esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 3 Green Computing for WBANs
### ELR-W: Operational Steps
#### 3.3.2 Next-Hop Selection Phase  
For the purpose of improving routing efficiency of a protocol, next-hop selection criterion is most important. In this phase, the proposed ELR-W protocol selects best available next-hop for the packet forwarding. The ELR-W makes this decision based on the path cost stated in Equation (4). The node with least value of path cost is preferred as the next-hop. The algorithm for next-hop selection is presented in Algorithm 2.
# Dynamic and Active THz Graphene Metamaterial Devices
## 3 Applications of Graphene Metamaterial
### THz Amplitude Modulation
Comparing the number of electron-hole pairs generated under the modulated beam in the graphene and the silicon substrates, graphene was negligible compared to silicon. Free carriers diffused from the silicon substrate into the graphene layer due to the charge gradient until a stable distribution was reached. Carriers diffusing into graphene possess a higher mobility than in silicon, thus resulting in a more pronounced change in conductivity than observed in silicon under the same beam. As discussed above, this results in enhanced modulation of terahertz waves in GOS compared to transmission through pure silicon. A maximum modulation depth of 99% was observed through mutual coupling between the two two-dimensional material layers.
# A Survey on Cyber Risk Management for the Internet of Things
## 4 Cyber Risk Assessment for IoT Survey Results
### IoT Cyber Risk Identification
#### 4.1.5 Identification of Controls  
Security controls are \"management, operational, and technical controls\" [88] that are used to protect assets and users in different ways. A limited number of papers consider the identification of security controls to facilitate IoT risk assessment. In the context of smart homes, [PERSON] et al. [67] consider the efficiency of safeguard measures that already exist within a smart home, assessing the quality of awareness-based and practical defences in addition to how these can influence the IoT risk score.  
Within the SKIP (self-assessment, knowledge, infrastructure, and practices) survey framework from [PERSON] et al. [39], knowledge-based questions consider IoT-specific cyber security areas, collecting information about a smart home's infrastructure and practices. Details about IoT controls are collected here, examining the existing security systems in place, and establishing the network within the home. On the other side, practice-centred questions relate to the self-reporting of best security practices and the extent implemented.  
In the context of security, readiness refers to how prepared users are to identify, prevent, and respond to cyber attacks. Within the work of [PERSON] et al. [43], readiness is used to understand the ease of an IoT attack based on the extent to which an IoT domain is prepared to detect, report, and respond when an attack occurs. Expanding this, [PERSON] et al. [60] also represent the readiness of a device to detect and react, considering IoT security functions, like encryption and intrusion prevention mechanisms, embedded within the device or controller (like smart phones). These authors also use the lack of security knowledge of the users, which reflects an increased probability of successful attacks. In addition, the authors address the cyber risks at the network level, storage, and processing level, which both incorporate control-based risk factors. Since IoT devices are limited in security capabilities, device readiness may be weaker than expected, with readiness relying on uses to carry IoT cyber security knowledge and training.  
_Insight 5_: Another factor of RQ1 is to identify pre-existing controls that reduce risk and the effectiveness in doing so. Surveyed papers involving control identification are limited, which is an issue for IoT domains that do not have clearly defined controls. In turn, controls that already reduce risk need to be factored into the risk assessment phase to ensure that the risk results are accurate. Overcoming this, the readiness of an IoT domain could be studied by assessing the ability to detect and react to threats from an asset and user perspective much like within the works of [PERSON] et al. [60] and [PERSON] et al. [43].
## 4 Experiments
### Power Consumption against Delay  
The average value of the measured power against the \(R_{on}\) for all experiments is shown in Figure 8 (blue solid line). We observe that power consumption increases almost proportionally to \(R_{on}\). Since \(R_{on}\) is the proportion of time that nodes stay ON, in order to save energy (smaller power consumption) \(R_{on}\) needs to be as small as possible. Obviously this comes at the price of a performance degradation since if nodes stay ON for less time (smaller \(R_{on}\)) the packet delay increases. This can be observed from the average delay curve in the same figure (green dotted line).  
