esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Viscosity and Transverse Ultrasonic Attenuation of Engineering Materials
## 3 Results and Discussion
### Attenuation Behavior of Fiber-Reinforced Plastics  
Eight fiber-reinforced plastics (FRP) were evaluated for transverse attenuation and the results are summarized in Table 5 in a similar format as before, but using C\({}_{3}\) and C\({}_{2t}\) coefficients in lieu of C\({}_{R}\) and C\({}_{Rt}\). Sources of the FRP samples were described in [13], but the types of epoxy used for glass fiber-reinforced plastics (GRP) and Kevlar fiber-reinforced plastics (KFRP) were unavailable. Cross-plied (XP) GRP and KFRP used woven rovings. Matrix resin types of AS4, G50, and T700 carbon fiber reinforced plastics (CFRP) were Hexcel 3501-6, Hexcel F584, and Toray 2510, respectively. G50 XP-CFRP had a layup of (0\({}^{\circ}\)2, 90\({}^{\circ}\)2)\({}_{4s}\). Eighteen transverse and two additional longitudinal tests were conducted at 25 \(\pm\) 2 \({}^{\circ}\)C. Except for two tests, the transverse attenuation coefficients, C\({}_{dt}\), were more than 1.5 times higher than the corresponding C\({}_{d}\) values, with the average C\({}_{dt}\)/C\({}_{d}\) ratio of 4.45. Even when waves move along the orientation parallel to the fibers, C\({}_{dt}\) values exceeded 300 dB/m/MHz, well above that of PMMA. This reflects low wave velocities parallel to fibers (1.75-2.15 mm/\(\upmu\)s), which are similar to the transverse or matrix wave velocities (1.3-1.9 mm/\(\upmu\)s). That is, the transverse wave propagation is controlled by the matrix primarily. When waves propagated normal to parallel fibers (Tests R4 and R16), the cubic frequency dependences were observed for the longitudinal attenuation (see for example, Figure 10, red curve), but the \(f^{3}\) contribution vanished and only linear frequency spectra were observed (see Figure 11, dark red curve). One case with non-zero C\({}_{R}\) term (Test R26) also lost the scattering contribution. Thus, the linear frequency spectra were predominantly observed for the transverse mode. Apparently, parallel fibers have a lower scattering cross section in the transverse wave mode. However, the wave velocity was always higher by 10% when the polarization direction coincided with the fiber orientation, implying the existence of transverse wav \(\times\) fiber interaction. Figure 9: Longitudinal (blue) and transverse (red) attenuation coefficients vs. nominal crystallinity of polymer samples. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline
**Test.
## 4 Implementation and Discussion
### Limitation and Future Work
#### 4.3.1 Prioritization of Decision Giving Entities  
This study introduced four decision-making entities (P, D, I\({}_{c}\), and C\({}_{p}\)) for a collaborative medical decision gathering. Although, PoF evaluated all success factors of an entity, prioritization was not considered among the entities (P, D, I\({}_{c}\), and C\({}_{p}\)). In this version of the study, the PoF consensus algorithm weighs each decision giving entities (P, D, I\({}_{c}\), and C\({}_{p}\)) equally. That is, PoF emphasizes the decision of P, D, I\({}_{c}\), and C\({}_{p}\) equally during decision-making for the patient, P from P, D, I\({}_{c}\), and C\({}_{p}\). Although, in real life, the impact of an individual's decision might differ based on the entities' nature. However, to do this prioritization, concrete evidence and empirical analysis is needed. Therefore, the study acknowledges the issue and plans to consider this in the future version.
