esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### C6 Volatile Level, Odor Contribution, and Cultivar and Vintage Effect
For example, its concentration was higher than its odor threshold in the \"Moldova\", \"Muscat of Alexandria\", and \"Christmas Rose\" cultivars at both vintages. However, the \"Xiangfei\" and \"Zaomeiguixiang\" cultivars showed a low concentration of (\(Z\))-3-hexen-1-ol at harvest. Its scent notes were only emphasized in the \"Tamina\" cultivar in 2013. C6 esters have been reported to be important volatiles that provide wine-making grapes (such as Merlot and Cabernet Sauvignon) and other fruits with varietal flavor notes [34; 35]. Their flavor scents have been depicted as fruity, floral, and sweet in fruits [34; 35]. In the present study, only ethyl hexanoate exhibited a concentration close to its odor threshold in the \"Tamina\" cultivars under the 2014 vintage (Figure 1d). (\(Z\))-3-Hexenyl acetate and hexyl acetate appeared to be barely above their threshold in these cultivars. Hexanoic acid was the only C6 acid found in these grape cultivars at harvest (Figure 1c). Compared to the C6 aldehydes and alcohols, this volatile exhibited a low concentration in these table grape cultivars (40 to 120 \(\upmu\)g/L). Among these cultivars, the \"Xiangfei\" and \"Tamina\" cultivars showed the highest level of hexanoic acid, whereas the lowest level of this acid was found in the \"Christmas Rose\" cultivar at harvest. This C6 acid has been reported to possess sweaty, cheesy, and fatty flavor notes [36]. However, its scent could not significantly contribute to the overall aroma of these grape cultivars due to its high odor threshold [37]. A two-way ANOVA was carried out in this study to elucidate the effect of cultivar, vintage, and cultivar x vintage interaction on the C6 volatile composition in these table grape cultivars (Table 1). It was observed that the grape cultivar played a primary effect on the composition of these C6 volatile compounds, except in ethyl hexanoate, whereas the vintage exerted a significant effect on the accumulation of these volatiles except for hexyl acetate. These results were consistent with the previously published study [24; 27]. Moreover, the cultivar \(\times\) vintage interaction played a role in affecting the level of these volatile compounds, except for ethyl hexanoate and 1-hexanol. These results indicated that these table grape cultivars had significant differences in terms of the C6 volatile composition and distribution.
###### Abstract  
This study investigated the effects of rowing with different seat cushion and cadence conditions on oxyhemoglobin (O2 Hb) and total hemoglobin (tHb) levels of the erector spinae (ES) as well as the effects on heart rate (HR) and ratings of perceived exertion (RPE). Thirty healthy adults completed tests under three unstable air seat cushion pressure levels (0, 80, and 140 mmHg) and three rowing cadences (slow: 18 ppm, medium: 30 ppm, and fast: 36 ppm) on a rowing machine, for a total of nine test conditions. During the exercise period, rowing on cushions set to 80 mmHg resulted in greater O2 Hb and tHb changes than did rowing at 0 mmHg (\(p<0.05\)). When rowing cadence increased, the O2 Hb and tHb decreased during the exercise period, whereas HR and RPE increased (\(p<0.05\)). During the recovery period, O2 Hb and tHb on cushions set to 140 mmHg during slow rowing were higher than those at 0 mmHg during slow rowing and 140 mmHg during fast rowing (\(p<0.05\)). Rowing on an appropriate pressure of seat cushion and using a slow cadence contribute to increasing muscle oxygenation of low back during exercise.  
unstable surface; stroke rate; blood volume; near-infrared spectroscopy; core muscles +
Footnote †: journal: Applied Sciences
# Topology Optimisation Using MPBILs and Multi-Grid Ground Element
## 3 Performance Enhancements of Multi-Objective, Population-Based Incremental Learning
### Opposite-Based MPBIL  
OMPBIL has been developed as an improved version of MPBIL [3]. Due to \(L_{R}\) affecting MPBIL performance, the issue is how to select a proper value of \(L_{R}\) for a general problem. It is expected to accelerate the convergence rate to find solution, as well as provide population diversity. Our previous work proposed the opposition-based concept embedded into MPBIL, which is an efficient technique that can upgrade MPBIL's performance. Therefore, the outline of OMPBIL algorithm includes the opposition-based concept, which is not included in this paper. More details can be found in [3].
