esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 3 Noise Injection in Cryptography  
Noise injection in cryptographic keys refers to the intentional introduction of random changes into the key generation or key handling process. The purpose of injecting noise is to enhance the security and resilience of cryptographic systems against various attacks, including side-channel attacks, differential power analysis, and fault attacks [27,28]. By adding noise to cryptographic keys, the keys become less predictable and exhibit increased entropy, making it more challenging for an attacker to derive the original key. The noise acts as a form of obfuscation that masks the underlying key data, thus increasing the level of difficulty in exploiting vulnerabilities in cryptographic algorithms or implementations.  
There are several techniques for injecting noise into cryptographic keys. One common approach is to use physical properties such as heavy magnetic noise, random electrical noise, a random number generator, quantum effects, or thermal noise (as a source of randomness during key generation [29, 30, 31, 32]). This ensures that the resulting keys have a high degree of entropy and are resistant to deterministic attacks. Another method is to apply random noise injection during key handling or cryptographic operations. For example, in key exchange protocols, random noise can be introduced to obscure the exchanged key material, preventing eavesdroppers from gaining information about the shared secret. Additionally, noise injection is used to protect cryptographic keys stored in hardware devices by introducing difficulty for an attacker attempting to extract or analyze the key through side-channel attacks or invasive probing.  
The effectiveness of noise injection depends on the quality and randomness of the introduced noise. It is important to ensure that noise does not compromise the security of keys or create unintended vulnerabilities. Additionally, noise injection techniques should be carefully designed to prevent reverse engineering or statistical analysis, making sure the noise remains unpredictable and does not expose any sensitive information.
## 4 Conclusions  
Based on the information derived from a study of these lakes, information about the vegetation and the composition of the water and sediments, the following can be concluded:  
1. Only two of the lakes (Louro and Vixan) can be considered Coastal lagoons (habitat 1150). Both Bodeira and Xuno are natural eutrophic lakes (habitats 3150).  
Figure 10: (**a**,**b**) represent the evolution of Xuno coastal lake between 1989 (**a**) and 2017 (**b**–**d**) show the evolution of Bodeira coastal lake between 1989 (**c**) and 2017 (**d**). Data: (IGN, [20]). An important factor to highlight in these images is the increase of alluvial forest in 2017 in relation to 1989, this habitat rises since 0.53 ha of continuous forest to 6.75 ha.  
2. The presence, designation and distribution of the habitats defined by PDRN should be revised in depth. Each lake is per se a functional unit and habitat, formed by different environments such as open waters, channels, salt marshes and reedbeds. Classifying each unit as a different habitat contradicts the philosophy of the Habitats Directive and complicates management of the ecosystems involved.
3. The Vixan, Xuno, and Bodeira lakes are characterized by advanced states of eutrophication and poor water quality. Restoration of hydraulic functionality should be investigated further.
4. The expansion of alluvial forest is an example of the rapid evolution of these systems in relation to halting anthropogenic pressure.
5. The delimitation of lakes should be revised by using technologies such as LiDAR and remote sensing.  
Conceptualization, A.P.-A., X.L.O.; methodology, A.G.-P., X.L.O., A.P.-A. and P.F.-S., formal analysis, A.P.-A., A.G.-P., M.S.-S., P.F.-S. and X.L.O.; writing-original draft preparation, A.P.-A., X.L.O. and A.G.-P.; writing-review and editing, A.P.-A., X.L.O., M.S.-S. and A.G.-P. All authors have read and agreed to the published version of the manuscript.  
This work is part of a research project entitled \"Monitorizacion de los procesos biogequimicos en las lagunas litorales en relacion con su calidad ambiental y respuesta al cambio climatico\" (Xunta de Galicia, PGIDIT08 MDS036000 PR), the program for consolidation of competitive research groups (Axudas a consolidacion e esturatacion de unidades de investigacion competitivas do SUG del Plan Galego IDT, Ambiosol Group ref. 2018-PG036), and Cross-Research in Environmental Technologies, CRETUS (Xunta de Galicia GRUP2015/02, ref. 2018-PG100). [PERSON] is supported by an FPU predoctoral contract by the Spanish government (Ministerio de Educacion, Cultura y Deporte). Grant Number: FPU16/03050.  
