esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Channel Model
### Noise  
For UWSNs, the noise sources are categorized into four classes. These noise sources are known as turbulence, shipping, wind, and thermal noise [43]. In the following equations, each noise source power spectral density (PSD) in dB re \(\mu\) Pa per Hz is given as:  
\[\begin{split} 10 logN_{t}(f)&=17-30 log(f)\\ 10 logN_{s}(f)&=40+20(s-0.5)+26 log(f)-60 log(f+0.03)\\ 10 logN_{w}(f)&=50+7.5w^{1/2}+20 log(f)-40 log(f+0.4)\\ 10 logN_{th}(f)&=-15+20 log(f)\end{split} \tag{1}\]  
\(N_{t}\), \(N_{s}\), \(N_{w}\), and \(N_{th}\) represent the PSD of turbulence noise, shipping noise, wind noise, and thermal noise, respectively. The frequency \(f\) is measured in kHz. The shipping parameter \(s\) varies from 0-1 for low and high shipping, respectively. The wind speed \(w\) given in m/s is the dominant source of the noise. The combination of all these noise sources affects the desired signal. The overall noise PSD is given by the following equation:  
\[N=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{2}\]
## 5 Thermal Analysis and Results Discussion
### Thermal Analysis of 6U satellite with Thermal Straps  
In this case, a flexible copper thermal strap of the dimensions as indicated in Figure 3 is considered to be thermally coupled to the HX and the deployable radiator panel. With higher applied joint pressure, thermal coupling between the radiator and HX plate results from a large surface area attachment and both HX and the radiator panel are aluminum which has a relatively higher thermal conductivity. Since the thermal strap is coupled to the HX plate, it is assumed that the thermal strap is isothermal with HX temperature. The radiator panel is thermally isolated from the structure so that no parasitic heat loads flow into the radiator panel except through the deployable mechanism and only the thermal strap is the effective thermal path between the HX and the radiator panel for heat conduction. Considering the length of the thermal strap in this configuration, a large temperature gradient is expected between the end points of the strap. It is also assumed that deep space is 0 K and the radiator has a full view factor to space, a thermal simulation with the thermal strap and the radiator was carried out. Figures 20 and 21 show the temperature profiles of the CCU, HX and the radiator panel. HX needs to be maintained between 10 \({}^{\circ}\)C to 20 \({}^{\circ}\)C and the simulation shows that the HX exceeds its operational limit with thermal straps. As the temperature of the radiator panel varies across its surface area, a few locations including radiator edges and middle regions were picked to plot the distributions and it is depicted in Figure 21.  
Figure 19: 6U Satellite deployable radiator with heat pipes.  
Figure 18: 6U Satellite deployable radiator with heat pipes—Thermal model.  
Passive TCS studies carried out for a typical high powered 6U satellite platform using thermal straps and heat pipes clearly show that the proposed satellite configuration need to be equipped with an active TCS in order to keep the interface temperature within the desirable range, and the passive solutions described here are not efficient for such satellites with a cryogenic instrumentation.
## 2 Spatial Distribution Characteristics of the Signal and Noise Fields
### Investigation of the Spatial Distribution of the Signal Field
#### 2.1.1 Simulation Testing  
In order to verify the theoretical distribution of the signal field, the signal sound intensity fluctuations of different center frequencies, bandwidths, and horizontal distances of the transceiver were simulated and examined.  
1. Simulation of the signal interference at different center frequencies  
Simulation parameters: the seabed was absolutely hard and flat; water depth: 100 m; sound source deployment depth: 95 m (near the seabed); horizontal distance of the transmitting and receiving end: 200 m; signal bandwidth: 10 Hz; frequencies were 500 Hz, 2 kHz, and 5 kHz.  
Figure 1 shows the signal interference diagrams at different frequencies. The frequencies from (a) to (c) were 500 Hz, 2 kHz, and 5 kHz. The abscissa is the signal sound pressure level and the ordinate is the depth. Comparing the figures, it can be seen that as the center frequency of the signal doubled, the vertical fluctuation range of the sound intensity decreased by a factor of half; the higher the mark frequency, the smaller the spatial fluctuation. In underwater acoustic communication, the center frequency of the signal should be appropriately increased within the tolerance range of the high-frequency absorption and attenuation of the signal.  
Simulation of signal interference at different horizontal distances  
Simulation parameters: the seabed was absolutely hard and flat; water depth: 100 m; sound source deployment depth: 95 m (near the seabed); frequency: 5 kHz; mark bandwidth: 10 Hz; the horizontal distances of the transceiver end were 100 m, 300 m, and 1000 m.  