# Pipeline Leakage localization Based on FBG Hoop Strain Sensors Combined with BP Neural Network
## 1 Introduction
In the next section, a case study of a long-distance pipeline is presented, including cases considering different levels of signal noise. Finally, conclusions are drawn.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 5 Ontology in Wireless Sensor Networks
### The Importance of Ontology in Wireless Sensor Networks (WSNs)  
According to [64], ontology is generally an interesting approach for converging the description of a data model and the related rule base into a single application. Ontologies developed in the web ontology language (OWL) derive several benefits afforded by the semantic web stack. The purpose of OWL is to represent complex knowledge of entities in a domain through a logic-based language, via a computation, in such a way that the knowledge encapsulated can be verified for consistency, uniformity, or applied as a foundation for inferences on that specific knowledge. Flexibility in defining any concept to the preplanned level of details is a well-known feature of the ontological model.  
Addressing the concept of ontology into the wireless sensor networks (WSNs) involves a well-structured implementation of the existing security measures to every class. Each class (whether parent or child class) should be properly defined in terms of their intending activities. Implementing the ontology into the network requires us to understand:  
(1) The origin of the attacker, (2) what information is provided by the attacker and where it is provided, (3) how can we distinguish the clone node attack information from innocent input or an accidental change, (4) what other metadata are needed when trying to analyze the network behavior, (5) what information is monitored by IDS/IPS systems or firewalls, etc.  
Some significant factors indicating the importance of ontology are highlighted below:  
(a) To share a customary understanding of the structure of data between people or software agents.  
To facilitate the reuse of domain knowledge (i.e., of the most universal classes, of the knowledge base).  
(b) To make knowledge-base assumptions of the WSN explicit.  
(c) To separate domain knowledge from functional knowledge.  
(d) To scrutinize the knowledge-base of the network and how data are roaming into the WSN environment.
## 1 Introduction
* Conducting a comprehensive network performance analysis, evaluating key metrics, including throughput, latency, success rate, failure rate, and security. The rest of the research paper is organized as follows: Section 2 presents the related works in blockchain-based patient records tracking systems. Section 3 outlines the methodology used in this study. Section 4 depicts the implementation of the proposed system. Section 5 concludes the paper and suggests future research directions.
# Cluster-Based Routing Protocol with Static Hub (CRPSH) for WSN-Assisted IoT Networks
## 3 System Model  
### Assumptions  
The WSN-assisted IoT network is made up of numerous sensor devices that are connected via a radio communication link operating within the radio range R. When the distance between two devices is less than the radio range R, the communication link is considered direct (one-hop); otherwise, the communication link is considered indirect (multihop). The network is comprised of \(k\) sensor devices and an IoT hub. The IoT hub is equipped with an infinite amount of memory, processing, and battery power. The devices can estimate the Received Signal Strength Indication (RSSI) value of the received signal. This protocol is well-suited for applications that require periodic sensing.
# The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme
## 6 Experimental Results
### Experiment II: The Influence of Pressure on the PAD Subsystem Accuracy
#### 6.3.2 PAD Subsystem Generalizability: Pressure versus Ordinary Presentations  
To assess the PAD subsystem generalizability, the second part of Protocol II is applied. Accordingly, leave-one-out cross-validation model is applied for each scenario considering all feature extractors. Considering the data division, each combination between a scenario and feature extraction method is evaluated 11 times; once for each fold. To describe each cross-validation model from 11 testing sets, let us assume that \((BPCER20)_{i=1}^{11}\) is the sequence of error rates, where i is the fold number. The vector \(BPCER20_{i}\) is analyzed by showing: (i) minimum, (ii) maximum, (iii) mean, (iv) median, and (v) standard deviation values.  
