esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Discussion
### Model Results Presentation
#### 4.3.2 Analysis of the Results of the Emotional Design Parameters  
In the process of developing the predictive model, the intuitive, layer-based elements of the sustainable design aspect were the color and appearance of the product and the materials used. The behavioral layer encompasses two elements: screen size and product weight. The reflective layer elements were usage scenarios. In the analysis of emotional design elements, the Mac series product line was employed, with the distinction of usage scenarios (as shown in Figure 7).  
Firstly, from the perspective of usage scenarios, this model analyzes the experimental environment of the same product under different use scenarios. For the Mac product line, it distinguishes between fixed workstation office use and portable office use scenarios, adjusting the design parameters accordingly. According to Figure 7, which shows emotional design parameters and related user satisfaction attributes, it is evident that regardless of whether the device is portable, the product color significantly affects user satisfaction. The appearance of the design is always a key focus for users. In the global experimental environment, it is evident from the comparison between the MBP13 and MBA13 control groups, both with nearly the same screen size, that a reduction in product width and thickness can enhance user satisfaction with the product's handling. Comparing the MBA13, which has the highest satisfaction level, with the MBP16, which has the largest  
Figure 6: Demonstration of the correlation between sustainable design parameters.  
screen size, reveals that the extra-large screen design of the MBP16 significantly increases its width, body thickness, and weight, yet does not decrease user satisfaction with the MBP16's handling. The changes in user satisfaction relative to the product's weight, thickness, and width are relatively minor compared to the impact of screen size design. An increase in product thickness due to a larger screen does not significantly affect user satisfaction with handling. This indicates that, for notebook product design, users prioritize screen size over the weight and thickness of the product. The design parameters related to weight and thickness are not particularly sensitive in terms of affecting user satisfaction.
## 2 Mathematical Model of Multi-Satellite Scheduling
### Objective Function  
In this paper, the optimization goal is to maximize the benefits of observation, the objective function \(Q_{1}\) is as shown in Equation (5).  
\[Q_{1}=\max\ (\ \sum V_{i}x_{i}),\ i\ \in\ I \tag{5}\]  
\(V_{i}\)--The benefit of observing the target \(t_{i}\).  
\(x_{i}\)--Whether the target \(t_{i}\) is selected for observation. If it is, \(x_{i}=1\), otherwise \(x_{i}=0\).
## 3 Results and Discussion
### Comparisons of Deposition Efficiency Predictions Using DPM Approach
#### 3.2.4 Gravity Orientation  
As shown in Figure 11, deposition efficiency, which is a function of generation number and gravity orientation, is investigated with the two-way DPM approach for three orientations: \(x\)-axis [\(-1\), \(0\), \(0\)], \(y\)-axis [\(0\), \(-1\), \(0\)] and \(z\)-axis [\(0\), \(0\), \(-1\)]. Deposition efficiency is defined as the percentage of trapped particles compared to the injected particles to the inlet segment of the model. It is clear that the deposition efficiency increases by increasing the particle size and generation number, regardless of the model orientation. The obtained data demonstrate the significant role of orientation in the amount of deposition and are in agreement with the results of [PERSON] and [PERSON] [18].  
The lowest deposition amplitude is computed as gravity acts on the \(y\)-axis [\(0\), \(-1\), \(0\)] orientation. Such a phenomenon is due to the same direction of gravity and motion of particles. Indeed, gravity increases the particle velocity and Reynolds number of airflow, which reduces the viscosity forces. The other two orientations, \(x\)-axis [\(-1\), \(0\), \(0\)] and \(z\)-axis [\(0\), \(0\), \(-1\)], exhibit approximately similar results because of the radial walls of the alveolar model in this study. The radial walls as shown in Figure 1 are ignored in many previous 1D and 2D studies. However, [PERSON] and [PERSON] [18] show that the radial walls are significant regions of deposition for 0.01-5 \(\upmu\)m particles due to sedimentation. Indeed, the models composed of radial walls modify the efficiency of deposition patterns.
