esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
## 3 Results
### Neural Network
The residuals of the neural network model are more variable than what one would expect from a normal distribution, based on the discrepancy between the two distributions. In summary, while the neural network performs reasonably well, the residual analysis reveals some key deficiencies: residuals show slight departures from normality, temporal autocorrelation is present, and extreme values are not well captured. Based on these results, it seems like the model might use some tweaks to make it more resilient to outliers and temporal dependencies. In Figure 9, we see the occurrence of extreme values in the series of residuals \(\{\epsilon_{t}\}_{1\leq t\leq n}\) and a significant correlation with lag \(\tau=1.2\). From the Q-Q plot we see the existence of \"fat tails\". We also see important differences between the theoretical distribution function with a normal distribution and the empirical distribution function. The tests examined the normality of the distribution of residuals using Shapiro-Wilk, [PERSON], [PERSON]  
Figure 9: Residuals analysis for neural network model. [PERSON], [PERSON] (Kolmogorov-Smirnov), the Jarque-Bera test, and D'Agostino omnibus tests and the independence of residuals using the Ljung-Box test for shifts \(1\leq\tau\leq 10\). Table 7 gives the values of statistics and the value of the test probability. From the obtained results, we can see that, at the significance level of 0.05 based on [PERSON], [PERSON], [PERSON], [PERSON] (Kolmogorov-Smirnov), the Jarque-Bera test, and D'Agostino omnibus tests, the working hypothesis, which assumes normality of residuals, should be rejected in favour of the alternative hypothesis. Additionally, based on the Ljung-Box test, the hypothesis concerning the independence of residuals in the linear model should be rejected (see also Figure 9, 'Autocorrelation function', which may mean that, at certain points during the test, additional power consumers may have been activated that were not taken into account or the reading of the predictors may have occurred with a certain delay). NN explains the variability of instantaneous energy consumption and recuperation in over 90%; however, as the analysis of residuals shows, additional factors influencing energy consumption and recuperation or predictors used in the model with lags due to readings delay should be considered.
## 2 Background and Proposed System Model
### Cfcpsc
#### 2.4.1 CFCPSC with SF  
For the CFCPSC with SF, denoted as CFCPSC-SF, the CFCPSC algorithm treats each antenna as a separate receiver. This adaptation from the original CFCPSC algorithm [11] is similar to having \(LU\) single-antenna SUs instead of \(U\) SUs with \(L\) antennas each. The corresponding modified algorithm acts in Steps 1 to 5 of the original CFCPSC, leading to the following full algorithm:  
1. By using the discrete Fourier transform (DFT), calculate the PSD of the signal at the \(l\)-th antenna of \(u\)-th SU in (1) as: \[\mathbf{F}_{l,u}^{\prime}=\frac{\left|\text{DFT}[y_{l,u}(n)]\right|^{2}}{N},\] (23) for \(n=1,2,3,\ldots,N\).
2. Compute the modified circular-even component of \(\mathbf{F}_{l,u}^{\prime}\) by making: \[\mathbf{F}_{l,u}(k)=\begin{cases}\frac{[\mathbf{F}_{l,u}^{\prime}(1)+\mathbf{ F}_{l,u}^{\prime}(N/2+1)]}{2},&k=1,\\ \frac{[\mathbf{F}_{l,u}^{\prime}(k)+\mathbf{F}_{l,u}^{\prime}(N-k+2)]}{2},&k=2, 3,\ldots,N.\end{cases}\] (24)
3. Split the sensed band into \(S\) sub-bands and compute the signal power in the \(s\)-th sub-band, with \(s=1,2,\ldots,S\), as: \[F_{s_{l,u}}=\sum_{k=1}^{N/(2S)}\mathbf{F}_{l,u}[k+(s-1)V].\] (25)Calculate the total amount of signal power in the band that is sensed as: \[F_{\text{full}_{l,u}}=\sum_{k=1}^{N}\mathbf{F}_{l,u}(k).\] (26)
5. Find the average of the ratios \(F_{s_{l,u}}/F_{\text{full}_{l,u}}\) in order to eliminate the effect of the noise variance, leading to: \[r_{u,s}=\sum_{l=1}^{L}\frac{F_{s_{l,u}}}{LF_{\text{full}_{l,u}}}.\] (27)
6. At the FC, compute the test statistic for the \(s\)-th sub-band as: \[T_{\text{CFCFPSC}_{s}}=\frac{1}{U}\sum_{u=1}^{U}r_{u,s}.\] (28)
7. Compare \(T_{\text{CFCFPSC}_{s}}\) with the decision threshold \(\gamma_{\text{FC}}\) in order to reach a decision on the occupation state of the \(s\)-th sub-band: decide \(\mathcal{H}_{0}\) if \(T_{\text{CFCFPSC}_{s}}<\gamma_{\text{FC}}\) or \(\mathcal{H}_{1}\) if \(T_{\text{CFCFPSC}_{s}}\geq\gamma_{\text{FC}}\).
