esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 5 Experimental Results
### Results of the Interference Detection Experiment  
Validating the time consumption and accuracy of the IQDN, we conduct experiments using test data of the MID Dataset mentioned in Section 3. The IQDN is trained on an NVIDIA GeForce RTX 3060 GPU. IQDN utilizes the Adam optimizer, and the batch size is set to 64. The learning rate is set to 0.01 and descends with a step-wise strategy. Figure 15(a) shows the growing tendency of training accuracy as more steps are taken by IQDN and some other CNNs. The IQDN accuracy gradually ascends to 100%. Figure 15(b) shows the loss curve with training steps. The loss drops rapidly as the model gradually converges. The curve demonstrates that the VGG model converges faster because pre-trained parameters are used during its training.  
Then, we conduct a Monte Carlo experiment by selecting 100 images from the dataset that was not used in the training process and test them with trained model parameters. During this, the total time consumption is recorded. We divided the total time consumption by 100 to get the average inference time required to test a picture; the results are shown in Table 3 below:  
We can draw a conclusion from the table that the IQDN network proposed in this paper can achieve faster inference speed than other CNNs. This means we can quickly screen out the data with RFI from massive SAR data and lay a solid foundation for subsequent work.  
While detecting RFI efficiently, the proposed model also has a smaller number of parameters than VGG, ResNet, SENet, and MobileNet, as shown in Table 4, which theoretically proves that the IQDN is lighter than other CNNs.  
Figure 16: The training accuracy and loss curve with training steps: (a) training accuracy; (b) loss.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **VGG** & **ResNet50** & **SENet** & **MobileNetV2** & **Ours** \\ \hline time (ms) & 8.01 & 7.02 & 9.04 & 6.99 & 6.10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the mean inference time per image of different models.
## 3 Case Study  
### Experimental Dataset  
The displacement monitoring data of the Shuizhuyuan landslide in the Three Gorges Reservoir area is selected as the time series' experimental dataset of this study. This time series is collected from seven GNSS monitoring sensors at the Shuizhuyuan landslide. The time interval is from 15 July 2017 to 1 December 2021, with a total of 1600 days. The data monitoring cycle is to obtain one monitoring data at the site every day, so the tensor size of the constructed dataset is 7 \(\times\) 1600 \(\times\) 1 (sensors \(\times\) time points \(\times\) day) tensor structure. The monitoring data are shown in Figure 3.
# Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues
## 3 Measurement Techniques
### Range-Based Technique  
In range-based algorithms, node locations are estimated by considering point-to-point distance or angle between the node with some reference. Some important approaches such as angle of arrival, time of arrival, time difference of arrival and received signal strength indicator are discussed in the section.
# Clustering Approach for Reliable Wireless Communication
## 4 Results and Discussion
### Scenario 2: Two Antennas at BS/20, 30, and 40 Users/MUSA Code Length of 4  
The codewords in Table 1 are split up into 20 clusters for this scenario, but the number of users \(K\) is increased to 30 and 40. Hence, instead of 10 users, as considered previously, the codewords from 20 clusters are used by 20, 30, and 40 users, respectively. The Rayleigh fading channel was used to obtain Figures 12(a) and 12(b). It can be seen from Figure 12(a) that clustering still performs better than allocating codes randomly. The three simulations with 20, 30, and 40 users, respectively, show that the difference is only observable at an SNR higher than 10 dB. Additionally, as Figure 12(b) illustrates, the clustering approach results in a marginal improvement in performance for \(K=20\) when the data length is increased ten times.  
