esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Engineering Application
### Degradation Trend Prediction of the FTU  
To forecast the degradation trend of the FTU according to the historical PDIs, the TPA-LSTM model was established. The main parameters are listed in Table 3. The mean square error was selected as the loss function, and the Adam optimizer was adopted to obtain a dynamic update of the learning rate. The obtained PDI curve included 7776 points, which were divided into a training set and test set in a ratio of 7:3. The model was trained for 300 epochs and the final prediction result is shown in Figure 15.  
Figure 14: PDI curve of the FTU.  
Figure 13: The healthy-state model based on GPR: (**a**) the three-dimensional surface of the mean value; and (**b**) the standard distribution at specific conditions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Length of the input & 12 \\ Length of the output sequence & 1 \\ Number of hidden units in the LSTM layer & 32 \\ Number of kernels in the 1 DCNN layer & 10 \\ Batch size & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main parameters of the TPA–LSTM.  
The root mean square error (\(RMSE\)), mean absolute error (\(MAE\)) and \(R^{2}\) were selected as the metrics of the prediction result, defined as:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{y}_{i}-{y_{i}}^{*})^{2}} \tag{28}\]  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert\hat{y}_{i}-{y_{i}}^{*}\rvert \tag{29}\]  
\[R^{2}=\frac{\sum_{i=1}^{N}(\hat{y}_{i}-\overline{y})^{2}}{\sum_{i=1}^{N}(y^{*} _{i}-\overline{y})^{2}} \tag{30}\]  
where \(N\) is the length of the sequence, \(\hat{y}_{i}\) means the prediction value, \(y_{i}^{*}\) is the actual value and \(\overline{y}\) is the mean value of the actual sequence. Lower \(RMSE\)s and \(MAEs\) indicate the better accuracy of the prediction result. The \(R^{2}\) value is between 0 and 1. An \(R^{2}\) close to 1 means the correlation between the predicted sequence and the actual sequence is strong. The metrics of the prediction result are listed in Table 4. The accuracy and the correlation of the TPA-LSTM model are high in the degradation trend prediction task. The criteria of the training set and the test set are similar, indicating that the model has good generalization performance. The performances of the TPA-LSTM model and other prediction methods are further compared and discussed in Section 4.
## 3 Methodology: Operating System and Hardware Selection
### Operating Systems  
In our considered scenarios, Contiki-NG would be the top choice; however, its relatively small compatibility with systems that are not micro-controller based hinders its adoption. Contiki-NG was utilised within the simulated section of experimentation; however, it is not the best choice for a physical study. This fact allows for the consideration of several other operating systems that will be compatible with other instruction sets. It is also especially important to diversify the hardware that can be used, as discussed in Section 3.2.  
In our experiments, we decided to take forward three operating systems that differ from each other and add another vector of comparison to the study, whilst bringing more data to the research community. The three chosen are: Tiny Core Linux, which is essentially a bare-bones Linux installation that is not IoT specific and features a graphical user interface (GUI); Yocto OS, which is an IoT focused distribution with a minimal footprint; and Raspberry Pi OS, which also depends on a GUI-based platform. The latter, however, is based on Debian which is not known for running on low power hardware. This will likely provide a harsh contrast to how a fully-fledged operating system reacts to a similar attack. We studied the available operating systems and created Table 1 to showcase key areas of operating systems and to provide a basis for cross-comparison of the options available. This comparison is based on the architecture support, community support, low-power protocol support, kernel used, and complexity of installation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Architecture** & **Large** & **Low Power** & **Kernel** & **Complexity** \\  & **Support** & **Community** & **Protocol** & & **(S,M,L)** \\  & **(S,M,L)** & **Support** & **Support** & & \\  & & **(Y,N)** & **(S,M,L)** & & \\ \hline Contiki-ng & S & Y & L & Contiki OS & M \\ \hline RIOT OS & M & N & L & Unix & M \\ \hline Free RTOS & L & N & L & RTOS & L \\ \hline Ubuntu Core & M & N & L & Linux & S \\ \hline Linux Arch & M & Y & S & Linux & S \\ \hline Raspbian & S & Y & S & Linux & S \\ \hline Tiny Core & L & Y & L & Linux & S \\ \hline Buildroot & L & Y & L & Embedded & S \\  & & & & Linux & \\ \hline Android & S & N & S & Android & N/A \\ Things & & & & & \\ \hline Yocto OS & L & N & L & Linux & L \\ \hline Tiny OS & M & N & L & Component & L \\  & & & & based & \\ \hline Windows 10 & L & N & L & Windows & S/M \\ IoT Core & & & & & \\ \hline Azure & S & N & M & Linux & L \\ Sphere & & & & & \\ \hline Kali & L & Y & S & Linux & S \\ \hline \hline \end{tabular}
\end{table}
Table 1: Operating systems available. Key: Y = Yes, N = No, S = Small, M = Medium, L = Large.
