esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Window Size** & **Time Resolution** & **Frequency Resolution** \\ \hline
128 & 0.12 ms & 1953.12 Hz \\
256 & 0.25 ms & 976.65 Hz \\
512 & 0.51 ms & 488.28 Hz \\
1024 & 1.00 ms & 244.14 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Window size and its resulting resolution in time and frequency. Figure 5: Exemplary spectrograms for a sample H\({}_{23\text{H}1}\) obtained for various alignments of the transducer and sizes of the computational window. Figure 6: Exemplary spectrograms obtained for various sizes of the computational window: before and after the dimension-fitting procedure. Furthermore, from the spectrogram computational point of view, the smaller the resolution in time, the greater the energy accumulated in a smaller number of cells, which causes a higher energy value visible in the spectrogram in a single cell. Conversely, if the resolution increases, the energy is distributed over a larger number of cells. Nevertheless, the influence of the window width on the obtained representations can be clearly observed, while it is still possible to observe local MBN activities even for the extreme case of the window width. The spectrograms that were obtained using various window sizes had different resolutions on the time and frequency axes. Thus, in order to perform the experiments using them by a single neural network, all had to be fitted to the same dimensions of 145 \(\times\) 94 (this size was applied in the previous article [23]). The process was required to train all data on a single specific artificial neural network architecture and compare the obtained accuracies. The exemplary spectrograms before and after the interpolation process are shown in Figure 5. Each of prepared databases contained 20,800 records in the training set, 2600 records in the validation set and 2600 in the testing set as well. The second type of database that was used during the experiments combined all sub-databases (all spectrograms obtained for the four window sizes), consisting of a total of 83,200 records in the training set and 10,400 in each of the validation and testing sets. The first type of database was applied to conduct an experiment in which the CNN training process was realized independently for data generated with a specific window size. The purpose of this experiment was to assess the impact of window size on the training process and to determine whether alternative processing could enhance classification accuracy. This approach was driven by the observation that adjusting the window size could reveal certain signal details. Additionally, this database enabled examining whether similar information patterns were present across certain window sizes. The second database was created from all data stored in independent sub-databases. The reason for this experiment was to see how well the CNN could generalize information across the entire dataset in the database, and to verify whether there was a difference in performance when checking with individual databases, each containing specific window sizes.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
###### Abstract  
Following the increasing popularity of unmanned aerial vehicles (UAVs) for remote sensing applications, the reliable operation under a number of various radio wave propagation conditions is required. Assuming common outdoor scenarios, the presence of trees in the vicinity of a UAV or its ground terminal is highly probable. However, such a scenario is very difficult to address from a radio wave propagation point of view. Recently, an approach based on physical optics (PO) and the multiple scattering theory (MST) has been proposed by the authors, which enables fast and straightforward predictions of tree-scattered fields at microwave frequencies. In this paper, this approach is developed further into a generative model capable of providing both the narrowband and wideband synthetic time series of received/transmitted signals which are needed for both UAV communications and remote sensing applications in the presence of scattering from tree canopies. The proposed channel synthesizer is validated using both an artificially-generated scenario and actual experimental dataset.  
physical optics; scattering; time series; vegetation +
Footnote †: journal: Remote Sensing  
1
Footnote 1: email: {[EMAIL_ADDRESS]}; Tel.: +42-022-435-2297
## 6 Security Analysis and Results
### Computation Cost  
We view the computation cost as the number of public key signature and verification operations per sensor node. As discussed earlier, a sensor node deployed outside its home zone only performs a single signature generation operation in its lifetime. As computed earlier, the probability of a node to reside outside its home zone is \(e^{-\frac{r^{2}}{2\sigma^{2}}}\), where \(r\) is the home zone radius and \(\sigma\) is the standard deviation of the Gaussian distribution. In our deployment of 50 nodes per territory, on average, about 45 nodes were deployed within the home zone and five nodes outside the home zone. Each of these \(t\) out zone nodes contact \(v\) verifiers in the home zone. The cost of signing/verification of a message \(m\), \(S_{\$}(m)\), where \(m\) is 96 bits (from our earlier calculation), is 135 mJ. Thus, each of the \(t\) out zone nodes and the \(v\) verifiers spend 135 mJ for signing operations. When these out zone nodes present their evidence to a neighboring node, they perform a verification, as well, which costs the same 135 mJ of energy. Sincethese are expensive operations, we ensure that this is a one-time expense in **Discard**, and the costs are distributed across the nodes in the home zone and potentially across time, as well.
