esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Handover Scenario  
The characteristics of the handover for a B-UWAN are outlined as follows:  
* The handover for a B-UWAN is horizontal, indicating that the transition occurs within an acoustic communication network with the same frequency band, modulation and coding scheme, and network configurations.
* Handover triggering is performed by an AUV. In other words, when the handover conditions are satisfied, the AUV initiates a handover request to the BS, negotiating to maintain the continuity of its service between the current BS and the target BS.
* An AUV moving along a planned trajectory can predict its current location with the help of location-tracking equipment, such as an ultra-short baseline. Unlike terrestrial cellular networks, where node movement is random, in a B-UWAN, it is unnecessary to consider handovers in every region within a cell. Instead, handovers can be considered only when the AUV is located in the overlapping region. This characteristic stands out, as it aptly captures the distinctive attributes of the AUV, and, when effectively leveraged, it substantially enhances the efficiency of handover by minimizing associated overheads.  
When an AUV moves from BS1 to BS2, the handover processes for a B-UWAN can be briefly described as follows:  
* Handover is considered when an AUV approaches the overlapping region, receiving acoustic signals not only from BS1 but also from BS2.
* When the AUV is present in the overlapping region and the handover conditions are met, it requests handover from BS1. As mentioned above, the AUV-BS distance is considered as a handover decision parameter; it is determined by using the transmission time information of messages sent by the BS and the time information received by the AUV, along with the propagation speed of acoustic signals.
* Upon receiving a handover request from the AUV, BS1 initiates a handover negotiation with BS2 using available maritime communication. During this process, BS1 communicates the QoS requirements of the AUV, and BS2 allocates resources that satisfy the QoS of the AUV.  
BS1 notifies the AUV of a successful handover and, simultaneously, terminates the network connection to the AUV. As the AUV enters the network of BS2, the handover process concludes.
* Given the challenging conditions of underwater communication, it is common to experience frequent transmission failures during the handover process. To mitigate this, it is necessary to configure parameters such as handover timeout and retry attempts, effectively preventing unnecessary resource consumption in the handover procedure.  
The scenarios described in Section 2.3 represent one simple example of the handover processes tailored for a B-UWAN. Scenarios for underwater handovers can be extended or include more complex procedures.
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 6 Prototyping and Testing
### Cross-Sensor Calibration  
Cross-calibration of depth and thermal cameras was carried out with a standard \(7\times 9\) checkerboard pattern, 26.5 mm per square, moved at a distance of about 1 m from the prototype helmet. Pictures of the pattern were taken using the depth sensor's reflectivity camera and the thermal sensor's RGB camera. Calibration was performed in accordance with the pipeline established by [35]: (i) estimate the intrinsic and extrinsic parameters in closed-form solution; (ii) refine all parameters, including lens distortion, with a non-linear minimization. We used the implementation available in Matlab R2019a.  
The procedure returned the following reprojection errors, in unit of pixels:  
\[\epsilon_{D} = 0.1571,\] \[\epsilon_{T} = 0.2542,\]  
with an average error \(\bar{\epsilon}=0.2056\). A visualization of the estimated extrinsic parameters is provided in Figure 7, where one can verify how the thermal camera (in red) correctly resembles its collocation on top of the helmet, above the AR viewer and the associated depth camera (in blue).  
Given the obtained sub-pixel reprojection error, we can conclude that a standard stereo calibration procedure is suitable as well for cross-calibrating heterogeneous sensors, combing visible and reflectivity information.  
Figure 7: Extrinsic parameters visualization for the cross–calibration of ToF–sensor (blue) and thermal camera (red) using a moving checkerboard pattern, combining ToF reflectivity with thermal RGB information.
