esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Impact of Meteorological Factors on NPP Changes  
The Tibetan Plateau has undergone significant warming and humidification over the past four decades [56]. Mean annual precipitation and mean annual temperature increased at a rate of 3.45 mm/yr and 0.04 \({}^{\circ}\)C/yr, respectively (Figure 10). In contrast, solar radiation displayed a decreasing trend, with a rate of \(-0.11\) W-m\({}^{2}\cdot\)yr\({}^{-1}\). Global warming has an impact on vegetation productivity, ecological processes and biodiversity dynamics. The length of the growing season (LOS) grassland becomes longer [57], and the normalized difference vegetation index (NDVI) and mean species abundance (MSA) show increasing trends [58; 59].  
NPP increased in most areas of the TP except in the eastern and southeastern of the Hengduan Mountains. We found that the precipitation was the main factor leading to the increase in vegetation productivity with the rate of 2.4 (g C\(\cdot\)m\({}^{2}\cdot\)yr\({}^{-1}\)) (Figures 6 and 7). This conclusion is consistent with the research from [PERSON] et al. and [PERSON] et al. [60; 61]. The vegetation growth is influenced by water and heat conditions [62]. Soil moisture content is directly influenced by precipitation and plays a crucial role in determining plant available water capacity and photosynthetic processes [63]. At the same time, adequate soil moisture ensures nutrient transport and accelerates the mineralization of organic matter in the soil [64; 65].  
The temporal and spatial trends of NPP on the TP were found to be significantly correlated with precipitation but not significantly correlated with temperature. This result is consistent with previous investigations [66]. The temperature increased significantly on the TP, and higher temperatures can promote enzymatic activities in photosynthesis, thus enhancing the intensity of plant photosynthesis and increasing the absorption of organic matter by plants [67]. However, our study has revealed that the temperature increase did not have a significant impact on the NPP change. Previous studies have discovered that temperature impacts both the rates of photosynthesis and autotrophic respiration (AR). The rising temperatures have a greater impact on the AR than the GPP [68], especially on the TP [69], which explains the slight downward trend of the NPP.  
Solar radiation, as the sole energy source for plant photosynthesis, can directly affect vegetation photosynthesis trends. In our study, the slight decreasing trend in solar radiation over the 40-year period resulted in a negligible effect on the NPP, with a rate of \(-0.10\) (g C\(\cdot\)m\({}^{2}\cdot\)yr\({}^{-1}\)), suggesting no significant impact (Figure 7).  
Figure 9: Trends in NPP for high, medium, and low coverage grasslands under different meteorological factors for the period 1979–2018, the dashed lines represents a linear trend. (**a**) Precipitation factor. (**b**) Radiation factor. (**c**) Temperature factor.
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 5 Antimicrobial Applications
### Antimicrobial Mechanism
#### 5.1.3 Cell Trapping  
Another major mechanism for the antibacterial activity of graphene-based membranes is cell trapping, which describes bacterial cells being captured by graphene-based membrane sheets when they come into contact with them. These bacteria are isolated from the external environment, and the way they get nutrients is limited. This entrapment effect is related to the size of the graphene-based membranes. Larger graphene oxide flakes have a stronger antibacterial effect, which is attributed to the ability of larger graphene oxide flakes to completely cover bacterial cells and prevent their proliferation. The study found that the antibacterial activity of GO increased with the increase in its lateral size [97]. It is possible that the trapping effect temporarily limits the growth of bacteria rather than killing them. Interestingly, it was found that the size of graphene-based membranes has a  
Figure 5: Schematic diagram of the antibacterial mechanism of graphene-based nanomaterials, including sharp edge cutting effect, oxidative stress and cell trapping. (Printed image with permission from Ref. [91], copyright 2019, Elsevier Ltd.).  
significant and complex effect on its antibacterial activity by affecting both cleavage efficacy and cell-trapping efficiency. Simply put, a large size favors the entrapment effect, but a small size is equivalent to high edge density, which is good for the cutting effect. The size of GO and rGOs also affect their UV absorption, probably because ultrasound reduces the size during oxidation. The size of GO and rGO also affects their UV absorption, which may be due to the possible size reduction caused by ultrasound during the oxidation process [91].
