esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Enterprise Internet of Things Maturity Models: A Review
## 7 Threats to Validity  
This study has conducted a review to identify and analyze the state of the art of maturity models for IoT in enterprises. However, this study has some limitations and threats to validity that need to be recognized and addressed.  
Search strategy: A detailed search strategy involving multiple scientific databases, keywords, and inclusion and exclusion criteria was used to retrieve relevant studies in the domain. However, some papers not indexed in the selected databases used different terms or synonyms or were published in other sources, such as books, reports, or these, which may have been omitted. In addition, only studies published in English were considered, which may have introduced a linguistic bias and excluded relevant studies in other languages. To mitigate this threat, we also performed a hand search of references of the primary studies and the related literature to identify additional studies that might be relevant to the research questions;  
Selection bias: A two-stage screening process was applied to select primary studies based on their titles, abstracts, and full texts. However, this process could be subject to selection bias due to the reviewers' subjective interpretation of the inclusion and exclusion criteria. To mitigate this threat, a rigorous protocol was followed, including using a screening tool and defining clear and objective criteria. When necessary, disagreements were resolved by discussion or consultation with a third reviewer;  
Quality assessment: The quality of the primary studies was assessed using a customized quality assessment checklist based on the criteria proposed by [PERSON] et al. [32]. However, this assessment may have been subjective and inconsistent, as different reviewers may have different perceptions and expectations of study quality. A transparent and objective scoring system was used to address this threat; the checklist was adjusted, and inter-reviewer concordance was calculated;  
Data extraction and synthesis: Data were extracted from the primary studies using a data extraction form and a narrative synthesis approach. However, this process could be affected by incomplete or inconsistent study reporting, heterogeneity of maturity models, or reviewer interpretation. Data extraction forms were collated to mitigate this threat; a common framework was used to compare maturity models, and themes were validated against the primary studies and the existing literature.
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
## 6 Conclusions  
We presented a minimally-invasive wireless neural interface system with the detailed description of the core IC and microelectrode array designs, and the in vivo ECoG measurement results. The interface circuits can perform 16-channel low noise, low power recording, filtering, and digitization, and data transfer through the proposed intra-skin communication (ISCOM). The interface system has been realized in an all-in-one platform by assembling the flexible microelectrode array, the signal processing core chip, the wireless data transmission module and a rechargeable battery. The platform is modular and expandable to be a distributed system over the large brain area to extend the system coverage and sensing modalities. The IC chip has been fabricated in 0.25 \(\upmu\)m CMOS technology, and the core area of the IC is 3.2 \(\times\) 0.9 mm\({}^{2}\). The measured power consumption per channel is 2.5 \(\upmu\)W and the total power consumption of the system operation from a rechargeable battery is 365 \(\upmu\)W. The measured NEF and PEF are 1.69 and 2.86 at 10-10 kHz, respectively, and 5.2 and 27.04 at 1-500 Hz, respectively. The 16-channel flexible microelectrode array has also been fabricated by using bio-compatible Pt and Parylene-C. The impedance of the microelectrode array was measured as 1.2-5.6 k\(\Omega\), which is at least 1000\(\times\) smaller than the input impedance of the interface circuits. The implemented system has successfully recorded 16-channel neural signals from a primate in vivo and transmitted the recorded data wirelessly through the skin at a rate of 10 kb/s at 160 \(\upmu\)W power consumption with \(<\)10\({}^{-6}\) of BER.  
The authors would like to acknowledge Prof. [PERSON] and his lab members at Washington University in St. Louis for in vivo validation of the wireless neural interface system presented in this work. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 3 The Framework of the Present Study  
The current problem focuses on the wireless rechargeable sensor network (WRSN), sensor nodes deploy over a two-dimensional area in an open environment in \(N\) distributed ways shown in Figure 1. Furthermore, notations used in this paper appended in Table 1.  
Figure 1: A WRSN with a wireless charging vehicle deployed over single-hop and multi-hop link.  
