esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
On the other hand, the main obstacle is how to represent the spatial and temporal relations of the incomplete node sensing data and further integrate these constraints to the current LRMA data recovery model. In this paper, we present a new data collection method based on LRMA, which improves the performance of the current method both at the data sensing and data recovering stages. At the data sensing stage, we propose to use Gini index to measure the spatial distributions of the selected sensing nodes and the evenness of energy status of the network. Under this measurement, the selection of sensing nodes is formulated as an optimization problem with the constraint of the network energy status. At the recovering stage, observing that the sensing data from a sensor generally has temporal consistency and the sensing data from different nodes has spatial correlation excepting for some noise and outliers. We give the representations of the spatial and temporal relationship of the node sensing data and add these constraints into the basic LRMA model and construct a correlated spatio-temporal constrained LRMA model (ST-LRMA). Then the ST-LRMA model is solved by an iteration method. The proposed ST-LRMA based data collection method is tested on both simulated and real wireless networks compared with other relevant methods. The experimental results demonstrate the proposed method achieves better data recovery accuracy and improves energy efficiency to prolong network lifetime. Additionally, it is robust to noise and outliers. The main contributions of this paper are:  
1. We propose a correlated spatio-temporal constrained low rank matrix approximation model, a novel data collection scheme in WSNs;
2. We propose an optimized node sampling model for reducing energy consumption of WSNs, in which Gini Index is adopt and used as a tool to improve the current uniformly random sampling method. The rest of the paper is organized as follows. In Section 2, we summarize the related work. Section 3 introduces the basic LRMA model based on random sampling. Section 4 presents the proposed ST-LRMA model. Section 5 elaborates the new optimized node sampling method. We will show the experimental results of our proposed methods compared with state-of-the-art methods, such as basic LRMA model [14], T-LRMA model [15, 16] and S-LRMA model in Section 6. Section 7 concludes the paper with a discussion on future research.
## 4 Experiment
### Main Components
#### 4.3.2 Modeling ML Forecast  
As mentioned earlier, the primary focus of the ML method in Acme's context is improving returns forecast accuracy. Since sales (split by the three customer groups) serve as an early indicator for future returns (except, potentially, historical returns), the intuition is apparent behind using an ML algorithm that can learn how the two relate without explicit instructions. More technically, an ML model can learn, from training samples (consisting of input/output pairs), the transformation from the input (set of early indicators) to the output (future returns) that minimizes prediction errors. This description corresponds to a supervised learning regime.  
However, the requirements for sequential data (in this context, time series) are slightly more stringent--the architecture must be capable of maintaining temporal ordering. From this perspective, there are two basic ML architectures: feedforward networks that flatten the inputs, hence their lack of means to carry forward information meaningfully, and architectures with a feedback loop. A recurrent neural network (RNN) is an architecture that falls into the latter type, which the evaluation framework uses. An RNN can use its memory about earlier periods in a time-series setting and combine it with the current period while making a prediction. One can best imagine the process by \"unrolling (the network) through time\" [74]; that is, imagining the network processing each of the periods sequentially. In the simplest case of a network of a single artificial neuron, it receives as input both the current period value and the output of the previous period (usually initialized to zero at the start). Thus, at any given period, the additional input--the output of the previous period--is akin to the memory of the entire past, which influences predictions.  
For the RNN implementation, the framework uses the Keras API, which offers convenient routines for training deep learning models [70]. In Keras, there are three types of RNN available: SimpleRNN, long short-term memory (LSTM), and the gated recurrent unit (GRU). Each type shares the basic idea of carrying over information when processing sequential information such as time series. The crucial difference between RNN and both LSTM and GRU lies in the latters' relative ability to handle long sequences. Since the backpropagation procedure has to deal with a significantly deeper network, given the unrolling through time, SimpleRNN (the vanilla implementation) suffers from a debilitating memory loss problem. It is a problem that LSTM and GRU specifically address, partly by being more discerning about what to retain and what to forget [74]. In the experiment, the sales model uses LSTM given the long historical sales horizon (36 months since there are seasonal effects). For the returns scenario, the experiment uses GRU as it performed better during the parameter tuning phase.  
