esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reflective Augmented Reality Integral Imaging 3D Display by Using a Mirror-Based Pinhole Array
## 1 Introduction  
With the development of display technologies, electronic display devices have to meet people's higher requirements. Augmented reality (AR) displays, which allow the overlaying of digital information onto a direct view of the real world so that the two-dimensional (2D) or three-dimensional (3D) images are seamlessly combined with the real-world scene, have been widely of interest to researchers and the industry [1; 2; 3; 4]. There has been great desire for AR in many applications, such as medical visualization, surgical training, flight simulation, and entertainment [5; 6; 7; 8]. Basically, there are two types of AR displays, which are optical see-through AR displays and video see-through AR displays [9; 10; 11; 12; 13]. Optical see-through AR displays, compared to video see-through AR displays [14; 15], can provide a non-obstructed visualization of the real environment that guarantees that the visual and proprioception information will be synchronized and maintains see-through vision to the real world, and therefore has attracted much research interest. Additionally, integral imaging (II), a 3D imaging technique which provides auto-stereoscopic images without the help of special glasses [16; 17; 18], is very suitable for displaying 3D images in the optical see-through AR display system. Moreover, the structure of II is very compact, which makes it suitable for the AR system to display 3D images.  
Some works have been proposed to achieve AR display with 3D images, a method of using free-form optics to magnify the 3D images reconstructed by a micro-II display unit and to overlap the 3D images with the real world, was reported by [PERSON] [19]; another useful way to transfer 3D images into the viewer's eyes through total internal reflection in an optical waveguide was also proposed, and achieved a good transmittance of the real-world lights [20]. Researchers have also proposed to realize an AR display by folding virtual 3D images into space with a semi-transparent mirror [21]. However, AR systems using these methods are more suitable for near-eye displays because of the size limitation, known as one of the common shortcomings, of the display screen or view box. To achieve amore compact structure for an AR system, the use of a lens array holographic optical element (HOE) has been proposed [22; 23], but the size of the lens array HOE only reached several centimeters [24; 25].  
In this paper we propose, for the first time, a reflective AR system using a mirror-based pinhole array (MBPA). The real-world scene is reflected by the mirror area of the MBPA, and the 3D images are reconstructed by a pinhole array-based II unit. Due to its compact form factor, the proposed AR display system has the advantages of lower cost, no distortions, and more importantly, ease of manufacturing large-sized display screens.
## 3 Results and Discussion
### Cell Uptake  
One important aspect of a PDT study is to determine when to expose the cells to light following their treatment with the sensitizer. This is called the \"optimized time\" and occurs when dye uptake is maximized. Reaching the maximum uptake into cells is key to reaching the highest phototoxicity.  
Cell uptake of the encapsulated nanoparticles of ZnPcBCH\({}_{3}\) was studied at different dye concentrations and incubation times. Figure 8 shows that increasing incubation time from 4 h to 15 h increased the amount of dye uptake. Also, a higher concentration of encapsulated dye resulted in higher cell uptake.
## 2 Materials and Methods
### Data
#### 2.2.2 Climate Data  
Climate data from 2015 to 2017 were retrieved from the DAYMET database, which provides gridded weather parameters for North America at a spatial resolution of 1 km [68]. The data used include daily precipitation (P), maximum air temperature (T), and water vapor pressure. Vapor pressure deficit (VPD) was further calculated using VP and T [72]. P, T, and VPD were selected according to the previous study in a similar study region [46]. The monthly means of T, P, and VPD were calculated from corresponding daily values for further analysis.
# Chemical Characterization and Volatile Profile of Trebbiano di Lugana Wine: A Case Study
## 3 Results
### Aroma Profile
#### 3.2.3 Aldehydes  
The two aldehydes detected were furfural and benzaldehyde. The presence of furan compounds in wines leads to sensory changes, including the color and aroma of wines [42]. 2-furfural, 5-methyl-2-furfural and 5-hydroxymethyl-2-furfural derive from sugars and they have been strongly correlated with the presence of sotolon in aged wines [43]. In addition, the above-mentioned furan compounds are considered aging markers of oxidized wines, such as the Madeira ones [43; 44]. In wines produced under the oxidative winemaking process, increased concentrations of these aromas have been reported following five year aging in barriques [43; 45]. Furfural is also present in passito wines, such as Caluso passito DOC; the origin of furan compounds in passito wine has been associated with the degradation of residual sugars occurring during long aging times [46]. Furthermore, furfural was also detected in Champagne wine after two years of aging in a bottle [47] and in young wines produced with Cabernet Sauvignon and Chardonnay grapes [48]. Furfural was detected in the wine stored longer than three years; however, its influence on the sensory profile of white wine might be negligible due to its high perception threshold (14 mg/L) [35]. This furan compound showed a constant increase during storage, reaching the highest levels in wine with the longest storage (13 years) (39.04 \(\pm\) 6.17 ug/L), therefore potentially more oxidized wines. A further evidence that this compound can be considered a marker of storage was obtained by its positive correlation with storage time (0.87) (Table S2). Benzaldehyde is characterized by a lower perception threshold in comparison to furfural (5 ug/L) and it can confer notes of sugar and almond [42]. This compound was detected only in the youngest wines in concentrations of 45.08 \(\pm\) 1.53 ug/L and 49.65 \(\pm\) 4.55 ug/L, respectively, for wine produced in 2017 and 2016.