# Hybrid Uncertainty Metrics-Based Privacy-Preserving Alternating Multimodal Representation Learning
## 2 Related Work  
### Multimodal Learning  
Multimodal learning leverages data from diverse sensory modalities to improve model performance and robustness. However, a persistent challenge in existing methods is modality laziness, where models disproportionately rely on dominant modalities, sidelining others and undermining overall effectiveness. To address this issue, researchers have proposed several strategies. [PERSON] et al. [3] investigated training challenges in multimodal classification networks, identifying gradient imbalance as a primary driver of modality laziness. They introduced gradient modulation to equalize contributions across modalities. Building on this, [PERSON] et al. [9] developed dynamic gradient modulation (OGM-GE), which adjusts gradient weights dynamically to foster balanced learning. Other approaches include modality dropout [11], which randomly excludes certain modality inputs during training to encourage reliance on all available modalities, and curriculum learning [12], which incrementally increases the complexity of modality combinations to mitigate the issue.  
To further tackle modality laziness, [PERSON] et al. [7] proposed the Multimodal Learning with Alternating Unimodal Adaptation (MLA) framework. Unlike gradient modulation or modality dropout, MLA alternates optimization between modality-specific encoders and a shared head, effectively balancing modality contributions and achieving superior performance on benchmark datasets. Despite its strengths, MLA faces limitations: its entropy-based uncertainty quantification performs suboptimally during inference, struggling to provide reliable estimates, and it lacks built-in privacy-preserving mechanisms, restricting its use in privacy-sensitive applications such as healthcare or finance. Additionally, methods like OGM-GE require meticulous hyperparameter tuning and may falter in dynamic environments, while modality dropout risks discarding critical information from dominant modalities and curriculum learning, though promising, is complex to design and scale for large-scale multimodal tasks.
## 3 Methodology of Construction of Architecture of e-STMS Based on IoT and WSNs
Advances in machine-to-machine communication (M2M) solutions and WSNs are significant for terminals. They are used not only to find or track freight on its way during delivery. In addition, WSN helps to monitor freight delivery conditions during its journey. It also alarms if the container is opened with the help of sensors (e.g., magnetic contact  
Figure 3: Integration of stages for registration, authentication, and removal of moving transport objects in the system based on the IoT technological platform. sensors) or shows temperature fluctuations (with the use of temperature sensors), indicating all details about the occurrence of these events and reporting the data over the Internet. With the help of sensors placed in the containers (which become smart), emergency cases (drops, fires, floods, or others) could be reported by sending SMSs about the event to insurance agencies to call for emergency assistance. It could also be checked if working hours correspond with GPRS/3G alarm timing. Three main features are enabled by WSN using in the infrastructure of smart containers:  
* detecting unexpected opening of doors,
* monitoring freight delivery circumstances,
* identifying storage conditions by adding sensors measuring environmental variables for freight's sensitivity to risky conditions [127; 128]. In the case of fragile goods, detecting shock and vibration exposure 3-axis accelerometer sensors can help to identify the issues. Finally, sensors can actively use RFID and Near Field Communication (NFC) technologies to exchange information with other nearby stored pallets or containers. For example, the alarm could be generated when dangerous freight is placed next to flammable substances [129].
