esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Line Camera-Based Calibration for Optical See-Through Head-Mounted Displays
## 5 Experiments and Results  
A dedicated software application was developed in MATLAB to validate the accuracy of the calibration technique. In the application, we generated a virtual scene whose virtual viewpoint (i.e., rendering camera) was controlled according to the extrinsic and the intrinsic parameters of the eye-display model determined from the calibration. In this way, we created an AR OST visualization, whose overlay accuracy could be considered as an objective evaluation metric for the calibration procedure. We therefore elaborated the image frames of the viewpoint camera and measured the overlay error, or reprojection error, between real and virtual features.  
Figure 7: Conceptual schematic of the optical see-through display calibration procedure.  
As ground truth real features, we considered the corners of the validation checkerboard, whereas as virtual features, we used virtual circles. The overlay error was computed as the Euclidean distance (\(o_{\text{2D}}\)) between the image coordinates of the real and virtual features (Figure 8).  
We exploited the hybrid nature of the HMD, which can work both under OST and the video see-through modality [28], to measure the overlay error as follows:  
* The image coordinates of the checkerboard corners were extracted by processing a frame of the viewpoint camera acquired with both the display and the optical shutter turned off. In order to do this, we used the MATLAB corner detection algorithm (Figure 9b).
* The image coordinates of the virtual landmarks were extracted by processing the viewpoint camera image without the real-world background, hence with both the display and the optical shutter turned on. The coordinates of the centroids of the virtual circles were retrieved using a semi-automatic centroid detection algorithm (Figure 9c).
# Nanomechanical, Structural and Antioxidant Characterization of Nixtamalized Popcorn Pericarp
## 1 Introduction  
Popcorn grain is composed by endosperm (83%), germ (11%), pedicel (1%), and pericarp (3.4 to 9.5%). The pericarp contains hemicellulose (67%), cellulose (23%), starch (7%), proteins (1.4%), lipids (1%), lignin (0.1%), and sugars (0.5%) [1]. In addition, it contains phenolic acids such as ferulic acid. It is recognized that the pericarp of popcorn grain is strongly linked to the expansion process, the nutritional quality, and the sensory properties of popcorn kernels [2]; and the antioxidant activity varies significantly with the color of the pericarp and the expansion process [2, 3]. Pericarp is a semipermeable barrier covering the endosperm and germ [4] that influences the drying rates [5] and diffusion of calcium and water into the inner kernel during nixtamalization [1].  
The wide versatility of the nixtamalization process for detaching the pericarp allows for increasing the soluble fiber, decreasing the lipid content and promoting higher proteincontent in the derived product, such as tortillas. In addition, the nixtamalization increases the solubility and availability of amino acids with major nutritional value, such as glutenin and tryptophan, and the calcium is accumulated in the pericarp [6]. Therefore, the use of nixtamalization could modify the nutrimental quality of the pericarp; however, the information about it is scarce.  
Regarding the studies on popcorn pericarp, technological development could improve the knowledge of its properties with the use of several analytical tools such as sensorial and physicochemical analyses, microscopy, spectroscopy techniques, and antioxidant assays [2; 3; 7; 8; 9]. The published studies are about the effect of the expansion process on its biochemical composition [10; 11] and the effect of pericarp thickness on the expansion process [12] and genetic traits [13], but the studies about the nanomechanical and structural properties, which explains, in part, the macro-scale material changes and properties, are scarce.  
Regarding the nutrimental composition and technological functions of the pericarp, this work aimed to perform a physicochemical, protein structure, antioxidant activity, and nanomechanical characterization of nixtamalized popcorn using microscopy techniques, image analysis, nanoindentation, antioxidant assays, and FT-IR. Consequently, this work provides valuable and unpublished information on nixtamalized popcorn pericarp regarding its cellular architecture, physicochemical, nanomechanical, antioxidant power, and structural properties. Based on the information obtained, it is proposed that this pericarp may be an alternative source of antioxidants and others compounds to apply in different industries and could provide guidelines for the characterization of another type of corn pericarp.
## 7 Performance Evaluation of NDA-EVM-AM on an NLOS Scenario  
Evaluating the performance of the adaptive modulation over a \(\eta-\mu\) fading channel can provide a useful reference for the transmit design of WSN. The accuracy of evaluating the quality of a fading channel, the effectiveness of an adaptation strategy and the average spectral efficiency are the three most important performance indexes in an adaptive modulation system of WSN. To justify NDA-EVM-AM in an NLOS scenario, we employed the Monte Carlo approach to simulate MQAM signals transmitted over \(\eta-\mu\) channels. We simulate the adaptive modulation with MQAM signals in physical uplink shared channel (PUSCH) specified in the 3 GPP LTE-Hi standard. The simulation parameters are set as shown in Table 2.