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 4 Proposed Multi-Frequency Universal Code Generator
Since the ROM use is reduced by 1 depth per 1 bit width for the bit widths applied by the hardwiring technique, a large amount of ROM use can be reduced in cases where satellite numbers are received as inputs. Therefore, in order to check the reduction rate of unnecessary hardware resources, the normalized multiplexer use, normalized XOR logic use, and normalized ROM use for each of the GNSS shown in Figures 4 and 5 are shown in Table 6. Figure 5a shows the proposed multi-frequency universal code generator for GPS signals, indicating that the use of multiplexers, XOR logics, and ROMs decreased by 43%, 39%, and 4%, respectively, compared to Figure 4a. Figure 5b shows the proposed multi-frequency universal code generator for BDS signals, indicating that the use of multiplexers, XOR logics, and ROMs decreased by 29%, 29%, and 5%, respectively, compared to Figure 4b. Figure 5c shows the proposed multi-frequency universal code generator for GLONASS signals, indicating that the use of multiplexers, XOR logics, and ROMs decreased by 54%, 59%, and 5%, respectively, compared to Figure 4c. Figure 5d shows the proposed multi-frequency universal code generator for Galileo signals, indicating that the use of multiplexers, XOR logics, and ROMs decreased by 73%, 69%, and 7%, respectively, compared to Figure 4d. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Codes**} & **Feedback** & **Feedback** & **Output ROM1** & **Output ROM2** & **Initial ROM1** & **Initial ROM2** \\  & **ROM1** & **ROM2** & **(10 bit)** & **(10 bit)** & **(10 bit)** & **(14 bit)** & **(10 bit)** \\ \hline L1 OF & \(-\) & 0100010000 & \(-\) & 0001000000 & \(-\) & 011111111 \\ L2 OFF & \(-\) & 01000100000 & \(-\) & 0001000000 & \(-\) & 0111111111 \\ L10 Cd & 0000100100000 & 11010001000 & 00001000000000 & 10000000000 & 00000000100100 & xxxxxx00000 \\ L10 Cp & 0011001010000 & 0000100001 & 00100000000000 & 0001000000 & 0010100011000 & 00000 xxxxx \\ L20 CP & 11000010001000 & 0001100000 & 1000000000000 & 000110000000 & 0001110010100 & 0000 xxxxx1 \\ L30 Cd & 1100001000100 & 0001100000 & 1000000000000 & 00010000000 & 0001110010100 & 000 xxxxx00 \\ L30 Cp & 11000010001000 & 0001100000 & 1000000000000 & 00010000000 & 0001110010100 & 000 xxxxx00 \\ \hline Summary & xxxx00x0x0x00x00 & xx0 xxx0x & x00x000000000 & x00 xxxx000 & 000 xxx0000 & xxxxx00000 & xxxxx00000 \\ \hline \end{tabular}
\end{table} 
Table 4: The fixed parameters of code generators for GLONASS signals. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Codes**} & **Feedback** & **Feedback** & **Output ROM1** & **Output ROM2** & **Initial ROM1** & **Initial ROM2** \\  & **ROM1** & **ROM2** & **(13 bit)** & **(14 bit)** & **(13 bit)** & **(13 bit)** & **(14 bit)** \\ \hline E5a-I & 100000100001 & 1010001101100 & 10000000000000 & 100000000000 & 111111111111 & x Figure 5: Proposed multi-frequency universal code generator architecture of each GNSS signal. (**a**) GPS. (**b**) BDS. (**c**) GLONASS. (**d**) Galileo.
