esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Chicken (Gallus Domesticus): Age-Dependent Stress Condition Classification
#### 3.3.1 Age dependent living condition detection  
The goal of the first task is age-dependent living condition classification. Newly hatched chicks were recorded at the ages of 1, 2 and 3 months, in six different stress factor conditions. These conditions include: No stimulus (Control), Heat (H), Human Presence (HP), Heat + Human Presence (H + HP), Crowding (C), Heat + Crowding (H + C). The baseline Control condition was without any humans present and an ambient temperature of 60 degrees Fahrenheit, the Heat condition was conducted at an ambient temperature of 100 degrees Fahrenheit, the Human Presence condition was conducted with one individual present in the barn, and the Crowding condition was conducted with space per chicken reduced by 1/3 over standard recommended conditions. A total of 1,355 vocalization segments were recorded across the three age groups and six conditions.  
Classification is run over each age category separately using 10-fold cross validation. An example confusion matrix for the 3 month old data subset is shown in Table 6, and overall accuracies are given in Table 5. Chance accuracy in all cases is between 25-35%. The results suggest that vocalizations are affected by condition and also that vocal production patterns become more consistent over time. It can be seen that, as might be expected, the highest confusion is between conditions with overlapping stimuli, for example Heat _vs_. Heat + Human Presence _vs_. Heat + Crowding. Accuracy between the disjoint stimuli Heat, Human Presence, and Crowding is much higher.  
To examine the change in vocalization patterns as a function of age, a cross testing experiment is done by training and testing on disjoint age groups. An example of these results, training on one month old data and testing on two and three month old data, is shown in Table 7. Accuracy drops in proportion to age differences between training and test set conditions, dropping from 60.5% within the same age group (from Table 6), to 49.1% on 2 month olds (a one month age differential), to 34.9% on 3 months (a two month age differential).
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 3 Experimental Results
### Quantitative Results  
In this section, we conduct a comprehensive quantitative assessment of our knee pseudo-CT synthesis approach, employing the suite of objective metrics described in Section 2.3 to evaluate the synthesized pseudo-CT images in comparison to the ground-truth CT scans.  
Figure 3: Sagittal slices of the MR images (**first column**), pseudo-CT images (**second column**), CT images (**third column**), and Relative Change between the pseudo-CT and the CT (**fourth column**) for 4 representative subjects. Green arrows highlight the presence of knee implants. Blue values in error maps denote decreased HUs in the pseudo-CT images, whereas red values denote increased HUs.  
A fundamental component of our quantitative analysis is the assessment of HUs, as they are integral for characterizing tissue properties in CT imaging. A thorough comparison of HU values for the analogous voxels in pseudo-CT and ground-truth CT images is presented in Figure 4, including a voxel-by-voxel correlation plot together with a Bland-Altman plot across all participants. The correlation plot showed an excellent correlation between the pseudo-CT HUs and the ground-truth CT HUs (m = 0.9785; adjusted \(R^{2}\) = 0.9988; \(\rho\) = 0.9849; \(p\) < 0.001). The Bland-Altman plot showed that the average of differences was low (\(HUL_{CT}-HUL_{pseudo-CT}\) = 0.7199 \(\pm\) 35.2490; 95% confidence interval [\(-\)68.3681, 69.8079]); the difference between methods was negligible below 200 HU (soft tissues) and tended to decrease as the average increased, especially above 900 HU (metallic implants). The main dispersion is located within the HU values associated with the cancellous and cortical bones.  
The remaining quantitative assessments are summarized in Table 2, and serve as a robust and rigorous analysis of the accuracy and efficacy of our method. All metrics demonstrate that our method performs well when synthesizing the pseudo-CT images. The MAE, PSNR, SSIM, ZNCC, and MI metrics demonstrate the excellent agreement between synthesized pseudo-CT images and the ground-truth CT. The RC metric reveals that errors are not systematically biased towards positive nor negative values, while the absolute RC shows that errors in synthesized pseudo-CTs are below 5% when compared to their corresponding ground-truth CTs.