Figure 12: BER vs. SNR for Scenario 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scenario No.** & **Features** \\ \hline
1 & 2 antennas at BS/\(K\) = 20/MUSA code length of 4/Rayleigh fading/BPSK \\
2 & 2 antennas at BS/\(K\) = 20, 30, 40/MUSA code length of 4/Rayleigh and Rice fading/BPSK and QPSK \\
3 & 4 antennas at BS/\(K\) = 20, 30, 40/MUSA code length of 4/Rayleigh and Rice fading/BPSK and QPSK \\
4 & 4 antennas at BS/\(K\) = 40 and 62/MUSA code length of 31 \\
5 & 4 antennas at BS/\(K\) = 20, 40, and 70/MUSA code length of 31 \\
6 & 2 and 4 antennas at BS/\(K\) = 20, 30, and 40/MUSA code length of 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenarios definition.  
Given the fact that extending the data length does not result in a noticeable improvement in performance, the simulation results shown in Figure 13c,d were conducted with a 700 bits data length. The results shown in Figure 13c were obtained by testing Rice fading under the same conditions as Rayleigh fading, with a K-factor of 5 dB. It is obvious that using clustering also improves the performance of the channel. Furthermore, better results can be seen when comparing them to those shown in Figure 13a because the Rice fading has a better channel gain due to the presence of the line-of-sight (LoS) component.  
To compare the effectiveness of different modulation schemes, QPSK was evaluated in a Rayleigh fading channel transmission, shown in Figure 13d. QPSK transmits two bits per symbol, while BPSK transmits only one. Although QPSK has a bandwidth that is twice more efficient than BPSK, it also requires 3 dB more SNR to achieve the same BER. This is the reason why it was expected to see additional gains from clustering at 10 dB (where clustering gains can be observed for BPSK with 20 users in Figure 13a plus 3 dB). However, for QPSK with 20 users, the clustering gains become visible starting from 5 dB SNR. This outlines the benefits of the proposed algorithm even more compared to a random code allocation. The same trend can also be observed for higher numbers of users, 30 and 40, respectively.
## 2 Methodology
### Discussions
There are two purposes: (i) to discuss the prediction accuracy when the training sets and testing sets are divided according to the construction sequence; (ii) to analyze the prediction accuracy of the proposed model under large excavation. The data used in this study are the monitoring data from the 70 th to the 180 th day of excavation. Among them, the data of the first 80 days (i.e., from the 70 th to the 150 th day) are the training samples, the data of the middle 23 days (i.e., from the 151 st to the 173 rd day) are the testing samples, and the data of the last 7 days (i.e., from the 174 th to the 180 th day) are used as the prediction samples. Figure 10 shows the prediction results for the settlement of buildings at JGY-22, using the SVM model, the BP model, and the BP-SVM model. It is found that the variation trends of the predicted results are consistent with the measured results, and the prediction accuracy of BP-SVM model is higher than that of the other two models. The prediction accuracy for the prediction samples is lower than that of the testing samples, but the values of \(R^{2}\) are all greater than 0.6 which is acceptable in the project. Figure 11 presents the history curve of \(R^{2}\), which indicates that the accuracy of prediction results decreases for long-term prediction with the advance of excavation, but it will gradually tend to stable. The above results demonstrate that the proposed method can be used as a supplement to field monitoring to predict the response of existing buildings induced by excavation, and the prediction accuracy is related to the machine-learning algorithm used. Meanwhile, the data of training samples needs to be updated continuously according to the excavation process in order to make better predictions, due to the fact that the model obtained through training sets is an approximate solution and the model parameters will change with the advancement of excavation. Figure 11: History curve of \(R^{2}\). Figure 10: Prediction results for the settlement of building at JGY-22.