WCV is responsible for refilling energy for all the sensors. Each sensor node equipped with identical energy. The energy consumption rates with a maximum energy E\({}_{max}\) and it is assumed to be charged fully at the initial level. If the battery level of sensor node exhausted below the threshold E\({}_{min}\), a recharge request is immediately triggered and would be sent to the base station. Further, the base station will inform about the application to WCV and responses back either to recharge the battery or to replace the battery. In our proposed model, each node sends data to the base station through multi-hop routing, and WCVs is responsible for recharging the battery of all the nodes. Each sensor node generates sensing data with a rate of \(R_{i}\). WCV is equipped with a robust battery pack capacity of E\({}_{max}\) and consumes \(C_{ij}\) KJ energy while moving. During each cycle, WCV starts from the service station to charge each sensor nodes on its charging cycle. The WCV travels as a given velocity (V m/s) and visits each node in the network to recharge the sensor nodes. The sensor nodes are chosen by the WCV for recharging based on the shortest distance. The WCV has its communication radius which is denoted by (\(R_{c}\)). The WCV calculates the distance to each neighboring node within its communication radius and chooses the node with the minimum distance for recharging. Additionally, when the battery of WCV is about to be exhausted completely, it returns to the service station for SOS action, such as recharging of the cell or replacement of the battery.
## 2 System Model and Problem Formulation
### Transmission Model
Therefore, the transmit power of \(R_{m}\) without covert information can be expressed by:  
\[P_{R_{m},0}=\frac{\gamma_{th}\sigma_{U}{}^{2}}{\left|h_{R_{m}U}\right|^{2}}. \tag{8}\]  
On the other hand, if \(R_{m}\) transmits source information to \(U\) with covert information, the received signal at \(U\) is given by:  
\[y_{U_{1}}(k)=\sqrt{P_{R_{m_{1}}}}h_{R_{m}U}x_{R}(k)+\sqrt{P_{C}}h_{R_{m}U}x_{C }(k)+n_{U}(k), \tag{9}\]  
where \(P_{R_{m},1}\) is the transmit power of \(R_{m}\) for \(x_{R}\), and \(P_{C}\) is the fixed transmit power of \(R_{m}\) for transmitting its own covert information \(x_{C}\), which also satisfies \(E\Big{\{}\left|x_{C}(k)\right|^{2}\Big{\}}=1\). When \(U\) receives \(y_{U_{1}}\), \(x_{C}\) is seen as an interference signal in the first phase of decoding \(x_{R}\). Thus, the signal-to-interference-plus-noise ratio (SINR) for decoding \(x_{R}\) is obtained as:  
\[\gamma_{U}=\frac{P_{R}|h_{R_{m}U}|^{2}}{\sigma_{U}u^{2}}. \tag{2}\]0  
Due to the constraints of Equation (6), \(\gamma_{U_{1}}=\gamma_{th}\) should be ensured at least. Hence, the \(P_{R_{m},1}\) can be denoted by:  
\[\gamma_{U}=\frac{P_{R}|h_{R_{m}U}|^{2}}{\sigma_{U}u^{2}}. \tag{2}\]1  
Moreover, it is apparent only when the condition \(P_{R_{m},1}+P_{C}\leq P_{M}\) is satisfied, \(R_{m}\) is able to transmit covert information, which is given by:  
\[\gamma_{U}=\frac{P_{R}|h_{R_{m}U}|^{2}}{\sigma_{U}u^{2}}. \tag{2}\]2  
when the set \(\Lambda\) is met, the \(R_{m}\) can have enough ability to realize the covert information transmission. After successfully decoding \(x_{R}\), it will be stripped by \(U\). Thus, the SNR for decoding \(x_{C}\) is expressed as:  
\[\gamma_{U}=\frac{P_{R}|h_{R_{m}U}|^{2}}{\sigma_{U}u^{2}}. \tag{2}\]3
## 2 Seismological Data and Results  
In the Ionian Islands there are historical reports of 94 earthquakes from the years 358 to 1898 [9; 61]. A significant number of historical earthquakes are located onshore, most possibly due to the limitations that arise from the use of historical sources. Instrumental seismicity, since 1900, is concentrated around the islands of Lefkada, Cephalonia and Zakynthos [28]. As described above, epicenters in Lefkada and Cephalonia are linked to local faults and mainly to the CLTFZ, whereas seismicity offshore and close to Zakynthos is related to the border between the Eurasian and African plates. Focal depths are limited to the upper 30-40 km of the crust, while foci at greater depths are related to the submerged African plate. The high seismicity rate is reflected in the high seismic hazard of the central Ionian Islands that belong to the highest category (Zone III) of the current Greek Building Code, with a Peak Ground Acceleration (PGA) value of 0.36 g for a return period of 475 years [62].  