## 4 Hardware Implementation  
### MRAM Device  
The MRAM device utilized in this research was the Everspin MR4A16B 54 TSSOP version. This commercially available device is organized as 1,048,576 words of 16 bits, providing a high-density memory solution. The MR4A16B employs toggle MRAM cells, which consist of one transistor and one magnetic tunnel junction (MTJ) cell. The MTJ cell comprises two ferromagnetic films with parallel magnetic field orientations. A visual representation of a toggle MRAM cell can be found in Section 2.3.  
The circuit that we designed allowed us to design differential PUFs. By selecting a pair of cells, one from the upper byte and another from the lower byte, and applying a small voltage to the device's upper (UB) and lower (LB) pins, two currents can be generated. These currents flow from the source into the chosen MRAM cells. The amount of current flowing into a cell makes it possible to analyze particular MRAM cells. Additionally, a small printed circuit board (PCB) adapter was employed to facilitate the easy mixing and matching of multiple devices on a single board.
## 3 Results  
### MEPs in Muscles of the Stationary Arm During Rest and During TB Vibration  
Figure 2A illustrates an example of MEPs in different muscles of the stationary arm in one participant. MEPs were successfully elicited in most muscles in all subjects. Since the latencies were \(\sim\)15-17 ms, we calculated the MEP amplitudes in the interval 10-40 ms following the stimulus. The background levels of EMG activity were not different between conditions (no vibration vs. TB vibration, paired \(t\)-test, \(p>0.05\) for each muscle pair, Figure 2B), likely because the arm was relaxed and the tonic vibratory reflex was not elicited in the vibrated (TB) and not-vibrated muscles (note also relatively low levels of background EMG values, \(\sim\)2 uV, that could likely reflect noise in EMG recordings). Despite some variability across muscles and subjects (in part due to differences in skin impedance across muscles and subjects), there were no significant differences in the MEPs of most muscles between \"no vibration\" and \"TB vibration\" conditions (\(p>0.05\), Wilcoxon matched pair test), except for TB (\(p<0.05\)) (Figure 2C).  
Figure 2: Effect of TB vibration on motor response in different upper limb muscles during stationary conditions. (**A**) Examples of MEPs. (**B,C**) Background EMG activity and MEPs, respectively (mean + SE, n = 13 subjects). BB, biceps brachii; TB, triceps brachii; DA, deltoideus anterior; DP, deltoideus posterior; FCR, flexor carpi radialis; ECR, extensor carpi radialis. Asterisk (*) denotes significant differences.
## 4 Discussion
### Strength and Limitations  
The most important limitation of this study is the lack of comparable pre-pandemic data. Therefore, the study cannot conclusively demonstrate if it was SARS-CoV-2 that caused the reduction in physical activity levels and the deterioration of body composition and physiological characteristics. A strength of the study is that it proves the new 12-MCTRE method to be an effective tool for measuring HR\({}_{\rm max}\) in less physically active individuals, not just athletes. The ergonomics of the rowing ergometer also afford the possibility of testing people who are overweight or obese, which is inadvisable in the case of running tests.