Figures 3 and 4 below provide a schematic representation of the ML (using RNN for illustrative purposes) and heuristic approaches.
## 4 Research Methodology
### Evaluation Methodology for PRDFS and BRDFS  
To compare PRDFS and BRDFS, we simulate two CPS. For simplicity purposes, each CPS consists of two layers of nodes. The first layer contains 14 sensors that monitor demand continuously. The second layer contains a base station. All sensor nodes are directly connected to the base station. They transmit their measurements to the base station at regular intervals. PRDFS resides in the base station of one CPS, and BRDFS resides in the base station of the other CPS. PRDFS and BRDFS are implemented in MATLAB and their performance is tested on High Performance Computing (HPC). PRDFS and BRDFS both play multiple games with the adversary model under different percentages of malicious nodes. The important rules of the games are stated below: (1) at the beginning of each game, the attacker compromises a fixed number of malicious nodes. (2) If the defender detects a malicious node, it becomes a normal node. From the next step onwards, the attacker cannot modify its measurements. (3) An attacker cannot compromise a new or recovered sensor node. (4) The duration of each game is 1000-time steps. (5) The adversary model has partial knowledge. At the start of the game, the adversary model gets the most recent 500 measurements of all malicious sensors, whereas PRDF and BRDFS get the most recent 1000 measurements of all sensors. (6) At each time step, the data generator (described in Section 4.5) generates the measurements of sensors at that time step. The adversary model modifies the measurements of malicious sensors. All measurements are then transmitted to the base station. PRDFS and BRDFS estimate the most trustworthy measurement based on the noisy sensor measurements, append the most trustworthy measurement to the aggregate demand time series, retrain the forecasting models on the updated aggregate demand time series, and forecast one-step ahead of demand using the updated forecasting model, and classify each sensor node into one of the two types: malicious and normal. The adversary model also adapts itself based on the new measurements of malicious sensors.
## 3 Results and Discussion
### Experimental Results  
The surface morphology of the fabricated MRR was successfully captured using SEM, as shown in Figure 7. The fabrication process using the Elionix F125 EBL system for pattern writing and HSQ as a negative tone resist mask for pattern transfer successfully produced the morphology of MRR with good repeatability and accurate dimensional control. We observed that the fabricated MRR had a ring radius, gap, and waveguide width of \(-4.35\), \(-4.95\times 10^{-2}\), and \(-5.02\times 10^{-1}\)\(\mu\)m, respectively. The differences in fabricated MRR compared to the proposed design were also considerably low: \(3.13\%\), \(0.10\%\), and \(0.32\%\) for ring radius, gap, and waveguide width, respectively.  
Characterization of the quality factor value has been done by measuring the through-port power of MRR transmission using OSA, while TLS, which covers wavelengths in the range of 1525 to 1575 nm, was used to supply the light that propagates in the MRR waveguide. Figure 8 shows the measurement results of the MRR device's output power at the through-port point, including the value of the quality  
Figure 6: The simulation result of the transmission at the through-port of MRR for index background of air (n = 1).  
Figure 7: SEM image of fabricated MRR from (**a**) bird’s-eye view and (**b**) top view.  
factor and FWHM from each resonance peak of the different samples. Each device, namely samples 1-6, was fabricated using the same method and configuration. The purpose of measuring all samples is to show the repeatability and consistency of the fabrication process. The similarity between the value of the quality factor and FWHM from each resonance peak of the different samples has, once again, proven the consistency of dimensional control of the fabrication process.  
The quality factor of the fabricated devices varied from 2969.117 nm to 3195.583 nm, with Sample 5 having the best values. There was a good agreement of the quality factor value between the experimental result (3195.583 nm), as shown in Figure 8e, and the simulation result (3385.307 nm) from Figure 6. Therefore, the 3D FDTD can be utilized to predict the sensitivity for MRR-based sensors, as it depends on the value of the quality factor [32].  
