esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ambient Parameter Monitoring and Sensor Technology
### Oxygen and Carbon Dioxide  
In controlled atmosphere (CA) storage, altered oxygen and carbon dioxide concentrations are used to reduce respiration, ethylene production and enzymatic activity, which lead to better nutrient retention and slower microbial growth [5; 54]. For example, [PERSON] et al. [31] found that broccoli heads stored in 1.5 kPa O\({}_{2}\)/15 kPa CO\({}_{2}\) at 18 \({}^{\circ}\)C were still green after 10 days whereas broccoli heads in other CA storage conditions turned yellow. [PERSON] et al. [55] showed that for apples of the variety \"Fuji\", the flesh firmness was retained significantly better under CA conditions than in air storage. Comparable beneficial effects are observed for many commodities, but only for some the beneficial effects are large enough for a profitable commercial use [22]. CA storage is commonly applied for apples, pears, cabbages and, to a lesser extent, to bananas, onions, kiwifruit, avocados, kakis, strawberries during long-distance transport [52; 54]. Injuries and physical disorders can occur when the concentrations shift outside the suitable range [54]. Therefore, real-time monitoring of oxygen or carbon dioxide concentrations can be valuable when using CA. For both gasses, several sensing technologies are established, but not all of them are suitable for an IoT application [56]. [PERSON] and [PERSON] [6] propose the use of electrochemical or fluorescence sensors for oxygen detection and NDIR sensors or metal oxides sensors for carbon dioxide detection because they are inexpensive and sufficiently accurate [6].
## 2 Materials and Methods  
### Study Design and Study Population  
The study was approved by the Ethics Committee of the Faculty of Medicine of Gaziantep University (Approval No: 2023/12, Date: 8 December 2023). Informed consent forms were obtained from all patients and/or their first-degree relatives included in the study. The study was conducted according to the guidelines of the Declaration of Helsinki. From March 2018 to January 2020, a total of 31 patients with complaints of tinnitus (11 males and 20 females, aged 26 to 73 years; mean age: 50.42 \(\pm\) 9.64 years) and 24 healthy individuals as controls (12 males and 12 females, aged 24 to 58 years; mean age: 39.67 \(\pm\) 10.63 years) were included in this study.  
Patients with tinnitus were monitored for three months in an outpatient clinic. The control group was matched by age and sex and consisted of individuals with normal hearing and unremarkable otorhinolaryngologic examinations. Both groups underwent comprehensive evaluations, including routine physical examinations, pure tone audiometry (PTA), blood tests (lipid profile, fasting glucose levels), and systolic and diastolic blood pressure (SBP, DBP) measurements. Vertebrobasilar Doppler ultrasonography was also performed to assess vascular parameters. Blood pressure readings were taken on three separate days, and the mean values were calculated for each participant.  
Exclusion criteria included middle ear pathologies affecting ossicular integrity or sound transmission, such as chronic otitis media, otosclerosis, or previous middle ear surgeries. Patients with chronic systemic conditions, including diabetes mellitus, hypertension, chronic liver or kidney failure, pulmonary insufficiencies, or neurologic conditions affecting cerebrovascular flow (e.g., transient ischemic attacks or cerebrovascular accidents), as well as individuals with obesity (body mass index \(\geq\) 35 kg/m\({}^{2}\)), were also excluded (Figure 1).  
Pure tone audiometry was conducted in a soundproof room at four frequencies (500, 1000, 2000, and 4000 Hz) using an AC40 Interacoustics device (Middelfart, Denmark). The testing was performed by an experienced audiologist, with each frequency assessed for one minute. Participants reporting whistling, buzzing, or other forms of sound perception were classified as having tinnitus. Additionally, the side of the tinnitus (unilateral or bilateral) was documented for each patient.
