esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Hardware Design of the Proposed Algorithm  
### XSG Design Flow Diagram  
For rapid prototyping, a development environment in which the hardware and software modules can be designed, tested, and validated is required. For this purpose, the Xilinx System Generator tool for DSP (Digital Signal Processor) development was used. It permits a high level implementation of DSP algorithms on FPGA circuit. It consists on a graphical interface under Matlab/Simulink environment used for modeling and simulating dynamic systems. A library of logic cores, optimized for FPGA implementation, is provided to be configured and used according to the user's specifications. When implementing System Generator blocks, the Xilinx Integrated Software Environment (ISE) is used to create \"netlist\" files, which serve to convert the logic design into a physical file and generate the bitstream that will be downloaded on the target device through a standard JTAG (Joint Test Action Group) connection. Simulation under Matlab Xilinx System Generator allows us testing the FPGA application with the same conditions with the physical device and with the same data width of variables and operators. The simulation result is \"bit-to-bit\" and \"cycle\" accurate, which means that each single bit is accurately processed and each operation will take the exact same number of cycles to process. Thus, there will be almost no surprises when the application is finally running in the FPGA.  
Figure 7 presents the processing flow of System Generator tool from the hardware architecture design to the generation of the reconfiguration file used for the RTL (Register Transfer Level) design generation and the co_simulation process. The architecture designed using XSG blocks will be simulated under Simulink to compare calculation results obtained by the Matlab code algorithm and those obtained by the designed hardware module.  
Figure 7: XSG design flow diagram.
## 5 Results: Quantitative Analysis and Most Important Publications
### A Deeper Discussion of Papers in MainReview\({}_{New}\)
The paper conducted a thorough analysis of highly effective models across five domains, proposed a taxonomy to classify these models according to their respective tasks, and explored future directions and challenges for transformer-based models. The survey covers publications from 2017 to 2022. The journal topic is computer science and applications. Ref. [46], by [PERSON], cited 28 times, belongs to medical applications, specifically radiology, and it reviewed the limitations of LLMs and mitigation strategies, as well as potential uses of LLMs, including multimodal models. [PERSON] et al., in [47], published in a journal whose topics are biochemistry and biology, providing a systematic literature review categorizing 82 papers into eight major areas, including treatment and medicine, patient care, and medical imaging. However, it focused on the impact of chatbots from a clinical and medical point of view, highlighting the need for human judgment. This paper has been cited 27 times. In Ref. [48], [PERSON] and [PERSON] analyzed the current state of generative AI in healthcare, identifying opportunities and the privacy and security challenges posed by integrating these technologies into existing healthcare infrastructure. They proposed strategies for mitigating associated risks, emphasizing the importance of addressing security and privacy threats to ensure the safe and effective use of generative AI systems in healthcare. The journal subject is medicine and the citation count is 25. [PERSON] et al. in [49] explored the current status and understanding of ChatGPT's potential applications in routine diagnostic pathology. They emphasized that any use of the ChatGPT knowledge, coming from a large but not verified sources, at the patient care level must be carefully merged with established medical information sources and expertise. The authors suggested that, with the ever-expanding knowledge base required for personalized, precision anatomic pathology, improved technologies like future versions of ChatGPT could serve as key allies to magnosticians. The citation count is 25 and the journal subject is strongly connected with medicine and clinical applications. In summary, the majority of reviews have primarily addressed ethical considerations regarding the application of AI in medicine rather than delving into technical aspects, algorithms, or model architectures. These reviews have emphasized clinical implications and the societal responsibilities tied to AI's integration into healthcare. Moreover, many of the papers are published in journals specifically dedicated to medicine or closely related fields. Notably, some of these journals are not currently indexed or classified in databases like SJR, potentially reflecting their niche focus or emerging status in academic publishing.
# Containerised Application Profiling and Classification Using Benchmarks
## 5 ML Classification Algorithm  
Although there have been strides on the field of artificial intelligence (AI) more complex training models such as artificial neural networks excel when data is high-dimensional [23], but the training is significantly more time consuming. Given that the number of features that can describe an application's footprint on the hardware is very specific and falls into particular categories (CPU, RAM, Storage IO, Network), the ML type of classification algorithms were more suited for this work. In order to identify the ideal ML classification algorithm, a comparative study of known ML algorithms was conducted.  
