esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Research Contributions  
In accordance with the findings from the literature review and to the best of our knowledge, research is still lacking in the field of PD-NOMA for TI applications. The performance analysis and evaluation of PD-NOMA in TI have not been comprehensively conducted in the past. Therefore, in this research, we have developed a novel downlink PD Single-Input Single-Output (SISO) NOMA communication scenario for TI, employing multiple sensors and actuators (e.g. users), focusing on transmitted signals from a BS and processed received signals at the user's end. Furthermore, the Key Performance Indicators (KPIs) are derived, discussed and evaluated to compare the performance characterisation of the NOMA and OMA schemes. The main contributions of this paper are highlighted below.  
1. We developed an analytical system model incorporating Signal-to-Interference and Noise Ratio (SINR), sum rate, fair Power Allocation (PA) coefficients and latency among the available SISO-NOMA users.  
2. We compared and analysed BER for SISO-NOMA and OMA schemes with varying path loss exponent and fixed PA coefficients using two- and three-user scenarios. To this end, we compared the achievable sum rate and latency trends for the SISO-NOMA and OMA schemes for fixed PA coefficients.
3. The outage probability, achievable sum rate and latency trends are compared and analysed for fixed and fair PA coefficients. To this end, the performance trend of outage probability for SISO-NOMA users in varying fixed PA coefficients is also analysed.
4. Finally, the achievable sum rate and latency are compared and analysed between SISO-NOMA and 4 \(\times\) 4 Multiple-Input Multiple-Output (MIMO) NOMA, incorporating a zero forcing-based beamforming and a round-robin scheduling process.
# TSINet: A Semantic and Instance Segmentation Network for 3D Tomato Plant Point Clouds
## 2 Materials and Methods  
### Data Acquisition  
For the semantic and instance segmentation tasks in this research, we utilized 3D tomato plant point cloud data from the Pheno4D [23] dataset. Pheno4D is a large-scale spatio-temporal plant point cloud dataset that includes two crop types: maize (seven plants over 12 days) and tomato (seven plants over 20 days). All plants were cultivated in pots within a greenhouse and scanned using a high-precision 3D laser scanning system with spatial accuracy reaching the sub-millimeter level (less than 0.1 mm). Figure 1 shows three tomato plants at different growth stages.  
Figure 1: Demonstration of some point clouds from Pheno4D.  
The dataset contains continuous scanning data of seven tomato plants over multiple growth stages during a three-week period following initial sprouting, resulting in 140 high-resolution tomato plant point clouds. Among them, 77 point clouds were manually annotated, with each point labeled as \"soil\", \"stem\", or \"leaf\". In addition, Pheno4D provides temporally consistent organ labeling, meaning that the same plant organ (e.g., a specific leaf) is assigned an identical instance label across different time points. Each individual leaf on a given plant is annotated with a distinct instance label to set it apart from other leaves, as illustrated in Figure 2b.
# Recent Developments in Plasmonic Sensors of Phenol and Its Derivatives
## 2 Plasmonic Resonance-Based Colorimetric Sensor for Phenolic Compounds
Furthermore, some specific studies on smart MOF nanomaterials can effectively detect contaminants of phenolic compounds in the environment, which are extremely significant for human health protection. For example, the dual-functional Co-MOF-74-based Co\({}_{3}\)O\({}_{4}\) nanoparticle-decorated cellulose derivative membrane has been well-synthesized for colorimetric detection of phenol [69]. Moreover, Zr-based MOFs capped with polyvinylpyrrolidone have been reported to successfully contribute to the application of colorimetric detection of phenol [70]. However, relying on the self-assembly of copper ions and DNA, the authors have successfully fabricated a novel copper hybrid nanoflower, which induces a new paper-based microfluidic device. This device has displayed effective application for the colorimetric detection of catechol, dopamine, and hydroquinone [20]. In addition, UV/Vis detection of 2,4-dichlorophenol has been successfully performed using an enzyme mimic that has been well-synthesized by Cu ion and adenosine monophosphate [71]. Through a solvothermal route, a new core-shell nanostructured metal of Au and Ni (Au@Ni) with a size \(<\) 8 nm on reduced graphene oxides (rGOs) was designed to obtain a novel nanomaterial composite as Au@Ni/rGO (Figure 3A). Subsequently, the peroxidase mimetic feature of this nanocomposite was checked by analyzing the process oxidation of 3,3\({}^{\prime}\),5,5\({}^{\prime}\)-tetramethylbenzamideidine in H\({}_{2}\)O\({}_{2}\). As shown in Figure 3B, the examined results indicated that this nanocomposite exhibited excellent peroxidase mimetic activity, which has been successfully applied in colorimetric detection of phenol, relying on its oxidative reaction with 4-aminoantipyrine, thereby inducing a change from colorless to pink of the quinoid dye in the presence of H\({}_{2}\)O\({}_{2}\). The detection limit and range of this method have been estimated for phenol concentrations as low as 1.68 uM and 1-300 uM, respectively. In addition, under natural irradiated sunlight, these Au@Ni/rGO nanostructures exhibit an excellent photocatalytic reaction, thereby degrading over 87% of phenol and phenolic substances as 2-chlorophenol and 2-nitropenol [72]. However, reduced graphene-based magnetic MOF nanocomposites exhibited an excellent enzyme-like feature. Phenol can be identified based on this property using a visual colorimetric method in water solution by oxidization of 4-aminoantipyrine in the presence of H\({}_{2}\)O\({}_{2}\). Simultaneously, these nanocomposites have also been exposed to a specific feature as a Fenton-like catalyst, which shows a high ability to degrade phenol effectively [73].
