esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiment Conclusions
### Experimental Results and Analysis
#### 5.3.4 CPU Time  
The time complexity of DNC is \(O(|E|)\). However, solely comparing time complexity may have limitations in some networks. Therefore, this paper also compares the CPU running time of DNC and the baseline methods. To facilitate a better comparison of the CPU running time of DNC and other comparative algorithms across various networks, we used the Python programming language, version 3.9.0, and employed the same coding style for execution on an identical computer. Figure 5 illustrates a comparison of CPU running time for Adjnoun, Jazz, Dolphins, and USAir97. Table 6 displays the runtime comparison between DNC and the baseline methods across various networks.  
The data presented in Figure 5 and Table 6 make it clear that, across all networks, the K-Shell algorithm exhibits the highest efficiency. K-Shell only requires iteratively removing nodes with a degree less than or equal to \(k\), which is the most efficient operation in this situation. However, the accuracy of K-Shell is quite low. The DNC and SC algorithms also exhibit relatively high efficiency and require minimal CPU running time. This is because SC only involves a simple addition of first-degree and second-degree values. Although the CPU running time for DNC and SC is quite comparable, DNC's accuracy is significantly higher than that of K-shell and SC. Therefore, our proposed DNC algorithm combines both time efficiency and accuracy.  
Figure 4: Subfigures (**a**), (**b**), (**c**), and (**d**) respectively depict the rank distribution plots of DNC and the baseline methods on the Dmela, Email, Yeast, and Hamsterster networks. The x-axis of each subfigure represents the ranking of nodes, while the y-axis represents the count of nodes with the same ranking.
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 4 Results and Discussion
### Investigating the Sources of Exergy Destruction
#### 4.5.1 Irreversibility in the Solar Field  
The exergy destruction in the solar field is due to heat transfer between the sun and the absorber, heat transfer between the absorber and the HTF, and the friction of the viscous HTF. The exergy loss is due to the optical efficiency (the ratio of sunlight capture to incident sunlight) and the heat transfer to the surroundings.  
The solar collector is considered to be the main source of exergy destruction in the solar field due to the high temperature difference in the collector. The major contribution to the exergy destruction in the solar collector is due to the heat transfer between the sun and the absorber, while the major exergy loss occurs due to optical errors [28].  
It was reported that exergy destruction due to heat transfer between the sun and the absorber accounts for 35% to 40% of the total exergy destroyed. Exergy losses to the surroundings account for 5% to 10% of the total exergy destroyed [28].  
It is thought that to decrease the exergy losses from the solar collector (i.e., increase the collector energetic efficiency), attention should be pointed toward improving the optical parameters of the collector (such as mirror reflectivity, transmissivity of the glass envelope, absorptivity of the heat collection element selective coating, focal length of the collectors etc.).  
Figure 9: ISCC power plant exergetic efficiency at different ambient temperatures for different solar heat inputs.  
Regarding the exergy destruction due to heat transfer, improving that part may involve great challenges because of the existence of the finite temperature differences which are essential for the heat transfer process and cannot be avoided.
# Flavour Volatiles of Fermented Vegetable and Fruit Substrates: A Review
## 3 Results
### Suplhur Compounds  
Seven studies found that sulphur compounds were present in fermented juices, and the major compounds detected were methanethiol, dimethyl sulfide, dimethyl disulfide, dimethyl trisulfide, methyl isopentyl disulfide, methyl ethyl disulphide, diethyl disulphide, 3,5-dimethyl, 1,2,4-Tithiolane (isomer 1), 3,5-dimethyl, 1,2,4-Tithiolane (isomer 2), methyl propyl sulfide, and methyl (methylthio) methyl disulfide. Suplhur compounds such as methanethiol, dimethyl sulfide, and dimethyl disulfide, depending upon concentration, have been found to cause off-flavour in fruit and vegetable substrates [79]. Of the seven studies, two reported that the concentration of sulphur compounds was reduced after fermentation: (1) In durian pulp fermented by either _L. casei_ monoculture or _L casei_ co-culture with the yeast _W. saturus_, the diethyl disulfide concentration was 6.9 and 8.9 times lower, respectively, compared to the concentration in the unfermented pulp [30]; and (2) in pomergranate juice fermented by different _L. plantarum_ strains, the methanethiol, dimethyl sulfide, and dimethyl disulfide concentrations were reduced compared to the concentration in the unfermented juice [24]. However, in non-pH-adjusted (2.7) sea buckthorn juice, the dimethyl disulfide concentration was 2.1 ng/mL, which was increased after fermentation for 36 and 72 h by _L. plantarum_ to 12.2 and 10.9 ng/mL, respectively [42], and in watermelon juice fermented by either _L. plantarum_, _L. brevis_, _P. pentosaceus_, _L. casei_, or _L. rhamnus_, the dimethyl disulfide concentration was 6.4, 3.4, 4.1, 5.8, and 9.6 ng/mL, respectively, compared to 1.1 ng/mL in the unfermented juice [48].