Figure 2 shows the signal interference patterns at different horizontal distances. The distances from (a) to (c) were 100 m, 300 m, and 1000 m, where the abscissa is the mark sound pressure level and the ordinate is the depth. Comparing the figures, the range of vertical fluctuations in sound intensity increased exponentially as the horizontal distance increased, which was consistent with the inference obtained using Formula (11). In the case of the same bandwidth, as the signal frequency and the horizontal distance increased, the sound intensity fluctuation was inversely proportional to the signal frequency and proportional to the horizontal distance; as the depth increased, the sound intensity fluctuation decreased.
# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 3 Methodology
### Traffic Volume Calculation  
To compute traffic volume, a virtual box is created within each lane (see Figure 3). As a vehicle passes, it intersects with the virtual box, creating a shadow area. When the shadow area grows from 0% to over 70% and then reduces back to 0%, this indicates that a vehicle has passed. The traffic volume is then incremented by one. The choice of 70% as the threshold is empirically based. Figure 4 shows the traffic volume detection logic through a virtual box and shadow area movements when a vehicle passes.
# Bit-Level Construction for Multiplicative-Repetition-Based Non-Binary Polar Codes +
###### Abstract  
In this paper, we discuss non-binary polar codes using a \(2\times 2\) matrix over a Galois field GF(\(2^{q}\)) as the kernel. Conventional construction of non-binary polar codes divides the synthesized channels into frozen channels and information channels. Each information channel carries one symbol, i.e., \(q\) bits. However, there are many middle channels with insufficient polarization, which cannot carry one symbol of \(q\) bits but only \(i\) bits, \(1\leq i<q,i\in\mathbb{Z}\), at finite block length. In this paper, we consider bit-level construction for multiplicative repetition (MR)-based non-binary polar codes and propose a bit-level construction based on the two following methods. We first calculate the error probability and channel capacity lower bound of each synthesized channel based on the channel degradation method, and then determine both the number and index of the carried bits for each synthesized channel according to the symbol error probability and capacity. To reduce complexity, we also introduce a Monte-Carlo method. We compute the error probability of each synthesized channel carrying \(i\) information bits and select the optimal construction that can minimize the union bound of the error probability. Finally, an improved construction-based probabilistic shaping method for MR-based non-binary polar codes is considered. Simulation results show that the proposed construction significantly improved the decoding performance compared with the conventional construction scheme.  
non-binary polar codes; channel degradation; construction algorithms; probabilistic shaping +
Footnote †: This work is an extended version of our conference paper: Improved construction for multiplicative repetition based non-binary polar codes. In Proceedings of the IEEE Information Theory Workshop, Shenzhen, China, 24-28 November 2024.
## 3 Mathematical Modeling
### Governing Equations and Numerical Method  
The commercial software FLUENT was used to simulate the flow and heat transfer characteristic in the shell side. The realizable \(k\)-\(\varepsilon\) model was adopted to the numerical simulation [30]. The governing equation for the mass, momentum, energy conservations, and for turbulent kinetic energy \(k\) and turbulent energy dissipation \(\varepsilon\) can be expressed as follows [28; 31; 32]:  
Continuity equation:  
\[\frac{\partial u_{i}}{\partial x_{i}}=0 \tag{8}\]  
Momentum equation:  
\[\frac{\partial u_{i}u_{j}}{\partial x_{i}}=-\frac{1}{\rho}\frac{\partial P}{ \partial x_{i}}+\frac{\partial}{\partial x_{j}}\left[(v+v_{\tau})(\frac{ \partial u_{j}}{\partial x_{i}}+\frac{\partial u_{i}}{\partial x_{j}})\right] \tag{9}\]  
Energy equation:  
\[\frac{\partial u_{i}T}{\partial x_{i}}=\rho\frac{\partial}{\partial x_{i}} \left[\left(\frac{v}{Pr}+\frac{v_{\tau}}{Pr_{\tau}}\right)\frac{\partial T}{ \partial x_{i}}\right] \tag{10}\]  
Turbulent kinetic energy equation:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{j}}(\rho ku_{ j})=\frac{\partial}{\partial x_{j}}\left[\left(v+\frac{v_{\tau}}{\sigma_{k}} \right)\frac{\partial k}{\partial x_{j}}\right]+\Gamma-\varepsilon \tag{11}\]Turbulent energy dissipation equation:  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{j}}( \rho\varepsilon u_{j})=\frac{\partial}{\partial x_{j}}\Bigg{[}\left(v+\frac{v_ {\tau}}{\sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{\partial x_{j} }\Bigg{]}+C_{1}\Gamma\varepsilon-C_{2}\frac{\varepsilon^{2}}{k+\sqrt{v \varepsilon}} \tag{12}\]  
where \(\Gamma=-\overline{u_{i}u_{j}}\frac{\partial u_{i}}{\partial x_{i}}=v_{\tau}( \frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{\partial x_{i}}) \frac{\partial u_{i}}{\partial x_{i}}\), \(v_{\tau}=\rho C_{\mu}\frac{k^{2}}{\varepsilon}\).  
