esa-sceva/satcom-chunk-collection huggingface dataset:
# Agrochemical Contamination and Ageing Effects on Greenhouse Plastic Film for Recycling
## 1 Introduction  
In civil and industrial buildings, plastic materials are usually limited to some complementary applications (e.g., pavement; door or window-frame; insulating/facing wall). Instead, in agricultural buildings, such as greenhouses and tunnels, they constitute the main constructive element, which delimits the external surface of the building, thus protecting the internal confined airspace against adverse meteorological events. Moreover, by virtue of their transparency properties to solar radiation, they develop a function of valorization of the incoming solar energy, which is used by crops for photosynthesis, at the same time being captured inside, hence improving the microclimatic characteristics of the internal environment as well [1].  
The agricultural plastic film market mainly covers greenhouse and mulching applications. Globally, in 2016, China had the largest area under protected cultivation, mainly under plastic greenhouses, estimated at 2.8 \(\times\) 10\({}^{6}\) ha. Together with Korea and Japan, they account for 80% of the total area covered by greenhouses [2]. According to the most recent estimations, in Europe in 2018, a total amount of 460,000 tons/year agricultural films were put on the market. Of these, 117,000 tons/year were for greenhouse covering application and 77,000 tons/year were for mulching. The rest was for livestock production [3].  
The progressive increase in the use of plastic material for crop protection needs a similar increase in studies on their technical characteristics, so as to suitably use and recycle them at the end of their useful life, in the framework of a Circular Economy approach, hence contributing to reducing the plastic footprint in agriculture [4; 5; 6]. Indeed, the use of plastics in agriculture may create serious environmental problems, such as those connected with the management of large amounts of post-consumer material in areas characterized by a fragile environment and a marked tourism industry [7]. A further increase in the use of agricultural plastic material should be therefore accompanied by scientific and technical efforts, aimed to reduce their environmental impact. To this aim, a better understanding of the factors hindering its mechanical recycling potential is paramount. In this sense, the conditions in which plastic materials conclude their useful life after application in agriculture are decisive. Among the main barriers to mechanical recycling, the low value of recycled products plays a considerable role. Therefore, it is crucial to analyze the most suitable conditions for the removal of plastic materials after ageing.
# Missed Detection of Entanglement in Two-Mode Squeezed States Based on the Inseparability Criterion
## 2 Theoretical Analysis
### Missed Detection of Entanglement
Specifically, Figure 2a shows the analysis of missed detections by the inseparability criterion in the case of asymmetry in the intermodal correlation orthogonal components, where we fix \(n=m=3\) and analyze the evolution of the criterion by varying the values of \(\tilde{\epsilon}_{1}\) and \(\tilde{\epsilon}_{2}\), with \(\tilde{\epsilon}_{i}=c_{i}/\epsilon_{\text{max}}\), where the state is maximally entangled when \(\tilde{\epsilon}_{i}=1\); Figure 2b illustrates the analysis of missed detections by the inseparability criterion in the case of asymmetry in the entanglement modes, where we fix \(\tilde{\epsilon}_{1}=-\tilde{\epsilon}_{2}=1/\sqrt{2}\) and vary the values of \(n\) and \(m\). In each subfigure, the orange region represents the entanglement range defined by the inseparability criterion, while the blue region shows the more comprehensive and rigorous entanglement definition given by the PPT criterion. The green region, a non-physical region, is defined by the constraints of Equation (3), reflecting the range of quantum states that are unattainable in actual physical systems. From the figures, it is evident that compared to the PPT criterion, the inseparability criterion covers a smaller region, entirely contained within the blue region defined by the PPT criterion. This indicates that the inseparability criterion fails to identify some entangled states recognized by the PPT criterion, highlighting the presence of missed entanglement detections. More specifically, in the case of ideal symmetric states (diagonally distributed from top left to bottom right in Figure 2a and anti-diagonally distributed from top right to bottom left in Figure 2b), the boundaries of the two criteria coincide, indicating that their judgments are equivalent: the inseparability criterion is both a sufficient and necessary condition for two-mode squeezed states. However, when this symmetry is broken, whether due to asymmetry in the intermodal correlation orthogonal components or asymmetry inherent in the entanglement modes--a significant divergence between the boundaries of the two criteria emerges, which deepens as asymmetry increases. This phenomenon reveals that under such circumstances, the inseparability criterion serves only as a sufficient condition, rather than a necessary condition, for determining whether a two-mode Gaussian state possesses entanglement properties. Consequently, when dealing with two-mode squeezed states with significant asymmetry, it may be necessary to apply the inseparability criterion more cautiously or consider using a more robust and rigorous criterion (such as the PPT criterion) that is less sensitive to asymmetry to ensure accurate entanglement detection.
