esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
## 4 Radar Depth and Velocity Estimation
### Velocity Estimation
(**a**) Linear motion model needs multiple detections for the object. (**b**) Curvilinear motion model requires either two radars to observe the same objects or the determination of the vehicle boundary and rear axle. by using temporal consistency between adjacent radar frames. [PERSON] et al. [91] estimated pointwise velocity by the fusion of radar and cameras. They first estimated the dense global optical flow and the association between radar points and image pixels through neural network models. Next, they derived the closed-form full velocity based on the geometric relationship between optical flow and Doppler velocity. [PERSON] et al. [125] estimated the scene flow for the 4D radar point cloud in a self-supervised learning framework. Scene flow is a 3D motion field and can be roughly considered as the linear velocity field. Their model consists of two steps: flow estimation and static flow refinement. In the flow estimation step, they adopted a similar structure with PointPVCNet [126]. To compensate for the positional randomness of detections between frames, a cost-volume layer is utilised for patch-to-patch correlation. The features and correlation maps are then sent to a decoder network for flow regression. In the static flow refinement step, they assumed that most radar detections are static and, therefore, used the Kabsch algorithm [127] to robustly estimate the ego-motion. They then filtered out moving objects based on the coarse ego-motion and applied the Kabsch algorithm again to all static points for fine-grained ego-motion estimation. The self-supervised loss consists of three parts: a radial displacement loss, which penalises errors between the estimated velocity projected along the radial axis and the measured Doppler velocity, a soft Chamfer distance loss, which encourages temporal consistency between two consecutive point clouds, and a soft spatial smoothness loss, which encourages the spatial consistency for the estimated velocities with their neighbours. The soft version of loss is used to model spatial sparsity and the temporal randomness of the radar point cloud.
# A LiDAR-Based Backfill Monitoring System
## 4 Experiment
### The Operational System Workflow in Mines  
The operational workflow of the proposed system applied in underground mines is illustrated in Figure 7. The initial information of the tunnel before backfill should be obtained by a technician before starting filling the tunnel with the backfill material. The LiDAR is used to scan the target tunnel from the top to the end, point cloud data are recorded and save as an ROS bag (a file format used for storing message streams in Robot Operating System [35]). The SLAM (Simultaneous Localization And Mapping) [36; 37] technology is used to build the three-dimensional point cloud map of the tunnel. This initial map is detailed and precise, showcasing physical characteristics and arrangement of rocks. When the retaining wall is under construction, it is necessary to consider grooving the top of the wall to fix the backfill monitor. As the backfill material slowly enters the tunnel, the LiDAR begins operating.  
The backfill 3D geometric model generated in the NUC can be stored in various digital formats, which can be directly imported and visualized in most 3D model processing software. After the three-dimensional model of the backfill body is obtained, the tunnel model and the slurry model are visualized with different marks and colors to visually display the backfilling situation. Based on the communication networks used in the mine where the backfill monitor is deployed, the calculation results are transmitted to the mining control center via LAN (Local Area Network), 5G, or WiFi [38; 39; 40]. Therefore, the control center can monitor the backfill progress and make corresponding control measures according to all kinds of emergencies.  
Figure 6: Calculation of signed volume.
## 5 Numerical Simulation Results and Discussion
### Practical Test 2: Imbalanced Vertical Shift with Friction and Fluid
To track the operating conditions of the drill string and the trend deterioration to predict the downtime, it can be seen from the overall measurement that the fluid-free vibration amplitude suddenly increases (Figure 21). The level of vibration on impact is still normal, so no action needs to be initiated, but the vibration should be measured regularly from then on, and any increase in amplitude should be accurately assessed.