## 4 Real-Scale Vehicle Tests and Result Analysis
### Sensor Arrangement  
In order to accurately obtain the dynamic response of the suspension system, multiple acceleration sensors (Model No. X901, Sinocera Piezotronics, Inc., Yangzhou, China) needed to be placed at the axle, at the junction of the suspension and frame, and at the frame, as seen in Figure 12. During the operation of the dump truck, the suspension dynamic response is mainly caused by the vertical excitation of the road surface irregularity. Therefore, only unidirectional acceleration sensors were vertically arranged in all mounting points. Considering the structural characteristics of the dump truck with more degrees of freedom, 12 measuring points were symmetrically selected on both sides of the vehicle, including: the front and rear lifting lugs of front suspension, front axle (unsprung), the fourth beam of frame and the intermediate and rear axles (unsprung). The specific mounting position of the acceleration sensors on the left side of the suspension is shown in Figure 12. All of the vibration signals were recorded simultaneously by a multiple channel acquisition system (Model No. YMC9232, YMC Piezotronics, Inc., Yangzhou, China), and thereafter processed offline for making corresponding comparisons against 11-DOFs dynamic model predictions. In keeping with the sampling rate of the simulation system, the frequency of data acquisition is set to 200 Hz.  
Figure 11: Identification of rear modal energy difference.
# Feature Deep Continuous Aggregation for 3D Vehicle Detection
## 2 Related Works
### Point-Cloud-Based Methods
#### 2.2.2 Voxel Representations  
Voxel representation methods usually divide the raw point cloud into several voxels according to a certain size. Some methods perform a 3D convolution operation to detect objects directly in 3D space, while other methods extract features from each voxel to form a pseudo image, and then use a 2D convolution backbone and a 3D detection head to output the detection results.  
In 3D fully convolutional network (3 DFCN) [22], the point clouds are represented as a binary volumetric, which is fed to a 3D fully convolutional network to predict the 3D bounding boxes and the orientation estimation of the vehicles. The expensive 3D convolution operation limits the real-time performance. Vote3 Deep [23] divides the point cloud into 3D grids; then, the 3D sliding window method is used to determine whether each window contains vehicles or not. In order to reduce the model's complexity, sparse convolution layers are used to avoid many empty convolution operations. Sparsely embedded convolutional detection (SECOND) [24] also divides the point clouds into grids and improves the sparse convolution method, which can greatly increase the real-time performance. Pointpillars [25] proposes a novel encoding method that exploits PointNet [26] to learn the feature vector from each vertical pillar; then, these feature vectors are treated as pseudo images, which are fed to a 2D convolution backbone and detection head to perform 3D detection.
## 1 Introduction
Primary hepatic malignancies can occur when liver cells become abnormal, grow out of control and spread to other areas of the body [25]. The stage of hepatic malignancies usually depends on the location and size of the malignancy [26]. In clinical practice, accurate measurements of the size, location and shape of the liver and tumors in CT images can help physicians make a more comprehensive assessment of and plan for the condition of a patient. However, this geometric information on the liver and tumors is often measured manually by experienced doctors, who subjectively examine the CT images, thereby costing a lot of time. Therefore, an automatic segmentation method for the liver and tumors of CT images is urgently needed in clinical practice, which would have practical significance. Since CT images are complicated and not clear, it is difficult to effectively segment the liver and the tumor. As shown in Figure 1, the Hounsfield Unit (HU) values of the liver and tumor not only have a large span but also has a large number of overlapping areas. In the HU histogram of Figure 1, HU values of the liver are distributed in [50; 200]; HU values of tumor are distributed in [0; 150]; and HU values of the overlap area is [50; 150]. Such a large range of HU values overlap brings great challenges to the automated segmentation task of the liver and tumor. In addition, intra-slice(from 0.45 mm to 6.0 mm) and inter-slice(from 0.45 mm to 0.98 mm) resolutions in CT images are vastly different, which further create difficulties for the automatic segmentation of the liver and tumor. These problems prevent the accurate segmentation of medical images. To solve the above problems, we propose a novel cascaded 2.5D FCNs learning framework for the segmentation of the liver and tumor in medical images and design a new boundary loss function for network optimization. The boundary loss function can effectively help the convolutional neural network(CNN) deliberately learn boundary and contour features in medical images. The proposed method can effectively segment medical images and can reduce false-positive cases. Furthermore, the proposed 2.5D FCNs can reduce the cost of VRAM and the utilization of computing resources. Specifically, the contributions of this paper are as follows:  
1. A boundary loss function is proposed to help capture more boundary and contour features of the liver and tumor, from CT images, and to make the segmentation boundaries smoother;
2. A cascading 2.5D FCNs based on the residual network is proposed, which can effectively segment the liver and tumor in CT images and can reduce VRAM cost;
3. A post-processing method for the image boundary is presented to reduce false-positive cases, which can further improve segmentation accuracy. The rest of the paper is organized as follows--related work is reviewed in Section 2. The proposed method is described in detail in Section 3. Details and experimental results are presented in Section 4. In Section 5, a discussion and summary of the proposed method are given.
