esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 SPS: Grid-Based Single Phantom Node Source Location Privacy Protection Scheme
### The Phantom Node Determination Phase  
(1) When the monitored target appears in the network, the node closest to the target becomes the source node. The SoN sends a request packet Msg_request to the SN, requesting the SN to help it determine the PNCS. The transmission mode of the request packet is: The SoN randomly selects a near-hop neighbor node from its neighbor table as the next hop node. The next hop node also randomly selects a near-hop neighbor node as its own next hop node. This process continues until the request packet reaches the SN;  
(2) As shown in Figure 3, after the SN receives the request packet, it randomly selects \(M\) grids from the grids near the SoN while not in the visible area of the SoN (the grids outside the \(\text{P}_{1}\text{OP}_{2}\) area). Then, the SN randomly selects one node in each of the selected \(M\) grids to form a PNCS;  
(3) The SN sends the PNCS and the grid number of each node in the PNCS to the SoN;  
(4) The SoN randomly selects one node from the PNCS as the actually used PN with equal probability. All the nodes within the PNCS are chosen with equal probability.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Node ID** & **Minimum Hop Count from Sink Node** & **Grid Number** \\ \hline \(u\) & \(\text{Hop}_{u,\text{sink}}\) & \(G_{i\times j}\) \\ \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Neighbor table of node \(v\).  
Figure 3: The sink node helps select phantom node candidate set.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 7 Conclusions  
In this paper, we propose a novel tensor completion method based on low-rank approximation with nonconvex nonlinear transformations (LRANNT). This method fully leverages the underlying low-rank structure and significantly improves the completion results both numerically and visually. By combining the advantages of nonconvex norms and nonlinear transformations, we are able to more accurately estimate missing data. In the process of model solving, we employ the proximal alternating minimization (PAM) algorithm, which plays a crucial role in enhancing both the efficiency and accuracy of the solution. Additionally, we fine-tuned three key model parameters (\(\alpha\), \(\beta\), and \(d\)) through experiments to optimize model performance. We conducted experiments on three common public datasets: grayscale videos, color images, and MRI images. The experimental results  
Figure 6: Sensitivity analysis of parameters \(\alpha\), \(\beta\), and \(d\), exploring variations in PSNR and SSIM values under different parameter settings and sampling rates.  
demonstrate that our method outperforms several existing tensor completion methods, including TNN, UTNN, FTNN, and NTTNN, both visually and numerically. Our method shows exceptional performance under conditions of low sampling rates.  
In the future, we will further explore methods for nonlinear transformations using various nonconvex functions and approaches based on deep learning, in order to enhance the numerical completion performance of our models. Additionally, we will develop automatic parameter adaptation methods. Ultimately, we envision applying the model in various industries such as medical, sports, and transportation for video or image data analysis.  
Conceptualization, Y.M. and X.S.; methodology, Y.M. and X.S.; software, Y.M. and H.L.; validation, Y.M., X.S. and H.L.; formal analysis, Y.M.; investigation, X.S.; resources, X.S. and H.G.; data curation, Y.M. and X.S.; writing--original draft preparation, Y.M.; writing--review and editing, Y.M. and X.S.; visualization, Y.M.; supervision, X.S.; project administration, X.S.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62301056, the Fundamental Research Funds for Central Universities No. 2024 JCYJ005, and the National Natural Science Foundation of China under Grant 12371094.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 5 Reactor Systems and Process Parameters
### Energy and Efficiency Metrics  
The photocatalytic systems must be evaluated not only by contaminant removal efficiency, but also by their energy and cost performance. Common performance indicators include the following:  
* Quantum yield (QY): The ratio of degraded pollutant molecules to incident photons, reflecting light utilization efficiency.  
QY measures the efficiency of photon utilization in driving photocatalytic reactions, defined as the ratio of reacted molecules to absorbed photons. A higher QY indicates more effective use of light energy. [PERSON] et al. (2023) synthesized a reduced graphene oxide-cadmium telluride (RGO-CdTe) composite using a solvothermal method [163]. This composite achieved an apparent quantum yield (AQY) of 22.29% in the photocatalytic degradation of tetracycline antibiotics under visible light, a 2.63-fold increase compared to pure CdTe. The enhancement was attributed to improved charge separation and increased surface area provided by the RGO.  
* Electrical energy per order (EEO): The energy required to reduce the pollutant concentration by one order of magnitude in a unit volume, often used for comparing treatment technologies.  
