esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 4 Micro-Supercapacitor Electrolytes
### Ionic Liquid Electrolytes  
As the electrochemical stability of the interface, meaning the maximum voltage that can be applied before the redox process is activated, is an important factor determining the energy that can be stored and released. Besides the carbon interfacial area, room temperature ionic liquids are of high interest because of their wide potential stability window (more than 5 V) and high thermal stability. They have a lower sensitivity to moisture and oxygen in comparison to organic electrolytes. However, their disadvantages involve their lower conductivity, lower power density due to lower ionic diffusion, and their higher viscosity [117].  
Ionic liquid, 1-butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([BMIM][NTfZ]), acted as the electrolyte in a planar thin film supercapacitor with cluster-assembled nanostructured carbon, which reached a capacitance of 75 F/g [120]. A flexible micro-supercapacitorwas made with laser-induced graphene from a polyimide substrate and decorated with red mud nanoparticles, as well as a solid-state ionic liquid electrolyte, composed of poly(vinylidene fluoride), 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide, and 1-ethyl-3-methylimidazolium tetrafluoroborate, which achieved an energy of 0.018 mW/cm\({}^{2}\) at a power of 0.66 mW/cm\({}^{2}\)[121].
# Stomatal Response of Maize (_Zea mays_ L.) to Crude Oil Contamination in Soils
## 4 Discussion  
### Soil Properties in Crude Oil Contaminated Soil  
Soil properties play an important role in soil microorganism activity and plant growth. According to previous work, plants exposed to soils contaminated with petroleum hydrocarbons were subjected to growth limitations, due to low water uptake and reduced nutrient availability [8]. Mineral nutrient availability can be reflected by soil electrical conductivity. Therefore, soil water content and soil electrical conductivity need to be well understood in phytoremediation of soil contaminated with petroleum hydrocarbons. In this study, water contents increased in soil contaminated with certain TPH levels (3700-17,800 mg/kg) but decreased significantly when TPH levels reached 48,800 mg/kg. Water-stable aggregates in soil are related to the content of soil organic matter [32]. The addition of crude oil to soil increased the soil organic matter content, possibly resulting in enhanced water holding capacity at a certain limited range, as soil organic matter was an important determinant of the available water capacity [33]. However, the high concentration of crude oil in soil might prevent water from entering the pores of soil, which decreases water holding capacity.  
Soil electrical conductivity increased in crude oil contaminated soils compared to the control soil in this study. This result was in agreement with previous work, which showed that the value of electrical conductivity in contaminated soil was higher than that of the control site [5]. Soil electrical conductivity represents soil salinity, which is mainly composed of cation ions such as Na\({}^{+}\), K\({}^{+}\), and Ca\({}^{2+}\). The result of this study illustrated that availability of these ions in soil was not the limiting factors for plant uptake. The addition of crude oil in soil leads to higher soil electrical conductivity, possibly resulting from the production of metabolites from crude oil biodegradation [34].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **TPH** & **SW** & **EC** & **LW** & **TR** & **SL** & **SD** & **K** & **Na** & **PHE** \\ \hline TPH & 1.00 & & & & & & & & & & \\ SW & \(-\)0.82 \({}^{\star}\) & 1.00 & & & & & & & & \\ EC & 0.55 & \(-\)0.22 & 1.00 & & & & & & & \\ LW & \(-\)0.10 & \(-\)0.25 & 0.23 & 1.00 & & & & & & \\ TR & 0.27 & 0.10 & 0.65 & \(-\)0.28 & 1.00 & & & & & \\ SL & \(-\)0.59 & 0.10 & \(-\)0.83 \({}^{\star}\) & 0.05 & \(-\)0.55 & 1.00 & & & & \\ SD & \(-\)0.91 \({}^{\star}\) & 0.65 & \(-\)0.78 & \(-\)0.10 & \(-\)0.46 & 0.81 & 1.00 & & & \\ K & \(-\)0.62 & 0.11 & \(-\)0.66 & 0.30 & \(-\)0.67 & 0.92 \({}^{\star\star}\) & 0.79 & 1.00 & & \\ Na & \(-\)0.71 & 0.27 & \(-\)0.54 & 0.44 & \(-\)0.72 & 0.75 & 0.78 & 0.94 \({}^{\star\star}\) & 1.00 & \\ PHE & \(-\)0.59 & 0.09 & \(-\)0.71 & 0.11 & \(-\)0.40 & 0.98 \({}^{\star\star}\) & 0.76 & 0.89 \({}^{\star}\) & 0.72 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simple correlation coefficient (r) between parameters.