# Influence of Soil Heterogeneity on the Contact Problems in Geotechnical Engineering
###### Abstract  
Contact problems are widely encountered in geotechnical engineering, such as the contact between soils and concrete used in earth and rockfill dams, tunnels and coastal levees. Due to the unknown contact region and contact forces, the contact problems have strong boundary nonlinearity. In addition, soils have been recognized as heterogeneous materials in geotechnical engineering. The existence of the soil heterogeneity increases the nonlinearity of the contact problems. Currently, the contact problems are mostly analysed without considering the soil heterogeneity, which may not reflect the contact behavior well. In order to investigate the influence of soil heterogeneity on the contact problems, in this paper, a simple plane-strain contact problem is analysed as an example. In this example, Young's modulus is taken to be a spatially variable. The local average subdivision (LAS) is used to model the heterogeneity of Young's modulus. The penalty method is utilised to determine the contact behavior. By the first use of linking the penalty method with the LAS, the proposed approach can be used to analyse the contact problems considering soil heterogeneity. The results show that the influence of soil heterogeneity on the elastic contact problems is significant. The contact forces of the heterogeneous case present apparent variation compared to the results of the homogeneous case. The distribution of the contact force at a specific point is also normal when Young's modulus is normally distributed, moreover, the coefficient of variation (COV) and the horizontal scale of fluctuation of Young's modulus affect the extent of variation of the normal contact forces. The standard deviation of the normal contact force increases with the increase of the COV and decreases with the increase of the horizontal scale of fluctuation of Young's modulus. From the analyses, to better predict the deformation/stress in the contact problems, heterogeneity needs to be considered.  
elastic contact problems; soil heterogeneity; local average subdivision (LAS); penalty method; Young's modulus +
Footnote †: journal: Applied Sciences
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 2 Technology Background of Physical Layer
### Frequency Bands in 5G
#### 2.1.3 5G Spectrum Expansion to FR3 FR4 FR5 Bands  
There is ongoing discussion [36; 37] aimed at utilizing other frequency bands in 5G. The current hot topic is a discussion about the use of frequency ranges FR3, FR4, and FR5. Basically, the plan is to reserve frequencies ranging from 7.125 GHz to 24.25 GHz, and from 52.6 GHz to 71 GHz for future releases of 5G. Each manufacturer (Qualcomm, Apple, Lenovo, Nokia,...) pick different names for each range. Some manufacturers use the FR3 label for range 7.125 GHz to 24.25 GHz, and some manufacturers use FR3 naming for the frequency range 52.6-71 GHz. However, regardless of the names, it is likely that the mentioned frequency bands will be used in the future.
## 1 Introduction
This study investigated the application of GAs for optimizing the goods flow process in logistic networks with a complex, fully-connected topology. The goods distribution was governed by the order-up-to (OUT) inventory policy:  
* Taken in its classical form [22], i.e., implemented in a distributed way, with each node preserving a full local autonomy regarding taking the ordering decisions,
* Its centralized, networked (NOUT) version [23], which is deployed under the auspices of a common organization governing the network. The optimization objective was to adjust the policy parameters (reference stock levels at the controlled nodes) such that the holding costs were minimized, while maintaining a specified service level in the presence of time-varying, uncertain demand. The value of the fitness function was obtained by investigating the system's response to various demand patterns, which required intensive computational effort, as is typical of real-life optimization problems. The policy parameters have a continuous range. Thus, in contrast to the standard-binary-GA form, the application of continuous GAs (CGAs) was considered. This study's contribution is thus twofold:  
* Investigate how to adjust the parameters of the popular inventory management policy when operating in a complex networked structure such that the cost and service level objectives are attained. * Show how to select the CGA parameters for the task of optimizing the logistic network performance, which requires significant computational effort to evaluate the population fitness owing to non-trivial node interaction and the uncertain nature of the demand. The paper is organized in the following way. First, a literature review related to configuring the classical inventory management policy and tuning the soft computing techniques for efficient performance in the considered class of problems is conducted in Section 2. Then, in Section 3, the system model is introduced and presented in a form that is convenient for computer implementation. The inventory policies are expressed using the model variables in a vector form. Next, in Section 4, the characteristics of CGAs and a way that they can be implemented for solving the problems related to the dynamic goods distribution in logistic networks are discussed. The CGA's numerical performance is examined in Section 5. Section 6 comprises the discussion of the results and the conclusions.
## 3 Methodology
### Data Sources
#### 3.1.1 Digital Elevation Model (DEM)  
We incorporated NASA's 90-m resolution Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) to subtract topographic phase components from the interferograms and facilitate geocoding procedures.