# Biocompounds and Bioactivities of Selected Greek Boraginaceae Seeds
## 4 Discussion
### Phenolic Compound Content  
TPC values (Table 2) showed significant differences among species when assessed through the F-C method. By using this methodology, most seeds showed significant amounts of phenolic compounds, although all figures were similar to other ones previously reported for Boraginaceae taxa, e.g., _B. officinalis_, which was reported to contain phenolic acids at 245-1090 mg GAE/100 g dw [42]; however, [PERSON] and [PERSON] [22] indicated for _B. officinalis_ 1490-1690 mg of phenolic acids as caffe acid equivalents by 100 g dw); for _E. vulgare_ and _B. officinalis_, 122.6 and 130.4 mg GAE/100 g dw were reported [43]; and for _Echium pycranthum_, 2731 mg GAE/100 g dw were found [44]. Concerning other GLA-rich species, evening primrose (_O. biennis_ L.) contains higher amounts than most Boraginaceae taxa, for instance 736.47 mg/100 g dw [43], a figure in line with the results of this work;for _R. nigrum_ L. cultivars, it ranged from 160.4 to 230.7 mg/100 g [45]. As for flavonoids, the figures for the species analyzed in this work were higher than those reported for _B. officinalis_ by [PERSON] and [PERSON] [22], 10-14 mg QE/100 g dw, but lower than the ones obtained for _E. pycranthum_ by [PERSON] et al. [44], 1626 mg of catechin equivalents by 100 g dw.  
Assessed as the sum of individual phenolics through HPLC-DAD (Table 3), phenolic compounds ranged from 247.8 in _S. bullosum_ SB2 to 1524.7 mg/100 g seeds in _A. methananea_. Using the same methodology, within Boraginaceae taxa, in _Buglosoides_ species such content ranged from 62.1 to 645.5 mg/100 g dw [14]; in _Borago_ species, it was between 55.36 and 255.86 mg/100 g seeds [23]. Concerning other GLA-rich taxa, total phenolic compounds assessed by HPLC-DAD typically reach \(\sim\)2/3 of the amount evaluated by the F-C method, as reported by [PERSON] et al. [45]. This disagreement can be explained considering the following: (i) the F-C method provides the total amount of phenolic compounds, while the HPLC reports only amounts for identified compounds, and (ii) the unspecificity of this methodology toward phenolics, which leads to an overestimation of the actual phenolic content [46].  
Overall, the samples checked here contain higher amounts of phenolic compounds than other previously reported Boraginaceae taxa. This fact can be due to the fact that warm summer Mediterranean climates, like the one in which the analyzed species occur, lead to stressful conditions for plants, which can activate a multi-gene response that induces the synthesis of secondary metabolites, for instance phenolic compounds [14].  
Need to be considered that refined oils usually contain low amounts of polyphenols, which are mainly removed during the refining process due to their hydrophilic nature. Accordingly, in the event that any of the oils from the analyzed seeds are commercialized, it is recommended that to benefit from the bioactive compounds contained in the seeds, such as phenolics, the target oils are extracted by cold pressing.
## 3 Method Architecture
### Feature Fusion Coding
#### 3.5.1 Pinyin-Semantic Fusion  
The interactive fusion network diagram of this paper is shown in Figure 2, where K is the key vector matrix, V is the corresponding value vector matrix, and Q is corresponding the query vector matrix. First, the multi-head attention mechanism is used to mine features with interactive information from pinyin features and semantic features, which are recorded as interactive features A and interactive features B, and then the correlation vector between pinyin features and semantic features is obtained. The obtained correlation information vector is used as the weight matrix, the pinyin feature itself is weighted, and the corresponding semantic information of the POS encoding is given; at the same time, the correlation between the semantic feature and the pinyin feature is obtained, and the obtained correlation information vector is used as the weight matrix, the semantic feature itself is weighted, and the pinyin encoding is given the corresponding POS information. Then, the two weighted vectors are concatenated and fused, and the concatenated vector is reduced in dimension to obtain contextual features containing high-order semantic associations between semantic features and pinyin features. Finally, dimensionality reduction is applied to obtain the final fused feature vector. Here, \(H_{py}=[h_{py}^{1},h_{py}^{2},\dots,h_{py}^{i},\dots,h_{py}^{n}]\) represents the sequence pinyin embedding feature, \(H=[h_{x}^{1},h_{x}^{2},\dots,h_{x}^{i},\dots,h_{x}^{n}]\) represents the sequence semantic embedding feature, and the pinyin-semantic fusion is shown in Formula (4):  
\[H_{py}^{h}=Cross-ATT\big{(}H,H_{py}\big{)} \tag{4}\]  
This method effectively integrates semantic features and phonetic features, and can better capture the relation between phonetics and semantics, thereby improving the model's ability to understand the semantics of text context.