## 6 Proposed Design Guidelines  
One objective of this review is to provide a preliminary set of guidelines to design effective non-safety related in-vehicle messaging content. Previous attempts have been made to provide human factors guidelines for roadway signage and interface design for elderly drivers [124]. NHTSA proposed a series of guidelines for in-vehicle displays, but these guidelines focus primarily on manual driving, whereas the conditions of automated driving could involve differing concerns [7]. As research on in-vehicle message delivery of road signage information during automated driving is still in its infancy, the preliminary guidelines proposed here are intended to provide a starting-point for how in-vehicle messaging should be designed to leverage aspects of vehicle automation. The guidelines are meant to motivate designers to address specific human performance issues associated with automated driving vs. identifying exact solutions for specific driver disengagement situations. Table 1 presents the proposed guidelines and their description as well as the supporting literature.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Design Aspect** & \multicolumn{2}{c}{**Guidelines Based on Existing Literature**} & \multicolumn{2}{c}{**Related Research Questions**} \\ \hline \multirow{8}{*}{Presentation of Road Signage} & \(\bullet\) & Road signage should use structures familiar to drivers from other physical signs [18]. & \(\bullet\) & Should the presentation of road sign information follow the standard road conventions, when presented during highly automated driving? \\  & \(\bullet\) & Signage should be physically representative of what it is trying to convey (i.e., lane closure signs should represent a lane being closed) [18]. & \(\bullet\) & If both on-road signage and in-vehicle displays are available, how would drivers cope with potential incongruence between the two information sources? \\  & \(\bullet\) & Pictorial information with text is more effective for conveying sign information [20]. & \(\bullet\) & Are auditory, visual, or multimodal cues most effective for presenting non-safety related information to a driver? \\  & \(\bullet\) & Multimodal cues (auditory and visual) should be leveraged to guide driver attention effectively [75; 77; 78]. & \(\bullet\) & How do these cues need to be altered to accommodate older drivers when presenting non-safety-related information? \\  & \(\bullet\) & Keeping drivers engaged during highly automated driving can benefit safety [126; 127]. & \(\bullet\) & Can we leverage in-vehicle presentation of non-safety-related information to periodically engage a driver with the road environment? \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed design guidelines and outstanding research questions for the presentation of non-safety related information via in-vehicle display.
## 5 Conclusions  
We suggest an innovative neural network design to boost the learning capabilities of infrared small targets aimed at extracting features across multiple scales and performing boundary regression on UAV targets against intricate backdrops. The introduction of the iAFF module allows us to compile diverse contextual data across channel dimensions, focusing on both extensive targets with worldwide distributions and minor targets with localized distributions. Our proposed OAAM strengthens the connections between channels in partially occluded regions, enabling the model to more effectively handle issues such as local occlusion and the loss of target features under partial occlusion. Altering the loss function to the NWD enables the network to discern the significance of each semantic layer, focusing more on the superficial aspects of infrared minor targets. The newly implemented NWD metric aids in producing superior positive and negative samples, thereby efficiently resolving the sensitivity concerns of the IoU metric in the context of infrared small targets. The outcomes of experiments using the public IRSTD-1k dataset and our custom IRMT-UAV dataset reveal that our technique surpasses cutting-edge (SOTA) methods, showing remarkable precision and resilience in diverse application situations, such as situations with intense clutter, small-scale targets, and partial occlusion.  
Our proposed algorithm can identify and locate small targets with lower false-positive and false-negative rates and higher accuracy against complex backgrounds and in adverse conditions, such as conditions with low contrast and high noise. Enhancing the detection capability of small targets in dynamic scenes is crucial in many application domains, such as drone surveillance and autonomous driving. Our next goal is to reduce the model's parameter size and computational complexity while maintaining the current algorithm's performance, thereby lowering hardware costs and energy consumption, which is critical for spaceborne and airborne devices.  
The process of conceptualization: [PERSON] and S.Z.; techniques: S.Z.; software: S.Z.; affirmation: S.Z.; methodical examination: S.Z.; references: [PERSON], Y.L., N.Z. and Z.L.; curating data: W.D. and S.Z.; composing--initial draft creation: S.Z.; authoring--evaluating and refining: W.D., Y.L. and S.Z.; overseeing: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The study did not obtain any funding from outside sources.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available because they are confidential.  
