esa-sceva/satcom-chunk-collection huggingface dataset:
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 5 Results
### Effects of Fresnel Zone
The average height of the mangrove trees (coconut trees) were calculated because it useful to find out how much it can encroach through the Fresnel zone, so in our experiment they calculated this using the following equation  
\[\text{Average Height of Coconut trees}=\frac{25+12}{2}=\] 18.5 m (8)  
where the 12-25 is the range of coconut trees heights  
For example, the Fresnel zone for the first kilometre distance between the transmission and receiver nodes can be calculated using the above Equations (5)-(8) as the following  
\[r=8.656\times\sqrt{\frac{1\times 10^{3}}{921.9\times 10^{6}}}=9.01\text{ m}\]  
\[\text{Fzone Diameter}=9.01\times 2=18.02\text{ m}\]  
Fresnel zone centre height = Fzone midpoint height \(-\) Fzone Radius = \(22.5-9.01\) = \(13.49\text{ m}\)  
Subtracting this value from the average height of coconut trees roughly gives us the distance of the coconut trees which crossed the Fresnel zone. Figure 12.0  
After this, it is easy to find out how much the Fresnel zone clearance is using the following calculations  
Figure 12.1  
Figure 12.2  
Thus, at the first distance (1 km) between the transmitter and the receiver, the Fresnel zone was more than 60% clear of obstruction. This means the coconut trees will not affect the clear line of sight between the transmitter and the receiver (see Figure 13). After applying the same calculations for the rest of the distances, the following results were found (see Table 8):  
Figure 12.3 The calculation of the Fresnel zone between the transmitter and the receiver at the first distance (1 km). It is obvious from these results (Table 8) that the clear Fresnel zone is less than 60% from the 5 th km and above, where the wireless link performance will definitely be affected [27]. Therefore, the coconut trees at the place of measurement and were an obstruction in this scenario and prevent the clear line-of-site status between the transmitter and the receiver nodes. This is the reason for packet loss in the experiment.
## 2 Methods
### United SCF Beamformer  
Based on the united framework presented here, we propose the united sign coherence factor beamformer (uSCF). The uSCF combines the sign coherence factor [18] and united framework (Section 2.3). The united framework allows the uSCF beamformer to make full use of phase information in both the spatial and angular dimensions.  
The matrix \(X\)(k) in Equation (4) is the received RF signal from the point (\(x\), \(z\)), which contains all information in both spatial and angular dimensions. Based on the SCF theory in Section 2.1, the 2D sign phase bit \(b_{i,j}\) uses the same representation as (8), where \(i\) and \(j\) are the index of angles and elements in transducer aperture, respectively. Different from the \(\sigma\) in aSCF, the standard deviation of \(b_{i,j}\) in uSCF is  
\[\sigma(k)=\sqrt{1-\left(\frac{1}{MN}\sum_{j=1}^{M}\sum_{i=1}^{N}b_{i,j}(k) \right)^{2}}, \tag{17}\]  
where the square value of the averaged sign data is inversely proportional to \(\sigma^{2}\), which represents the similarity of the matrix \(X\)(\(k\)). It is obvious that \(\sigma^{2}\) ranges from 0 (all the elements are same) to 1 which is also similar to the sSCF and uSCF. The uSCF can be defined as:  
\[uSCF(\mathrm{k})=1-\sigma=1-\sqrt{1-\left(\frac{1}{MN}\sum_{j=1}^{M}\sum_{i=1 }^{N}b_{i,j}(k)\right)^{2}} \tag{18}\]In this expression, we only consider the \(x_{i,j}\)'s polarities. Similar to the 1D beamformer, uSCF reaches to its max (one) when all discrete signal data belongs to the positive or negative classification interval. Correspondingly, uSCF takes the minimum (zero) when all discrete signal data could be divided equally into two intervals. Based on (16), the final output of uSCF could be shown as:  
\[Y_{u}(\mathrm{k})=\frac{1}{N}uSCF(\mathrm{k})\sum_{i=1}^{N}X_{c}^{i}(\mathrm{k}). \tag{19}\]
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 3 Methodology and Design Process
### Phase 2: Empirical Content Analysis of Theses  
To inform our metadata field selection, we conducted a content analysis of fourteen graduate sociology theses (6 quantitative, 6 qualitative, 2 mixed-method) from the University of the Aegean's institutional repository. These theses were manually coded against a draft metadata template derived from DDI-Lite's \"Study Description\" and core Dublin Core fields.  