Figure 8: The measurement results at the through-port of the MRR device from (**a**) sample 1, (**b**) sample 2, (**c**) sample 3, (**d**) sample 4, (**e**) sample 5, and (**f**) sample 6. The values of quality factor, Q, and FWHM for each resonance peak are also shown.
# Additive Manufactured Waveguide for E-Band Using Ceramic Materials
## 2 Manufacturing Process  
The manufacturing process for a ceramic waveguide can be separated into four different steps. In a first step (Section 2.1), the geometry of the waveguide is prepared and modified in order to enable an AM process. Then, the waveguide is fabricated by a DLP printer (Section 2.2) before it is sintered (Section 2.3) and finally metallized (Section 2.4).
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 2 Stakeholder and System Interoperability
### Ecosystems of Industrial Production
#### 2.1.1 Key Stakeholders  
The participants, as shown in Figure 3, define the main directions and invest the most in developing the actual communication technologies. Firstly, the **Standardization Agencies** as 3 GPP, ITU, IETF and IEEE, etc., are responsible for recommending and defining the communication standards. They also describe methodologies and create models on how to interconnect different systems. Their global presence and historical background qualify them to work globally, while HW and SW **Vendors** can use the published standards as the base for their development and maintenance processes. **Network Operator (NO)**--be it public or private--is a crucial participant as they provide the data connectivity for the production platforms. To fulfill this, they need to provide physical connection either as wired cabling or wireless connectivity systems, including licensed and non-licensed solutions. NOs use the existing standardized solutions and devices made by the Vendors to provide scalable services and cover a broad spectrum. Vendors are developing and manufacturing devices and network solutions based on the standards, but their products can differ significantly, leading to interoperability issues. Due to the limited Vendors and possible interoperability problems, the initial Vendor selection by NOs is crucial for further network development. **Research laboratories** are also fundamental players in this ecosystem as they can both bring the new ideas and materialize them and be the catalyzers between the standard and the implementation at NOs and Vendors. Research Lab developments are generally the playground for Vendors and NOs before investing in public rollout with strict service and quality requirements.
## 3 Model Architecture
### Construction of the Spatial-Temporal Graph Based on the Skeleton  
#### 3.2.1 Delaunay Algorithm  
In the process of Delaunay triangulation, each point in the set of insertion points needs to undergo local optimization to judge the insertion points, so as to determine the local subdivision map.  
The specific implementation process of the local optimization treatment is as follows:  
1. Two adjacent triangles form a quadrilateral;
2. Make a circumferential circle for two triangles to check whether other points in the point set are in the circumferential circle;
3. If a point in the point set is within the circumferential circle of any triangle, the diagonal is swapped to complete local optimization. This is shown in Figure 4.  
Points A, B, C, and D in the figure represent four points in the point set, Triangle ABD and triangle BCD are two adjacent triangles, and the two triangles form a quadrilateral, ABCD. Point A is inside the circumferential circle of triangle BCD. The diagonal swapping rules are used to change the diagonal BD in the quadrilateral ABCD into diagonal AC, so that the circumferential circle does not contain any point in the point set.
# Quantum Multi-User Broadcast Protocol for the \"Platform as a Service\" Model
## 3 Multi-User Broadcast Protocol Based on _Qcm_  
### Neighbor Quantum Node Discovery Process  
The quantum multicast router starts to discover its neighbors with probe packet messages, which contain the important informations: a list of addresses for neighbors from which the originating router has received probe packet messages, a generation ID used to detect changes in status of neighbors, and so on. After receiving the probe packet messages, the quantum multicast router records the address of the original router and the interface of received messages.
# Multivariate Algorithmics for Finding Cohesive Subnetworks
## Average \(s\)-Plex
### Absolute Relaxation of Vertex-Connectivity  
A further connectivity-based clique relaxation is to relax the connectivity demand by an absolute amount \(s>1\). That is, instead of demanding vertex-connectivity \(k-1\) for \(k\)-vertex graphs, we demand vertex-connectivity \(k-s\).  
