esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
The influence of V-shaped support geometric parameters on BWB aircraft support interference was obtained by numerical simulation and validated by wind tunnel tests. The main conclusions are as follows:  
1. The influence of V-shaped support geometric parameters on the pitching moment interference of BWB aircraft is significant, while the changing of geometric parameters has a little influence on the drag coefficient.
2. The sensitivity of the inflection angle is the highest of the four geometric parameters, and the pitching moment interference of BWB aircraft can be reduced significantly by increasing the inflection angle. It is recommended that decreasing the straight-section length and increasing the straight-section diameter or inflection angle is an effective way of reducing the support interference of the pitching moment.
3. The pressure distribution near the cavity of the lower surface is influenced by geometric parameters of the V-shaped support, resulting in different aerodynamic characteristics of the BWB aircraft.  
The research could be used to design special V-shaped support for BWB aircraft in wind tunnel tests, reduce support interference, and improve test accuracy. Furthermore, the test model was designed to be stiff enough to focus our investigations on the aerodynamic interference of V-shaped support. The aeroelastic effects were not detailed in the current research. In future, the blended wing body concept needs to go further to exhibit aeroelastic effects, especially the flutter, and effects of structural displacements on the cruise aerodynamic efficiency need to be evaluated.  
Funding acquisition, D.L. and D.C.; investigation, X.X.; methodology, X.X. and D.L.; project administration, D.L. and D.C.; software, Q.L.; supervision, K.C.; validation, X.X.; writing--Original draft, X.X.; writing--Review and editing, X.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors
## 2 Overview of Polymer Additives in Crystallization
### Semicrystalline Polymers  
Semicrystalline polymers may strongly compete with organic semiconductors in terms of crystallization when blended with them, offering in-situ crystallization control that amorphous polymers lack [69]. Moreover, employing nonconjugated semicrystalline polymers prevents polymer-induced \(\pi\)-\(\pi\) interactions during crystallization. [PERSON] et al. explored the effects of incorporating polyethylene oxide (PEO), a semicrystalline polymer, as an additive to the organic semiconductor TIPS pentacene. They selected PEO due to its ability to influence the crystallization and charge transport properties within the organic semiconductor matrix. The morphology of TIPS pentacene films are shown in the optical images  
Figure 2: Optical micrographs of the TIPS pentacene based thin films, which incorporates four different conjugated polymer additives including **(a)** P3 HT, **(b)** PnBT-RRa, **(c)** PBTTT, and **(d)** F8 BT. Reproduced from reference [67; 68], with permission from ACS Publications and Wiley.  
of Figure 3a-f, based on the different molecular weight (Mw) of 8k, 100k, 300k, and 900k, and mixing ratios of PEO of 1%, 3%, 5%, 10%, and 20%. Lower Mw PEOs (8k and 100k) significantly improved the crystallinity and charge carrier mobility of the TIPS pentacene films. These PEOs facilitated better crystal formation, leading to enhanced film morphology and higher crystallinity. Specifically, the optimal performance was observed at 3% and 5% weight ratios, where the hole mobility increased by up to 11 times compared to pure TIPS pentacene films. Conversely, higher Mw PEOs (300k and 900k) exhibited reduced molecular chain activity, which resulted in lower crystallinity and less effective charge transport. The authors also found that as the Mw of PEO increased, the ability of the polymer chains to form well-organized crystals diminished, leading to phase separation and reduced charge carrier mobility. The high dielectric constant and thermoplastic properties of PEO also contributed to phase separation, further impacting the film electronic performance. Overall, this research highlighted the potential of low Mw PEOs as effective additives in enhancing the performance of organic semiconductors in OTFTs, paving the way for the development of advanced organic electronic devices.