# Calculation of Main Circuit Steady-State Parameters for Capacitor Commutated Converter System
## 3 Calculation Model and Method of the Main Circuit Parameters
### Calculation Formulas of Main Circuit Parameters
#### 3.2.1 Calculation of the DC Voltages  
The formula for calculating the DC voltage across the six-pulse rectifier is:  
\[U_{\text{dR}}=U_{\text{dioR}}[\cos\alpha-(d_{\text{xR}}+d_{\text{rR}})\frac{I_ {\text{d}}}{I_{\text{dN}}}\frac{U_{\text{dioNR}}}{U_{\text{dioR}}}]-U_{\text{T}} \tag{29}\]  
The DC voltage of the six-pulse inverter \(U_{\text{dI}}\) is calculated according to (20).  
For the monopolar ground return mode, the voltage \(U_{\text{dR}}\) and \(U_{\text{dI}}\) can also be calculated from the system point of view:  
\[\left\{\begin{array}{c}U_{\text{dR}}=U_{\text{dLR}}+(R_{\text{eR}}+R_{\text {gR}})I_{\text{d}}\\ U_{\text{dI}}=U_{\text{dLI}}-(R_{\text{eI}}+R_{\text{gI}})I_{\text{d}}\end{array}\right. \tag{30}\]  
For the bipolar mode, the voltage is:  
\[\left\{\begin{array}{c}U_{\text{dR}}=U_{\text{dLR}}\\ U_{\text{dI}}=U_{\text{dLI}}\end{array}\right. \tag{31}\]  
where \(U_{\text{T}}\) is the forward voltage drop of the LCC. \(U_{\text{dLR}}\) and \(U_{\text{dLI}}\) are the pole to ground DC voltages at the rectifier side and the inverter side, respectively. \(U_{\text{dioR}}\) is the ideal no-load DC voltage of the six-pulse rectifier and \(U_{\text{dioRN}}\) is the rated ideal no-load DC voltage of the six-pulse rectifier. \(I_{\text{d}}\) is the DC current and \(I_{\text{dN}}\) is the rated DC current. \(d_{\text{xR}}\) and \(d_{\text{rR}}\) are the relative inductive voltage drop and the relative resistive voltage drop, respectively. \(R_{\text{eR}}\) and \(R_{\text{eI}}\) are the resistances of the rectifier and inverter electrode line, respectively. \(R_{\text{gR}}\) and \(R_{\text{gI}}\) are the resistances of the rectifier and inverter electrode, respectively. \(\alpha\) is the firing angle at the rectifier side.
# Switchable Chiral Metasurface for Terahertz Anomalous Reflection Based on Phase Change Material
## 1 Introduction
Here, we theoretically investigate a switchable chiral metasurface based on a phase change material GST, which can switch between a right-handed circularly polarized (RCP) mirror and a left-handed circularly polarized (LCP) mirror. The metasurface is composed of asymmetric split-ring resonators (ASRRs) arranged periodically. The simulated results show that when the conductivity of GST \(\sigma\) is equal to 0 S/m, ASRR_1 will reflect incident right-handed circularly polarized light (LCPL) and absorb incident left-handed circularly polarized light (LCPL) at 0.76 THz. As \(\sigma\) rises to 3 \(\times\) 10\({}^{5}\) S/m, the response of ASRR_1 to circularly polarized light will be reversed. That is, it reflects the incident LCPL and absorbs the incident RCPL at 0.66 THz. In addition, we also simulate ASRR_2, the mirror structure of ASRR_1. Then, the effect of conductivity on the properties of ASRR_1 was also studied. In addition, the physical principle of ASRR_1 was explained. Finally, we arrange ASRR_1 into a phase continuous metasurface. Before and after the GST phase transition, it can anomalously reflect the incident LCPL and RCPL out of the whole metasurface at different angles.