## 3 Results and Discussion
### Air Duct Hole Shape  
Different air duct hole shapes were introduced in the +4\(D\) separator model, as separation is superior in this model. The effect of duct hole shapes on velocity magnitude at points 1 and 2 is  
Figure 4: Effect of various duct hole diameters on (**a**) velocity magnitudes at point 1, (**b**) velocity magnitudes at point 2 and (**c**) pressure distributions at point 1 at an air inlet velocity of 35 m/s and bed slanting angle (\(\theta\)) of 0\({}^{\circ}\).  
presented in Figure 5. The triangular-shaped duct hole resulted in higher velocity magnitude because of its sharp corner [23]. The start of the graph shows that the recirculation zone on the vibrating bed increased, which in turn increased the collection of waste materials (Figure 5a). The increase in diameter of the triangular- or round-shaped duct hole reduces the velocity magnitude, resulting in low separation of PET and PVC materials. However, the round-shaped duct hole with a diameter of 1.5\(D\) showed higher value of velocity magnitude at point 2 because of the smooth shape (Figure 5b). Thus, the kinetic energy of the ballistic trajectory is not significantly reduced [23]. The triangular-shaped duct hole with a diameter of 1.5\(D\) increased separation by increasing the velocity magnitude of the ballistic trajectory. The effect of duct hole shape was also investigated by estimating the pressure distributions in the density separator models (Figure 6). The minimum pressure value is situated near the vibrating bed, whereas the maximum is near the duct hole (Figure 6a). The round-shaped duct hole showed the highest pressure distribution values. The duct hole with a diameter of 2.5\(D\) resulted in the highest pressure and lowest velocity magnitudes at point 1. These results suggest that the triangular duct hole with a diameter of 1.5\(D\) results in an enhanced separation process, as the shape decreases the pressure distribution. Similarly, the pressure magnitude is high for the round duct hole shape as compared with the triangular shape in point 2 (Figure 6b). The increase in the size of the triangular or round duct hole also increased the pressure magnitudes. This finding implies that duct hole shapes with higher diameters are not suitable for the density separator, because the large duct holes reduce the velocity magnitude of the ballistic trajectory and increase the pressure in the separator model.  
Figure 5: Effect of various duct hole shapes on velocity magnitude at (**a**) point 1 and (**b**) point 2 at an air inlet velocity of 35 m/s and bed slanting angle (\(\theta\)) of 0\({}^{\circ}\).  
Figure 6: Effect of various duct hole shapes on the pressure distributions at (**a**) point 1 and (**b**) point 2 at an air inlet velocity of 35 m/s and bed slanting angle (\(\theta\)) of 0\({}^{\circ}\).
