esa-sceva/satcom-chunk-collection huggingface dataset:
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 2 PRACH Architecture and Random Access Procedure
#### Steps of the Random Access Procedure  
The UE that wishes to establish communication with eNodeB needs to the perform random access procedure on PRACH [15] using the four-way handshake (Figure 3), which consists of the following steps:  
Figure 2: PRACH resource configurations, after [2].  
1. The terminal randomly selects the preamble from the available 54 preamble sequences and transmits it over PRACH to eNodeB.
2. The eNodeB transmits an RA Response (RAR) to the terminal through the physical downlink shared channel (PDSCH). RAR contains temporary Cell Radio Network Temporary Identifier (CRNTI) identity information. It also contains scheduling information for the third step.
3. Then, the terminal sends its CRNTI and scheduling information to eNodeB through the Physical Uplink Shared Channel (PUSCH) radio resources assigned in Step 2.
4. Finally, the eNodeB responds with the confirmation of the identity of the terminal and finishes the contention procedure.  
Preambles are mutually-orthogonal Zadoff-Chu (ZC) sequences; by default, each cell has a pool of \(N=64\) preambles. A small number of preambles (typically, 10) is reserved for time critical actions like handoff, while the remaining 54 are available for random access. The duration of a preamble depends on the cell size since larger cells have higher signal attenuation and larger propagation delays. Signal attenuation can be countered by a longer preamble time (1600 us, as opposed to the default value of 800 us). Propagation delays can be countered by extending the time intervals. As a result, Preamble Format 0 fits in a single 1-ms subframe, Formats 1 and 2 fit into two consecutive subframes, while Format 3 fits into three consecutive subframes [2; 15]. The structure of preamble formats is shown in Figure 4, and in the text that follows, we will refer to the format number as \(PF=0\ldots 3\).  
The rules for calculating preamble sequence length are based on optimizing the number of ZC sequences with respect to cross-correlation properties and minimizing interference from PUSCH in neighbouring cells [2; 16; 17]. For example, in a typical LTE system with a 5-MHz bandwidth, the bandwidth dedicated to the PRACH channel is \(W=1.08\) MHz. For For  
Figure 4: Different preamble formats.  
Figure 3: Four-way handshake between UE and eNodeB (after [15]).  
length \(N_{\text{ZC}}=839\) elements is transmitted within 800 \(\upmu\)s, which gives the preamble element rate of \(R=1.048\) M elements per second.  
Unfortunately, the first and third steps of handshaking are prone to collisions and the overload condition due to the limited number of preambles. The resulting low SINR value prevents completion of the RA handshake [10] in the case of high traffic volume, which is likely in the case of massive M2M access. In a nutshell, the use of the four-way handshaking procedure of LTE for massive network access is inefficient and leads to SINR outage at the eNodeB [2]. This has motivated our research efforts towards a PRACH overlay architecture.
# A Packet Collision Reduction Based on Reselection for LTE V2X Mode 4
## 1 Introduction
Nevertheless, the effect is not sufficient for the reselection that occurred immediately before evaluating the received power. An enhancement to the SPS algorithm has been introduced in which each vehicle announces the reservation information for the reselection in [32]. A balanced resource allocation scheme has been proposed in [33]. In this paper, we propose two resource reselection methods in consideration of other UE reselection, using only the information originally included in the control information [34]. In the proposed methods, the UEs can use the released resources by evaluating the received power only for the resources after other UEs' reselection. Furthermore, it can avoid packet collisions by selecting from the resource range not selected by other UEs. The main novelty of this paper is the effective use of information contained in the already operational Resource Reservation Interval (RRI) in the wireless resource allocation  
Figure 1: LTE V2X communication. algorithm using the SPS. Various methods have been studied to improve the performance of the wireless resource allocation problem by using additional information, but these methods require extensions that do not conform to the standard. In contrast, this study proposes and examines a new method for using the value of RRI included in the standard to improve performance while maintaining compatibility with the standard. The proposed method is highly feasible because it does not require any special extensions to existing standards. Since our method is designed to maintain compatibility with existing standards, it may or may not have a significant effect, but there are no disadvantages.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using SPSS 29.0.1.1 (IBM, Ehningen, Germany) and data visualization using syntax from [PERSON] [51]. The best performance values were used in the data analyses for all performance tests. Data are presented as the means, standard deviations, and 95% confidence intervals. The level of significance was set a priori at \(p<0.05\) for all statistical methods. Data were checked for a normal distribution using the Shapiro-Wilk test.  
