esa-sceva/satcom-chunk-collection huggingface dataset:
# Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches
###### Abstract  
Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.  
image semantic segmentation (ISS); weakly supervised; conditional random fields (CRF); image patches +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
It is measured in dB and is related to the ECC as follows:  
\[\Delta L=0.412h\frac{\Big{(}\epsilon_{reff}+0.3\Big{)}\Big{(}\frac{W}{h}+0.264 \Big{)}}{\Big{(}\epsilon_{reff}-0.254\Big{)}\Big{(}\frac{W}{h}+0.8\Big{)}} \tag{2}\]3The channel capacity loss (CCL) metric measures the reduction in the maximum achievable data rate of the MIMO system. It is calculated using the following expression:  
\[ \begin{split} CCL=-\text{log}_{2}\big{|}\beta^{R}\big{|}\\ \text{where}\\ \beta^{R}=\begin{pmatrix}\beta_{11}&\beta_{12}&\dots&\beta_{1N}\\ \beta_{21}&\beta_{22}&\dots&\beta_{2N}\\ \vdots&\vdots&\vdots&\vdots\\ \beta_{N1}&\beta_{N2}&\dots&\beta_{NN}\\ \end{pmatrix}\\ \text{where}\\ \beta_{ii}=1-\sum\limits_{n=1}^{N}\big{|}S_{in}^{*}S_{ni}\big{|}\\ \text{and}\\ \beta_{ij}=-\sum\limits_{n=1}^{N}\big{|}S_{in}^{*}S_{nj}\big{|}\\ \end{split}  \tag{15}\]  
The matrix \(\beta^{R}\) represents the received signal coefficients in an MIMO system. The diagonal elements \(\beta_{ii}\) correspond to the power received by antenna ii, accounting for the influence of signals from all other antennas. The off-diagonal elements \(\beta_{ij}\) describe the correlation between the received signals by antennas i and j, in the MIMO array.
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 4 Target Distortion Optimization
### Problem Formulation  
Since the target distortion optimization comprises bandwidth, power, and distortion, it is considered a three-dimensional problem. As will be demonstrated later, bandwidth and power constraints are exchangeable. Consequently, the optimization problem (5) is decomposed into two two-dimensional subproblems, where one constraint is fixed in each subproblem. For instance, we formulate the minimization of power resource usage given a distortion constraint \(\overline{MSE}\) and a bandwidth resource usage \(K\) as a power distortion optimization problem:  
\[\begin{split} SNR_{clk}(\rho)=&\arg\min_{\rho} \frac{1}{N}\sum_{i=1}^{r}\rho_{i}\\ &\text{s.t.}\frac{1}{N}\Big{(}\sum_{i=1}^{r}\frac{\lambda_{i}}{ \rho_{i}+1}+\sum_{i=r+1}^{N}\lambda_{i}\Big{)}\leq\overline{MSE}\\ & 1\leq r\leq K,\qquad r\in Z\end{split} \tag{19}\]  
On the other hand, we also formulate the minimization of bandwidth resource usage given a target distortion \(\overline{MSE}\) and a power budget \(SNR_{clk}\) as a bandwidth distortion optimization problem:  
\[\begin{split} K(\rho)=&\arg\min_{\rho}K\\ &\text{s.t.}\frac{1}{N}\Big{(}\sum_{i=1}^{K}\frac{\lambda_{i}}{ \rho_{i}+1}+\sum_{i=K+1}^{N}\lambda_{i}\Big{)}\leq\overline{MSE}\\ &\frac{1}{N}\sum_{i=1}^{K}\rho_{i}\leq SNR_{clk}\end{split} \tag{20}\]
## 4 Signal Processing Method  
### Adaptive LMS Algorithm  
Obviously, the signals received in the wireless system contain not only temperature sensing signals but also various noises including baseline noise, quantization noise (\(\pm\)25 \(\mu\)V) of active components in the circuit, and some unexpected reflected and scattered signals in the environment [27,28]. To obtain accurate temperature information, the received signals should be filtered to improve its SNR. The received signal was converted into a digital signal by sampling after filter, amplifier, and signal conditioning circuit of the receiving module. The amplitude of the digital signal is between 0.2\(\sim\)3.1 V, and the number of data points is 800. Then, the time domain response can be obtained through fast FFT, and the reflection peak can be observed.  