## 3 Results
### The Effect of Map Orientation on Spatial Knowledge Development
#### 3.2.1 Pointing Direction Test by Cognitive Style  
Table 4 shows the means and standard deviation of the angular error on the pointing direction test in each map orientation condition, categorized by the two cognitive style groups. The results indicated that FD participants had a higher angular error when using the north-up maps (M = 85.568, SD = 16.373) than when using the forward-up maps (M = 75.78, SD = 14.022). FI participants, on the other hand, had no considerable amount of difference in angular error between the north-up map (M = 70.576, SD = 26.281) and forward-up map (M = 71.245, SD = 25.688) conditions (see Figure 8). These results indicate that FD individuals may be affected by map orientation favoring the forward-up map when acquiring survey knowledge. On the other hand, FI individuals are not dependent on map orientation for acquiring survey knowledge.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Cognitive Style** & **Mean** & **Standard Deviation** & **Min** & **Max** \\ \hline \multirow{2}{*}{North-Up} & FD & 85.57 & 16.37 & 61.25 & 119.8 \\ \cline{2-6}  & FI & 70.58 & 26.28 & 32.42 & 105.08 \\ \hline \multirow{2}{*}{Forward-Up} & FD & 75.78 & 14.02 & 47.75 & 95.75 \\ \cline{2-6}  & FI & 71.25 & 25.69 & 14 & 100.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The angular accuracy in the pointing direction test by cognitive style. Note: “FD” stands for field dependence and “FI” stands for field independence.  
Figure 8: The descriptive plot of the angular accuracy in the pointing direction test by cognitive style. Note: “FD” stands for field dependence and “FI” stands for field independence.  
To investigate whether the difference between cognitive groups with respect to the map orientation was statistically significant, a repeated measures ANOVA was performed on the pointing direction test with map orientation (North-up vs. Forward-up) as a within-subjects variable and cognitive style (FD vs. FI) as a between-subjects variable. As Table S2 in the Supplementary Materials shows, the main effect of map orientation on the pointing direction test's accuracy was insignificant: F (1, 38) = 1.24, \(p\) = 0.273. There was no significant interaction between map orientation and cognitive style, F (1, 38) = 1.56, \(p\) = 0.219.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 4 Discussions  
This study demonstrates the synergistic effects of recycled fine aggregate (RFA) and stabilizers in improving the mechanical properties of soft soils, particularly red clay. The results indicate that both the stabilizer content and recycled aggregate content play critical roles in enhancing strength. Specifically, higher stabilizer content contributes to the formation of cementitious products like calcium silicate hydrate (C-S-H) and calcium aluminate hydrate (C-A-H), which improve bonding between particles and densify the soil structure. Conversely, excessive recycled aggregate content may introduce voids, leading to reduced strength. Curing conditions further impact the stabilization process. Extended curing periods allow for more complete hydration reactions, resulting in improved compressive and shear strength. This study also highlights the significance of particle size distribution in enhancing particle interlocking and minimizing void spaces, contributing to overall stability.  
The primary practical application of this research lies in the stabilization of soft soils, particularly in urban areas where construction waste, such as recycled fine aggregate (RFA), is abundant. The use of RFA, in combination with soil stabilizers, presents a sustainable approach for improving the mechanical properties of soft soils, making them more suitable for construction purposes. This method offers an environmentally friendly solution by recycling construction waste and reducing the need for natural aggregates, thereby conserving natural resources. Additionally, this research contributes to the field of sustainable construction and waste management by providing a feasible option for recycling construction waste, such as RFA, in soil stabilization. It also opens new avenues for future research to explore the feasibility and economic viability of large-scale applications.  
While the results are promising, limitations exist. Variability in recycled aggregate properties and site-specific soil conditions may affect the generalizability of these findings. Future studies should explore long-term performance under diverse environmental conditions and assess the economic feasibility of large-scale applications.
# Analysis of the Effect of Pulse Length and Magnetic Field Strength on Nonlinear Thomson Scattering
## 4 Conclusions  
In summary, we have investigated the electron motion properties and space radiation properties resulting from nonlinear Thomson scattering of stationary single electrons by a tightly focused circularly polarized laser in the presence of an applied magnetic field. Creatively, we analyze it from the point of view of the radiation emitted during the motion. The trajectory and radiated power variations under different magnetic field values and different pulse length values are investigated. And the effects of pulse length and magnetic field on the collimation of radiation of electrons are analyzed.  
From the perspective of the radiated power of the electrons, the maximum radiated power of the electrons increases with the pulse length L when the magnetic field strength \(B_{0}\leq~{}0.3\). When the magnetic field intensity is greater than 0.3, the maximum radiated power of the electrons occurs at \(L=6\lambda_{0}\), after which it decreases slowly.  
Analyzing from the point of view of the collimation of electrons, the collimation of the radiation of the electrons consistently improves with the increase in pulse width. When the pulse length \(L\) is small, collimation increases with the increase in magnetic field; while when the pulse length \(L>6\lambda_{0}\), collimation slowly worsens with the increase in the magnetic field.  