# Upscaling Northern Peatland CO2 Fluxes Using Satellite Remote Sensing Data
## 2 Materials and Methods  
### Study Sites  
The study includes five Nordic peatland sites located in Sweden and Finland: Abisko-Stordalen, Degero, Mycklemossen, Siikaneva, and Lompolojankka (Figure 1, Table 1). Mycklemossen is part of the SITES (Swedish Infrastructure for Ecosystem Science) infrastructure, whereas the other sites belong to the ICOS (Integrated Carbon Observation System) infrastructure. The sites were selected to cover peatlands exposed to different climatic conditions (hemi-boreal, boreal, and subarctic), at northern latitudes from 58\({}^{\circ}\)N to 68\({}^{\circ}\)N. Four of the sites are fans, and one, Abisko-Stordalen, is classified as a bog, although it also contains areas of fen. The study period spanned between 2017 and 2019, though data availability varied among the sites. Further information on each site can be found in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Site Name and** & **Location** & **Peatland Type** & **Vegegation Cover** & \
\begin{tabular}{c} **Annual Precipitation** \\ **and Air Temperature** \\ \end{tabular} & **Data Years** & **Reference** \\ \hline Abisko-Stordalen & & & & & & \\ (SE-Sto) & 68.356\({}^{\circ}\)N, 19.045\({}^{\circ}\)E &
\begin{tabular}{c} Sub-arctic \\ _
# Dynamic Group Management Scheme for Sustainable and Secure Information Sensing in IoT
## 2 Related Work
### Group Sensor Network and Distance Bounding Work  
Management techniques in group sensor network topology are classified into the following three types [12]. The first management technique is the centralized group management technique. This technique allows only one group administrator to manage all group members. Moreover, headers are generated with an increase in the number of group sensor nodes. The second management technique is the decentralized management technique. It allows several group administrators to manage their own group sensor nodes. There might be an overload when modifying group confidential information or changing group members. Lastly, the contribution type management technique enables all sensor nodes to cooperate to maintain groups without a specific administrator. It has the problem associated with the optimization of group size.  
Authentication protocols and management schemes for group sensor networks have been proposed by many researchers ([PERSON] et al. [13], [PERSON] et al. [14], etc.). Nonetheless, there are many vulnerabilities such as replay and relay attack and the session key duplication problem. Moreover, the existing group sensor management studies have designed management plans without taking into account the modification of cluster header. As a result, they are not suitable for the environment in which cluster headers are changed continuously.  
There are some measures secure to the relay attack that implement authentication via data connection between transmission devices such as sensor or cluster header. Some of these measures are the wireless network topology analysis using radio frequency signal-based method proposed by [PERSON], [PERSON] et al. [15] and the distance verification-based method proposed by [PERSON] et al. [16]. The method using distance validation is more efficient than the wireless network topology analysis using radio frequency signal-based method. Because of only focusing on the simple distance validation, mafia attack has a high success rate.
