esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results  
To evaluate the performance of the proposed combined methods, we performed a simulation (Monte Carlo) with 100 repetitions. The length of each repetition was fixed to 700 realizations. We choose 700 observations to guarantee that the number of extrema becomes sufficient to fit and estimate the parameters of GPD distribution. The simulation experiences were made with an AR (1) demand process while the point forecasting is an SES method following the same procedure as in [PERSON] and [PERSON] [41]. The choice of the AR (1) demand process is explained by the fact that is widely used to describe the series in datasets (see for instance [PERSON] et al. [44] and [PERSON] et al. [45].  
In this study, we have compared these different empirical methods mentioned in Section 2 for computing safety stocks assuming foremost that the forecast error \(\varepsilon_{\mathrm{t}}\) is i.i.d normally distributed with zero mean and a variance of \(\sigma^{2}\). We found that the CEVT method always gives good results for most values of quantiles followed by the FHS and GARCH methods. We show in the first following subsection the overview results of this case. Thus, this work is interested to confirm these results when the forecast errors follow a non-normal distribution (log-normal and gamma distribution). The log-normal distribution is used to model demand when products are subject to promotional periods characterized by observed demand that is higher than the baseline demand. The gamma distribution and Beta distribution have previously been used in the literature ([46; 47; 48; 49; 50]) to assess violations of the normality assumption. Following [PERSON] [46], the gamma distribution is an especially effective choice because it is flexible and has non-negative values as the real demand. [PERSON] [47] justify the applicability of the gamma distribution and show that a particular advantage of the gamma distribution is that it may be used to deal with fixed lead times and that it can be extended to situations where the lead time is a random variable.  
Detailed results are provided in Supplementary Tables S1, S2, S3 and S4. Table S1: contains the Tick loss values corresponding to Lead Time 1 week, when forecasting errors follow a Log-Normal distribution, and \(\phi\) range from \(-0.90\) to \(\phi\) = 0.90. Table S2 contains the Tick loss values corresponding to Lead Time 4 weeks, when forecasting errors follow a Log-Normal distribution, and \(\phi\) range from \(-0.90\) to \(\phi\) = 0.90. Table S3 contains the Tick loss values corresponding to Lead Time 1 week, when forecasting errors follow a gamma distribution, and \(\phi\) range from \(-0.90\) to \(\phi\) = 0.90. Table S4 contains the Tick loss values corresponding to Lead Time 4 weeks, when forecasting errors follow a gamma distribution, and \(\phi\) range from \(-0.90\) to \(\phi\) = 0.90.
## 5 Path Following Control Design  
The control law is mainly divided into three parts: 3 DOFAPLOS guidance law, heading controller and speed controller. Firstly, 3 DOFAPLOS guidance is used to determine the desired heading of AUV considering drift angle. Then, by combining the FOPID heading controller, the error between the current heading and the desired heading can be reduced. The system block diagram of path following is depicted as Figure 3.  
As shown in Figure 3, 3 DOFALOS guidance is the basis of the path following system to determine the desired heading of AUV. Four inputs need to be known, including forward speed, cross-track error, curvature of path and slide angle. The speed controller based on proportion integration differentiation (PID) requires two inputs, including mission speed and current forward speed, and has an output propeller speed. The aim is to make forward speed achieve the desired mission speed by adjusting the propeller speed of AUV. Similarly, the heading controller based on FOPID requires two inputs, including desired heading and current heading, and has an output rudder angle. The purpose is to make the heading error between desired heading and current heading tend to zero.
# Flexible UHF RFID Tag for Blood Tubes Monitoring
## 4 Fabrication and Experimental Characterization of the Proposed Tag Antenna
### Realized Gain  
A useful parameter to assess the performance of the tag is the realized gain (_Gr,tag_ = _Gtag_ \(\times\)\(\tau\)), which is defined as the tag antenna gain (_Gtag_) times the power transmission coefficient (\(\tau\)). \(\tau\) is given by \(\tau=(1-|\Gamma|^{2})\), where \(\Gamma\) is defined in (1). Figure 9 depicts a good agreement between the simulated and measured values of this parameter for the proposed tag attached to full and empty blood tubes. At the resonant frequency \(f0=867\) MHz, the realized gain of the tag is of -13.5 dBi for measurements (-12.3 dBi for simulations). In this case, it is useful to compare this value (-13.5 dBi) with data reported in [23] for a tag that operates in water, which is -9.8 dBi. Our tag exhibits quite good matching to the IC-chip as reported in Figure 5. Therefore, the lower realized gain as compared to [23], is essentially due to a higher power absorption of the blood, and a higher directivity of the tag presented in [23] due to its larger dimensions. However, when the tag is attached to an empty tube, the realized gain drops to -24.7 dBi (-23 dBi for simulations) at the same frequency of 867 MHz. In absence of blood, the transmission coefficient and the gain of the tag decrease drastically.  