## 3 Industrial Consumption, BESS, and Grid Modelling  
The mathematical and simulation models used in Section 4 for performing a case example of the aforementioned methodology are described in this section. Three main models are considered: an industrial consumption, a BESS, and a power grid. The BESS is connected to the same point as the industrial consumption, the point of common coupling (PCC), and both of them are connected to the grid, as it can be seen in the general model diagram in Figure 2. The complete model outputs several electrical-variables time series (P, Q, V, and I) for the three main components. Additional electrical variables such as frequency or harmonic distortion are also evaluated and calculated by the model, but the authors decided not to output them since they are not relevant for the purpose of this paper.  
The models are developed with MATLAB Simulink, under the Simscape Electrical environment. When developing simulation models, a higher complexity and completeness usually implies a higher accuracy [17; 18]. However, complex models tend to require a substantial computational effort to run them, and a huge amount of information to parametrize them. Moreover, they are commonly valid only for a specific device, making them non-generalizable [19; 20].  
On the other hand, simplified models, usually composed by combination of resistors, inductors, and capacitors, are easily parametrized, they require low computational effort, and are valid for several devices with similar characteristics. Furthermore, even though they are not as accurate as complex models, they can mimic the behaviour of the device with sufficient precision, allowing for the extraction of general conclusions [21; 22].  
Hence, given the objective of this paper, the approach taken by the authors was to develop simplified models, that are generalizable and easily parametrized, which is sufficient for obtaining accurate general conclusions when dimensioning BESS.  
Figure 1: Flow chart of the proposed BESS dimensioning methodology.  
Figure 2: General model diagram.
## 3 Hand Orthosis Development  
Hand orthoses, also known as splints, are wearable devices that are an integral part of rehabilitation and are indicated for diseases and injuries in the traumatological, orthopedic, rheumatological, and neurological fields, as well as for congenital deformities of the upper extremities. They fulfill several roles, such as protecting joints and bones after trauma, reducing pain and inflammation, correcting deformities, and supporting people in activities of daily living (ADL). They can be divided into static and dynamic [10; 11; 12]. Static splints are usually used to stabilize a joint [10; 12; 13]; however, their function extends beyond stabilization. They are also employed to protect the joint during ADL or at night [14], and to correct improper posture or deformities [11; 15]. In contrast, dynamic splints are designed to mobilize one or more joints, while controlling the range of motion, providing stabilization and supporting specific movements [12; 14]. These splints consist of a static base and at least one movable part [16; 17]. These devices can be passively actuated using springs or other elastic elements, or actively actuated, often referred to as exoskeletons, which are powered, for example, by electrical motors.  
The classification of orthoses is not limited to static and dynamic categories [10; 15; 16; 17; 18]. For instance, static progressive splints apply constant torque to a joint for gradual mobilization, which can be adjusted progressively. To ensure splinting is an effective treatment, it must be tailored to the specific condition being addressed [19].  
Hand splints serve various roles in rehabilitation. In arthritis they are used for pain relief [20], while in neurological disorders like stroke or multiple sclerosis, they compensate for muscle weakness [21] and prevent contractures, to reduce the risk of muscle spasticity [15]. Splints are also used to correct joint misalignment in trauma [22] and stabilize post-surgery structures [23]. Dynamic splints promote mobility in stiff joints or after tendon injuries [21].  
During the workshops, it was identified that discussions between technicians and therapists may lead to differing perspectives on splinting. The technical approach to splint fitting typically involves selecting a specific splint model customized for a particular clinical condition. However, it is important to recognize that multiple options for splinting are usually available in a therapeutic setting. A single splint with the same technical and design features can be used for various clinical conditions, while different types of splints can achieve the desired outcome for the same clinical condition. Additionally, combinations of static, dynamic, and redressing splints can greatly benefit the patient.  