Another way of presenting this relationship is by ploting the average measured value of power against the observed average packet delay for each experiment, shown in Figure 9. It is interesting to observe that the relationship between the average measured value of power consumption against the observed packet delay is not linear and the graph shows a knee. This is mainly due to the special case where all  
Figure 8: Average power consumption and average delay _versus_\(R_{on}\).  
Figure 7: Probability that all nodes of a packet’s path are ON.  
nodes are ON (\(R_{on}=1\)) and the delay is very small since there is no congestion in the network. Also, as the nodes remain turned OFF for longer periods (smaller \(R_{on}\) values) there are more packets being queued up in the nodes. Thus, there is an additional delay when the packets are waiting to be processed after the next hop is turned back ON.  
We do observe though a clear tradeoff between the power savings that can be achieved and the degradation in QoS that packets may experience presented in terms of delay. Power consumption is reduced by up to 88%, at the expense of increased delay that can be unacceptable for some of the traffic in the network. Through this relationship, one can choose the appropriate operating state of the network that offers the desired power savings and QoS constraints. For instance, in Experiment 6 (\(R_{on}=0.7\)) we observe a 35% savings in network power consumption, while the end-to-end packet delay shown in Figure 4 is small for the most of the traffic.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 5 Experimental Verification
### Fault Classification of Planetary Gearbox Based on VPMCD  
Under a single operation condition, 100 groups of data were selected as the VPMCD training set, and the remaining 100 groups of data were used as the VPMCD test set. Similarly, under variable operation conditions, there were 75 groups of data used as the training set and the remaining 75 groups of data as the test set. The feature vector after PCA dimension reduction was used as the input of VPMCD, and the fault type was used as the output of VPMCD. The corresponding relationship between the number and the fault type is shown in Table 4.  
The classification results are shown in Figures 22-26. It can be seen from the classification results that the diagnostic accuracy rate reached 100% under the single operation condition and variable operation conditions.  
In order to reduce the randomness of the diagnosis, we selected 75 groups of data randomly as training sets and the remaining 75 groups of data as test sets. After 100 random classifications, the average classification accuracy of VPMCD was 100% and no testing sample was misclassified.
## 6 Discussion  
In this work, the effectiveness of the proposed transceiver-joint equalization filter is evaluated and compared with the conventional PPE and the PE-only schemes. The performance evaluation is conducted using the HOM formats over a \((2\times 2)\) SDM-based optical MIMO system with a 1.4 km MMF transmission link. The average optical received power \(P_{\mathrm{r}}\) required to reach a BER of \(10^{-4}\) using the PAM-4 formats is improved by \(\approx\)2.68 dB with just a four-tap optimally designed pre-equalizer with post-equalization. Similarly, an enhancement of \(\approx\)2.58 dB in the \(P_{\mathrm{r}}\) gain is achieved by the joint-PPE using PAM-2 format. Despite using a longer MMF channel with twice the bit-rate compared to [19], the improvement of \(\approx\)0.65 dB and \(\approx\)0.84 dB in the \(P_{\mathrm{r}}\) gains at \(10^{-4}\) BER is still accomplished. Additionally, with the improved numerical optimization of the PPE taps, an enhancement of nearly two folds in the required Pr gain at \(10^{-4}\) BER is attained using PAM-2 constellation compared to [23]. In contrary to the existing joint PPE schemes, the efficiency of the proposed transceiver-joint equalization is verified on a critical dispersion-impaired channel with the MIMO configuration. Further research should focus on the channel robustness in a dynamic environment.
# Skin Cancer Metabolic Profile Assessed by Different Analytical Platforms
## 4 Skin or Blood Metabolomic Profiling for Diagnosing and Treating Skin Conditions  
Skin metabolomics analysis has a positive impact on disease diagnosis and health improvement. Recently, non-invasive wearable biosensors have been developed as an alternative way for health monitoring, aiming to detect biofluid composition, such as sweat. Metabolites in the interstitial fluid have been detected by the development of slightly invasive microneedle array sensors. The operation steps of the invented biosensor are: electrode collection on a substrate, combining a selective recognition component for the analyte, and applying a physicochemical transducer to transform any sensed variations into legible signals [26]. Skin metabolomic analysis reflects metabolic changes caused by skin conditions, such as skin cancer, while blood metabolomic analysis offers more information about how skin conditions affect the body's metabolic profile (Figure 2) [27]. The presence of target analytes detected in biofluids is evaluated for any correlation of them in blood using the developed biosensors. However, verifying this correlation is affected by variabilities in sampling protocols, such as the composition and thickness of skin layers (i.e., dermis and epidermis). [26].  