For the longitudinal data, differences between groups at baseline were determined using one-factor ANOVA. The effect sizes for pairwise comparisons were calculated using [PERSON]'s d [52]. To analyze the changes in performance within and between groups, a 3 \(\times\) 2 ANOVA with repeated measures (baseline to 10 weeks) was performed using the factors 'time' and 'interaction' (group \(\times\) time). In the case of significant F-values, post hoc Scheffe tests were conducted for pairwise comparisons. The effect sizes for the global effects were calculated via the partial square of eta (\(\eta^{2}\)). In general, effect sizes of \(\eta^{2}\geq 0.25\) are classified as large, \(\eta^{2}\geq 0.1\) as moderate, and \(\eta^{2}\geq 0.01\) as small. The effect sizes for pairwise (inner-subject) comparisons were calculated using [PERSON]'s d. In general, effect sizes of [PERSON]'s d defined as d \(>0.8\) can be interpreted as large effects, d \(>0.5\) can be interpreted as medium effects, and \(>0.2\) can be interpreted as small effects [52]. The analysis included calculating the number of participants who showed improvements exceeding the smallest worthwhile change (SWC) from baseline to 10 weeks, defined as 0.5 of the between-athletes standard deviations, reflecting a [PERSON] effect size of 0.50 [53].  
The relationships between pre- to post-changes in strength parameters and upper-body endurance parameters were analyzed with one-tailed bivariate Pearson correlation coefficient (r). Additionally, a 95% CI for correlation coefficients was reported. Correlation coefficients were squared (r\({}^{2}\)) to examine the variance (in percent [%, r2 \(\times\) 100]) explained by each variable.
## 3 Analysis and Design of the Proposed System
### Communications Architecture  
Figure 1 shows the communications architecture of the proposed system. At the bottom of the figure are two collaborative AR networks that are composed by HMD devices. Each network has a master device, which synchronizes the details of the overall AR scene, and several slave devices, which follow the indications of the Master device, but which also can communicate among them to share relevant events (e.g., an event that is triggered when a user clicks on a shared virtual object).  
The devices of a collaborative AR network are usually physically close and communicate among them, but they can also communicate with devices of other remote collaborative AR networks in order to share the same AR experience. Such communications are carried out through WiFi Access Points (APs) and over the deployed wired communications infrastructure.  
At the top of Figure 1 is the cloud, which in the case of Navantia runs on a cluster of PCs that execute the software that is necessary for the creation of AR content (e.g., NX or FORAN for Computer-Aided Design (CAD) model creation) or for providing contextual data to an AR scene (e.g., Production Engineering and Operations (PEO) and ERP software from SAP).
###### Abstract  
This study proposes a method for selecting suitable edge hardware and Artificial Intelligence (AI) models to be deployed on these edge devices. Edge AI, which enables devices at the network periphery to perform intelligent tasks locally, is rapidly expanding across various domains. However, selecting appropriate edge hardware and AI models is a multi-faceted challenge due to the wide range of available options, diverse application requirements, and the unique constraints of edge environments, such as limited computational power, strict energy constraints, and the need for real-time processing. Ad hoc approaches often lead to non-optimal solutions and inefficiency problems. Considering these issues, we propose a method based on the ISO/IEC 25010:2011 quality standard, integrating Multi-Criteria Decision Analysis (MCDA) techniques to assess both the hardware and software aspects of Edge AI applications systematically. For the proposed method, we conducted an experiment consisting of two stages: In the first stage of the experiment, to show the applicability of the method across different use cases, we tested the method with four scenarios on UAVs, each presenting distinct edge requirements. In the second stage of the experiment, guided by the method's recommendations for Scenario I, where the STM32H7 series microcontrollers were identified as the suitable hardware and the object detection model with Single Shot Multi-Box Detector (SSD) architecture and MobileNet backbone as the suitable AI model, we developed a TensorFlow Lite model from scratch to enhance the efficiency and versatility of the model for object detection tasks across various categories. This additional TensorFlow Lite model is aimed to show how the proposed method can guide the further development of optimized AI models tailored to the constraints and requirements of specific edge hardware.  