To compare the different classification algorithms applied to our training dataset, we used the tenfold cross-validation method and produced for each algorithm the mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and simple accuracy of the predictions. In these results, all the Sysbench and the Phoronix network benchmarks are not only characterised by their operation, but also by their workload. Specifically, the same benchmarks with different workloads correspond to different labels in our model and are thus differentiated.  
As seen in the Table 1, PART Decision List, SPAARC, random tree, Bayes network, ForestPA and random forest all perform adequately. In terms of MAE, random forest, Bayes network, and random tree have the best results. With the exception of the last one, this is also true for RMSE. Taking into account RAE, things get a little more complicated because random forest has the third-best performance, and all of the aforementioned algorithms perform very closely to each other. The actual accuracy clarifies the field and reveals that even though random forest is a little worse in some error metrics (but still comparable), it surmounts every other algorithm as far as the actual predictions are concerned. In particular, for the random tree, this was expected because, apart for some exceptions, random forests typically outperform random trees.  
Figure 6: Number of benchmarks that surpass the 5% threshold of \(p\)-value for every selected feature.  
In continuation of the analysis of variances, in Figure 7 the number of features that do not reject the null hypothesis are depicted, for every benchmark. Having in mind how a random tree works, the more features are possible to not reject the null hypothesis, as described in Section 4, the more possible it is for the algorithm to make the right decision on a prediction node. Figure 7 reveals the level of stability of the results among different configurations for a specific benchmark. If these results are also differentiated between different benchmarks, then the algorithm will be able to predict with a high accuracy.
## 4 Diversity Performance
### Total Active Reflection Coefficient (TARC)  
TARC is the ratio of the square root of total reflected power to the square root of the total incident power. The TARC shows the return loss of a single MIMO antenna when placed in the arrangement of the array, in the presence of mutual coupling. The TARC is calculated using the S-parameters of the antenna.  
\[\text{TARC}=\sqrt{\frac{(S_{ii}+S_{ij})^{2}+(S_{ji}+S_{jj})^{2}}{2}} \tag{8}\]  
The TARC should be less than \(\leq 0\) dB for the MIMO antenna system. The TARC result is shown in Figure 11. The graph shows that the total active reflection coefficient is less than 0 dB for the entire band.
# Influence of Hot Carrier and Thermal Components on Photovoltage Formation across the p-n Junction
## 4 Conclusions  
Hot carrier photovoltage across the GaAs p-n junction was evidenced experimentally and supported by an empirical model. In general, photovoltage across a p-n junction consisted of three simultaneous components arising due to electron-hole pair generation, hot carrier effect and  
Figure 5: Dependence of the relative change of the hot carrier photovoltage (\(U_{hc}\)_{\({}^{biased}}-U_{hc}\)_{\({}^{nonbiased}}\))/ \(U_{hc}\)_{\({}^{nonbiased}}\)_ on laser light intensity at \(+0.45\) V forward bias voltage (red circles) and \(-5\) V reverse bias (blue triangles). In inset: typical experimental photovoltage pulse traces at zero (**a**), forward (**b**) and reverse (**c**) bias voltage; the scale is 50 mV/div and 50 ns/div.  
semiconductor lattice heating after the thermalization. The first two of them could be evaluated experimentally at some extent, while the lattice heating-caused component could not be detected separately. The proposed model of a p-n junction as a first-order LTI system allowed revealing the individual input of each component. The magnitude of the net photovoltage resulted from mutual competition between all three components. Under certain conditions, depending on light wavelength, intensity, semiconductor bandgap and bias voltage, the hot carrier component could even exceed the classical photovoltage. As for application, the hot carrier photovoltage might be the reason of the still experimentally unattainable Shockley-Queisser limit, and the so called below \(E_{g}\) loss should be revised by taking into account the formation of this photovoltage of opposite polarity. On the other hand, maximum reduction of the hot carrier photovoltage contribution will boost the efficiency of a single-junction solar cell.  
Conceptualization and methodology, S.A. and J.G.; sample fabrication, A.S. and A.S; formal analysis, V.V. and A.C.; experimental investigation, E.S., O.Z and O.M.; calculations, O.M.; writing, reviewing and editing, J.G., S.A. and A.S.; visualization, O.Z. and O.M.; project administration and supervision, S.A. All authors have read and agreed to the published version of the manuscript.  