# Tire Bubble Defect Detection Using Incremental Learning
## 2 Proposed Method
### The Incremental YOLO Architecture  
Previous methods merge new data into the training datasets before retrain to reduce missed detections due to incorrect identification of defective images by the original model. Although this method improves the detection rate of the model, it is a time-consuming process. We propose an incremental YOLO approach with the concept of transfer learning. Since we can only train the object detection model on images with objects, we have manually relabeled and added the misidentified images to the incremental YOLO training dataset. For incremental training, we retained the shallow features in the original model while training the deeper features of the network layer. As a result, the bubble defects missed during inspection accumulate and are used to rapidly update the model to improve the detection rate.  
We froze the Backbone section for incremental training and only trained the Neck and Prediction network layers to improve the detection rate. Since the backbone network mainly extracts low-level features such as edges, colors, and textures of images. The neck layer is mainly to process and enhance the low-level features extracted by the backbone, so that the model can learn features suitable for tire bubbles. To reduce the computational load of feature extraction and efficiently learn the image features of the field, we froze the backbone layer and retrained the neck layer in incremental learning to strengthen the bubble features of the field. As shown in Figure 7, the blue box indicates the frozen Backbone section, while the red box indicates the sections where the incremental YOLO architecture performs parameter adjustments.  
Figure 7: Steps of the incremental learning method.
## 3 Problem Description and Model Formulation
### Problem Description  
This study focuses on perishable fresh products, such as fish and meat. Figure 1 illustrates the inventory status at time \(t\). Using inventory age as an indicator to represent the number of days elapsed since stocking, \(i_{j,t}\) denotes the inventory quantity with age \(j\) at time \(t\). As shown in this figure, it is assumed that perishable products \(i\) with different shelf lives are displayed on the shelf simultaneously. \(i_{m-1}\) represents a perishable product with an inventory age of \(m-1\) days. Thus, perishable products with an inventory age of \(m\) or more are discarded, while other products with expiration periods of \(m-1,m-2,\ldots,1\) can be sold in the next period.  
This study considers a periodic reviewed, single-item, single-echelon inventory system with a positive lead time, perishable products, dynamic pricing, and uncertain and age-dependent demand. Order quantity and pricing for fresh items with each shelf life are determined based on the number of fresh items at each shelf life at the start of the period.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Sets and Indexes** \\ \(M\) & Set of inventory age, \(M=\{1,2,\ldots,m\}\) \\ \(P\) & Set of discount settings \\ \(i,j\) & Index for inventory, \(i,j\in M,\ i\
eq j\) \\ \(t\) & Index for time \\ \hline
**Parameters** \\ \(l\) & Lead time \\ \(m\) & Shelf life \\ \(q^{max}\) & Maximum order quantity \\ \(i_{j,t}\) & Inventory at inventory age \(j\) at time t \\ \(d_{t}\) & Stochastic demand \\ \(g_{t}\) & Gross profit at time \(t\) \\ \(c^{h}\) & Per-unit holding cost \\ \(c^{o}\) & Per-unit order cost \\ \(c^{l}\) & Per-unit lost sales penalty \\ \(c^{p}\) & Per-unit perishable cost \\ \hline
**Variables** \\ \(q_{t}\) & Order quantity at time \(t\), where \(q_{t}\in\{0,1,\ldots,q^{max}\}\) \\ \(p_{j,t}\) & Discount setting at inventory \(i_{j,t}\), where \(p_{j,t}\in P\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Notation of inventory management problem.  
Figure 1: A sales system for fresh products with different shelf lives.  
The goal is to obtain an optimal policy for ordering and pricing that maximizes total profit across potential inventory states.