## 2 Mathematical Model of Permanent Magnet Synchronous Motor  
The voltage equation for a permanent magnet synchronous motor in the d-q coordinate system is expressed as follows:  
\[\left\{\begin{array}{l}V_{d}=R_{s}I_{d}+L_{d}\frac{d}{dt}I_{d}-\omega_{e}L_{ q}I_{q}\\ V_{q}=R_{s}I_{q}+L_{q}\frac{d}{dt}I_{q}+\omega_{e}\Big{(}L_{d}I_{d}+\psi_{f} \Big{)}\end{array}\right. \tag{1}\]  
Here, \(\rm V_{d}\) and \(\rm V_{q}\) represent the d-q axis components of the stator voltage, \(\rm I_{d}\) and \(\rm I_{q}\) denote the d-axis and q-axis current components, \(\rm L_{d}\) and \(\rm L_{q}\) represent the d-axis and q-axis inductance components, respectively, while \(\psi_{f}\) represents the permanent magnet flux linkage. For surface-mounted motors, \(\rm L_{d}=\rm L_{q}\). \(\rm\omega_{e}\) represents the angular velocity (rotational speed) of the motor.  
Based on Equation (1), we can derive the equivalent voltage circuit in the synchronously rotating d-q reference frame, as depicted in Figure 1.  
The electromagnetic torque equation for a permanent magnet synchronous motor in the d-q coordinate system is given below [37]:  
\[T_{e}=\frac{3}{2}p\Big{[}\psi_{f}I_{q}+\big{(}L_{d}-L_{q}\big{)}I_{d}I_{q} \Big{]} \tag{2}\]  
This equation describes the electromagnetic torque \(\rm T_{e}\) produced by the motor, where \(\rm P\) represents the number of pole pairs.
# Automated Wildlife Bird Detection from Drone Footage Using Computer Vision Techniques
## 2 Materials and Methods
### Object Detection Models and Configurations
#### 2.3.1 YOLOv4 Architecture  
YOLOv4 is a advanced convolutional neural network model, based on CSPDarknet53 as the backbone Figure 7. Darknet-53 is also known as YOLOv3 [25].  
\begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{2}{c}{**Hyperparameters**} & \multicolumn{1}{c}{**Description**} \\ \hline
**Batches** & 64 & Number of samples processed in one batch \\ \hline
**Subdivisions** & 16 & Number of batches the GPU will process at once \\ \hline
**Max Batches** & 30,000 & Total batches until training finishes \\ \hline
**Learning Rate** & 0.002 & The rate at which the network adjusts the weights \\ \hline
**Steps** & 24,000, 27,000 & The batch milestones when the learning rate is scaled \\ \hline
**Network Size** & 640 \(\times\) 640 & The (WidthxHeight) resolution of the network; the images were scaled down to fit the network’s size \\ \hline \hline \end{tabular}
\end{table}
Table 1: The hyperparameters used during the training session of our model.  
Figure 6: Performance comparison of various object detection models, including YOLOv4 and YOLOv4-tiny.  
YOLOv4 comprises various layers, the input layer, the backbone, the neck, and finally, the classification layer. The input layer is simply where the network is fed images. YOLOv4's backbone layer extracts valuable information out of the image, into a feature map. The backbone layer also applies various data augmentation techniques in order to improve the model's overall mAP. YOLOv4's neck layer is similar to the backbone; it extracts valuable information from the feature map. Finally, the classification layer, also known as the head layer, is decomposed and is similar to the YOLOv3 model. This layer is responsible for detecting any objects within the images and calculating the confidence value of each detection. If any object is detected by the model, it will create several detections for each class. The detections of the same object and the highest confidence value will be displayed by the model in the output.
# Adaptive Learning in AI Agents for the Metaverse:
## 4 ALMA: A Framework for Adaptive AI in the Metaverse
### Implementation Considerations  
#### 4.3.1 Practical Aspects  
Practical issues associated with deploying and operating the Adaptive Learning Model for AI Agents (ALMAA) within the Metaverse need to be resolved. These characteristics affect the model's integration, scalability, and sustainability in a very dynamic and resource-hungry virtual environment.  
(1) Existing Systems Integration.  
The first practical aspect relates to the smooth blending of ALMAA with the existing Metaverse grassroots. This necessitates the design of interoperable APIs that will help AI agents run by ALMAA interact with different components of the Metaverse, such as user interfaces, data management systems, and other AI functionalities. Compatibility across various technological stacks and platforms is the main factor in the popularization of ALMAA.  
(2) Resource Management  
Resource management is crucial, considering that the real-time operation of sophisticated machine learning algorithms is computationally intensive. ALMAA should be optimized to work along with the resources, reducing the demand for server capacities and minimizing latency. The process includes modifying the model to operate on distributed computing systems using cloud technologies and edge computing that distributes the load and improves performance.  