## 3 Experimental Results and Analysis
### Experimental Results
#### 3.4.3 Ablation Experiments of MLGW-VIS  
In this paper, the CrossVIS network is modified into a weakly supervised video instance segmentation network, and MSL-FPN and GWM are proposed to improve the problems existing in the network. This section conducts ablation experiments on the two modules on the basis of CWVIS to verify its effectiveness on the network. The experimental results are shown in Table 3. When MSL-FPN is used on the basis of CWVIS alone, the segmentation accuracy is 36.6%, which is 4.4% higher than that of the baseline CWVIS. When GWM is added only at the mask branch, the average segmentation accuracy of the network reaches 36.2%, which is 4% higher than that of the baseline CWVIS. The combination of the above two modules in CWVIS achieves the best segmentation accuracy of 37.9%, which is 5.7% higher than that of the baseline CWVIS. According to the experimental results in Table 1, the segmentation accuracy of MSL-FPN and GWM separately added to CWVIS is significantly improved, and when the two modules are used in the network at the same time, the average segmentation accuracy is better than the performance of the two modules alone, which verifies the effectiveness of the proposed MLGW-VIS algorithm.
# Self-Supervised Spatio-Temporal Graph Learning for Point-of-Interest Recommendation
## 2 Related Works
### Point-of-Interest Recommendation  
Most of the traditional POI recommendation models are based on matrix factorization [2; 3; 4; 5]. LRT [2] focused on the impact of temporal information on POI recommendation, while IRenMF [4] exploited geographic location information to model each location's neigh bors at both geographic location and geographic region levels. In addition, LGLMF [3] used local geographic information to obtain popular locations within the user's primary activity area. STACP [5] considered both geographic and temporal information, and they studied the behavior of users at different periods. Recently, some methods based on graph neural networks have been proposed [9; 10; 11]. For example, GPR [9] built the POI-POI graph by connecting adjacent POIs in the interaction sequence of users and POIs, and they used an exponential function to measure the physical distance between POIs to learn POI representations. HGMAP [10] additionally constructed a user-user graph based on social relationships, as well as a POI-POI graph based on geographic location, and combined the embedding of multiple graphs to obtain multiple user preference scores. GEAPR [11] learned user representations with the help of several factors, including user attributes, and used attention mechanisms to achieve interpretable recommendations. Data sparsity poses a great challenge to GNN-based recommendation [12]. Although social information and node attributes can alleviate the data sparsity problem to some extent, this auxiliary information is often not available due to the need to protect user privacy.
# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 5 Results and Discussion
### Performance Comparison
#### 5.4.2 Malicious Node Detection vs. Adversarial Density  
To evaluate robustness under varying levels of adversarial infiltration, we compare the detection accuracy of our framework with [PERSON] et al. [39], [PERSON] et al. [40], and [PERSON] et al. [33] under increasing percentages of malicious nodes (5%, 10%, 15%, and 20%). Figure 6 presents the comparative results.  
In low adversary settings (5%), all models perform competitively, with detection rates ranging between 88 and 90%. Our framework achieves 90%, matching or exceeding all  
Figure 5: The detection rate of malicious nodes against different network sizes as compared to [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40]  
Figure 6: Detection rate of malicious nodes against adversarial density as compared to [PERSON] et al. [33], [PERSON] et al. [39], and [PERSON] et al. [40]  
baselines. As adversary density increases, the gap between traditional models and the proposed approach becomes more evident.  
At 10-15% adversarial presence, while [PERSON] et al. [39] and [PERSON] et al. [33] experience a 3-6% decline in detection accuracy, our framework maintains a relatively stable performance at 87-85%, indicating superior resistance to moderate-scale trust disruption. At the 20% mark, our method outperforms all baselines, achieving 85% detection--4% higher than [PERSON] et al. [33], 4% higher than [PERSON] et al. [39], and 2% higher than [PERSON] et al. [40].  
This stability under pressure is attributed to our model's use of cross-layer trust signals, including both communication-based metrics and immutable behavioral logs recorded on the IOTA Tangle. Malicious nodes struggle to generate legitimate Tangle contributions. This leads to rapid and consistent detection, even when attackers outnumber defenders.