Therefore, under the premise that the radius of the laser pulse waist and the peak amplitude of the electrons are certain, obtaining high-energy and highly collimated X-rays can be realized by increasing the pulse length up to \(6\lambda_{0}\) and increasing the magnitude of the applied magnetic field. We can also achieve this purpose by continuously increasing the pulse length and applying a smaller magnetic field. The choice between the above two options can be flexible and based on different requirements.  
This provides a good modulation scheme for the use of nonlinear Thomson scattering in scientific experiments, as well as some potential applications.  
Conceptualization, Y.Z., Y.W., Q.Y. and Y.T.; Methodology, H.W., F.G., Y.Z. and Y.W.; Software, H.W., Y.Z., Y.W., Q.Y. and Y.T.; Validation, H.W. and F.G.; Writing--original draft, H.W.; Writing--review & editing, H.W.; Visualization, H.W.; Project administration, Y.T.; Funding acquisition, Y.T. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the National Natural Sciences Foundation of China under grants No. 10947170/A05 and No. 11104291; the Natural Science Fund for Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006; the Natural Sciences Foundation of Shanghai under grant No. 11 ZR1441300; the Colleges and Universities in Jiangsu Province under grant No. 10 KJB140006; and the Natural Science Foundation of Nanjing University of Posts and Telecommunications under grant No. 202310293146Y.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Experiments and Analysis
### Experimental Settings  
#### 3.1.1 Datasets  
The CHN6-CUG [37] and Massachusetts [38] datasets are widely used for evaluating road extraction models in remote sensing imagery, ensuring the robustness and generalization ability of the evaluated models. Below is a detailed introduction to these two datasets utilized in this experiment.  
CHN6-CUG (50 cm/pixel): This dataset, created by [PERSON] and others from China University of Geosciences (Wuhan), is a large-scale satellite remote sensing road dataset. It contains high-resolution satellite imagery from six representative cities in China, including Beijing and Shanghai. The dataset features pixel-level annotations, categorizing pixels into road and non-road classes with high precision. It comprises a total of 4511 labeled images, each with a resolution of 512 \(\times\) 512 pixels. Among these, 3608 images are used for model training, while the remaining 903 images are reserved for testing and result evaluation.  
Massachusetts (1 m/pixel): This dataset contains a total of 1171 remote sensing images of urban, suburban, and rural scenes. Each image is 1500 \(\times\) 1500 pixels in size, covering an area of 2.25 km\({}^{2}\) per image and more than 2600 km\({}^{2}\) in total. The labeled images are derived from rasterized road centerlines generated using OpenStreetMap vector road data, with a line width of 7 pixels. A random selection of 995 images (85%) is designated as the training set, while 176 images (15%) are used as the test set. To preprocess the data, each 1500 \(\times\) 1500 image is padded with 18 rows and 18 columns of zero-value pixels on the top, bottom, left, and right sides, converting it to a size of 1536 \(\times\) 1536 pixels. Subsequently, each image in both the training and test sets is divided into 9 non-overlapping patches of 512 \(\times\) 512 pixels, resulting in 8955 patches for training and 1584 patches for testing. To reduce training costs, patches containing only zero-value pixels are removed, leaving a final dataset of 8239 training patches and 1355 test patches, along with their corresponding labeled images.