(3) Data Management and Security  
Securing large amounts of data is yet another vital practical issue. To ensure that ALMAA protects user data and complies with international data protection regulations, it must be endowed with strong data encryption and privacy-protecting features. Further, the model should be able to handle large data through data streaming and batch processing techniques, which are used in managing real-time user-generated data.  
(4) Scalability  
ALMAA's ability to scale is important for its use in the growing Metaverse. The model must be capable of scaling smoothly with growing users and the size of virtual environments. This also includes the dynamic reallocation of AI resources and capabilities to ensure that AI agents can maintain personalized and responsive experiences even when the performance is degraded.  
Covering these practical issues allows ALMAA to be successfully and productively deployed in the Metaverse, thus achieving its goal of improving the interaction between AI agents and users in various virtual worlds.
# Integrated Components and Solutions for High-Speed Short-Reach Data Transmission
## 2 Transmitter for Short-Reach Transmission Systems  
Unlike long-haul transmission systems that prefer to use expensive IQ modulators to obtain high-speed transmission rate, short-reach transmission systems are very sensitive to component cost because these systems are often used in a data center, enabling data exchange between many servers and racks [3]. Thus, low-cost IM/DD technology has been firstly considered for 100-Gbit/s short-reach transmission systems. In this section, we review recent applications of the five types of transmitters (e.g., VCSEL, DML, EML, MZM, and monolithic integrated transmitter) in IM/DD systems.
# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 4 Results
### Study Case 1--SMOTE and ROS  
The oversampling technique specifically targets the minority class, which possesses a lower number of instances. Both methods, SMOTE and ROS, aimed to address class imbalance by increasing the number of data points in the SMS class, the minority one. This helps to create a more balanced dataset where the minority class has a greater influence on the model's learning process.  
Random oversampling operates by duplicating or synthesizing minority class examples, effectively increasing their representation within the dataset and aiming to achieve a more balanced class distribution. On the contrary, SMOTE creates synthetic data points in the feature space between existing minority class data points. Consequently, there was a deliberate effort to evaluate the performance and advancements of both methodologies using the dataset outlined earlier. To accomplish this objective, each methodology was executed iteratively ten times, and, subsequently, the mean outcomes along with their corresponding standard deviations were meticulously documented, as illustrated in Table 1. The results obtained for the balance accuracy were 88.5% and 88% for SMOTE and ROS, respectively. An analysis of the data revealed a close correspondence between specificity and sensitivity values. Both methods achieved an accuracy of 80% in correctly classifying SMS samples. However, SMOTE exhibited a slight advantage in correctly identifying normotypic samples, achieving a value of 96% compared to ROS's 95%. Finally, it is noteworthy that both methods correctly classified 75 SMS samples. However, a distinction emerged in their performance with normotypic samples. SMOTE achieved a higher number of correctly classified normotypic samples (1396) compared to ROS (1382).
# A Distance-Vector-Based Multi-Path Routing Scheme for Static-Node-Assisted Vehicular Networks
## 1 Introduction
Now note that, although RDV has a strong characteristic providing probabilistic expectation on packet delivery ratio, the performance of RDV has not still been optimized. SADV has two major advantages on RDV that may further improve performance of RDV, that is, multi-path utilization and delay routing metric. Thus, in this paper, as an extension of a conference paper [14], we try to introduce those two features into RDV to improve the communication performance. First, we incorporate the multi-path utilization into RDV to relax the concentration of packets on the shortest paths so as to expand the traffic capacity of RDV. Second, we compare two major routing metric criteria, i.e., traffic volume and delivery delay, in RDV routing schemes in order to make better selection of the metrics. Specifically, in this paper, we propose MP-RDV (Multi-Path RDV) that extends RDV to utilize multiple paths. MP-RDV forks forwarding paths at each intermediate static node and creates duplicated packet copies for each of the paths to improve the load balancing performance as well as the delivery delay. Inheriting the property of RDV, MP-RDV minimizes the total traffic loads to achieve the preconfigured delivery ratio by computing the minimum number of packet copies to forward on each of the forked paths. In addition, we designed a delay routing metric and its computational mechanism that work with RDV to provide an option of delay-based metric utilization in RDV. Since the delivery delay of packets consists of waiting delay on nodes and transportation delay between nodes, those two values are measured separately in cooperation with related nodes and summed up to compute the delay metric values, and the metric values are advertised by the distance-vector messages. The simulation results show that MP-RDV provides well load-balanced communications so that larger traffic than both RDV and SADV can be forwarded while simultaneously holding the preconfigured expected delivery ratio. In addition, by utilizing multiple paths simultaneously, delay performance is reduced as the same level as SADV. As for comparison of metrics, we found that the built-in traffic-volume metric has totally better performance because the delay metric is found less stable and reliable against fluctuation of vehicular traffic. The results lead to the conclusion that MP-RDV with traffic-volume metric has totally better communication performance than the conventional methods in the literature. The reminder of this paper is organized as follows. In Section 2, we describe the related work in the literature and clarify the contribution of this study. In Section 3, we introduce the distance-vector routing scheme RDV, which is the base single-path scheme that we extend in this paper. In Section 4, we present the proposed scheme MP-RDV in detail, and introduce the delay routing metric to MP-RDV. Evaluation is done through simulation in Section 5, which examines the performance of MP-RDV in terms of several measurement criteria. Finally, in Section 6, we conclude the work.