EEO quantifies the energy required to reduce a pollutant's concentration by one order of magnitude in a unit volume, expressed in kilowatt-hours per cubic meter per order (kWh-m-3-order-1). Lower EEO values signify more energy-efficient systems [164]. The researchers calculated the EEO values to assess energy efficiency, finding that optimal conditions led to significant pollutant degradation with reduced energy consumption. This approach demonstrated the practicality of using EEO as a metric for comparing energy efficiencies across different advanced oxidation processes. These examples underscore the importance of incorporating both QY and EEO metrics when evaluating and optimizing photocatalytic systems for environmental remediation.
## 4 Results and Discussion
### Corrosion Tests  
#### 4.4.1 Bare Steel Plates  
Figures 5 and 6 show, respectively, the free corrosion potential values and the corrosion rates of bare steel plates embedded in cracked concrete as a function of wet-dry cycles. Just after the exposure to the chloride environment, all the steel plates assumed activation values lower than \(-500\) mV/SCE, reflecting a general high corrosion risk, regardless of the type of concrete mixture. At the same time, the related polarization resistance values did not change significantly with the addition of FA or when natural aggregates were replaced with recycled ones, thus indicating similar corrosion rates for the different concretes. Therefore, the total replacement of natural aggregates, with or without high FA volume, does not seem to negatively affect the corrosion behavior of embedded steel reinforcements when an adequate strength class is guaranteed. Moreover, despite the reduction in concrete pore solution alkalinity due to the pozzolanic reaction of fly ash, the corrosion behavior of steel reinforcements in high-volume fly ash concrete seems not to be negatively affected, at least for cracked concrete.  
To better analyze the electrochemical behavior, all specimens were autopsied after 7 wet-dry cycles in order to visually evaluate the corroded area and to assess the weight loss of the bare steel plates after pickling. A visual observation of the corrosive attack on the steel plates embedded in the different concrete mixtures is reported in Figure 7. All the bare steel plates showed significant  
Figure 5: Corrosion potential of bare steel plates in cracked concrete as a function of wet–dry cycles.  
Figure 6: Polarization resistance of bare steel plates in cracked concrete as a function of wet–dry cycles.  
corrosive attack in the crack area, due to the high Cl concentration detected on the steel reinforcement surface (2-5% by weight of cement), thus overcoming the concentration threshold (0.4% by cement weight), which is considered the critical value able to induce the corrosion of bare steel. However, from a morphological point of view, in the presence of recycled aggregate (Rec-0.3) or when a high amount of fly ash is used (Nat + FA-0.6), the corrosive attack appears more diffuse and less penetrating (see Figure 7).
## 3 Results and Discussion
### Effect of Thrust Force  
Figure 11 shows the effect of thrust force on the vibration transmissibility of the wrist. As can be seen from Figure 11, the trend of the vibration transmissibility of the wrist part is similar under different grip forces and arm posture, and the resonance frequency increases correspondingly with the increase of the thrust force, while the resonance peak value increases with the increase of the thrust force. The trend of the increase of the resonance peak value is related to the size of the grip force: the smaller the grip force the faster the increase of the resonance peak value, and the larger the grip force the slower the increase of the resonance peak value. At the grip force of 60 N, the resonance peak increases slightly with the increase of the thrust force. The figure also shows that left of the intersection point, the vibration transmissibility of the wrist part under the SP1 posture decreases with increasing thrust force, while the vibration transmissibility of the wrist part under SP2 posture decreases slightly with increasing thrust force.  
The trend of the effect of thrust force on the transmissibility at the wrist site is the same as that of the literature [14], and the resonance peak value at the wrist site in the literature [14] increases slightly with increasing thrust force; this difference may be due to the relatively small thrust force (20 N), which was used in this experiment, and the relatively large thrust force (100 N) used in the literature [14]. The two transmissibility curves in Figure 11c for a thrust force of 50 N and 80 N are also particularly close, which is consistent with the results of the literature [21] (transmissibility is almost constant when grip and thrust forces reach a certain value).