In fact, studies have proved that in skin diseases, skin metabolomic profiles are more accurate in following the progression of treatment and reflecting the recovery than blood profiles. As an example, a previous study was performed to examine the metabolic profile of psoriatic skin and blood plasma during treatment with ustkeinumab and adalimumab, anti-TNF-\(\alpha\) drugs used to treat plaque psoriasis, and to explore the correlation between selected blood and skin metabolites, which are glutamic acid, choline, citruline, lactic acid, phenylalanine, and urocanic acid, during the treatment, and to compare their levels in both biological matrices. Blood samples were collected from the enrolled patients with time intervals of 1-3 months. Furthermore, for comparison, metabolites present in the psoriatic skin were collected using non-invasive hydrogel micro patch probes that allow the passive diffusion of skin metabolites from the skin to water within the hydrogel matrix.  
Study results proved that several metabolites helped monitor treatment response with disease severity and confirmed that skin metabolomic profiles were more reliable in tracing treatment. This explained the reason behind prefering skin metabolites over blood  
Figure 2: Correlation between Skin and Blood Metabolites.  
metabolite in skin diseases [27]. However, for most metabolites of clinical interest in other skin diseases, this preference has not yet been confirmed, and blood is still considered the typical biofluid, which has aided in the detection of most human metabolome findings. So, further studies are necessary to find out which is more suitable in the case of other skin conditions [26].
# Ultrasonic Attenuation of Ceramic and Inorganic Materials Using the Through-Transmission Method
## 4 Results and Discussion
### Hard Ceramics
SiC additions to Si\({}_{3}\)N\({}_{4}\) have been examined using Xray and microscopic methods [69; 74]. Only the two constituents, Si\({}_{3}\)N\({}_{4}\) and SiC, were present in Xray diffraction spectra. Transmission and scanning electron micrographs also showed mixtures of Si\({}_{3}\)N\({}_{4}\) and SiC. These results imply that observed decreases in the density due to SiC additions (3.18 to 3.08 and 2.97 Mg/m\({}^{3}\)) are caused by additional porosity of 2.1 and 6.6%. The porosity contents correspond to the reduction of the Young's modulus of Si\({}_{3}\)N\({}_{4}\) by 2.4 and 10.7% when the SiC contribution is ignored. These two sets agree well. However, since the E of SiC is 46% higher than the E of Si\({}_{3}\)N\({}_{4}\) (taken as 344 GPa), E reduction is higher than the effects expected from the porosity. This additional E reduction may arise from low intergranular bonding between Si\({}_{3}\)N\({}_{4}\) and SiC grains. Si\({}_{3}\)N\({}_{4}\) with 20% SiC with Y\({}_{2}\)O\({}_{3}\) showed the fracture toughness of 5.2 MPa\(\cdot\)m\({}^{0.5}\), which was at 71% of that of Si\({}_{3}\)N\({}_{4}\)[64]. As noted earlier, the 20% SiC addition increased E only slightly. Thus, systematic studies are needed to clarify elastic responses of Si\({}_{3}\)N\({}_{4}\)-SiC ceramics since the methods of ceramic consolidation must be taken into account. The poor intergranular contact and porosity in Si\({}_{3}\)N\({}_{4}\)-SiC ceramics are expected to increase attenuation through multiple reflections and scattering. It is possible that frictional loss at the boundaries contribute to ultrasound absorption. While the presence of similar internal nano-scale flaws has not been known at this time, it is plausible in some hard ceramics. In oxide ceramics containing alumina and zirconia, polygonal grain structures have often been observed [65]. However, these showed higher attenuation than carbide ceramics as discussed above. Thus, separate mechanisms for attenuation must be conceived. Presently, no viable physical mechanisms exist for attenuation in ceramics with varied microstructures and intercrystalline bonding conditions. It is also necessary to consider the presence of the glassy phase at the boundaries. In turn, this indicates that the attenuation characteristics can be used to explore the condition of intergranular boundaries.
