esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
###### Abstract  
The inherent characteristics and limitations of Internet of Things networks make it hard to avoid facing adverse network conditions. Addressing high performance in extreme situations still remains a challenge even for a standardized routing protocol like the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). No effective support is provided by the design of RPL to guarantee high network performance in the presence of such challenging conditions. To address such a compelling need, an innovative approach referred to as Dynamic-RPL is proposed in this research paper. With only limited in-protocol modifications to RPL, Dynamic-RPL provides effective support of dynamic topology management in a distributed manner. Seamless optimization of network topology is realized with dynamic topological adjustments to sustain high network performance and stability. It incorporates modified RPL topology establishment, customized RPL objective function and parent selection, a new dynamic topology management algorithm, and additional inter-routing support. The evaluation results demonstrated the ability of Dynamic-RPL to maintain high overall network performance irrespective of the adversity of ongoing network conditions. Considering varying-scale experimental setups, high QoS performance and low energy consumption were achieved without much increase in network overhead. Dynamic-RPL succeeded in adapting responsively with little time required to have the network performance successfully restored and network topology completely converged.  
Internet of Things (IoT); wireless networks; network architecture; topology +
Footnote †: journal:
###### Abstract  
Photovoltaic (PV) systems are increasingly recognized as a viable renewable energy source due to their clean, abundant, silent, and environmentally friendly nature. However, their efficiency is significantly influenced by environmental conditions, necessitating advanced control strategies to ensure optimal power extraction. This study aims to enhance the performance of PV systems by developing and evaluating maximum power point tracking (MPPT) algorithms capable of operating effectively under both uniform irradiance and partial shading conditions (PSCs). Specifically, two metaheuristic algorithms--Particle Swarm Optimization (PSO) and Cuckoo Search Optimization (CSO)--are modeled, implemented, and tested for tracking the global peak power (GPP) in various static and dynamic scenarios. Simulation results indicate that both algorithms accurately and efficiently track the GPP under static uniform and PSCs. Under dynamic conditions, while both the PSO and CSO can initially locate the GPP, they fail to maintain accurate tracking during subsequent intervals. Notably, CSO exhibits reduced oscillations and faster response time compared with PSO. These findings suggest that while metaheuristic MPPT methods are effective in static environments, their performance in dynamic conditions remains a challenge requiring further enhancement.  
Keywords:photovoltaic systems; global peak power; metaheuristic; static and dynamic environments; partial shading; uniform irradiance
## 4 IDAIC Framework
### IDAIC Phases
#### 4.1.3 Data Assessment  
Once the high-level capabilities of the dataset are understood, the data may be assessed in further detail to evaluate the quality of the data. The data assessment phase is focused on managing the bad quality issue but may also provide insight into the completeness of the data (broken) and the operation of the asset (background). The area of measuring data uncertainty, or data quality assessment, is not a mature area of research in industrial applications. In 2015, [PERSON] and [PERSON] [55] proclaimed that a unified and approved data quality standard had not been agreed for big data and that research in the area had just begun. However, many data quality assessment methodologies have been developed in other domains, which have been systematically reviewed by [PERSON] et al. [56] in 2009. The authors found accuracy (Acc), completeness (Comp), and consistency (Cons) to be the dimensions of highest consistency in the literature [56]. Therefore, in the data assessment phase we propose that the assessment of these dimensions is the main focus, and the associated metrics to assess these dimensions are heavily influenced by domain knowledge. In Figure 9, a decision tree is proposed to manage the 3B's based on the outcome of the data assessment. If the required data is missing, then a digitalization plan is required to obtain the data, such as the approach proposed by [PERSON] et al. [57]. If the data is inaccurate, then the sensors need to be manually checked and may require recalibration or replacement. Finally, if the consistency of the data is low, or the data is not in accordance with the domain expert's experience, sensors may also be in need or recalibration or replacement. The difference from the accuracy dimension is that inconsistent data may be caused by a component or control fault; therefore, an operation assessment is needed to determine the source of the anomaly. However, the data must firstly be appropriately prepared.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 3 System Model and Problem Formulation
### Scenario Description
According to the service placement and request scheduling decisions obtained from _Provision Planner_, each MEC node will trigger corresponding actions. Please note that these decisions are made for a certain period during which the user demand is fixed and forecast. Since the demand may change over time (e.g., after some hours), the _Provision Planner_ will have to periodically forecast new demand and adjust the policy accordingly. The adaptation of service placement requires service migration, which causes the cost of replicating service data through the network. Services may migrate/replicate between MEC nodes, or from the core cloud to a MEC node. In the scope of this paper, we investigate the service provisioning problem in an offline setting, which does not consider service migration cost and user mobility. In addition, the user demand is assumed to be predicted in advance and remains unchanged during the placement period. The online setting, which includes service migration, user mobility and other dynamic changes in the network can be considered to be future work.