**Definition 14** ([5]): _A set \(S\) in a graph \(G=(V,E)\) is an \(s\)-bundle if \(G[S]\) is \((|S|-s)\)-vertex-connected._  
As for degree-based and density-based clique relaxations, there are relations between \(s\)-bundles and their relative counterpart, the \(\gamma\)-relative-vertex-connected sets. In addition, every \(s\)-bundles is also an \(s\)-plex, since \(\kappa(G)\) is upper-bounded by the minimum degree of \(G\). Hence, the \(s\)-bundle property is more restrictive than the \(s\)-plex property. For \(s=2\), the two properties coincide, that is, every \(2\)-bundle is also a \(2\)-plex. For \(s=3\), the properties are different. Consider, for example, a graph consisting of two isolated edges. This graph is a \(3\)-plex (the minimum degree is \(1=4-3\)), but not a \(3\)-bundle (it is not connected, but a \(3\)-bundle on four vertices needs to be connected).  
\(s\)-Bundle  
**Input:**: An undirected graph \(G\) and an integer \(k\).
**Question:**: Does \(G\) contain an \(s\)-bundle \(S\) of size at least \(k\)?  
As this problem has not received any attention so far, it is open to study its complexity, in particular its multivariate complexity, in more detail.  
**Open Topic 6**: Perform a multivariate complexity analysis for \(s\)-Bundle.  
Since being an \(s\)-bundle is hereditary, Theorem 2 shows that \(s\)-Bundle is W[1]-hard with respect to \(k\) for every constant \(s\). Identifying differences between \(s\)-Bundle and \(s\)-Plex for other parameters could be one interesting aim of a multivariate complexity analysis. Furthermore, given that \(s\)-Plex can be solved quite efficiently in practice [29; 30; 31; 32], an implementation of efficient fixed-parameter algorithms for \(s\)-Bundle seems clearly within reach.
## 2 Materials and Methods
The information is based on recent findings from the six European projects shown in Table 1. Figure 1 outlines the crop selection process, which filtered the crops from these projects based on their characteristics, technological maturity, and applicable management practices. These assessments were drawn from the Biomass Policies, S2 Biom, MAGIC and PANACEA projects, which have performed modelling, field trials, and have consolidated knowledge for industrial and non-food crops in Europe [14; 15; 16; 17]. As noted before, one pathway to additional agricultural harvest would be to bring unused agricultural land back into production. In the case of land that has been abandoned due to natural conditions (for example high local temperatures, water stress, or rocky soil), we seek to assess and select crops with good tolerance to these specific non-ideal growing conditions. The PANACEA project used the TRL [14] as a method to evaluate the readiness of twenty-nine near-to-practice non-food crops that are suitable to grow in different European regions as feedstocks for biofuel and bioenergy. The ranking (Table 2) was performed for the crop productivity and ability to be grown and harvested using existing machinery. In this paper we narrowed down the selection to include only oil and lignocellulosic crops. Table 2 presents the scales of the TRL for the criteria used in assessing the crops. Only crops with expected TRL for 2030 above 7 are analyzed for the economic opportunities in the second part of Section 3. The information and relevant metrics for sustainable agricultural practices and potential yield increases were based on research conducted within the MAGIC, SoilCare, and BIO4A projects. Two categories of biomass crops suitable as feedstocks for advanced biofuels are reviewed in this paper. They include seven oil and nine lignocellulosic crops, and they are selected based on their adaptability to the European AEZ and their technological readiness level within the period 2020-2030 (see Figure 1). They are a combination of conventional and new species with improved traits for climate change related risks (e.g., drought, high temperature, etc.) [16; 17]. Figure 1: Methodological approach for the crop selection process.
## 4 Experimental Results and Analysis
### Analysis of Different Tampering Test Results
#### 4.2.3 Splicing Attack  
Unlike a copy-move attack, a splicing attack extracts a portion of a watermarked image and then splices it into another watermarked image. Figure 9 displays the tamper location and recovery results for the different methods and Table 4 lists their PSNR and SSIM values. Figure 9c shows that a car from the watermarked image Car1 was inserted into the same position in another watermarked image, Car2. In Figure 9e, the tamper detection result locates only a portion of the boundary of the tampered region but the other three methods detect the tampered region correctly. This results occurs because the method in Reference [8] cannot achieve independent block authentication. Comparing the images in Figure 9j-1 and the values listed in Table 4, the proposed method and the methods of [7,9] all achieve high performance; however, the proposed method is the best. The reasons are as follows. In this experiment, the tampered region in the Car2 image contain only smooth information. Therefore, no jagged edges or confused areas affect the recovery quality. In addition, median filtering removes some noises without affecting the edge characteristics, further improving the subjective and objective qualities of the final image.  