8. Make the final decision as \(\mathcal{H}_{0}\) when each decision in each sub-band is \(\mathcal{H}_{0}\) or as \(\mathcal{H}_{1}\) when at the minimum one sub-band decision is \(\mathcal{H}_{1}\).
## 3 Results
### CO\({}_{2}\) Fluxes  
The monthly integrated values of NEE, GPP, and RE are shown in Figure 2, while the seasonal and annual integrated values are presented in Table S1 (Supplementary Material). A positive sign of NEE indicates net CO\({}_{2}\) emissions to the atmosphere, while a negative sign denotes CO\({}_{2}\) assimilation or uptake by the ecosystem. Generally, the magnitude of NEE, RE, and GPP are larger in SS than in AW. This behavior is more evident in monthly values than in daily values, as shown in Figures 2 and S1 (Supplementary Material), respectively. The ecosystem-level NEE was a carbon sink in all periods and approximately two times greater in SS than in AW. However, the understory NEE acted as a source, with its value in SS nearly 25% greater than in AW. NEE was controlled by GPP in the overstory and by RE in the understory. Annually, GPP in the overstory was 73% higher than in the understory, accounting for 63% of the ecosystem's total GPP. The Understory RE accounted for 94% of the ecosystem's total RE. At the ecosystem level, the RE/GPP ratio was higher in AW (0.92) than in SS (0.88), indicating that the Araucaria forest ecosystem emitted less by RE than was absorbed by GPP in SS compared to AW. This pattern led to a greater CO\({}_{2}\) absorption in SS than in AW. Generally, the RE/GPP ratio was higher in the understory (RE/GPP \(\geq\) 2.0) than in the overstory (RE/GPP \(<\) 0.1) for all analyzed periods.  
The annual net ecosystem productivity (NEP = \(-\)NEE) was 298.96 g C m\({}^{-2}\) yr\({}^{-1}\), indicating that the Araucaria site acted as a CO\({}_{2}\) sink, considering the annual integration  
Figure 1: Monthly average of (**a**) air temperature (Tair), (**b**) solar radiation (Rg), (**c**) pressure vapor deficit (VPD), and (**d**) monthly accumulated precipitation (Prec). The dashed line in (**a**) and dashed bars in (**d**) represents the Climatic Normal for the region. The non-shaded and shaded areas represent the spring–summer (SS) and autumn–winter (AW) periods, respectively.  
Figure 2: Monthly integrated values of (**a**) NEE, (**b**) GPP, and (**c**) RE in g C m\({}^{-2}\) month\({}^{-1}\); (**d**) LE and (**e**) H in (MJ m\({}^{-2}\) month\({}^{-1}\)) for the understory and ecosystem; and (**f**) LAI (m\({}^{2}\) m\({}^{-2}\)) and EVI. The non-shaded and shaded areas represent the spring–summer (SS) and autumn–winter (AW) periods, respectively.  
