esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of the Fault State and Establishment of the Fault Data Sample  
The content of the fault diagnosis should be clearly defined first before diagnosing the fault. The abnormality of the engine control system mainly includes the fuel injection failure of the injector, low fuel supply pressure, leaks of the exhaust pipe, and the intermittent flameout of engine. Combined with the normal state of the 3 W engine control system, a total of five status types are defined. In this paper, fault injection technology is used to simulate these faults. An engine control system fault diagnosis experimental platform built using various sensors and actuators is shown in Figure 1. With this platform, the fault injection method is used to simulate the control system fault, and the corresponding data is collected and filtered to realize the data sample establishment. In the following analysis process, 80 sets of data are selected from each type of data in the fault sample, among them, 30 sets are used as training sample and 50 sets are used as test sample. All of them make up the data sample.
## 3 Architecture
### Packet Format Design  
Within an evolutionary ICN environment compatible with TCP/IP, IPCRSA designs a computing resource request packet format based on the IPv6 extension header, as illustrated in Figure 4. Firstly, two crucial fields, Source ID and Destination ID, are added to the packet header. The Source ID is filled with USER_ID to identify the User requesting resources, while the Destination ID is filled with the computing resource service identifier CRS_ID_T\({}_{x}\) to specify the type of computing resource service requested by the User. The inclusion of these two ID fields enables the implementation of an ID-based routing mechanism in the ICN. Combining this mechanism with SDN technologies allows the convenient implementation of late-binding for IP, enabling modification of the Destination IP field during the packet routing process based on actual requirements, which enhances routing and scheduling flexibility.  
Following these two ID fields are the QoS field, Cost field, and Resource Request Quantity field. The QoS field is filled with the User's QoS constraint for resource scheduling delay: Scheduling_Delay. The Cost field is filled with the maximum cost that the User is willing to accept for CRN usage: Max_Price. The Resource Request Quantity field is filled with the specific resource request quantities in CPU, memory, storage, and bandwidth dimensions: RRQ_Core, RRQ_Memory, RRQ_Storage, and RRQ_Bandwidth, respectively. The values of these three fields, along with the Destination ID field's computing resource service identifier, collectively constitute the User's hard constraints on resources.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**CRN Type** & **Type Name** & **ID** \\ \hline T\({}_{1}\) & Universal-CRN & CRS\_ID\_T\({}_{1}\) \\ T\({}_{2}\) & Super-CRN & CRS\_ID\_T\({}_{2}\) \\ T\({}_{3}\) & Intelligent-CRN & CRS\_ID\_T\({}_{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: CRN types in IPCRSA.  
Next is the Preference field, which is filled with the User's specific resource request preference: RRP\({}_{x}\), indicating the User's inclination toward resources. This field represents a soft constraint.  
Finally, there are the Scheduling State field and the Scheduling Target field. The Scheduling State field, filled with the IS_Scheduled flag, is set to 0 when the computing resource request has not been scheduled, and the corresponding Scheduling Target field is left empty. When set to 1, it signifies that the computing resource request has been scheduled, and the corresponding Scheduling Target field is filled with the sequence number of the destination CRN: Sequence_Number.  
Each SN in IPCRSA can receive computing resource request packets, read the relevant fields of resource demands and demand preferences in the packet header, make intelligent decisions, and forward packets based on the corresponding computing resource scheduling strategy to accomplish resource scheduling.
# Personal Picocell Scheme Using Adaptive Control CRE in Heterogeneous Mobile Networks +
## 4 User Throughput in Multi-Band HetNet
### Comparison with Conventional Method  
Figure 10 shows the average and 5-percentile user throughput of the adaptive control CRE when using the optimal parameters determined as described in Section 4.1, i.e., \(\alpha\) = 0.4, \(\text{CSO}_{\text{low}}\) = 3 dB, and \(\text{CSO}_{\text{high}}\) = 12 dB, compared with the conventional method and HomoNet.  
For the conventional method, five kinds of fixed CSO are set: \(\text{CSO}\) = 0,3, 6, 9, and 12 dB. The \(\text{CSO}_{\text{high}}\) can be greater than that in single-band HetNet because there is no interference between macrocell and picocells. The blue and orange bars show the average and 5-percentile user throughput, respectively.  