The writers affirm the absence of any conflicting interests.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FPN & Feature Pyramid Network \\ PAN & Path Aggregation Network \\ iAFF & Iterative Attentional Feature Fusion \\ OAAM & Occlusion-Aware Attention Module \\ NWD & Normalized Wasserstein-Gaussian Distance \\ MS-CAM & Multi-Scale Channel Attention Mechanism \\ \end{tabular}
## 4 Results and Discussion
After the phantoms were produced, a monostatic radar-based imaging system was set up to detect the tumor inside the phantom, as shown in Figure 11.  
Figure 11: Setup of the imaging system in the experimental environment. Figure 10: Heterogeneous breast phantom production: (**a**) adding mixtures suitable for the properties of the tissues; (**b**) skin tissue; (**c**) tumor tissue; (**d**) diameter of the tumor; (**e**) tumor positioned after fat and glandular tissue; and (**f**) breast phantom left to dry. The S11 parameter response of the heterogeneous phantom with and without a tumor in VNA with the established system is shown in Figure 12.  
As a result of all signal processing stages, images of tumors located in heterogeneous and homogeneous breast were obtained. Structured images of the tumor located in the homogeneous and heterogeneous phantoms are shown in Figure 13. In Figure 13 (a-homogeneous and b-heterogeneous), the location of the tumor in the breast phantoms was obtained in the correct location in the X-Y plane (top view). In addition, in Figure 12 (c-homogeneous and d-heterogeneous), the image of the tumor in the X-Z (side view) plane was obtained in the correct location. It is shown that the RMWBI system developed using the proposed Vivaldi antenna can detect and scan small tumors in the breast at the correct location. Figure 12: VNA S11 image: (**a**) heterogeneous phantom without a tumor and (**b**) heterogeneous phantom with a tumor. Figure 13: Image of the 16 mm tumor placed into the breast phantom: (**a**) homogeneous phantom X–Y plane; (**b**) heterogeneous phantom X–Y plane; (**c**) homogeneous phantom X–Z plane; and (**d**) heterogeneous phantom X–Z plane. Since the dynamic range of the VNA device used in the experimental setting was not high enough to detect very small tumors, tumor detection in the homogeneous phantom resulted in a less noisy image compared to the heterogeneous phantom. Additionally, the glandular tissue found in the heterogeneous phantom weakens the tumor response. Tumor sizes detected in studies in the literature are shown in Table 5.
## 6 Evaluation and Result Analysis  
In this section, to evaluate the performance of the proposed algorithms, numerical simulations and a lake trial were conducted. The parameter settings of CP-OFDM in the simulations and lake trial are shown in Table 2. The bandwidth, carrier frequency and sampling frequency of the lake trial are closely related to the transducers used in the trial, and these values are set smaller in the simulation system to increase the simulation speed. Because of the high cost of the lake trial, the CP length, the number of pilot subcarriers and the number of null subcarriers are designed to be larger than the simulation system to ensure the availability of trial data. In the simulation system (the specifications of our computer are as follows: Intel(R) Core(TM) i5-6400 CPU 2.7 GHz (4 cores), 16 GB RAM, 1 TB memory with Windows and MATLAB installed), it is assumed that the UWA channel has 12 randomly generated paths, and that these paths remain fixed within one OFDM frame. The maximum path delay is less than CP length, and the amplitudes of paths are Rayleigh distributed with the average power decreasing exponentially with delay. The Doppler scale factor \(a\in(0,10^{-3})\) is randomly chosen. Moreover, there is no channel code module in the simulation system.
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 5 Experiments
### Results of the Coarse Localization Stage
#### 5.1.1 Training Process of the Classification CNN  
A transfer learning process was carried out with the aim of re-training the AlexNet network and address the room retrieval classification task. The training of the model was carried out with the Colab tool and a sensitivity analysis was performed to set the optimal value of the the following hyperparameters as follows:  
* 30 epochs;
* Initial Learn Rate: 0.001;
* Optimization algorithm: Adam;
* Loss function: Categorical cross entropy loss;
* Batch size: 50.  