## 2 Model Description
### EVs Participation in DR Programs  
EVs can be considered in three different modes: grid-connected mode, idle mode, or driving mode. In grid-connected mode, the MG operator can schedule charging/discharging process of EVs batteries. EVs can exchange power with the MG based on their state of charge (SOC), stop time in the parking lot (PL) and the electricity price in each DR program. In this case, EVs are considered to be probabilistic loads or generations which can be evaluated by stochastic methods [28]. The exchange power between each EV and the network can be obtained as [28]:  
\[P_{k,t}^{EV}=\eta_{c}P_{k,t}^{c}-\frac{P_{k,t}^{d}}{\eta_{d}}\quad\forall t\in u _{k} \tag{11}\]  
The SOC of EVs connected to the network is updated by Equation (12) [28].  
\[BC_{k}\cdot SOC_{k,t}=BC_{k}\cdot SOC_{k,t-1}+P_{k,t-1}^{EV}\quad\forall t\in T,\forall k\in Nk \tag{12}\]  
where \(SOC_{k,t-1}=SOC_{k,t}\), if \(t=1\). \(BC_{k}\) is battery capacity of EV in kWh and \(SOC_{k,t}\) is the initial SOC of \(k\)th EV. It is important to control the charge and discharge energy of the parked vehicle \(w\) such that the SOC of the battery could be kept within the allowed range \(SOC_{k}^{\text{min}}\) and \(SOC_{k}^{\text{max}}\).  
Besides, in idle or driving mode, there is no power exchange between EV and the network, however the stored energy might decrease depending on the EV trip length (\(L_{k}\)) and its energy consumption rate (\(r_{k}\)). It is assumed that each EV returns to the PL after driving \(L_{k}\) km and is plugged back into the network. Thus, the SOC at the time of arrival (\(SOC_{k}^{ent}\)) can be estimated by Equation (13) [28].  
\[SOC_{k}^{ent}=SOC_{k}^{\text{int}}-L_{k}\times r_{k}\;\;\;\forall k\in Nk \tag{13}\]  
where, \(SOC_{k}^{\text{int}}\) is the initial SOC at the beginning of the trip.
# Active Noise Control over Space: A Subspace Method for Performance Analysis
## 5 Simulation Results
### Comparison of the Effect of Different Noise Source Positions  
After investigating the cancellation performance in two different noise source positions, we move the noise source position to different elevations (\(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\)) and different azimuthal angles (\(0^{\circ},45^{\circ},90^{\circ},135^{\circ},180^{\circ},225^{\circ},270^{\circ},3 15^{\circ}\)). As shown in Figure 5a, 24 source position candidates are chosen on the sphere with radius of 2 m. We measure the primary noise field coefficients and secondary noise field coefficients by microphones with different SNR levels, which are 60 dB and 30 dB, respectively, for each source position candidate.  
Figure 4: Energy of the residual noise field, when the noise field is generated by one primary source using different methods. Here, pink point is the projection of the primary source on the x-y plane and blue points are the loudspeaker points located on the x-y plane: (**a**) the WDLS method in case 1; (**b**) the WDLS method in case 2; (**c**) the subspace method in case 1; (**d**) the subspace method in case 2.  
Figure 5: _Cont._Figure 6 demonstrates the noise reduction performance for different source positions. For most of the positions, the WDLS method can achieve slightly better noise reduction than the subspace method. This is because the subspace method only uses the principal components while the WDLS method exploits all the information of the secondary path. Since 8 out of 12 loudspeakers are located in the x-y plane, the noise source positions indicated better noise reduction levels in Figure 6a,b are position No. 2, 5, 8, 11, 14, 17, 20, 23, which are the source candidates on the x-y plane. As the accuracy of the microphone recordings is reduced, the performance prediction becomes less accurate. For example, as shown in Figure 6b, at the noise source position 15, using both methods, the noise reduction levels are positive, which indicates the opposite result from that of Figure 6a.  
Figure 5: Two different array setups, when the noise source moves around a sphere, where in both setups, pink points are the primary source positions, blue points are loudspeaker positions, and red points are microphone positions: (**a**) Case 3; (**b**) Case 4.  
Figure 6: _Cont._  
Figure 7 demonstrates the energy of the loudspeaker driving signals (\(E_{d}\) in (35)) using different methods (Here, we evaluate the summation of squared driving signals, for all the loudspeakers.). As shown in Figure 7a,b, in both cases, compared with the WDLS method, the proposed subspace method can reduce the total energy on the loudspeakers significantly, which can avoid the overloading of the secondary sources.  