The average user throughput decreases according to the increase in the CSO from 0 to 12 dB in the conventional CRE method. By increasing the CSO up to 6 dB, the 5-percentile user throughput increases, although the average user throughput decreases. When CSO is more than 6 dB, the conventional method degrades both the average and 5-percentile user throughput. The reason for this phenomenon is the same as in single-band HetNet. Adaptive control CRE can improve the 5-percentile user throughput by 8% while maintaining the average user throughput compared with those of conventional method (CSO of 6 dB), as shown in Figure 10. Compared with a CSO of 0 dB, the proposed method can improve 5-percentile user throughput by 1.4 times approximately, although the average user throughput slightly decreases.  
As shown in Figures 8 and 10, the average user throughput slightly decreases as the CSO increases. However, the performance behavior in user throughput depends on the number of UE installations in the microcell or macro sector, and the type of UE distribution such as cluster distribution and uniform distribution. The system bandwidth of the picocell, i.e., the number of radio resources, also affects the performance behavior in user throughput. If the signal bandwidth of the picocell is wider than that of the microcell, the performance behavior in average user throughput is different from the results as shown in Figures 8 and 10. In ref. [29], it is shown that average user throughput increases as the CSO increases up to a certain value. In this paper, it is assumed that the signal bandwidth  
Figure 10: User throughput of adaptive control CRE compared with conventional CRE and HomoNet in multi-band HetNet.  
of picocell is the same as that of microcell, and the number of UE is relatively high, such as 30 per macro sector. Therefore, the average user throughput slightly decreases as the CSO increases.
## 4 Design Trade-Offs of Distributed Open Systems
### CAP Theorem  
The _CAP_ Theorem [51] states that a networked data system can only hold two of these three desirable properties:  
1. Consistency: The requests of the distributed system should behave as if they were handled by a single node with updated information.
2. Availability: Every request should be responded to.
3. Partition resistance: The system should be able to operate in the presence of network partitions.  
Given that the framework considers open systems where agents with partial information can join or leave at any moment, partition resistance is a necessary property for our proposal. Therefore, one of the most important design decisions for the systems built within this framework is to find the best balance between consistency and availability.
## 6 Simulation and Results
### Residual Energy Balancing Channel Assignment  
Another type of simulation result is the network residual energy balance. Figure 19 shows the comparison of the network lifetime with the MAB policy, the random scheme and the EKF. Figure 19 verifies the design goal of the residual energy balancing channel assignment is met. The red line represents the random scheme. some nodes run out of battery power after \(8.95\times 10^{5}\) s. A considerable part of the nodes run out of battery after \(9.075\times 10^{5}\) s. Although some nodes still have power after \(9.075\times 10^{5}\) s, since most of the nodes have run out of battery it means the whole network almost cannot run. The blue line represents the MAB adaptive packet size. Before \(9.075\times 10^{5}\) s, almost no nodes fail, and the entire network can run well. The death of sensors is distributed in a narrow time period. This is desirable because the total unused energy is reduced when the network dies. The MAB scheme can maintain the network residual energy balance. The green line represents the EKF. The EKF is slightly better than the random scheme regarding the network residual energy balance. The residual energy balancing effect of the MAB is clearly better than the random scheme and the EKF.