# Evaluation of Machine Learning Assisted Phase Behavior Modelling of Surfactant-Oil-Water Systems
## 3 Results and Discussion
### Prediction Under Varying Salinities Using the SVM Model  
After validating the cubic SVM model, we evaluated its performance under varying salinity conditions: under-optimum, optimum, and over-optimum. This allowed us to test its ability to interpolate and predict phase transitions. The results, shown in Figure 7, demonstrate the model's ability to capture key physical behaviors.  
For under-optimum salinity, the SVM accurately identified the shrinking of type II+ region and consistent growing of type II\(-\) region. Similarly, for over-optimum conditions, the SVM model accurately predicted the shrinking II\(-\) region and growing II+ two-phase region. For optimal conditions, the model correctly predicted middle-phase microemulsion formation and equilibrium states, aligning closely with the physical model. It is worth noting that even though the shrinking of the two-phase region size was small, the model could still identify transitions from three-phase to two-phase to single-phase regions in compositional space.  
Figure 8 illustrates the cubic SVM model's ability to predict phase behavior across varying salinities (y-axis) and surfactant concentrations (x-axis) at a fixed water-oil ratio (WOR = 1). The model captured the expected transitions between Winsor Type I, II, and III regions, with clear phase boundaries. The accurate phase predictions within an interpolative setup demonstrate the model's robustness and ability to generalize physical behavior from the training data. The SVM model's smooth interpolation between phase regions under varying conditions underscores its value for practical applications, such as in enhanced oil recovery (EOR) reservoir simulation modeling, where both salinity and surfactant concentrations fluctuate dynamically.
# Blockchain-Assisted Verifiable and Multi-User Fuzzy Search Encryption Scheme
## 6 Program Analysis
### Security Analysis
\begin{array}{l}Pr[Ind_{\mathcal{A}}(\lambda)=1]=\frac{1}{2}+Adv_{\mathcal{A} }(C,C^{*})+Adv_{\mathcal{A}}(I_{real},I^{*})+Adv_{\mathcal{A}}(T_{\mathbb{Q}}, T^{*})\\ =\frac{1}{2}+|Pr[Encrypt(F_{i},K_{i})\to C]-Pr[Sim\to C^{*}]|\\ +|Pr[BuildIndex(KS,F_{i},KW_{F})\to I_{real}]-Pr[Sim\to I^{*}]|\\ +|Pr[Trapdoor(KS,W_{q}^{*})\to T_{\mathbb{Q}}]-Pr[Sim\to T^{*}]|\\ \leq\frac{1}{2}+negl_{1}{}^{*}(\lambda)+neg_{2}{}^{*}(\lambda)+negl_{3}{}^{*} (\lambda)\end{array}  \tag{4}\]  
In summary, the outputs of \(Rel_{\mathcal{A}}(\lambda)\) and \(Ideal_{\mathcal{A},\,B}(\lambda)\) obtained for any polynomial adversary are indistinguishable. \(\square\)  
Theorem 4.2: _If the blockchain is tamper-proof, the scheme ensures that user transactions are fair._  
Proof: If the \(CS\) is dishonest, it will return incorrect file numbers or falsify search results. According to the fair-trade rules set by \(SC\), \(CS\) will not receive service fees and will be penalized with loss of deposit. If \(CS\) enforces the contract rules honestly, \(DU\) gets the correct result and \(CS\) gets the corresponding service fee. In addition, the transaction specifies a limit time \(L_{t}\), when the transaction time is greater than \(L_{t}\), the user's deposit will be refunded, and the transaction will be ended. \(\square\)  
Theorem 4.3: _For malicious cloud servers and other external adversaries, no information can be learned about plaintext files except for ciphertext files._  
