esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Formulation  
The mathematical formulation for the coordination of directional OCRs in MGs is presented in Equations (1)-(8). The proposed model minimizes the total operating time, guaranteeing the OCR coordination between the main and backup relays:  
\[min\sum_{i=1}^{m}\sum_{f=1}^{n}t_{if} \tag{1}\]  
subject to:  
\[t_{if}-t_{if}\geq CTI \tag{2}\]  
\[t_{imin}\leq t_{if}\leq t_{imax} \tag{3}\]  
\[TMS_{imin}\leq TMS_{i}\leq TMS_{imax} \tag{4}\]  
\[PSM_{if}=\frac{I_{fi}}{ipickup_{i}} \tag{5}\]  
\[ipickup_{imin}\leq ipickup_{i}\leq ipickup_{imax} \tag{6}\]  
\[PSM_{imin}\leq PSM_{if}\leq PSM_{imax} \tag{7}\]  
\[t_{if}=\frac{A\cdot TMS_{i}}{PSM_{if}^{B}-1} \tag{8}\]  
\[PSM_{if}^{B}>1 \tag{9}\]  
Equation (1) is the objective function, where \(t_{if}\) is the operating time for relay \(i\) under a fault \(f\), \(m\) is the total number of relays, and \(n\) is the number of faults. Depending on the fault location, each relay within the MG experiences a different fault current. Ideally, the relay closest to the fault (main relay) must operate first, so that the fault can be isolated. In the event that this relay does not operate, the backup relay should operate within a coordination time margin. Equation (2) represents the coordination margin between the main and backup relay for each fault, where CTI is defined as the coordination time interval, while \(t_{if}\) and \(t_{if}\) are the operation time of the main and backup relays, respectively, when fault \(f\) takes place. Minimum and maximum limits of \(t_{if}\) are given by Equation (3).  
Equation (4) represents the minimum (\(TMS_{imin}\)) and maximum (\(TMS_{imax}\)) limits for \(TMS_{i}\). In this case, \(TMS_{i}\) is the dial of the relay operation curve. Equation (5) defines the plug setting multiplier \(PSM_{if}\) that is seen by relay \(i\) under a fault \(f\). \(PSM_{if}\) is the ratio that relates the fault current (\(I_{fi}\)) with the pickup current (\(ipickup_{i}\)) of relay \(i\). Equation (6) indicates the minimum (\(ipickup_{imin}\)) and maximum (\(ipickup_{imax}\)) limits of \(ipickup_{i}\). Equation (7) indicates the minimum (\(PSM_{imin}\)) and the maximum (\(PSM_{imax}\)) limits of \(PSM_{if}\). Finally, Equation (8) represents the operating curve of the main relay which allows calculating its operating time. In this case, \(A\) and \(B\) are the constants that define the characteristics of the relay operation curve. Finally, Equation (9) prevents the denominator of Equation (8) from being zero.
## 1 Introduction
### Why the CubeSat Software Is Complex to Develop?  
When we look into what actually makes CubeSats development a complex and challenging task, we can see a few major factors. The first and widely underestimated factor is the cyclomatic complexity of making CubeSat software. It is rather easy to see that even on the first order of a functional breakdown, CubeSat software demonstrates a complex set of sub-systems and their functions. The second factor is the skillset of the undergraduate and/or postgraduate students who did not have the option to obtain the industry practices and did not make their own so-called rule of 10,000 h of experience. It leads to a bunch of issues related to both architectural and implementation errors, and low awareness of the more effective tools and approaches for making software solid and robust. Last but not least, a major factor lies in the attempt of \"blind\" reuse of the open-source software (OSS). The push towards this reuse is normally given by the following factors: other team successes, strict and tight project deadlines, and focusing on the payload rather than on a whole CubeSat system. Such reuse leads to the low quality and maturity of the source code, and a lack of time to properly overview the entire code being developed. At the same time, CubeSat systems, including ground stations, communication signals, and CubeSat spacecraft, are subject to various cyberattacks, the classification of which is given by the standard ISO/IEC 15408 [21]. There are various papers that analyze [22] and propose methods for analyzing CubeSat security threats and solving those, for example, based on the analysis of attack trees [23].