Considering that the noise characteristics of the signal are unknown and constantly changing, the filter should track the noise changes and constantly adjusting the filtering parameters to optimize the filtering effect. Therefore, the adaptive LMS filtering algorithm was employed to eliminate the noise [29], and low computational complexity, excellent convergence, and easy implementation are expected, which is appropriate in this work.  
The output signal \(y(n)\) of the adaptive LMS filtering algorithm is defined by  
\[y(n)=w^{T}(n)*x(n), \tag{2}\]  
The error signal \(e(n)\) is denoted by  
\[e(n)=d(n)-y(n), \tag{3}\]  
where \(d(n)\) denotes the expected signal and \(w^{T}\) is the weight coefficient. The weight coefficient can be adjusted to adapt to the statistical characteristics of the changing signal and noise. The adjustment standard is to minimize the mean square value of the error between the expected signal and the filtered output signal. The recurrence relation of the weight coefficient is defined by  
\[w(n+1)=w(n)-\mu*e(n)*x(n), \tag{4}\]  
where \(\mu\) represents the adaptive step size, which determines the stability and convergence speed of the algorithm.  
Figure 3: The wireless system and corresponding setup for temperature sensing.  
Here, the sampled sensing signal was regarded as the expected signal \(d(n)\), which contains both noise signal and sensing signal. The collected noise of the system was used as the input signal \(x(n)\), which signifies that the noise signal is strongly correlated to the noise \(d(n)\). The error signal \(e(n)\) was the desired filtered signal. The number of digital data points was taken as the N value. The flow chart of the specific filtering algorithm is shown in Figure 4a. The adjustable parameters of the filtering algorithm were determined by multiple debugging, and the adaptive step size (\(\upmu\)) and the filter order are 0.0001 and 150, respectively. Figure 4b indicates the comparison of responses in time domain before and after filtering. It can be seen that the signal peak sharpness corresponding to two reflectors increases significantly after filtering. In other words, the SNR of the system response of the sensor increased obviously after filtering.
# Failure Response of Simultaneously Pre-Stressed and Laser Irradiated Aluminum Alloys
###### Abstract  
The failure response of aluminum alloys (Al-6061 and Al-7075) under the condition of simultaneously pre-stressing and laser heating was investigated. Specimens were subjected to predetermined preloading states and then irradiated by continuous wave fiber (Yb) laser. For all specimens, it was found that the yield stress decreased with increasing laser power density. This implies that the load-bearing capacity of the specimens reduced under increased thermal or tensile loading. Consequently, the specimen's failure time was shortened by increasing either laser power density or preloaded speed. For Al-6061, a remarkable reduction in failure time by the increase of laser power density is found. However, for Al-7075, under higher preloaded speeds, comparatively smaller impact of laser power density on the failure time is reported. Moreover, for Al-6061, relatively a more non-uniform variation in the average failure time with the increase of laser power density or preloaded speed is observed. The failure mode of Al-6061 turned from brittle to ductile at higher laser power densities; whereas for Al-7075, it changed from quasi-brittle to ductile. At higher preloaded speeds, a greater degree of melting and ablation phenomenon can be seen due to relatively higher temperatures and higher heating rates.  