# Mining Pool Selection under Block WithHolding Attack +
## 3 Evolutionary Game for Mining Pool Selection
### Game-Theoretic Analysis
#### 3.3.3 Evolutionary Stability of NEs  
Generally, ESSs are NEs, while NEs are not necessarily ESSs. Thus, we need to analyze the stability of NEs to identify the ESSs of the game. Again, we interpret the population states as mixed strategies. Suppose the entire population adopts the strategy \(\mathbf{x}^{*}\) and there is another mutant strategy \(\mathbf{x}^{\prime}\) that attempts to invade a fraction \(\epsilon\in(0,\overline{\epsilon})\) of the population. If the following inequality holds, then \(\mathbf{x}^{*}\) is an ESS:  
\[\sum_{i\in\mathcal{M}}x_{i}^{*}y_{i}((1-\epsilon)\mathbf{x}^{*}+\epsilon\mathbf{x}^{ \prime})\geq\sum_{i\in\mathcal{M}}x_{i}^{\prime}y_{i}((1-\epsilon)\mathbf{x}^{*}+ \epsilon\mathbf{x}^{\prime}). \tag{13}\]  
More precisely, \(\mathbf{x}^{*}\) is an ESS if the following two conditions are met [46]:  
1. \((\mathbf{x}^{*}-\mathbf{x})^{\top}Y(\mathbf{x}^{*})\geq 0,\quad\forall\mathbf{x}\in\mathcal{X}\)
2. If \((\mathbf{x}^{*}-\mathbf{x})^{\top}Y(\mathbf{x}^{*})=0\), then \((\mathbf{x}^{*}-\mathbf{x})^{\top}Y(\mathbf{x})>0\) holds.  
The first condition means that an ESS must be an NE. The second condition indicates that if a miner choosing strategy \(\mathbf{x}\) can earn as much revenue as a miner who chooses the NE strategy when other miners choose the NE, then a miner who chooses the NE must earn more revenue than a miner who chooses \(\mathbf{x}\) when other miners choose \(\mathbf{x}\).
## 1 Introduction
We align our model closely with the natural behavior of bird sounds by employing phase-type distributions, such as Erlang and Hyper-Exponential distributions. * We develop a novel steganography technique that inserts information into bird sounds with minimal distortion. This technique ensures that the covert nature of the communication channel is maintained, avoiding detection by potential attackers. * We investigate the Slotted ALOHA protocol to derive key performance metrics for a WSN utilizing a covert channel based on sound. This analysis provides insights into the proposed system's throughput, transmission rate, and energy consumption. The rest of the paper is organized as follows. Section 2 presents a survey of related work. In Section 3, we review a reference framework for our study on information insertion and wireless communication. Then, in Section 4, we introduce a mathematical model based on Markov chains. We also discuss the insertion of information in this section. In Sections 7 and 8, we present our test and results, where we analyze different metrics that were mentioned earlier. Finally, Section 9 concludes this paper and outlines future research directions.
## 2 Results and Discussion
### RelBe-XRE-ReIE Composite  
RelBE is one of the best-described TA families [86]. In the canonical system, the toxin, RelE (Relaxed E) is associated with the antitoxin, RelB (Relaxed B) [9,86]. However, the \"mix and match\" phenomenon also applies, where the RelE may associate with other antitoxins such as AbrB- and RHH-domain harboring proteins, XRE (Xenobiotic Response Element) family proteins, and Phd antitoxin [69]. We are presenting the XRE-RelE TA composite, which belongs to the RelBE TA family [69], since it is the one found in the strains analyzed here (for more information on the RelB antitoxin and the other possible composites, the reader is referred to [86]).  
RelE and ParE toxins both belong to the ParE superfamily [86]. RelE has a bacteriostatic effect [17]. The XRE protein family is a large family of transcriptional regulators, with an HTH DNA-binding motif, that controls different functions in prokaryotic cells [75,87]. The _xre_ gene regulates the transcription of its own gene as well as other neighboring genes [88]; therefore, it has the potential to function as an antitoxin. Moreover, XRE proteins can inactivate toxins [89]. Since proteins functioning as antitoxins are not necessarily specialized in functioning that way exclusively, XRE proteins may act as antitoxins [75,90] that pair with e.g., RelE toxin [7,8,75,86,91,92]. Three of the four _Geobacillus_ strains have a putative XRE-RelE TA composite, as discussed below.
# Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification
## 1 Introduction
This method extracts information from the three frequency bands and this significantly reduces the quantity of data, however it also compresses the structure information of each trail. This study proposes a novel approach to enhance the features of spectral maps and apply some milestone achievements in deep learning to the EEG-based workload classification area. In the feature enhancement part, 3-channel feature-enhanced maps are obtained and combined with original 3-channel spectral maps, thus building the parallel mechanism of spectral feature-enhanced maps which have 6-channels. In the feature extraction part, the shortcut connection in ResNet and short paths in DenseNet improve the ability of feature extraction from the spectral map, and are undertaken for the first time in the EEG-based cognitive workload classification area [25; 26]. The final accuracy of cognitive workload classification using a public dataset is improved from 91.1% to 93.6% and the generalization of the network is also improved.
# An Analysis of the Use of Feed-Forward Sub-Modules for Transformer-Based Image Captioning Tasks
## 6 Conclusions  
We presented simple yet effective transformer-based image captioning models and performed detailed analyses of the use of feed-forward layers to both encode the images and text captions. Whereas we did not outperform the current state-of-the-art models, the results obtained where only marginally lower than those obtained in fully attentive self-attention-based image captioning models. We demonstrated that most of the transformer self-attention layers could be replaced by fully connected feed-forward layers. In this setup, the self-attention mechanism is really only required in the cross-attention sub-module. While not explicitly stated, our experiments and results also provide an insight into the significance of attention mechanisms in transformer models. The captions produced were mostly as good as those produced by their self-attention counterparts, but did exhibit some problems when it came to capturing the long-distance intra-sequence relations between the caption tokens and the detected features. We also showed that in spite of being a larger model with many more parameters than similar self-attention based models the feed-forward models used only slightly more memory at lower batch sizes and less memory at higher batch sizes that we tested. We demonstrated that there is need to revisit some of the earlier algorithms, because they could provide alternative perspectives with which to address current issues. This could lead to alternative research directions that would greatly benefit from current hardware. As part of our future work, we plan to develop much simpler models that can compete evenly with current more complex state-of-the-art models. This would make deep learning research more easily available to researchers who have no access to high-end hardware.  
**Author Contributions:** Conceptualization, R.I.O.; methodology, R.I.O.; software, R.I.O.; validation, R.I.O., Z.Y. and J.L.; formal analysis, R.I.O., Z.Y. and J.L.; investigation, R.I.O.; resources, R.I.O. and J.L.; data curation, R.I.O.; writing--original draft preparation, R.I.O.; writing--review and editing, R.I.O., Z.Y. and J.L.; visualization, R.I.O.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant U2003208, in part by the Science and Technology Plan of Hunan under Grant No. 2016 TP1003, and in part by the Key Technology R&D Program of Hunan Province under Grant No. 2018 GK2052.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
In this paper, the analytical solution to the vibro-acoustic response of a vibrating clamped P-FGM plate is analyzed. Several validation studies and parametric effects are presented. Listed below are the conclusions of the present research:  
1. The sound radiation fields validated with ANSYS and literature are in good agreement.
2. The sound power level monotonically increases with an increase in the power-law index because of a reduction in the stiffness of the FGM plate.  
Figure 14: Radiation efficiency of (**a**) Al/Al\({}_{2}\)O\({}_{3}\), (**b**) Al/ZrO\({}_{2}\), (**c**) SuS304/Si\({}_{3}\)N\({}_{4}\), and (**d**) Ti6 Al4V/ZrO\({}_{2}\) P-FGM plates under different damping loss factors.  
Figure 13: Variation of radiation efficiency with power-law indexes, \(k\): (**a**) normal and (**b**) log-log scale.  