While the DDI standard was not originally designed for theses, our empirical analysis revealed that many of its high-level descriptive fields (such as research questions, sample characteristics, data collection dates, and geographical scope) are sufficiently general to apply across all research methodologies. We therefore selectively adapted these fields for use in quantitative, qualitative, and mixed-method theses, focusing on those that capture universal aspects of empirical design rather than quantitative-specific constructs like variables or datasets. This approach supports the hybrid model's goal of balancing expressiveness with usability across methodological types.  
Our analysis measured the presence, absence, and usability of 25 candidate metadata fields. Fields were selected for inclusion in the final schema if they were  
* Present in at least 70% (10 out of 14) of the theses, leaving the remaining fields optional;
* Cross-methodologically relevant (usable across qualitative, quantitative, and mixed methods);
* Essential for understanding empirical research design (e.g., sampling, data collection);
* Realistically identifiable and fillable by non-specialist users.
## 3 Methods and Samples  
### Thinning Defect Sample  
The sample applied in this study was a square plate-shaped sample made of S275 material, which is used in CLPs. Figure 4 shows the geometric information of the thinning defect sample. The thinning defect sample is 300 \(\times\) 300 mm, and the thinning size in subsurface of the sample is constant at 40 \(\times\) 40 mm. The thickness of the sample considered was 6 mm, which is the CLP thickness of KSNPP, as described in Section 1. As for the thinning depth, 9 defects of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, and 90% of the total thickness of the specimen were considered. A rows A to C and columns 1 to 3 show the respective thinning defect identification information. Table 1 shows quantitative information for each defect ID. Figure 5 shows a picture of the sample. Figure 5a shows the front of the test sample, where matte black paint was applied to the surface to maintain the emissivity above 0.9, and Figure 5b shows the thinning defect generated on the back side.  
Figure 4: Details of thinning defect samples with various thinning rates considered on the back side.  
Figure 5: Pictures of the thinning defect sample: (**a**) front side coated with matte black to maintain an emissivity above 0.9, and (**b**) thinning defects in nine different conditions on the back of the sample.
## 3 Materials and Methods
### Dataset  
In these experiments, two different types of parallel corpora in the two language pairs (EN-EL and EN-IT) are used. The first dataset (\(C1\)) consists of the test sets developed in the TraMOOC project [29]. It is a small and noisy dataset as it is comprised of educational video lecture subtitles, lecture presentation slides and assignments, while it contains mathematical expressions, spoken language features, fillers, repetitions, and many special characters, such as /, @. The second formal dataset (\(C2\)) consists of parallel corpora from European Union legal documents, found on EUR-Lex, the online gateway to European Union Law, under the category \"Consolidated texts\". The chosen sentences are from Directives, Decisions, Implementing Decisions, Regulations and Implementing Regulations of the European Council and the European Commission, on the following issues: general, financial and institutional matters, competition and development policy, energy, agriculture, economic, monetary and commercial policy, taxation, social policy and transport policy. As pointed out, \(C1\), is not a well-structured corpus as it contains linguistic phenomena which are unorthodox and ungrammatical, like misspellings, repetitions, fillers, disfluencies, spoken language features and so forth. On the other hand, \(C2\) is formal language text. For the \(C1\) corpus it was necessary to perform data pre-processing, that is, removal of special symbols (@, /), and alignment corrections. For the \(C2\) corpus no pre-processing was required. Two MT outputs were used - one generated by SMT models, that is, the Moses toolkit [30] for _C1_ and Google Translate [31] for _C2_, and the second was generated by Neural Machine Translation (NMT) models, that is, the Nematus toolkit [32] for _C1_ and Google Translate for _C2_. The Moses and Nematus prototypes are trained in both in- and out- of domain data. The Nematus is trained on additional in-domain data provided via crowdsourcing, and also includes layer normalization and improved domain adaptation. In-domain data included data from TED, Coursera, and so forth [33]. Out-of-domain data included data from Europal, OPUS, WMT News corpora and so forth. The Google Translate prototype was trained on over 25 billion examples. More details about the corpora are presented in Table 1.