This work was supported by CRETUS Institute. [PERSON] was in receipt of an FPU predoctoral contract with reference FPU16/03050. We would like to thank [PERSON] for her assistance with laboratory work and [PERSON] for providing language support.  
Authors declare no conflict of interest.
# VDGA-Based Resistorless Mixed-Mode Universal Filter and Dual-Mode Quadrature Oscillator
## 3 Proposed Resistorless Mixed-Mode Universal Filter
### VM Filter  
If \(i_{in}=0\), namely open-circuited, the electronically tunable SITO universal filter in VM can be obtained with the following voltage transfer functions:  
\[T_{VLP}(s)=\frac{v_{o1}}{v_{in}}=T_{LP}(s), \tag{2}\]  
\[T_{VBP}(s)=\frac{v_{o2}}{v_{in}}=-T_{BP}(s), \tag{3}\]  
and  
\[T_{VHP}(s)=\frac{v_{o3}}{v_{in}}=-T_{HP}(s). \tag{4}\]  
In the above relations, the transfer functions \(T_{LP}(s)\), \(T_{BP}(s)\), and \(T_{HP}(s)\) are in the form of, respectively:  
\[T_{LP}(s)=\frac{\Big{(}\frac{g_{mA1}g_{mB1}}{C_{1}<2}\Big{)}}{D(s)}, \tag{5}\]  
\[T_{BP}(s)=\frac{\Big{(}\frac{g_{mA1}}{C_{2}}\Big{)}s}{D(s)}, \tag{6}\]  
and  
\[T_{HP}(s)=\frac{\Big{(}\frac{g_{mA1}}{S_{mA2}}\Big{)}s^{2}}{D(s)}, \tag{7}\]  
Figure 2: Proposed resistorless mixed-mode universal biquad filter.  
where:  
\[D(s)=s^{2}+\left(\frac{g_{mB2}\eta_{1}}{C_{2}}\right)s+\left(\frac{g_{mA1}g_{mB1}} {C_{1}C_{2}}\right). \tag{8}\]  
In the expressions above, the parameters \(g_{mbi}\) and \(\eta_{i}\) (\(i=1\), 2) refer to the gains \(g_{mkl}\) and \(\eta\) of the corresponding VDGA. As indicated by Equations (2)-(4), the output voltage \(v_{01}\) exhibits a noninverting LP response, \(v_{02}\) displays an inverting BP response, and \(v_{03}\) presents an inverting HP response. In addition to Equation (7), the realization of the HP filter requires an equal element condition \(g_{mA2}=g_{mB2}\).
## 2 System Model and Problem Formulation  
### System Model  
As depicted in Figure 1, we consider a UAV-enabled RF-based wireless energy transfer (WET) system where one UAV simultaneously transfers power to \(U\) energy-harvesting devices (EHDs) on the ground, such as IoT devices and sensors. The UAV is equipped with \(N\) transmit antennas, and the EHDs are equipped with a single antenna. The UAV hovers over the central EHDs with an altitude \(h\in[H_{\text{min}},H_{\text{max}}]\) in order to conserve power for flight, where \(H_{\text{min}}\) and \(H_{\text{max}}\) denote the minimum and maximum altitude of the UAV, respectively. In addition, the maximum WET coverage of the UAV on the ground is denoted as \(r_{\text{max}}\). The horizontal distance between the UAV and \(u\)-th EHD, where \(u\in\mathcal{U}\triangleq\{1,...,U\}\), is denoted by \(r_{u}\leq r_{\text{max}}\).  
