esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Diphenylsulfone Based Emitters
Devices based on **9** proved to be more efficient than **10**-based devices (EQE = 11.7%, CE = 19.4 cd A\({}^{-1}\)). The authors ascribed this improvement to the relatively small \(\Delta E_{\rm ST}\), facilitating the RISC process. Due to the facilitated intramolecular charge transfer and a higher degree of conjugation between the HOMO and LUMO energy levels in **9**, a red shift of the emission was observed and a sky-blue EL with CIE coordinates of (0.18, 0.19) was obtained. Following this strategy, [PERSON] et al. synthetized a series of highly soluble triarylamine/_bis_(phenylsulfonyl)benzene emitters suitable for printed OLEDs and the bluest emitter (**11**) is given in Figure 1 [82]. The soluble molecules exhibited an increased steric hindrance of the amine donors and a small \(\Delta E_{\rm ST}\) as low as 0.32 eV. Application of these TADF molecules in solution-processed OLEDs resulted in high brightnesses of \(\leq\) 10000 cd/m\({}^{2}\), CE of \(\leq\) 9.5 cd/A, and EQE of \(\leq\) 8.5%, while retaining deep blue emission ranging from 466 to 436 nm with colour coordinates low as CIE\({}_{y}\) = 0.08 in the case of compound **11**. In conclusion, _bis_(phenylsulfonyl)benzene has been proved to be a potential accepting building block for constructing blue solution processable TADF emitters.
## 2 Fundamentals  
### Convective Heat Transfer by Impingement Jet  
Convection is the transfer of energy associated with the flow of material. There are two types of convection: forced convection and free convection. Forced convection is caused by external forces, for example by the use of a fan, while free convection is caused by forces within the fluid itself. In general, these are differences in density that cause buoyancy forces in the earth's gravitational field. Convective heat transfer refers to the transfer of energy to a solid in the form of heat at a location where it has been transported by fluid flow. These phenomena include both the transfer of energy by molecular movement in the form of diffusion and the directional movement of particles. In conjunction with a temperature difference, convection leads to localised heat transfer, which is described by [PERSON]'s law of cooling, as shown in Equation (1) [4]:  
\[q_{conv}^{n}=h\cdot(T_{s}-T_{\infty}) \tag{1}\]  
with  
\(h\)  
as the heat transfer coefficient in W/(m\({}^{2}\)K);  
as the convective heat flow density in W/m\({}^{2}\);  
as the strip temperature in K;  
\(T_{\infty}\)  
as the fluid flow temperature in K.  
To calculate the transferred heat flow, the heat flow density is multiplied by the transfer area according to Equation (2) [4]:  
\[\dot{Q}_{conv}=q_{conv}^{n}\cdot A \tag{2}\]  
with  
\(\dot{Q}_{conv}\)  
as the convective heat flow in W;  
as reference area in m\({}^{2}\).  
When determining the heat transfer coefficient for both free and forced convection, it is important to consider a large number of dependencies. These include the geometry of the body around which the flow passes, the surface properties of the body and the material properties of the fluid. Additionally, the flow conditions of the impingement jet, which are shown schematically in Figure 2, also have a significant influence on the heat transfer coefficient.  
The impingement jet can be divided into three characteristic regions. First, there is a free jet region. After leaving the nozzle, the jet flows according to the given initial conditions. Under technical conditions, a turbulent flow structure is expected. The free jet spreads out as a result of mixing with the ambient fluid, forming a shear layer and subsequently slows down. As the distance to the wall surface decreases, the free jet transforms into a stagnant flow. The velocity is separated into a vertical component and a horizontal component. The vertical component is decelerated to a velocity of \(u=0\) m/s, while the horizontal component is accelerated to its maximum value. The stagnation region is identified by the stagnation point, where the total velocity is zero. The subsequent wall jet region has similar flow structures to the free jet. As the flow rate increases, it becomes a wider decrease in velocity. At some point, it encounters the other jet from the adjacent nozzle, forming a mixed region, and they flow out again. In the industrial furnace, the return flow is fed back to the fan and the cycle starts all over again [5].
