}\leq 1,\ \forall j\in J \tag{2}\]  
\[\sum_{j=1}^{N_{m}}x_{ij}\leq L_{m},\ \forall i\in I \tag{3}\]  
\[\sum_{i=1}^{N_{r}}\sum_{j=1}^{N_{m}}x_{ij}=N_{min} \tag{4}\]  
\[x_{ij}\in\{0,\ 1\} \tag{5}\]  
where \(t_{e}(j,\ p_{i})\) is finish time of mission \(j\) if robot \(i\) conduct mission \(j\) along the path \(p_{i}\); \(x_{ij}\) is the flag that is \(1\) if mission \(j\) is taken to robot \(i\) and \(0\), or otherwise, \(N_{r}\) and \(N_{m}\) are the number of robots and missions, respectively, \(L_{m}\) is the maximum number of missions that the robot can take, and \(N_{min}\) is the minimum number between \(N_{m}\) and \(N_{r}L_{m}\). \(I\) and \(J\) are robot and task group, respectively, so the number of elements in \(I\) is \(N_{r}\) and the number of elements in \(J\) is \(N_{m}\). Note that one mission is taken to only one robot as described in Equation (2).  
The mission planning is composed of two parts: mission allocation and mission alignment. The mission allocation is to decide which robot will take which mission. The mission alignment is to calculate the start time and the finish time of each mission based on the result of mission allocation. Some mission planning algorithms conduct mission allocation at first for all missions; then, the mission alignment is performed once based on the mission allocation result. The other mission planning algorithms conduct mission allocation for one mission, and then the mission alignment is performed for one mission. This procedure repeats for all missions. This sequence is used when the mission allocation uses the start time or the finish time of each mission for the results of mission alignment.
## 2 Methodology
### Statistical Methods  
Various metrics and visualization techniques are employed below to gauge the performance of algorithms. Retrieval parameters are compared with the same parameter collected in situ for both the optimization/tuning dataset (i.e., using in situ reflectances) and satellite validation. In addition to common metrics such as the number of samples (N), the standard deviation (_STD_), and the squared correlation coefficient (r\({}^{2}\)), we evaluate the adjusted r\({}^{2}\) (r\({}^{2}\)):  
\[r^{2^{\prime}}=r^{2}-\left(1-r^{2}\right)\left(\frac{\beta_{n}}{N-\beta_{n}-1 }\right), \tag{2}\]  
(where \(\beta_{n}\) is the number of regressors) which adjusts the \(\mathbf{r}^{2}\) downward to correct for the number of predictive values relative to the number of samples in, for example, multiple linear regression. The root mean square difference (_RMSD_) was also calculated:  
\[RMSD=\sqrt{\frac{\sum_{i}^{N}(mod_{i}-ref_{i})^{2}}{N}}, \tag{3}\]  
where \"mod\" is the model retrieved parameter and \"ref\" is the field measurement. The centered-unsigned (or unbiased) _RMSD_ (_RMSD\({}^{\star}\)_) was defined as follows:  
\[RMSD^{\star}=\sqrt{\frac{\sum_{i}^{N}(mod_{i}-mean(mod_{i}))-\left(ref_{i}- mean(ref_{i})\right)^{2}}{N}}, \tag{4}\]  
and the signed _RMSD\({}^{\star}\)_ is simply the _RMSD\({}^{\star}\)_ multiplied by the sign of the difference between the _STD_ of the model retrieval and the _STD_ of the field data (_RMSD\({}^{\star}\)_(\(\sigma_{\text{d}}\))). The bias and the normalized bias (_Bias*_) are also employed:  
\[Bias^{*}=\frac{\sum_{i}^{N}(mod_{i}-ref_{i})}{N\times STD(ref)} \tag{5}\]  
as well as the percent bias (%_Bias_),  
\[\%Bias=100\times\frac{mean(mod-ref)}{mean(ref)}, \tag{6}\]  
and the mean average percent difference,  
\[MAPD=100\times mean\bigg{[}abs\bigg{(}\frac{mod-ref}{ref}\bigg{)}\bigg{]}. \tag{7}\]  
While most of these metrics are fairly straightforward, a few warrant further explanation and context. A powerful graphical tool for assessing the skill of model performance--and comparing one model to another--is the Taylor diagram [57], which combines the _RMSD\({}^{\star}\)_, _STD_, and correlation into a single figure in which proximity to the field data indicates how well the pattern of the modeled data matches the observations. This is made possible in two dimensions because of the relationship between the \(RMSD^{\star}\), the correlation, and the variances of model and reference. Because the means of the model and reference are removed prior to calculating higher statistics shown in Taylor diagrams, they represent the comparisons between the patterns with any bias removed. For this reason, we have added color here to Taylor diagrams to include %_Bias_. Another graphical assessment used here which accounts for the bias (_Bias_*) and adds a sign to the \(RMSD^{\star}\) is the target diagram [58], in which the \(y\)-axis represents normalized bias of the model, the \(x\)-axis is signed-centered \(RMSD\), and distance in any direction from the origin to the model is the total \(RMSD\). Here, we also include color in our target diagrams to help visualize the \(MAPD\).