# Rapid Soil Tests for Assessing Soil Health
## 3 Results
### Step 3: Soil Health Indicator Report
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**EU** & **ABCDE** & **A** & **B** & **C** & **D** & **E** & **Unit** \\ \hline A1 & ECe & \textless{}2 & 2–4 & 4–6 & 6–8 & \textgreater{}8 & dS m\({}^{-1}\) \\ A3 & SOC:clay & \textgreater{}1/8 & 1/8–1/10 & 1/10–1/13 & 1/13–1/16 & \textless{}1/16 & - \\ B1 & P-Olsen & 30–50 & 20–30 or 50–60 & 15–20 or 60–70 & 10–15 or 70–80 & \textless{}10 or \textgreater{}80 & mg P kg\({}^{-1}\) \\ B2 & Heavy metals (Cd) & \multicolumn{4}{c}{pH dependent system, see Figure 4} & \multicolumn{1}{c}{\(\mu\)g Cd kg\({}^{-1}\)} \\ C2 & Soil pH & 5.5–6.5 & 5.2–5.5 or 6.5–7.0 & 5.0–5.2 or 7.0–7.5 & 4.5–5.0 or \textgreater{}7.5 & \textless{}4.5 & - \\ C4 & Biodiversity & \multicolumn{4}{c}{SOC dependent system, see text} & \multicolumn{1}{c}{mg PLFA kg\({}^{-1}\)} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thresholds used for an integrated assessment of soil health, an ABCDE score, based on a recent proposal of the European Union [51]. <4 dS m\({}^{-1}\)[51] has been converted to EC. Further, although there is some debate about the optimal ranges for the SOC:clay ratio [102; 103; 104; 105] for mineral soils, we used target values of 1/8, 1/10, 1/13 and 1/16 for'very good', 'good','moderate' and 'degraded' levels [100]. For the ABCDE score we considered A = very good, B = good, C = moderate, D = degraded and E = very degraded (i.e., SOC/clay ratio > 1/16). Table 4 presents the ABCDE score system used in the soil health indicator report, with ranges for each class. For soil contamination by cadmium, the ABCDE scores depend on the soil pH (see Figure 4). The average scores for the randomized test set, stratified by postal code, were 6%, 41%, 37%, 14%, and 2% for groups A, B, C, D, and E, respectively. If a 'one out, all out' scoring approach had been applied, then--based solely on soil P status--85% would have been classified as group E.
## 3 Use-Case-Based Categorisation of Technologies
### Conclusions  
The network types, use cases and resulting requirements are now directly connected to technologies. These can now be mapped onto the data rate and range space, which to some extent also aligns with acceptable cost and the ability to adapt to changing environmental conditions. Each axis has a gap between the technologies. The most notable gap can be seen in Figure 4, which shows that there is no technology that offers both a high data rate and a high range-this is due to the lack of penetration.  
According to the requirements from the use cases, automation and teleoperation networks can serve digitalization applications, while tracking and tracing as well as Long-Range Underground Monitoring technologies can offer lower data rate connectivity at potentially lower costs and with less infrastructure installation, making them suitable for a wider range of applications. Each network type connected to technologies creates a space supporting the different use cases (Figure 6).
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 2 Mathematic Model of Hybrid Power System
### Lithium-Ion Battery  
A lithium-ion battery is used to smooth the power supply of the PEMFC, to provide instantaneous power fluctuation response for the hybrid power system. A simplified lithium-ion battery model is adopted without considering the influence of temperature and degradation [33]. This model consists of an ideal resistance \(R_{bn}\) and an open circuit connected to an ideal open voltage source \(V_{OC}\) in series whose value is associated with SOC [34].  
According to the Rint model of lithium batteries [35], the relationship between the terminal voltage \(V_{bn}\) and \(V_{oc,bn}\) in the equivalent circuit can be described as:  
\[V_{bg}=n_{cell}V_{ba}=n_{cell}(V_{oc,bn}-I_{ba}R_{ba}) \tag{22}\]  
where \(I_{bn}\) is terminal current and \(V_{bg}\) stands the total voltage of \(n_{cell}\) series-connected batteries.  
In fact, \(V_{oc,bn}\) is closely related to SOC, and this study uses a fitting method to obtain the correspondence between \(V_{oc,bn}\) and SOC:  
\[V_{OC}=a_{1}SOC+a_{2}SOC^{2} \tag{23}\]  
where \(a_{1}\) and \(a_{2}\) are constants.  