Al-alloys; CW laser; tensile loading; structural failure +
Footnote †: journal: Applied Sciences
## 4 Sampling the Aperture
### Compressed Sensing for Aperture Gap Recovery
The problem is an underdetermined linear system (22) that provides approximated 2D Fourier coefficients of the sliding window in the ideal case. The resulting vector represents the 2D Fourier coefficients \(\vartheta\) of size \(N_{v}\cdot N_{u}\). The number of vertical frequency components is then \(N_{v}=(N_{s}+N_{z})\) and the horizontal components \(N_{u}=N_{t}\). Note that the number of sensors is now complemented by the number \(N_{z}\). After reliable coefficients are found, it is possible to recover the signal. The main idea in the seminal publication on CS [32] is to substitute the _NP-hard 10 norm_ by the closest convex norm in form of the _l1 norm_. Accordingly, the coefficients \(\vartheta\) can be determined by an optimization process (23). Nevertheless, there are still varieties of solutions (e.g., spikes within the gaps), which are unlikely to occur in this data. The approach here is to restrict the number of frequencies, which provide a decent approximation of the slow-time domain. In (21), a range is defined with corresponding restricted symmetric basis functions. \(N_{r}\) is the number of effective frequencies. Hence, the corresponding matrix \(\Psi\) has a rank of \(N_{rank}=2\cdot N_{r}+1\). The required inverse \(\Psi\) is determined by the pseudo inverse using the Singular Value Decomposition (SVD) (24). The notation \([A]_{ij}\) describes the \(ij\)-submatrix, i.e., A with \(i.\)th row and \(j.\)th column deleted. By deleting the row/columns of \(D\) and \(U\) according to (24) respectively, the desired parameters \(\vartheta\) can be determined as in (25). The parameters \(N_{t}\) and \(N_{s}\) are used as measurement data and are set as follows: \(N_{t}=3\) and \(N_{s}\) is half the sensor-channels from each side of the neighboring array data. A range of 20% to 30% of \(N_{r}\) as a range of low frequencies yields a sufficient approximation using the pseudo inverse. The restoration of the missing array data is finally achieved by applying an inverse 2D FT on \(\vartheta\) (26). \[M(\mathbf{u},\mathbf{v},\mathbf{x_{v}},\mathbf{y_{v}})=e^{-i2\pi(\frac{uN_{z} }{N_{t}}+\frac{v\pi_{v}}{N_{s}})} \tag{15}\]  
\[\Psi=\left( \begin{array}{c}vec(M_{u=0,v=0}(\mathbf{x_{v}},\mathbf{y_{v}}))\\ vec(M_{u=0,v=1}(\mathbf{x_{v}},\mathbf{y_{v}}))\\ \vdots\\ vec(M_{u=0,v=(N_{s}+N_{z}-1)}(\mathbf{x_{v}},\mathbf{y_{v}}))\\ \vdots\\ vec(M_{u=(N_{t}-1),v=(N_{s}+N_{z}-1)}(\mathbf{x_{v}},\mathbf{y_{v}}))\\ \vdots\\ vec(M_{u=(N_{t}-1),v=(N_{s}+N_{z}-1)}(\mathbf{x_{v}},\mathbf{y_{v}}))\\ \end{array} \right) \tag{16}\]\[y_{v} = 0,\...\,\ \frac{N_{s}}{2},\ (\frac{N_{s}}{2}+N_{z}),\...\,(N_{s}+N_{z}-1) \tag{17}\] \[x_{v} = 0,\...\,\ (N_{t}-1)\] (18) \[u = 0,\...\,\ (N_{u}-1)\] (19) \[v = 0,\...\,\ (N_{s}+N_{z}-1)\] (20) \[v_{r} = 0,\...\,\ N_{r},\ (N_{v}-N_{r}),\...\,(N_{v}-1)\] (21) \[\Psi\cdot\vartheta=vec(B)\] (22) \[min\ \|\vartheta\|_{1}\ \text{subject to}\ \Psi\cdot\vartheta=vec(B)\] (23) \[\Psi=UDV U U =[U]_{j\ 0\to N_{rank}} D=[D]_{i\ 0\to N_{rank}}\] (24) \[\vartheta=VD^{-1}U^{T}vec(B)\] (25) \[A=\mathcal{F}^{-1}\ (\vartheta)\cdot(Nu\cdot Nv) \tag{26}\]
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 5 Simulation Results
### Simulation 1: Effect of OFDM Carriers  
Figure 10 shows the BER performance with respect to the pilot power ratio \(\zeta\) and the reflection coefficient \(\alpha\). The BER performance improves as the symbol power allocated in pilot subcarriers increases and tags reflect a larger amount of the incident power at their antenna. In the TD-AmBC, the reader is difficult to detect AmBC signals even if the values of \(\zeta\) and \(\alpha\) are sufficiently large because ambient OFDM carrier signals are not fully utilized in TDMA whose codewords are allocated in an orthogonal manner as we discussed in Figure 6. So only a small number of OFDM carriers are used to convey data for each tag while a large number of OFDM carriers are available for ambient backscatter  
Figure 9: Simulation settings for the MSC-AmBC system. (**a**) Deployment scenario with \(U=10\) sources, \(N=10\) tags, and a reader located at the origin. (**b**) Common system parameters [3; 18; 25; 41; 45].  