# Pedestrian Counting Based on Piezoelectric Vibration Sensor
## 2 Related Work
### Vibration Signal-Based Approaches  
Although the approaches based on the vibration signal present the advantage of protecting privacy compared with camera-based approaches [18; 19; 21; 36], there are significant challenges to detect the number of pedestrians.  
The vibration signal pedestrian identification-based approaches for pedestrian counting [23; 25; 26] require that the step event (SE) signals must not be mixed. This means that the system can only work when a maximum of one person is walking at the same time. In indoor multi-person scenarios, it is quite common for multiple people to move together at the same time. Such approaches cannot handle the use cases of normal daily life and only work in a well-defined environment such as a lab experiment. This dramatically limits their practical applications.  
The room-level pedestrian counting approach [14] requires that there is a maximum of one person in the detection area. When the person leaves the detected area and enters a room, the counter will increase by one. This kind of approach can count the number of pedestrians when people go into and leave the detection area one by one. This works well in experimental scenarios, but is not suitable for practical use cases such as pedestrian counting in a large shopping mall.  
The studies [15; 16] support multiple people walking simultaneously in the same area where the signals between people can be mixed. Nevertheless, these studies require that there is only one group of pedestrians in the detected area, and this group of people should walk close together. In addition, the distance between each individual in the group should not be too far. In [PERSON] et al.'s work [15], the signal of interest (SoI) is defined as the ambient vibration signal induced by occupant footsteps. In other words, a Sol is a piece of signal from the sensor when someone passes the sensor. The four features used in [15] are given in Table 1.  
However, in real-world scenarios such as shopping malls, it is more likely that more than one group of pedestrians walk with different walking patterns in the same area. Because the method proposed in [15] deployed the sensors sparsely in a room, the problem  
\begin{table}
\begin{tabular}{c p{284.5 pt}} \hline \hline  & **Features for Pedestrian Counting** \\ \hline (1) & Space-differential: Cross-correlation between Sols from different sensors for the same footsteps. \\ (2) & Time-differential: Cross-correlation between Sols for consecutive footsteps from the same sensor. \\ (3) & Sol duration. \\ (4) & Energy-specific: Sol signal entropy. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature selection of previous work [15]. The features capture the information in vibration signals for footstep events from different perspectives.  
cannot be solved by deploying sensors with a different grid. Furthermore, when there is more than one group of people in the area, features (2) and (3) in Table 1 will not be available.
## 5 Damping System
### Influence of Damper Parameters on the Seismic Responses  
To improve the seismic performance of super-long-span suspension bridges, the influence of the damper parameters on the seismic response reduction was parametrically analyzed. Based on the discussions summarized in the last two sections, a friction damper combined with a shear-link beam (FD-SLB) damper and a BMD damper arrangement scheme were adopted.  
Figure 19: Displacement along the height of the tower with different damper arrangement schemes: (**a**) El Centro, PGA = 400 gal; (**b**) Chi-Chi, PGA = 400 gal.  
Figure 18: Classical types of damper arrangements: (**a**) full-uniform distribution; (**b**) full-trilaminar distribution; (**c**) bottom-middle distribution.  
The initial sliding force of the friction damper was selected as one optimized parameter. The initial sliding forces of the friction damper were set to 1000 kN and 1500 kN, respectively. The displacement responses along the height of tower with different initial sliding forces of the friction damper are shown in Figure 20.  
As shown in Figure 20, the displacement at the tower top decreased with a decline in the initial sliding force of FD-SLB when exposed to a far-fault earthquake. In the results of the finite element model, damper sliding did not occur at an initial sliding force of 1500 kN, but it did occur at an initial sliding force of 1000 kN. Hence, FD-SLB with an initial sliding force of 1000 kN worked with the energy dissipation to decrease the seismic response. In comparison, a larger displacement caused damper sliding when exposed to a near-fault earthquake, even though the initial force was only 1000 kN. FD-SLB with an initial sliding force of 1500 kN had larger energy dissipation, which resulted in smaller displacement responses.  
Thus, dampers should be designed with a reasonable initial sliding force to ensure damper sliding during the whole process of earthquake excitation. The initial sliding force could be increased when super-long-span suspension bridges are located close to near-fault areas.
# Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path
## 2 Literature Survey
#### Problem Statement  
In air transport, the basic constraint for the measurement of efficiency is flight time. All air transport is calculated in terms of the flight time and so the basic GPS and thus the tracking system is useless, as the shortest distance might not give the shortest time. This research paper concerns this aspect [25; 26; 27; 28].  
The UAVs need a new set of instructions as they are self-driven and the algorithm has to feed in the coordinates in real-time to the UAVs to keep the drone on track and keep it from deviating from its path. Additionally, it has to check the coordinates for the destination and mid-point of the journey to change its modes. Moreover, the natural terrain and the legal boundaries have to be kept in mind. There is also a set of precautionary or emergency instructions too in case of any failure or damage. The UAVs should also be sending this information to a server or storing it to keep track and recover in case of system failure.  
How to reduce time by using basic physics and algorithms to follow that path so that less time is used for reaching the destination and even considering the external interferences like wind speed, height, thrust, and even the weight of the carriage are investigated.
## 3 Methodology
### Octree Algorithm  
The octree algorithm is a commonly used tree-like data structure for managing three-dimensional data [54]. The fundamental idea behind octrees is to partition three-dimensional space into a series of cubes (also known as volume elements) and build a tree-like structure based on this division. Each node in the octree represents a volume element in the form of a cubic region, and every node has eight child nodes. The combined volume of these eight child nodes is equal to the volume of their parent node, as illustrated in Figure 10. The octree algorithm exhibits significant advantages in handling high-dimensional data and adaptability [55]. It finds wide applications in various fields of three-dimensional computer graphics, such as ray tracing, collision detection, 3D modeling, and more [56]. The processes of creating an octree are as follows:  
1. Set a maximum recursion depth to prevent infinite recursion and performance issues caused by excessive subdivisions;
2. Determine the maximum size of the scene, which serves as the initial size of the cubic region;
3. Add individual elements successively to the cubic regions that can contain them and do not have child nodes;
4. If the current cubic region has not reached the maximum recursion depth, divide it into eight equal parts and allocate all individual elements to the child cubic regions;
5. If the number of individual elements assigned to a child cubic region is the same as that of its parent cubic region, an additional subdivision is considered meaningless, and the subdivision process stops;
6. Repeat steps 3-5 until the maximum recursion depth is reached.  
Using point cloud data as an example, each cubic region records the number of point cloud points it contains. When this count decreases to below a certain threshold, the subdivision of that cubic region is halted. This approach helps avoid performance issues associated with excessive subdivisions and filters out some noisy point cloud data, enabling a more precise delineation of each individual object.  
Figure 10: Octree partitioning process.
## 1 Introduction
In contrast, in food applications, HS calyxes are valued for their sensory attributes, pigments, and antioxidant properties [11; 12]. HS aqueous extracts have been used as impregnation solutions to study the transport kinetics of hibiscus anthocyanins in apple cubes [12] and papaya cubes [4]. Additionally, VI has been applied to various food matrices, including potatoes [10], partially dehydrated strawberries [13], and chockberry fruits [14], prior to dehydration. Furthermore, a variety of natural juices and natural extracts have been utilized as impregnating solutions to enhance the content of phenolic compounds, anthocyanins, and antioxidant capacity in food matrices, including _Vaccinium myrtillus_ juice [13], pear-apple based juice [14], and _C. sinensis_ aqueous extracts [15]. In general, VI enhances the sensory properties and antioxidant content of dehydrated or minimally processed fruits. Research has focused on optimizing the VI variables, including vacuum pressure, treatment time, restoration time after treatment, type and concentration of the impregnation solution, and the size, thickness, and geometry of the fruit. Studies agree that each VI process involving a new food matrix or modifications to the impregnating solution should be optimized to maximize the advantages offered by this technology [6; 16; 17; 18]. In this context, the response surface methodology (RSM) has been investigated to develop models for optimizing vacuum impregnation processes. RSM has been used to optimize grape juice incorporation into apple tissues using a Box-Behnken design, considering vacuum pressure, juice concentration, and processing time [19]. [PERSON] et al. [20] also used RSM to optimize the vacuum impregnation of apple slices with sucrose. Moreover, the vacuum impregnation of apple tissues with black carrot concentrate was optimized by RSM coupled with a rotatable central composite design [21]. Thus, this study aimed to optimize the vacuum impregnation conditions (vacuum pressure, impregnation time, and restoration time) of apple slices with _H. sabdariffa_ aqueous extracts, using response surface methodology. The effect on soluble phenol and flavonoid content, antioxidant capacity, and physicochemical parameters before and after vacuum impregnation and dehydration procedures in apple slices were also evaluated.