Figure 6: Noise reduction performance in case 3, when the noise field is generated by one primary source moving around the sphere using different methods: (**a**) with \(\mathrm{SNR}=60\) dB white noise on the microphone recordings; (**b**) with \(\mathrm{SNR}=30\) dB white noise on the microphone recordings.  
Figure 7: _Cont._
## 1 Introduction  
Optical measurements play a crucial role in examining brain physiology, particularly in exploring the connection between neural activity and changes in hemodynamics. In neuroimaging studies, diffuse optical tomography (DOT) has been extensively utilized to detect functional alterations related to visual, motor, somatosensory, or cognitive stimuli [1]. Previous research has introduced a novel approach to processing DOT data, treating them as functional magnetic resonance imaging (fMRI) volumes. This approach was successfully validated in the prefrontal cortex using a cognitive paradigm [2] and in sensory and motor areas [3]. The DOT technique also enables the precise measurement of hemodynamic changes during intricate brain processes, such as motor imagery, which reveals more subtle cerebral activations compared to motor execution [4].  
The presence of noises from non-target anatomical regions, such as the scalp or the external layers of the skull, poses a significant challenge in DOT measurements. These signals introduce short-term variability that affects spatial and temporal changes throughout the brain and scalp [5]. To overcome this challenge, various methods have been employed that do not rely on assumptions about the hemodynamic model. Approaches like principal component analysis (PCA) or independent component analysis (ICA) have been utilized to generate cerebral activation maps [6] and reduce noise originating from these external layers. Similar to common practices in neuroimaging studies, a widely used method is region of interest (ROI) analysis, which relies on the structural anatomy of the brain. This approach involves selecting specific regions within the brain based on their anatomical landmarks or predefined brain atlases. By focusing on these regions of interest, researchers can analyze the DOT data specifically within these areas, allowing for more targeted and accurate examination of the functional activations. This structural-ROI-based analysis in DOT is an approach commonly employed in neuroimaging studies, where researchers define specific regions of interest based on structural anatomy to investigate brain activity and connectivity patterns [7], particularly when a magnetic resonance (MR) device is available. However, a notable drawback of using DOT in human brain studies is the reliance on a structural MRI image for the accurate localization of functional activations within the anatomy. This requirement for an MRI scan limits the usability of DOT techniques in situations where an MRI scan is not easily accessible.  
Here, we investigate whether an ROI analysis could be constructed based on the intrinsic signal/brain characteristics across voxels rather than relying on a structural-ROI. To test this hypothesis, we analyzed DOT data recorded from the well-studied sensory-motor area, where the spatial locations of activations are known. The data were collected from a group of healthy subjects while they performed finger movements with their right hands. The cerebral maps obtained from DOT were compared with the maps derived from fMRI data, providing a basis for comparison and validation.
## 3 Case Study: Campulung Town Hall Building
### Energy Analysis--A Pre-Intervention, Non-Invasive, Multicriterial Study
#### 3.3.2 Result from the In Situ Measurements of Temperature and Humidity
Consequently, this means more chances of thermal bridge formations appearing, and even mold can occur over time, given the change in temperatures and the lack of necessary layers for thermal insulation. All this comes as a research validation tool for the energy audit conducted, showcasing with real-time data that the construction elements do not meet the requirements of thermal insulation and energy saving. The south office room has a wider temperature range, with the lowest and highest recorded readings being 21.7 \({}^{\circ}\)C and 24.4 \({}^{\circ}\)C, respectively, with an average temperature of around 23.31 \({}^{\circ}\)C. In comparison, the north office room maintains a temperature range between 19.8 \({}^{\circ}\)C and 23.6 \({}^{\circ}\)C, with an average temperature of approximately 21.74 \({}^{\circ}\)C, which is lower. This suggests that the temperature in the south office is consistently higher than that in the north office. The humidity levels in the north office room typically exceed those in the south office room. The humidity levels in the north office range from 19.5% to 41.7%, with an average humidity of 29.83%. In comparison, the south office has humidity levels ranging from 17.1% to 39.1%, with a mean humidity of 26.89%. This indicates that the air in the north office consistently has a higher level of humidity. The north office reveals a little more noticeable fluctuation in temperature and humidity. The north office exhibits a wider spectrum of temperature and humidity variations, which could have a more pronounced impact on thermal comfort and air quality, compared to the relatively milder south office.