## 3 Experimental Results and Analyses
### Experiment in Waters with Flat Seabed Topography  
In the flat terrain area, the seabed gradually changes from northwest to southeast, with an average depth of 40 m. Additionally, this region features fewer underwater features or obstacles, as shown in Figure 12. During this experiment, a 1-meter-diameter object model was submerged underwater to enhance the texture richness of the seafloor.
## 2 Material and Structure of Frustum Observation Window  
### Material  
The selection of the observation window material is based on considerations such as strength, impact resistance, density, and transparency. PMMA is a classic viscoelastic material that exhibits the dual properties of both a viscous liquid and an elastic solid. This material exhibits noticeable creep characteristics which can keep the stress at a low level and do not result in significant stress concentrations. The mechanical characteristics of the PMMA frustum observation window under current investigation are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Property** & **Value** \\ \hline Elastic Modulus, \(E/\)MPa & 2740 \\ Density, \(\rho/\)kg\(\cdot\)m\({}^{-3}\) & 1190 \\ Poisson’s ratio, \(u\) & 0.38 \\ Compressive yield strength, \(\alpha_{\mathrm{s}}/\)MPa & 115 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of PMMA.
## 2 The Proposed Method
### Invertible Denoising Network
#### 2.2.1 Invertible Wavelet Transform  
To facilitate the learning of low-resolution clean images during forward propagation, the InvDN incorporates the Invertible Discrete Wavelet Transform (DWT). The Haar wavelet transform is specifically employed to enhance the feature channels and downsample the feature maps. The wavelet transform converts an input image of size (H, W, C) into a new image of size (H/2, W/2, 4C). The input image is decomposed by the Haar wavelet into a low-frequency representation and three high-frequency representations in the vertical, horizontal, and diagonal directions [13]. In this study, the Haar DWT is employed; however, alternative DWTs, such as the Daubechies and Coiflet wavelet transforms, can also be utilized.
# Rare Earth Elements Uptake by Synthetic Polymeric and Cellulose-Based Materials: A Review
## 6 Factors Affecting REE Uptake  
### Sorption and Functional Groups  
The usage of polymeric materials dominates REE uptake research, and most materials studied for this purpose are polymeric in nature [24; 25; 26]. In general terms, REE sorption processes for batch experiments have been described using adsorption isotherms. Adsorption is defined as the increase in the concentration of a chemical substance in an interphase in which one of the phases is a solid substance. The nature of the surface interactions determines the resistance of the retained substance (adsorbate) for being removed from the solid surface (adsorbent) [27; 28]. Langmuir and Freundlich isotherms are probably the most used models for this purpose. Although other adsorption isotherm models have also been evaluated for experimental results, the Langmuir and Freundlich isotherms are preferred for their simplicity. A Langmuir model states that all active sites are equivalent and have the same affinity for the adsorbate (monolayer model), while a Freundlich model states that the active sites have different affinities for the adsorbate, and they are occupied starting from the sites with the highest affinity for the adsorbate and constantly generating new active sites with lower affinity (multilayer model) [27]. Applying those models for REEs, which are found as cations in aqueous media, should consider interactions between REEs and a proper moiety present in the surface of the material. The nature of these interactions could be by means of electrostatic attraction (ionic interactions with a moiety of opposite charge) or through an electron free pair present in the moieties of the surface of the adsorbent material (coordination) [27; 28; 29] (Figure 3). Polymers and their derivatives incorporate into their surface SO\({}_{3}\)\({}^{-}\), -OH, COOH, -NH\({}_{2}\), -NH, and -MO functional groups and moieties [24; 25; 26; 27; 28]. Due to the chemical properties of REEs, ionic-type interactions are preferred due to REEs' high charge (3+) [27; 28; 29]. Most of the materials evaluated for REE uptake fit into the Langmuir sorption model due to the fact that most polymeric materials evaluated include only one type of functional group, thus presenting active sites with the same affinity for REE cations. Incorporation of different functional groups or combining different polymers in the same material could make possible the application of the Freundlich sorption model in some cases [27; 28].