## 5 Evaluation
### Technology Acceptance, Intention, and Use Questionnaire  
The objective questionnaire, consisting of 37 items, was administered to evaluate user satisfaction with the Augmented TD. It was given after the interaction with the artifact, allowing participants to express their agreement. The questions are structured using a 7-point scale. Low scores are better than high scores due to the anchor points used (completely agree/completely disagree), except for the anxiety construct (where higher scores are better, due to the phrasing of the negative statements). Figure 14 presents the compilation of results--the Technology Acceptance, Intention, and Use Questionnaire; the graph shows the agreement scale for each participant, with their data compared column-wise.  
Overall, participants favored the Augmented TD over the conventional TD. Almost unanimously, participants appreciated the ease of use, enjoyment, aesthetic and informational quality, perceived usefulness, and overall interface quality provided by the mediation tool. However, constructs of perceived behavioral control and facilitating conditions diverged among some participants; these were associated with the resources (adequate devices) for using the Augmented TD.  
The main criticism that the Augmented TD received was regarding its compatibility with devices. The versions of devices used by the workers and the available model of the set design result in a poor experience when a large number of textures are loaded, for example, in Augmented TD Sheet 1. However, [PERSON] expressed interest in replacing the devices so that the technique could be immediately implemented into the workflow (in the short term).  
In positive analyses, participants found it intuitive and very easy to use. The Augmented TD offered them a clear, smooth, and faster interaction experience to understand the project. Additionally, some participants mentioned excellent perceived behavioral control, both when they already had the knowledge to use it and when provided with the necessary resources, opportunities, and knowledge to use the platform. The tool also received good emotional evaluations concerning the constructs of anxiety, intention, and attitude toward the use and control of the platform. Throughout the research, the instructions given in the first minute of interaction with each of the four sheets allowed participants to instantly grasp the technique. In light of this, some participants noted that manipulating the object virtually directly with their hands is ergonomically more pleasant than manipulating it directly on the screen.  
Figure 14: Technology Acceptance, Intention, and Use Questionnaire: the graph displays the agreement scale for each participant, segmented by column. Low scores are better than high scores due to the anchor points used (completely agree/completely disagree), except for the anxiety construct (negative statements).
## 4 System Model, Algorithms, and Security Model
### System Model  
Figure 2 shows the major entities in our SP-MAACS system for cloud-based data sharing. The entities are:  
1. _Data Owner (DO):_ The data owner decides on an access policy and formulates it using the attributes present in the attribute universe. Then they encrypt the data under this policy. These encrypted data are stored on the cloud servers, but the access policy is kept partially hidden (Steps 1 and 2 in Figure 2);
2. _Central Authority (CA):_ The responsibilities of a CA can be defined as: (1) Generate global public parameters for the system. (2) Service the user's request for registration and issue identity keys based on their global identifier (gid) (Step 3 in Figure 2);
3. _Attribute Authorities (AAs):_ The responsibilities of an AA can be defined as: (1) Generate public keys for the attributes they manage. Each AA may have the authority to issue any number of attributes, but a single AA is authorized to issue each attribute. (2) Verify the user's possession of the attribute and issue a secret key for the user's attributes;
4. _Cloud Service Provider (CSP):_ The CSP essentially acts as a resource provider in place of the cloud, replicating that role for the cloud. The data owners use its data storage service and the users send a query for required data to access it. Furthermore, there is an assumption that the CSP is curious about obtaining the knowledge of data, but at the same time, it is honest;
5. _User:_ A unique global identity is allotted to every user. They receive a secret key issued for numerous attributes from the responsible AA. The user sends the request for data access to the CSP along with their acquired secret keys, and if the attributes possessed by them are required in satisfying the access policy, they can obtain the data (Steps 5 and 6 in Figure 2).  