Proof: In the scheme, the file is encrypted with the corresponding symmetric key \(K_{i}\) before it is uploaded to the \(CS\), then \(DO\) encrypts \(K_{i}\) as \(C_{K_{i}}\) based on CP - ABE and stores it in the index tree \(Index_{tree}\). Even though the \(CS\) and external adversaries eavesdrop on the ciphertext files in the channel, the difficulty of trying to get the plaintext information is equal to the difficulty of decrypting \(K_{i}\). Since \(\overline{C}=k_{i}e(g,g)^{as}\) in \(C_{K_{i}}\), \(e(g,g)^{as}\) must be computed to decrypt \(K_{i}\). Under the discrete logarithm problem, the external adversary and \(CS\) to compute \(e(g,g)^{as}\) from \(C=h^{s}\) and \(e(g,g)^{as}\) is difficult. Based on the \(Diffe-Hellman\) computational problem, the adversary cannot use the Lagrange interpolation formula to recursively compute \(e(g,g)^{rs}\) without the attribute key \(SK_{u}\) that conforms to the access policy and obtain \(K_{i}\). \(K_{i}\) and the plaintext file can be decrypted when \(SK_{u}\) satisfies the access structure. Since \(SK_{u}\) is privately stored by the \(DU\), it is not possible for \(CS\) and external adversaries to learn any plaintext information other than the ciphertext file. \(\square\)
# Design Approach and Mechanism Analysis for Cavitation-Tolerant Torque Converter Blades
## 3 Application of Suppression Slot in Stator Cascade
### Hydrodynamic Performance Comparison of Slot Designs  
The hydrodynamic performance of the HTC with the different suppression slots is shown in Figure 8. \(K\) deteriorates over the entire \(SR\) range after the suppression slots are added to the stator blade. Slot Plan 3 shows the greatest \(K_{0}\) degradation with the widening of the slot and the decrease in the mass flow rate in the mainstream domain. By contrast, Plan 1 exhibits the smallest \(K_{0}\) degradation relative to the baseline. The \(T_{\text{bg}}\) curve shows that the addition of the suppression slot heavily degrades \(T_{\text{bg}}\). Under the overall operating conditions, except that the \(T_{\text{bg}}\) values of Plan 4 under \(SR\) = 0.7 and 0.8 are higher than  
Figure 6: Evolution of 10% vapor volume fraction distribution in turbine domain under stall operating condition.  
The cavitation distribution of the stator with the different suppression slots is shown in Figure 9. After the suppression slots are placed, the stator cavitation shifts from being an unstable cloud cavitation to being a stably attached cavitation, and the cavitation volume is greatly reduced. As shown in Figure 9, the cavitation of Plan 1 includes three types of cavitation, attached cavitation (located near the suction front edge of the stator), slot cavitation (located at the outlet of the suppression slot), and blade tail cavitation (at the trailing edge of the pressure side of the stator). Cavitation on the leading edge of the blade covers only half of the blade in the spanwise direction. Figure 9 shows that Plan 2 cavitation still covers the entire stator blade near the leading edge of the suction surface. In addition, the cavitation in the stator domain belongs to the attached cavitation. In Figure 9, the area covered by the cavitation of Plan 3 is smaller than that covered by Plan 2 cavitation due to the widening of the slot. The cavitation in the stator domain still belongs to the attached cavitation. The difference is that, as shown in Figure 9, the stator cavitation of Plan 4 is almost completely suppressed. There are only a few scatter cavitations on the stator blades, and there is no attached cavitation, and the stator cavitation is suppressed to a great extent.
## 3 Review of the Scheme of [PERSON] et al. [19]
### System Setup  
The system setup phase includes the following steps:  
1. The TA initializes an elliptic curve \(E_{p}(a,b)\), with \(P\) as its generator in \(\mathbb{G}\). A secret master key \(s_{TA}\), a one-way hash function \(h(\cdot)\), and a fuzzy verifier \(l\) (\(2^{4}\leq l\leq 2^{8}\)) are chosen by the TA. Secret and shared keys are deployed to each RSU.