Since the implementation of the HUSN in 2007 [63], the monitoring of seismic activity in Greece has been enhanced, with increased detectability and improved location accuracy. This was particularly important for the region of the Ionian Islands, as it is situated at the western margins of the network and a significant part of its intense seismicity is located offshore. Herein, a catalogue of ~62,000 earthquakes was compiled. These events have occurred in the broad Ionian Islands area, including part of mainland western Greece. The seismological data were collected from the database of the Geodynamics Institute of the National Observatory of Athens (GI-NOA) for the period from February 2011 to May 2018. For the consecutive period from June 2018 to November 2021, the seismological data were extracted from the database of the Seismological Laboratory of the National and Kapodistrian University of Athens (SL-NKUA). Furthermore, in the present analysis, relocated seismicity catalogues were incorporated for the 2014 Cephalonia, 2015 Lefkada and 2018 Zakynthos aftershock sequences [29; 32; 64; 65; 66; 67].
# Cellular Antioxidant Effects and Bioavailability of Food Supplements Rich in Hydroxyrosol
## 3 Results
### Bioavailability  
Several cellular effects have been evaluated in vitro to test the antioxidant potential of olive water concentrates. Although, the bioavailability of such components is a prerequisite to any health claim made based on cellular tests. To investigate whether the hydroxytyrosol and trans-resveratrol consumed with the food supplements here studied are bioavailable, we conducted a pilot internal study with four volunteers (two male, two female). Volunteers in a fasted state received a single dose of 50 mL of P-2 in period 1 and 50 mL of P-1 in period 2, separated by a one-week wash-out period. Trans-resveratrol and hydroxytyrosol in its free form were undetectable in urinary samples collected immediately before intake. Free hydroxytyrosol, but not free trans-resveratrol, was detectable in urinary samples collected 30 min after the intake of both supplements and in all the volunteers (Figure 3).  
Figure 3: UHPLC-MS/MS chromatograms of the MRM 153 > 123. (**A**) hydroxytyrosol standard in urine; (**B**) representative urine sample taken 30 min after ingestion; (**C**) representative urine sample before intake of the supplement/product.  
Similarly, previous human studies have showed that plasma and/or urinary hydroxyol increase following the oral administration of hydroxyytrosol, consumed with olive oil [39; 40; 41], liquid or encapsulated olive leaf extract [42], and encapsulated extract from oil mill wastewater [8]. In these experiments, the absorption and excretion of orally administered hydroxyytrosol, collected mainly in conjugated forms and in a dose-dependent manner, have been shown. Furthermore, it has been shown that human absorption may differ depending on the composition of the food matrix through which the hydroxyytrosol is dispensed. Besides this, the bioavailability is likely influenced by wide interindividual variability in the absorption and metabolism [43].  
Our preliminary results show that hydroxyytrosol consumed together with a hydrophilic vehicle is bioavailable in the human body, independently of the interactions of the combined fruit extracts used. Moreover, hydroxyytrosol can be detected in urine in its free form. This is relevant as the absorption of hydroxyytrosol is dependent on the vehicle of administration [9]. This is likely because the interaction between olive water and grape or lemon juices influences the hydroxyytrosol absorption and recovery yield. Further studies should be done to verify this hypothesis.
## 4 Simulation Results and Discussion
### Offline Results  
To confirm the proposed controller in the offline calculation, the search interval for the Lagrange multiplier is bounded to within the following two maximum and minimum values \(\lambda_{min}\) = 5 \(\times\) 10\({}^{-5}\) and \(\lambda_{max}\) = 7.0114 \(\times\) 10\({}^{-5}\).  
Before setting the primary initialization parameters of the bisection method, the trial-and-error method was applied to the global PHEV model in order to ensure the existence of the desired co-state and to confirm the choice of the boundary search interval. Figure 11 illustrates the state of the charge of the battery variation according to \(\lambda\)-Control parameters iteration in the offline calculation.  
It reveals that the searched multiplier meeting the above hypothesis can be localized between the two parameters \(\lambda_{7}\) and \(\lambda_{8}\) for a fuel consumption rate of less than or equal to 1.95 L per 100 km.  
The co-state search problem \(\lambda(0)\) is solved after 25 iterations with an allowable \(\Delta\)SoE tolerance error of 0.05 and a 0.1 s sampling period. After running through iterations, the found value \(\lambda(0)\) for ten consecutive NEDC driving cycles is equal to 6.5505 \(\times\) 10\({}^{-5}\). Thus, it is assumed to be the most effective value leading to optimal control results (in terms  
Figure 11: State of the charge variation according to L-Control parameters iteration using the Bisection method in the offline calculation.  
of fuel consumption) in blended mode with the target SoC performance of around 30% at the end of the trip. The resulting value is then fed into the system to test its evolution as well as the SoC variation and the use of the fuel rate. Figure 12 represents the vehicle velocity, the required vehicle velocity, the state of charge, and ICE and EM torques for the proposed offline \(\lambda\)-control algorithm for PHEV using the Bisection Method on the blended mode.  