## 2 Materials and Methods
### Estimation of Reactive Oxygen Species by DCFH and Cytochrome-C Release  
The intracellular reactive oxygen species levels were determined by \(2^{\prime}\),\(7^{\prime}\)-dichlor fluorescein diacetate (DCFH-DA) (Thermo Fisher Scientific, Waltham, MA, USA) as described by the producer's manual. DCFH-DA fluorescence was determined using a fluorescence spectrophotometer at Ex/Ew wavelength of 485/520 nm. Cytochrome c release was estimated using standard kits according to the manufacturer's instructions (GeneTex, India) and the results are represented in relative fluorescence units.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene** & **Direction** & **Sequence** \\ \hline \multirow{2}{*}{CASPASE-3} & Forward & \(5^{\prime}\)-CCTGCATGCGCCTTAGGTC-3\({}^{\prime}\) \\  & Reverse & \(5^{\prime}\)-ATGGTGTGGATGGTGCGTCCA-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{CASPASE-7} & Forward & \(5^{\prime}\)-GGGGCCCATCAATGACACAGA-3\({}^{\prime}\) \\  & Reverse & \(5^{\prime}\)-GTCTTTCCGCTCCTCCA-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{APAF-1} & Forward & \(5^{\prime}\)-ICTTTCCAGTGAAAAGATTCAGTT-3\({}^{\prime}\) \\  & Reverse & \(5^{\prime}\)-TTGCAAGCATCAGAATGCG-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{BAX} & Forward & \(5^{\prime}\)-GAGCTAGGGTCAGAGGGTCA-3\({}^{\prime}\) \\  & Reverse & \(5^{\prime}\)-CCCCGATTCATACCCTGC-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{BCL2} & Forward & \(5^{\prime}\)-ACCTACCCAGCCTCCTATT-3\({}^{\prime}\) \\  & Reverse & \(5^{\prime}\)-GAACTGGGGAGGATTGTG-3\({}^{\prime}\) \\ \hline \multirow{2}{*}{\(\beta\)-ACTIN} & Forward & \(5^{\prime}\)-ACTACTCATGAAGATCCTC-3\({}^{\prime}\) \\  & Reverse & \(5^{\prime}\)-TAGAAGCATTGCGGTTGACGATG-3\({}^{\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The primer sequences of various genes involved in apoptosis and anti-apoptotic genes; ACTB (\(\beta\)-actin) was used as a housekeeping gene.
## 2 State of the Art  
### Technology Modelling and Workflows  
The state of the art in manufacturing, (as shown though European projects and Research, Technology Development and Innovation [25]) seems to dictate integrating a bundle of technologies and implies their smooth functionalities. To this end, a classification of technologies could be useful. This could be the toolset of an engineer designing and operating a digital twin [26] and making it work both technically and in a business sense. Such a classification system is described hereafter and is depicted in Appendix A.  
In terms of previous works, the Business Process Model and Notation (BPMN) is one of the first formal approaches for production workflow modelling [27], while Petri networks can be introduced [28] to this end, also as a part of authorization models [29]. Another well-known concept that is helpful in this and can enhance optimization is metamodelling [30]; an even more generic term.  
As far as (meta)modelling techniques are considered, there are specific grammar techniques that can boost workflows towards preventing errors [31]; these methods are highly useful in taking into account cyber-physical systems (CPS) [32] as well; however, they can be quite hard to apply. In the same context, functional and sequence modelling has also been suggested [33]. Regarding workflows, it is imperative that automation is also regarded; this, however, introduces additional needs [34]. Partially, this can be addressed by (entities') identity management [35], leading also potentially to certification. Finally, it is noted that there are dedicated pieces of software [36] that can help with workflow modelling. At this point, it can be stated that there may be three different drives towards modelling, beyond optimization: standardization, simplification and transformation [37].
## 3 Results and Discussion
### Potential Practical Applications  
Depending on the total composition of molten steel, deoxidation regime, and inclusion modification procedure, for example, via calcium treatment, duplex oxide-sulphide inclusions such as CaS-CA, CaS-C12A7, and CaS-Al2O3, can be formed in the steel melt. This study contributes to the applicability of Raman spectroscopy to non-metallic inclusion characterization with a current focus on how to quantify duplex oxide-sulphide inclusions. The most suitable Raman peak ratios for the study exhibited values for a mean absolute error (MAE) for the full data set ranging between 6.23 and 8.87 wt. % and that of the coefficient of determination (R2) between 0.94 and 0.98 for synthetic phases of C12A7, Al2O3, CA, C3A, and MgO-Al2O3.  
This study, therefore, offers the prospect of applying Raman spectroscopy to estimate duplex oxide-sulphide, and combined with previous studies [15; 16], to characterize inclusions found in steel samples, because the steel matrix is known to be Raman inactive. Additionally, the potential use of Raman spectroscopy will lead to reduced sample preparation and characterization times and can complement existing steel cleanliness analysis techniques. It is noteworthy that future studies could consider using Raman spectroscopy for a multiphase analysis consisting of three or more phases such as CaS, calcium aluminates, and spinel, because industrial steel samples can contain multiphase inclusions.