The output power of the battery is expressed as:  
\[P_{ba}=V_{ba}I_{ba}=V_{oc,bn}I_{ba}-I_{ba}^{2}R_{ba} \tag{24}\]  
where \(P_{ba}\) is the output power for the battery.  
The battery current \(I_{bn}\) can be calculated through solving Equation (25) as follows:  
\[I_{ba}=\frac{V_{oc,bn}-\sqrt{V_{oc,bn}^{2}-4P_{ba}R_{ba}}}{2R_{ba}} \tag{25}\]  
The relationship between SOC and \(I_{bn}\) can be described by the Ampere-time integral method:  
\[SOC=-\int\frac{I_{ba}}{3600C_{ba}}dt \tag{26}\]  
where \(C_{ba}\) is the capacity of battery (Ah).  
Combining the above equations, the SOC dynamic change of the battery can be represented as:  
\[\frac{dSOC}{dt}=-\frac{I_{ba}}{3600C_{ba}}=\frac{-V_{\alpha,ba}+\sqrt{V_{\alpha, ba}^{2}-4P_{ba}R_{ba}}}{7200R_{ba}C_{ba}} \tag{27}\]
## 3 Results and Discussion  
### RSID\({}^{\rm TM}\) Blood and RSID\({}^{\rm TM}\) Reader Experiments  
Assuming 100% extraction efficiency, 50 \(\mu\)L of whole blood deposited on a sterile cotton swab and extracted in 1 mL of RSID\({}^{\rm TM}\) extraction buffer will produce an extract that contains 50 nL of blood per microliter of extract. When 2, 4 and 10 \(\mu\)L of blood extract is adjusted to a total volume of 200 \(\mu\)L with RSID\({}^{\rm TM}\) running buffer, 100 \(\mu\)L of this extract will contain 50, 100 and 250 nL of blood, respectively. Two different Readers were evaluated in this study, each with two sets of RSID\({}^{\rm TM}\) cassette replicates. All results were concordant.  
Figure 2 shows results of RSID\({}^{\rm TM}\) Blood as they appear in a digital photograph (Nikon's Coolpix 5600) and in the digital images generated by the RSID\({}^{\rm TM}\) Reader. Negative controls (Figure 2, cassettes 1 and 2) and saliva-semen-urine cross-reactivity controls (Figure 2, cassettes 9 and 10) were read as \"negative\" by both RSID\({}^{\rm TM}\) Readers tested. Samples with calculated amounts (50, 100 and 250 nL) of blood were read as \"positive\" by both RSID\({}^{\rm TM}\) Readers tested (Figure 2, cassettes 3-8).  
Cassettes 3 and 4, on which 50 nL of blood were tested had very light positive signals, which was expected since 50 nL of blood is at the detection limit of RSID\({}^{\rm TM}\) Blood Test [1]. To measure the reproducibility of the RSID\({}^{\rm TM}\) Reader, cassette 3 was analyzed three times in a row on both RSID\({}^{\rm TM}\) Readers. Both RSID\({}^{\rm TM}\) Readers detected signal and registered positive for blood in all three replicates.
## 5 Experimental Results
### Performance Metrics
#### 5.2.3 User Response Time  
It refers to an important measurement indicator that demonstrates how much time the user gets for the task completion. It is the time that is required to complete the given cybersecurity tasks. Two different experiments are conducted. In the first experiment, time is measured for the basic cybersecurity tasks. In the second experiment, time is measured for the advanced tasks. Therefore, the basic task is calculated as  
\[C_{bt}=\frac{n_{u}}{t_{w}}\times 100\% \tag{40}\]  
where \(C_{bt}\) is the correlation between the number of users and time wasted when solving basic cybersecurity tasks, \(n_{u}\) is the number of users, and \(t_{w}\) is the time wasted when solving basic cybersecurity tasks.  