Let \(\mathbf{w}_{b}\in\mathbb{C}^{N}\) be the \(b\)-th energy beamforming vector transmitted from the UAV to the EHDs where \(b\in\mathcal{B}\triangleq\{1,...,B\}\) and \(B\) (\(\leq\)\(N\)) is the number of energy beams to be determined. Then, the transmitted signal vector of the UAV for the power transfer, denoted by \(\mathbf{x}\in\mathbb{C}^{N}\), is given as  
\[\mathbf{x}=\sum_{b=1}^{B}\mathbf{w}_{b}s_{b} \tag{1}\]  
where \(s_{b}\) is the energy-bearing signal with zero mean and unit variance, i.e., \(\mathbb{E}[|s_{b}|^{2}]=1\), which can have an arbitrary distribution, since \(s_{b}\) does not carry any information [1; 2; 3]. In this case, the transmit covariance matrix at the UAV, denoted by \(\mathbf{X}\in\mathbb{H}^{N}\), becomes  
\[\mathbf{X}=\sum_{b=1}^{B}\mathbf{w}_{b}\mathbf{w}_{b}^{H}\succeq 0 \tag{2}\]  
where \(\mathbb{H}^{N}\) represents the set of \(N\)-by-\(N\) Hermitian matrices. It is assumed that the UAV has a transmit sum power constraint of \(P\); then, the transmit covariance matrix of (2) must satisfy \(\text{Tr}(\mathbf{X})\leq P\).
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 3 Fusion of Improved A* and DWA Algorithm
### Sampling Speed
#### 3.2.3 Environmental Obstacle Limitation  
Because of the need to consider real-time obstacle avoidance, the obstacles around the handling robot should be considered. The constraint condition that it does not collide with the surrounding obstacles is  
\[V_{a}=\left\{(v,\omega)\left|\begin{array}{l}v\in[v_{\min},\sqrt{2\cdot dist (v,\omega)\cdot a_{\max}}]\\ \omega\in[\omega_{\min},\sqrt{2\cdot dist(v,\omega)\cdot a_{\omega\max}}] \end{array}\right.\right\} \tag{15}\]  
\(dist(v,\omega)\) represents the nearest distance between the simulated trajectory and the obstacle at the current speed. In the absence of obstacles, \(dist(v,\omega)\) is a large constant. When the sampling speed of the robot is within its range, it can achieve safe deceleration with the constraint of maximum deceleration until it avoids obstacles.  
The speed combination \(v_{r}\) that the final handling robot can take needs to meet the intersection of the following three constraints.  
\[V_{r}=v_{m}\cap v_{d}\cap v_{a} \tag{16}\]  
After the constraint is determined, the DWA algorithm samples in this space with a certain resolution. \(E_{p}\) and \(E_{\omega}\) are used to represent the sampling resolution of linear velocity and angular velocity, respectively, then \(n\) sets of velocities will be taken in this sampling space.  
\[n=\left[\frac{v_{high}-v_{low}}{E_{v}}\cdot\frac{\omega_{high}-\omega_{low}}{E_{ \omega}}\right] \tag{17}\]  
\(v_{high}\), \(v_{low}\), \(\omega_{high}\), and \(\omega_{low}\) are the upper and lower limits of the velocity space.  
When the Dynamic Window Approach (DWA) samples a series of candidate linear velocities \(v\) and angular velocities \(\omega\) within the dynamic window, the system sequentially invokes the kinematic model of the differential-drive chassis to 'predict' each velocity pair over a short time horizon. First, the model enforces the robot's physical limits by clipping sampled velocities according to its maximum and minimum linear speeds and maximum linear/angular accelerations, ensuring all candidates lie within the chassis' actual performance envelope. Next, using the robot's circumscribed circle as a safety boundary, each predicted trajectory undergoes collision checking, and any trajectory that may intersect with environmental obstacles is automatically discarded. Finally, the remaining feasible trajectories are mapped directly to executable drive-command velocities. Through this process, the kinematic model acts both as a physical-constraint filter--guaranteeing that velocity samples do not exceed hardware limits--and as a safety validator--preserving only collision-free paths--ultimately yielding a set of safe, reliable, and truly executable motion plans.
