esa-sceva/satcom-chunk-collection huggingface dataset:
# Compact SOI Dual-Mode (De)multiplexer Based on the Level Set Method
## 4 Discussion  
Table 1 shows the simulation and experiment results of various reported mode (de)MUX designed by inverse design methods. Our designed device exhibited a remarkable performance with a low IL and CT. Compared to the simulation results of other works, our work demonstrated the lowest insertion loss below 0.35 dB, which validated the advancement of our work. The outstanding performance of our reported device can be explained below. In cases where devices contain more holes and cracks, light tends to scatter and dissipate as it passes through the interfaces, and this could introduce higher scatters and dissipations, resulting in a higher IL. Compared with the optimized structures generated by other DBSs and the density method, our device is simply connected, containing no holes and cracks inside. Therefore, a single-connected structure offers the advantage of minimizing IL.  
Moreover, some of the topologically optimized structures that performed well in the simulation are difficult to manufacture by nanofabrication standards with a restriction of minimum feature size [31]. In contrast, the fabrication of single-connected structures only  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Nodes** & **Simulation IL** & **Experiment IL** & **Experiment CT** & **Ref.** \\ \hline \multirow{3}{*}{Direct binary search} & TE\({}_{0}\); TE\({}_{1}\) & \(<\)0.47 (1530–1590 nm) & \(<\)1.0 (1530–1590 nm) & \(<\)\(-\)24 & [18] \\  & TE\({}_{0}\); TE\({}_{1}\) & \(<\)1.53 (150 nm in C-band) & \(<\)3.0 (138 nm in C-band) & \(<\)\(-\)18.6 & [19] \\  & TE\({}_{0}\); TE\({}_{1}\) & \(<\)0.83 (1500–1630 nm) & \(<\)\(-\)1.57 (1525–1565 nm) & \(<\)\(-\)10.91 & [20] \\ \hline \multirow{3}{*}{Density topology optimization} & TE\({}_{0}\); TE\({}_{1}\) & \(<\)1.5 (1530–1600 nm) & \(<\)\(-\)25 & [22] \\  & TE\({}_{0}\); TE\({}_{1}\); TE\({}_{2}\) & \(<\)1.2 (1520–1620 nm) & \(<\)3.0 (1520–1620 nm) & \(<\)\(-\)12 & [23] \\  & TE\({}_{0}\); TE\({}_{1}\) & \(<\)2.6 (1520–1580 nm) & - & - & [24] \\  & TE\({}_{0}\); TE\({}_{1}\) & \(<\)0.63 (the whole O-band) & - & - & [27] \\ \hline \multirow{3}{*}{Bayesian DBS} & TE\({}_{0}\); TE\({}_{1}\) & \(<\)0.9 (1270–1330 nm) & \(<\)4.2 (1270–1330 nm) & \(<\)\(-\)22 & [21] \\  & & \(<\)1.1 (1530–1570 nm) & \(<\)3.4 (1530–1570 nm) & \(<\)\(-\)13 & [21] \\ \hline Gradient-probability-driven search algorithm & TE\({}_{0}\); TE\({}_{1}\) & \(<\)1.0 (1525–1610 nm) & - & - & [25] \\ \hline Digitized adjoint method & TE\({}_{0}\); TE\({}_{1}\) & Avg. 0.68 (1530–1570 nm) & \(<\)1.36 (1530–1570 nm) & \(<\)\(-\)20 & [26] \\ \hline Adjoint and level set method & TE\({}_{0}\); TE\({}_{1}\) & \(<\)0.35 (1500–1600 nm) & \(<\)0.89 (1530–1570 nm) & \(<\)\(-\)24 & This work \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the performances of (de)MUX, where the results were obtained from the simulation and the experiment.  
requires attention to the contour edges, making the experimental results more consistent with the simulation.
# Simulating Resource Management across the Cloud-to-Thing Continuum: A Survey and Future Directions
## 2 Related Works  
### The Cloud-to-Thing Continuum  
Traditional cloud computing aims to provide a virtually unlimited pool of resources available for an on-demand provisioning. Consequently, by design, cloud data centers are hyper scale systems characterised by hardware homogeneity, a common management layer, single organisation control, and a strong focus of cost efficiency [16]. They largely assume centralised compute and storage and are often located outside of major urban areas due to the cost and size of these operations.  