Figure 8: Tamper detection and recovery results of copy-move attack on Goldhill image: (**a**) original image; (**b**) watermarked image; (**c**) tamper reference; (**d**) tampered image; (**e**) tamper detection result of [8]; (**f**) tamper detection result of [9]; (**g**) tamper detection result of [7]; (**h**) tamper detection result of the proposed method; (**i**) recovery result of [8]; (**j**) recovery result of [9]; (**k**) recovery result of [7]; (**l**) recovery result of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **[PERSON] et al. [8]** & **[PERSON] et al. [9]** & **[PERSON] et al. [7]** & **Proposed** \\ \hline PSNR (dB) & 25.90 & 35.05 & 36.13 & 36.34 \\ SSIM & 0.6861 & 0.7910 & 0.9925 & 0.9930 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Objective comparison of the recovery performance for a copy-move attack on the Goldhill image.
## 4 Discussion  
### Evaluation of Fracture Prediction Results  
Possible limitations and pitfalls in the application of finite element modelling, including:  
(i) The rock assemblage in Basin A is laterally highly variable, with different rock types exhibiting different rock mechanical properties, creating non-homogeneity. However, within the finite element model, Basin A is divided into facets that are treated as nearly homogeneous. This situation is almost impossible to avoid unless the focusing region is reduced to a small area, such as drilling profiles [53].  
(ii) The finite element model uses regional boundary conditions that ignore specific localised stresses in some regions.  
Figure 9: Late Cretaceous Maastrichtian Formation rupture coefficients for Basin A. W1–W5 (well name).  
The distance from each point to the fracture zone on the three profiles was measured. Exponential regression analysis was performed on the fracture rupture coefficient versus the distance from the fracture zone using the horizontal coordinate to represent the distance from the fracture zone (km) and the vertical coordinate to represent the rupture coefficient.  
According to the graph of rupture coefficient versus distance from the fracture zone (Figure 10), it can be seen that the rupture coefficient decreases exponentially with the increase in distance from the fracture zone; the equations of the exponential curves obtained from the fitting are respectively y = 0.28771e(-x/0.197525) + 1.0316, y = 0.29338e(-x/0.202231) + 1.04179, y = 0.25033e(-x/0.157353) + 1.04877, where y represents the rupture coefficient and x represents the distance from the fracture zone.  
According to the regression curve analysis (Figure 11), the rupture coefficient decreases sharply with the increase in the distance from the fracture zone within the range of 5 km from the fracture zone. In comparison, the rupture coefficient decreases slowly with the increase in the distance from the fracture zone outside the range of 5 km from the fracture. The fracture zone mainly controls the structural fractures within 5 km. Still, beyond 5 km, the fracture coefficient has exceeded the limit of the fracture zone control, and it is a regional tectonic control range.
## 2 Three-Dimensional Printing Program of Artificial Vertebrae and Analysis of Influencing Factors
### Effect of 3D Printing Process Parameters on Bone Density
#### 2.2.2 Effect of Layer Thickness on Specim Density  
To investigate the influence of layer thickness on the density of the specimen, six distinct layer thicknesses are chosen: 0.10 mm, 0.15 mm, 0.20 mm, 0.25 mm, 0.30 mm, and 0.35 mm. During the experiments, all other parameters are kept constant, with the specific settings detailed in Table 2.  
In order to more intuitively see the relationship between fill density and specimen density, Figure 3 shows the layer thickness-specimen density curve; it can be seen that the specimen density with the increase in layer thickness changed in a small manner but did have an impact, the layer thickness increased from 0.10 mm to 0.35 mm, and the specimen density increased from 1.240 to 1.249.