## 2 Data and Preprocessing
### Phenology Camera Data  
The PhenoCam tracks vegetation phenology in various ecosystems, initially in North America and then globally [43,44]. Cameras are pointed in the dominant wind direction in order to match the eddy covariance tower footprint [45]. Digital images are stored as JPG files (image resolution of about 1900 \(\times\) 960 pixels, three color channels with 8-bit RGB color information) at half-hourly intervals for several hours per day with exposure mode. If digital images contain devices or the sky, several regions of interest (ROIs) are used to extract vegetation enclosing all areas in the foreground. As GCC (green chromatic coordinate) is widely used for monitoring vegetation canopy development and quantifying canopy greenness [45,46], it is estimated from the following equation:  
\[\mathrm{GCC_{cam}}=\frac{\mathrm{DN_{G}}}{\mathrm{DN_{R}}+\mathrm{DN_{G}}+ \mathrm{DN_{B}}} \tag{1}\]  
where the subscripts \(\mathrm{DN_{G}}\), \(\mathrm{DN_{R}}\), and \(\mathrm{DN_{B}}\) represent the red, green, and blue channels, respectively. The daily GCC is smoothed by a filter based on the official algorithm to reduce some uncertainty [47] and aggregated at 8-day intervals to match EVI temporal resolution for extracting phenology. Because there were no time periods overlapping the FLUXNET and PhenoCam databases, the GCC from the US-Ne2 site (dryland environment) could not be used in this analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site\_ID** & **Site\_Name** & **PFT** & **Latitude** & **Longitude** &
\begin{tabular}{c} **Data** \\ **Availability** \\ \end{tabular} & \(\mathbf{T_{min}}\) & \(\mathbf{T_{max}}\) & \(\mathbf{T_{opt}}\) \\ \hline US-Ho1 & Howland Forest (main tower) & ENF & 45.204 & \(-\)68.740 & 2013–2015 & \(-\)20.64 & 25.66 & 18.87 \\ US-Ha1 & Harvard Forest EMS Tower (HFR1) & DBF & 42.537 & \(-\)72.171 & 2010–2012 & \(-\)15.83 & 27.52 & 20.13 \\ US-MMS & Morgan Monroe State Forest & DBF & 39.323 & \(-\)86.413 & 2012–2014 & \(-\)12.08 & 29.85 & 22.82 \\ US-Ne2 & Mead-irrigated maize–soybean rotation & CRO & 41.164 & \(-\)96.470 & 2010–2012 & \(-\)18.26 & 29.56 & 23.02 \\ US-Twt & Twitchell Island & CRO & 38.108 & \(-\)121.653 & 2012–2014 & 2.60 & 28.65 & 20.46 \\ US-Myb & Mayberry Wetland & WET & 38.049 & \(-\)121.765 & 2012–2014 & 4.14 & 27.27 & 20.02 \\ US-Var & Vaira Ranch, Ione & GRA & 38.413 & \(-\)120.950 & 2012–2014 & 3.45 & 31.58 & 23.73 \\ \hline \hline \end{tabular} ENF: evergreen needleleaf forest, DBF: deciduous broadleaf forest, CRO: cropland, WET: wetland, GRA: grassland. Site US-Ne2 is covered by dryland, and site US-Twt is covered by paddy field. \(\mathrm{T_{min}}\), \(\mathrm{T_{max}}\) and \(\mathrm{T_{opt}}\) are the minimum temperature, the maximum temperature and the optimal temperature, which were used in previous studies based on site observations.  