# Distributed MIMO Measurements for Integrated Communication and Sensing in an Industrial Environment
## 4 System Calibration
### Carrier Frequency Offset  
Even if a good reference clock is provided and distributed, there might be frequency drifts or offsets due, for example, to hardware impairments and/or temperature variations. To identify and remove possible carrier frequency offsets, we use a part of the measurement where _all_ antennas are static. If there is a carrier frequency offset, it comes from the oscillators and is not due to Doppler caused by movements. Inspired by [30], we identify carrier frequency offsets as follows. We collect the snapshots \(\mathbf{r}_{n}^{(hh^{\prime})}\) received at the antenna \(h\) from the antenna \(h^{\prime}\) in the \(N_{\text{f}}\times N_{st}\) matrix \(\mathbf{H}^{(hh^{\prime})}=\left[\mathbf{r}_{0}^{(hh^{\prime})},\mathbf{r}_{ 1}^{(hh^{\prime})},\mathbf{r}_{2}^{(hh^{\prime})},\ldots,\mathbf{r}_{N_{st}- 1}^{(hh^{\prime})}\right]\), where \(n\in\{0,1,\ldots,N_{\text{st}}-1\}\) are all static snapshots, and define the \(N_{st}\times N_{st}\) shift matrix \(\mathcal{S}\) as follows:  
\[\mathcal{S}\triangleq\begin{pmatrix}0&0&\cdots&0&0\\ 1&0&\cdots&0&0\\ 0&1&\cdots&0&0\\ \vdots&&\ddots&&\vdots\\ 0&0&\cdots&1&0\end{pmatrix}\]  
This is applied \(s\) times to shift the columns of \(\mathbf{H}^{(hh^{\prime})}\) as follows:  
\[\mathbf{C}^{(hh^{\prime})}=\mathbf{H}^{(hh^{\prime})}\odot\Big{(}\mathbf{H}^{ (hh^{\prime})}\cdot\mathcal{S}^{s}\Big{)}^{*}. \tag{3}\]  
Discard the \(s\) last columns of \(\mathbf{C}^{(hh^{\prime})}\) since they are all zeros  
\[\mathbf{C}_{s}^{(hh^{\prime})}\triangleq\Big{[}\mathbf{C}^{(hh^{\prime})} \Big{]}_{1:N_{t}:1:N_{st}-s}. \tag{4}\]  
The average carrier frequency offset can now be estimated as follows:  
\[\hat{n}^{(hh^{\prime})}=\angle\left(\sum_{n_{f}=1}^{N_{t}}\sum_{n=1}^{N_{st}- s}\Big{[}\mathbf{C}_{s}^{(hh^{\prime})}\Big{]}_{n_{f},n}\right) \tag{5}\]  
The correction factor then becomes  
\[\exp\left\{\text{j}n\frac{\hat{n}^{(hh^{\prime})}}{s}\right\},\qquad n\in\{0, 1,\ldots,N_{s}-1\}\,. \tag{6}\]
# Matching-Based Resource Allocation for Satellite-Ground Network
## 3 System Model
### Performance Analysis  
**Theorem 1**.: _After a limited number of iterations, the algorithm will terminate and produce a stable match._  
**Proof of Theorem 1.** The algorithm will finish after a set number of steps because there are only so many LEO satellites and users on each user's preference list. To prove that output matching is stable, we suppose that the output set \(\Sigma^{t}\) is unstable. It implies that there must be a blocking triple \((u_{i}^{t},\,l_{j}^{t},\,g_{k}^{t})\), following that: \(g_{k}^{t}\in PL_{j,t}^{l}\), \(\mathcal{N}(\Sigma^{t},\,\,l_{j}^{t})<N_{L}\), \(\,l_{j}^{t}\succ_{u_{i}^{t}}\Sigma^{t}(u_{i}^{t})\), and \(u_{i}^{t}\succ_{g_{k}^{t}}\Sigma^{t}(g_{k}^{t})\). Thus, \(u_{i}^{t}\in\Pi^{+1}(\Sigma^{t},\,g_{k}^{t})\), \(\,l_{j}^{t}\in\Pi^{+1}(\Sigma^{t},\,u_{i}^{t})\), \(\,l_{j}^{t}\in\Pi^{-1}(\Sigma^{t},\,g_{k}^{t})\), and \(u_{i}^{t}\in\Pi^{-2}(\Sigma^{t},\,g_{k}^{t})\). Then, \(\Pi^{+1}(\Sigma^{t},\,g_{k}^{t})\cap\Pi^{-2}(\Sigma^{t},\,g_{k}^{t})\
eq\varnothing\), and \(\Pi^{+1}(\Sigma^{t},\,u_{i}^{t})\cap\Pi^{-1}(\Sigma^{t},\,g_{k}^{t})\
eq\varnothing\). In this situation, the algorithm will not terminate, which is paradoxical. Therefore, the output result \(\Sigma^{t}\) must be stable [12]. \(\square\)  
**Theorem 2**.: _In \(O(KTM\sum_{u_{i}\in U}|PL_{i,j,t}^{u}|)\) iterations, the algorithm can always identify a stable match._  
**Proof of Theorem 2.** If a LEO satellite has a large capacity \(N_{L}\), only one user will be assigned to that LEO satellite during each iteration. Thus, the total time \(T\), user' number \(N\), LEO satellite' number \(M\), and GEO satellite' number \(K\) determine the maximum amount of time needed, hence, this algorithm's time complexity is \(O(KTMN)\). However, when \(N_{L}\) is very limited, just one user is assigned to a more suitable LEO satellite in its preference list during each sign iteration, continuing until the sign equals 0 and this algorithm ends. In reality, each user pre-matches every LEO satellite in its preference list when the algorithm is running, which is the worst scenario. Therefore, the length of the user's preference list, rather than the matching set \(\Sigma^{t}\), dictates the maximum time required, so the time complexity is \(O(KTM\sum_{u_{i}^{t}\in U^{t}}|PL_{i,j,t}^{u}|)\). \(\square\)
## 3 Hierarchical Control Approach  
Hierarchical control is a practical approach to managing power sharing in a Microgrid. The primary control level determines the amount of power to be generated by each DG based on factors such as power demand, availability of energy, and system constraints, as Figure 1 shows. The secondary control level manages the power-sharing between the Microgrid and the utility grid, ensuring that the Microgrid operates within acceptable limits. System optimization and long-term planning are carried out at the tertiary control level to ensure optimal power sharing among the energy sources, energy storage systems, and loads.  