Edge AI; systematic evaluation; ISO/IEC 25010:2011 quality standard; Multi-Criteria Decision Analysis (MCDA); Unmanned Aerial Vehicles (UAVs) 2020 IEEE International Conference on Robotics and Automation (ICRA), Vol., pp. 1-10, 2012-2017, 2012.
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### BVOCs and Plant-Derived Essential Oils  
Apart from being plant-emitted components of the atmosphere, forest VOCs can be found in plant-derived products and, in particular, in distilled essential oils, the lipophilic result of steam distillation of aromatic plants [52]. Although essential oils do contain BVOCs, not all BVOCs are present in essential oils, and some molecules included in essential oils are not part of the BVOC molecular suite but are rather artefacts of distillation. Essential oils are dominated by volatile terpen and terpenoids (C10 and C15) and, in specific cases, they contain phenylpropanoids, oxylipins, and, more rarely, N- and S-containing molecules. They do not contain appreciable quantities of isoprene, highly reactive chemical species such as GLVs (although occasionally found in essential oils from _Camelia sinensis_, _Viola odorata_, _Carpinus betulus_, _Fragaria vesca_, _Rubus ideaus_), or any of the volatile but hydrophilic compounds found in hydrodsols [53]. Essential oils have a higher percentage of heavier compounds (sesquiterpenes and sesquiterpenoids) compared to atmospheric BVOCs, due to higher temperatures involved in distillation that allow vaporization of the less volatile compounds (the high-boiling ones), and are mainly obtained from plant species with storage structures (glandular trichomes, resiniferous ducts, etc.). Many essential oils seem capable of exerting interesting pharmacological activities, even if not all essential oils are active and no single essential oil exerts all these activities. Moreover, given that essential oils are inherently perceptively salient, they can have mood-altering effects, which are mediated not by classical pharmacological mechanisms but by olfactory images.  
A simplified list of the most common and noteworthy activities of essential oils upon inhalation includes general antimicrobial ones and specific effects [54; 55; 56; 57]:  
* antimicrobial effects on antibiotic-resistant bacterial strains;
* antitussive, mucoactive, bronchodilation, and antitapsamodic activities on the respiratory system;
* non-olfactory-mediated psychopharmaceutical effects on arousal, activation, memory loss, dementia, cognitive performance, anxiety, quality of life, quality of sleep;
* antioxidant effect;
* antinociceptive, anti-inflammatory, and cytotoxic activity;
* anti-nausea and spasmolytic effects on the intestine.
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 5 Numerical Analysis
### Discussions and Insights
In addition to these benefits, SPN models can also be used to analyze the impacts of various external factors on the performance of the service. For example, an SPN model can be used to evaluate the impacts of changes in the regulatory environment, market conditions, or customer demand on the performance of the service. This can be useful for planning and decision-making, as it can help service providers to anticipate and to respond to changes in the operating environment. In summary, performability modeling and evaluation using SPN models can provide valuable insights and perspectives on the performance and reliability of a video-on-demand service platform. By capturing the complexity and uncertainty of real-world systems, SPN models can help service providers to optimize the design and operation of the service, and to anticipate and respond to changes in the operating environment. The goal of this work is to introduce new models. The case studies are not intended to provide broad insights, as each system exhibits specific behaviors. In addition, we present a set of models in the article that can be widely applied by adjusting the parameters and using the Mercury tool to calculate the relevant metrics. To achieve this, the user must modify the values of the transitions and markings. However, the model is highly customizable, with the ability to alter 11 transition time parameters and 2 capacity markings (Front-End and Back-End) in the base model alone.