## 3 Results  
### Chemical Composition of the Essential Oil  
The yield of the essential oil obtained from the aerial parts of \(D\). _reboudii_ was 0.9% (w/w). The chemical composition obtained by GC-MS is summarized in Table 1. Characteristic MS spectra of the identified compounds are reported in the Supplementary Materials. Twenty-eight compounds were identified, representing 96.6% of the whole oil. A high content of oxygenated monoterenes (82.1%) was observed, mainly because of the high percentage of (E)-anethol (59.4%) and estragol (21.2%). The second most representative chemical class of constituents was that of non-terpene derivatives (6.6%), mostly characterized by dodecanal (4.4%) and tetradecanal (0.7%), followed by monoterene hydrocarbons (4.9%), largely represented by limonene (3.6%), myrcene (0.6%) and \(\alpha\)-pinene (0.3%). Sesquiterpene hydrocarbons (1.7%) were another important class of volatiles in \(D\). _reboudii_ essential oil, represented mainly by \(\beta\)-caryophyllene (0.4%) and germacrene D (0.4%). Oxygenated sesquiterpenes were the less represented (1.3%). In previous studies, the chemical composition of essential oils from different _Daucus_ species has been largely investigated. These studies revealed the predominance of monoterpenes and/or sesquiterpenes, and sometimes phenylpropanoids, as major fractions. Indeed, the essential oils of _D. gingidium_ ssp. _gingidium_ and _D. carota_ ssp. _carota_ (from Italy, Lithuania and Poland) were dominated by the monoterpenes \(\alpha\)-pinene and sabinene [5,17,18], whereas the main constituents of the oil of _D. carota_ (from China) and _D. carota_ var. _sativa_ (from Egypt) were the sesquiterpenes carotol and daucol [19,20]. In the case of _D. sahariensis_ and _D. carota_ ssp. _hispanicus_, the major compound was the phenylpropanoid myristicin [21,22]. Moreover, in the oil of _D. carota_ ssp. _maximus_, trans-methyl isoeugenol and \(\beta\)-bisabolene were identified as the main volatiles [8]. Nevertheless, in the aerial parts of _D. reboudii_ growing in El Tarf, Algeria, [PERSON] et al. [7] found the monoterpenes \(\alpha\)-pinene (39.7%) and sabinene (21.2%) as the main constituents. Our results showed that the chemical composition of the _D. reboudii_ essential oil growing in Bejaia, Algeria, was significantly different, mainly due to the presence of the oxygenated monoterpenes (E)-anethol and estragole. This result suggests that the _Daucus_ species considered in this work could represent a new chemotype of _D. reboudii_, although further genetic and epigenetic investigations will be required.
## 3 The Modeling and Simulation Architecture
### Component Infrastructure in the Cloud Platform  
Some of the benefits of using the cloud platform are flexible system capacity, scalability adjustment, and elasticity. Cloud providers such as Amazon Web Services and Google Cloud Platform offer the use of virtual machines where we can deploy the components of the architecture. They also provide Kubernetes as a portable, extensible, open-source platform for managing containerized workloads and services [22]. Thus, we use images of containerized system components by using Docker technology to deploy our architecture [23].  
The main component that makes possible the union of the WSAN simulation environment and the cloud components is an MQTT Broker. The MQTT protocol is useful due to its low bandwidth requirement, is easy to implement, and can be used for some types of environments where devices with limited resources are used, such as in a WSAN [24]. In this paper, we used the Erlang MQTT Broker (EMQ/eMQTT). eMQTT is a distributed,  
Figure 1: Modeling and simulation architecture for IoT applications in a smart city.  
massively scalable, highly extensible MQTT message broker written in Erlang/OTP [25]. In Docker Hub, there are many images about an eMQTT [26].  
Furthermore, we used Apache Kafka to facilitate the construction of fast data ingestion architectures, making it possible to build near-real-time web applications through the management of streaming data pipelines [27].  
Moreover, we used MongoDB as a system data repository. MongoDB is a distributed, document-based database that offers more flexibility and high performance than relational databases [28].  
Finally, IoT software services are containerized and deployed as microservices. The microservices are designed using an asynchronous, nonblocking, message-driven approach to improve the user experience [29].
## 3 Quantum Modelling of FMO Quantum Excitation
### LHCs As Open Quantum Systems
#### 3.3.2 Redfield Approach to FMO Complexes Modelling  
The Redfield master equation:  
\[\dot{\rho}_{S}=-\frac{i}{\hbar}[H_{S},\rho_{S}]-\frac{1}{\hbar^{2}}\sum_{ \alpha}[\Sigma_{\alpha},\Lambda_{\alpha}\rho_{S}-\rho_{S}\Lambda_{\alpha}^{ \dagger}] \tag{23}\]  