Based on the result, we determine that a maximum of 90 users complete their basic cybersecurity tasks in 29.58 min with the proposed WBCA program, whereas M-TISM, ISF, FDCA, CSA, and CKS help the users to complete their tasks in 39.58, 37.31, 35.01, 32.41, and 49.59 min, respectively. In the second experiment, the proposed WBCA program takes 33.01 min to help the 90 users to complete their advanced cybersecurity tasks, while the  
Figure 4: **(a)** Cybersecurity topic consistency using proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS). **(b)** The quality ratios of the advanced cybersecurity topics using proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS). **(c)** The correlation between the number of users and the time consumed when solving basic cybersecurity tasks with the proposed WBCA and contending programs (M-TISM, ISF, FDCA, CSA, and CKS).  
contending programs M-TISM, ISF, FDCA, CSA, and CKS support the users to finish their advanced cybersecurity tasks in 42.05, 45.01, 51.09, 39.03, and 57.23, respectively.
###### Abstract  
The efficient coverage of underwater wireless sensor networks (UWSNs) has become increasingly important because of the scarcity of underwater node resources. Complex underwater environments, water flow forces, and undulating seabed reduce the coverage effect of underwater nodes, even leading to coverage holes in UWSNs. To solve the problems of uneven coverage distribution and coverage holes, a three-dimensional iterative enhancement algorithm is proposed for UWSN coverage hole recovery using intelligent search followed by virtual force. Benefiting from biological heuristic search algorithms, improved particle swarm optimization is applied for node pre-coverage. With the change in iteration times, the adaptive inertia weight, acceleration factor, and node position are constantly updated. To avoid excessive coverage holes caused by search falling into local optimum, underwater nodes are considered as particles in the potential field whose virtual forces are calculated to guide nodes towards higher coverage positions. In addition, based on the optimal node location obtained by the proposed algorithm, the monitoring area is divided based on the clustering idea. The underwater routing protocol DBR based on depth information is subsequently used to optimize node residual energy, and its average is calculated comprehensively and compared with the other three coverage algorithms using the DBR routing protocol. Based on the experimental data, after 100 iterations, the coverage rates for BES, 3D-IVFA, DABVF, and the proposed algorithm are 83.28%, 88.85%, 89.31%, and 91.36%, respectively. Moreover, the proposed algorithm is further verified from the aspects of different node numbers, coverage efficiency, node movement trajectory, coverage hole, and average residual energy of nodes, which provides conditions for resource development and scientific research in marine environments.  
UWSNs; node three-dimensional deployment; iterative enhancement; coverage hole recovery; node action force +
Footnote †: journal: Marine Science and Engineering  
0
Footnote 0: _J. Mar. Sci. Eng._**2023**, _11_, 2365. [[https://doi.org/10.3390/jmse11122365](https://doi.org/10.3390/jmse11122365)]([https://doi.org/10.3390/jmse11122365](https://doi.org/10.3390/jmse11122365))
# Edge-Aided Reliable Data Transmission for Heterogeneous Edge-IoT Sensor Networks
## 3 System Design
### Discussion  
**Asymmetric links.** One limitation would be that the edge servers cannot sense the packet losses at the receiver side due to the limited communication range of sensor nodes. To deal with this problem, we can employ a multi-hop estimation scheme to estimate the number of retransmissions on an indirect link. Following Equation (6), we can obtain the link quality of any link in the edge server's communication range. Suppose the target link \(l_{1}\) is in the edge's communication range but the edge is out of its communication range. Another link \(l_{2}\) and the edge server are in each other's communication range and at the same time \(l_{1}\) and \(l_{2}\) are in each other's communication range. Our goal is to estimate the link quality of \(l_{1}\) via \(l_{2}\). Note that \(l_{1}\)'s sender node is the receiver node in \(l_{2}\), as illustrated in Figure 4. \(l_{1}\) is the link from node S to R and \(l_{2}\) is the link from node R to D. In this case, the feedback of node D cannot be heard by the edge servers.  