# An Automatic Software Defect Repair Method Based on Multi-Objective Genetic Programming
## 2 Related Concepts
### Multi-Objective Optimization Problem  
Most previous genetic programming approaches only considered the number of test cases passed when selecting offspring variants. However, in real-world tasks, there are often multiple competing objectives that need to be optimized simultaneously, which can be formulated as a multi-objective optimization problem (MOP) [22]. Mathematically, a general MOP can be expressed as:  
\[\text{min}F(x)=\left(f_{1}(x),f_{2}(x),\ldots,f_{n}(x)\right)^{T} \tag{1}\]  
The value of F(x) is composed of n objective functions, and the value of x is an n-dimensional decision vector. The n objective functions in Equation (1) are often conflicting, meaning that increasing one objective function may lead to a decrease in others. Therefore, there is usually no single solution that can optimize all objective functions simultaneously. To solve MOPs, attention needs to be paid to approximating the Pareto Front (PF), which represents the best trade-off among objectives. The concept of PF is formally defined as follows:  
**Definition 1**.: _Pareto Dominance [23], where vectors \(p=\left(p_{1},p_{2},\ldots,p_{m}\right)^{T}\) and \(q=\left(q_{1},q_{2},\ldots,q_{m}\right)^{T}\), \(p\) dominate \(q\), denoted \(p\prec q\), if and only if \(\forall i\in\left\{1,2,\ldots,m\right\}\), \(p_{i}\leq q_{i}\), and \(p=\left(p_{1},p_{2},\ldots,p_{m}\right)^{T}\), \(\exists j\ \in\ \left\{1,2,\ldots,m\right\}\), \(p_{j}\leq q_{j}\)._  
**Definition 2**.: _The PF is the subset of solutions that are not dominated by any other solutions._  
Due to the population-based nature of Evolutionary Algorithms (EAs), they are able to approximate the PF of a MOP in a single run by obtaining a set of non-dominated objective vectors, from which a decision-maker can select one or more to satisfy their specific needs. These evolutionary algorithms are referred to as Multi-Objective Evolutionary Algorithms [24]. In the context of a suitable multi-objective scenario, Multi-Objective Genetic Programming utilizes a Multi-Objective Evolutionary Algorithm approach to evolve a set of candidate patch programs for multiple objectives.
## 4 Deployment of CATIM in an Industrial Case Study
### Technical Assets  
We defined the technical asset types to be used in our case study and, starting from the available technical information, we generated the correspondent technical assets:  
* Short text, taken verbatim from the real manual: \"Open shut-off valve 7 until you read a work pressure value on the pressure gauge\".
* Long text, that details the instruction for novice users: \"Turn counterclockwise the shut-off valve 7 that you can find at the bottom right, until you read a pressure value of 35 bar on the pressure gauge, then stop\".
* Short audio, that is the short text delivered by a speech synthesis program.
* Long audio, that is the long text delivered by a speech synthesis program.
* Annotated photograph, that is a picture of the valve with annotations useful to understand the tasks to accomplish (Figure 5a).
* Product model, that is the animation of the CAD model of shut-off valve 7 to indicate the direction of rotation, and of the gauge to locate it (Figure 5b).
* Video tutorial, that is the recording of the operation previously accomplished by an operator (Figure 5c). As for the annotated photograph, the video can be either screen-fixed or world-fixed.  
Figure 5: Multimedia asset generated to convey the instruction: (**a**) an annotated photo, (**b**) an animated CAD models superimposed on valve, a (**c**) video tutorial shows the task performed by an operator.  
Through AR devices, visual assets can be either directly superimposed on real-world referents (world-fixed) or fixed to a designated area within a display's field of view (screen-fixed). A photograph can be rendered at a fixed location on the GUI (i.e., screen-fixed) or at a specific location in the real world (i.e., world-fixed).
## 5 Synergistic Effects of Integrating IoT, LLMs, and Federated Learning
### Theme 3: Scalable and Adaptive Domain Specialization at the Edge  
The Challenge: Deploying large, general-purpose LLMs directly onto resource-constrained IoT devices is often infeasible due to their size and computational requirements [62]. While smaller, specialized models can run on the edge, training them from scratch for every specific IoT application or location is inefficient and does not leverage the power of large pre-trained models [15]. Centralized fine-tuning of large models for specific domains requires access to potentially private or distributed IoT data [13].  
The Synergy (IoT + LLM + FL): FL combined with PEFT techniques like LoRA [70] provides a highly scalable and resource-efficient way to specialize pre-trained LLMs for diverse IoT domains using distributed edge data [13; 60]. IoT devices/edge servers provide the specific local data needed for adaptation [14]. PEFT ensures that only a small fraction of parameters need to be trained and communicated during the FL process, drastically reducing computation and communication overhead [61; 82]. The base LLM provides the powerful foundational knowledge, while FL+PEFT enables distributed, privacy-preserving specialization [71].  
Emergent Capability: This synergy enables the mass customization and deployment of powerful, specialized AI capabilities directly within diverse IoT environments. Key outcomes include the following:  
* Locally Optimized Performance: Models fine-tuned via FL+PEFT on local IoT data will likely outperform generic models for specific edge tasks (e.g., a traffic sign recognition LLM adapted via FL to local signage variations [14]).
* Rapid Adaptation: New IoT devices or locations can quickly join the FL process and adapt the shared base LLM using PEFT without needing massive data transfers or full retraining [10].
* Resource-Aware Deployment: Allows for leveraging powerful base LLMs even when end devices can only handle the computation for small PEFT updates during FL training [79], or optimized inference models (potentially distilled using FL-trained knowledge [86]). Frameworks like Split Federated Learning can further distribute the load [17; 18].