This work was in part supported by the Research Council of Lithuania (grant No. 01.2.2-LMT-K-718-01-0050).  
The authors declare no conflict of interest. Additionally, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 5 DASH7 Communication System Implementation
### The Receiving Process
#### 5.2.1 Reception  
The reception of a signal while using an SDR happens when the user tunes the local oscillator to or close to the frequency of interest. For FSK modulation, the center frequency of the SDR front-end needs to be tuned to the same center frequency of the FSK waveform. Accordingly, any deviation in the frequency between the front-end and the received modulated signal will result in a detection error and can cause a high bit error rate (BER).  
In our implementation, the frequency of the received DASH7 signal is assumed to be known by the receiver (in general, in every communication system, this should be the case). Therefore, if the signal is received with an IF frequency, the signal has to be shifted to the center frequency of the receiving bandwidth. This can be achieved in GNU Radio by multiplying the received signal with a sinusoidal signal, which has a frequency equal to the IF frequency. Figure 12 presents the flowgraph of the digital mixer operation. The signal source provides a complex single-tone signal with a frequency equal to the IF frequency.  
Figure 11: Frequency response of a baseband-modulated DASH7 signal.  
Figure 10: Flowgraph of the GFSK modulation. The FSK modulation mainly consists of an IIR filter with configurable taps that act as an integrator and a phase to complex value block.
## 2 Underwater Wireless Sensor Network
### Acoustic Link and Its Constraints
#### 2.1.4 Low Bandwidth  
Applying the acoustic waves in underwater environments revealed many challenges such as high propagation delay, high energy consumption, high path loss, and low bandwidth. These factors decrease the bandwidth and are significantly dependent on range and frequency [14; 27]. The long-range systems may have few bandwidths, and short-range systems may have high bandwidth. In both situations, these factors pose low bit rates. Also, the communication range is significantly reduced compared to the radio channels that are used in terrestrial environments. Based on the sound wave direction, acoustic links are classified into two parts, namely vertical and horizontal. Also, their propagation characteristics differ separately, especially in delay variance, scattering, spreads, and multipath. The main factors that influence acoustic channels are path loss, noise, multipath, high and variance delay, and Doppler spread [14; 17].
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 2 Analysis of the Game Process in Collaborative Allocation of En-Route Network Resources
### Game Process and Elements Analysis  
Based on the basic framework of the Stackelberg game model, the game process of collaborative allocation of en-route network resources mainly involves three stages, as shown in Figure 1:  
Stage 1: ATC acts as the leader and calculates the initial allocation plan based on the available trajectory and slot information, as well as the set of trajectory options (TOS) submitted by airlines, with the goal of optimizing the overall system state (maximizing efficiency and fairness).  
Stage 2: Each airline engages in the game under the initial allocation plan provided by ATC and pursues to minimize its total delay time while considering the impact of the plan chosen by other airlines on itself, so as to measure the preference for different plans and finally choose the optimal plan for itself.  
Stage 3: The airlines feedback the selection situation of plans obtained from the game to ATC, then ATC finally selects the allocation plan that maximizes the benefits of each airline and maximizes the overall operational efficiency from the obtained initial allocation plans, which is the optimal resource allocation plan.  
In the second stage, the game between airlines is a non-cooperative game. To simplify the game process and reduce the calculation complexity of the game model, the game is transformed into a two-player game process, then each airline is treated as an individual player to play a multi-round game. This game problem mainly needs to clarify the meaning of the following three elements:  
1. Set of participants: This is the set of all airlines \(A=\{\alpha_{1},\alpha_{2},\cdots,\alpha_{N}\}\), in each round of the game, and the airlines are divided into two opposing entities. One entity represents an individual airline \(A_{1}\) and the other entity represents the set of airlines \(A_{2}\).  
Figure 1: Flow chart of the game process.  
Set of strategies for each participant: this is the set of allocation plans \(S=\{s_{1},s_{2},s_{3},\ldots s_{m}\}\) that each airline can select, which includes the reasonable and viable plans obtained from the initial allocation plan.
3. Payment function (utility): When one airline chooses a particular plan \(s_{i}\) and the other set of airlines participating in the game chooses the plan \(s_{j}\), the delay time of the airline can be obtained. This delay time is the payment function \(F(s_{i},s_{j})\) of the corresponding airline.