Figure 8: Simulated and measured impedance of the proposed tag antenna. (**a**) Real part of the impedance; (**b**) imaginary part of the impedance. Green dashed line denotes the impedance of the chip (NXP UCODE G2 XL).
## 3 Results
### Packet Loss Rate of Multi-Node End-to-End Transmission  
Figure 9 illustrates the simulation results of the packet loss rate for multi-node end-to-end transmission under different loads. As shown in the figure, when the network load is \(5.66\times 10^{9}\) b/s, the packet loss rate before optimization is 1.1%, while the packet loss rate after optimization is only 0.05%. Thus, the optimization algorithm proposed in this paper can reduce the packet loss rate during end-to-end transmission.
## 2 OTFS Modulation  
In this section, the employed models for the OTFS signal, the propagation channel, and receiver-side processing are presented.
## 4 Dependence of the FPC Antenna Position on the PCB  
Once the location is fixed on the PCB for the embedded antenna booster, no mounting instabilities impact the total efficiency performance since the antenna booster is an SMD component that can be automatically embedded on a PCB following a pick-and-place assembly process. This process can get accuracies down to 0.01 mm [13]. However, the FPC antenna does not fit in a pick and place assembly procedure but on a human-dependent peel and stick process. In this regard, the total efficiency of the FPC antenna has been compared in three different positions: top, middle, and bottom (Figure 4). For illustrative purposes, the sticker above 15 mm of the 130 mm \(\times\) 60 mm PCB is discussed (Figure 14). As clearly drawn from the results, the total efficiency is very dependent on the mounting process, which is not convenient since performance is not repetitive when building hundreds of thousands of IoT devices. For example, a variation in average of 4.2 dB in total efficiency is observed (Table 6). Similar trends are found for the small and large PCBs.  
Figure 13: Measured total efficiencies for the antenna booster and the FPC. The best efficiency among the three positions (top, middle, and bottom) is shown for the FPC antenna at a given height.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Average Total Efficiency (\%)** & **698–960 MHz** & **1710–2690 MHz** \\ \hline FPC antenna h = 2.4 mm & 0.5 & 18.4 \\ FPC antenna h = 5 mm & 2.6 & 25.2 \\ FPC antenna h = 10 mm & 4.0 & 35.6 \\ FPC antenna h = 15 mm & 4.0 & 45.6 \\ FPC antenna h = 20 mm & 7.0 & 40.8 \\ Antenna Booster & 17.1 & 56.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measured total efficiency for the FPC antenna and antenna booster.  
At the frequency of 698 MHz of the low-frequency region, total efficiency can change from 6.4% at the middle of the PCB down to 0.6% at the top edge, representing a considerable variation of about 10 dB. At 2690 MHz, the performance also changes about 3 dB depending on the position of the FPC antenna.
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 2 Advantages of Enzymes Immobilization  
Immobilization positively affects lipase stability at high temperatures [21; 22]. Immobilized lipase exhibits higher activity compared with free lipase [23; 24; 25; 26]. A study showed that the immobilized thermophilic _Bacillus_ sp. lipase still retained 31.81% of the activity but deactivated free lipase activity [27]. The significant shift in the optimum reaction temperature occurs in free lipase from 37 to 50 \({}^{\circ}\)C for immobilized lipase [28]. The high temperature has less influence on the lipase activity, but it increases thermal stability [29]. The optimum temperature of immobilized lipase from _Candida rugosa_ (ICRL) is from 20 to 50 \({}^{\circ}\)C, while that of free _Candida rugosa_ lipase is only from 40 to 50 \({}^{\circ}\)C [24]. The enzymatic activity of ICRL has a broader range of temperature, because yolk shells carriers help retain the lipase conformation in its active form [24; 30]. The electrostatic charges changed after immobilization, which shifts the optimum pH of the immobilized enzyme to a slightly alkaline region [29]. The number of acidic groups increases after immobilization, which makes the enzyme gain more polyanionic character [13]. In alkaline environments, immobilized lipase has higher activity than free lipase [24; 30].  