# Toward Intelligent Roads: Uniting Sensing and Communication in Mobile Networks
## 3 Cooperative Data Fusion in JSC Network for Vehicular Scenarios
### Monostatic Multisensor Data Fusion and Target Recognition
#### 3.3.2 Multiple Target Tracking  
Tracking algorithms represent an essential tool in the context of JSC systems, as they enable continuous surveillance of moving objects and enhance target detection and localization accuracy owing to their ability to distinguish targets from clutter, especially in scenarios involving extended targets with multiple scatterers detected [31]. Furthermore, tracking can be vital in proactively allocating resources for dynamic target sensing, leveraging its capability to predict future target positions and behaviors.  
Bayesian filtering theory is a prominent approach for implementing target tracking in JSC systems. This framework focuses on predicting the probability density function (p.d.f.) of a target's state at a specific instant of time \(t\), based on its state at the previous scan at time \(t-1\). The predicted distribution is subsequently updated using measurements obtained at time \(t\). To handle an unknown and variable number of targets over time, the probability hypothesis density (PHD) filter offers an efficient, yet effective, solution. In its Gaussian mixture (GM) implementation, this approach approximates the target intensity function as a GM with a predetermined number of components [32]. More specifically, an a posteriori target intensity function at time step \(t-1\) is expressed as  
\[D_{t-1|t-1}(\mathbf{x})=\sum_{h=1}^{\mathcal{H}_{t-1|t-1}}w_{t-1|t-1}^{(h)} \mathcal{N}_{\mathbf{x}}(\boldsymbol{\mu}_{t-1|t-1}^{(h)}\mathbf{P}_{t-1|t-1 }^{(h)}), \tag{2}\]  
where \(\mathbf{x}\) is a target's state; \(\mathcal{H}_{t-1|t-1}\) represents the number of Gaussian components in the prior intensity function; and \(w_{t-1|t-1}^{(h)}\boldsymbol{\mu}_{t-1|t-1}^{(h)}\), and \(\mathbf{P}_{t-1|t-1}^{(h)}\) represent the importance, mean, and covariance of the \(h\)th component. The integral of the intensity function can be interpreted as an estimate of the number of targets present in the scenario. An alternative approach is the multi-Bernoulli mixture (MBM) filter, which leverages association probabilities between measurements and targets. The MBM distribution to represent the prior multiobject p.d.f. is given by  
\[\text{MBM}_{t-1|t-1}(\mathbf{x})=\sum_{g=1}^{\mathcal{G}_{t-1|t-1}}w_{t-1|t- 1}^{(g)}\text{MB}_{t-1|t-1}^{(g)}(\mathbf{x}), \tag{3}\]  
where \(\mathcal{G}_{t-1|t-1}\) represents the number of multi-Bernoulli (MB) components or global hypotheses, and \(w_{t-1|t-1}^{(g)}\) stands for the \(g\)th component importance. This filter demonstrates increased robustness in challenging scenarios, particularly in managing weak targets effectively [33].
# Thermal Stability Design of Asymmetric Support Structure for an Off-Axis Space Camera
## 4 Finite Element Analysis of the Support Structure  
We obtained the low-CTE design of the off-axis remote sensing camera structure in the asymmetric direction by adjusting the CFRP ply angle. In order to verify the thermal stability of the designed support structure, we take the whole space remote sensing cam-era as the analysis object to calculate the thermal deformation of the support structure.
## 4 Propulsion System Design and Selection
### Propulsion System Selection
#### 4.1.1 Pulsed Plasma Thruster Discharge Process  
It is possible to describe the pulsed plasma thruster discharge process, assuming a capacitor is employed as a storage device and the electrodes are aligned in parallel plate configuration. The electrode separation should be larger than the electron width [4]. It has been assumed that the propellant bar is constructed from Teflon, with discharge initiated by a sparkplug.  