# Energy Generation and Attenuation of Blast-Induced Seismic Waves under In Situ Stress Conditions
## 3 Numerical Study of Blasting Seismic Energy Generation and Attenuation under In Situ Stress
### Conceptual Model of Multiple-Hole Blasting under In Situ Stress  
In this numerical study, a circular tunnel with a radius of 5.0 m is considered to be excavated in a homogeneous and isotropic rock medium. The rock medium suffers from horizontal in situ stress \(\sigma_{x}\) and vertical in situ stress \(\sigma_{y}\). The circular tunnel is excavated by the millisecond-delay blasting technology with six rounds of blastholes, as presented in Figure 1. The cutting holes in the innermost round are first fired, followed by the stoping holes in the second to fifth rounds and the contour holes in the outermost round. The distances from each round of blastholes to the center of the tunnel are 0.5, 1.2, 2.2, 3.2, 4.2, and 5.0 m, respectively. In the present study, the stoping hole blasting in the fourth round is simulated. There are twenty blastholes with a spacing of 1.0 m in this round. The blastholes are 0.04 m in diameter and are fully filled with explosive columns of the same diameter. The twenty blastholes are considered to be fired simultaneously, regardless of detonator delay errors. Before blasting the stoping holes in the fourth round, the blastholes in the previous rounds have been detonated, and a new free surface has been created, as shown in Figure 1. In the numerical study, the new free surface aligns the connecting line of the blastholes in the third round without considering over-break or under-break caused by blasting.
## 2 Materials and Methods  
### Theoretical Background  
When inscribing a BG into the core of GI-MMF the PMG formation causes that the Bragg condition is satisfied for modes of the same PMG or modes of neighboring PMGs [6]. The Bragg condition is given by:  
\[\beta_{1}-\beta_{2}=2\pi/\Lambda \tag{1}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are the propagation constants of the forward and backward propagating modes to be coupled and \(\Lambda\) is the grating period. In case of the coupling between modes of the same PMG, the propagation constants are equal \(\beta=\beta_{1}=-\beta_{2}=2\cdot\!\pi\cdot\!n_{\textit{eff}}\!/\lambda\), where \(n_{\textit{eff}}\) is the effective RI of the modes to be coupled, and from this the reflected Bragg wavelength for the coupled modes is calculated as [6]:  
\[\lambda_{B}=2n_{\textit{eff}}\Lambda. \tag{2}\]  
However, in case of the mode coupling between modes of neighboring PMGs the propagation constants are unequal with \(\beta_{1}=2\cdot\!\pi\cdot\!n_{\textit{eff}1}\!/\lambda\) and \(\beta_{2}=2\cdot\!\pi\cdot\!n_{\textit{eff}2}\!/\lambda\) and thus the corresponding reflected Bragg wavelength can be determined as follows [6]:  
\[\lambda_{B}=\Big{(}n_{\textit{eff}1}+n_{\textit{eff}2}\Big{)}\Lambda \tag{3}\]  
where \(n_{\textit{eff}1}\) and \(n_{\textit{eff}2}\) are the effective refractive indices of the coupled modes of the neighboring PMGs. From this it follows that the spectrum of a GI-MMF comprising a BG contains several peaks in reflection mode and several dips in transmission mode, where each peak and dip represents the mode coupling within the PMG of the same order and neighboring PMGs, respectively [6]. Consequently, lower order and higher order PMGs can be distinguished relatively easily by monitoring the reflection and/or transmission spectrum of the GI-MMF with BG, respectively, and observing the corresponding Bragg wavelength [6].  
Furthermore, in Equations (2) and (3) the effective refractive indices depend on the field distribution of the modes to be coupled and thus this parameter can be sensitive to SRI, depending on whether a fraction of the electrical field of the mode is propagating inside the cladding or in the surrounding region.