The model constants have been established to ensure that the model performs well for certain canonical flow as follows:  
\[C_{1}=\max\left[0.43,\frac{\eta}{\eta_{\tau}+5}\right],\ C_{2}=1.0,\ \sigma_{k}=1.0,\ \sigma_{ \varepsilon}=1.2\]  
where \(\sigma_{k}\) and \(\sigma_{\varepsilon}\) are the Prandtl number for \(k\) and \(\varepsilon\), respectively. The enhanced wall treatment was adopted for the compute of flow characteristic in the near-wall region. The range of the dimensionless length \(y^{+}\) is \(y^{+}\leq 5\).  
The governing equations were based on finite volume method [33]. The SIMPLE algorithm was adopted for velocity-pressure coupling. The second order upwind scheme was used to discretize the convective terms. The convergence criterion was that the mass residual less than \(10^{-4}\) and the energy less than \(10^{-7}\). A parallel computation with 16 numbers of processes was performed on workstation with 32 Intel Xeon-core CPUs and 32 GB RAM. It takes approximately 36 h to acquire converged solutions.
# Adaptive Multi-Grained Buffer Management for Database Systems
## 2 Related Work
### Key-Value Cache Management  
Key-value caches are often used to decrease data latency, increase throughput, and ease the load off back-end systems. For example, Memcached is a general-purpose distributed memory caching system. It is used to speed up dynamic database-driven websites by caching data and objects in DRAM to reduce the number of times an external data source must be read. Compared with Memcached, Redis supports different kinds of abstract data structures, such as strings, lists, maps, and sets. In general, popular key-value caches use LRU or variants of LRU as their eviction policy. By deploying a key-value cache in front of a disk-based database, hot tuples can be extracted from the disk-based database and managed by the separate cache system [12]. Thus, hot tuples rather than hot pages are cached in memory.  
However, for two-layer architectures, there are two problems [26; 27; 28]. First, hot tuples may reside both in the key-value cache and in the buffer pool, which leads to wasting precious memory resources. Second, databases and key-value caches are two separate independent systems. Thus, a synchronization mechanism is necessary to ensure data consistency. For example, when a tuple is modified, the update is sent to the back-end database. Now, the states of the tuple in the database and key-value cache are different. If the application needs up-to-date tuples, the application must update the object in the cache.  
In summary, key-value caches manager data in a fine-grained manner. However, it suffers from data-redundant and complex synchronous mechanisms. Instead of the two-layer architecture, we embed the fine-grained data management into the buffer management to benefit from their respective advantages.
# On Training Road Surface Classifiers by Data Augmentation
## 3 Data Augmentation  
A key aspect of the previous scheme is the training of the classifier. In the experiments of Section 4, it is demonstrated that significant saving of signal acquisitions can be obtained by data augmentation. This latter has been implemented by generating synthetic feature vectors that preserve the structural properties of the original features while providing an oversampling of the feature space.  
We have considered the method Generative Adversarial Network Synthesis for Oversampling (GANSO) as described in [30]. This method has demonstrated its superiority regarding classical oversampling methods, particularly for small training sets (small data problem), and where all classes could be deficient. Other data augmentation methods are mostly applied for the imbalance problem, where only one class is deficient. Let us comment on the main ideas of GANSO; specific mathematical details of the general method may be found in [30]. Specific details of the adaptation of GANSO to the application considered in this work are given in Section 4. A Generative Adversarial Network is composed by two blocks; the first one (generative) provides examples to the second one (discriminative), which is to decide if the example provided by the generative block should be accepted or rejected. Both blocks collaborate in the sense that the discriminative block \"informs about why\" the example is rejected so that the generative block can modify the example until it is accepted. This is shown in Figure 2.  