Moreover, immobilized lipase has better storage stability than free lipase based on previous studies [31; 32]. In general, immobilized lipase displays full activity over a long period, indicating excellent stability of biocatalysts [25; 26]. Immobilized enzymes are very stable, with a half-life of more than 1800 h under plant-operating conditions [33]. Immobilization enhances the enzyme properties by increasing the enzyme rigidity and heat tolerance, which is caused by low conformational flexibility of the enzyme and is generally indicated by an increase in the optimum temperature and stability against inactivation [13; 29]. The improved thermal behavior of immobilized enzymes forms the basis of enzyme applications for many industrial processes requiring high temperatures [27; 33].  
In addition, the main factors important for industrial applications are the storage and operational stabilities of immobilized lipase [34]. A study showed the relative activity of ICRL maintained over 90.85% of the initial activity while that of free lipase was only 40% of the initial activity after 28 days of storage [24]. The storage stability of _Candida rugose_ lipase was improved significantly, showing that the stable configuration of immobilized lipase was maintained after one month [24; 34]. The _Candida rugose_ lipase immobilization on yolk shells reduced its freedom to undergo changes in the stable conformation, which stabilized it against denaturing for an extended period compared to that of free lipase [35].
# A Community-Based Construct Method for an Inter-Satellite Communication Network of Satellite Swarm
## 2 Complex Network Models of the Satellite Swarm
### Pre-Link Network  
If there is no ISL to establish in a satellite swarm, the ICNS will not be created. Under the circumstances, the satellite swarm can be modeled as a pre-link network (PLN). The PLN is an imaginary network that plays a vital role in constructing an ICNS. The PLN modeled to construct \(G_{k}\) is denoted by \(\widetilde{G}_{k}=(V,\widetilde{E}_{k})\), where \(V\) is the node set and \(\widetilde{E}_{k}=\left\{\widetilde{e}_{ij}|v_{i},v_{j}\in V,i<j\right\}\) is the edge set. The edge \(\widetilde{e}_{ij}\) means the couple of \(v_{i}\) and \(v_{j}\) between which an ISL can be established during slot \(k\) rather than a real ISL. In additionrather than a real ISL. In addition, there is \(|\widetilde{E}_{k}|\leq\binom{\Theta}{2}\).  
Although the satellite swarm actually creates an ICNS by establishing some ISLs, we can give a new explanation for the construction of an ICNS, which is based on the theory of complex networks: we first tease out all satellite pairs that meet the condition for establishing ISLs and create a PLN through them; then, we convert the PLN, which has more edges, into an ICNS, which has fewer edges, by selecting some particular edges.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 2 Theoretical Background
### Fog Attenuation Models in FSO  
Fog is one of the most detrimental weather conditions for FSO links due to the high density of water droplets, which cause both scattering and absorption. While Mie theory provides a rigorous approach for modeling such effects, it requires detailed input data--such as the particle size distribution and refractive index--that are typically unavailable or impractical to obtain in real-time applications [6; 7; 17]. As a result, various empirical models based on visibility have been developed.  
The Kruse model estimates specific attenuation \(\gamma\) as a function of wavelength \(\lambda\) and visibility V (in km) [11]:  
\[\gamma(\lambda)=\frac{3.912}{\text{V}}\left(\frac{\lambda}{550}\right)^{-\text {q}} \tag{4}\]  
where \(\lambda\) is in nanometers and the exponent q depends on the visibility range  
\[\text{q}=\left\{\begin{array}{ccc}1.6\,,&V>50\\ 1.3\,,&6<V<50\\ 0.585\text{V}^{\frac{1}{3}}\,,&V<6\end{array}\right. \tag{5}\]  
The Kim model refines the Kruse formulation by introducing improved empirical values for q, particularly in dense fog conditions [12]:  
\[\text{q}=\left\{\begin{array}{ccc}1.6\,,&V>50\\ 1.3\,,&6<V<50\\ 1.6V+0.34\,,&1<V<6\\ V-0.5\,,&0.5<V<1\\ 0\,,&V<0.5\end{array}\right. \tag{6}\]  
This model is considered more accurate in low-visibility regimes.  