## 5 Conclusions  
In order to reduce the noise level of the developed high-speed two-stage centrifugal air compressor for vehicle fuel cells, the experimentally obtained near-field noise characteristics are used to identify the sound source by the independent component analysis method, and the CFD-BEM coupling model is further applied for acoustic calculation and analysis. Based on the analysis results, the structure of the compressor is optimized, and a perforated muffler is applied in the pipe system for the reduction in the noise. The main conclusions are as follows:  
1. The \"buzz-saw\" noise at the rotating fundamental frequency and its low order harmonic frequency in the aerodynamic noise is prominent in the noise spectrum, while the sound pressure level of the discrete single-tone noise is relatively low;
2. There are flow separation phenomena in the process of impeller acceleration in the compressor, including the secondary flow in the blade tip clearance, and the flow separation at the leading edge and the trailing edge, which ultimately leads to uneven pressure distribution in diffusers and solutes. In order to reduce the \"buzz-saw\" noise of the air compressor, the blade outlet angles, the radius of the impeller outlets, the diffuser width, and the outlet area of the first stage solute are increased, so as to reduce the flow separation phenomenon and the interference within the volute tongue;
3. After the structure optimization, the thermal performance of the air compressor maintained the same level, and the noise level was significantly reduced. The near-field noise at the first and second stage solutes is reduced by 5.3 dBA and 3.3 dBA, respectively, and the near-field noise at the inter-stage pipeline is reduced by 7.4 dBA;
4. The application of the perforated muffler has an obvious noise attenuation effect on broadband noise and \"buzz-saw\" noise in the range of 0-4000 Hz. The sound pressure  
Figure 26: Noise spectrum before and after the application of the perforated muffler.  
level reduces from 74.7 dBA to 68.9 dBA, specifically by 5.8 dBA, under the rated condition at 1 m from the compressor in the horizontal direction.  
The research in this paper realizes the key technologies of the whole chain from sound source identification and induced mechanism exploration to noise control. The results provide the acoustic theoretical support and a practical basis for the forward development and upgrading of the air compressor for vehicle fuel cells.  
Investigation, S.S.; Methodology, S.S., W.C. and M.Z.; Project administration, Z.X.; Software, M.Z.; Supervision, Z.X.; Validation, C.W. and H.C.; Visualization, S.S.; Writing--original draft, S.S.; Writing--review & editing, W.C. and C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, grant number 2019 YFB1504604.  
Not applicable.  
The support from Gree Electric Appliances Inc and HPC Platform, Xi'an Jiaotong University is appreciated.  
The authors declare no conflict of interest.
## 4 Results  
### Exploratory Analyses with the Calibration Data  
In the first instance, a box-cox transformation of the SDRD data (renamed to SDRD.T) is applied, as it very slightly improves the linearity between it and WD (with a small correlation improvement of \(-0.87\) to \(-0.88\)). The transform results in an estimated exponent of lambda = 2.5 (_i.e._, this transform is applied, SDRD[2, 5]). Regardless of whether a transform is applied or not, it is clear that the SDRD data will be a strong predictor of WD. Histograms and scatterplots of this analysis are given in Figure 3a-e. Note also, that the correlations between WD and the coordinate data are \(-0.69\) and \(0.23\) for Eastings and Northings, respectively. This confirms the expected East-West trend in WD, as WD in the Bay generally increases towards the East, as we move further from the shore.  
Conditional boxplots for WD and SEABED display a worthy WD discriminating value for this predictor variable, especially for seabed class 2 (Figure 3f). The use of this categorical predictor variable in addition to SDRD.T also provides a stronger exploratory MLR fit; The Akaike Information Criterion (AIC) without this variable is 395.3, whilst AIC with this variable is reduced to 375.5. Thus, SEABED is retained as a predictor variable in addition to SDRD.T, and all five study prediction models will be calibrated as such. The spatial distributions of WD, SDRD.T and SEABED are given in Figure 4. Visually, both predictor variables appear suited to help explain the variation in WD, in some way.  
In order to investigate relationship non-stationarity, GW correlations are found for WD _versus_ SDRD.T, firstly using (a narrow) 10%, and secondly using (a wide) 50% bandwidth (each with bi-square kernels). Note that the bandwidths for the GW correlations are user-specified and are not optimally found. Details on how to conduct a GW correlation analysis can be found in [PERSON] _et al._, [59], and follow a similar procedure to a GWR analysis. From Figure 5, it can be seen that this relationship varies across space, where the relationship tends to get weaker as water depth increases (see Figure 4a). This local analysis confirms the value in a calibrating a non-stationary relationship predictor (_i.e._, GWR, GWRK and KED-LN). Observe that we have not explored the non-stationary relationship between WD and SEABED.  