# A Novel Cross-Layering Power Control Mechanism for AODV +
## 5 Performance Evaluation
### MANET with RandomWaypoint  
Now, we consider a MANET with the RandomWaypoint mobility model. Simulations were carried out in an area of 500 \(\times\) 500 m, considering three different densities of nodes and xx different speeds. For transmission, a fixed node will be the server and the remaining nodes will act as UDP clients that will send the traffic. See Figure 4. All client nodes will send 100 data packets with a size of 1024 bytes in a total simulation time of 600 s.  
In addition, for the performance evaluation, we consider two options of the mechanism. DPCM 1 refers to the power control mechanism using an upper bound (\(ub\)) equal to five. On the other hand, DPCM 2 employs an upper bound equal to six. With these considerations, we analyze the performance of DPCM with respect to throughput varying the network density and speed of nodes.
## 2 System Model  
In the paper, for concise analysis, the proposed method is analyzed based on the baseband communication model where two elements simultaneously transmit different user signals and two elements are used to implement diversity reception. It should be noted that based on actual requirements, the proposed method can be applied to communication scenarios with more transmit and receive elements.
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
## 6 Numerical Results
### Routing Metrics  
Recall that the main aim of the proposed UVCS algorithm is to perform efficient routing between possibly mobile nodes. At the same time, observe that even perfect topology matching does not guarantee that the routing performance will be similar for  
Figure 4: Topology similarity index.  
different routing algorithms. For this reason, we now proceed to investigate the routing performance of the proposed UVCS approach using different routing metrics including practical geographical routing protocols.  
Here, we start with the path similarity index provided in Figure 5 and show how fast the routes may converge to the shortest ones based on the Dijkstra algorithm. Recall that the closer the value is to 0 the better the match in terms of selected paths is. Here, we can see that the proposed algorithm again converges to its final solution much faster, even though the similarity index is not as good as for the GAR or Geo algorithms--0.6 against 0.5 and 0.4, respectively.  
One of the weakest points of the GAR, originally proposed in [19], is a low number of total discovered paths in the network. Even though the proposed UVCS algorithm does not show the best performance in terms of topology similarity metric, Figure 6 shows that the total number of discovered routes is higher compared to the GAR algorithm. This number is even comparable and is getting very close to the Geo algorithm that relies on external information for routing. To achieve these performance gains in terms of the number of discovered paths, the proposed algorithm in some cases finds longer, less optimal routes to the destination, shown in Figure 7, which may be considered a minor drawback for delay critical applications. However, recalling that the UVCS does not rely on any external positioning information, unlike the original Geo algorithm, this slight increase in average path length enables a much higher number of discovered paths together with a faster convergence time.  
Figure 5: Routing similarity index.  
Figure 6: Total number of discovered paths.
## 5 Field Trial and Validation
### Hardware Implementation  
A low-power SN is designed to implement and test the proposed LDAP using an 8-bit microcontroller (MCU), namely the ATMega328, with only 32 kB of ROM (Read Only Memory), 4 kB of RAM (Random Access Memory), and running at 16 MHz. Figure 8a shows the hardware block diagram of the SN with LoRa and sensor connections. With the SX1276 LoRa module at 915 MHz, the MCU is capable of interfacing four analog sensors and four digital sensors simultaneously through the built-in ADC (Analog to Digital Converter) and I2C (Inter Integrated Circuit) interface, respectively. The MCU used in the SN is powered by a 12 V to 5 V voltage converters, and the sensors connected with it also need to operate at 5 V. The total SN is powered by three 18,650 Li-Ion batteries, each of 6000 mAH, providing 10.8 V to 12 V, which can also be replaced by a 12 V lead-acid battery with a 12 V solar panel for external charging for lengthy remote operations. Figure 8b,c show two SNs connected with a LoRa wireless module, battery, and the different types of sensors used in the field trial. The DS and LoRa-IoT gateway are implemented using a Raspberry-Pi with LoRa, Wi-Fi, and a dedicated LTE-Wi-Fi access point.