It proves good speed tracking and good SoC battery management over the journey. The third curve below presents the distributed energy flow of the thermal (red) and electric parts (blue). The system favors the use of the ICE in high energy demand to delay the earlier complete discharge of the battery and reach its required SoC value towards the end of the drive pattern. Only the electric motor supplies the negative power demand and allows the energy storage system to recharge during deceleration and braking phases.  
It has been mentioned that throughout the system performance test, it is necessary to make a well-balanced compromise between the required set objectives. In this case, it can be assumed that the obtained \(\lambda(0)\) value assisted the system in improving the fuel economy rate to 1.86 L per 100 km for a final SoC found at the end of the trip close to the set target SoC balance value \(-70.02\%\) (\(\Delta SoC=SoC_{final}-SoC_{initial}\)).
# GNSS-Based Monitoring Methods for Mining Headframes
## 4 Correlation Analysis and Risk Assessment  
Based on the data analysis, the main body of the headframe and the sheave wheels exhibit uneven settlement. To further clarify the settlement patterns and their relationship with factors related to the headframe and to qualitatively study how these factors affect the headframe deformation, this work uses correlation analysis to calculate the correlation coefficients of the main factors with the deformation quantities and identifies the dominant factors of headframe deformation on the basis of the magnitude of the correlation coefficients. At the same time, using the combination of the maximum and minimum values of the deformation quantities, a reasonable risk assessment threshold is estimated, and the correctness of this threshold is verified with monitoring data over 10 consecutive days.
## 5 Results
### Effectiveness Analysis of SAVOA Path Planning
#### 5.5.2 Effectiveness Analysis of Urban Terrain  
Figure 13 shows the average values of the four-performance metrics for GA, WOA, ABC, SSA, AVOA, and SAVOA in the urban environments. In the city 1 scenario, SAVOA's four performance metrics remained at their lowest values, where the optimal fitness decreased by 13.04%, 10.19%, 6.94%, 20.11%, and 6.34% over GA, WOA, ABC, SSA, and AVOA, respectively; for average fitness, SAVOA was reduced by 12.60%, 10.46%, 7.34%, 20.77%, and 8.31%, respectively; above the flight time, the reduction was 25.92%, 27.01%, 22.93%, 34.39%, and 24.21%, respectively; and the smoothness was 93.73%, 94.75%, 91.24%, 98.84%, and 94.34%.  
For the city 2 scenario, the optimal fitness of SAVOA decreased by 16.97%, 17.22%, 8.17%, 18.40%, and 9.57%; the average fitness decreased by 15.95%, 17.48%, 8.30%, 19.55%, and 11.86%; the flight time decreased by 33.73%, 34.12%, 32.47%, 37.10%, and 26.73%; in terms of smoothness, SAVOA was reduced by 93.33%, 99.00%, 97.07%, 99.04%, and 98.19%, respectively.  
According to the above analysis, SAVOA outperforms the other five algorithms in all four metrics. In the four scenarios, its average fitness is only 1.37%, 1.64%, 1.28%, and 2.10%, higher than the optimal fitness, respectively, which is a small difference, which shows that SAVOA converges faster while achieving the lowest fitness. In addition, it having the least flight time and the best smoothing also indicates the efficiency and safety of the algorithm for UAV path planning.
## 3 The Cerenkov FEL Oscillator
This will be relevant for the analysis of the velocity modulation induced on the remaining part of the macro-bunch itself. The peak intra-cavity peak power, as deduced by Figure 3b), is about \(P_{SAT}\)-4.2 \(\times\) 10\({}^{7}\)\(W\); considering an average transverse radiation dimensions, obtained from the data reported in Table 1, of about \(\Sigma_{L}=1\) cm\({}^{2}\), the electric field associated to the intracavity radiation at saturation is:  
\[E_{SAT}\ =\ \sqrt{2I_{SAT}/(\varepsilon_{0}c\Sigma_{L})}\approx 1.782\cdot 10^{ 7}[\mathrm{V/m}]\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Cerenkov FEL Gain Parameters @ \(\lambda=250\)\(\upmu\)m**} \\ \hline Electron & Angular & Transverse & Peak Gain & Total & Intracavity \\ Bunch & Distribution & Distribution & Coefficient & Losses & Equilibrium Power \\ \hline \(I_{0}=20.0\) & \(\sigma^{\prime}=5\times 10^{-4}\) & \(\sigma_{x}=1\) mm & & & \\  & (r.m.s.) & (r.m.s.) & \(g_{0 MAX}=0.4\) & \(\Gamma=0.042\) & \(I_{e,i}=7.22\times 10^{7}\)\(W\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cerenkov FEL Gain Parameters @ \(\lambda=250\)\(\upmu\)m.  