The IoT revolves around a number of key concepts and enabling technologies including object (thing) identification, information sensing, communications technologies for data exchange, and network integration technologies [17]. Due to the scale, heterogeneity, distributed geographies, mobility, and variation in use and context inherent in IoT, traditional cloud computing and telecommunication networks can struggle to meet QoS requirements. As a result, new paradigms in computing have emerged to locate processing and storage along a C2T continuum-in the cloud, locally (at the edge), or somewhere in between (the fog). For the purposes of this study, the following NIST definitions will be used:  
* _Fog Computing_ is a layered model for enabling ubiquitous access to a shared continuum of scalable computing resources. The model facilitates the deployment of distributed, latency-aware applications and services, and consists of fog nodes (physical or virtual), residing between smart end-devices and centralised (cloud) services [3].
* _Edge Computing_ is the network layer encompassing the end devices and their users, to provide, for example, local computing capability on a sensor, metering or some other devices that are network-accessible [3].  
Edge devices and fog nodes often have limited capabilities due to form and limited power. Their geographic position may move regularly and unexpectedly, e.g., smart vehicles, and they may have intermittent connectivity to the Internet or other latency issues. Resource management in fog and edge computing environments directly impacts both quality of service (QoS) and quality of experience (QoE). Note that QoS is network-centric, whereas QoE is user-centric [18]. In addition, poor resource management may result in degradation of the useful working life of the end point device.
## 3 Results and Discussions
A hexagonal grid was chosen, because it can be better exploited for spatial surveys, simulations, and continuous process monitoring [38], since it will be used to apply more complex spatial analyses in future investigations of this research (Part II). Indeed, a hexagonal grid has proved to be more suitable compared to other shapes, as reported in several kinds of geospatial research [48; 49; 50; 51]. In this case, an arbitrarily constructed grid was used on the basis of the maximum size of APS found. For a planning purpose, a local authority can define a grid on the basis of subjective evaluations and use it for all monitoring and planning phases so as to have a univocal spatial reference in time. In addition, the realization of a density grid in vector format is more useful, because it can be easily associated with a complex geodatabase and then integrated with other types of information in a more immediate and practical way than raster data [52]. The work carried out has proved to be fundamental for the automated mapping of surfaces covered by plastics. It could be extended to much larger study areas, where the results can provide even more application insights. Being a preliminary work, in the future the same techniques can be used to perform a differentiation of the different types of plastics used by simplifying the semi-automatic satellite image classification techniquesalready in use and to detect plastic used in other seasons as well. In addition, the same methodologies can be applied to high-spatial-resolution satellite imagery and spectra that would provide higher accuracy. Furthermore, by exploiting advanced spatial analysis tools from open-source GISs, it is possible to start from plastics distribution data to identify potential locations of disposal centers, through the analysis of spatial connection networks.
# MapReduce Algorithm for Location Recommendation by Using Area Skyline Query
## 2 Related Works  
### Skyline Query  
Studies on skyline computation have a long history. [PERSON] et al. [1] first introduced skyline queries in large database applications and proposed Block Nested-Loop (BNL), Divide-and-Conquer (D&C) and B-tree based algorithms. [PERSON] et al. [2] proposed an efficient skyline computation algorithm, Sort-Filter-Skyline (SFS), which improved BNL by presorting. [PERSON] et al. [3] proposed two progressive algorithms, called _Bitmap_ and _Index_, to improve the computation of skyline queries. Currently, Brach and Bound Skyline (BBS) has been the most effective algorithm, proposed by [PERSON] et al. [4], which is a progressive algorithm based on the Best First Nearest Neighbor (BFNN) algorithm. Due to the increase of the data dimensionality, many research are proposed to address the dimensionality problem of skyline queries such as skyline frequency [6], \(k\)-dominant skyline [7] and \(k\)-representative skylines [8]. These research works focused on the non-spatial database. However, spatial relationships between data points are playing an important role in our real life.
# Barrel Shifter Physical Unclonable Function Based Encryption
## 3 Block Encryption Protocol
### Asymmetric Encryption
Note that, in the following protocol, we do not specify which \(m\)th bit of the delay is used for entanglement. We will decide that later based on experimental entropy and reproducibility of the delay bits. Consider \(PUF_{1}\) with \(\pi_{key_{1}}=(0\mapsto 1,1\mapsto 2,2\mapsto 3,3\mapsto 0)\) for a 4-bit input \(x_{0},x_{1},x_{2},x_{3}\) and \(PUF_{2}\) with \(\pi_{key_{2}}=(0\mapsto 2,1\mapsto 3,2\mapsto 0,3\mapsto 1)\). Composition of \(f_{PUF_{1}}\circ f_{PUF_{2}}=(0\mapsto 1,1\mapsto 2,2\mapsto 3,3\mapsto 0) \circ(0\mapsto 2,1\mapsto 3,2\mapsto 0,3\mapsto 1)=(0\mapsto 3,1\mapsto 0,2 \mapsto 1,3\mapsto 2)\). By going over the communication protocol in Figure 1 step by step, a defect becomes apparent. The complete verification process is shown in Figure 4.  