In the SP-MAACS scheme, attribute authorities in the open domain can be hospitals, clinical research centers, etc. These authorities issue attributes to users such as doctors, nurses, etc. The EHR data owner plays the role of an AA for issuing secret keys to the users in a closed domain, e.g., friends, relatives, etc. (Step 4 in Figure 2).
## 3 Results  
### Microbial Growth Inhibition by Essential Oils  
#### 3.1.1 Antifungal Activity  
Among the 11 EOs tested, oregano, clove bud, thyme, and cinnamon cassia showed equally potent antifungal activity against strains F1 (_F. oxysporum_) and F2 (_Mortierella_ sp.). The oregano EO formed the largest inhibition zone (4.8-19.3 mm), followed by the cinnamon cassia EO (4.6-19.3 mm), thyme EO (4.3-19.3 mm), and clove bud EO (3.0-19.3 mm). The other EOs tested showed varying levels of antifungal activities depending on the fungal strain. With F1, lavender, tea tree, peppermint, and basil EOs formed small inhibition zones (0.3-0.4 mm), whereas no inhibition zone was observed for eucalyptus, rosemary, and grapefruit EOs. With F2, peppermint and rosemary EOs formed inhibition zones of 1.0-3.8 mm, whereas eucalyptus, lavender, tea tree, grapefruit, and basil EOs did not form an inhibition zone (Table 2).
## 2 Materials and Methods  
### Study Participants and Data Collection  
A total of 132 participants, including 52 stroke patients with the mean age of 72.7 (SD 6.6) and 80 control subjects with the mean age of 75.5 (SD 3.4), were recruited for this study. The stroke subjects who participated in this study were the outpatient volunteers diagnosed with stroke within one year at the time of data collection capable of independent walking and using assistance tools. The control subjects are those without physical or cognitive impairments caused by cerebrovascular disease such as stroke. The subjects with an acute or chronic infections such as liver failure, renal failure and cancer were excluded in this study. The ECG signals were measured using the BIOPAC MP150 (BIOPAC Systems Inc., Goleta, CA, USA) with the standard three lead measurements, conducted in Chungnam National Hospital, Daejeon, South Korea from 2017 to 2018. Resting ECG signals were obtained for a period of 5 min in the lying down position. The blood samples from the study participants were taken after overnight fasting before ECG recordings. All the clinical parameters were determined in the laboratory of Chungnam National Hospital. The demographics and clinical data such as age, gender, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), hemoglobin, low-density lipoprotein (LDL), high-density lipoprotein (HDL), total cholesterol and cardiovascular risk factors such as hypertension and dyslipidemia were included in this study. Hypertension was assessed with the repeated measures of systolic blood pressure \(\geq\)140 mmHg or diastolic blood pressure \(\geq\)90 mmHg, and diabetes was assessed with the blood glucose level \(\geq\)126 mg/dL. A family history of stroke was evaluated if one or both of the parents of the subjects had a stroke. Smoking was determined as having smoked at least once per day or more for one year and reported current smoking. Alcohol consumption was classified as current drinkers who drink on a daily or weekly basis at least once per week at present. Smoking and alcohol levels were assessed using a questionnaire, and the National Institute of Health Stroke Scale (NIHSS) was used to evaluate the stroke severity of the patients. The individual scores of each feature are summed to calculate the total NIHSS score. The maximum severity score is 42, and the minimum is 0. In a total of 52 stroke patients, there were 8 patients with minor stroke (NIHSS: 1-4), 12 patients with moderate stroke (NIHSS: 5-15), 13 patients with moderate-to-severe stroke (NIHSS: 16-20) and 19 patients with severe stroke (NIHSS: 21-42).