## 4 Numerical Results
### Pollutant Advection in a Uniform Flow Field  
We next model the pure advection of a pollutant, in which all the diffusion tensor elements are zero (\(K_{xx}=K_{yy}=0\)). Transport occurs longitudinally along a 30 m-long channel with a flat bottom. The flow is taken to be uniform with a velocity \(u=1\) m/s and \(v=0\). The initial condition for the pollutant concentration is taken to be Gaussian, placed at the point (\(x=5\) m, \(y=5\) m),  
\[c(x,y,t=0)=\frac{q_{c}(x,y,t=0)}{h(x,y,t=0)}=c_{0}\exp\left(-\frac{(x-5)^{2}+( y-5)^{2}}{\sigma^{2}}\right) \tag{27}\]  
where we take \(\sigma=1\) and \(c_{0}=1\). Once in the flow, the pollutant moves along the \(x\) axis at a constant speed of \(u=1\) m/s. Figure 4 depicts the plume transport and displays the pollutant concentration at times \(t=6\), 12 and 20 s. It is seen from the figure that the shape of the pollutant is correctly preserved. Furthermore, the correct position of the plume is reflected by an accurate calculation of the propagation speed of the concentration wave. Nevertheless, the calculation of the maximum concentration is affected by numerical diffusion when the number of points used is small. We also provide the maximum and minimum concentration values in the same figure. These values indicate that the concentration is preserved positively, as well as the maximum concentration being accurate.  
In Table 1, we present the errors obtained for the concentration \(c\) at times \(t=6\) s, 12 s, and 20 s for the same point distribution, using the Roe-MUSCL finite volume scheme and the LRBF methods. By examining the values of the \(L^{1}\) and \(RMS\) errors, it is clear that the LRBF method is much more accurate.  
We performed a comparison between our simulation results, those obtained by the Roe-MUSCL finite volume scheme, and the analytical solution. We present in Figure 5 a longitudinal section at \(y=5\) m of the pollutant concentration for the same simulation times as those in Figure 4. This figure shows the results of numerical analysis performed on distributions with 8000 points for the present meshless method; a mesh contained 8000  
Figure 3: Comparisons of water level cross-sections at \(x=5\) m calculated using the Roe-MUSCL finite volumes method and the present LRBF meshless method at times \(t=0.3\) s and \(0.6\) s.  
points and 15,680 volumes. Clearly, the results obtained by our numerical method agree very well with those of the analytical method.
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
## 4 Simulation and Results  
### Simulation Condition  
To analyze the effectiveness of the proposed adiabatic PUF, a 4-bit cascaded PUF was simulated using a 0.18 \(\upmu\)m standard CMOS process provided by ROHM Corporation. Table 1 summarizes the MOS device parameters and simulation conditions.  
Figure 6: Proposed 4-bits PUF architecture.  
Figure 7: Input and output signals of the proposed adiabatic PUF.  
The ability of PUF to uniquely distinguish a chip among the group of other chips was demonstrated by conducting 100-runs of the Monte Carlo simulation to emulate the behavior of 100 IC PUF chips. This simulation aimed to analyze the effect of process variation, such as VTH, TOX, and ambient temperature variations. For VTH and TOX, a tolerance variation of 10% was set in the Monte Carlo simulation to evaluate the uniqueness and reliability of the proposed adiabatic PUF cell. The temperature variation was set at \(-40\), 0, 27, 50, and 100 \({}^{\circ}\)C.