[PERSON] Model  
[PERSON] et al. proposed models that differentiate between advection fog and radiation fog, based on empirical measurements in varying climatic conditions [13]:  
\[\gamma_{\text{advection}}=\text{a}_{\text{adv}}\cdot\lambda^{-\text{b}_{ \text{adv}}} \tag{7}\]\[\gamma_{\text{radiation}}\;=\;a_{\text{rad}}\cdot\lambda^{-b_{\text{rad}}} \tag{8}\]  
where a and b are empirically derived coefficients reflecting fog type and regional characteristics. These models provide improved accuracy in practical deployment scenarios by accounting for specific fog morphologies.
## 3 Numerical Methodology and Procedure
### User-Defined Function and Solver Setting  
For this study, a user-defined function, developed in a parallelized format, is utilized to depict the movement of the fluid dynamics grid. The program describes how the rigid body piston is moved by the pressure difference across the two surfaces and specifies the parameters of the piston, including its mass, gravitational acceleration, maximum stroke length, final velocity rebound ratio, initial displacement, velocity, and acceleration. In the simulation, the \"DEFINE_CG_MOTION\" macro command is employed to define the distinctive movement trajectory of the piston's center of gravity.  
In the numerical experiment conducted, a solver grounded in the pressure-based methodology was employed. This solver incorporates the coupled pressure-velocity coupling technique as its fundamental discrete control equation for the effective management of flow dynamics. The convergence criterion is based on the observation of the change in the residual, and usually the reduction in the residual to three orders of magnitude of the original value can be considered to have converged. We tighten the energy parameters to a threshold of \(10^{-6}\), while all other criteria are maintained at a threshold of \(10^{-3}\).  
The transient calculation is initialized with the results from the steady-state computation, and the transient time step is typically set within a range of 0.001 s. However, due to the high impact frequency of the RC-DTH hammer, the flow channel state within the cylinder undergoes six changes per cycle. This complexity significantly increases the challenge of achieving convergence, necessitating a reduction in the time step to 0.0001. Because of the short closing stage of the inlet when the piston is moving, the calculation of the pressure continuity equation cannot converge. To ensure that the fluid domain computation converges, we specify the maximum number of iteration steps, set to 2000, to ensure that the computation is pushed toward convergence when the normal criteria for convergence are not met. Within the Fluent, the pressure contour plot of the primary section is saved every eighth time step to track the piston's movement throughout its cycle, as depicted in Figure 7.  
Figure 6: Grid independence verification.
## 4 Numerical Simulation  
Upon Equations (22)-(24), the performances of the HEH depend on the magnetic force, mass, stiffness, and mechanical dampings of the system. In this section, we will discuss the effects of theabove parameters on the harvesting performances of the HEH. The theoretical model is numerically simulated by using the ordinary differential equation solver \"ode45\" in MATLAB(r) (MathWorks, Natick, MA, USA). The piezoelectric material used is PZT-5H [43]. The geometric and physical properties of the proposed HEH are given in Table 1. Note that magnet A stands for the proof magnet, while magnet B stands for the magnetic oscillator. In the following parts, the electromechanical coupling coefficients \(\theta_{\mathrm{p}}\) and \(\theta_{\mathrm{em}}\) are determined based on these properties. The reference resistances connected to the HEH are assumed to be \(R_{1}=1/C_{\mathrm{p}}\omega_{1}\), \(R_{2}=Rc=240\)\(\Omega\). The excitation acceleration is set 2 m/s\({}^{2}\) and the initial separation distance \(d_{0}\) is 52.5 mm. The equilibrium position of the center of magnetic oscillator is set on the upper surface of the coil, _i.e._, \(z_{2}=h_{\mathrm{c}}\). The mechanical damping ratio of each oscillator is set 0.02.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 3 Proposed Methodology
### Optical Flow Convolutional Networks  
Although for certain activities, a single frame can be enough for recognizing object-oriented activities, however, time-oriented activities such as running, jogging, etc., need special input. Here, we describe the input to the ConvNet model for the temporal stream.  