Figure 3: Exploratory analysis: (**a**) histogram of WD, (**b**) histogram of SDRD, (**c**) histogram of SDRD.T, (**d**) scatterplot of WD _versus_ SDRD, (**e**) scatterplot of WD _versus_ SDRD.T, and (**f**) conditional box-plots for WD _versus_ SEABED.  
Figure 4: Maps of the study variables: (**a**) WD, (**b**) SDRD.T, and (**c**) Seabed class (see Table 1).  
Figure 5: GW correlations between WD and SDRT.T using: (**a**) 10% and (**b**) 50% bandwidths.
## 3 Home Area Network
### Smart Home HAN Requirements  
The communication requirements related to HAN are defined by the services and applications which run in the household; for example, diverse processes have to be executed among all devices in a smart home network. According to this condition, HAN have to provide reliable communication between application area devices and from the functional area devices to HAN devices for different indoor scenarios. If such is the case, then all HAN devices have to be dependably accessible as well.  
Communication can be seen from the general and individual point of view. Therefore, it is important to identify what is needed to ensure an efficient exchange of information in a functional area. However, since we have more than one functional area in the same physical space of a smart home, we are required to find a way for them to coexist in a superior infrastructure of communication inside the same house.  
It may not be advantageous and economical to have identical communication requirements for all smart home appliances; for example, a temperature monitoring device needs only low speed communication infrastructure, whereas facial recognition communication infrastructure is greater.  
Next, the organization and classification in four sections of main characteristics and requirements for HAN [4,41,50] is made, as shown in Figure 2.
## 2 Mathematical Model Description
### Turbulence Modeling  
[PERSON] et al. (2019) compared experimental results with the numerical simulation data for four turbulence models. The \(k\)-\(\omega\) SST model showed better performance under global and local flow phenomena predictions. [PERSON] et al. [35] performed numerical simulations of a single-phase R134a supersonic ejector. They pointed out that the \(k\)-\(\omega\) SST model can accurately predict ejector performance and capture the shock wave structure. [PERSON] et al. [36; 37] and [PERSON] et al. [38] also found that the \(k\)-\(\omega\) SST model had the best agreement with experimental data when simulating the steam ejector with CFD. Therefore, the \(k\)-\(\omega\) SST model was selected in this study. It can be described as follows:  
\[\frac{\partial}{\partial x_{i}}(\rho ku_{i})=\frac{\partial}{\partial x_{j}} \left[\Gamma_{k}\frac{\partial k}{\partial x_{j}}\right]+G_{k}-Y_{k}+S_{k} \tag{10}\]  
\[\frac{\partial}{\partial x_{j}}\big{(}\rho\omega u_{j}\big{)}=\frac{\partial} {\partial x_{j}}\left[\Gamma_{\omega}\frac{\partial\omega}{\partial x_{j}} \right]+G_{\omega}-Y_{\omega}+D_{\omega}+S_{\omega} \tag{11}\]  
where \(k\) is the turbulent kinetic energy and \(\omega\) is the specific dissipation rate. Definitions of terms G, Y, S, and D can be found in [39].
## 5 Research Methodology and Empirical Findings
### Multilayer Perceptron  
Because of data censorship, the introduction of machine-learning models into the simulation system can increase the accuracy of system analysis. Many AI models, such as random forest, support vector regression and MLP, can be used to analyze reliability [45]. Artificial neural network is an algorithm derived from the concept of the human brain. It uses neurons, or so-called perceptrons, as a unit to calculate outputs in response to given inputs. For complex or nonlinear analysis, this algorithm can use big data to deduce predictions. This study will use the conjugate gradient method with the iterative search concept to define the weighting coefficients. In MLP models, the hidden layers and sampling of the batch should also be defined. The connecting and weighting mechanism by two hidden layer structures [67] is shown in Figure 6. The feed-forward characteristics make the system prediction accuracy largely dependent on the data input [68]. The optimal solution for the weights connecting to the gradient is defined after the model is tested.  
Figure 5: Simulation and modeling process.  