## 5 Experiments and Results
### Comparison Results
#### 5.3.3 Statistical Tests of G-QMIX and G-MAPPO  
We use the data in Tables 4 and 5 to conduct independent \(t\)-tests of G-QMIX against other value-based algorithms, and G-MAPPO against other policy-based algorithms to prove the significance of our results. We use Scipy to generate the normal distributions using the means and standard deviations from the tables with the size being 500, and then run the independent \(t\)-tests. The results of \(t\)-tests are shown in Table 6. The t-statistics are shown in the table with the \(p\)-values following. We only conduct \(t\)-tests in MMM2, 6m2m_15m, and 6m2  
of G-QMIX against other value-based algorithms, and G-MAPPO against other policy-based algorithms to further prove the significance of our results. The results of One-way ANOVA tests are shown in Table 7. The F-statistics are shown in the table with the \(p\)-values following.  
It can be observed that only the \(p\)-values of G-QMIX against QMIX and ASN are greater than 0.05, indicating that the two paired algorithms have no significant difference in their means. All of the other compared groups have p-values smaller than 0.05, indicating a significant improvement brought by the GAPO method.
# Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
## 4 Methodology and Implementation
### Data Pre-Processing  
The data pre-processing phase consists of multiple steps, including filtering network flow, creating a network graph, deriving statistical data from the graph, calculating autocorrelation from edges, determining the degree centrality of the source node, and normalizing the data. Additionally, for the PHMM, a binning step is required to convert data into alphabetical strings, forming sequences in multiple sequence alignment (MSA) format. These sequences are stored in FASTA format for PHMM input. Figure 7 outlines the common data pre-processing steps required for both the HMM and PHMM.  
We outline the steps involved in pre-processing our data as follows:  
* **Basic data cleaning.** Basic data cleaning involves checking for null values and repetitions and sorting the data in ascending order by StartTime.
* **Filter network flow.** From the 15 available features mentioned in Table 4, 10 significant features are selected, and unwanted protocols and states are removed based on the communication characteristics associated with botnets. Table 5 displays sample data entries after network flow filtering.
* **Update labels**. Botnet labels in the CTU-13 dataset are modified, and unnecessary labels are dropped based on the analysis. The revised labels distinguish between botnet and normal traffic effectively.
* **Create network graph**. A network graph is constructed with nodes representing computer systems and edges indicating communication between these systems. The network graph is created using IPs from SrcAddr and DstAddr. The design and algorithm for constructing the network graph are outlined in Figure 8 and Algorithm 1, respectively.
* **Extracting statistical features from network graph.** Statistical analysis is performed on features such as Dur, TotPkts, and TotBytes for meaningful feature extraction. The derived statistics include mean, median, standard deviation, minimum, maximum, and range, resulting in 18 new statistical features.
* **Extracting autocorrelation features from network graph.** Autocorrelation features are calculated for attributes such as Dur, TotPkts, and TotBytes. Periodicity and strength of autocorrelation are determined, resulting in 9 new autocorrelation features. Figure 9a,b illustrate the periodicity observed in total bytes and duration, respectively.  
Figure 6: Implementation overview.  
Figure 7: Data pre-processing overview.
## 4 Application Example
### Discussion of Results  
Finally, the performance of the proposed method was validated comparing the abovementioned results with the ones provided by a conventional one, the optimum damping coefficient of the Pacheco's  
Figure 8: Maximum displacement at the mid-span of the stay cable damped by the friction damper for the different elements of the sample.  
Figure 7: Maximum displacement at the mid-span of the stay cable with the elastomeric damper for the different elements of the sample.  
universal curve [25]. This optimum value for a viscous damper can be determined using the following relationship:  
\[c_{opt}=0.10\frac{mL\omega_{1}}{\frac{x_{c}}{L}}, \tag{24}\]  
where \(\omega_{1}=2\pi f_{1}\) is the fundamental angular natural frequency of the stay cable [rad/s] and \(x_{c}\) is the distance between the anchorage of the cable and the point where the damper is implemented (Figure 2). As in the remaining cases, the viscous damper is located at the point, \(x_{c}=0.03L\), with respect to the lower anchorage. The optimum damping coefficient, according to this conventional method for the viscous damper was \(c_{opt}=1.64\times 10^{3}\) sN/m.  
Thus, two main conclusions may be obtained via the comparison of the abovementioned results: (i) the motion-based design method under uncertainty conditions allows reduction of the characteristic parameter of the viscous damper by about 35% with respect to the conventional method; and (ii) for this case-study, the viscous damper appears to be the best choice to control the dynamic response of the longest cable of the Alamillo bridge, as a minimum value of the damping coefficient was obtained for this passive damper. The proposed method allows a better adjustment to the design requirements of the problem, reducing, as consequence, the size and the cost of the passive damping devices. Hence, the performance of the motion-based design method, for this particular problem, has been validated.