# Removal of Chromium from Synthetic Wastewater Using Modified Maghemite Nanoparticles
## Materials and Methods
### Adsorbent Preparation and Characterization  
The NPs obtained from Sigma-Aldrich (Bengaluru, India), after suitable modification, were used as the adsorbent. SDS was used to modify the surface characteristics of NPs, according to the method described elsewhere [25]. In this method, different volumes of 5% (_w/v_) SDS solution (1, 2, 3, and 4 mL) were tested to identify the most effective concentration for chromium removal. The SDS solution was added to 0.1 g of NPs and stirred for 90 s. Thereafter, the MNPs were separated using magnetic decantation and then washed with double-distilled water (4 times). After this step, drying was done overnight at 60 \({}^{\circ}\)C in a hot air oven. The dried particles were crushed in a mortar and pestle (made of marble to avoid any external particle contamination).  
To determine the suitable SDS concentration, the NPs (0.1 g) were added to 10 mL samples of 2000 mg/L of chromium solution at pH = 5.0 in a 250 mL flask and placed in an orbital shaker at 200 rpm, at \(\sim\)35 \({}^{\circ}\)C room temperature for 30 min [26]. Thereafter, magnetic decantation was done to the samples and the resulting solution was stored for further analysis. The concentration of chromium was determined using atomic absorption spectroscopy (AAS). It was observed that the most effective SDS concentration for chromium removal was 2 mL. Hence, the NPs modified using 2 mL SDS were selected as the model adsorbent in this study.  
FTIR analysis (IR Affinity-1 spectrophotometer, Japan) was done to determine the specific functionality of the NPs (Figure 1a) and MNPs (Figure 1b), respectively, in transmission mode. An average of 5 scans was collected for each measurement in the range of 400-4000 cm-1. The crystalline structure of these nanoparticles was characterized by XRD analysis (T-8 Brooker Advanced, Germany), operated at 30 kV and a current of 40 mA with Cu-K\(\alpha\) radiation (\(\lambda\) = 0.154 nm). A scan mode was used to collect the 2\(\theta\) data from 20\({}^{\circ}\) to 90\({}^{\circ}\).
## 2 Materials and Methods
### Validation and Performance Evaluation
#### 2.5.2 Sensitivity  
Although the absolute sensitivity is completely independent of the positioning methods investigated in this study, this parameter was assessed for the sake of completeness. To this end, we calculated the sensitivity by moving the \({}^{22}\)Na point source (180 kBq) from one side to the other of the scanner's axial FOV with 5 mm step size and for an acquisition time of 10 min.
## 3 Proposed Method
### Preliminaries
#### 3.1.3 Cross-Layer Routing  
Figure 2 shows the model representation of cross-layer routing. The development of cross-layer routing is a protocol design approach which exploits the interaction among the layers [30].  
The information shared among the various layers is used to create adaptability, which mainly depends on the protocol design method. Next, this design considers the channel features in the higher layers, as well as considering the traffic's stochastic arrival in the lower layers. The definition of the cross layer is derived from the clear dissimilarity among the cross-layer design and layered protocol design, without considering the layered architecture. Similar to the seven-layer open systems interconnection model, the layered architecture separates the whole networking task into various layers and expresses the service's hierarchy, which is required for using the individual layers. The development of the protocol for various layers is used to realize the service in various layers. Moreover, the direct interaction among the non-adjacent layers is avoided by the architecture design, and the communication among the adjacent layers is restricted to the process calls and responses. The addition of several headers to the data results in data overhead. The requirement for at least one protocol standard per layer is another drawback. It takes a while to establish and implement the standards, since there are so many layers.  
Figure 2: Model of cross-layer routing.