# Nonlinear Vibrations of Carbon Nanotubes with Thermal-Electro-Mechanical Coupling
###### Abstract  
Carbon nanotubes (CNTs) have wide-ranging applications due to their excellent mechanical and electrical properties. However, there is little research on the nonlinear mechanical properties of thermal-electro-mechanical coupling. In this paper, we study the nonlinear vibrations of CNTs by a thermal-electro-mechanical coupling beam theory. The [PERSON] method is used to discretize the partial differential equation and obtain two nonlinear ordinary differential equations that describe the first- and second-order mode vibrations. Then, we obtain the approximate analytical solutions of the two equations for the primary resonance and the subharmonic resonance using the multi-scale method. The results indicate the following three points. Firstly, the temperature and electric fields have a significant influence on the first-mode vibration, while they have little influence on the second-mode vibration. Under the primary resonance, when the load amplitude of the second mode is 20 times that of the first mode, the maximal vibrational amplitude of the second is only one-fifth of the first. Under the subharmonic resonance, it is more difficult to excite the subharmonic vibration at the second-order mode than that of the first mode for the same parameters. Secondly, because the coefficient of electrical expansion (CEE) is much bigger than the coefficient of thermal expansion (CTE), CNTs are more sensitive to changes in the electric field than the temperature field. Finally, under the primary resonance, there are two bifurcation points in the frequency response curves and the load-amplitude curves. As a result, they will induce the jump phenomenon of vibrational amplitude. When the subharmonic vibration is excited, the free vibration term does not disappear, and the steady-state vibration includes two compositions.  
carbon nanotubes; thermal-electro-mechanical coupling beam model; Galerkin method; nonlinear vibration; multi-scale method +
Footnote †: journal: Applied Sciences
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 3 Recommended Deep Learning for Inverter Sizing
### Results  
In this study, a system with a range of 1-5 kWp solar power capacity and an inverter of 2 kWp installed at longitude 44\({}^{\circ}\)28\({}^{\prime}\) E and latitude 33\({}^{\circ}\)14\({}^{\prime}\) N were considered. The system cost and power records were obtained with the aid of the system advisor model (SAM) [111]. According to [112], the annual weather temperature and irradiance records of Baghdad, Iraq-can be depicted in Figure 7.  
Figure 6: Diagram of a recommended approach to optimize PV array DC/AC inverter power, while maximizing yearly energy yield for on-grid photovoltaic systems that use Deep Learning networks.  
The observations show that the irradiance-to-temperature ratio does not remain consistent throughout the year. In addition, there is a difference in the ratio of irradiance to temperature between the year's growth and deterioration halves. To verify the proposed approach, we employed Bayesian optimization CNN with the main specifications listed in Table 5, while the main interesting result showing the relationship between the system cost with respect to PV capacity and the DC/AC ratio for this system is shown in Figure 8.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Description** & **Dimensions** \\ \hline Minimum Batch Size & 128 \\ Initial Learning Rate & 0.0003 \\ Maximum Epochs & 15 \\ layers convolution 2d Layer 3 & 3 \\ batch Normalization Layer & 1 \\ relu Layer & 1 \\ Maximum Pooling 2d Layer 3, Stride = 2 & 3, 2 \\ convolution 2d Layer 3, 2 \(\times\) Number of filters & 3, 2 \(\times\) 12 \\ batch Normalization Layer & 1 \\ relu Layer & 1 \\ maximum Pooling 2d Layer 3, Stride = 2 & 3, 2 \\ convolution 2d Layer 3, 4 \(\times\) Number of filters & 3, 4 \(\times\) 12 \\ batch Normalization Layer & 1 \\ convolution 2d Layer 3, 4 \(\times\) Number of filters & 3, 4 \(\times\) 12 \\ batch Normalization Layer & 1 \\ convolution 2d Layer 3, 4 \(\times\) Number of filters & 3, 4 \(\times\) 12 \\ batch Normalization Layer & 1 \\ batch Normalization Layer & 1 \\ Maximum Pooling 2d Layer (time Pool Size 1) & 1 \\ dropout Layer & 1 \\ fully Connected Layer (12 = numClasses) & 12 \\ Soft-max Layer & 1 \\ classification Layer & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: List of the main specifications of the proposed Bayesian optimization architecture.  
Figure 7: The annual weather temperature and irradiance records of Baghdad, Iraq.  
The obtained results demonstrate that there is a global minimal point for a particular system of specified environmental conditions when minimizing the cost of a system with a 2 kW inverter. Moreover, a minimal cost of 2223 USD was obtained associated with an optimal DC/ AC system ratio of 1.287 and a required PV capacity of 2.32 kWp.