# Does Augmented Reality Affect Sociability, Entertainment, and Learning? A Field Experiment
## 3 Results
### Learning Performance  
Table 5 shows the results regarding quiz performance. We excluded those who did not finish the quiz from this analysis. Based on the results, AR users received lower scores from the quiz (\(M=8.59\), \(SD=2.14\)) than the two control groups: pen-and-paper participants (\(M=12.11\), \(SD=2.05\)) and non-AR users (\(M=10.75\), \(SD=2.56\)). The difference among the experimental groups was statistically significant (\(\chi^{2}\) [\(2\), \(N=155\)] = 47.36, \(p<0.001\), with ties). [PERSON]'s pairwise comparison with Bonferroni corrections post hoc test shows a statistically significant result for all pairwise comparisons. We did additional regression analysis and found that AR users who reported higher satisfaction on the app were more likely to finish the quiz (\(OR=1.65\), \(p<0.001\)). In turn, participants who finished the quiz had higher quiz scores (\(B=7.41\), \(p<0.001\)).  
Interviews with AR users after the experiment revealed that AR participants assumed a high potential of AR technology in teaching and providing engaging learning tools, as was apparent in one comment:  
_Definitely, I can see it could support learning, mine and others as well. It was especially nice that it was a part of the exhibition space. I also liked the scoring. If those were utilized more, I think it would have supported the learning more. (Interviewee 16)._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Learning** & \(M\) & \(SD\) & \(n\) & **Rank Sum** & **1. AR App** & **2. Non-AR App** \\
**Performance** & \(M\) & \(SD\) & \(n\) & **Rank Sum** & **1. AR App** & **2. Non-AR App** \\ \hline Quiz score & 10.54 & 2.67 & 155 & & \\
1. AR app & 8.59 & 2.14 & 49 & 2170.50 & \\
2. Non-AR app & 10.75 & 2.56 & 53 & 4378.50 & \(-\)4.34 *** & \\
3. Pen \& paper & 12.11 & 2.05 & 53 & 5541.00 & \(-\)6.82 *** & \(-\)2.53 * \\ \hline \hline \end{tabular}  
* \(p<0.05\). **\(p<0.01\). ***\(p<0.001\). Note. The dependent variable: Learning performance based on quiz score.  
\end{table}
Table 5: Means (\(M\)), Standard Deviations (\(SD\)), Frequencies (\(n\)), Rank Sum, and [PERSON]’s Multiple Nonparametric Pairwise Post Hoc Test with Bonferroni Corrections for Differences in Dependent Variable of Learning Performance (\(N=372\)).  
High engagement in the competitive features of finding and answering all the questions was a common theme. However, some interviewees had doubts that AR technology could actually improve learning and performance outcomes: \"I believe using augmented reality was more meaningful, but I don't believe it increased learning\" (Interviewee 10). The previously reported results of comparing AR users' quiz performance to the two control groups provide support for this suspicion, but the interviews still highlight the potential entertainment value in learning, which cannot be measured by performance.
## Classification
### Modelling Techniques
Finally, it is important to note that the performance of class modelling approaches mainly depends on the settings of several metaparameters such as the confidence level at which the class space is constructed and/or the number of latent variables extracted from the original data. Different approaches for the optimization of these metaparameters exist, whose efficiency and robustness can vary according to the specific scenario faced (e.g., high class overlap, presence of outliers in the training set, etc.) [144; 145; 146; 147; 148; 149]. For their particular nature and for the main implication resulting from their use (i.e., each category of objects is handled independently and separately), modelling classification strategies are nowadays regarded as ad hoc solutions for coping with food authentication problems (see Table 2). In particular SIMCA has lately been widely resorted to for the traceability of high value-added foodstuffs like wine, beer, extra virgin olive oil, olive seeds, walnuts, coffee and rice [132; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161; 162; 163], and has been found to guarantee extremely satisfactory performances when PDO and/or PGI products are to be discerned from lower-quality ones. In this regard, several application studies involving the combination of NIR spectroscopy and SIMCA for the characterization of Avola almonds [164], Gagnano pasta [165], Italian hazelnuts [166] and Vallerano chestnuts [167] have been recently reported in literature. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Methods**} \\ \hline
**Name** & **Aim** & **Reference** \\ \hline UNEQual class spaces (UNEQ) & Exploratory & [137; 138] \\ Soft Independent Modelling of Class Analogy (SIMCA) & Regression & [139; 140] \\ Non-parametric class modelling & Regression & [151] \\ Neural networks-based class modelling & Regression & [152; 153] \\ Partial Least Squares Density Modeling (PLSDM) & & [154] \\ Potential function (kernel density) method (POFTUN) & Regression & [168; 169; 170] \\ Pattern Recognition by Independent Multicategory & Regression & [171] \\ Analysis (PRIMA) & Regression & [172] \\ Multivariate Range Modeling (MRM) & Regression & [173] \\ Support Vector Domain Description (SVDD) & & [173] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main class modelling methods and application examples in the field of spectroscopy-based food analysis. PGI and PDO stand for Protected Geographical Indication and Protected Designation of Origin, respectively.