* **Step 1:** Apply \(f_{PUF_{1}}\) to \((x_{0},x_{1},x_{2},x_{3})\) resulting in \((1,2,3,0)(x_{0},x_{1},x_{2},x_{3})\), which equals \((x_{3}\oplus D(3,0)_{m},x_{0}\oplus D(0,1)_{m},x_{1}\oplus D(1,2)_{m},x_{2} \oplus D(2,3)_{m})\). * **Step 3:** Apply \(f_{PUF_{2}}\) to \(f_{PUF_{1}}\)'s output as in \((2,3,0,1)(1,2,3,0)(x_{0},x_{1},x_{2},x_{3})\). This equals \((x_{1}\oplus D(1,2)_{m}\oplus d(2,0)_{m},x_{2}\oplus D(2,3)_{m}\oplus d(3,1)_ {m},x_{3}\oplus D(3,0)_{m}\oplus d(0,2)_{m},x_{0}\oplus D(0,1)_{m}\oplus d(1, 3)_{m})\). * **Step 5:** Now invert the output. Apply \(f_{PUF_{1}}^{-1}\) to \((2,3,0,1)(1,2,3,0)(x_{0},x_{1},x_{2},x_{3})\). \(f_{PUF_{1}}^{-1}\) results in \((1,2,3,0)^{-1}(2,3,0,1)(1,2,3,0)(x_{0},x_{1},x_{2},x_{3})\), which equals \((x_{2}\oplus D(2,3)_{m}\oplus d(3,1)_{m}\oplus D^{{}^{\prime}}(0,1)_{m},x_{3} \oplus D(3,0)_{m}\oplus d(0,2)_{m}\oplus D^{{}^{\prime}}(1,2)_{m},x_{0} \oplus D(0,1)_{m}\oplus d(1,3)_{m}\oplus D^{{}^{\prime}}(2,3)_{m},x_{1} \oplus D(1,2)_{m}\oplus d(2,0)_{m}\oplus D^{{}^{\prime}}(3,0)_{m})\). \(D^{{}^{\prime}}(i,i^{{}^{\prime}})\) denotes the backward path delay from output \(i^{{}^{\prime}}\) to input \(i\). According to post-layout simulations, \(D^{{}^{\prime}}(i,i^{{}^{\prime}})\) is always equal to \(D(i,i^{{}^{\prime}})\) in BS-PUFs. * **Step 7:** Further applying \(f_{PUF_{2}}^{-1}\) as in \((2,3,0,1)^{-1}(1,2,3,0)^{-1}(2,3,0,1)(1,2,3,0)(x_{0},x_{1},x_{2},x_{3})\) results in \((x_{0}\oplus D(0,1)_{m}\oplus d(1,3)_{m}\oplus D^{{}^{\prime}}(2,3)_{m}\oplus d ^{{}^{\prime}}(0,2)_{m},x_{1}\oplus D(1,2)_{m}\oplus d(2,0)_{m}\oplus D^{{}^{ \prime}}(3,0)_{m}\oplus d^{{}^{\prime}}(1,3)_{m},x_{2}\oplus D(2,3)_{m}\oplus d (3,1)_{m}\oplus D^{{}^{\prime}}(0,1)_{m}\oplus d^{{}^{\prime}}(2,0)_{m},x_{3} \oplus D(3,0)_{m}\oplus d(0,2)_{m}\oplus D^{{}^{\prime}}(1,2)_{m}\oplus d^{{}^{ \prime}}(3,1)_{m})\). This logical result is correct in routing \(x_{i}\) back to the \(i\)th bit position, but the physical delay terms are completely mixed up and do not cancel each other. Figure 3: Flowchart of 1-bit encryption; \(d_{i}(m)\) is the \(m\)th bit of signal delay in selected path. This path depends on all bits of key \(k\). Keyed path selection achieves confusion.
# Effects of Phase Shift Errors in Recurrence Plot for Rotating Machinery Fault Diagnosis
## 2 Methods
### Recurrence Plot  
The RP allows the visualization of the dynamics of phase space trajectories owing to the preservation of equilibrium between the velocity and position of a particle according to the Hamilton's principle. The trajectory along the phase plane can be represented as a discrete vector \(\vec{x}(t)\), as illustrated in Figure 1b, that represents the system state at time \(t\):  
\[\vec{x}(t)=(\vec{x}_{1},\vec{x}_{2},...,\vec{x}_{n}) \tag{4}\]  
Then, the recurrence plot [6] is defined as:  
\[\mathbf{R}_{i,j}=\Theta(\epsilon_{i}-\|\vec{x}_{i}-\vec{x}_{j}\|) \tag{5}\]  
where \(\epsilon_{i}\) is a predefined cut-off distance, \(\|\cdot\|\) is the norm and \(\Theta(x)\) is the Heaviside function.  