This section describes the process followed to set the values of each one of the hyperparameters showed above. Concerning the number of epochs, preliminary studies performed in a local computer with limited resources established this hyperparameter as 5. The accuracy obtained was around 54%. As a consequence, it turned out to be necessary to increase that value and, thus, to perform the training with a more powerful machine. As for the optimization algorithm, the choice of Adam is supported by the broad use of this method by the scientific community due to its effectiveness and computational efficiency in a number of applications [18]. This justifies the selection of this algorithm. As for the initial learn rate, this hyperparameter is considered one of the most crucial. A high value of this hyperparameter can imply that the network is not capable of learning and a low value can imply a low learning speed. Therefore, the dynamic performed in this work to tune this value has been to establish initially a default value and then reduce it in later epochs as the network is learning, thus leading to the value selected. Finally, regarding the loss function, the cross entropy is the suitable option since the union of this function with a softmax activation function provides a value that indicates how likely it is that the input image belongs to a specific room. This feature makes this function suitable for the desired task. The definition of the cross entropy loss is shown in Equation (1).  
\[CE=-\sum_{i}^{C}t_{i}\log(f(s)_{i}) \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Code** & **One-Hot Vector** \\ \hline CR & [1, 0, 0, 0, 0] \\ PA & [0, 1, 0, 0, 0] \\
2 PO1 & [0, 0, 1, 0, 0] \\ ST & [0, 0, 0, 1, 0] \\ TL & [0, 0, 0, 0, 1] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Labeling transformation. The room information label is transformed to one-hot vector with the aim of addressing the CNN classification training.  
where \(C\) is the number of output neurons, \(s\) the vector of scores, \(t\) is the one-hot vector with a positive and negative classes and \(f(s)\) is the softmax function that squashes the output scores \(s\) in the range (0, 1). The outputs of this function can be interpreted as class probabilities.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## 2 Related Work
### Fault-Tolerance by Software Redundancy  
Regarding fault-tolerance by software redundancy, it is common to use redundant multithreading techniques for transient fault detection and recovery of multi-core processors working in Symmetric Multi-Processing (SMP) mode [10; 11; 12; 13; 14]. Some of them are based on Simultaneous Multi-Threading (SMT) where the systems run identical copies of a process as independent threads. However, in these works, the details about the validation of the proposed techniques are not provided. Moreover, the principle of redundancy in multi-core processors is used to recover the system from permanent hardware faults [15].  
In [16] is presented a software technique for transient-fault tolerance: the Process-Level Redundancy (PLR), which leverages multiple cores for low overhead. This approach is evaluated by Single Event Upset (SEU) fault-injection on redundant processes. The faults are injected by means of the Intel tool called Intel Pin dynamic binary instrumentation, which changes one bit of a randomly-selected instruction. Nonetheless, there is no complete independence between processes since this approach uses a unique Operating System (OS).  
The work in [17] proposes a reliable resource management layer applied to many-core architectures. The scheduler applies a Triple Modular Redundancy (TMR) on multi-threading applications, detects errors and isolates faulty processing elements. This approach is evaluated at the simulator level by injecting one fault per run. The evaluation was done on a single cluster of 16 cores.  
In [18] is proposed a framework to improve the reliability of COTS many-core processors. The framework is illustrated through an adaptive NMR system that selects the better number of replicas to maximize the system reliability. To achieve this, they present an analysis of the reliability of the system that shows a trade-off between the reliability of the voter and the number of replicas. This analysis is only theoretical and has not been experimentally evaluated.  
The work in [19] proposes a soft NMR that improves the robustness of classical NMR systems by using error statistics. Its effectiveness is illustrated by an example in image coding.  
Lastly, the authors of [20] implement four independent fault-tolerance techniques on the Radiation Hardened By Design (RHBD) Maestro processor aiming at mitigating hardware and software errors. This work describes the implementation of these techniques, which include a kernel-level checkpoint rollback, a process and thread level redundancy and a heartbeat. Nonetheless, there is no evaluation of the effectiveness of their proposal.