## 6 Experimental Verification
### Temperature Estimation Under Complex Conditions
Except for moments when the load condition changes sharply, the steady error of the stator temperature estimation was observed to be around 0 \({}^{\circ}\)C. The temperature error is smaller regarding the end cap, with a variation of 2 \({}^{\circ}\)C.
4. The estimation performance of the state equation estimation and the Kalman filter algorithm was compared. As for the stator and rotor, the temperature error of the Kalman filter method is smaller than that of state equation estimation. The reason lies in the fact that the state equation estimation method adopts an open-loop structure, and the temperature prediction error is easy to accumulate over time. Since the estimation error of the end cap is smaller by only around 2 \({}^{\circ}\)C, the performance of these two methods can be thought to be the same. 5. In the experiment, the temperature estimation error may result from the following reasons: The temperature measurement device is affected by the interference of the motor itself; the thermal model comes from the simplification of LPTN; and the open-loop estimation error based on the state equation is susceptible to time accumulation. Figure 9: Experiment under complex condition: Measured (red); state equation (blue); Kalman filter (green). In summary, the five-node LPTN proposed in this paper is not only suitable for estimating the motor temperature at a fixed speed but also for the temperature of the motor under complex conditions. At the same time, the experimental results also verify the correctness of the model parameter identification algorithm based on the multiple linear regression method, and the accuracy of the temperature estimation algorithm based on the state equation and the Kalman filter.
## 3 Review of Sensor Requirements for A3R
### Safety Considerations  
Safety is a key consideration for all aircraft operations. The sensors onboard the UA support safety directly by ensuring critical functions and indirectly by providing redundancy to operational and monitoring capabilities. For example, the COA defined by DGPS information ensures that, while the UA is in the COA, the UA can exercise a higher level of autonomy by navigating based on information provided by the CV system. The COA also helps ensure that the UA does not inadvertently navigate towards an incorrectly identified object. By confining the approach to a DGPS defined corridor, additional assurance can be provided to airworthiness officials that the UA will operate in a safe manner when a human cannot monitor operation in real time.  
Depending on the criticality of the system, additional redundancy may be required for sensors onboard the UA. For example, if the mission of the UA requires a combat radius that exceeds the unrefueled range of the platform, A3R becomes a safety critical function to the success of the flight. For a mission that requires A3R to succeed, redundancy may be required for specific sensors, such as the camera system, to ensure that a single failure does not eliminate the capability for the platform to receive fuel airborne. This requirement will be derived from the CONEMPS and will inform requirements for redundancy throughout the system.  
In training, crewed refueling is typically conducted in special use airspace (SAU), as defined by the FAA, to include restricted areas, warning areas, or military operations areas (MOA) containing a published tanker track [2]. Similarly, T&E of new capabilities and platforms will generally occur in the same SAUs, due to the limited presence of other aircraft not involved in T&E. Once a UA capable of A3R has been certified and delivered to warfighters, the UA would then be capable of conducting A3R wherever the mission planning factors allowed for operations. Key considerations here would be deconfiction from other flight operations around friendly forces and the avoidance of threat engagement ranges due to the vulnerability of both platforms during A3R and the strategic importance of tanker aircraft.  
The extended flight time enabled by aerial refueling may introduce additional safety considerations for UAs. Previous research has explored the use of sensors and measurements for UA safety and advocates for standard testing to ensure reliability and assess performances [105].