with \(\Sigma_{i}\) and \(\Lambda_{i}\) comprising the physical information (see Appendix B) of coupling between the system and the environment. This master equation has been used successfully to model physically the quantum FMO dynamics [36; 43] and adaptations of it has been used to analyze and to propose notable quantum processing applications based on FMO complexes [10]. A concrete implementation to such a system is given explicitly by [48] in the form of the Bloch-Redfield equation:  
\[\dot{\rho} = -\frac{i}{\hbar}\left[H_{S},\rho\right]-\frac{1}{\hbar^{2}}\sum_{j,k }\left(S_{j}Vq_{jk}V^{\dagger}\rho-Vq_{jk}V^{\dagger}\rho S_{j}+\rho V\dot{q}_{ jk}V^{\dagger}S_{j}-S_{j}\rho V\dot{q}_{jk}V^{\dagger}\right) \tag{24}\] \[\text{with:} C_{jk}(\omega)=\int_{-\infty}^{\infty}e^{i\omega\tau}\langle B_{j }(\tau)B_{k}(0)\rangle d\tau\] (25) \[\langle a_{n}|q_{jk}|a_{m}\rangle=\frac{1}{2}\langle a_{n}|V^{ \dagger}S_{k}V|a_{m}\rangle C_{jk}(\omega_{m}-\omega_{n})\] (26) \[\langle a_{n}|\dot{q}_{jk}|a_{m}\rangle=\frac{1}{2}\langle a_{n}| V^{\dagger}S_{k}V|a_{m}\rangle C_{kj}(\omega_{n}-\omega_{m}) \tag{27}\]  
where \(V^{\dagger}H_{S}V=\text{diag}(\{e_{i}\})\) and \(|a_{n}\rangle\) can be any basis of states of the quantum system. If particularly the eigenstates of the system Hamiltonian \(H_{\text{S}}\) are chosen as basis \(|a_{n}\rangle=|e_{n}\rangle\), then the matrices \(V\) become a unitary operator. There, \(C_{jk}(\omega)\) is the spectral function of the environment defining the noise spectrum as well as the strength of spatial correlations \(q_{jk},\dot{q}_{jk}\) between sites \(j\) and \(k\). The system operators \(S_{j}\) define which part of the system couples to the noise environment.
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 2 Theoretical Background  
When an electromagnetic wave travelling in free space impinges on an obstacle, only part of the energy of the incident wave is transmitted across that obstacle. Another part of the energy is reflected back while a further part may be dissipated within the obstacle itself. In any case, the transmitted energy is a fraction of the incident energy; thus, the wave has suffered attenuation. This attenuation depends on the size and the shape of the obstacle, but also on its electromagnetic properties; in addition, it may be computed using [PERSON]'s equations.
## 3 Results and Discussion
### Cluster Analysis  
A multidimensional statistical analysis (cluster analysis) was carried out to divide groups of stirred yogurts (objects) similar in terms of selected parameters: total acidity, pH, EI, MVI, firmness, stickiness, degree of syneresis, instability index and sedimentation velocity. Three groups (clusters) of products were obtained, the binding distance was: y = 8.3803 (Figure 11). Yogurt with 15% inulin content was assigned to cluster 1, which was characterized by, among others, the lowest acidity (approx. 39 \({}^{\circ}\)SH), highest viscosity (MVI = 7.074 10\({}^{-2}\) nm\({}^{-2}\)), firmness (0.997 N) and the lowest values of syneresis degree (9.5%) and instability index (0.185). Cluster 2 included yogurts with 0%, 3% or 6% inulin addition, showing the highest average acidity (approx. 4.60), the lowest elasticity (approx. 1.380 10\({}^{-2}\) nm\({}^{-2}\)), stickiness value (approx. 1.20 N), and the greatest average degree of syneresis (approx. 38%) and sedimentation velocity (approx. 6.66 \(\upmu\)m/s). Cluster 3 yogurts with 9% or 12% inulin concentration showed average values of determined parameters between cluster 1 and cluster 2.  
Figure 9: Instability indexes and sedimentation velocity of particles values (position in 120–130 mm) of yogurts with 0%, 3%, 6%, 9%, 12% or 15% _w/w_ inulin addition measured using CSA method—LUMiSizer test (a, b, c...—various letters for average values mean statistically significant differences, \(p<0.05\)).  
Variability coefficients of parameters within clusters present a large variation (\(>\)20%) in cluster 3 for medium firmness (39.4%), stickiness (21.6%), and degree of syneresis (24.5%).  
Figure 10: Transmission profiles (“fingerprints”) on the example of yogurts containing 0% or 15% _w/w_ inulin addition—the LUMiSizer test.
## 3 Deep-Learning-Based Channel Estimation Methods
### Model-Driven Deep-Learning-Based Channel Estimation Methods  
The model-driven approach is built on known mathematical models, and its central idea is to combine deep learning with conventional algorithms to improve or expand existing methods. Moreover, it does not depend on extensive labeled data to select the right standard neural network, thus making deep learning more interpretable and predictable. Figure 16 illustrates the components of the model-driven approach. Currently, most research on model-driven channel estimation primarily unfolds on the basis of the LS algorithm and the algorithms in compressive sensing (CS) that include the OMP algorithm and the approximate message passing (AMP) algorithm. This section will discuss model-driven deep-learning-based channel estimation methods based on these three algorithms.