The cross recurrence plot is similarly defined as:  
\[\mathbf{R}_{i,j}=\Theta(\epsilon_{i}-\|\vec{x}_{i}-\vec{y}_{j}\|) \tag{6}\]  
where \(\vec{x}(i=1,...,N)\) and \(\vec{y}(j=1,...,M)\) are the first and second trajectory to be analyzed.  
RQA is used to quantify the similarity between phase space trajectories by calculating the following parameters [7; 39]: recurrence rate \((RR)\), is the ratio of all recurrent states; determinism \((DET)\), is the ratio of recurrence points forming diagonal structures to all recurrence points; Shannon entropy of diagonal length \((ENTR)\), is a measurement of the disorder in the phase plane; laminarity \((LAM)\), is the percentage of recurrence points that create a vertical line; averaged diagonal length \((L)\), is the average time in which two segments of the trajectory are close to each other; length of the longest diagonal line \((L_{max})\), is the longest diagonal line found in the RP; trapping time \((TT)\), is the average length of the vertical lines; and length of the longest vertical line \((V_{max})\).  
In mechanical system applications, the phase plane is determined from the differential equations or by the measurement of the dynamic response. The RP for a single time series is calculated using the time delay method by numerical integration of data taken from an accelerometer, which produces significative uncertainties [16]. Noise and vibration signal integration, conditioning, and acquisition methods cause errors [40; 41]. Among these errors, there is the phase shift, which is the focus of this paper.  
The proposed method to analyze phase shift effects on asynchronous functions and smoothing by phase plane, RP and CRP is presented in Figure 2.  
Figure 2: Proposed flow chart to analyze the phase shift effects on functions and smoothing.
## 1 Introduction
Furthermore, we investigate whether or not these proposed VC-based approaches can detect different shapes and sizes of forgeries in the images. The contributions of this research based on visual cryptography watermarking approaches can be summarized as follows:  
1. This is a novel approach to detect and localize forgeries in the digital images-based on visual cryptography-based watermarking schemes and feature extraction algorithms. 2. This approach has the ability to detect and localize forgeries of different sizes and shapes in the digital images even at the pixel level. 3. The proposed approach can be used for image copyright in addition to its main purpose of detecting and localizing forgeries in images. To the best of our knowledge, this is the first time a VC-based watermarking scheme has been used for the purpose of image forgery detection and localization. 4. The proposed approach overcomes the limitations of the watermark size, resistance to attacks, pixel expansion, distorted cover image quality and the sending of side information that exist in the previous approaches. The rest of the paper is arranged as follows: the materials and methods are presented in Section 2, the experimental results and analysis are presented in Section 3, a discussion is presented in Section 4, and finally, Section 5 is devoted to the conclusions, limitations and future work.
## 5 Experimentation, Results, and Analysis
### Performance Metrics  
To assess the performance of the proposed DCAT model, we utilize both standard and custom-designed evaluation metrics. These metrics aim not only to evaluate linguistic accuracy but also the contextual richness of the generated captions.
## 3 Methods and Samples
### Experimental Setup for LIT  
Figure 6 shows the configuration of the LIT experimental device applied in this study. The IR camera was the FLIR SC655 long-wave infrared camera model with a wavelength range of 7.5 to 14 \(\upmu\)m and a resolution of 640 \(\times\) 480 pixels. The infrared camera has a FOV of 15\({}^{\circ}\), and a lens with an aperture of f = 41.3 mm was applied. Sinusoidal heating was performed by applying two 1 kW halogen lamps as external heat sources which were controlled in synchronization with the function generator and power amplifier. In the LIT experiment, six excitation frequencies were applied: 0.1 Hz, 0.05 Hz, 0.04 Hz, 0.03 Hz, 0.02 Hz, and 0.01 Hz. The distance between the camera and the sample was approximately 1.5 m, and it was placed so that the sample on the screen could be filled as much as possible. The irradiance (radiation flux) of the sample plane by a halogen lamp with an output of 2 kW was irradiated in the form of a sinusoidal wave ranging from a minimum of 0 to a maximum of 11.1 kW/m\({}^{2}\). Raw data of LIT testing was acquired at 50 frames per second and analyzed using commercial software MATLAB 2022a coding. Table 2 shows the raw data information of LIT considered to determine phase through the 4-point method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Defect ID**} & \multicolumn{4}{c}{**Geometric Value**} \\ \cline{2-4}  & **Size** & **Depth** & **Thinning Rate** \\  & **(mm)** & **(mm)** & **(\%)** \\ \hline A1 & & 0.6 & 10 \\ A2 & & 1.2 & 20 \\ A3 & & 1.8 & 30 \\ B1 & & 2.4 & 40 \\ B2 & 40 \(\times\) 40 & 3.0 & 50 \\ B3 & & 3.6 & 60 \\ C1 & & 4.2 & 70 \\ C2 & & 4.8 & 80 \\ C3 & & 5.4 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of each defect and its dimensional value.  