over the ecosystem. During this period, the GPP of the ecosystem was approximately 8% greater than the RE, with GPP at 2898.78 g C m\({}^{-2}\) and RE at 2599.83 g C m\({}^{-2}\) yr\({}^{-1}\). [PERSON] et al. [47], studying the Amazonian forests in Brazil, found that the annual NEP accumulation was 450 \(\pm\) 388 g C m\({}^{-2}\) yr\({}^{-1}\), with GPP of 3413 \(\pm\) 333 g C m\({}^{-2}\) yr\({}^{-1}\) and RE of 2963 \(\pm\) 235 g C m\({}^{-2}\) yr\({}^{-1}\). Therefore, the Araucaria forest, in our study, absorbed CO\({}_{2}\) from the atmosphere at rates similar to those of the Amazonian forest in terms of NEE absorption and partitioning.
## 4 Experiments and Results
### Experimental Results  
To comprehensively evaluate the effectiveness of the proposed method in this chapter, this section conducts a comparative analysis of the evaluation metrics for the proposed DFs and the anomaly detection model based on the fine-tuned Llama3 model. This analysis verifies the effectiveness of the proposed improvements. Finally, the proposed method is compared with the more advanced existing methods in the field of industrial control device anomaly detection, demonstrating the performance level of the proposed method.
## 2 The Proposed Method
### Data Pre-Processing  
For simplicity, we assume that the retinal blood vessel dataset is \(D\), the retinal blood vessel images in the dataset are denoted as \(d^{1}\), \(d^{2}\),..., \(d^{n}\), the images of labels in the dataset are denoted as \(l^{1}\), \(l^{2}\),..., \(l^{n}\). Taking \(d^{n}\) as an example, \(h\) and \(w\) are the length and width of \(d^{n}\), respectively. Firstly, padding the image \(d^{n}\), the padded image is represented by \(d^{n}\). The length and width of \(d^{n^{\prime}}\) are denoted by \(H\) and \(W\), respectively. The size of the extracted image block is \(p\times p\), the padded retinal image \(d^{n^{\prime}}\) is shown in Equation (1).  
\[{d^{n}}_{(H,W)} = d^{n}_{(h+p/2,w+p/2)} \tag{1}\]  
Secondly, starting from the first pixel of the retinal blood vessel image, traversing the \(n\)-th entire image to obtain \(m\) image blocks with the size of \(p\times p\), the whole blocks extracted from \(n\)-th image are denoted as \(\{x^{n}\}_{i=1}^{m}\), each block respects the attribute of the center pixel.  
Thirdly, getting the label \(y^{n}_{i}\) of the corresponding image block \(x^{n}_{i}\) from the image \(d^{n}\) of labels via traversing, all of the labels in \(n\)-th image of labels are denoted as \(\{y^{n}_{i}\}_{i=1}^{m}\). The image block and the corresponding label compose the sample pair, which is denoted as \(\{x^{n}_{i},y^{n}_{i}\}\).  
Taking the open fundus retinal image dataset DRIVE as an example, there are 40 fundus retinal images in the dataset, 20 images are retinal vessel images and the others are label images.  
The whole sample pairs are extracted by the above three steps, which are denoted as \(\{x^{n}_{i},y^{n}_{i}\}_{i=1}^{m},\)\(N\), where, \(N\) denotes the number of the retinal vessel images.
## 3 Examples of New Empirical Laws from \(y_{2\text{A}}(x)\)
### Allowable Soil Pressure in Geotechnics  
In civil engineering, the resistance of the ground to footing foundations is important for the construction of road structures and buildings. The so-called allowable soil pressure \(q_{a}\) is conventionally used to characterise this geomechanical resistance. This parameter provides the pressure which the foundation footings (indeed, the ground) can withstand according to a target structure and without exceeding the subsidence limit determined in the standard formulation (e.g., 25 mm settlement in Part 2 of Eurocode 7 for geotechnical design). This standard also includes the widespread Bowles relation [26], which relates \(q_{a}\) with penetrometric data \(N\), footing radio \(B\), and embedment \(D\) as the standard equation. However, these standard equations have two flaws. (1) Two different expressions are required depending on the footing dimensions, which involve unnatural discontinuities. (2) These equations are only valid up to a depth that is limited by the footing width. The empirical data from which the conventional relationships are derived show that the ground subsidence gradually changes from a relatively constant behaviour to a critical value from which the ground tends asymptotically to a different constant value. This can lead to the conclusion that the footing is supported on the ground up to a certain size but sinks into the ground beyond that size, resulting in a different response behaviour. From a theoretical point of view, it would, therefore, be possible to search for a function that reflects both behaviours with a corresponding continuous transition between them.  