# Multi-Objective Optimized GPSR Intelligent Routing Protocol for UAV Clusters
## 3 Mathematical Modeling of Multi-Objective Routing Optimization Problems
### MDP Modeling of the Routing Forwarding Process
The principle is shown in Figure 1, if node \(i\) chooses the neighbor j as the next hop (\(j\) is closest to the destination), when the packet is forwarded to \(j\), there is a high probability of a routing hole after the packet is forwarded. Therefore, calculating \(d^{\min}_{(i^{(hop_{2}},dst)}\) in node \(i\) based on \(j\)'s neighboring topology information can prevent data from being forwarded to node \(j\) and falling into a routing hole. * Relative movement trends \(\cos_{j,d}\). \(\cos_{j,d}\) indicates the relative movement trend of the candidate neighbor node with respect to the destination node \(dst\), which is defined by the computation method as Equation (17) shown: \[P_{l}(d)=P_{l}(d_{0})+n10\log_{10}(\frac{d}{d_{0}}) \tag{6}\]2 (17) where \(\overset{\rightarrow}{V}_{j}\) denotes the speed vector of node \(j\), \(\overset{\rightarrow}{l}_{dst}\) denotes the distance vector from the node \(j\) to the destination node. (2) Action space: \(A=\left\{n_{1},n_{2},n_{3}....n_{n}\right\}\), denotes the set of all neighbor nodes of the current node \(i\), where \(n_{j}\) denotes the \(j\)-th neighboring node, and n denotes the total number of nodes. If there is a packet forwarding requirement for the current node, the next hop node needs to be decided as routing in this set. (3) Transfer probabilities: DDQN belongs to a model-free approach, which does not require explicit knowledge of the state transfer probabilities of MDP, and can learn the optimal policy through interaction with the network environment. (4) Reward function R: the reward mechanism is an important factor in refining the strategy. If the chosen action leads to a weak reward, the agent can choose other actions in the future under the same conditions to obtain other reward possibilities [34]. \[P_{l}(d)=P_{l}(d_{0})+n10\log_{10}(\frac{d}{d_{0}}) \tag{6}\]3 Routing hole avoidance for neighboring nodes. In this study, we give all the objective metrics of the multi-objective routing optimization problem in the reward mechanism of DDQN-MTGPSR to provide behavioral guidance for routing decisions [35]. The reward mechanism \(r_{i,j}\) of DDQN-MTGPSR is as (19) shown:  
\[r_{i,j}= \begin{cases}k_{1}e^{-T_{j}^{wait}}+k_{2}e^{-P_{hwr2}}+k_{3}e^{-d_{j,dt}} +k_{4}SNR_{i,j}+k_{5}E_{j}^{grade}&,j\;is^{\prime}t\;dst\\ r_{\max}&,j\;is\;dst\end{cases}  \tag{18}\]  
where \(k_{1}\) to \(k_{5}\) are the weight values and \(\sum_{i=1}^{5}k_{i}=1,k_{i}>0\). Finally, the algorithm will tend to select the next hop node with less delay, better link quality, more energy left, lower possibility of routing hole, and shorter distance to the destination. In this study, the Min-Max Scaling method is chosen to normalize the state eigenvalues.
# SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition
## 3 Stacking of Distilled Vision Transformers (SDViT)
### Vision Transformer
#### 3.1.1 Linear Projection of Flattened Patches  
Before feeding the patches into the transformer encoder blocks, the image patches undergo a linear projection process. Each individual patch is transformed into a vector and associated with a lower-dimensional embedding through the utilization of a trainable matrix denoted as \(\mathbf{E}\). In order to incorporate positional details and support the learning process, one-dimensional positional embeddings denoted as \(\mathbf{E}_{\text{pos}}\), which are acquired through training, are incorporated. The role of positional embedding is to inject information about the relative or absolute position of each patch in the image by using sine and cosine functions with different frequencies to encode positional information. As ViTs do not have a built-in understanding of the spatial arrangement of pixels, positional embedding helps the model differentiate between different regions of the image and capture spatial relationships. Without positional embedding, the model might treat all patches equally, neglecting the essential spatial structure of the input. Additionally, a trainable class embedding \(\mathbf{x}_{\text{class}}\) is included in the sequence of embedded patches, similar to the concept of the class token used in bidirectional encoder representations from transformers (BERT). The outcome of this linear projection process, labeled as \(z_{0}\), can be expressed as follows:  
\[\mathbf{z}_{0}=\left[\mathbf{x}_{\text{class}};\mathbf{x}_{P}^{1}\mathbf{E}; \mathbf{x}_{P}^{2}\mathbf{E}\dots;\mathbf{x}_{P}^{N}\mathbf{E}\right]+\mathbf{ E}_{\text{pos}}, \tag{1}\]  
where \(\mathbf{E}\in\mathbb{R}^{\left(P^{2}\cdot C\right)\times D}\), \(\mathbf{E}_{\text{pos}}\in\mathbb{R}^{\left(N+1\right)\times D}\), and \(\mathbf{x}_{P}^{n}\) represents the flattened patch vector for the nth patch. The patch embeddings are employed as the input for the transformer encoder, enabling the ViT model to grasp overall patterns and relationships within the image. Simultaneously, it retains a degree of spatial information by utilizing patches.  
Figure 2: Network architecture of the vision transformer in the proposed SDViT approach.