## 3 Theoretical Background
\(H\left(X1\,Y\right)\) is expressed in Equation (4):  
\[H(X)=-\sum_{X}\,\text{p}(x)\text{log}\,\text{p}(x)=-\text{Blog}\,\text{B}-(1- \text{B})\text{log}(1-\text{B}) \tag{3}\]  
\[H(X\text{IY})=\frac{-\sum_{x}\sum_{y}p(x)p(y\text{lx})\text{log}[\text{p}(x) \text{p}(y\text{lx})]}{p(y)}{=-\text{B}\left(1-\gamma\right)\,\log\,\text{PPV}- \text{B}\gamma} \tag{4}\]  
\[\log\left(1-\text{NPV}\right)-(1-\text{B})(1-\alpha)\log\text{NPV}-(1-\text{B })\,\alpha\log\left(1-\text{PPV}\right)\]  
Equation (5) is obtained by substituting the equations and is the expression for \(C_{ID}\):  
\[C_{\text{ID}}=\frac{-\text{Blog}\,\text{-}(1-\text{B})\text{log}\,\text{-} (1-\text{B})\,\text{log}\,\text{PPV}-B\gamma\,\text{log}\,(1-\text{NPV})-(1- \text{B})(1-\alpha)\,\,log\,NPV-(1-\text{B})\text{log}\,(1-\text{PPV})} \tag{5}\]  
The Intrusion Detection Capability (\(C_{ID}\)) formula, which gauges how well a system detects intrusions, is shown in Equation (5). This equation defines several parameters: the  
Figure 3: An Intrusion Detection Model with Binary Symmetry. symbols for base rate (\(B\)), false negative (\(FN\)) rate (\(\gamma\)), false positive (\(FP\)) rate (\(\alpha\)), positive predictive value (\(PPV\)), and negative predictive value (\(NPV\)), which are as follows [1]:  
The base rate (\(B\)) characterizes the Intrusion Detection System's (IDS) operational environment. A value of \(B\) = 0 or \(B\) = 1 indicates that the input solely consists of regular or intrusive events, respectively. On the other hand, figuring out or managing the base rate in practice could be difficult for practitioners. This challenge stems from the fact that they frequently utilize base rate as an operational metric to assess the IDS environment. References [30; 31] provide a detailed discussion on estimating base rate \(B\) and prior probabilities. The probability that an alert would sound without an intrusion is an Intrusion Detection System's false positive (\(FP\)) rate (IDS). The false negative (\(FN\)) rate represents the likelihood that the Intrusion Detection System fails to raise the alarm when an intrusion occurs [30; 31]. When the IDS raises an alarm, the positive predictive value (\(PPV\)) measures the likelihood that an intrusion is present. It shows the percentage of IDS alarms that are related to real incursions. The \(PPV\) can be expressed mathematically as Equation (6):  
\[\mathrm{PPV}=\frac{\mathrm{B}(1-\gamma)}{\mathrm{B}(1-\gamma)+(1-\mathrm{B})\alpha} \tag{6}\]  
A negative predictive value (\(NPV\)) shows the probability of no incursion when the Intrusion Detection System (IDS) does not sound an alarm. Put otherwise, it quantifies the probability that the absence of Intrusion Detection System (IDS) alerts accurately implies the absence of invasions. Equation (7) provides the mathematical expression for \(NPV\)[32; 33; 34; 35]:  
\[\mathrm{NPV}=\frac{(1-\mathrm{B})(1-\alpha)}{(1-\mathrm{B})(1-\alpha)+\mathrm{ B}\gamma} \tag{7}\]
# Light-Weighted Password-Based Multi-Group Authenticated Key Agreement for Wireless Sensor Networks
## 4 Security Analysis
### Resistance to Sensor Node Capture Attacks  
In the proposed scheme, each sensor node \({SN_{i}}\) has its secrets (\(ID_{i}\), \(pw_{i}\)). An attacker A who has captured \({SN_{j}}\) and obtained \(ID_{j}\) cannot derive other sensor node \({SN_{i}}\)'s secrets (\(ID_{i}\), \(pw_{i}\)), and thus cannot impersonate \({SN_{i}}\) and \(AS\).
# Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
## 3 Properties of Linear Photoacoustic
### Frequency Domain Analysis  
Figure 6 shows a general geometry of the optoacoustic mechanism where a laser beam with radius \(a\) has a normal incidence in the water medium. The pressure spectral response at point \(P\) which is \(R\) distance away from the source can be expressed as [66]:  
\[P(R,\omega)=A\left(\frac{x^{2}}{x^{2}+1}\right)e^{-(\frac{S\tau}{P})^{2}\frac{ \sin\left(\frac{N\delta x}{2}\right)}{\sin\left(\frac{\delta x}{2}\right)}} \tag{10}\]  
where,  
\[A=-(\frac{\beta P_{o}}{2\sqrt{\pi}c_{p}})\frac{e^{iR}R}{R}(\frac{\tau}{\tau_{ \mu}})e^{-\frac{\omega^{2}\tau^{2}}{4}}e^{i\frac{(N-1)\omega T}{2}} \tag{11}\]  
Here, \(x\) is referred to as the angular frequency which is normalized by a time constant \(\tau_{\mu}\), where \(\tau_{\mu}=\frac{\cos\theta}{\mu c}\). In Equation (10), \(S\) is defined as the \"slenderness\" of the optoacoustic source and calculated as \(S=\frac{\tau_{\mu}}{\tau_{\mu}}\), where \(\tau_{\alpha}=\frac{a\sin\theta}{\hat{c}}\) is the characteristics transit time across the laser beam diameter. \(\delta\) is the normalized inverse repetition rate of the laser source and is defined as \(\delta=\frac{T}{\tau_{\mu}}\). All the other parameters in Equations (10) and (11) are defined in Abbreviations. Equation (10) shows the spectral response of the generated acoustic signal for various laser parameters such as laser beam diameter, \(a\), laser pulse repetition rate (\(\frac{1}{P}\)), laser power, \(P_{o}\) and some environmental parameters such as optical absorption coefficient of water, \(\mu\) observation angle, \(\theta\) etc. Generally, this spectral response is very broadband. Yet, by choosing proper laser repetition rate and observation angle, narrow band signal generation is also possible.
## 4 Online Control Strategy  
The global optimization-based algorithm needs to predict all the information of the working conditions and has a large amount of computation, which limits the real-time application of the algorithm. However, the optimal equivalent factor sequence obtained by the DP-A-ECMS method can  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cycle Name** & \multicolumn{3}{c}{**11-SC03**} & \multicolumn{3}{c}{**9-HWFET**} \\ \hline
**Initial SOC** & **0.9** & **0.6** & **0.3** & **0.9** & **0.6** & **0.3** \\ \hline \multirow{3}{*}{Fuel consumption (L)} & DP & 1.131 & 2.947 & 4.943 & 5.297 & 5.602 & 6.093 \\  & DP-A-ECMS & 1.133 & 2.958 & 4.995 & 5.321 & 5.631 & 6.175 \\  & \% & 0.18 & 0.37 & 1.05 & 0.45 & 0.52 & 1.35 \\ \hline \multirow{3}{*}{Calculating time (min)} & DP & \multicolumn{3}{c}{58.4} & \multicolumn{3}{c}{123.1} \\  & DP-A-ECMS & \multicolumn{3}{c}{27.7} & \multicolumn{3}{c}{59.6} \\ \cline{1-1}  & \% & \multicolumn{3}{c}{\(-\)52.57} & \multicolumn{3}{c}{\(-\)51.58} \\ \hline \hline \end{tabular} Note: The main configuration parameters of the computer are a 2.8 GHz Intel Core i5-8400 and 8 GB of memory.  
\end{table}
Table 2: Offline optimization of the fuel economy and simulation calculation time.  
Figure 6: Optimal equivalent factor sequence, (**a**) 11-SC03, Initial SOC = 0.9; (**b**) 9-HWFET, Initial SOC = 0.9; (**c**) 11-SC03, Initial SOC = 0.6; (**d**) 9-HWFET, Initial SOC = 0.6; (**e**) 11-SC03, Initial SOC = 0.3; (**f**) 9-HWFET, Initial SOC = 0.3.  
be used as a reference for the online controller. Considering that energy management strategies are influenced by many different factors, it is difficult to obtain deterministic equations or relationships. Neural networks can not only approximate any complex nonlinear mapping with any degree of accuracy, but also have the ability of learning and generalization [24]. Therefore, the nonlinear relationship between the optimal equivalent factor and state variables was learned based on the neural network in this study, and the relationship was then used to generate a real-time control strategy, such that the vehicle can obtain the optimal fuel economy performance during practical application.