To adequately resolve these issues, [PERSON] et al. [5] modified the standard formula for calculating the allowable pressure value as a function of footing width, among other variables. Figure 3 shows the comparison between the resulting curve from the two existing relationships with discontinuous gradients and the resulting curve from the new single relationship with continuous gradients. This new formulation also makes it possible to establish a single parameter \(q_{1}(N)\) that includes a new relationship between \(N_{\text{SPT}}\) (obtained by the standard penetration test in boreholes) and the more extensively used \(N_{\text{DPSH}}\) (obtained by the dynamic penetration test). This parameter is independent of footing and reflects the terrain characteristics that affect its allowable pressure. A new relationship \(q_{3}(D/B)\) was also developed to evaluate the variation of the allowable pressure for appreciably greater depths than the footing width. Thus, correcting the continuous increasing linear expression (1 + 0.33-\(D/B\)) from [PERSON] [27], established for \(D<B\), on the influence of \(D/B\) for allowable pressure values. The three relationships \(q_{1}(N)\), \(q_{2}(B)\), and \(q_{3}(D/B)\) were developed using the \(y_{2\text{A}}(x)\) function.
## 4 Implementation
### IoT Nodes
#### 4.1.2 Sensor Nodes
For instance, in the case of the TMP36, when powering it at 3 V, 1.2 V are encoded approximately as 70 \({}^{\circ}\)C, which is a temperature that will not be reached in practice by most HAS. Nevertheless, other sensors like the ones for measuring the relative humidity and luminosity, cannot neglect the voltage range limitation, since they require a much wider range. For such cases, a voltage divider could be included at the output of the sensors, which moves the 0 V to 3 V range to an output between 0 V and 1.2 V. If sensors actually need more precision, external hardware would have to be added to the node and, therefore, current consumption and economic cost would be increased. The most straightforward solution would consist of adding an external microcontroller and/or an ADC (if the microcontroller's internal ADC has not enough precision), and then feeding the collected data into the Xbee module through the serial interface. In addition, it should be pointed out that battery load may influence both the measurements and the calibration of the sensors. The impact of the battery load also depends on other factors like the specific sensor model or the existing environmental conditions, so further proper, well-designed experiments would be needed in order to evaluate the accuracy of the sensors through time. Figure 8 shows the schematic of a sensor node, which basically includes the previously mentioned components and a couple of LEDs (one for determining the status of the Xbee module and another one for debugging the operation of the PIR sensor). An actual prototype of a sensor node is shown in Figure 9.  
Figure 8: Schematic of the sensor node.
### Study on the Influence of Fault Dip Angle on Fault Activation
#### 2.3.1 The Expound of the Barrier Effect of Fault Dip Angle on Rock Mass Movement  
Space being limited, only the stress field distribution of the surrounding rock around the fault is shown with the fault dip angle is \(30^{\circ}\) and \(80^{\circ}\) (Figures 13 and 14). The stress concentration in the fault hanging wall in the mining area on both sides of the rock grows with the fault dip angles increase. The tensile stress is mainly near monitoring point A, and its value is increasing. Figure 15 shows the displacement distribution of vertical and horizontal surrounding rock along the monitoring line of different fault  
Figure 16: The shear stress distribution at the fault plane under various buried depths of the upper boundary of fault.  
Figure 15: The displacement distribution of surrounding rock along monitoring lines of different fault dip angles, they should be listed as: (**a**) Vertical displacement distribution; (**b**) Horizontal displacement distribution.  