## 2 Methods
### Partitioning the RP Distribution  
The RP score distribution (i.e., for upregulated features) is an ordered set, beginning with a steep ascent, converging to a linear distribution for features following the null hypothesis and finally diverging again for the last few features (i.e., downregulated) (see \"Supplementary material 1--Method\"). The set of features most significant in describing the phenomenon investigated consists of the first \(n\) elements of that distribution, necessarily encompassing at least part of the steep ascent of RP scores. Conversely, the linear part of the distribution corresponds to stochastically behaving features, which should be excluded. Moreover, the RP score distribution carries information regarding each feature's consistency of behavior across replicates and is rigidly determined by the structure and form of the original data, deflecting external computational bias (in contrast to pfp calculation). Consequently, \(n\) will be robustly determined by partitioning the RP distribution.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**GEO Accession**} & \multirow{2}{*}{**Type**} & \multicolumn{2}{c}{**Original**} & \multirow{2}{*}{**Entropic Ranks S**} & **RP Maximum** \\  & & **Publication S** & & & \(p\)**-Value** \\ \hline \multirow{2}{*}{GSE12288} & Differentially & \multirow{2}{*}{160} & 18 * & \(<\)0.0001 \\  & expressed & & 86 ** & \(<\)0.0001 \\  & Uregulated & 127 & 135 & 0.0027 \\  & Downregulated & 330 & 89 & 0.0083 \\ GSE60767 [winter & Uregulated & 0 & 29 & \(<\)0.0001 \\
2009] & Downregulated & 0 & 53 & \(<\)0.0001 \\ GSE60767 [summer & Uregulated & 0 & 33 & \(<\)0.0001 \\
2009] & Downregulated & 0 & 26 & \(<\)0.0001 \\ GSE60767 [winter & Uregulated & 0 & 34 & \(<\)0.0001 \\
2010] & Downregulated & 0 & 41 & \(<\)0.0001 \\  & Uregulated & NA & 24 & \(<\)0.0001 \\ GSE42861 & Downregulated & NA & 98 & \(<\)0.0001 \\ SRP127667 & Uregulated & NA & 32 & \(<\)0.0001 \\  & Downregulated & NA & 37 & \(<\)0.0001 \\ \hline \hline \end{tabular}
*: upregulated, **: downregulated.  
\end{table}
Table 1: Number of differentially expressed features identified in the initial study compared with entropic rank results. The last column notes the maximum \(p\)-value calculated through rank products for these features. The number of features shows no correlation with sample size and the number identified in the initial research.
# Spectrum Based Power Management for Congested IoT Networks
## 5 Test Results and Analysis
### Retransmission Power Consumption  
Our test result proved, after implementing spectrum management algorithm at FREE-DEVPLA to improve the power use for congested IoT environment, the retransmission power consumption decreased significantly. Since our method efficiently managed communication channels, success-fullness of low power consumption is high. In other words, the average power consumption of the IoT device is very less compared to the conventional way of IoT environment. Figure 11 present the power spent for retransmission in a conventional network. This is power spent for retransmission attempt. From the figure, it is clear that the conventional method spends significant amount of power for retransmission purpose in a congested IoT environment. Obviously, if the environment get more congested the number of retransmission will be increased which results in even more power consumption.  
In our implementation this power consumption is almost not visible, very low. This comparison is essential for how the proposed method avoids retransmissions and power consumption of IoT devices simply by managing channels at FREDEVPLA level. Furthermore, we like to remind our readers that the result presented in Figure 11 is power spent only for trying retransmission in a network with out FREDEVPLA.  
This opens the opportunity to vastly improve the IoT network system by opening up avenues to begin testing an array of managing power and transmission from spectrum angle.