## 4 Forest Fire Emissions Estimation in China  
Biomass burning emissions including greenhouse gases such as CO\({}_{2}\), CH\({}_{4}\) and aerosol particles have significant influences on air quality, atmospheric chemical composition, the earth's radiation budget, and global climate change [75-79]. In recent years, the forest fires have significantly aggravated and affected national forest ecosystem due to human activities and some extreme climate events. The forest fires are believed to have generated much carbon emissions in China [35,36]. An approach for addressing biomass burning emissions is to accurately quantify the amount of emissions spatially and temporally based on observations of active fires. Figure 6 presents a flowchart of the Chinese Forest Fire Emission Estimation System (named CFFEES).  
Figure 6: Forest fire emissions estimation system in China.  
Figure 6 shows the forest fire emissions estimation system in China. In the CFFEES, the forest fire burned area (BA) and fuel loading (FL) detection by using multi-sensors including NOAA/AVHRR, FY-series, EOS-MODIS, Landsat-TM, ENVISAT _etc._; and which are integrated with forest type (or land use), forest inventory data, the fraction of fuel consumed (FF) in live vegetation data, and the associated emission factor (EF). The CFFEES may calculate the forest fire emissions of various gases (_i.e._, CO\({}_{2}\), CO, CH\({}_{4}\)) or PM by using the emission model showed in Figure 6. All the source data are spatially and temporally distributed and daily assimilated according to the forest burning hot spot defined by using the satellite observations. The entire estimated emissions of forest fire using the CFFEES system can be calibrated by using the flux tower observation data.
## 5 Performance Study  
In this section, we first present a simulation architecture namely ConMoSim that can simulate nodes' controllable mobility in a network environment. Then, we evaluate the performance of RoCoMAR by comparing it with other on-demand, proactive, and geographical forwarding based ad hoc routing protocols, such as AODV, DSR, OLSR, and Greedy Forwarding Routing (GFR).
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## VANET Component Model
### Roadside Unit (RSU)  
Roadside units are essential nodes to provide communication support between vehicles. This support may be in the form of reporting an accident, safety warnings, and traffic congestion. A model of an RSU is shown in Figure 5. Each RSU is equipped with a network interference card (NIC) and the application layer software, appl. The NIC unit also includes the AODV router.  
Figure 4: Adaptive cruise control (ACC) with vehicle [28].
## 2 Review of Smart Metering Systems  
Smart Grid is a wide scope concept that in most energy distribution utilities starts with the so-called Smart Metering [12, 13, 14, 15]. Smart Metering systems are also referred to as Automatic Meter Reading (AMR) in literature [16, 17, 18, 19, 20, 21], and the infrastructure they rely on is referred to as Automatic Meter Infrastructure (AMI). However, Smart Metering is more than the basic remote meter reading capability and the ability to operate over smart meters (e.g., connect/disconnect), and may become the door to new services for energy efficiency [20, 21, 22]. These new services for energy efficiency are a subgroup of all the functional elements included in the wider Smart Grid for electricity distribution utilities, inside the advanced grid control systems that a Smart Grid requires. These new services are [23]:  
* _Distribution Automation Systems_ to control remotely the substations;
* _Energy Management Systems_ to gather information from remote sensors, meters and other devices so as to analyze, control and plan the entire electricity grid;
* _Intelligent Network Agents_, as intermediate points collecting data of the grid, interacting with control centers and taking non-centralized decisions on local switching and control functions;
* _Demand Side Management Systems_ to reduce consumption in the grid and optimize the global performance;
* _Asset Management Systems_, as complete and updated inventory systems of the facilities and equipment of the grid;
* _Geographic Information Systems_ to add geographic data to the electricity grid operations for the optimization of the planning activities, and the detection and recovery of faults;
* _Grid Modeling, Simulation, and Design Systems_ to obtain the best results minimizing operational risks.  