## 3 Results and Discussion  
### Synthesis and Characterization  
#### 3.1.1 UV-Visible Characterizations  
GO was synthesized using the modified Hummers method for the subsequent impregnation of natural zeolite. The initial material was analyzed using a UV-visible spectrophotometer in aqueous solution for the characterization and identification of its characteristic spectrum.  
The UV-Vis spectrum of graphene oxide (GO), shown in Figure 1a, indicated that the predominant absorption peaks were localized at a wavelength of 234 nm, corresponding to the \(\pi-\pi*\) transitions of the aromatic C=C bond. Furthermore, a small shoulder at 301 nm was identified, attributed to the \(n-\pi*\) transition of the C=O bond, explained by the presence of carboxylic groups in GO, in accordance with previously reported studies [29; 31; 40].  
The spectrum depicted in Figure 1b belongs to natural zeolite; here, a peak appears at 192 nm corresponding to \(n-\sigma^{*}\) electronic transitions involving the oxygen-silicon charge transfer, specifically (O\({}^{2-\infty}\) Si\({}^{4+}\)) transitions, within the SiO\({}_{2}\) tetrahedral framework. This indicates that the framework predominantly consisted of Si--O--Si bonds with minimal Al substitution, which exhibit higher charge transfer energy compared to Si--O--Al bonds. This fact explains the high-energy transitions occurring in the deep UV region.  
Regarding the impregnated composite, Figure 1c shows the spectra of zeolite impregnated with graphene oxide (ZGO2.5, ZGO5, and ZGO10). A peak is visible at 196 nm, 192 nm, and 194 nm, respectively, corresponding to the n\(-\sigma^{*}\) transition, as explained above for the zeolite spectrum (Figure 1b). The peaks at 194 nm and 196 nm underwent a slight redshift compared to those in the natural zeolite spectrum. Meanwhile, the peaks located at 233, 234, and 232 nm correspond to the GO \(\pi-\pi*\) transitions of the aromatic C=C bond. Additionally, a shoulder around 300 nm is present, identifying the \(n-\pi*\) transition of the C=O bond.
# Safe and Policy Oriented Secure Android-Based Industrial Embedded Control System
## 4 Experiment and Evaluation
### Operability on an Actual Industrial Network  
To validate the operability of the developed system on an actual industrial network, we have connected four industrial servo drives slaves to the JECS-600 ITX via EtherCAT. A simple Android application enforced with the Android application layer security policies was created, leveraged with the shared memory procedures discussed in Section 2. The application sends the target velocity as a message to a standard Linux Message Handler task that bridges the Android application and a Xenomai RT task. The RT task runs constantly with a cycle period of 1 ms and configured to the highest priority of 99 in accordance with Xenomai specifications. It is responsible for receiving the target velocity from the Android application through an XDDP port. The trapezoidal velocity profile is generated according to the target velocity received from the Android application. The task also handles the initialization of an EtherCAT master instance by utilizing the functions and services offered by IgH EtherLab.  
The experiment was conducted for 60 s, resulting in 60,000 samples, and the results are shown in Figure 8a. The same reference velocity was supplied to all servo drives to easily observe whether any of the EtherCAT slaves could track the given reference. We have configured the Android application to periodically send target velocities of 940 and \(-\)940 RPM every 15 s. These commands were sent to the Message Handler task, which then transmits them to the RT task via XDDP. To measure the end-to-end transaction time, we have measured the timestamps before the velocity commands are sent from Android and after they are received in the Xenomai RT task. The average transaction time for this one-way communication is 67 \(\upmu\)s. In the figure, the generated reference velocity profile is represented by the black solid line. All of the slaves were able to follow the reference with a minimal tracking error, as shown by the overlapping lines representing each of the slaves. In our observation, the main cause of this error is the high ratio (1:100) of the reduction gear connected to each motor. Figure 8b illustrates the first 5 s of the experiment to give a closer look of the results. Based on these results, we verified the operability of the developed Android-based industrial embedded control system when connected with an actual industrial EtherCAT network. According to [PERSON] et al., [35] systems that meet hard real-time constraints can track a given reference accurately in comparison to their non real-time counterparts. Thus, these results verify that the system can satisfy real-time requirements, which is essential in determining the safety operation of industrial control systems. For the quantitative results of the real-time performance, we have performed real-time analysis in the next subsection.