# Mdp1 _sciences_
## 3 Results & Discussion
### Correlations and Empirical Kernel Density Estimates for Different Forecast Methods  
As discussed in Section 2.3, six versions of Kalman Filter-based forecast methods (KF1-KF6) have been implemented in this work. Method KF1, 2, 3, 5, and 6 assimilate exogenous data, whereas method KF4 only uses data from the target site. From Table 2, it can be seen that the methods using exogenous data have similar standard errors (with an rms error of the order of 0.55 m/s), whereas method KF4 has a somewhat higher error (rms = 0.71 m/s), i.e., some 30% higher. While this does point to an inferior performance by method KF4, the rms error does not fully capture the impact on the capability of the method to predict future incompliance events. Based on the considerations described in Section 2.2, it is convenient to inspect the empirical joint probability density function for the wind speed changes \(y\) and the prediction error \(\delta y\) for those changes, both for two time steps into the future.  
The results for method KF4 and a representative method with exogenous data (KF2) are shown in Figure 7. It can be seen that not only is the forecast error (vertical axis) dramatically smaller for method KF2, but also is there a significant difference in the correlation structure between the two methods. Whereas the bi-pdf of method KF4 is reminiscent of a bi-normal pdf, the probability density for KF2 shows two distinctive lobes or modes, and also a larger concentration of the density near the origin. Both methods show a negative correlation between the wind speed changes and the forecast errors, but the effect is much less pronounced in the case of method KF2. From the comparison of Figure 7 and Figure 2, it can be concluded that a much larger fraction of the events will fall into the \"lost\" area in Figure 2, i.e., correspond to incompliance events that go undetected. Method KF2, on the other hand, while also showing some overlap with the lost area, mainly because of the lobe with a high negative slope (of around \(-1\)), an important fraction of the data falls  
Figure 6: Example time series of the true wind speed changes, the forecast wind speed changes, as well as the true and the forecast curtailment margins, all for two time steps into the future, shown for methods KF1 and KF4; see Table 2. The critical slope was set to \(a=1\) m/s in this example.  
onto the lower lobe, with its much less negative slope, and thereby increases the probability for the detection of incompliance events.
# A Channel Compensation Technique Based on Frequency-Hopping Binary Offset Carrier Modulated Signal
## 2 Overview of the Designed System
### Signal Model
To develop compatibility, the raised cosine filters are adopted as matched filters for up-sampling OFDM signal, turning discrete OFDM symbols into a continuous one, as \(v(n)\times h_{s}(t)=v(t)\). Now, we present the frequency-domain description of the matched filter as \(H_{s}(f)\), and also the impulse response as \(h_{s}(t)\). Additionally, for the sake of clarity, the expression on matched filters will be discussed in next section. Hence, the combined signal can be described as  
\[s(t)=m\cdot u(t)\cdot\cos(2\pi f_{c}t)+n\cdot[v(n)\times h_{s}(t)]\cdot j\sin( 2\pi f_{c}t), \tag{7}\]  
where \(v(n)\times h_{s}(t)=v(t)\) means the convolution of OFDM signal and the impulse response of the matched filter, \(f_{c}\) is carrier frequency. Additionally, the parameters \(m\) and \(n\), which have been normalized by \(\sqrt{m^{2}+n^{2}}=1\), are the coefficients for adjusting the power ratio between the in-phase component and quadrature component.
# Preamble-Based Adaptive Channel Estimation for IEEE 802.11p +
## 3 Channel Estimation Schemes
### LS Estimation Scheme  
The LS scheme performs channel estimation using predefined long training symbol \(X_{0}(k)\) in frequency domain. Since the preamble is composed of two identical long training symbols, the LS channel estimation is obtained as:  
\[H(0,k)=\frac{Y_{0}(1,k)+Y_{0}(2,k)}{2X_{0}(k)}, \tag{2}\]  
where \(Y_{0}(1,k)\) and \(Y_{0}(2,k)\) are the frequency-domain received symbols at subcarrier \(k\). The channel estimates obtained by (1) are employed for equalizing all the received symbols in the packet; the channel variations in vehicular environments are very fast, so channel estimate \(H(0,k)\) is not valid over time. Hence, the LS scheme is difficult to use in vehicular environments.