## 3 Multi-Parametric Formulations
### Deterministic mp-Milp  
To give the closed form of deterministic mp-MILP, we first define the following vectors:  
* Parameter variable vector: \(\theta=\left[P_{j}^{s},E_{j}^{s},\forall j\in S\right]^{T}\).
* Binary variable vector: \(\hat{y}=\left[b_{jt}^{s},\forall j\in S,\forall t\in T\right]^{T}\).  
* Uncertain variable vector: \(\hat{w}=\big{[}p_{kt}^{m},\forall k\in W,\forall t\in T\big{]}^{T}\).
* Continuous variable vector: \(\hat{x}\) is a column vector and encloses all the remaining continuous variables in (1)-(7).  
Then, any equality in (1)-(7) can be replaced by two opposite inequalities; for example, (6) is equivalent to the following:  
\[\sum\limits_{\forall t\in T}p_{it}^{sc}\eta_{j}^{sc}\leq\sum\limits_{\forall t \in T}p_{it}^{sd}/\eta_{j}^{sc},\sum\limits_{\forall t\in T}p_{it}^{sc}\eta_{ j}^{sc}\geq\sum\limits_{\forall t\in T}p_{it}^{sd}/\eta_{j}^{c},\forall j\in S \tag{9}\]  
By far, the closed form of deterministic mp-MILP can be written as follows:  
\[\begin{split} v^{D}(\theta)=\min\limits_{\hat{x},\hat{y}}\hat{x }\\ \text{s.t.}\hat{A}\hat{x}+\hat{B}\hat{y}\leq\hat{b}+\hat{C}w+\hat{F} \theta,\theta\in\Theta\end{split} \tag{10}\]  
where \(\hat{A},\ \ \hat{B},\ \ \hat{b},\ \ \hat{C},\ \ \hat{F},\ \ \text{and}\ \ \hat{c}\) are coefficient matrices or vectors derived from (1) to (8). Note that, in the deterministic model (10), \(w\) is given; \(\theta\) is the parameter, so it is not decision variable. \(v^{D}(\theta)\) is the optimal value function parameterized in \(\theta\); in other words, it gives the optimal value with any \(\theta\) in \(\Theta\) where the parameter set \(\Theta\) is assumed to be a polyhedron:  
\[\Theta=\big{\{}\theta|S\theta\leq H\big{\}} \tag{11}\]  
Set \(\Theta\) consists of the concerned constraints of decision-makers on \(\theta\). Basically, \(\theta\) includes the power and energy capacities of ESUs, so it should be non-negative; taking the investment cost for example, which relates to the power and energy capacities, the total cost cannot exceed a given budget \(\Pi\). Nevertheless, \(\Theta\) can be freely defined.
## 3 Proposed TH-CPSK-CEMC System
### MC Channel  
Channel modeling is a crucial aspect of MC systems, dictating how molecules propagate through various media. In this study, we focus on channels characterized by free diffusion in air media, defined by the channel quantum response. In a free-diffusion channel, the movement of signaling molecules is driven by the concentration gradient and Brownian motion, rather than active transport mechanisms. The channel quantum response captures the dynamic behavior of molecules as they move through the channel. The channel model is given as follows [15; 24]:  
\[C(r,t)=\frac{1}{(4\pi Dt)^{3/2}}\cdot\exp\biggl{(}-\frac{r^{2}}{4 Dt}\biggr{)}, \tag{2}\]  
where \(C(r,t)\) is the concentration of molecules at distance \(r\) and time \(t\), \(D\) represents the diffusion coefficient of the channel in \(\mathrm{cm}^{2}\,\mathrm{s}^{-1}\), and \(r\) is the distance between the TN and the RN in cm. The diffusion coefficient, \(D\), depends on the medium of the MC channel [15]. Note that Equation (2) assumes that the concentration of molecules diffuses over time from a point source. In particular, we can observe a Gaussian distribution as a function of time and distance, reflecting the random nature of molecular diffusion.  