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 6 Conclusions  
The launch environment is one of the most hostile environments a satellite will encounter throughout its lifetime. It involves significant risks and the possibility of failure. As a result, it is critical to develop a separation mechanism that is both safe and reliable. In this paper, the separation mechanism for the MF satellite is designed, analyzed, and tested.  
The following conclusions can be obtained from the separation mechanism's theoretical analysis, simulation calculation, ground environment test, and separation test: (1) There are no explosives or redundant separation residues, and the system can be utilized several times on the ground. The separation mechanism is fully integrated, with a basic structure and straightforward processing. The mass ratio of the satellite to the separation mechanism is only 0.29, which is much less than the conventional separation mechanism's mass ratio, reducing launch costs dramatically. Simultaneously, the interface with the rocket is straightforward, decreasing reliance on the rocket and therefore expanding satellite launch alternatives. (2) The simulation analysis shows that the structural strength of the separation mechanism meets design criteria and has a sufficient safety margin. The fundamental frequency of the assembly is higher than 60 Hz, and there is no resonance with the carrier launch vehicle. The random response value of the satellite is minimal, which conforms to the design index. During the separation process, the MF satellite can efficiently avoid colliding with the door cover and other structures, and the separation velocity and angular velocity match design criteria. (3) The principle prototype test verifies the design and analysis of the separation mechanism. It allows for the efficient and reliable separation of micro-nano satellites.  
However, the separation mechanism still has a large optimization space; if the entire structure adopts a method of additive manufacturing, it will improve the separation mechanism's stiffness and integration while also reducing weight; therefore, this method needs further study.  
Conceptualization, X.Z. and J.L.; data curation, X.Z. and Y.G.; formal analysis, X.Z.; funding acquisition, L.Z.; investigation, X.Z.; methodology, X.Z. and S.C.; project administration, L.Z.; resources, S.C. and L.Z.; software, X.Z.; validation, X.Z.; visualization, L.Z.; writing--original draft, X.Z.; writing--review and editing, X.Z. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Province Science and technology development plan project of China, Grant No.20200401011 GX.  
Not applicable.  
The authors declare no conflict of interest.
# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 3 Results
### Relationships among MODIS Products with GPP and NPP Fluxes  
Figure 7a-f shows a series of scatter plots, which illustrate the relationship between the MODIS products (EVI, LSWI and LST) and GPP/NPP flux data values from linear-regression analysis. In most of the models, the effects of water and temperature on plant photosynthesis were estimated as a relationship function. Thus, we explored the relationships between the LSWI-EVI and LST-EVI to determine an inter-relationship that could yield more information on vegetation succession (Figure 7g-i). We calculated the average annual values based on the different burn severities to obtain the correlations for the multiyear data. The results in Figure 7 show that the correlations of the EVI and LSWI with the GPP/NPP were greater than those of the LST. The correlations were strongest in the high-severity fire areas; the correlation coefficients with the EVI were 0.86 and 0.81, respectively, and those with the LSWI were 0.88 and 0.83, respectively. As the burn severity weakened,  
Figure 6: Variation trajectories of GPP and NPP in post-fire forests. (**a**–**c**) represent the annual changes in the GPP. Theinter-annual trajectories of: GPP (**d**–**f**); and NPP (**g**–**i**). GPP and NPP’s differences between the control and focal pixels (**a**,**d**,**g**); inter-annual trajectories under different fire intensities (**b**,**e**,**h**); and differences under different fire intensities between the control and focal pixels (**c**,**f**,**i**).  
the correlation coefficient also gradually fell. The LST and GPP had a negative correlation, but this correlation was not strong. At all burn severities, the EVI-LSWI correlations of the sites were positive and the EVI-LST correlations were negative, but the EVI-LSWI relationships appeared to be stronger than the EVI-LST relationships; as the fire intensity weakened, the correlation coefficient decreased from 0.9 to 0.81.