## 2 Measurements and Quality Control Processes
### Frequency Distribution of Longwave Irradiances  
Figure 8 shows the annual frequency distributions of the hourly downward and upward irradiances. As can be seen \(L_{dn}\) irradiances follow the normal distribution function, while \(L_{up}\) follows an almost normal distribution but with a long positive tail. The annual cumulative density function curves (_CDF_) of the two variables are presented in Figure 9. As indicated from the graph, in 75% of the irradiances, \(L_{dn}\) at Athalassa are lower than 350 W m\({}^{-2}\). For the upward longwave irradiance (\(L_{up}\)), for the same probability, the irradiances are lower than 480 W m\({}^{-2}\).  
Figure 8: Marginal plot of the hourly \(L_{dn}\) and \(L_{up}\) with the annual frequency distributions of the two variables at Athalassa.  
Figure 9: Cumulative density function (_CDF_) for \(L_{dn}\) and \(L_{up}\) at Athalassa.  
The monthly variation of both variables is shown in Figure 10. The outliers are presented with an asterisk and as seen, significant number of outliers of \(L_{dn}\) is observed in June and during the period of September to November. In the case of \(L_{up}\), outliers are observed at the beginning of the year (in the first three months) and towards the end of the year (in the last two months).
# Analytic Hierarchy Process and Multilayer Network-Based Method for Assembly Line Balancing
## 1 Introduction
Among the wide range of heuristic methods capable of achieving reasonable solutions [20], SA is the most widely used search algorithm [21], so it has already been applied to solve mixed and multi-model line-balancing problems [22]. To deal with the complexity of this problem, an SA algorithm was also developed. The proposed algorithm utilises a unique problem-oriented sequential representation of the assignment problem and applies a neighbourhood-search strategy that generates feasible task sequences for every iteration. Since the algorithm has to handle multiple aspects of line balancing, the analytic hierarchy process (AHP) technique is used to quantify the importance of the objectives, also known as Saaty's method [23]. AHP is a method for multi-criteria decision-making which is used to evaluate complex multiple criteria alternatives involving subjective judgments [24]. This method is a useful and practical approach to solving complex and unstructured decision-making problems by calculating the relative importance of the criteria based on the pairwise comparison of different alternatives [25]. The method has been widely applied thanks to its effectiveness and interpretability. Two papers were found in which it has already been applied to determine the cost function of multi-objective SA optimisation problems. In the first case study of supplier selection, AHP was applied to calculate the weight of every objective by applying the Taguchi method [26] (Adaptive Tabu Search Algorithm--ATSA [27]). In contrast, in the second report, this concept was applied to the maintenance of road infrastructure [28]. The novelties of the work are the following:  
* In Section 2, the main problem formulation is introduced, including the multilayer network representation of multiple aspects concerning the balancing of production lines, and the details of the objective function. * In Section 3, an SA algorithm will be introduced based on a novel sequential representation of the line-balancing problem and the search algorithm that guarantees the fulfilment of the precedence constraints. * Section 4 demonstrates how AHP can be used to aggregate the multi layer network-represented objectives of the line-balancing problem for SA.