* However, contrary to what other authors [29] have claimed, the acoustic response of P-FGM plates does not appear to always increase with an increase in the power-law index in the current study.
* The current findings lead to conclude that there exists a key value of the power-law index for which the sound power level is at minimum in a specific frequency range.
* Sound power level peak decreases and level shifts to higher frequencies with an increase in \(E_{rat}=E_{c}/E_{m}=\) 2, 5, 10, 20, and 40.
* It can also be said that the P-FGM plates' material distribution (due to variation in power-law index '\(K\)') plays the major role in the computation of the sound radiation of the FGM plate because it characterizes the stiffness variation of the structure.  
Hence, it is found that the power-law index does not appear to have a significant impact on radiation efficiency at low frequencies of less than 250 Hz. Overall, the mono-ceramic P-FGM plate can be considered as a better sound radiator because of higher stiffness.  
Conceptualization, V.R.; Formal analysis, B.N.S.; Investigation, B.N.S.; Methodology, V.R.; Software, S.D.; Supervision, V.R. and R.N.H.; Writing--original draft, B.N.S.; Writing--review & editing, R.J. and K.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
###### Abstract
MINLP allows for the simultaneous handling of discrete and continuous variables, facilitating precise modeling of complex nonlinear constraints [8], including throughput, communication latency, and task dependencies. This approach enables the construction of the optimization problem with an objective function centered on system throughput. Moreover, our model's constraints comprehensively encompass hardware limitations, such as device computational power, memory capacity, and communication latency, while ensuring balanced processing speeds across stages to avoid pipeline bottlenecks. In summary, the overall contributions of this paper are as follows:  
* **Non-Uniform Deployment Framework**: We propose a non-uniform deployment-based inference framework (NUDIF) that allocates sub-model instances across heterogeneous devices to optimize inter-stage processing speed alignment. This framework enhances overall resource utilization through non-uniform deployment. * **Optimization Modeling and Solving**: A mixed-integer nonlinear programming (MINLP) model is formulated to maximize system throughput through the joint optimization of sub-model deployment, load balancing, and communication efficiency, providing theoretical guidance for heterogeneous resource adaptation. * **Experimental Validation**: Evaluations on real-world edge device clusters demonstrate an average throughput improvement of 9.95% compared to traditional single-pipeline optimization methods, validating the effectiveness of non-uniform deployment strategies in batch inference scenarios.
## 2 Result and Discussions
### Does P Expression from pcIpR-P-timm perturb \(\lambda\) Vegetative Growth?  
As previously noted, although present in very limited amounts in the cell, DnaB is a multifunctional protein involved in replication fork movement [3], serving as a mobile promoter in priming reactions [4, 5], in the progression of Holliday junctions [6, 84], and in replication restart reactions [70, 71], some of which are likely involved in \(\lambda\) replication beyond the _orin_-O-P-dependent initiation step. Since the interaction of P and DnaB yields a P-DnaB-ATP dead-end ternary complex [10], we wondered if the expression of P from fully induced pcIpR-_P_-timm in cells shifted to 42 'C could serve to limit \(\lambda\) replication, as monitored by phage burst. Lysogenic cultures with a \(\lambda\)_cl_[Ts]857 Sam7 prophage defective for natural cell lysis (because of the nonsense mutation in _S_) were synchronously induced by shifting the cells from 30 to 42 'C. One of the parallel cultures contained pcIpR-_P_-timm. Following induction, the cells were artificially lysed. Relative phage burst from each of the cultures was determined by dividing the released phage titer by the cell titer pre-induction. The cells with pcIpR-_P_-timm showed an increased relative burst by 60 min post induction over parallel cells without the plasmid (i.e., bursts of 248 (cells with pcIpR-_P_-timm) and 42). Clearly, \(\lambda\) replication / maturation is not curtailed (and appears enhanced) by combined expression of \(P\) from both induced prophage and plasmid, which is dramatically opposite to the effect of \(P\) expression on ColE1 plasmid replication/maintenance.