## 3 Results and Discussion
### Effects of Electrospinning Voltages on Morphology and Diameter of PVA/Zinc Citrate Nanofiber Mats  
In Section 3.5, the zinc citrate content is constant as 3 wt %, and different high voltages of 60, 70, or 80 kV are applied in order to obtain nanofibers with different diameters. The antibacterial efficacy against _S. aureus_ is then evaluated in terms of the diameter of nanofibers. Figure 7A indicates the morphology and diameter distribution of PVA/zinc citrate nanofiber mats as related to various electrospinning voltages. The nanofibers have an uneven morphology and some amount of spindle-shaped nanofibers when they are electrospun with a voltage of 60 kV. The nanofibers then have a satisfactory morphology and a smooth surface when they are electrospun by using a voltage of 70 kV. Finally, the nanofibers have some bead-structure and a smaller diameter when they are made with 80 kV. The diameter with a corresponding voltage is 167 \(\pm\) 39 nm (60 kV), 107 \(\pm\) 22 nm (70 kV), and 75 \(\pm\) 24 nm (80 kV), as indicated in Figure 8A. The diameter of PVA/zinc citrate nanofiber mats has a decreasing trend as a result of the increasing voltage. A high voltage causes a powerful electric field force, and the Taylor cones are thus converted into a finer jet that eventually forms a finer nanofiber.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Concentration of** & **Electrospinning** & **Size of Inhibition** \\  & **Zinc Citrate (wt \%)** & **Voltage** & **Zone (mm)** \\ \hline a (control) & 0 & 80 kV & 0 \\ b & 0.5 & 80 kV & 5.0 \(\pm\) 1.1 \\ c & 1 & 80 kV & 7.0 \(\pm\) 0.8 \\ d & 3 & 80 kV & 11.8 \(\pm\) 0.5 \\ e & 5 & 80 kV & 13.5 \(\pm\) 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The antibacterial efficacy of PVA/zinc citrate nanofiber mats against _S. aureus_.  
Figure 7: The antibacterial efficacy of PVA/zinc citrate nanofiber mats against (**A**) _S. aureus_; (**B**) _E. coli_ and (**C**) optical density. The mats are composed of (**a**) 0 wt %, (**b**) 0.5 wt %, (**c**) 1 wt %, (**d**) 3 wt %, and (**e**) 5 wt % zinc citrate.  
The inhibition zones caused by the PVA/zinc citrate nanofiber mats have different sizes, as indicated in Figure 8B, indicating the voltage influences them. The diameter of inhibition zone with corresponding voltages is 8.0 \(\pm\) 1 (60 kV), 9.3 \(\pm\) 0.57 (70 kV), and 11.7 \(\pm\) 0.5 mm (80 kV) as indicated in Table 4. Namely, the diameter of inhibition zone is proportional to the electrospinning voltage. The diameter of nanofiber becomes finer when the high voltage is increased. The nanofibers are electrospun with a zinc citrate-contained polymer solution, and thus when nanofibers are finer, they have a larger specific area that allows for a greater amount of the zinc citrate to expose, and thereby improving the antibacterial efficacy.
# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 4 Experimental Simulation and Result Analysis
### Construction of Datasets
Next, the new frame sequence \(\{I_{t-3},I_{t-2},I_{t-1},I_{t},\hat{I}_{t+1}\}\) was used to obtain a new predicted frame sequence \(\{\hat{I}_{t-2},\hat{I}_{t-1},\cdots\hat{I}_{t+2}\}\). Then, the newly constructed frame \(I_{t-3}\) was removed, and \(\hat{I}_{t+2}\) from the new prediction frame sequence was appended to the end of the input frame sequence. This process continued, and finally, the predicted frame sequence \(\{\hat{I}_{t+1},\hat{I}_{t+2},\cdots\hat{I}_{t+5}\}\) was obtained. The experiment followed a 7:3 ratio, in which the first 35 consecutive frames out of every 50 frames were assigned to the training set, and the last 15 consecutive frames were assigned to the test set. A sliding window with a width of six frames and a step size of one was then used to generate training and test sequences from the training and test sets, resulting in a dataset containing 1380 training sequences and 460 test sequences. On this basis, 5 consecutive test sequences were concatenated to evaluate the model performance in rolling predictions of 5 frames, resulting in a total of 276 test sequences with a width of 10 frames.