Figure 5 illustrates the channel quantum response for the diffusion-based MC channel, showing how the concentration of signaling molecules varies over time (\(t\)) for different distances (\(r\)) from the TN. We can observe that initially, the concentration increases, reaching a peak value before gradually decreasing. The maximum indicates the peak concentration for each distance. As the distance from the transmitter increases, the peak concentration value decreases. This is observed with the highest peak at \(r=1\) cm, followed by lower peaks at \(r=2\) cm and \(r=3\) cm. This trend is consistent with Equation (2), which predicts lower concentrations at greater distances due to the spreading of molecules. In addition, with the decrease in amplitude, we can observe a shift in the received peak concentration. This is due to the channel bias. Particularly, for \(r=3\) cm, the delay is at approximately \(4\) s, and the spread is also high.  
Figure 4: An example of a simple TH-CPSK signaling structure, where \(M=2\), \(N_{h}=5\), and \(N_{p}=3\). The total signal time \(T_{\mathrm{tx}}\) is divided into three slots, and based on the position of the molecular concentration, corresponding bits are transmitted.
## 4 Validation of Model; Discussing and Comparing the Model With Experimental Data
Figure 3 in the aforementioned paper shows the experimental results for the transport of H\({}_{2}\) in the presence in the system of strongly adsorbed CO\({}_{2}\) molecules. It is seen from this figure that the permeance of H\({}_{2}\) in the presence of CO\({}_{2}\) is smaller than the permeance of pure H\({}_{2}\). This result follows also from Equation (19) in the case \(\theta_{\rm C}>0\) and \(f_{2}>f_{1}\). It is worth to note that the influence of resonance radiation on the sticking coefficient and the adsorption time of the molecules of CO\({}_{2}\) due to excitation of these molecules can lead to a decrease in the values of above-mentioned parameters for molecules of CO\({}_{2}\) and accordingly to a decrease in the value of \(\theta_{\rm C}\). This in turn leads to an increase in the permeance of H\({}_{2}\). The aforementioned effect can be used in particular in the separation of H\({}_{2}\)/CO\({}_{2}\). mixtures. Heating of the membrane by radiation also leads to a decrease in \(\theta_{\rm C}\) on the walls of slit-like pores of the GO membrane and accordingly to an increase in the permeance of H\({}_{2}\). The aforementioned effects can be used to manage processes such as the membrane separation of gas mixtures and cleaning of gases from impurity molecules. We hope to perform in the near future experiments associated with the verification of the presented theoretical results. Note that the shape of gas molecules can also affect the transport of gases through GO and NG membranes [10; 27]. The influence of the molecule shape on the kinetic diameter of molecules, and accordingly on the transport of gas through GO membrane is discussed in [10]. In [27] it was shown by means of molecular dynamics simulations how the size, shape, and functionalization of pores in NG membranes affect the permeance of CO\({}_{2}\), H\({}_{2}\)S, and CH\({}_{4}\) molecules.
# A Brief Review on the Recent Experimental Advances in Thermal Rectification at the Nanoscale
###### Abstract  
The concept of thermal rectification was put forward decades ago. It is a phenomenon in which the heat flux along one direction varies as the sign of temperature gradient changes. In bulk materials, thermal rectification has been realized at contact interfaces by manufacturing asymmetric effective contact areas, electron transport, temperature dependence of thermal conductivity and so on. The mechanism of thermal rectification has been studied intensively by using both experimental and theoretical methods. In recent years, with the rapid development of nanoscience and technology, the active control and management of heat transport at the nanoscale has become an important task and has attracted much attention. As the most fundamental component, the development and utilization of a nanothermal rectifier is the key technology. Although many research papers have been published in this field, due to the significant challenge in manufacturing asymmetric nanostructures, most of the publications are focused on molecular dynamics simulation and theoretical analysis. Great effort is urgently required in the experimental realization of thermal rectification at the nanoscale, laying a solid foundation for computation and theoretical modeling. The aim of this brief review is to introduce the most recent experimental advances in thermal rectification at the nanoscale and discuss the physical mechanisms. The new nanotechnology and method can be used to improve our ability to further design and produce efficient thermal devices with a high rectification ratio.  