2. The TA generates a distinct identifier \(ID_{j}\) for \(RSU_{j}\), computed as \(s_{j}=h(ID_{j}||s_{TA})\) and \(k=h(ID_{TA}||s_{TA})\). The triplet \((ID_{j},s_{j},k)\) is then shared with \(RSU_{j}\).
3. Upon receiving this information, \(RSU_{j}\) calculates a public key \(P_{j}=s_{j}P\) and securely stores the values \((k,s_{j})\). This exchange occurs via a secure communication channel.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Description** \\ \hline \(V_{i}\) & i-th vehicle \\ \(ID_{i}\) & Identity of \(V_{i}\) \\ \(PW_{i}\) & Password of \(V_{i}\) \\ \(RSU_{j}\) & j-th RSU \\ \(RSU_{k}\) & k-th RSU \\ \(ID_{j}\) & Identity of \(RSU_{j}\) \\ \(ID_{k}\) & Identity of \(RSU_{k}\) \\ \(E_{p}(a,b)\) & An elliptic curve \\ \(SC_{i}\) & Smart card \\ \(P\) & Generator of \(\mathbb{G}\) \\ \(RID_{i}\) & Pseudo-identity of \(V_{i}\) \\ \(TID_{i}\) & Temporary identity of \(V_{i}\) \\ \(Y,y\) & Public key and private key of TA \\ \(k\) & Shared key of TA and RSUs \\ \(Sig_{j}(\cdot)\) & Signature of \(RSU_{j}\) \\ \(SK_{ij}\) & Session key of \(V_{i}\) and \(RSU_{j}\) \\ \(s_{j}\) & Private key of \(RSU_{j}\) \\ \(P_{j}\) & Public key of \(RSU_{j}\) \\ \(n_{j},n_{k}\) & Random nonces \\ \(T_{a}\) & Timestamp \\ \(h(\cdot)\) & One-way hash function \\ \(\oplus\) & Exclusive OR operation \\ \(\parallel\) & Concatenation operation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notation Guide.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 4 Applications of LLMs in Maritime Operations
### Decision Support Systems
#### 4.3.1 Real-Time Risk Assessment  
Maritime operations generate vast amounts of data from sensors, navigational systems, weather reports, and other sources. LLMs can analyze this heterogeneous data to identify potential hazards and assess risks in real time. By recognizing patterns and anomalies, the model can alert operators to emerging threats such as adverse weather conditions, navigational obstacles, or equipment malfunctions [127; 128].  
Furthermore, LLMs can provide recommendations during critical operations by synthesizing information from multiple sources [129]. For instance, during complex maneuvers or emergency situations, the model can suggest optimal courses of action based on historical data and established best practices. This support enhances situational awareness and aids in mitigating risks proactively.
## 3 PPAD-Hardness  
In this section, we prove that it is PPAD-hard to find a stable multicommodity flow. This is true even if the digraph is acyclic, a special case relevant for example in economic applications like supply chains (see [1]). The problem that we reduce to it is the following stable fractional hypergraph matching problem. We are given a hypergraph \(H=(V,\mathcal{E})\), and for every node \(v\in V\) a linear order \(\prec_{v}\) on the hyperedges containing \(v\). A _fractional matching_ of \(H\) is a vector \(x\in\mathbb{R}_{+}^{\mathcal{E}}\) such that  
\[\sum_{e\
i v}x_{e}\leq 1\text{ for every }v\in V\,.\]  
A fractional matching is _stable_ if for every \(e\in\mathcal{E}\) there exists \(v\in e\) for which  
\[\sum_{e^{\prime}\succeq v}x_{e^{\prime}}=1\,.\]  
[PERSON] _et al_. [8] showed that finding a stable fractional hypergraph matching is PPAD-complete. We reduce this problem to the stable multicommodity flow problem in order to prove the following theorem.  