The initial process in discharge is pre-ignition [60]. During this phase a power supply feeds electrical energy to the thruster's capacitor until saturation is achieved [19]. The rate of charge of the capacitor is determined by the magnitude of the incoming power supply, and therefore this also dictates the discharge frequency. Once the capacitor is saturated the PPT is primed for the discharge process to begin [4].  
The secondary phase of the discharge process is ignition. The process consists of a sparkplug operating between the two main parallel electrons. This erodes the electrode and its respective insulating ceramic to generate plasma [4]. The generated plasma then enters the discharge chamber producing a conduction path. This path allows the main substantial discharge to occur.  
The third phase in discharge is the LCR [19]. The ignition phase establishes the current loop as described above, and current begins to flow through this loop, causing energy to be stored in the thruster's magnetic field. The resistance of the closed loop is typically less than 1 ohm, leading to the production of strong magnetic fields [4]. Eventually the capacitor will fully discharge creating zero potential difference across the plate configuration. The current however continues to flow as the electrode acts as an inductor trying to resist the flow of the current. To overcome this flow resistance energy is consumed from the magnetic field. Once the energy stored in the magnetic field is fully consumed the current flow will cease and energy will once again begin to be stored in the thruster's capacitor. The cycle described then begins again, but with an opposite flow direction [4]. This repetitive process then generates a constant supply of plasma into the discharge chamber (Figure 38).  
Once mass generation has occurred, the final stage of the discharge process is the plasma propagation and acceleration. As the current flows through the strong magnetic field, a Lorentz force is produced due to the plasma's interaction with the field. The vector of the Lorentz force is perpendicular to the magnetic field and respective flow of current [39]. The Lorentz force accelerates the generated plasma particles along the respective vector producing acceleration upon the CubeSat in an opposite direction to the path of the accelerated plasma particles [4].  
Figure 38: Pulsed Plasma Thruster Model [4].
# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 3 Proposed Method
### Joint Optimization of Model Split Points and Bandwidth  
In the optimization phase, the optimizer searches for dynamic split points based on the current bandwidth of edge devices through the reduced model in the cloud server training phase and acceptable latency requirements. Algorithm 1 gives the complete process. For the compressed CenterNet model with N pre-split points, this paper firstly establishes a relationship mapping table based on different split points and the computing resources of edge devices and cloud server devices. The relational mapping table will be transferred to the optimizer during the optimization phase. The relational mapping table takes a similar idea with Neurosurgeon [12] and Edgent [28] interlayer prediction models. This algorithm simplifies the operation in the experiment. According to the prior knowledge of the actual operation, a one-to-one correspondence is established between the interlayer running delay of different devices and the split point, and then the relationship mapping table is formed. In addition, the feature extraction network structure adopts the resnet residual network. In this paper, each residual block is regarded as a whole, and its prediction delay is the sum of the prediction delays of each layer, which simplifies the selection of split points. As for the layers before the first residual block, this paper also treats it as a whole, because after the maxpool pooling layer [29], the size of the output data is subject to change. Note that the relational mapping table needs to be tested. Once the relational mapping table is established, it becomes a simple linear index, and the decision-making speed is very fast.  
**Input:**  
\(N\): numbers of pre-partition points  
\(\{L_{i}|i=1,2,\cdots,N\}\): numbers of layer before partition point  
\(\{D_{j}|j=1,2,\cdots,N\}\): each layers output data size after partition point  
\(f(L_{j})\): relationship mapping table of different partition point  
\(B\): current bandwidth  
_latency_: the target latency  
**Output:**  
Split point selection  
// Procedure  
```
1:for\(i=1\); \(i<=N\); \(i++\)do
2: TM\({}_{i}=f_{mobile}(L_{j})\)
3: TU\({}_{i}=D_{i}\)/B
4: TC\({}_{i}=f_{cloud}\big{(}L_{j}\big{)}\)
5: \(T=\sum_{j=1}^{N}TM_{i}+\sum_{j=i+1}^{N}TC_{i}+TU_{i}\)
6:if\(T\leq\)latency and\(T\) is min latency then
7:return Selection of partition point
8:else
9:return NULL
10:endif
11:endfor
```  
**Algorithm 1** The partition algorithm  
The optimization problem defined in (4) is actually to minimize the end-to-end inference latency. It consists of three parts: mobile Terminal execution delay (\(TM_{i}\)), transmission delay (\(TU_{i}\)), and cloud execution delay (\(TC_{i}\)). The delay of these three parts is affected by the dynamic split point, which is obtained by the split point algorithm we give. In short, we will adaptively find the best split point in the current state according to the delay requirements given by the user and the current network bandwidth to minimize the delay.  