# On the Energy Performance of Iridium Satellite IoT Technology
## 2 Related Work
### Energy Performance of IoT Technologies  
Since many IoT devices are powered by limited energy sources, energy performance is a crucial aspect of an IoT technology. Many studies have focused on modeling, evaluating, and/or improving the energy performance of a wide range of IoT technologies such as IEEE 802.15.4 and ZigBee [17], Bluetooth Low Energy (BLE) [18; 19], LoRaWAN [20], Sigfox [21],or NB-IoT [22], among others. However, to our best knowledge, no work in the literature has evaluated the energy performance of a satellite technology for IoT.
## 8 Experimental Results  
We demonstrate the CHP2 prototype ranging capabilities using both cabled and over-the-air experiments. This implementation includes several ToA and ToF methods; the advanced estimators (cabled) demonstrate the system's best ranging performance while the simple estimators (over-the-air) are more robust and suitable for a field demonstration. This initial implementation uses a fixed-rate protocol, so the communications rate is not variable and we limit the operating range (10 km) to comfortably ensure that the bit error rate at the output of the decoder is 0. We explore the manifold optimization of data rate, ranging precision, and timing synchronization and the use of adaptive rate protocols in companion studies. The objective of the following experiments is to demonstrate the successful implementation of the proposed system on a COTS experimental testbed and to provide the initial validation of the previously-claimed 10 cm ranging resolution.
# Two-Dimensional Vortex Solitons in Spin-Orbit-Coupled Dipolar Bose-Einstein Condensates
## 4 Semi-Vortices in Spinor SOC BEC with DDI and ZS
### Shape Control of the SVS by ZS  
It is found that the strength of the ZS can change the shape of the SVS. As shown in Figure 5, when the strength of ZS \(\Omega=0\), the system goes back to Equation (4), and similar SVS are produced (Panels (a1), (a2)). Just like the SVS in Figure 1, the anisotropic nature of DDI deforms the fundamental and vortex component in the horizontal direction (longitudinal directions compared to the dipole  
Figure 4: Motion of the c.m. of kicked asymmetric vortex solitons (AVS) with \(N=0.15\). (**a**) AVS with \((X_{ps},Y_{ps})=(0.12,0)\). The kick \(\mathbf{k}=(k_{x},k_{y})=(+0.5,0)\) (blue curve), \((-0.5,0)\) (red curve) and \((0,+0.5)\) (green curve). (**b**) \((X_{ps},Y_{ps})=(0.26,0)\) with the same kick [58].  
Figure 5: Symmetric vortex solitons (SVS) with \(N=5\). (**a**) Fundamental (**a1**) and vortex (**a2**) component of SVS with \(\Omega=0\); (**b**) with \(\Omega=10\); (**c**) with \(\Omega=4.23\)[59].  
orientation), creating two density maxima separated in this direction. For this reason, these SVSs are called _horizontal SVS_[59]. When \(\Omega\) increases and exceeds a certain critical value \(\Omega_{c}\), the shape of the SVS changes, especially for the vortex component (Panels (b1), (b2)), which now has two density maxima along the vertical directions (transverse directions compared to the dipole orientation; compare Panels (b2) and (a2)); therefore, it is called the _vertical SVS_. This change of shape is continuous with \(\Omega\) increasing from zero, so at \(\Omega=\Omega_{c}\), the SVS features an elliptic ring with no density maxima for any specific direction (Panel c2). The value of \(\Omega_{c}\) can be determined numerically by defining a shape parameter:  
\[\Delta=\frac{|\phi_{-}^{max}(x,0)|^{2}-|\phi_{-}^{max}(0,y)|^{2}}{|\phi_{-}^{ max}(x,0)|^{2}+|\phi_{-}^{max}(0,y)|^{2}} \tag{11}\]  
and the results are displayed in Figure 6.  
Figure 6 shows clearly that the strength of ZS, \(\Omega\), can be used as a shape control parameter to transform the SVS between horizontal and vertical mode. The critical value of this transition \(\Omega_{c}\) has an almost linear relation with the total norm \(N\), which can be explained qualitatively by noticing that at large \(\Omega\) and a large \(N\) limit, the energy of DDI and ZS scales as \(N^{2}\) and \(\Omega N\), respectively [59]. Since this transition is the result of competition between DDI and ZS, the transition then must occur at \(\Omega=const.\times N\), as indicated in Panel (b) of Figure 6.