To notify the edge servers about the link status, we require each sender to add an \"additional delay\" field in its packet and record the sequence difference for its next hop link. The value of \"additional delay\" field, \(d_{a}\), is calculated as:  
\[d_{a}=\tau(s_{s}-s_{r}) \tag{8}\]  
Figure 4: An illustrating example of the impact of asymmetric links in retransmissions.  
where \(\tau\) is the duration of a packet transmission. With this information, the edge server will be able to infer how many packets are lost in \(l_{1}\) with \(d_{a}\) from node S (as S will transmit the difference between \(l_{1}\) and \(l_{2}\)).  
**Placement of the edge servers.** The placement of edge servers can affect the performance improvement. Specifically, for the same sensor network, if the edge server is placed in an area covering more sensor nodes, more transmissions can be migrated from sensor nodes to the edge servers. If the edge server is placed in an area covering fewer sensor nodes, there will be less chance to exploit edge servers and thus the improvement is expected to decrease. For deploying an edge server to an existing sensor network, it would be better to place it in a dense area to enhance the chance of communication offload.  
**Power consumption.** It is worth noting that the basic idea of our work is to migrate the power consumption from sensor nodes to the edge nodes. The total energy consumption may not be reduced or even be increased. The reason is although a smaller number of packets are transmitted by the sensor network, the transmission power of edge servers is much larger than that of the sensor nodes. However, considering that the sensor networks are powered by batteries, the power migration can effectively enlarge the lifetime of sensor networks.  
**Security communications.** To avoid security risks in the sensor-to-sensor and sensor-edge communications, we can apply a series of different cryptography and authentication techniques that are designed for resource-constrained devices [35; 36].
# Improvements of the xAAL Home Automation System
## 4 The Basic Schema
### Changes in xAAL Schemas
#### Guidelines for Writing Schemas  
Former versions of xAAL used the _Camel Case_[30] writing convention for naming the elements of xAAL schemas. This choice was consistent with the habits of JSON (and JavaScript) communities.  
The new version of xAAL moves from JSON to CBOR, leaving JSON communities. The main developers are closer to Python and C communities. One, therefore, decided to move to the _snake_case_ writing convention.  
Action Verbs for Methods  
A key to get an engaging API is to select a wording for elements that carry an intuitive and well-known meaning. This makes the talent of the designer. There is no universal rule. However, this sounds reasonable selecting action verbs to name methods, for example, turn_on() and not just on().  
Similarly, programmers usually like _accessors_ to _attributes_, i.e., set_XXX() get_XXX(). (Note that the latter may be redundant with the generic get_attributes() method of the basic.basic schema.)  
Describes Functionalities from the Perspective of Others  
Amazon Alexa [31] and Google Home [32] propose means to design its own device by composing elementary so-called _capabilities_ (Alexa) or _traits_ (Google Home). For example, for a color-changing lamp this means _power on-off brightness HSV color_, etc.  
Such APIs are oriented towards the description of products. For instance, an innovative startup may sell an all-in-one connected fan with an oscillating feature, a colored dimmable lamp, a camera and a thermometer. The API of such a device can be written by composing corresponding capabilities or traits. This is the responsibility of the client application to seek devices and to turn on the lamp of this fan if the user wants light.  
Moreover, this may require sub-addressing. For instance, a weather station may provide both temperature and humidity measures, and be exposed with corresponding capabilities. However, if this weather station has two thermometers, the temperature capability has to be instantiated twice, with a sub-addressing to retrieve each of the two thermometers into the device. From the client application point of view, these two thermometers are not retrieved like other thermometers of the house.  
On the contrary, xAAL proposes schemas oriented towards the description of functionalities from the client point of view. Concretely, multi-feature products, such as a weather station, are split into several devices (thermometers, hygrometers, etc.). Each feature is implemented by a dedicated xAAL device. Such xAAL devices may then be grouped thanks to a _group identifier_. Client application may retrieve all thermometers in the same way whether they are a single thermometer or embedded into an all-in-one product.