**Theorem 3.1**.: _It is PPAD-hard to find a stable multicommodity flow in a multicommodity network with preferences, even if the network is acyclic._  
Proof.: Let \((H=(V,\mathcal{E}),\prec)\) be an instance of the stable fractional hypergraph matching problem. We use the notation \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) and \(|\mathcal{E}|=m\). We construct a network whose node set consists of nodes \(s_{e},t_{e}\) (\(e\in\mathcal{E}\)) and \(v_{i}^{\prime},v_{i}^{\prime\prime}\) (\(i\in[n]\)). There are \(m\) commodities, one corresponding to each hyperedge \(e\in\mathcal{E}\), with source \(s_{e}\) and sink \(t_{e}\). The arc set of the network contains the arcs \(v_{i}^{\prime}v_{i}^{\prime\prime}\) (\(i\in[n]\)) with cumulative and individual capacities all 1. Furthermore for each hyperedge \(e\), whose elements are \(v_{i_{1}},v_{i_{2}},\ldots,v_{i_{k}}\) with increasing indices, we add the arcs \(s_{e}v_{i_{1}}^{\prime}\), \(v_{i_{1}}^{\prime\prime}v_{i_{2}}^{\prime},\ldots,\)\(v_{i_{k-1}}^{\prime\prime}v_{i_{k}}^{\prime}\), \(v_{i_{k}}^{\prime\prime}t_{e}\). On these edges we set the cumulative capacity and the capacity of the commodity \(e\) to 1, and the other individual capacities to 0.  
By the construction, each commodity has only one possible path, so we do not have to define precedence orderings for the nodes. In addition, only the arcs \(v_{i}^{\prime}v_{i}^{\prime\prime}\) can be used by more than one commodity, so it is enough to define precedence orderings for these arcs: let the ordering \(\prec_{v_{i}^{\prime}v_{i}^{\prime\prime}}\) be the same as \(\prec_{v_{i}}\).  
It is easy to see that the feasible multicommodity flows of this network correspond to the fractional matchings of the hypergraph \(H\). Furthermore, the stability of a multicommodity flow is equivalent to the stability of the corresponding fractional matching. Since the constructed network is acyclic, this proves the theorem.
## 4 Results  
The way we obtained results for the system that is the subject of the case study, after applying the methods described in Section 3.2, is presented in the following subsections, which delve into the intricate details of the proposed method's procedural steps and their application to the case study.  
Section 4.1 presents the method of modeling the profile during load peaks that satisfies predefined performance requirements. Section 4.2 describes the approach to experimentally obtain cost-optimal parameters for autoscalers, enabling the entire system to meet the specified QoS requirements.
# FEL Pulse Duration Evolution along Undulators at FLASH
## 3 FEL Measurements

Table 1: Main parameters derived from the measurements shown in Figure 3. The values were deduced from scaling the experimental data to the 3D simulation, with \(z_{sat}\) as saturation length in “number of undulators” with a maximum number of undulators of 12 at FLASH2, \(\tau_{ph}^{min}(FWHM)\) as minimum pulse duration, and \(E_{sat}\) as the saturation pulse energy. In order to compare the experimental data to the 1D simulation, the normalized electron pulse duration \(\tau_{el}\) (equivalent to the number of modes M) was determined for all measured wavelengths using Equation (4), and the coherence time at saturation \(\tau_{el}^{sat}\) (FWHM) was taken from Refs. [19; 33]. For all measurements except at 34 nm, the undulators were not tapered (all undulators contributing to the lasing had the same gap settings, i.e., the same K-value), in order to compare the experimental values with the simulations. For each wavelength, the pulse duration was first measured when all 12 undulators were closed (thus, all undulators contributed to the lasing). Afterward, starting with the undulators closest to the experiments, one pair of undulators at a time was opened until no measurable XUV pulse energy could be detected. Since only the downstream undulators were opened, the trajectory in the first (lasing) undulators was kept constant. The resulting shift of the source point of the XUV radiation enlarged the XUV focal spot in the streaking interaction region slightly; however, it was still sufficiently smaller (\(<\)300 \(\upmu\)m FWHM) as compared to the THz focal spot (\(\sim\)1 mm FWHM) and did not lead to significant changes for the THz streaking measurements. The energy of the XUV pulses was simultaneously measured with an absolutely calibrated pulse energy detector (GMD) [34] provided by the FLASH photon diagnostic. Figure 3: Evolution of the pulse duration (red circles) and pulse energy (blue circles) along the undulators. The actual undulator length z is normalized by the saturation length z\({}_{ssl}\). The pulse energy is normalized to the saturation energy and the pulse duration to the minimum pulse duration (Equation (4)). The experimental results (shown as points) represent the average over several thousand single pulse measurements and were recorded for FEL wavelengths of 8, 10, 12, 16, 20, and 34 nm and agree very well with the chirped 3D simulations (shown as red and blue solid lines). The 3D simulations without energy chirp (shown as red and blue dashed lines) are showing less agreement with the experimental data. The “error bars” denote not the experimental uncertainty but rather the width of the measured distribution induced by SASE and technical fluctuations to indicate by how much the FEL pulse energy and duration fluctuate during measurement (see also Figure 4).