Figure 6: Configuration of lock-in thermography testing.
## 3 UWB Characteristics
### IEEE 802.15.4 UWB Physical Layer (PHY)  
The UWB physical layer (PHY) waveform operates on a signaling scheme called the impulse radio, which uses band-limited data pulses. Three different frequency bands are defined by the IEEE 802.15.4 standard: the sub-gigahertz band, low band and high band. The sub-gigahertz band has one channel that spans from typically 249.6 MHz to 749.6 MHz. The low band covers the spectrum from typically 3.1 GHz to 4.8 GHz and consists of four channels (channel 1 to channel 4). The high band occupies the range 5.8 GHz to 10.6 GHz and includes 11 channels (channel 5 to channel 15), which are listed in Table 2. These optional channels can increase the communication range and positioning accuracy, as well as improving resistance to multipath interference. Figure 4 shows the UWB packet format. The packet or frame consists of three parts: a synchronization header (SHR), which includes the preamble sequence and the start of frame delimiter (SFD), a physical layer header (PHR), and the data portion. The preamble is made up of pulses that are used to detect the frame and are composed of two parts: the preamble and the SFD. The SFD marks the end of the preamble and the start of the PHY header, and it is used to determine the accurate frame reception timestamp that is essential for precise localization. The UWB PHY contains a mandatory short SFD of 8 symbols for the default and medium data rates, and an optional SFD of 64 symbols for the nominal low data rate of 110 kbps. The number of symbols in the preamble can be classified as 16, 64, 1024, or 4096 symbols, and the options are defined based on the application requirements. One of these preamble lengths has to be supported in order for the device to comply with the UWB standard.  
Figure 4: UWB PHY frame.  
Figure 3: Comparison of the attributes of UWB spectrum with various positioning technologies.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion
### Aquifer Management and Racharge Strategies  
To address Peru's groundwater crisis, we propose three evidence-based strategies:  
1. Managed Aquifer Recharge: Pilot projects in high-risk aquifers (e.g., Chillon-Rimac, Ica, Tacna), modeled on Mexico and Spain, could recover 10-15% of GWS annually [67]. California has pioneered innovative MAR approaches to address critical groundwater depletion. The state's Flood-MAR initiative strategically uses controlled floodwaters to replenish aquifers, particularly in agricultural regions with permeable soils [68]. Research demonstrates that Agricultural MAR (Ag-MAR) can effectively recharge groundwater by applying surplus water to active croplands during fallow periods, with potential recharge rates up to 3.7 million acre-feet annually under optimal conditions [69].
2. Efficient Irrigation and Governance: Drip irrigation, saving 20-30% of water [70], paired with extraction quotas based on natural recharge rates, can curb overexploitation. The impact of phasing out flood irrigation on recharge requires further study [71]. The landmark Sustainable Groundwater Management Act (SGMA) establishes a comprehensive regulatory framework, requiring local agencies to develop and implement sustainability plans by 2040 [72]. Complementing these policy measures, the agricultural sector--responsible for approximately 80% of groundwater use--has adopted precision irrigation technologies and water banking systems to optimize efficiency [73,74].
3. Integrated Monitoring: A GRACE-based early warning system, validated with expanded in situ networks, could predict shortages 6-12 months ahead [75]. Protecting Amazon recharge through agroforestry incentives can mitigate deforestation's 5-10% impact [76]. California employs a sophisticated groundwater monitoring network combining:  
- In situ well sensors.  
- NASA's GRACE-FO satellite system.  
Predictive hydrological modeling.  
This integrated approach enables real-time tracking of aquifer levels and informs adaptive management strategies [74]. Stanford researchers emphasize the importance of combining these technical solutions with stakeholder engagement through platforms like the SGMA Portal for transparent governance [77].  
These measures should prioritize critical regions (Lima, Ica, Tacna) and integrate interregional cooperation to align economic goals with long-term water security.