# Reliability-Based View Synthesis for Free Viewpoint Video
## 3 Proposed Framework
### Depth Refinement
\(W_{H}\) and \(W_{L}\) are the weighting values with high reliability and low reliability, respectively. The weighting factor for each direction can be formulated as:  
\[W_{direction}=\left\{ \begin{array}{ll}WD_{direction}\times W_{H},&\text{if pixel in this direction has high reliability}\\ WD_{direction}\times W_{L},&\text{if pixel in this direction has low reliability}\end{array} \right., \tag{1}\]  
where the subscript _direction_ can be either \(t\), \(b\), \(l\), or \(r\). The four weighting factors are normalized as \(WN_{direction}\), then the unreliable depth value \(D_{r}\) can be interpolated by Equation (2):  
\[D_{r}=\sum_{d=t,b,l,r}WN_{d}\times D_{d}, \tag{2}\]  
where \(D_{d}\) is the nearest reliable depth value in one of four directions.
# Calculation Method for Settlement Deformation of Existing Tunnel Induced by Underpass Construction
## 5 Analysis of Influencing Factors  
When the soil and construction conditions surrounding the tunnel are uniform, factors such as the spatial relationship between the old and new tunnels, stratigraphic conditions, and other elements significantly influence the deformation of the existing tunnel. Consequently, leveraging the previously discussed theoretical approaches, the new sewage pipe project in Macao examines the depth and diameter of the new tunnel, as well as the influence of ground loss rate on tunnel settlement deformation. This analysis is based on the specific working conditions to aid in determining the gradient of parameter changes. Take the initial parameters of the new tunnel depth of 12 m, the new tunnel diameter of 4 m, and the rate of loss of the ground to remain unchanged.
## 3 Research Framework  
As shown in Figure 1, the research framework mainly includes three parts: predicting the real-time physical and mechanical indices, establishing the comprehensive evaluation model of compaction quality and addressing a case study. First, based on the RTCM technology, the prediction models for real-time physical and mechanical indices are established by using SVR with CFA. The material and compaction parameters are selected as the inputs for the physical indices' prediction, and the roller acceleration spectrum is selected for the mechanical indices. Secondly, the comprehensive evaluation model of compaction quality is established, in which the \(i\)-AHP method is introduced for real-time hybrid evaluation and \(i\)-GAM is presented to analyze real-time compaction quality heterogeneity. Finally, the proposed method is applied to a high earth-rock dam project in China.
# Computerized Generation and Finite Element Stress Analysis of Endodontic Rotary Files
## 2 Materials and Methods
### Definition of the Geometry of the Raw Material for the Endodontic File  
The raw material (Figure 1b) has an axisymmetric geometry that consists of two different parts:  
* A cylindrical part, which corresponds to the shaft, characterized by its length (\(L_{total}-L_{ap}\)) and its diameter (\(d_{sh}\));
* A truncated cone, which corresponds to the active part. It is characterized by its length (\(L_{a}\)) and its diameters at the front (\(d_{ap}\)) and back ends (\(d_{sh}\)).  
The position of a point \(P\) on the surface of the raw material is given by the parametric equation \(\mathbf{r}_{f}^{{}^{\prime}}(\theta,v)\), which provides the coordinates of point \(P\) in coordinate system \(S_{f}\) from its parametric coordinates \(\theta\in[0^{\circ},360^{\circ}]\) and \(v\in[0,L_{total}]\):  
\[\mathbf{r}_{f}^{{}^{\prime}}(\theta,v)=\left[\frac{\lambda^{{}^{\prime}}(v)}{ 2}\cdot\cos\left(\theta+\varphi^{{}^{\prime}}(v)\right)\qquad\frac{\lambda^{{} ^{\prime}}(v)}{2}\cdot\sin\left(\theta+\varphi^{{}^{\prime}}(v)\right)\qquad v \right]^{T} \tag{1}\]  
where angle \(\varphi^{{}^{\prime}}(v)\) is defined as a function of \(v\); assuming a constant pitch \(p_{z}\) along the endodontic file, it can be defined as  
\[\varphi^{{}^{\prime}}(v)=\frac{v}{p_{z}}\cdot 2\pi\;\text{rad} \tag{2}\]  
On the other hand, \(\lambda^{{}^{\prime}}(v)\) represents the diameter of the raw material at coordinate \(v\), and it is defined as  
\[\lambda^{{}^{\prime}}(v)=\begin{cases}d_{ap}+v\cdot\frac{d_{sb}-d_{ap}}{L_{ap }},&\text{if }0\leq v<L_{ap}\\ d_{sh},&\text{if }L_{ap}\leq v<L_{total}\end{cases} \tag{3}\]