In contrast, the utilization of OFDM carriers can be further enhanced by using sparse codes which can allow for non-orthogonal use of those carriers for multiple tags. When the signal dimension \(K_{1}\) increases in the MSC-AmBC, the backscatter signals can be modulated over a larger number of OFDM carriers. Further spreading the backscatter signals can increase the detection gains in the MSC-AmBC, leading to significant BER improvement as shown in Figure 10.
###### Abstract  
Marine resources are valuable assets to be protected from illegal, unreported, and unregulated (IUU) fishing and overfishing. IUU and overfishing detections require the identification of fishing gears for the fishing ships in operation. This paper is concerned with automatically identifying fishing gears from AIS (automatic identification system)-based trajectory data of fishing ships. It proposes a deep learning-based fishing gear-type identification method in which the six fishing gear type groups are identified from AIS-based ship movement data and environmental data. The proposed method conducts preprocessing to handle different lengths of messaging intervals, missing messages, and contaminated messages for the trajectory data. For capturing complicated dynamic patterns in trajectories of fishing gear types, a sliding window-based data slicing method is used to generate the training data set. The proposed method uses a CNN (convolutional neural network)-based deep neural network model which consists of the feature extraction module and the prediction module. The feature extraction module contains two CNN submodules followed by a fully connected network. The prediction module is a fully connected network which suggests a putative fishing gear type for the features extracted by the feature extraction module from input trajectory data. The proposed CNN-based model has been trained and tested with a real trajectory data set of 1380 fishing ships collected over a year. A new performance index, DPI (total performance of the day-wise performance index) is proposed to compare the performance of gear type identification techniques. To compare the performance of the proposed model, SVM (support vector machine)-based models have been also developed. In the experiments, the trained CNN-based model showed 0.963 DPI, while the SVM models showed 0.814 DPI on average for the 24-h window. The high value of the DPI index indicates that the trained model is good at identifying the types of fishing gears.  
f 2020; 10, 4010; doi:10.3390/app10114010
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 4 Experiments
### Comparison of Results for Different Datasets
#### 4.5.2 Evaluation on HRSC2016  
Table 8 presents the detection results of the selected comparative methods and MIHE-Net on the HRSC2016 dataset. MIHE-Net achieves an mAP of 92.43%, with Params of 13.69 MB and FLOPs of 71 G. While achieving the highest mAP, MIHE-Net also maintains a relatively low number of parameters and computational load; thus, while ensuring detection accuracy, it also has high operational efficiency and good potential for practical deployment. Figure 5 displays some of the detection results of MIHE-Net on the HRSC2016 dataset. Most of the ship targets in the HRSC2016 dataset are rotated in multiple directions, have extreme aspect ratios, and come in different sizes (the three images in the first row). This leads to target features becoming blurred and indistinguishable when the background mimics the target's appearance (first column of the second row), the image background is highly intricate (second column of the second row), or the image exhibits pronounced light-dark fluctuations (third column of the second row). Nonetheless, MIHE-Net demonstrates strong robustness by accurately detecting ship targets and shows some anti-interference capabilities, enabling the network to more accurately estimate target scales and boundaries.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Backbone** & **Parameters (MB)** & **FLOPs (G)** & **Size** & **mAP** \\ \hline RRPN [46] & ResNet101 & 44.5 & 125 & 800 \(\times\) 800 & 79.08 \\ RoI-Transformer [42] & ResNet-101 & 55.1 & 200 & 512 \(\times\) 800 & 86.20 \\ RSDet [47] & ResNet-50 & 28.9 & 73 & 800 \(\times\) 800 & 86.5 \\ DAL [39] & ResNet-101 & 36.4 & 216 & 416 \(\times\) 416 & 88.95 \\ R\({}^{3}\)Det [28] & ResNet-101 & 41.9 & 216 & 800 \(\times\) 800 & 89.26 \\ SLA [41] & ResNet-101 & 49.0 & 130 & 768 \(\times\) 768 & 89.51 \\ CSL [48] & ResNet-50 & 37.4 & 236 & 800 \(\times\) 800 & 89.62 \\ GWD [23] & ResNet-101 & 55.2 & 121 & 800 \(\times\) 800 & 89.85 \\ TIOE-Det [45] & ResNet-101 & 48.6 & 139 & 800 \(\times\) 800 & 90.16 \\ S\({}^{2}\)A-Net [25] & ResNet-101 & 38.6 & 198 & 512 \(\times\) 800 & 90.17 \\ ReDet [30] & ResNet-101 & 31.6 & 89 & 512 \(\times\) 800 & 90.46 \\ Oriented RCNN [44] & ResNet-101 & 41.1 & 199 & 1333 \(\times\) 800 & 90.50 \\ MIHE-Net & ResNet-101 & 13.69 & 71 & 800 \(\times\) 800 & 92.43 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance evaluation on the HRSC2016 dataset.  