2.3.2. The Expound of the Stress Change on Fault Plane Caused by Mining  
Figure 17 displays the shear stress to normal stress ratio distribution along the fault plane at various fault dip angles (\(30^{\circ}\leq\alpha\leq 80^{\circ}\)) during the downward excavation of the fault hanging wall. The calculation results show that as the fault dip angle increases, the ratio of shear stress to positive stress becomes larger and larger, demonstrating that the fault is easier to activate. For various fault dip angles, the shear stress and positive stress ratio distribution are consistent, which in the range from 0 m to \(-50\) m depth increases sharply to the peak, and then gradually decreases and stabilizes. For example, the maximum value of shear stress to normal stress ratio is from 0.79 to 0.66 as the fault dip angle from \(\alpha=80^{\circ}\) to \(\alpha=50^{\circ}\).
## 5 Experimental Simulation and Analysis  
### Simulation Setup  
In this paper, the performance of the SLP-MACGT model is compared with that of the stratification-based source location privacy scheme (SSLP) [11], the push-based probabilistic method source location privacy scheme (PP-SLPP) [12], and the multi-round game-based source location privacy scheme (MRGSLP) [35]. Simulations were performed using MATLAB 2021 to evaluate the performance of these models. In the simulations, all sensor nodes are randomly distributed in a space of 1000 m \(\times\) 1000 m \(\times\) 1000 m with the default simulation parameters listed in Table 4.  
The performance evaluation indexes used in this paper are  
* Packet delivery ratio (PDR): PDR is the probability of successfully forwarding data packets from the source node to the sink node;
* Network safety time: Network safety time refers to the time between the activation of the source node and the successful detection of the source node by the attacker under the premise that the source node continues to send packets;
* End-to-end delay: End-to-end delay represents the time required to transmit a data packet from a source node to the sink node;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Equilibrium Point** & **Eigenvalue 1** & **Eigenvalue 2** & **Eigenvalue 3** \\ \hline \((0,0,0)\) & \(C_{X2}-C_{X1}+R_{X1}-R_{X2}\) & \(C_{Y2}-C_{Y1}+R_{Y1}-R_{Y2}\) & \(C_{Z2}-C_{Z1}+R_{Z1}-R_{Z2}\) \\ \hline \((1,0,0)\) & \(C_{X1}-C_{X2}-R_{X1}+R_{X2}\) & \(C_{Y2}-C_{Y1}+R_{Y1}-R_{Y2}\) & \(C_{Z2}-C_{Z1}+R_{Z1}-R_{Z2}\) \\ \hline \((0,1,0)\) & \(C_{X2}-C_{X1}+R_{X1}-R_{X2}\) & \(C_{Y1}-C_{Y2}-R_{Y1}+R_{Y2}\) & \(C_{Z1}-C_{Z2}-R_{Z1}+R_{Z2}\) \\ \hline \((1,1,0)\) & \(C_{X1}-C_{X2}-R_{X1}+R_{X2}\) & \(C_{Y1}-C_{Y2}-R_{Y1}+R_{Y2}\) & \(C_{Z2}-C_{Z1}+R_{Z1}-R_{Z2}\) \\ \hline \((1,0,1)\) & \(C_{X1}-C_{X2}-R_{X1}+R_{X2}\) & \(C_{Y2}-C_{Y1}+R_{Y1}-R_{Y2}\) & \(C_{Z1}-C_{Z2}-R_{Z1}+R_{Z2}\) \\ \hline \((0,1,1)\) & \(C_{X2}-C_{X1}+R_{X1}-R_{X2}\) & \(C_{Y1}-C_{Y2}-R_{Y1}+R_{Y2}\) & \(C_{Z1}-C_{Z2}-R_{Z1}+R_{Z2}\) \\ \hline \((1,1,1)\) & \(C_{X1}-C_{X2}-R_{X1}+R_{X2}\) & \(C_{Y1}-C_{Y2}-R_{Y1}+R_{Y2}\) & \(C_{Z1}-C_{Z2}-R_{Z1}+R_{Z2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Eigenvalues at different equilibrium points.  