In this nonparametric model, we will use the sigmoid function as the perceptron for expressing the nonlinear features of the data. It is expressed as follows [67]:  
\[\mathrm{x_{i}(t+1)=f(\left(\sum_{j=1}^{N}w_{ij}x_{j}(t)\right)-\partial_{i})} \tag{8}\]  
In the N-dimensional network, the weighting \(w_{ij}\) and offset \(\partial_{i}\) (\(1\leq i\), \(j\leq n\)) are defined for the activation function used for MLP models. The gradient descent methodology achieves convergence of the result using the activation function. The test data verification mechanism can identify the optimal solution for the nonparametric model. The mean square error (MSE) and root mean square error will be regarded as the loss function of the MLP model to measure the performance of the system. Finally, companies can combine the reliability forecasts of different service items to define the optimal service program.
## 3 Results
### Analysis of the Spatiotemporal Influencing Factors of RSEI in the Shendong Mining Area
#### 3.3.5 Precipitation and Temperature  
As shown in Figure 9, from 1990 to 2023, the average contribution rates of precipitation and temperature to the \(RSEI\) index in the study area were 0.085 and 0.012, respectively, indicating an overall positive effect of changes in precipitation and temperature on the \(RSEI\) index. However, spatial heterogeneity is observed across different regions. Precipitation exerts a positive influence on the \(RSEI\) index in 70.02% of the study area, primarily located in the southwest and southeast portions. Conversely, precipitation has a negative impact on the \(RSEI\) index in 29.98% of the area, mainly distributed in the northeast and southern central parts. Temperature predominantly shows a positive influence on the \(RSEI\) index, with 98.65% of the area exhibiting this positive effect.
# Joint Iterative Decoding Design of Cooperative Downlink SCMA Systems
## 2 System Model
### The Cooperative SCMA System
To simplify the representation, the received signal of the first and second phase for the \(i\)-th user in the second group can be rewritten as  
\[\mathbf{y}^{f}=\mathrm{diag}(\mathbf{h}^{f})\sum_{i=1}^{J}\mathbf{x}_{i}^{b}+ \mathbf{n}^{f},\mathbf{y}^{s}=\mathrm{diag}(\mathbf{h}^{s})\sum_{i=1}^{J} \mathbf{x}_{i}^{r}+\mathbf{n}^{s}, \tag{6}\]  
where the subscript \"\(\gamma\)\" is removed and \(\mathbf{y}^{a}=[y_{1}^{a},y_{1}^{a},\cdots,y_{K}^{a}]^{T}\), \(\mathbf{h}^{a}=[h_{1}^{a},h_{2}^{a},\ldots,h_{K}^{a}]^{T}\), \(\mathbf{n}^{a}=[n_{1}^{a},n_{2}^{a},\ldots,n_{k}^{a}]^{T}\), \(a=f,s\).
# A Methodological Approach to Securing Cyber-Physical Systems for Critical Infrastructures
## 3 Open (Sub-)Challenges
### Detection Challenges  
**Slow DoS Attack Detection.** A relevant aspect to consider when protecting from slow DoS attacks is related to the behavior of the attacks themselves. In particular, from one side, such threats are characterized by low-bandwidth rate [4]. Therefore, it is not trivial to detect an ongoing attack by monitoring the overall network flow [55; 56]. In addition, as such threats typically alternate periods of activity to period of sleep, such behavior may resemble the behavior of legitimate clients actively communicating with the server (e.g., to ask for a specific resource), hence temporarily interrupting the communication (e.g., to read the received content). Moreover, if we consider the payload exchanged during the attack between the client(s) and the server, such payload is compliant to the exploited protocol [57], as no malformed packets or byte sequences are sent by the attacker. Finally, by considering the connection peaks which characterize the execution of a slow DoS attack--although at first sight, such peaks may identify a malicious activity--it is important to consider that very similar peaks characterize legitimate flash crowds [58]. In virtue of the considered aspects, to the best of our knowledge, two aspects are today considered open research challenges in the slow DoS attack scenario: on one hand, it is the design of effective intrusion detection algorithms that are able to identify ongoing threats [59]; and on the other hand, it is the design of novel attacks affecting specific protocols or contexts [60], such as Industry 4.0 environments.  
**Data Analysis and Interpretation.** Behavioral data from smart devices often include high-dimensional datasets that are dynamic and evolve over time. Analyzing these data to extract useful information and produce _interpretable_ results requires sophisticated machine learning models and real-time processing capabilities [31]. Developing algorithms that can accurately identify and interpret user behaviors without significant errors is challenging. This involves reducing false positives (incorrectly identifying a behavior that did not occur) and false negatives (failing to identify an actual behavior), which are critical for applications like fraud detection or personalized recommendations.
# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 2 Materials and Methods
### Bidirectional Cross-Scale Feature Fusion  
The Path Aggregation Network-Feature Pyramid Network has certain limitations in underwater fish detection tasks: the variations in lighting and noise in underwater environments can affect feature extraction, and high computational complexity could impact real-time detection capability. In this paper, as shown in Figure 4a, integrating RepBi-PAN as the neck component in underwater fish detection provides significant advantages by enhancing multi-scale feature fusion through bidirectional connections and top-down flow:  
\[F_{i}^{TD}=F_{i}^{\prime}+u\big{(}F_{i+1}^{\prime}\big{)},\{for\;i=n-1,n-2, \ldots,1\} \tag{5}\]  
Features from higher layers are passed down to lower layers and fused with the lower-layer features, where \(u\) represents an upsample operation.  
Bottom-up flow:  
\[F_{i}^{BU}=F_{i}^{TD}+D\Big{(}F_{i-1}^{TD}\Big{)},\{for\;i=2,3,\ldots,n\} \tag{6}\]  
Features from lower layers are passed upward to higher layers, where \(D\) represents a downsampling operation.  
The connections improve detection accuracy for fish of varying sizes. Its ability to transmit features bidirectionally helps preserve fine details and rich semantic information. After bidirectional connections, feature fusion is applied. Let the weights for fusion be denoted by \(\alpha_{i}\), which controls the importance of each scale. The final fused feature map \(F_{i}^{final}\) at scale \(i\) is:  
\[F_{i}^{final}=\alpha_{i}\times F_{i}^{TD}+(1-\alpha_{i})\times F_{i}^{BU} \tag{7}\]  
where \(\alpha_{i}\varepsilon[0,1]\) is a learnable parameter that dynamically adjusts the importance of the top-down and bottom-up features during training. The final output feature map \(F^{out}\) is the aggregation of the fused feature maps across all scales:  
\[F^{out}=\sum_{i=1}^{n}F_{i}^{final} \tag{8}\]  
Figure 4: The overall flowchart of the neck part in the fishway fish detection and the module operation process at each stage: (**a**) Overall flow structure diagram of RepBi-PAN; (**b**) Operation flowchart of the RepBlock module in the training phase of the RepBi-PAN structure; (**c**) Operation flowchart of the RepBlock module in the inference phase of the RepBi-PAN structure; (**d**) Structure diagram of the BiFusion module in the RepBi-PAN process.  
This output is passed to subsequent layers of the network for further processing, such as detection. Additionally, RepBi-PAN maintains computational efficiency, ensuring that enhanced detection performance is achieved without excessive computational overhead, thus supporting real-time applications in challenging underwater environments.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Technology Migration  
With the rapidly changing global scenarios with the latest cutting-edge technologies such as advancement in hardware, software, applications, and protocols, service providers must be well adapted to provide efficient, reliable, and fast services to customers. The latest technologies developed will be more robust, efficient, cost effective, and more operation friendly. Hence, encouragement to service providers towards timely migration to newer wired and wireless technologies like SDN enabled IPv6 networks, wireless virtualization, and the use of OpenBTS and OpenCellular technologies are required for sustainable services. However, SDN and IPv6 network migration is not in the scope of this paper, the benefit of its implementation as an energy efficient technology is presented in Section 4.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 2 Preliminaries
### Traditional Fast SC Decoding  
When we recognize a polar code with length \(N\) to a string of subcodes, we first judge if the whole polar code can be recognized as a subcode; if not, we will judge the former, and the latter \(N/2\) polar code can be recognized as a subcode. If so, the recognition stops, and if not, we divide the \(N/2\) polar code by the \(N/4\) polar code and continue the recognition, until the original is totally recognized as subcodes. When decoding a polar code with a code length of 32, as discussed in Section 2.1, the polar codes are first recognized as special nodes, as shown in Figure 5.  
Figure 4: Schematic diagram of the SC decoding time step.  
After node recognition, the nodes are decoded as discussed in Section 2.2. The decoding sequence is the same as that in Section 2.1.  
The decoding process is shown in Figure 6. Though the traditional fast SC decoding costs 19 decoding steps which is much lower than SC decoding, it cannot improve the bit-error performance. Maintaining the low time step of the fast SC algorithm while improving the bit-error performance is the goal of the algorithm in this paper.