One original feature vector (OFV) is provided to the generative block to generate a synthetic feature vector (SFV) by transforming the OFV using the complex graph Fourier transform [31], a mathematical operator developed in the framework of graph signal processing [32]. This requires a graph model of the connections between the different components of the feature vector. Moreover, from this graph model, we compute the maximal cliques (subsets of fully connected vertices that cannot be extended by adding more adjacent vertices). Thus, a structured representation is built into the feature vector space. Then, the maximal cliques of the OFV and SFV are, respectively, correlated with the maximal cliques of a reference subset formed by other OFVs--this is termed structured correlation in Figure 2. By doing so, we obtain two sets of discriminative features, respec  
Figure 1: Multisensor road surface classification system.  
tively corresponding to the true class and to the candidate class. Then, these two sets are used to train a two-class linear discriminant in the discriminative block. If the two classes cannot be properly separated, SFV is accepted; if not, SFV is rejected. In this latter case, the discriminative block informs the generative block about the linear separator used to discriminate both classes, so that a corrected SFV is provided, trying to obtain a better candidate. All the process is iterated until SFV is accepted.
## 4 Simulation Results and Comparisons
### Example (2): Proposed AUFPSO-NTE in Comparison with FVFP-PSO, FP-PSO, FV-PSO, and PSO

Table 14: Means obtained using the proposed AUFPSO-NTE and for FVFP-PSO, FP-PSO, FV-PSO, and PSO for 5 benchmark functions in 100 independent trials in Example (2). In Table 15, the \(D_{n}\) for functions\(f_{5},f_{8},f_{9},f_{11}\), and \(f_{12}\) were set to 30, the number of particles was set to \(S=30\), and the number of iterations was set to \(N=1000\). Tables 16 and 17 show the parameter settings for the proposed AUFPSO-NTE and for the AFO-DPSO, NCPSO, FO-DPSO, FPSO, APSO, DPSO, HPSO, and PSO. Parameters \(\omega_{\min}\), \(\omega_{\max}\), \(c_{\min}\), \(c_{\max}\), \(c_{\min}\), and \(c_{\max}\) of the proposed AUFPSO-NTE were set as in Example (1). For AFO-DPSO, \(\omega\) was 1; \(c_{1}\) and \(c_{2}\) were 1.5 to 2.5, respectively; and \(\delta\) was 0.05 to 0.1. For NCPSO, \(\omega\) was 0.7298, and \(c_{1}\) and \(c_{2}\) were both 1.4962. For FO-DPSO and FPSO, \(\omega\) was 0.9, \(\lambda\) was 0.632, and \(c_{1}\) and \(c_{2}\) were both 1.5. The APSO parameters were automatically set. In DPSO, \(\omega\) was set to 0.9. In HPSO, \(\omega_{\min}\) and \(\omega_{\max}\) were set to 0.2 and 0.8, respectively, and \(c_{1}\) and \(c_{2}\) were both set to 2.5. In PSO, \(\omega_{\min}\) and \(\omega_{\max}\) were set to 0.4 and 0.9, respectively, and \(c_{1}\) and \  
In Tables 19 and 20, the mean values obtained by the AUFPSO-NTE are compared with those obtained using AFO-DPSO, NCPSO, FO-DPSO, FPSO, APSO, DPSO, HPSO, and PSO given by [PERSON] et al. (2018). Mean values obtained using each PSO-based algorithm for \(f_{5},f_{8},f_{9},f_{11}\), and \(f_{12}\) in 30 independent trials were recorded. Tables 19 and 20 show that the means obtained using the proposed AUFPSO-NTE were better than those obtained using AFO-DPSO, NCPSO, FO-DPSO, FPSO, APSO, DPSO, HPSO, and PSO. In [PERSON] et al. (2018), the performance was evaluated in terms of variance in the optimum (12):  
\[\text{variances in optimum}=\sum_{i=1}^{30}\left|\frac{f_{i}-f_{avg}}{f_{max}} \right|^{2}, \tag{12}\]  
where \(f
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 5 Setting up the PrECast Infrastructure
### The PrECast Algorithm
#### 5.1.1 IP Address Is Configured to a Host Manually  
We assume that the IP address is assigned to Host A manually. The conventional CAM table build-up process is as follows: During the boot-up process (or during the IP address binding process), Host A will issue a G-ARP (Gratuitous ARP [1]) broadcast packet to everyone to ensure that the assigned IP address is unique in the network. We assume that Host A is connected to Switch S. The switch port to which Host A is connected is the first network device to receive this encapsulated Ethernet frame. In the conventional process, the switch will record the port from which this Ethernet frame is received and the source MAC address present in the frame to its CAM table. Once this is done, the switch will forward this frame to every other active port in the switch, except the port from which this frame was received. Other switches receiving this broadcast frame will follow the same process as that of Switch S. In the event of any IP address conflict, a host may send a unicast reply to this G-ARP request. The G-ARP reply will be switched back to Host A using the entries available in the CAM table of every switch in the reverse path.  