\[Z=\sum_{i=1}^{P-1}TM_{i}+TU_{i}+\sum_{i=P}^{N}TC_{i} \tag{4}\]
# Millimeter-Wave High-Gain Dual-Polarized Flat Luneburg Lens Antenna with Reflection Cancellation
## 4 Implementation, Measurements and Discussions
### Discussion  
The achieved performance of the FLLA was compared with the conventional spherical LL (discussed in Section 2) and other related works, as illustrated in Table 1. The spherical LL is used as a benchmark because its performance is the best except for its gain, because the used 12-layer structure is divided with a criterion of equal space but not equal permittivity step. The FLL in [13] is a type of horizontally placed cylindrical FLL, as shown in Figure 1c, and a metal grid is applied on its both top and bottom interfaces to improve its sidelobe level performance. Thus, the FLL in [13] cannot be operated with dual polarizations or 2D beam scanning. Our work exhibited better performance in terms of gain and aperture efficiency than the design reported in [20,21] due to the introduced reflection cancellation method. Moreover, the presented work is demonstrated in millimeter wave band. Hence, the smaller wavelength limits the adopted layers in our design, i.e., 10 layers, which is smaller than those used in [20,21]. Thus, the achieved impedance bandwidth is a little bit narrower as well. Actually, an \(X\)-band prototype was implemented following the method proposed in this article, and a 30% bandwidth was achieved due to the introduced reflection cancellation method, which is not reported here.  
Figure 15: Measured scanning beams of FLLA: (**a**) E-plane, (**b**) H-plane.  
In our design, all the air intervals in-between any two adjacent layers of the FLL were set to be identical for simplicity. If all the air intervals can be independently tuned, a better performance is expected.
## Materials and Methods
### RNA Isolation and Transcript Quantification  
Engineered strains were pre-cultured in 5 mL aliquots in an SC yeast minimal medium (1X yeast nitrogen base, 2% glucose, complete supplement mixture (CSM)) overnight and used to inoculate 5 mL fresh SC-mixed carbon source medium (1\(\times\) yeast nitrogen base, 1.8% galactose, 0.2% glucose, complete supplement mixture (CSM)) in 50 mL Falcon tubes to achieve an initial OD\({}_{600}\) of 0.05. The cultures were grown at 30 \({}^{\circ}\)C and 250 rpm in an orbital shaking incubator. After 24 h, a 1.2-1.5 mL aliquot of each culture was collected and centrifuged for 5 min at 3000\(\times\)\(g\). The pellets were washed with 5 mL of distilled water. Total RNA was extracted using the QIAgen RNeasy Kit under the manufacturer's protocol. Contaminating genomic DNA was removed from the RNA samples by DNaseI (Thermo Fisher Scientific, Waltham, MA, USA) digestion using the manufacturer's protocol. The RNA quantity was analyzed using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, NC, USA), and samples were stored at \(-\)80 \({}^{\circ}\)C until RT-PCR analysis. cDNA was obtained using RevertAid Reverse Transcriptase (Thermo Fisher Scientific, Waltham, MA, USA) using the manufacturer's protocol. Relative expression levels of _ERG10_, _ERG20_, _tHMG1_, _ERG8_, _ERG13_, _IDII_, _ERG19_, and _ERG12_ were quantified using iQ SYBR Green Kit (Bio-Rad, Hercules, CA, USA) on CFX96 Touch Real-time PCR Detection System (Bio-Rad, Hercules, CA, USA). Real-time PCR was performed in triplicate, and _TAF10_, a gene that encodes a subunit of transcription factor IID (TFIID), was used to normalize the amount of the total mRNA in all samples. Primers for real-time PCR are listed in Table S1.