Power sharing between DGs is usually carried out by parallel inverters connected to a common AC bus with multiple loads [42]. The proposed controller in this paper considers the microgrid as three Voltage Source Inverters (VSI) feeding an RL load at the point of common coupling. An RL load was chosen to study the microgrid's active and reactive power sharing. Each DG represents a power source implemented in The Simulation Platform for Power Electronic Systems (PLECS) [43; 44]. Droop control in parallel inverters is a widely used strategy to regulate MG power sharing. The main goal of primary droop control is to set a proportional load sharing among DGs, based on the well-known (P-Q) droop method [5]. Each inverter has an external droop control loop to improve performance and provide a decentralized control method. Figure 2 shows the strategy applied to regulate frequency and voltage for one VSI. The details of hierarchical control can be found in App  
Figure 1: Hierarchical control levels and communication restrictions for networked microgrids.  
The control strategy is evaluated according to different events within the microgrid. The droop control is activated during the first second until it reaches the steady state condition. As shown in Figures 3 and 4, there is a deviation in voltage and frequency that needs to be solved by the secondary control. For that reason, it is necessary to perform secondary control to reach the stability of the MG. The secondary control is activated after 10 seconds and remains until the end of the simulation.  
Research studies have used a hierarchical control approach with an SDN-based communication architecture to enhance overall system intelligence [13; 37; 45]. However, while this approach addresses some aspects of power sharing in networked microgrids, the communication system remains a critical factor that impacts the power system and hinders overall recovery. The monolithic architecture of the SDN controller, the integration of new control functionalities, and the excessive workload can be improved through a microservices-based architecture and automatic orchestration.
## 3 Simulation of Thermal Processes in the Food Industry Using CFD
### Sterilization Process
#### 3.2.4 Food Drying Processes  
Food drying is a critical thermal process aimed at reducing the moisture content in food products to inhibit microbial growth and extend shelf life. The modeling of drying processes using CFD provides valuable insights into the complex interactions between heat transfer and mass transfer, enabling the optimization of drying parameters [89]. The fundamental equations used in CFD simulations for food drying include the Navier-Stokes equations for fluid flow, the energy equation for heat transfer, and [PERSON]'s laws of diffusion for mass transfer. To effectively model convective heat transfer and the diffusion of moisture within the food matrix, numerical methods such as the FVM are employed. This approach allows for the efficient discretization of the governing equations in space. Additionally, turbulence models, such as \(k\)-\(\varepsilon\) or \(k\)-\(\omega\), are often used to improve the prediction of airflow distribution, thereby enhancing the accuracy of temperature and moisture profiles within the drying environment [90]. During the drying process, heat is transferred from the surrounding medium--usually air--to the food product, leading to moisture evaporation. The heat transfer equations are linked to the energy balance equation, which includes both convective and conductive heat transfer, as well as enthalpy changes associated with the moisture evaporation process. Moreover, the analysis of temperature gradients, moisture distribution, and airflow patterns helps in identifying the optimal operating conditions, such as temperature, relative humidity, and air velocity, which significantly influence the drying efficiency and product quality [27]. CFD also facilitates the exploration of various drying techniques, such as hot air drying, freeze-drying, and microwave drying. Each method presents unique challenges and advantages. For instance, hot air drying typically operates at higher temperatures, which may negatively affect the sensory quality of the product, while freeze-drying preserves texture and flavour but is more energy-intensive. CFD allows for the quantification of the impact of each technique on drying kinetics by modeling the heat and mass transfer coefficients specific to each method, as well as the corresponding drying rate equations [91]. In particular, the incorporation of phase change phenomena, such as the transition from liquid to vapor, poses a significant challenge in CFD modeling, as it requires the accurate representation of moisture content dynamics and enthalpy changes during the drying process. Despite these complexities, CFD remains a powerful tool for optimizing food drying processes, allowing for precise control over operational conditions and leading to improved product quality, enhanced energy efficiency, and extended shelf life. By utilizing CFD, food technologists can optimize drying processes, reduce energy consumption, and better preserve the sensory attributes of dried products [92].