Figure 3: Saturation behaviour: (**a**) gain as a function of the round-trip number; and (**b**) intracavity power as a function of the round-trip number. Before analysing the dynamical process, it is worth briefly addressing the question of the longitudinal modes in the Cerenkov FEL resonator. The wavenumber mode separation, as evident from Equation (6), is (\(\Delta\omega\)/\(c\)) = \(\pi\)/L. The relative gain bandwidth is [11; 15]:  
\[\frac{\Delta\omega_{G}}{\omega}\approx\frac{1}{4N_{eq}}\ =\ \frac{\gamma^{2} \lambda_{C}}{2L}\ \Rightarrow\ \frac{\Delta\omega_{G}}{c}\ =\ \frac{\omega_{C}}{c}\frac{\gamma^{2}\lambda_{C}}{2L}\approx\frac{\pi\gamma^{2} }{L}\]  
The number of modes contained in the gain bandwidth is therefore: \(\Delta\omega_{G}/\Delta\omega=\gamma^{2}\). It is well known [11] that any FEL based on the RF accelerator locks all the longitudinal modes in a natural way. The result of such mode-locking is that a single short pulse of radiation travels back and forth inside the tuned resonator with a group velocity that is slightly smaller than that of the electron beam, causing a kind of anti-lethargic effect [23], unlike what happens for undulator FELs.
## 3 Robot Control Paradigms and Cognitive Architectures
### The Hierarchical Paradigm  
The _hierarchical_ paradigm is historically the oldest method used in robotics, beginning in 1967 with the first AI robot, Shakey [18]. In the hierarchical paradigm, the robot senses the world to construct a model, plans its next actions to reach a certain goal, and carries out the first directive. This sequence of activities is repeated in a loop in which the goal may or may not have changed (Figure 1).  
Figure 2 shows an example of a hierarchical paradigm characterized by a horizontal decomposition as summarized by [PERSON] in Rodney (1969). The first module consists in collecting and processing the environmental data received through the robot's sensors. The processed data are used to either construct or update an internal world model. The model is usually constituted by a set of symbols composed by predicates and values that can be manipulated by a logical system. The third module, i.e., the planner, uses the world model and the current perception to decide a feasible plan of actions to be executed to achieve the desired goal. Once a suitable set of actions has been found, the fourth and fifth modules execute the actions by converting the high-level commands into low-level commands to control the actuators of the robot. This process is repeated continuously until the main goal of the robot has been achieved.  
Using a top-down design and sequential modules, the hierarchical paradigm lacks robustness because each subsystem is required to work and the failure of any one of the sub-modules causes the entire chain to fail. Moreover, it requires higher computational resources due to the modeling and planning phases.
# Electrically Small Water-Based Hemispherical Dielectric Resonator Antenna
## 2 Configuration  
The water-based DRA is shown in Figure 1. The hemispherical cavity has radius \(r_{\rm w}\), and the feed line is a coaxial transmission line with an inner and outer radii of \(r_{\rm i}=0.92\) mm and \(r_{\rm o}=2.99\) mm, respectively, and a dielectric material with the relative permittivity of \(\varepsilon_{\rm c}\approx 2.1\). The characteristic impedance of the feed line is 48.75 \(\Omega\), and its length was set to \(l_{\rm c}=10\) mm in the numerical model. The monopole of length \(l_{\rm m}\) is displaced from the center by distance \(x_{\rm m}\). A Cartesian coordinate system was introduced, as shown in Figure 1. The permittivity of water was described by the Debye model [12]. A model of the water-based DRA was built in COMSOL Multiphysics 5.3 [28], which was used in all numerical calculations. The model consisted of the DRA enclosed by a hemisphere of free space and with a perfect electric conductor (PEC) plane as the ground. An outer perfectly matched layer (PML) terminated the free-space hemisphere. A matched port was placed on the bottom of the transmission line, as shown in Figure 1. The input power was set to 1 W.  
See Appendix A for definitions and more details of the antenna parameters used in this work.