## 5 Conclusions  
The main goal of this study was to investigate the impact of orthogonal coded excitation for MLT crosstalk suppression. The results demonstrated that coded excitation could be used to suppress MLT crosstalk, although a Tukey-Tukey apodization is still required when the number of MLT beams is larger than the number of orthogonal codes. Moreover, coded excitation could help maintain the transmit energy (thus the SNR), which can be largely reduced as a result of increasing MLT beams. Overall, the benefit of applying coded excitation on an MLT imaging scheme to further suppress crosstalk artifacts remains limited, however when seeking a balance between frame rate and SNR, coded excitation could indeed be a valid alternative.  
The following are available [[http://www.mdpi.com/2076-3417/9/3/486/s1](http://www.mdpi.com/2076-3417/9/3/486/s1)]([http://www.mdpi.com/2076-3417/9/3/486/s1](http://www.mdpi.com/2076-3417/9/3/486/s1)), Video S1: Figure13_a; Video S2: Figure13_b; Video S3: Figure13_c.  
**Author Contributions:** Conceptualization, L.T., A.O., J.D.; Methodology, L.T., A.O.; Software, L.T.; Validation, L.T., Q.H., and A.R.; Writing-Original Draft Preparation, L.T.; Writing-Review & Editing, L.T., J.D., P.T., A.R., J.L., Q.H.; Visualization, L.T.; Supervision, J.D., J.L., P.T.; Funding Acquisition, L.T., A.R., J.L., J.D. and P.T.  
**Funding:** The research leading to these results has received funding from China Postdoctoral Science Foundation (2014 MP560094), National Natural Science Foundation of China (81471665, 61871251, and 81561168023), the National Key R&D Program of China (2016 YFC0102200 and 2016 YFC0104700), European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC Grant Agreement number 281748. [PERSON] is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 786027 (ACOUSTIC).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Validation and Data Analysis with ML  
### Selected Attacks  
To test and validate our contribution, we selected and tested several types of attacks on IIoT networks. For that purpose, we based our selection on the work of [PERSON] et al. [39] who presented the major attacks on a BC/ML-based application for IoT networks. Therefore, Table 4 exhibits the set of selected attacks, which are structured with respect to the concerned layers of our architecture.  
The upper layer concerns various attacks such as poisoning, evasion, impersonate and inversion attacks. The application layer incorporates the most important attacks against IIoT applications such as injection, brute force, buffer overflow and XSS attacks. Flooding and desynchronization are examples of transport layer attacks. Attacks against the network/protocol layer are considered as the bulky portion of attacks by including DoS, DDoS, MitM, HELLO flood and Sybil attacks. Finally, eavesdropping and RFID tracking jamming belong to physical sensing layer attacks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Attacks** & **Description** \\ \hline \multirow{6}{*}{ML, Data and cloud services} & \multirow{2}{*}{Evasion} & \multicolumn{2}{c}{Attack against ML via injecting adversarial samples to the training data in order to distort the model prediction} \\  & & & Samples are changed at the inferring phase to evade detection \\ \cline{3-3}  & & & Prefers to imitate data samples from victims, in particular for application scenarios related to image recognition \\ \hline \multirow{6}{*}{Application} & \multirow{2}{*}{Brute force Buffer overflow XSS} & Untrusted data that are sent to an interpreter or database An attempt to guess a password via sending various passwords This attack is targeting SCADA system and tries to overwrite a buffer to disrupt controller activity A kind of injection attack sent via a browser script \\ \hline \multirow{2}{*}{Transport} & Flooding & Repeating the request of a new connection until the IIoT system reaches maximum level Disruption of an existing connection \\ \cline{3-3}  & \multirow{2}{*}{DoS} & Attempt to stop or reduce activity of an IIoT \\ \cline{3-3}  & & DDoS & A distributed DoS attack from several location Violating data confidentiality or integrity during transfer \\ \cline{3-3}  & & HELLO flood & Uses HELLO packets as weapon to launch the attack on IIoT system \\ \hline \multirow{6}{*}{Physical sensing} & \multirow{2}{*}{Eavesdropping} & \multicolumn{2}{c}{Deducing data sent by IIoT devices across network Modifying a content of a tag or trying to disable it} \\ \cline{3-3}  & & RFID tracking Jamming & Creating radio interference and exhaustion on IIoT devices \\ \hline \hline \end{tabular}
\end{table}
Table 4: Layer-based attacks in IIoT networks.