## 6 Results
### Landmark Estimation  
The experimental results of the sensitivity analyses for the _Landmark Estimation_ are shown in Figure 14. Figure 13(a) shows the results of the particle-based sensitivity analysis (\(N_{l}=512\)), while Figure 13(b) shows the results for the observed landmark-based analysis (\(N_{p}=1024\)). The fully parallelized implementation (\(MBNT\)) demonstrates the best performance among all test cases. For a low number of landmarks, thread-based (\(MB1T\)) parallelization is the least efficient among the parallel implementations.  
The most efficient implementation reduces the processing time by a factor of around 250 for the highest numbers of particles (\(N_{l}=512\)), a factor of almost 270 for the highest values of \(N_{l}\), with \(N_{p}=1024\).
## 4 Results and Discussions  
### The Fuzzy Delphi (FDM) Analysis  
#### 4.1.1 The First Fuzzy Delphi Analysis  
This study integrated the opinions of scholars and experts with the \"double triangular fuzzy number method\" proposed by [PERSON] (2001), examined whether the expert opinions have reached convergence with the \"grey zone testing method\" [74], and applied the operating procedures of [PERSON] et al. (1993) [75] and Microsoft Excel 2007 software to conduct the pre-test analysis of the development of product design indicators for wearable devices. In the aspect of evaluation criteria, according to the 80/20 rule, the overall average of 6.976 was multiplied by 0.8 to obtain the threshold value of 5.581. Those with values lower than the threshold value were eliminated. As a result, all six measurement criteria were retained, which means that the measurement aspects have expert consistency and importance. As for the design indicators, 80% of the total average expert consensus value of 7.014 was used as the threshold, and the expert consensus threshold value of 5.611 was retrieved. Those with values lower than that were deleted. Consequently, a total of 16 evaluation indicators were retained, and \"Additional Function (5.575)\" was deleted.
# Optimal Scheduling of Hydro-PV-Wind Hybrid System Considering CHP and BESS Coordination
## 2 Coordinated Optimal Scheduling Model of Hybrid System
### Objective of Optimal Scheduling Model in Hybrid System
#### 2.2.1 The Model of Hydro Power Station  
Hydro power is a traditional renewable clean energy. In accordance with the regulation ability of water, the hydropower station can be divided into two categories, a run-off hydropower station and a pumped storage power station. The latter has a certain capacity of reservoir, which can store the appropriate capacity of water and have a certain ability to adjust and control electricity. Therefore, we assumed the hydropower station in this paper is a pumped storage power station to complement the renewable resources effectively. The output power of the hydro unit follows a function with the water flow. The relationship can be expressed as:  
\[P_{h}^{t,s}=F(Q^{t,s}) \tag{5}\]  
\[P_{h}^{\min}\cdot u_{h}^{g,t}\leq P_{h}^{t,s}\leq P_{h}^{\max}\cdot u_{h}^{g,t} \tag{6}\]  
\[Vol^{t,s}=Vol^{t-1,s}+R^{t-1}-Q^{t-1,s}-S^{t-1,s} \tag{7}\]  
\[Vol^{\min}\leq Vol^{t,s}\leq Vol^{\max} \tag{8}\]  
\[Vol^{NT,s}\geq Vol^{end} \tag{9}\]  
\[Vol^{0,s}=Vol^{begin} \tag{10}\]The relationship between output power and the water flow is shown in Equation (5), and the output power is limited by the limits of capacity in Equation (6). Equation (7) represents the reservoir volume constraint at time \(t\) and \(t-1\), and the right and left limits of the reservoir volume in each hour are expressed in Equation (8). Meanwhile, Equation (9) fixes the hydro reserve at the end of the period to be no less than the terminal volume limit.