# Energy-Efficient Hybrid Routing Protocol for IoT Communication Systems in 5G and Beyond
## 3 Problem Formulation and Existing Challenges  
Using IoT systems in a 5G environment, many difficulties and issues in the clustering approach need attention. This section addresses the challenges and the model of the network employed in our research.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 3 DPTracer Design
### Threat Model  
The goal of attackers is to covertly manipulate the DPN to generate incorrect outcomes and to thus exploit discrepancies in the target system for their gain. Attackers may infiltrate the network as nodes in order to inject incorrect information or omit crucial data. However, the DPLog ensures that such malicious activities are recorded, enabling the inspector to identify them. Thus, we assume that attackers may attempt either to manipulate the log record undetectably during and after data provisioning or to deliver inconsistent data to the log records. Additionally, we assume that attackers cannot find collisions in cryptographic hash functions.  
We consider the following internal threats and log-tampering attacks:  
* **Data modification**: attackers may compromise nodes to send incorrect data to subsequent aggregators while deceiving the logger to avoid detection.
* **Log modification**: attackers may compromise the logger to record incorrect information that deviates from the data specified in the nodes' log requests.
* **Data fabrication**: attackers may compromise nodes to generate a new data flow with arbitrarily fabricated outputs.
* **Delivery omission**: attackers compromise nodes to omit certain inputs to skew results deliberately.
* **Post-process tampering**: attackers access and modify log entries from previously completed results to hide misbehavior in earlier data provisioning.
technology, sensor technologies, and artificial intelligence (AI) are catalyzing this shift, leading to the development of autonomous maritime systems [2].  
Autonomous maritime systems encompass a wide array of technologies, including unmanned surface vessels (USVs), autonomous underwater vehicles (AUVs), and integrated systems for traffic monitoring, control, and management. These systems promise to revolutionize maritime operations by enhancing operational efficiency, reducing human error, and minimizing environmental impacts [3]. For instance, autonomous vessels can optimize fuel consumption through precise route planning and adaptive navigation, thereby contributing to significant reductions in greenhouse gas emissions [4; 5].  
The integration of AI into maritime systems enables real-time data processing and decision-making, which are critical for the safe and efficient operation of autonomous ships. AI-driven systems leverage vast amounts of data collected from various sensors--such as radar, LIDAR, Automatic Identification Systems (AIS), and cameras--to gain situational awareness, detect obstacles, and make informed navigation decisions [2; 6]. These capabilities are essential for navigating complex and dynamic maritime environments, where factors such as weather conditions, sea state, and traffic density can rapidly change [2; 7; 8].  
Despite the promising advancements, the transition to autonomous maritime operations introduces a suite of complex challenges [9; 10]. Ensuring the safety and security of autonomous vessels is paramount, as failures in AI systems could lead to accidents with potentially severe consequences, including environmental damage, loss of cargo, and threats to human life [11]. Moreover, the maritime industry must address issues related to cybersecurity, data integrity, and the reliability of AI algorithms under diverse and unpredictable conditions [7; 8].  
In addition to safety concerns, the adoption of autonomous systems must align with evolving regulatory frameworks aimed at promoting sustainable and environmentally friendly maritime practices. Regulations such as the International Maritime Organization's (IMO) Carbon Intensity Indicator (CII) and the European Union's Intelligent Transport Systems (EU ITS) mandate stringent reductions in carbon emissions and the adoption of advanced technologies to monitor and manage environmental impacts [12; 13]. Autonomous maritime systems, empowered by AI agents, play a crucial role in meeting these regulatory requirements by enabling the real-time optimization of vessel operations to enhance sustainability [5; 7; 14].