Figure 11 highlights the differences between those statistics by showing BPCER20 for the best (i.e., min. BPCER20) and worst (i.e., max. BPCER20) testing folds, and how the BPCER20 is distributed with respect to the average value using the median value. Moreover, the stability of a model is implied by the standard deviation values. In other words, a low average with low disparity confirms higher generalizability for the tested model.  
As can be seen from Figure 11, the differences between the max BPCER20 values are remarkable when comparing the pressure and ordinary scenarios. The best improvement is achieved considering VLPQ for the optical sensor, where BPCER20 is reduced from 50% to approximately 5%, i.e., BPCER20 is 10 times less for the pressure scenario. In addition, the average, median, and standard deviation values are reduced for all feature extractors for the pressure scenario. This implies a lower range of disparity at a lower average value for BPCER20.  
Figure 11: The influence of pressure on the PAD subsystem efficiency (Unshown bars on the _min_ refer to the value 0).  
Figure 10: PAD scores distribution for the thermal sensor using GIST 3-D features.  
The findings of this experiment emphasize the validity of the proposed model when it is compared to ordinary dynamic presentations. Primarily, significant enhancement in the PAD subsystem accuracy was obtained using the same sensors, feature extractors, and evaluation methodology. Furthermore, the proposed model illustrated lower error rate variance when each independent captured subject was tested individually, demonstrated in Figure 11.  
The observed enhancement in the accuracy, shown in Figures 9 and 10, is in line with the initial assumption which claims that fingerprint pressure produces more distinctive dynamic features that allow differentiating bona fide from attack presentations. On the other hand, the stability of the PAD model, which was confirmed by the leave-one-out cross validation, could be interpreted as being a result of obtaining more generic features that are less dependent on certain differences between capture subjects.
## 3 Computation of the principal components
### Distributed estimation of the principal components
#### Computation of \(\mathbf{C}\mathbf{v}\)  
\[\forall i\in\{1,..,p\}\,,\,\,\,(\mathbf{C}\mathbf{v})\,[i]=\sum_{j=1}^{p}c_{ ij}v[j]\]  
As we made the assumption that:\[\forall i\in\{1,..,p\}\;,\;\forall j\
ot\in\mathcal{N}_{i}\;,\;c_{ij}=0\]  
The sum can be simplified :  
\[\forall i\in\{1,..,p\}\;,\;\;(\mathbf{Cv})\left[i\right]=\sum_{j\in\mathcal{N}_ {i}}c_{ij}v[j]\]  
Recall that each node \(i\) has available the set of covariances \((c_{ij})\), \(j\in\mathcal{N}_{i}\). Therefore at each step \(t\), node \(i\) only has to broadcast \(v_{t}[i]\) and to receive the \(v_{t}[j]\), \(j\in\mathcal{N}_{i}\) from its neighbors for updating locally its \(\sum_{j\in\mathcal{N}_{i}}c_{ij}v[j]\;=\;\;(\mathbf{Cv})\left[i\right]\). The power iteration method can thus be implemented in a fully distributed manner.  
Normalization and orthogonalizationThe norm can be computed using the primitives detailed in Section 2.1.2, while the orthogonalization is obtained by means of these primitives:  
\[init(v_{t}[i]) = \langle(v_{t}[i]\mathbf{w_{l}}[i])_{1\leq l\leq k-1}\rangle\] \[f(\langle X\rangle,\langle Y\rangle) = \langle X+Y\rangle\] \[e(\langle X\rangle) = X.\]  
The resulting \(k-1\) scalar products \(\{\langle\mathbf{w_{l}},\mathbf{v_{t}}\rangle\}_{1\leq l\leq k-1}\) are then communicated to the sensors by means of an F operation, so that each sensor \(i\) can locally orthogonalize \(\mathbf{v}_{t+1}[i]\leftarrow\mathbf{v}_{t}[i]-\sum_{l=1}^{k-1}\langle \mathbf{v}_{t},\mathbf{w}_{l}\rangle\mathbf{w}_{l}[i]\).