\end{table}
Table 1: Basic information of the FLUXNET tower sites.
## 3 Results and Discussion
### Sensory Evaluation  
The sensory property of the yogurt samples was evaluated in terms of color and appearance, taste and odor, texture and overall acceptability, as shown in Table 3. Compared with the control (S1) plain yogurt, the symbiotic yogurt with 1% inulin (S3) had higher sensory scores for color and appearance, taste and odor. As reported by [PERSON] et al. [50], addition of inulin in dairy products improved mouthfeel and taste. Although addition of the probiotic yeast _S. boulardii_ decreased the scores for the texture of yogurt, production of alcohol and carbon dioxide (CO\({}_{2}\)) contributed to the enhanced flavor and taste. [PERSON] et al. [51] reported that carbon dioxide (CO\({}_{2}\)) and alcohol produced by yeast contributed to the refreshing and foamy taste of Kefir. Furthermore, addition of inulin in S3, S4, and S5 recovered the textural scores of yogurt gradually. However, inulin at higher concentration (2%) decreased the sensory scores for the taste, odor, and overall acceptability of symbiotic yogurt S5. Inulin was reported to provide creamy mouthfeel and sweet taste to yogurt [52]. Carbohydrate fat substitutes such as inulin in yoghurt production improved perception of color [53], and this would help overcome the problem of slight discoloration of symbiotic yogurt during the fourth week of storage, as observed in this study. [PERSON] et al. [54] also reported that color of functional yogurt could be significantly affected by storage time. Overall the symbiotic yogurt supplemented with 1% inulin (S3) possessed desired sensory properties. There were no significant differences among S1, S3, and S4 in overall acceptability, but S2 and S5 had relatively low acceptability.
## 3 Results
### Fourier Transform Infrared Radiation (FTIR) Analysis  
The FTIR measurement of glass and glass-ceramic samples was carried out for the sample precursor glass sample in the frequency of 400-1400 cm\({}^{-1}\) to investigate the vibration modes of the sample, as shown in Figure 3. The information on functional groups of glass and glass-ceramic samples can be studied using infrared spectra measurement. The related vitreous and crystalline compounds of previous research results were compared to the infrared spectra obtained [25; 26; 27; 28]. Figure 3 shows the small changes that occurred in bonding between atoms in the sample when heated to 600 \({}^{\circ}\)C. Based on the graph, it was found that the Si-O band shifted to a lower frequency, when compared to room temperature. These small changes prove that there were structural modifications to the different structural units in the sample. After the heating temperature increased, the IR spectrum of the glass-ceramics started to appear related to the IR spectra of the crystallized zinc silicate phase, which is at 457, 570, 611 and 710 cm\({}^{-1}\) and associated as asymmetric deformation of SiO\({}_{4}\), symmetric stretching of ZnO\({}_{4}\), asymmetric stretching of ZnO\({}_{4}\), and Si-O bond vibration, respectively. Additional IR spectra at 866 cm\({}^{-1}\) are attributed to symmetric stretching of SiO\({}_{4}\), and 900, 930 and 977 cm\({}^{-1}\) as asymmetric stretching of SiO\({}_{4}\) as shown in Table 2. Overall, the presence of SiO\({}_{4}\) and ZnO\({}_{4}\) related vibrations in the IR spectrum indicates the creation of the Zn\({}_{2}\)SiO\({}_{4}\) linkages inside the samples [28; 29]. Increased heating temperature may result in increased FTIR band intensities. Based on the analysis of [PERSON] et al., IR bands corresponding to asymmetric stretching vibration Zn-O of ZnO\({}_{4}\) units are found at 500-550 cm\({}^{-1}\) and 650-730 cm\({}^{-1}\), while for symmetric stretching vibration of Si-O-B bending, there was asymmetric stretching vibration of Si-O-B that overlapped with the B-O stretching modes of the BO\({}_{4}\) tetrahedral at 900-950 cm\({}^{-1}\)[30].  
Figure 2: X-ray diffraction (XRD) pattern of 55 ZnO–5B\({}_{2}\)O\({}_{3}\)–40 SLS glass and glass-ceramic sintered at various temperatures.