## 1 Introduction
In principle, high energy effects could be mitigated by operating at a partial bias of the sensor, reducing the amount of collected charge in the core pixel thanks to the enhanced charge diffusion to the adjacent ones [29; 30]. For this reason, the Timepix3 Radiation Monitor described in this paper is operated at a partial bias of 50 V, leading to a depletion layer of around 250 \(\upmu\)m instead of the full 300 \(\upmu\)m thickness of the silicon sensor. The main results of this paper consist of an experimental campaign dedicated to the calibration of the CERN Timepix3 Radiation Monitor with quasi-mono energetic proton (alpha) beams ranging from 0.6 (1) to 5 (5.6) MeV at the Centro Nacional de Acceleradores (CNA) particle accelerator facility [31; 32], following the methods described in Section 4. The measurements included the estimation of the thickness of the dead layer in front of the sensor, which was obtained by performing an irradiation scan of the setup with 597 keV protons at an angle ranging from 0\({}^{\circ}\) up to 45\({}^{\circ}\). The results of the test campaign are described in Section 5, starting with a discussion of the cluster reconstruction performance, which included a filtering procedure based on morphological parameters such as radius and density. Subsequently, the cluster-level calibration is presented, as well as the distributions of ToT and cluster size for both proton and alpha beams at different energies. The results of the angular scan for the dead layer estimation are also discussed, along with an investigation of the origin of high-energy saturation effects. Having established the key features of the Timepix3 Radiation Monitor and its calibration parameters, Section 6 investigates the suitability of the setup for the measurement of the particles from the LHC radiation field, using the FLUKA code to evaluate their expected energy depositions in the sensor. Such energy depositions are discussed in light of the results of the calibration campaign, with special attention paid to the high energy effects described above. Finally, Section 7 is devoted to a summary of the results and general considerations about the usage of the Timepix3 Radiation Monitor at the CERN accelerator complex.
# Passive Harmonic RFID System for Buried Assets Localization
## Harmonic Tag
### Tag Antenna Design  
The antennas have been designed using the Keysight Momentum simulator. The size of the receiver patch is 75 mm by 75 mm, and a 1-mm wide coplanar line is employed to match the antenna. The length of the coplanar line is 14 mm. The transmitter patch has a dimension of 40.5 by 40.5 mm, and the inset of the 1-mm wide coplanar line has a length of 12 mm.  
Patch antennas are narrow-band antennas, especially if thin substrates are used [45]. As the tag is buried in soil, a detuning due to the presence of high-permittivity material can be expected. In addition, the tag must be protected from contact with moisture through a plastic box (see Figure 3). By means of electromagnetic simulations, the effect of the height of the box and the soil permittivity is studied. Figure 5 shows the reflection coefficient of the antennas for different air spacers (placed between tag and soil due to the height of the protection box) varying between 3 mm and 12 mm. A relative permittivity of 7 and loss tangent of 0.25 is considered as a worst case in the simulations in order to simulate wet soil [46; 47]. The plastic box is made with polylactic acid (PLA) using a 3D printer. A 1-mm thick top cover has been considered in the simulations and in the prototype. The dielectric permittivity (\(\varepsilon_{\mathrm{r}}\) = 2.8 and tan\(\delta\) = 0.003) used in the simulations is taken from the work of [48]. It is shown that for a spacing of 12 mm, the detuning due to the presence of soil is small. Consequently, in order to avoid the antenna's detuning, the tag is inserted into a plastic box with an air gap of 12 mm between the antenna and the soil, which also protects the tag against water (see Figure 3).  
Figure 4: Schematic of the quartz crystal low-power oscillator.  
Figure 5: Reflection coefficient of the patch antenna at the fundamental frequency (**top**) and the second harmonic (**bottom**) as a function of frequency for different spacer thickness (h = 3 mm, 5 mm, 10 mm, 12 mm, and the tag in the air). The inset figure shows a schema of the cross-section of the box in the soil.