# Dual-Boost Inverter for PV Microinverter Application--An Assessment of Control Strategies
## 3 Control Strategies of the DBI
### Non-Linear Controls
#### 3.3.3 Sliding-Mode-Based Control  
The control for the DBI based on the sliding mode approach was presented in [18], where the experimental results were obtained with a fixed source at the input. In this case, the global switching control strategy for the DBI was used and implemented. The same control approach was considered in this work with an extra control loop (PV voltage control loop). The control scheme shown in Figure 11 is composed of two cascaded current control loops and a PV voltage control loop.  
The objective of the inner control loop is the current regulation of the boost converters. The difference between these currents is controlled through the sliding mode strategy, which is based on the equivalent control approach [17]. The selection of the switching surface is the most important aspect in the design of control, because it determines the behavior of the inverter. Then, the equivalent control was obtained to guarantee the feasibility of the sliding motion over the switching surface, and the existence condition was verified.  
In this case, the difference between the current of the inductors can regulate indirectly the output voltage of the inverter [16]. Therefore, the sliding surface (\(\sigma(t)\)) was defined as shown in Equation (25).  
\[\sigma(t)=-k_{2}(t)+i_{\text{L2}}(t)-i_{\text{L1}}(t) \tag{25}\]  
where \(k_{2}(t)\) is the output of external control loop.  
On the other hand, the outer loop regulates the grid current of the DBI. Since the current reference is sinusoidal, a PR controller was used, which was tuned to the grid frequency \(\omega_{\text{s}}\). The angle of \(i_{\text{s}}^{*}\) was obtained from a PLL, which was used to reconstruct a sinusoidal waveform enabling the synchronization with the grid [27; 32]. The amplitude for the current reference was provided by the PI controller, which regulates the input capacitor's (\(C_{\text{PV}}\)) voltage.  
Furthermore, a phase compensator \(C_{\phi}\) composed of a pole and a zero and an integral term were introduced in the control diagram to increase the phase margin and compensate the parameter differences between each DC-DC converter. A deeper analysis of this strategy can be found in [18].  
Figure 11: The cascaded control scheme of the grid-connected DBI, with external PR controller and internal sliding mode controller.
## 3 Wireless Communication Technologies
### Long-Range Communication Technologies  
Long-range communication technologies can be used as a backhaul between two sights to deliver data communication services over a large distance. These technologies may also be usefulfor aerial vehicles to enable them communicate directly with each other (through U2U) and with the fixed infrastructure (using U2I). In the following subsection, capabilities of the existing short-range communication technologies will be examined.