# Spatio-Temporal Changes in Water Use Efficiency and Its Driving Factors in Central Asia (2001-2021)
## 3 Results  
### Spatial and Temporal Variation of WUE  
The yearly spatial and temporal distribution of the annual average WUE in Central Asia in 2001-2021 is shown in Figure 3. The average WUE value during 2001-2021 is 3.011 gCkg\({}^{-1}\)H\({}_{2}\)O, with an overall trend of WUE decreasing in Central Asia by a small amount per year. The spatial heterogeneity of WUE is evident in the spatial variability, with high WUE values concentrated in the Aral Sea periphery to the Lake Balkhash Basin in southern Kazakhstan, northeastern Uzbekistan to the Syr Darya Basin, the southern edge of Turkmenistan, the southern edge of the Tian Shan Mountains, and the northern edge of the Tian Shan Mountains. The medium-WUE areas are concentrated in the Ural, Tobol, Ishim, and Erzis river basins in northern Kazakhstan. The most pronounced spatial and temporal variation is found in the high-WUE areas, indicating that the arid ecology of Central Asia has a strong and persistent influence on the high-WUE areas, and that the adaptation of the region to arid ecology has led to consistently high WUE values.  
Figure 3: Spatial and temporal variation of annual average WUE per year in Central Asia, 2001–2021.
## 3 Results and Discussion
### L. monocytogenes Behavior in DCH during Storage after HPP: Growth/No Growth
#### 3.2.2 L. monocytogenes  
Growth during the Storage of DCH  
In agreement with the observed results, _L. monocytogenes_ growth was predicted to occur (\(\Gamma>0\)) in products with \(a_{w}\geq 0.96\) (trials 56-64; Table 2). _L. monocytogenes_ was able to reach the maximum bacterial-population density (_ca._ 8 log cfu/g) even for DCH pressurized at 750 MPa (Figure 5).  
The growth kinetics were dependent on pressure level and DCH's \(a_{w}\) (which in turn determined the pathogen concentration at the beginning of the storage) (trials 56-64; Table 2). In this regard, the application of the lowest pressure level (300 MPa) caused little lethality. As a consequence, the high initial concentration (6.8-7.0 log cfu/g) of _L. monocytogenes_ after HPP did not allow for proper estimation of the growth rate (trials 56, 61; Table 2; Figure 5a). The results show that DCH's \(a_{w}\) affected the lag time (\(\lambda\)) of _L. monocytogenes_ in DCH pressurized at 300-450 MPa. The pathogen needed a \(\lambda\) of ca. 6 days before starting to grow in DCH with an \(a_{w}\) of 0.96 (trials 56-57; Table 2) but no \(\lambda\) was observed in DCH with an \(a_{w}\) of 0.98 (trials 61-62; Table 2). _L. monocytogenes_ seemed to start growing immediately after the HPP at 600-750 MPa. The estimated growth rate was similar for DCH with an \(a_{w}\) of 0.96 and 0.98 (trials 58-60, 63-64; Figure 5c,d). Nevertheless, it has to be considered that at pressure levels of 600-750 MPa, variability in the _L. monocytogenes_ concentration was observed between sample replicates along the storage time (i.e., from  
Figure 5: Growth of _L. monocytogenes_ during storage at 7 \({}^{\circ}\)C of pressurized dry-cured ham (DCH) with an \(a_{w}\geq 0.96\) pressurized at **(a)** 300 MPa (trials 56 and 61), **(b)** 450 MPa (trials 57 and 62), **(c)** 600 MPa (trials 58 and 63), and **(d)** 750 MPa (trials 59 and 64). Symbols correspond to log change values and lines to the fit of the Logistic growth model (Equation (3)) to data. Empty symbols correspond to replicates where the pathogen was not quantified but was detected after sample enrichment.  
7 log cfu/g to not detected), making the growth-kinetic-parameter estimates less accurate (Table 2; Figure 5c,d). Some works have associated this variability in the _L. monocytogenes_ counts after HPP to the different injury degrees and/or different recovery capacities of the _L. monocytogenes_ cells [27].