# Pollen Food Allergy Syndrome in Southern European Adults: Patterns and Insights
## 3 Results
### Cross-Reactivity Between Aeromalergens and Food Allergens in Southern Europe  
PFAS is clearly influenced by local airborne allergens, which act as primary sensitizers, as well as by the dietary habits of the local population. The Mediterranean climate, characterized by mild winters and dry summers with relatively high temperatures, supports a diverse range of vegetation typical of Southern Europe. Common plants in this area include Grass, Olive, and Cypress, as well as weeds like _Parietaria_ (Wall-pellitory), _Plantago_ (Plantain), _Chenopodium_ (Goosefoot), _Salsola kali_, _Artemisia_ (Mugwort), and _Ambrosia_ (Ragweed).  
The aerobiological sampling of pollen concentrations in Mediterranean cities has identified three distinct \"pollen seasons\" [66]:  
1. Mild winter season (December-March) with Cypress as the prevailing pollen.
2. Spring-Summer season (March-July) with Grass, Olive, and weeds as prevailing pollen.
3. Autumn period with Parietaria and Artemisia.  
An interesting review on the topic highlights numerous associations between aeroallergens and foods of plant origin within this geographical area, many of them implicated in PFAS. The region is particularly known for Olive tree growing, and sensitization to the Olive profilin Ole e2 has been linked to PFAS to peach (_Prunus persica_), pear (_Pyrus communis_), melon (_Cucumis melo_), and kiwi (_Actinidia delicosa_) [67]. The profilin Cyn d 12 from _Cynodon dactylon_ (Bermuda grass) is cross-reactive with profilins from tomato (Sola l1) and cantaloupe (Cuc m2) [68]. On the other hand, the profilin of Mugwort (Art v4) is associated with PFAS involving _Apiaceae_ foods such as celery (Api g4), carrot (Dau c4), and various spices, a cross-reaction often referred to as Celery-Mugwort-Spice syndrome [67; 69].  
Fruits of the _Cucurbitaceae_ family--including watermelon, cantaloupe, honeydew melon, zucchini, and cucumber--are widely consumed during summer in South Europe. PFAS to _Cucurbitaceae_ as well as to _Musacae_ family (e.g., bananas), is commonly observed in individuals sensitized to _Ambrosia artemisinin_, a phenomenon known as the Ragweed-melon-banana association [67; 69; 70]. PFAS to melon, banana, and peach has also been linked to respiratory sensitization to the profilin of Chenopodium album, while sensitization to Plantago lancoleata pollen has also been associated with PFAS to melon [67; 69].  
Finally, pistachio (_Pistacia vera_), a popular tree nut in Italy, Greece, Turkiye, and Cyprus has been implicated in PFAS due to cross-reactivity to _Parietaria_ sensitization [71].