As the diverse functions and classifications of hand orthoses have been established, it is important to consider the methods by which these devices are created. The manufacturing process influences the effectiveness and comfort of the orthoses, directly impacting patient outcomes
###### Abstract  
Mealybugs found on grapes during quarantine in Korea are fumigated with methyl bromide (MB). However, MB is an ozone-depleting, highly toxic pesticide; therefore, the International Plant Protection Convention, a representative organization involved in quarantine, recommends using MB alternatives. We evaluated the feasibility of using liquid ethyl formate (EF), a new EF formulation, to control merabulspying (_Pseudococcus constocki_) on grapes (Campbell Early). Large-scale tests and the comparative evaluation of EF and MB desorption from grapes were conducted during the simulated 72-h post-fumation period. Dose-response tests showed that the EF concentration and time product causing 99% mortality (LCt\({}_{99}\)) at 5 \({}^{\circ}\)C was 47.36 and 145.85 g h/m\({}^{3}\) for adults and eggs, respectively. EF treatment at 70 g/m\({}^{3}\) for 4 h at 5 \({}^{\circ}\)C with a loading ratio of 10% achieved an LCt\({}_{99}\) of 145.85 g h/m\({}^{3}\) on _P. constocki_, confirming EF efficacy on mealybugs without phytotoxic effects on grapes. EF fumigation may also be safer because EF concentrations were maintained at less than 100 ppm, the specified exposure limit of EF; meanwhile, those of MB were higher than the exposure limit (1 ppm). Therefore, liquid EF can be used as a safer alternative to MB in phytosanitary treatments of grapes to control _P. constocki_.  
e 27 September 2022  
28 September 2022  
**Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.**  
**Keywords:** ethyl formate; post-fumigation safety; pest control; sorption rate; mealybugs +
Footnote †: journal: Applied Sciences
# Dynamic Storage Optimization for Communication between AI Agents
## 6 Discussion
#### Future Research  
Avenues for future research could include the following:  
1. Performance Optimization: Further investigation into optimization methodologies could enhance the efficacy of AI agent communication in decentralized systems. This may include investigating ways to improve scalability, reduce resource bottlenecks, and accelerate data transfer rates among agents.
2. Security and Privacy Considerations: As decentralized systems raise security and privacy concerns, future research could focus on developing robust protocols that safeguard confidential data during communication among AI agents. This could involve looking at privacy-preserving protocols, access control systems, and encryption techniques.
3. Integration with Emerging Technologies: Given the rapid rate at which new technologies are developing, it would be advantageous to look into how the proposed communication model can integrate with technologies such as edge computing, the Internet of Things (IoT), or federated learning. This could enable AI bots to collaborate and communicate effectively under difficult conditions.
4. Real-World Deployment and Case Studies: Case studies and real-world implementations can be conducted to obtain useful information on the suitability and effectiveness of the proposed communication model. This could involve applying the model in particular industries or enterprises and evaluating how well it functions, how user-friendly it is, and how it influences choices.
5. Standardization and Interoperability: To facilitate widespread adoption and smooth communication among AI bots from different platforms or organizations, future research could also focus on standardizing communication protocols and promoting interoperability. This may involve developing common ontologies, semantic map pings, and communication protocols in order to facilitate smooth integration and cooperation among AI agents.  
By pursuing these research avenues, we can improve AI agents' capabilities in decentralized environments, encouraging their use across a range of industries and advancing the field of AI communication.  
Software, N.N.; Writing--original draft, A.T.; Supervision, H.K.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
[PERSON] is employee of Openfabric Network SRL company. The authors declare no conflicts of interest.
## 4 Application Analysis and Results  
### Application Background Description  
The SAA model for the general-configuration design was applied to aircraft manufacturing. We assumed that a batch of 50 aircraft were put into production. We considered nearly 20 option groups, with a major dependency between their options and their respective impact on the workload of the workstations on the assembly line. The option groups included the decoration of aircraft (e.g., the type of the cockpit window frame decorative cover) and airborne devices (e.g., the music playback system and emergency equipment).  
It was assumed that there were contracts promising delivery times for 20% of orders at the planned time. These orders were indicated as certain orders, a specially designed aircraft version for a specific customer.  
The options for the remaining 80% of orders were considered uncertain. Each scenario had 50 customer orders, of which 20% were certain orders and 80% were uncertain orders.  