* Energy consumption: Energy consumption represents the energy consumption of transmitting and receiving data and controlling data packets during a simulation run.  
According to [PERSON] et al. (2019), the energy consumed by a sensor node to send a data packet with \(l\) bits can be expressed as follows:  
\[E_{sent}(l,d)=lP_{0}A(d,f)=lP_{0}d^{k}\alpha(f)^{d}, \tag{10}\]  
where \(P_{0}\) is the received power level of the node, \(A(d,f)\) is the power attenuation coefficient relevant to the distance, \(k\) is the spreading factor of the propagation geometry, and \(\alpha(f)\) is the acoustic signal absorption coefficient.  
The energy consumed by the node to receive \(l\) bits data can be calculated by  
\[E_{received}(l)=lE_{1}. \tag{11}\]  
where \(E_{1}\) represents the reception coefficient, which is the energy consumption for receiving 1 bit data.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
## 1 Introduction
### Related Work  
In this section, we provide various techniques that have been proposed in the literature for traffic scheduling and protection in multipath transmission systems, LOS prediction and tracking and the application of RL in multipath traffic scheduling.
# A Reconfigurable Formation and Disjoint Hierarchical Routing for Reshapeable Bluetooth Networks
## 6 Scatternet Performance  
In this section, the performance of scatternet formation and the routing algorithm are simulated to demonstrate the performance of optimum mesh-tree topography, and compared to BlueHRT. The ring-shaped topology of BlueHRT contains only three nodes in the ring structure, and the tree-shaped topology is extended by all the other nodes. A simulation program is written to evaluate the system performance, including the scatternet, routing and energy efficiency.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 4 Proposed Scheme and Power Control  
In this section, the user-serving scheme for an unserved user is proposed. The proposed scheme in this paper is based on the observation and simulation result in Section 3, and this scheme supplements the limitations of the existing methods described in Section 4.1.
## 5 Introduction of a Domain Name System in a HIP DEX Opportunistic Mode  
In HIP DEX, if the initiator does not know the HIT_R, it can initiate the HIP handshake with the I1 packet where all bits of HIT_R are set to zero. This connection mode is called opportunistic. In Section 3.4, we detailed the arguments to revoke HIT_R and to minimize HIT_I size. We can optimize the communication overhead of HIP DEX packets by alleviating their size in omitting both HIT_I and HIT_R with the introduction of a Domain Name System (DNS) in an HIP DEX opportunistic mode. The proposed scenario implements the CTTP as an HIP DNS server and steps for generating and distributing HIT from HI are described in the following:  
1. After computing the \(PK_{\textit{resp}}\), the responder's HI (HI_R): the public key (see Figure 7, Section 4.1.2), the CTTP computes the HIT_R from HI_R using FOLD function to get the 96 bits-compressed format of HI_R and then stores a binding of HI_R and HIT_R corresponding to the responder in its local memory;
2. The CTTP sends the calculated HIT_R to the initiator;
3. After computing the \(PK_{\textit{init}}\) as the initiator's HI (HI_I) using FOLD function to get the 96 bits-compressed format of HI_I and then stores a binding of HI_I and HIT_I corresponding to the initiator in its local memory;
4. The CTTP sends the calculated HIT_I to the responder.  
Introducing a DNS server to distribute HITs for HIP peers has the major advantage of the minimization in the computation cost due to the delegation of the HITs' computation to the CTTP. This solution encourages the establishment of a secure association in HIP DEX in an opportunistic mode with minimal energy consumption. This solution can be generalized  
Figure 7: Proposed HIP DEX Secure Lightweight Association Establishment Phase.  
to generate the HIT_SUITE_LIST that represents a group of initiator's and responder's IDs. The HIT_SUITE_LIST is defined in HIP DEX to allow the initiator to identify the IDs supported by the responder and the same benefit for the responder. With our proposed solution, both the initiator and the responder receive the HIT of each other from a third party and be sure that it is a legitimate HIP peer. Security threats and mechanisms to secure the third party as it has a main task in the handshake are discussed in Section 8.