Smart Metering systems need to provide both the basic functions needed for the remote meter reading, while at the same time they provide the real-time capabilities to access the smart meters in real time for the purposes of connection and disconnection. Smart Metering systems should be based on technologies that will make them come closer to the Smart Grid desired extended functionally, making the infrastructure they rely on, capable of offering increasing throughput to the communications network, and developing that infrastructure with the support of the electricity grid in such a way that the communications network itself, through the usage of the proper communications technology, offers information on the grid status and geographical connectivity.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Green Corrosion Inhibitors  
The usage of green corrosion inhibitors is becoming more and more popular today due to traditional inhibitors being costly and harmful to the environment. For this reason, researchers have become interested in corrosion inhibitors that are economical, cost-effective, and highly effective in preventing corrosion.  
Roots, chitosan, oil, flour, yeast, natural honey, leaves, herbs, and phytochemicals from plants including other substances are examples of green corrosion inhibitors. In a corrosive environment, green inhibitors treat the surface of metals or alloys by being added in very small quantities. Plant extracts are thought to be rich in naturally occurring chemical compounds that, when added to many industrial systems, reduce the rate of corrosion by adsorbing useful species on metal surfaces. Figure 5 shows the natural sources of green CIs. The report claims that some organic and biodegradable compounds are powerful inhibitors [3]. Both nonpolar chemicals with aromatic rings, aliphatic chains, heterocyclic rings, and functional moieties, as well as polar molecules with oxygen and nitrogen, are abundant in plant extracts. These substances can absorb on the metal surface and prevent corrosion without affecting the environment, unlike inorganic substances [57]. [PERSON] et al. [58] prepared a CI by using spinach extract as a green CI and tested it on OPC-fly ash composite cement pastes. One of the main components for the synthesis of imidazoline is the lauric acid that can be derived from vegetable oil. [PERSON] et al. [59] developed CIs by adding CO\({}_{2}\) saturated oil to lauric acid; when this was applied to metal samples, the IE increased to 98.6%. Pomelo peel was used by [PERSON] et al. [60] to develop CIs. They investigated N80 rusting in 3.5 wt.% NaCl saturated with CO\({}_{2}\) solution by employing the weight loss technique (WL). The efficiency of corrosion inhibition increased as the extraction solution's concentration increased. The efficiency reached a maximum of more than 87%.  
Figure 5: Various sources for natural green corrosion inhibitors.
###### Abstract  
One of the technical possibilities to enhance the properties of lubricants and biolubricants is dispersing nanoparticles in them. Although conceptually simple, this operation faces challenges related to: (1) obtaining an initial good dispersion of the nanoparticles in the liquid and (2) ensuring the stability of this dispersion to avoid coalescence. The objective of this study is to verify possible improvements of the stability and characteristics of conventional and bio-based lubricants by efficiently dispersing in them surface-modified SiO2 nanoparticles. The silane coupling agent KH570 was utilized to modify the surface properties of SiO2 nanoparticles, facilitating their dispersion within the lubricants. Nanolubricants and nanobolubricants were prepared using a two-step technique. The dispersion stability of these lubricants was assessed using sedimentation photography, FTIR, and UV-Vis spectrophotometric analyses. The addition of SiO2 nanoparticles resulted in enhanced physicochemical properties of the resulting lubricant, including slight increases in density and viscosity, as well as a higher viscosity index. Chemical analyses, such as TAN and TBN measurements, confirmed that the nanoparticle addition at various concentrations (0.25%, 0.5%, 0.75%, and 1.0%) did not introduce critical acidity levels or compromise the alkaline reserve. ICP-OES analysis indicated minimal impact on essential additive concentrations, supporting the feasibility of SiO2 nanoparticles in enhancing lubricant properties without destabilizing additives. The stability of the nanolubricants was monitored over 77 days, with visible sedimentation beginning around the 30 th day and becoming more pronounced by the 54 th and 77 th days. Bio-lubricants exhibited slightly higher sedimentation than conventional lubricants. Optimizing the sonication time proved to be crucial, with longer sonication times (2.5 h) significantly improving the stability of nanolubricants across various concentrations of added nanoparticles. FTIR analysis confirmed the presence of SiO2 and KH570, indicating no alteration to the basic functional structures of the lubricants and biolubricants. UV-Vis spectrophotometry further underscored the importance of optimizing sonication time for enhanced stability. Overall, this study demonstrates that incorporating surface-modified SiO2 nanoparticles enhances the properties and stability of conventional and biolubricants, offering potential for improved performance in industrial and engine applications.  