## 3 Results and Discussion
### Objective Measures
With MR-subspace-enhanced motion and vision mapping, a comparable rate of improvement in completion time was attained for operators with minimal technical knowledge. Mean training completion time was 153.7 s, 98.3 s, and 71.8 s for the Baseline and MR with and without the imitation-based motion mapping modes, respectively. The learning time for the training tasks with the MR subspace approach decreased by 53% and 36%, compared to the Baseline and MR SpaceMouse approaches, respectively, indicating that additional learning was required for the two typical HRI modules to reach the same competency as for the proposed MR-subspace-enhanced imitation-based HRI module. It can also be observed that, even at the end of the training, subjects did not reach the same proficiency and dexterity as they immediately did while using the MR-subspace-enhanced imitation-based module. The MR subspace module has an important effect in saving training time even for non-skilled operators.
# A Novel Breast-Volume Self-Measurement Method with Improved Convenient and Accuracy
## 3 Results
### Breast-Volume Measurement Results  
In the operation of the breast specimen drainage-volume measurement method, the volume of water displaced by the unilateral breast simulation model was used to replace the volume of the breast model. The resulting volume of the unilateral medical breast model was 425 mL. To standardize the measurement units, the breast volume was converted to 425 cm\({}^{3}\).  
In the proposed measurement method for breast volume (Figure 10), the breast medical-simulation model showed that the coordinate of the nipple point \(A\) in the proposed breast shape-measurement coordinate system was \((x,\ y,\ z)=(5.5,-10,7.5)\), the arc distance of the upper breast border \(AE=12\), the arc distance of the lower breast border \(AD=7.5\), the arc distance of the front breast border \(AB=9\), the arc distance of the lateral breast border \(AC=8.5\), and the breast height \(AA^{\prime}=5.5\). It was assumed that \(C1G1=F1B1\), \(E1G1=A1C1\), \(E1F1=A1B1\), and \(h_{1}=L_{Ei-Ai}\). Based on Equation (45), we replaced the measurement parameters in the equations and obtained  
\[\begin{array}{ll}H_{[PERSON]^{\prime}}=AA^{\prime}=5.5,L_{[PERSON]}=\sqrt{AD^{2}- AA^{\prime 2}}=5.099\\ W_{Ai^{\prime}-B1}=\sqrt{AB^{2}-AA^{\prime 2}}=7.1239,W_{Ai^{\prime}-C1}=\sqrt{ AC^{2}-AA^{\prime 2}}=6.4807\\ h1=L_{[PERSON]}=\sqrt{AE^{2}-AA^{\prime 2}}=10.6654\\ V=\frac{\pi}{6}H_{[PERSON]^{\prime}}\cdot L_{[PERSON]}\left(W_{[PERSON]^{\prime}-B1}+W_{[PERSON] ^{\prime}-C1}\right)+\frac{\pi\cdot H_{[PERSON]^{\prime}}\cdot W_{[PERSON]^{\prime}-Bi} }{12{h_{1}^{2}}}\\ \cdot\left[h_{1}^{3}-(h_{1}-L_{[PERSON]})^{3}\right]+\frac{\pi\cdot H_{[PERSON]^{ \prime}}\cdot W_{[PERSON]^{\prime}-C1}}{12{h_{2}^{2}}}\cdot\left[h_{2}^{3}-(h_{2}-L_ {[PERSON]})^{3}\right]=408.7\end{array}\]Given that the breast volume of the reference group, measured by the gold standard, is 425 cm\({}^{3}\), the breast-volume results calculated using different methods for the comparison group were summarized. The calculated breast volumes and relative error data for each method in the comparison group are presented in Figure 11.  
As shown in Figure 11, a comparison reveals that the new breast-volume measurement method proposed in this study has the smallest relative error compared to the reference group, at only 3.8%, demonstrating the high accuracy of this method. The 3D scanning method follows, with a relative error of 4.8%. The breast-volume equation method yielded the largest relative error, reaching 86.3%.