# Application of Bonded-Block Models to Rock Failure Analysis
## 3 Generation and Application of Bonded-Block Models
### Solution Methods  
The bonded-block model presented herein was implemented in the discrete element code UDEC (Itasca [63]) by means of a user-defined contact constitutive law described in the previous section. This 2D block code is based on [PERSON]'s general approach [2], in which the solutions of both quasi-static and dynamic problems are executed by the same time-stepping algorithm. While in dynamic analyses, realistic block masses and damping are used, for quasi-static solutions, masses are scaled and artificial damping is employed to provide efficient convergence to the static equilibrium or to a collapse mechanism. Large-displacement analysis can be undertaken, taking into account block rearrangements and the updating of contact locations during the progressive deformation and failure process. This type of algorithm offers a robust solution for ultimate strength calculations, although  
Figure 2: Normalized contact post-peak softening curves. (**a**) Joint tensile stress ratio vs. joint normal displacement ratio; (**b**) joint shear stress ratio vs. joint shear displacement ratio.  
long run times may be required for large 3D problems. The disadvantage of the algorithm is tied to the requirements for numerical stability, which depends on the mechanical properties. As a consequence, to improve computational efficiency, it is advisable to avoid very high stiffness parameters; otherwise, convergence to the solution may require many calculation steps. Very stiff blocks are preferably represented as rigid, while joint stiffness should not exceed physically reasonable bounds. This is the reason why the stiffness of the bonds between inner blocks should not be taken as nearly infinite, as it is feasible in implicit solution codes, but they should be assigned at least a small fraction of the block deformability. The simulation of uniaxial tensile or compression tests is usually performed by prescribing displacement histories at the loading plate, keeping the applied movement rates slow enough to allow the response to progress as smoothly as possible.
## 2 Materials and Method
### Principal Component Regression (PCR) Method
#### 2.2.6 Principal Component Regression (PCR)  
In this study, the PCR analysis method has been employed to predict the UCS of cementitious materials at different curing times (7, 14, and 28 days). PCR integrates PCA for dimension reduction with regression modeling, enabling improved elucidation and prediction of complex multivariate systems. The selected PCs are treated as independent variables, and a regression model is developed to predict the target variable (UCS) using these PCs. The regression equation can be expressed as in Equation (5).  
\[\text{UCS}=\beta_{0}+\beta_{1}\text{PC}_{1}+\beta_{2}\text{PC}_{2}+\ldots+ \beta_{k}\text{PC}_{k}+\varepsilon \tag{5}\]  
where  
UCS = predicted unconfined compressive strength  
\(\beta_{0}=\) intercept or constant term  
\(\beta_{1}\), \(\beta_{2}\),..., \(\beta_{k}=\) regression coefficients associated with the respective principal components (PC\({}_{1}\), PC\({}_{2}\),..., PC\({}_{k}\))  
\(\varepsilon=\) residual or error term  
The regression coefficients (\(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\),..., \(\beta_{k}\)) are estimated using ordinary least squares (OLS) or other regression techniques to minimize the sum of squared residuals between the predicted and actual UCS values. The predictive performance of the PCR model is evaluated using appropriate metrics, such as the R-squared, RMSE, or MAE. These metrics provide quantitative measures of the model's accuracy and can be used to compare the PCR approach with other regression techniques or literature values. It is important to note that the PCR approach assumes linearity between the principal components and the target variable (UCS). If nonlinear relationships are present, alternative techniques like nonlinear PCR or kernel PCR may be more appropriate.