Stacked Optical Flow  
Dense optical flow refers to a collection of displacement vectors, denoted as \(d_{t}\), which represent the motion between pairs of consecutive frames in a sequence, where \(d\) is the displacement vector that represents the movement direction between two consecutive frames in frame \(t\), as in Figure 4a,b. The displacement vector is a point \((u,v)\), at locations \(u\) and \(v\) in a frame \(t\), which moves to the corresponding point in frame \(t+1\), thus, can be denoted by \(d_{t}(u,v)\) for the \(t^{th}\) frame. The horizontal component \(d_{t}^{v}\) and vertical component \(d_{t}^{v}\) of the displacement vector and their optical flow representation as an image can be seen in Figure 4.  
In the corresponding image, only the outlined region is being moved during two consecutive frames (an arm of the person). Figure 4a,b show the movement detection  
Figure 3: Convolutional model of our proposed architecture.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Model** & **Layers** & **Parameters** \\ \hline InceptionV3 & 159 & 23,851,784 \\ VGG-16 & 23 & 138,357,544 \\ Xception & 126 & 22,910,480 \\ MobileNet & 88 & 4,253,864 \\ MobileNetV2 & 88 & 3,538,984 \\ NASNetMobile & \(-\) & 5,326,716 \\ DenseNet121 & 121 & 8,062,504 \\ DenseNet169 & 169 & 14,307,880 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of models pre-trained on ImageNet.  
of the arm. Now, Figure 4c of the image shows the \(x\) component of the vector \(d\) and Figure 4d shows the \(y\) component of the vector \(d\). Both the \(x\) and \(y\) components in the images are represented by white and black colors in the respective images. Motion across the consecutive frames is represented by stacking the vertical and horizontal components' displacement vectors of \(L\) continuous frames to form a total of \(2L\) input channels. A ConvNet input volume \(I_{\mathrm{r}}\in\mathbb{R}^{w\times h\times 2L}\) for a video frame of width \(w\) and height \(h\), for temporal stream with an arbitrary input \(\tau\), can be denoted as in Equations (1) and (2).  
\[I_{\mathrm{r}}(u,v,2k-1) = d_{\tau+k-1}^{x}(u,v) \tag{1}\] \[I_{\mathrm{r}}(u,v,2k-1) = d_{\tau+k-1}^{y}(u,v)\] (2) \[u = [1;w],\] \[v = [1;h],\] \[k = [1:L].\]
## 5 Reconstruction Methods
### AE-Based Models  
As an innovative application in the field of unsupervised learning, models based on autoencoders (AE) [68] have made significant progress in the area of AD. DAGMM [37] is the pioneering model that integrates deep AE with GMM [65] for unsupervised anomaly detection. Unlike traditional techniques that perform density estimation after dimensionality reduction, DAGMM optimizes both processes simultaneously, ensuring that key information for anomaly detection is preserved. However, DAGMM relies on deep autoencoders, which face challenges in escaping local optima and require substantial computational resources. To address this issue, USAD [39] was proposed, utilizing adversarial training to enhance the stability and speed of autoencoder-based anomaly detection. The architecture of USAD enables it to train quickly and isolate anomalies effectively, making it suitable for large-scale applications. However, USAD may struggle with detecting subtle anomalies because it focuses on reconstruction error. In response to this challenge, [PERSON] et al. [38] introduced MSCRED, which incorporates a multi-scale convolutional recurrent encoder-decoder framework to capture temporal dependencies and inter-sensor correlations within multivariate time series data. Its approach using signature matrices and an attention mechanism allows the model to robustly diagnose anomalies of varying severity levels. However, the complexity of the model may pose challenges when dealing with extremely high-dimensional data or real-time applications. The next method NPSR, proposed by [PERSON] et al. [40], also faces the challenge of handling the complexity of high-dimensional data. NPSR modulates the anomaly detection process by introducing a Nominality Score, balancing the detection of point and contextual anomalies. The NPSR framework includes both point-based and sequence-based reconstruction models, and its performance surpasses existing techniques, offering a theoretically grounded method for anomaly detection. Further optimization is still needed for its application in high-dimensional settings to ensure computational efficiency.