# Detection of Volatiles by HS-SPME-GC/MS and Biological Effect Evaluation of Buddha's Hand Fruit
## 2 Materials and Methods
### Determination of Total Flavonoids  
The total flavonoid content of EEX and MEX was determined by the aluminum chloride colorimetric method [25], with some modifications. Briefly, 100 \(\upmu\)L of each suitably diluted sample was separately mixed with 300 \(\upmu\)L of methanol, 20 \(\upmu\)L of 10% aluminum chloride, 20 uL of 1 M potassium acetate, and 560 uL of distilled water. The final solution was incubated in the dark for 30 min at room temperature. Then, the absorbance was measured at 420 nm using a Jenway 6310 spectrophotometer (Keison, Chelmsford, Essex, UK). A calibration curve was prepared with quercetin as standard at various concentrations (12.5-100 ug/mL). The test was performed in triplicate three times, and the results reported as mg of quercetin equivalents (QE) per g of fruit part.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 4 MobilityFirst-Based IoT Architecture
### MobilityFirst-Based IoT Architecture Design  
As a robust and trustworthy mobility-centric architecture with abundant in-network services, MobilityFirst can address many challenges that today's IoT is facing, such as scalability, mobility, content retrieval, inter-operability, and security/privacy. Therefore, we propose a unified solution to support IoT based on the MobilityFirst network architecture [33]. Our IoT platform consists of four basic components as shown in Figure 4. Though this IoT architecture is designed in the context of the MobilityFirst network, it is general and can be applied to other similar network infrastructures, which adopts the separation of identifiers from network locators, such as [8; 34; 35; 36].  
* **Devices**: a wide variety of devices, e.g., sensors, actuators and tags, that use embedded techniques to sense, communicate and/or interact with the external environments.
* **MobilityFirst network**: MobilityFirst provides connectivity for different distributed IoT devices and applications. Due to the seamless mobility support of MobilityFirst, besides well-established IoT system, more dynamic IoT applications/systems can be developed on top of it.
* **IoT middleware**: MobilityFirst-based IoT middleware integrates three functional layers--_Aggregator_, _Local Service Gateway (LSG)_, and the _IoT server_. The aggregator supports sensor abstraction to hide the hardware specifics for sensors and presents a single interface to query and subscribe to the sensor data. LSG connects the local IoT system to the global Internet and provides necessary management, including naming resolution, key management and context processing services. The IoT server is a logically centralized server in the control plane that manages subscription memberships as well as provides a lookup service so that subscribers may query for the IoT data/services.
* **Applications**: end users who consume the IoT data and may feed back to the external environment through actuators.  
Figure 5 explains how the IoT architecture works with the support of the MobilityFirst infrastructure. The aggregator collects data and then the LSG sends the aggregated data to the storage  
Figure 4: The four basic components of the IoT MobilityFirst architecture: devices, IoT middleware, MobilityFirst network and applications.  
location. The raw data might be processed by the LSG for context refining/aggregating purposes. Meanwhile, the LSG publishes the data information, which contains a data GUID, the access control policy and the storage location information (e.g., human readable names or network address), to the IoT server so that end users may query the IoT server about where to fetch the data. The IoT server may decide how to enforce the access control policy: either at itself or push it to the NCRS/GNRS. The data consumer, typically an application, first makes a query at the IoT server for data information through its edge router and then fetches the data from the storage location or the aggregator directly.