###### Abstract  
With the development of pervasive sensing and machine learning technologies, automated epileptic seizure detection based on electroencephalogram (EEG) signals has provided tremendous support for the lives of epileptic patients. Discrete wavelet transform (DWT) is an effective method for time-frequency analysis of EEG and has been used for seizure detection in daily healthcare monitoring systems. However, the shift variance, the lack of directionality and the substantial aliasing, limit the effects of DWT in some applications. Dual-tree discrete wavelet transform (DTDWT) can overcome those drawbacks but may increase information redundancy. For classification tasks with small dataset sizes, such redundancy can greatly reduce learning efficiency and model performance. In this work, we proposed a novel redundancy removed DTDWT (RR-DTDWT) framework for automated seizure detection. Energy and modified multi-scale entropy (MMSE) features in a dual tree wavelet domain were extracted to construct a complete picture of mental states. To the best of our knowledge, this is the first study to employ MMSE as an indicator of epileptic seizures. Moreover, a compact EEG representation can be obtained after removing useless information redundancy (redundancy between wavelet trees, adjacent channels and entropy scales) by a general auto-weighted feature selection framework via global redundancy minimization (AGRM). Through validation on Bonn and CHB-MIT databases, the proposed RR-DTDWT method can achieve better performance than previous studies.  
EEG monitoring; DWT; DTDWT; automated seizure detection; machine learning +
Footnote †: journal: Applied Sciences
## 1 Introduction
To the best of our knowledge, we have not come across any work that has analyzed the aggregation delay for data gathering trees as a function of the structure of the data gathering tree itself. We hypothesize that there could be two distinct types of data gathering trees depending on the way they are constructed (bottleneck node-weight based and bottleneck link-weight based). Our hypothesis is confirmed through the simulations. We observe the bottleneck node-weight based data gathering trees to incur a smaller diameter and a larger number of child nodes per intermediate node, thereby incurring a larger aggregation delay compared to that of the bottleneck link-weight based data gathering trees that incur a relatively larger diameter and a fewer number of child nodes per intermediate node. We thus see a diameter-aggregation delay tradeoff for data gathering trees in sensor networks and this has been hitherto not reported in the literature. The rest of the paper is organized as follows: Section 2 discusses related work on the lower and upper bounds for aggregation delay in sensor networks and highlights our contributions. Section 3 presents the system model and presents in detail the Maximum Bottleneck Node Weight-based data gathering (MaxBNW-DG) algorithm. The section also presents the modifications to the MaxBNW-DG algorithm to determine the MinBNW-DG, MaxBLW-DG and MinBLW-DG trees, as defined above. Section 4 presents in detail the algorithms to determine the diameter of the DG tree and the minimum aggregation delay for a DG tree. Section 5 presents results of simulation studies conducted on the four categories of DG trees and discusses the results obtained for the diameter, aggregation delay, hop count per path and the distribution of the child nodes per intermediate node and the leaf nodes as well analyzes the tradeoffs. Section 6 concludes the paper and discusses plans for future work.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 4 Building a QKD Emulation System to Test Protocols
### Modifications to Include Two Wavelengths
#### 4.2.2 Software  
The software for the automated emulation will rotate [PERSON] and [PERSON]'s half-wave plates for each wavelength and interpret the resulting trit based on the detector analog read values. To accomplish this, we require a function to rotate each stepper motor (connected to a half-wave plate) accordingly. This includes a function to rotate [PERSON]'s 635 nm (red) laser half-wave plate in the position corresponding to polarizing the beam in 0\({}^{\circ}\), 90\({}^{\circ}\), 45\({}^{\circ}\), or 135\({}^{\circ}\) (a total of four separate functions). The functions are duplicated for [PERSON]'s 520 nm (green) laser half-wave plate (a total of four more functions). We also have a function to rotate each of [PERSON]'s stepper motors (connected to a half-wave plate) in the position corresponding to the rectilinear or diagonal basis choice, for a total of four more functions (2 for 635 nm and 2 for 520 nm). Prior to starting the protocol, we perform a calibration stage where we run through each scenario of [PERSON] and [PERSON]'s basis choices ten times and store an average expected detector analog read value for each detector. This helps reduce errors because the photodetectors are extremely sensitive to light and equipment alignment. By storing the expected analog read values in recent lighting and alignment conditions, we know what value to expect for each basis scenario. When interpreting the photodetector value, we calculate the difference between the actual analog read value and each of the stored analog read values from the calibration stage for both detectors. We add the two differences for each detector to get a total difference and find which stored analog read value scenario results in the minimum total difference. We interpret the bit according to which scenario is closest to our current reading values. All of these functions allow us to emulate the protocols for error rate testing purposes.  
Figure 6: Two-wavelength emulation system showing [PERSON], [PERSON], and [PERSON] prior to automating the kit.  
Figure 7: Automating polarization rotation design.