## 3 Proposed Method
### Complexity Analysis and Hierarchical Levels for Message Extraction  
We now analyze the time complexity of our scheme. Let \(W\times H\) represents the size of the image. In the message embedding, we adopt the OWCTE algorithm in Phase-1 and the WMRDH algorithm in Phase-2. Both algorithms embed the respective secret vector and message, thus requiring the time complexity of \(O(W\times H)\). Similarly, in the message-extraction process, we adopt similar algorithms but in a reverse order. Therefore, the time complexity is \(O(W\times H)\). In summary, the time complexity of our proposed scheme is \(O(W\times H)\). Our proposed algorithm provides a multi-hider reversible data-hiding mechanism. In particular, the sender sends dual stego images concealing three secret messages to recipients, in a hierarchical way. On the receiving side, a receiver who holds authorized secret key(s) can extract one or several parts of the secret data independently.  
We analyze the secret keys required by the authorized recipient to extract secret messages in Table 6. A total of seven levels for message extraction are available for a combination of three secret keys, \(key_{3}\), \(key_{2}\), and \(key_{1}\). As an example, a receiver has the lowest rank if a secret key is assigned; for example, the secret message vector, \(\mathbf{d_{du}}\), can be extracted by a receiver in level 1 who has a single secret key, \(key_{3}\), without even restoring the stego image. In contrast, a receiver holding three secret keys, \(key_{3}\), \(key_{2}\), and \(key_{1}\), has the highest rank (level 7), able to extract the entire secret messages. Our proposed algorithm offers two benefits due to the multi-hider mechanism. First, it enables application access to be varied under varying and dynamic secret key operating conditions, preferably in a sender-specific customized manner. Second, the secret keys and their hierarchical forms can be applied and then modified appropriately based on changing circumstances, for example, in the cloud server, thereby providing additional security guarantees.
## 2 Materials and Methods
### Data  
This study considered eight variables: wind energy production (dependent variable), time, environmental, technical, and locational variables. The characteristics of each variable are shared in Table 2. The study data were obtained as open access from www.github.com[26]. For this study, 157,930 data points covering the 2018-2019 hourly period were accounted for, and statistical and estimation analyses were performed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Variable Class** & **Variable Name** & **Unit** & **Type** & **Data Class** & **Notation** \\ \hline Time & Time & UTC & Independent & Meta & t \\ \hline Environmental & Wind Direction & (\({}^{\circ}\)) & Independent & Numeric & Wd \\ Wind Speed & (m/s) & Independent & Numeric & Ws \\ \hline Technical & Nacelle Direction & (\({}^{\circ}\)) & Independent & Numeric & Nd \\ Rotational Speed & (s\({}^{-1}\)) & Independent & Numeric & Rs \\ Wind Turbine & — & Independent & Text & Wt \\ \hline Locational & Wind Farm & — & Independent & Text & Wf \\ \hline Energy & Power Output & MW & Dependent & Numeric & Po \\ \hline \end{tabular}
\end{table}
Table 2: Features of dependent and independent variables.  
Table 2 summarizes the characteristics of the variables used in this study, classified according to their nature and role in the analysis. The time variable is t, a meta-independent variable measured in UTC (Coordinated Universal Time). Environmental variables include wind direction (Wd, in degrees) and wind speed (Ws, in meters/second), both numerical and independent. In the Technical category, nacelle direction (Nd, in degrees), rotation speed (Rs, s\({}^{-1}\)), and wind turbine ID (Wt, textual data) are classified as independent variables. Spatial data are represented by the wind farm ID (Wf), a textual and independent variable. Finally, in the Energy class, a dependent variable is the average power production (Po, in megawatts). This structure provides a clear framework for analyzing the relationships between the independent and dependent variables, such as power production.