## 2 Dual-Loop Decoupled Control Strategy of PWM Converter
\tag{3}\]  
where \(k_{\mathrm{ip}}\) and \(k_{\mathrm{i}}\) are the proportional and integral coefficients of the current loop PI controller, respectively, and \(i_{\mathrm{d}}^{*}\) and \(i_{\mathrm{q}}^{*}\) are the reference currents. According to the instantaneous reactive power theory, \(i_{\mathrm{d}}\) affects the active power of the system, and \(i_{\mathrm{q}}\) affects the reactive power of the system. In order to achieve unit power operation, \(i_{\mathrm{q}}^{*}\) was set to zero in this study. Moreover, to realize the zero steady-state error control of the \(d\)-axis reference current, PI control was used to control the outer voltage loop to generate \(i_{\mathrm{d}}^{*}\), which can be described as follows:  
\[i_{\mathrm{d}}^{*}=\bigg{(}k_{\mathrm{up}}+\frac{k_{\mathrm{ui}}}{s}\bigg{)} \Big{(}u_{\mathrm{dc}}^{*}-u_{\mathrm{dc}}\Big{)} \tag{4}\]  
where \(k_{\mathrm{up}}\) and \(k_{\mathrm{ui}}\) are the proportional and integral coefficients of the voltage loop PI controller, respectively, and \(u_{\mathrm{dc}}^{*}\) is the reference voltage of the DC bus. SVPWM is a pulse width modulation technique that has been improved by sinusoidal pulse width modulation (SPWM), which is often used for the control of three-phase bridge converters [28]. This modulation represents the eight working states of the three bridge arms with eight voltage space state vectors. The process of space vector composition is shown in Figure 2, where the state vector \(\mathbf{V}_{i}(i=1,2,\ldots,6)\) is the nonzero vector, and \(\mathbf{V}_{0}\) and \(\mathbf{V}_{7}\) are the zero vectors. As depicted in Figure 2, the input reference voltage \(\mathbf{V}_{\mathrm{ref}}\) can be represented by two adjacent nonzero state vectors and zero vectors. As the specific working principle and implementation method of SVPWM have been introduced in detail in [29], they will not be repeated here. This modulation technique can not only improve the utilization rate of the voltage but also effectively reduce the switching frequency and improve the reliability of the system [30]. Therefore, in this study, SVPWM was used to control the switching of the converter. Based on the above analysis, the dual-loop decoupled control strategy of the three-phase voltage-source PWM converter based on PI control and SVPWM control is illustrated in Figure 3.  
Figure 2: Principle of space vector composition.
## 6 Conclusions  
Several configurations of SPR sensors in TPOFs have been implemented and experimentally tested and compared. First, we compared two configurations (with and without a buffer layer) to determine the influence of this intermediate layer on the sensor's performances. Once the simpler configuration was chosen, we implemented and tested three different SPR sensors based on different TPOFs, made by a lab-built system, with taper ratios ranging from 1.3 to 1.5.  
We determined that with the increase of the taper ratio, the sensitivity increased as well. However, this improvement in sensitivity led to a deterioration in terms of signal-to-noise ratio. In particular, all three SPR-TPOF sensors presented FWHM values very similar to that obtained exploiting a non-tapered, small-diameter POF (250 \(\upmu\)m), but with triple the sensitivity [25]. Moreover, all the SPR-TPOF sensors showed better performances (sensitivity and SNR) with respect to the worst case obtained by an SPR-POF sensor with a nonoptimized D-shaped area [25; 27]. Consequently, the obtained results demonstrate that this approach can be used for biosensing applications when a bioreceptor or a biomimetic receptor is used on the gold film, as already demonstrated in [17; 25].  
Conceptualization, N.C. and A.M.; data curation, N.C., F.A., A.M., D.M., and L.Z.; formal analysis, N.C., F.A., A.M., D.M., and L.Z.; funding acquisition, N.C., A.M., and L.Z.; investigation, N.C., F.A., A.M., and D.M.; methodology, N.C.; supervision, N.C., A.M., and L.Z.; validation, N.C. and L.Z.; writing--original draft, N.C., F.A., A.M., D.M., and L.Z.; writing--review and editing, N.C., A.M., and L.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the VALERE program of the University of Campania [PERSON] (Italy). The authors declare no conflict of interest.
## 3 What Is SDN?
### Architecture of SDN  
The architecture of SDN can be divided into three layers, as shown in Figure 7:  
* **Application Layer:** This top layer contains all the applications that need to communicate with the network. These applications can include network topology builders, logging and monitoring tools, network Access Control Lists (ACLs), and other network services [64; 70]. The applications are agnostic to the Southbound protocols, whether they use OpenFlow or P4 Runtime.
* **Control Layer:** This central layer houses the SDN controller, which manages the flow of data traffic in the network devices based on the requirements of the application layer. The SDN controller uses Northbound APIs to communicate with the application layer and Southbound APIs to interact with the infrastructure layer [64; 70].
* **Infrastructure Layer:** This bottom layer consists of networking devices such as switches and routers. It is responsible for forwarding packets based on the decisions made by the control layer [64; 70].  