## 5 Simulation Setup and Result Analysis
### Throughput Performance of O-MAC and Other Hybrid MAC Protocols  
We analyze the throughput efficiency of O-MAC and other competing hybrid MAC protocols. We used combined static and mobile scenarios for determining the throughput based on the varying number of transmitting nodes. In Figure 12, we set 25% of the nodes to be mobile, including transmitting nodes throughout the simulation. O-MAC and other competing MAC protocols initially produce an average throughput of 458 to 500 kb/s, but when the number of transmitting nodes increases, the performance of O-MAC decreases only slightly compared with other hybrid MAC protocols. The O-MAC throughput decreases from 500 to 439 kb/s, whereas the throughput of the other hybrid MAC protocols decreases from 458 to 324 kb/s with the same number of transmitters. O-MAC is superior to the other competing MAC protocols and achieves a throughput that is 8.4% to 23% higher. This mobility analysis is measured using two methodologies: analysis based on synthetic traces and analysis based on real-world traces, as discussed in [41].
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 6 Evaluation
### Experiment Settings  
We consider an ultra-dense network (UDN) with a certain number of edge servers and users, which are randomly distributed in a \(1\times 1\) km area with some heterogeneous edge servers. In this multi-access UDN network, many edge servers are in the vicinity of a given IoT device. Thus, each user has various options in making the offloading scheme. Our goal is to find the appropriate offloading strategies for all the users in the UND efficiently. Each user has a task to be completed with the help of an edge network, which consists of inter-dependency subtasks, and their DAG is generated based on some popular user applications, such as object recognition [27], gesture recognition [10] and video navigation application [28].  
We also randomly generate some DAG to verify the adaptability of our offloading algorithm, in which the CPU cycles required by subtasks vary from 30 _Megacycle_ to 120 _Megacycle_. Other experimental settings are made as follows: The wireless channel bandwidth is \(B=150\) kHz. The transmission power is ranged from 100 to 200 mW [29] randomly among different devices, the background noise is set to \(-120\) dBm [30]. We also assume that the CPU frequency of the edge server (30 GHz) is ten times that of user devices (3 GHz) [29].
# Research on Localization Algorithms Based on Acoustic Communication for Underwater Sensor Networks
## 3 Localization Algorithm
### Communication between Nodes  
According to the feature of communication between reference nodes and ordinary nodes, we classify the localization algorithms of UWSNs into two taxonomies, namely, single-stage scheme and multi-stage scheme. The single-stage scheme is that all the ordinary nodes exchange the message directly with the reference nodes. When they are localized, they remain passive and cannot help obtain  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **The State of the Nodes** & **Coverage** & **Localization Time** & **Accuracy** & **Computational Complexity** & **Energy Consumption** \\ \hline
[49] & Stationary and active & Large & Long & High & Low & High \\
[50] & Stationary and active & Large & Depend & Low & Medium & Low \\
[51] & Stationary and active & Small & Long & Low & Low & High \\
[52] & Stationary and silent & Small & Short & Low & Low & High \\
[53] & Stationary and silent & Large & Depend & Average & Medium & Low \\
[54] & Stationary and silent & Large & Depend & Average & Medium & Medium \\
[55] & Mobile and active & Large & Long & High & High & High \\
[56] & Mobile and active & Large & Long & High & High & Low \\
[57] & Mobile and silent & Large & Short & Average & Low & High \\
[58] & Mobile and silent & Depend & Depend & Depend & Low & High \\
[59] & Hybrid and active & Large & Depend & Average & Low & Low \\
[60] & Hybrid and silent & Large & Short & Low & Low & Low \\
[61] & Hybrid and silent & Large & Depend & Depend & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of localization algorithms for using the state of nodes.  
the location information of the unknown nodes [38]. As for the multi-stage scheme, the ordinary node may not communicate with the reference node directly. Once the ordinary node is localized, they can be looked at as the new reference nodes and help to realize localization of the other ordinary nodes [38].