Figure 5: Example detection results of MIHE-Net on the HRSC2016 dataset.
# Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism
## 2 Related Work
### Mixed Game Models  
The authors of [25] studied the cooperative and non-cooperative game models for Slotted ALOHA with channel capture. The optimal threshold strategy is used to characterize the equilibrium in the case of cooperation, whereas the non-cooperative equilibrium is given as the Bayesian Nash equilibrium. The authors showed that in the non-cooperative game, users transmit with a higher probability than the cooperative scenario. This aggressive behavior comes from the fact that in non-cooperative games, users consider only their own payoffs.  
In [26], an extensive investigation of cooperation and selfishness was carried out using stochastic games and evolutionary game theory. According to the study, the system resource depends on the strategic choices of the users. Thus, it increases in the case of cooperation and decreases in the case of non-cooperation. The same behavior was found in SA and in SAZD systems [12].  
The authors of [27] proposed a game model with a mix of cooperative and non-cooperative users for Wifi networks. They found that the cooperation is beneficial even if some users choose to deviate. Additionally, they claimed that most defected users are penalized by obtaining the worst signal-to-interference-plus-noise ratio (SINR). However, the results focus mainly on the SINR; and the impact of the mixed user scenario on other performance metrics, e.g., throughput and delay, is not investigated.  
In [28], the authors developed an optimal caching algorithm using the social selfishness concept to achieve the best caching strategy for a mix of cooperative and non-cooperative users. The social selfishness concept adopted takes into account the social relationship between users. Thus, each user cares more about the cooperative users with whom he has a strong social relationship.
## 4 Description of the Application Problem and Employed Multi-Fidelity Models
### Low Fidelity: 0D (Perfect Mixing) Condensation Model  
In this work, a 0D mixing condensation model is considered a low-fidelity model. Figure 3 shows the parameters of this model. It can be regarded as an evolution of the one developed by [PERSON] et al. [41], in which a total (perfect) mixing between the inlet streams (air and EGR) is considered in an isobaric and isenthalpic way, as depicted in Figure 2. After calculating the mixing condensation, the 0D model employed in this work also evaluates the temperature decrease due to the area reduction (labeled \"nozzle\" in Figure 3) usually existing in LP-EGR junctions as a potential source of additional condensation. More  
Figure 1: Engine layout, highlighting EGR systems and main condensation locations.  
Figure 2: Psychometric diagram showing the process of humid streams mixed in a three-way junction as considered by the 0D model.  
details about the psychometric calculations to obtain the condensation considering perfect mixing can be found in the work of [PERSON] et al. [41].  
In the perfect mixing process, both streams are completely mixed in a homogeneous flow, obtaining the maximum amount of water condensed in perfect conditions.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 3 Methodology and Experimental Procedure  
As mentioned, this paper is focused on the study of a positioning system based on the UWB/IMU/Odometry fusion as a complementary system to those based on GNSS signals. Our approach is based on an UWB system formed by several fixed UWB nodes, called anchors, with a known position and one mobile UWB node, called tag, which is located in the vehicle. This system will compute UWB ranging estimates between the different UWB anchors and the UWB tag in order to correct over time the vehicle's position computed from its on-board data. To evaluate the performance of this positioning system, it is necessary to select an appropriate measurement procedure as well as a clear statistical analysis of the results.