# Secure Transmission in mmWave Wiretap Channels: On Sector Guard Zone and Blockages
Our diversified contributions and insights are listed as follows:  
* According to the characteristics of mmWave beam pattern, both the main lobe and side lobe are taken into consideration. Specifically, a sector secrecy guard zone model is considered to achieve theoretical design and analysis. Depending on the locations of eavesdroppers detected by a transmitter, an adaptive transmission scheme is proposed which chooses two types of transmission strategies adaptively. The fist-type is direct transmission when there exists no eavesdroppers in the sector secrecy guard zone and the second-type is the AN assisted transmission when one or more eavesdroppers in the sector secrecy guard zone. * Stochastic geometry is adopted in proposed mmWave wiretap network to characterize the random spatial locations of eavesdroppers. The closed-form expressions of secrecy outage probability (SOP),connection outage probability (COP) and secrecy throughput are derived in the proposed scheme. In addition, we provide a further insight of the system parameters, i.e., transmit power, power allocation factor, secrecy guard zone radius and central angle, blockage density, antenna gain, and the intensity of the eavesdroppers into secrecy performance. * The results show that enlarging the radius of sector secrecy guard zone improves secrecy performance. In addition, recruiting AN also enhances secrecy performance especially when the density of eavesdroppers is dense. In addition, blockage plays an important role in the transmission of mmWave, which can be utilized to improve secrecy performance. Furthermore, in our adaptive transmission scheme, increasing the density of eavesdroppers not always deteriorates the secrecy performance. Ultimately, simulations provide an easy choice of transmit power and power allocation factor for achieving higher secrecy throughput. The remainder of this paper is organized as follows. Section 2 presents the system model and the performance metric. Section 3 introduces the secure transmission strategies. Section 4 derives the expressions of secrecy performance for adaptive transmission scheme. Numerical and simulation results verified our theoretical analysis are presented in Section 5. Finally, we conclude this paper in Section 6.
## 3 Results and Discussion
### Results of a Delphi Survey of Experts
This visualization clearly shows collision and path deviation as high-priority concerns, clustering in the high severity and probability region. The heatmap (Figure 3b) effectively illustrates the variation in risk assessments across expert groups, highlighting the consistent pattern of higher ratings from academic experts and lower ratings from developers. To further illustrate the distinct risk perception patterns of each expert group, Figure 4 presents spider diagrams of the risk assessments. The spider diagrams clearly visualize how each group's risk perceptions differ across the nine risk factors, with academic experts (Group 4) showing consistently higher ratings forming a larger area, particularly for collision and path deviation, while robot developers (Group 3) demonstrate a more concentrated pattern with lower overall ratings. This visualization effectively highlights how professional background influences the assessment of different risk factors, with operators and safety experts often falling between these two extremes. Robot operators (Group 1) showed particularly high concern for explosion prevention performance (mean severity 5.0) and unauthorized access (mean severity 4.2). This emphasis on practical operational risks likely reflects their direct experience with the challenges of maintaining safety in actual working conditions. Their high rating of explosion prevention performance is especially notable given the critical nature of this risk in industrial settings. Safety management experts (Group 2) generally provided moderate assessments, often falling between the higher ratings of academics and lower ratings of developers. However, they rated unauthorized access risks particularly low (mean risk score 1.6), suggesting confidence in existing security protocols. This middle-ground position might reflect their balanced perspective, considering both theoretical safety principles and practical implementation constraints.
# Intra-Reliability of a Wearable Near-Infrared Sensor for Monitoring the Intensity of Exercise
## 4 Discussion
### Future Research Perspective and Practical Applications  
Further research is warranted to explore mathematical techniques for detection, including the capability to identify aerobic and anaerobic thresholds, the detection across multiple regions, and the impact of gender, performance level, and adipose tissue on threshold determination. For this sensor, comparing the reliability of SmO\({}_{2}\) with other common physiological measures such as oxygen uptake (VO\({}_{2}\)), heart rate (HR), blood lactate concentration ([La\({}^{-}\)]), and rating of perceived exertion (RPE) will provide a useful contrast for each of these common markers during the same exercise protocol. [PERSON] et al. [13] suggested the utility of training zones evaluated on muscle oxygenation rather than the traditionally used HR monitoring, given the various factors influencing HR responses [8; 13]. Additionally, consecutive analyses should explore the relationships between other physiological variables such as VO\({}_{2}\). For instance, the inverse relationship between SmO\({}_{2}\) and VO\({}_{2}\) expresses the increasing difference between the mean muscle capillary O\({}_{2}\) content and the rate of oxygen utilization by the working muscle [13]. Finally, future research areas could investigate the potential efficacy of the Q-LAC sensor for use during strength exercise, rest, and recovery strategies, such as muscle ischemia provoked by blood flow restriction or compression garments [31,32].