There were seven workstations on the aircraft assembly line, each of which was responsible for different assembly tasks. Considering the balance of workstation assembly, the proportion of assembly time reserved for optional configurations within the production cycle varied accordingly. Based on the current production schedule, the proportion of the reserved time for each workstation is shown in Table 3:  
It is worth mentioning that a dedicated workstation is set up for reworking and modifying the produced aircraft. The assembly time in the rework workstation was considered as the reworking time in our optimization model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SART** & \\
1. Initialize historical data set \(O^{H}\), sampling times \(N\), number of orders \(K\), converge gap \(Gap\), \\ iteration time \(t=0\), initial objective \(M^{0}=0\). \\
2. **While \(\delta<Gap\) Do** \\
3. Set \(t=t+1\), \(s=0\). \\
4. **For \(s<N\) Do** \\
5. Generate scenario \(s\), set \(o^{s}=\emptyset\), \(k=0\) \\
6. **For \(k<K\) Do** \\
7. Generate order \(k\) using Monte Carlo sampling in \(O^{H}\) and add it to \(o^{s}\). Set \(k=k+1\). \\
8. **End for** \\
9. Add the scenario \(s\) with the orders \(o^{s}\) to the objective formula (10). Set \(s=s+1\). \\
10. **End For** \\
11. Solve Problem with the updated objective formula (10) and obtain the optimal objective \(M^{t}\) \\
12. Set \(Gap=M^{t}-M^{0}\), and set \(M^{0}=M^{t}\). \\
13. **End while** \\
14. **Return \(M^{0}\)** \\
**END** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reserved time for optional configurations in different workstations.
## 2 UAV Network Communication Model and Architecture
### Decentralized Communication Architecture
#### 2.2.2 Multi-Group Swarm Ad Hoc Network  
To address the limitations of the \"single-group ad hoc swarm network\", the \"multi-group ad hoc swarm network\" (Figure 6) integrates both the centralized architecture and the \"single-group ad hoc swarm network\" architecture. Different types of groups are applied differently depending on the task. In general, the architecture is organized in a centralized manner to control and monitor the groups. In each group, UAVs communicate with each other in an ad hoc manner. The communications between groups (GTG) are performed through the infrastructure. This is still the responsibility of the gateway UAVs. When the mission scenarios require multiple types of UAVs, the \"multi-group ad hoc swarm network\" architecture can be used. However, the robustness of this architecture needs more attention to since the \"multi-group ad hoc swarm network\" architecture is semi-centralized. At the same time, the GTG communications between two UAVs in different groups still encounter high latency. A specific application of multi-group architecture is the joint multi-target operation on the battlefield in the military. The Norwegian military UAV operation [33] clearly describes the roles and responsibilities of each component in a military operation using UAVs. This includes standards for communication between the  
Figure 5: Communication architecture in the swarm: (**a**) ring, (**b**) star, (**c**) mesh.  
control station and the UAV groups, and the roles and responsibilities in the information exchange in military operations.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 2 Harmonic Detection Principles and Methods
### Discrete Fourier Transform  
Literature [14] introduced the detection method based on the Discrete Fourier transform. This method has been extensively used in literature for detection due to its convenience and numerous functions. The advantage of the technique is that a number of harmonic eliminations can be selected, and the environment has less influence on the method. However, one of the disadvantages is that the number of calculations is large, the selection of the current value takes considerable time, and the real-time performance of the calculation result is accurate up to a limit [15]. In the sampling process, when the signal frequency and the sampling frequency are inconsistent, the calculated signal parameters will have errors, and the phase will have a significant deviation. This Fourier transform method can be improved using an interpolation and windowing algorithm, a bimodal spectral line correction algorithm, and a sampling frequency synchronization method [14].
## 2 System Model
### Dataset Construction  
The steps involved in the construction of the dataset for training the neural architecture are mentioned in this subsection, as follows:Selection of clean speech signals corresponding to three speakers from publicly available speech databases such as LibriSpeech and WSJ0 mix. This is also mentioned in Section 4 on experimental results.
2. Generation of acoustic room impulse responses (RIRs) using the image source method (ISM).
3. Selection of reverberation times--RT60 of 550 ms, 250 ms and 50 ms--in three rooms of different dimensions.
4. Generation of noise samples at different acoustic SNRs.
5. Creation of reverberant, noisy mixture of speech signals by convolution of clean speech signals with generated RIRs, with the addition of reverberation and noise samples.
6. Estimation of position coordinates and label of each source using MS-GLMB filtering algorithm.
7. Enhanced speech signal corresponding to target source across different time frames using GSC-MVDR beamforming.
8. Construction of T-F mask from the output of beamformer as explained in Section 2.4 A.
9. Creation of training dataset (to train the neural architecture) comprising the reverberant speech mixtures as input data and T-F masks as corresponding labels. In addition, the dataset is split into training, validation and testing datasets. This is further elaborated in quantitative terms in Section 4, where the experimental setup is described.