## 5 Application of Our Proposed Approach
### Case Study-1: Demand Data for Transformer Tanks
#### 5.1.1 Determination of the Smoothing Constants  
MATLAB coding is used to simulate and determine the best trio of smoothing constants. As a primary objective of the simulation, the best combination of trio was selected against the lower _MAD_ values of the forecast. Optimized values of the trio (\(\alpha\), \(\beta\) and \(\gamma\)) are secured by converging the _MAD_ to its lowest possible value. The converging behavior of _MAD_ is demonstrated in Figure 4a-c, respectively, for the optimum values of \(\alpha\), \(\beta\) and \(\gamma\).  
Figure 3: Four-year demand data for the 750 KV transformer tank.  
Figure 4a presents the results of the simulation in which the _MAD_ values are changed in a line with the variation of \(\beta\) and \(\gamma\) while \(\alpha\) remains fixed at its optimal value (\(\alpha=1\)). Figure 4b illustrates the effect of \(\alpha\) and \(\gamma\) on _MAD_ values with a fixed value of the variable \(\beta\) (\(\beta=0\)), whereas Figure 4c shows the converging behavior of _MAD_ with respect to \(\alpha\) and \(\beta\) for a constant value of 0.2 for \(\gamma\). It can also be seen that in all of these three exhibits, the _MAD_ is converging to its unique minimal value of 0.89. Hence, the optimum values of the trio (\(\alpha\), \(\beta\) and \(\gamma\)) as obtained through simulation are 1.0, 0.0 and 0.2, respectively.
# Improving the Efficiency of Modern Warehouses Using Smart Battery Placement
## 3 The Proposed System
### Calculating Sub-Areas
```  
**Algorithm 1**Affinity propagation algorithm for sub-area assignment. Following the outlined AP algorithm, it is notable that its standard configuration is tailored to cluster cells (or data points) based on similarity. Thus, cells with akin traffic patterns will typically coalesce. While this trait suits numerous applications, it is not quite fitting for our specific challenge. For the context of our warehouse optimization, the goal is a more nuanced clustering: denser traffic areas should be condensed into smaller clusters, while lighter traffic zones should sprawl over more expansive clusters. This design choice  
Figure 2: (**a**) Visualizes the traffic of each cell within the warehouse. Darker shades of blue indicate higher traffic. Red cells indicate the exits of the warehouse, and black cells indicate the shelves. In (**b**), we can visualize the generated sub-areas using the proposed AP-based algorithm. Each color besides red (which is used to denote the exits) and black (which is used to denote the shelves) denotes a separate sub-area (cluster). Areas with more traffic are more likely to yield smaller clusters. maximizes battery utility and optimizes the likelihood of a UGV securing an available battery during its operation. Equation (1) shows how the self-similarity \(S[i,i]\) is calculated. Cells with more traffic are rewarded, while cells with less traffic (weight matrix \(W\)) are punished and are less likely to become exemplars. The constant \(c\) is a small positive constant that prevents the equation from becoming undefined, controls the influence of cells with low traffic values, and introduces a desired level of granularity in the clustering process. \[S[i,i]=-log(c+W[i]) \tag{1}\]  
Central to this adjusted clustering behavior is the recalibration of the self-similarity values within the similarity matrix \(S\). Notably, diagonal values within \(S\), which represent self-similarities, pivotally determine the cluster count. Rather than defaulting to the median of each row for these values, we adjusted them to the negative logarithmic value of the corresponding traffic value from the matrix \(W\). This adjustment introduces a distinct behavior: cells in high-traffic zones, with their elevated values, are predisposed to become exemplars, fostering a multiplication of clusters. In contrast, those in quieter zones are less apt to become standalone exemplars and would typically be subsumed into larger clusters. This nuanced approach achieves our objective: high-traffic areas see more clusters and low-traffic areas span larger ones. A visual representation of this can be seen in Figure 2, which exhibits a warehouse environment segmented into its respective sub-areas.