The architecture of SDN consists of five key elements that collaborate to form a flexible, programmable, and efficient network. These elements are described below [61; 64]:  
* **Applications and Services:** These programs and systems use the network to communicate and interact with the SDN controller via the Northbound Interface to request network services.
* **Northbound Interface:** This communication link between the SDN controller and the applications allows applications to request network services and receive network information.
* **Network Operating System (NOS):** The software running on the SDN controller provides the necessary functionality for managing the network.
* **SDN Controller:** The heart of the SDN architecture, the SDN controller acts as the network's \"brain\", making packet routing decisions. It interacts with network devices through the Southbound Interface and communicates with applications via the Northbound Interface. Examples of controllers include ONOS, OpenDaylight, Floodlight, IRIS, POX, and Ryu [71].
* **Southbound Interface:** This communication link between the SDN controller and network devices transmits instructions from the controller to the devices and provides the controller with network state information.
* **Network Devices:** The physical infrastructure, including switches, routers, and other networking hardware, receives instructions from the SDN controller and forwards or drops data packets accordingly.  
These elements collectively contribute to a flexible, programmable, and centrally managed network infrastructure. Although there are similarities between SDN and the Open Systems Interconnection (OSI) model, it is crucial to recognize that they represent different paradigms, and their mapping is not exact.
# Visual-Based Positioning of Aerial Maintenance Platforms on Overhead Transmission Lines
## 5 Discussion
### Comparison with Existing Positioning Techniques
#### 5.2.5 Electric and Magnetic-Field Change Sensing  
These sensors can be used to sense the presence of magnetic objects and fields and can be helpful in determining the position of the drone. However, the calculation of the electric and magnetic field is extremely complicated, and several electromagnetic interference sources and the noise can affect the measurements.
## 4 Experimental Results and Discussion  
### Datasets  
The effectiveness of our proposed method is evaluated with comprehensive experiments that were conducted on three medical datasets, as depicted in Table 1. Some examples of three dataset are shown in Figure 5. The comprehensive results reveal that our scAG can effectively increase the segmentation performance though an added small fraction for the model complexity.
# Social Internet of Vehicles for Smart Cities
## 6 SIoV Privacy Issues
### Privacy-Enhancing Technologies for the SIoV  
Combining privacy issues from social networks and vehicular networks, it is easy to see some of the privacy issues that will affect the SIoV. In particular, we envision three risks. First, the privacy threats against social and vehicular networks will become accessible to a wider range of adversaries. For example, it will be possible to eavesdrop on information about social networks via wireless vehicular transmissions, and it will be possible to find location information via online social networks. Second, the information available today will be augmented in two directions: social network profiles will be combined with ubiquitous geo-tagging, and vehicular network messages will be combined with information about a driver's preferences and behaviour. This will allow the inference of much more detailed driver profiles, which can be exploited for marketing or surveillance. Third, it is unclear how privacy policies can be effectively conveyed to a driver in a vehicle and how privacy controls may be offered to drivers. This may lead to even more inaccessible privacy policies and less accessible privacy controls than with today's social networks [124].  
To counter these risks, privacy-enhancing technologies for the SIoV are needed. To date, there are very few approaches in the literature that specifically target SIoVs. Most notably, the proposed SIoV architecture in [20] attempts to address privacy issues. However, their approach has three weaknesses. First, their approach relies on cloud storage, which means that the privacy issues associated with cloud computing apply, for example data privacy both towards the cloud provider and the provider's other customers [134]. Second, the privacy protection proposed in their architecture consists of tagging all messages with a privacy value (public, private and protected). This policy-based protection depends on whether the recipient of a message honours the transmitted privacy value and is ineffective against most adversaries. Third and most importantly, it does not include technical privacy protections, such as cryptographic means.  
Future privacy-enhancing technologies for SloVs should be based on proven privacy-enhancing technologies for social networks and vehicular networks. New privacy-enhancing technologies and those resulting from combinations of existing technologies need to be evaluated thoroughly to make sure that they provide an adequate amount of privacy. Because the SloV is a combination of social and vehicular networks, evaluations need to use a selection of privacy metrics from both domains [135; 136].