thermal rectification; nanotechnology; nanostructure; experiments +
Footnote †: journal: Applied Physics Letters
###### Abstract  
With rapid urbanization and industrialization, steel in-use stocks (SIUS) have experienced significant growth, playing an important role in urban mining and future renewable resources. Although previous studies have quantified SIUS at the provincial level, a comprehensive understanding of its spatial distribution remains limited. This study uses Henan Province as a case to assess SIUS and its spatial distribution at the provincial level. A spatio-temporal characterization framework is developed to systematically analyze SIUS dynamics, integrating the bottom-up model, the spatial autocorrelation model, the Tapio-LMDI model, and the stock-driven model. The findings show that total SIUS has been continuously increasing, reaching 499.35 Mt in 2023, with the buildings sector being the largest contributor, accounting for 67%. However, due to its large population, per capita SIUS was 5.09 t/cap in 2023, lower than that of China. Spatial analysis reveals significant autocorrelation in per capita SIUS, with notable spatial heterogeneity in its density. Moreover, the average annual growth rate of SIUS is projected to decline from 10% in 2023 to 5% in 2060, suggesting that SIUS in Henan is approaching a saturation phase, consistent with theoretical expectations.  
iron and steel industry; in-use stocks; temporal and spatial characteristic; spatial autocorrelation analysis; Tapio-LMDI model; stocks-driven model Article
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 2 ISF Quantization in AMR-WB  
A linear prediction analysis in AMR-WB is illustrated as follows: To begin with, a frame is applied to evaluate linear predictive coefficients (LPCs), followed by a conversion into ISF coefficients. Subsequently, ISF coefficients are quantized by following a process, as will be seen below.
# Numerical Simulation and Analysis of Fish-Like Robots Swarm
## 3 Results and Discussion
### The Diamond Swarm  
The thrust and drag at different spacing in the diamond swarm are shown in Figure 8a. It is verified by simulations that the net force of each fish is about zero. Thus, the fishes are able to maintain the diamond formation, and the Froude efficiency \(\eta\) is useful.  
We calculated the non-dimensional thrust, lateral power loss coefficients and the Froude efficiency of each fish. The results are shownin Figure 15. In the diamond swarm, the thrust coefficients of no.2 fish and no.3 fish in the symmetrical position were basically equal, and the No.4 fish had the largest thrust coefficient. None of the fish had an obvious advantage in the lateral power loss and the Froude efficiency. The no.2 fish and the no.3 fish basically had the same results after the spacing was larger than \(1.0L\).  
In the diamond swarm, only the no.4 fish was affected by the wake of the fish in front. When the spacing was large, there was almost no wake near the no.4 fish head as shown in Figure 16a, so the efficiency was not high. However, at close spacing shown as Figure 17a, the pressure generated by the adjacent fish swing affected the stability of the fish and reduced efficiency. Therefore, in the diamond swarm, the efficiency was best when the spacing was moderate (\(1.25L\)). Its wake and pressure field are shown in Figures 16b and 17b. The wake was weak, but it was better than no wake.  
Figure 14: The contours in pressure field of the phalanx swarm: (**a**) spacing = 0.75L, (**b**) spacing = 1.25L.  
Figure 15: Results of the diamond swarm: (**a**) non-dimensional thrust coefficients \(C_{t}\), (**b**) non-dimensional lateral power loss coefficients \(C_{p}\), (**c**) the Froude efficiency \(\eta\).
## 2 Materials and Methods
### Acquisition and Control System  
The weighing system consisted of five load cells, UTILCELL model 300, in accordance with the OIML R60 class C regulations. Four of the load cells used for the cultivation tank had a sensitivity of 2 mV/V and a nominal capacity of 150 kg. The load cell used for the drainage tank had same sensitivity that the other four load cells, but its nominal capacity was of 10 kg. The maximum load for each group of load cells was 300 kg and 20 kg, and its weighing accuracy was of 15 g and 1 g respectively.  
The data obtained from the load cells were recorded a model CR3000 datalogger (Campbell Scientific Spain, S.L., Barcelona, Spain). It had an analogue-digital convertor of 16-bit, 14 differential analogue inputs, four voltage excitation terminals and eight I/O ports. The control module was in charge of controlling the solenoid valves, closing or opening the solenoid valves. The accumulated water was drained for weight and subsequent evacuation with a model SWIFT weighing indicator (UTILCELL, S.A., Barcelona, Spain) and analogue-digital convertor of 24 bits. The system for controlling the solenoid valves was powered by a 24 V current circuit. The transmission system was carried out by means of a Wi-Fi network system connected to the cloud.