nano-additives; nanolubricants; nanobiolubricants; nanoparticles dispersion; nanoparticles sedimentation; UV-Vis analysis; stability of lubricants; internal combustion engines +
Footnote †: journal: Applied Sciences
# Structural Evolution of Polyimide-Derived Carbon during Phosphoric Acid Activation
## 3 Results and Discussion
### \({}^{31}\)P Mas Nmr  
Figure 7a shows \({}^{31}\)P MAS NMR spectra of polyimide-derived carbons obtained with phosphoric acid (H\({}_{3}\)PO\({}_{4}\) series) at different temperatures. A sharp peak at about 0 ppm and a somewhat broad signal at 7-12 ppm are visible in the \({}^{31}\)P NMR spectra. With confidence, phosphate structures may be attributed to the sharp peak. The second signal is rather broad and asymmetric, indicating the presence of a mixture of orthophosphates and/or quadruply attached phosphorus in an asymmetric environment. To reveal these components the spectra were deconvoluted into five peaks using a Lorentzian line shape (Figure 7b). The results of deconvolution are presented in Figure 7b. The peak P1 at around \(-\)10 ppm (Figure 8a) only shows up as a shoulder in the main peak P2. The peak P1 may be ascribed to polyphosphate end groups belonging mainly to PO\({}_{4}\) groups linked by one bridging oxygen atom [16; 32; 35; 67; 68; 69; 70; 71]. The content of polyphosphates is rather small but increases to 24-26% for carbons obtained at 800-900 \({}^{\circ}\)C (Figure 8b). The peak P2 around 0 ppm is sharp and with confidence may be ascribed to orthophosphate structural units linked to polyaromatic graphene framework [16; 32; 35; 67; 68; 69; 70]. The peak P2 shifts from 0 to \(-\)6.0 ppm with increasing carbonization temperature due to the increasing positive shielding from \(\pi\)-electrons of enlarged graphene layers [16; 22; 32] (Figure 8a). The peaks P3 and P4 could be ascribed to pyrophosphate diesters [72] and metal phosphates [73]. The formation of pyrophosphate diesters that crosslink the fragments of polymer structure at high temperature is reasonable and was proposed as activating mechanism of phosphoric acid [74]. The highest amount of pyrophosphate diesters is produced around 500 \({}^{\circ}\)C, however, when carbonization temperature rises above this point, cross-linking becomes less prominent until it nearly completely disappears at 800-1000 \({}^{\circ}\)C (Figure 8b). At high temperatures, orthophosphate structural units become the dominant structure of phosphorus compounds. The peak P5 at 25-30 ppm is ascribed to phosphonates [75]. The content of phosphonates is small at all temperatures and amounts to 1-9%. Thus, the \({}^{31}\)P MAS NMR investigation revealed the existence of different types of phosphates as well as phosphonates in the structure of polyimide-derived carbon obtained with phosphoric acid at different temperatures (H\({}_{3}\)PO\({}_{4}\) series).  
Figure 7: \({}^{31}\)P MAS NMR spectra of polyimide-derived carbons obtained with phosphoric acid (H\({}_{3}\)PO\({}_{4}\)-series) at different temperatures (**a**) and deconvolution of \({}^{31}\)P MAS NMR spectrum of polyimide-derived carbon obtained with phosphoric acid at 600 \({}^{\circ}\)C (**b**).