We modify this conventional process as follows. Note that we have not made any changes to the protocol standards and operation, thus even if a switch does not support the PrECast process, it can still coexist with the PrECast infrastructure:  
Since our PrECast protocol works transparently, Host A broadcasts a G-ARP packet through switch port \(S_{A}\) of the switch S. This packet is then be verified by the switch for any possible IP address conflict as follows:  
* Switch S will first check with its MCAM table for a possible IP address conflict by referring to its MCAM table. In case of any IP conflict, Switch S will craft a G-ARP reply and inform Host A.
* If there is no entry available for the same IP address in its MCAM table, S will multicast the G-ARP request to all other switches that are securely known to S. Switches that receive this G-ARP frame will check with their MCAM table for a possible address conflict. The switch that detects an address conflict will send a G-ARP reply to S; S will in turn craft a reply back to Host A.
* If the IP address is not mapped onto any other MAC address, S will not receive any reply from other switches until the time-out set out by the ARP-standard. After this time-out, switch S will add the switch-port \(S_{A}\), the MAC address of Host A and its IP address to the MCAM table.
* In the absence of any G-ARP reply, Host A will bind the IP address like the conventional process.
### System Configuration Optimization
#### 2.3.4 Configuration Optimization Solution  
The proposed optimization approach uses two separate layers of models. The planning layer sends system configuration combinations to the operating layer, and the operating layer transmits the system operating cost back to the planning layer. This exchange ultimately leads to obtaining the system configuration optimization results.  
The approach reformulates the optimization problem of PEDF system configuration into a constrained optimization problem, described as follows:  
\[\left\{\begin{array}{ll}\min F&=C_{\text{inv}}+C_{\text{ope}}+C_{\text{mail} }\\ \min F^{\prime}&=(f_{1},f_{2},f_{3})\\ \text{s.t.}&G(E_{\text{pv}},E_{\text{e}})\leq 0\\ &H(E_{\text{pv}},E_{\text{e}})=0\end{array}\right. \tag{18}\]  
where \(G(E_{\text{pv}},E_{\text{e}})\leq 0\), \(H(E_{\text{pv}},E_{\text{e}})=0\) denote the constraint condition for solving equations and inequalities in the model.
## 6 Conclusions  
This study investigated mechanical characteristics of atrazine-contaminated clay by performing a series of Atterberg limit, compaction, and torsional ring shear tests. Following conclusions can be drawn:  
* The results showed that enhancing atrazine content in soil increased the liquid limit (LL) and plastic limit (PL) characteristics of the tested clays due to viscous nature of the contamination agent.
* Increasing atrazine contamination content of the soil reduced maximum dry density (MDD) and optimum moisture content (OMC) of the tested clays.
* Peak shear strength and residual stress ratio of the tested soils reduced by increasing the atrazine contents. This reduction was attributed to an increase in the viscosity of the soil which caused the pore fluid in the clay to behave like a lubricant agent and let the clay particles easily slide when external stress is applied.
* Peak shear strength and residual stress ratio reduced even more when duration of the contaminating soil increased. This behaviour was attributed to simulation of the long-term contamination condition that caused a reduction dielectric constant and accordingly the shear strength characteristics of the tested soils.  
M.K. and A.C. conceived and designed the experiments; M.K. performed the experiments; M.K., A.C. and H.N. analyzed the data; Y.Y. contributed reagents/materials/analysis tools; M.K., A.C., H.N., Y.Y. wrote the paper. All authors have read and agreed to th published version of the manuscript.  
This research received no external funding.  
The authors acknowledge the use of Curtin University's Microscopy & Microanalysis Facility, whose instrumentation has been partially funded by the University, and State and Commonwealth Governments.  
The authors declare no conflict of interest.  
Figure 10: Effect of ageing of the atrazine contamination on AC, BC and KC specimens on (**a**) Peak shear strength and; (**b**) residual stress ratio.