# Design and Evaluation of Large-Scale IoT-Enabled Healthcare Architecture
## 3 Proposed IoT-Enabled Healthcare Architecture
### Simple Use Case  
Many patients with diseases such as Alzheimer's, diabetes, weakness, mental disability, and addiction may have unpredictable reactions. Sometimes, these types of diseases make the patient unbalanced and lose consciousness or awareness. In addition, most of these diseases have become widespread all over the world, and the most vulnerable to these diseases are elderly. Moreover, there are many accidents that have occurred for these types of patients due to leaving them without monitoring or tracking. Having permanent monitoring by a person is a very difficult process, especially since these patients may not have the money or qualified persons for this in addition to the safety factor. Moreover, placing monitoring cameras in their locations has its drawbacks because anyone who feels that he is being monitored may have bad feelings that hinder or delay the duration of his treatment. In addition, this type of monitoring requires that the patient should have limited movements, which is not desirable. This is why applying IoT technology in the healthcare field will help these types of patients.  
Based upon the above discussion, many technologies are needed to achieve the monitoring target. For example, the clothes of these types of patients can be supported with RFID through which we can track them. In addition, by strengthening their bodies with nanosensors, we can measure general health parameters such as blood pressure, pulse, and degree of awareness. As stated above, these patients are spread all over the world, including in poor and remote places, which means that the Internet service may be unavailable and the communication with them will be so difficult (if not impossible). To face this challenge and guarantee the communication/integration of patients to/in the proposed architecture, HAP and satellite are used. Hence, if there is a relapse for any patient, their data will be sent to the predetermined health organization in a real-time manner. In addition, a suitable ambulance, which is closest to the patient and equipped with all of the medical requirements and teams that are appropriate to the medical status, will be called and moved towards the patient's current location. In addition, the patient's health parameters are measured and sent to their treating doctor(s) and emergency specialists (if required). Furthermore, if the treating physician needs expert advice, full patient data can be made available to the selected expert in a real-time manner. Moreover, the initial treatment plan for the patient (such as the required medication, blood transfusion, preparing the surgical room, intensive care) will be constructed using artificial intelligence (AI) healthcare applications [37]. Finally, the patient's location will be accurately determined and all of their current specifications can be automatically sent to their relatives (see Table 2).
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 2 Prediction Method Based on Statistical Analysis Theory
### Disadvantages  
Although prediction methods based on statistical analysis theory have demonstrated good performance on small datasets with short observation periods and limited time series, the predictive capability of these statistical models is limited in traffic forecasting applications. This is due to their simple and transparent computational structure, their exclusive focus on time series data, and their lack of consideration for the complexity of spatiotemporal relationships. To address this challenge, some studies have developed extended time series methods, such as ST-ARIMA [11] and VARMA [12], which incorporate the interaction between space and time in a novel manner. However, because these methods rely on explicit parameterized functions with strong assumptions during the modeling process, they are not well suited for the simulation of real-world traffic scenarios.
## 4 Experiments
### Statistical Analysis  
This study aimed to verify the effect of dynamic speech level shifting by a dialog system on dialog with users. A statistical analysis was conducted to analyze this subject from the perspective of subjective evaluation, using the aforementioned questionnaire items as independent variables. By analyzing these questions as independent variables, the psychological effects of speech level shifts in dialog systems can be identified from multiple perspectives. The questionnaire evaluation values are depicted in Figure 3. One-way analysis of variance (ANOVA) [48; 49; 50; 51] that does not assume equal variances was adopted as the statistical analysis method to determine whether there was a significant difference in the average values of the three experimental conditions for each item (\(\alpha~{}=~{}0.05\)). As mentioned in Section 4.2, the 10-item questionnaire was classified into four categories: \"Human-like\", \"Recognition of speech level\", \"Dialog breakdown\", and \"Overall impression\". Therefore, one-way ANOVA was conducted three times for \"Human-like\", three times for \"Recognition of speech level\", two times for \"Dialog breakdown\", and two times for \"Overall impression\". In summary, comparisons were made between all items within all categories. The Bonferroni method was used to correct the \(p\)-values according to the number of times one-way ANOVA was applied to each category. \(\eta^{2}\) was calculated as the effect size [52]. This value was calculated as follows: _Sum of squares of a factor/Total sum of squares_. The approximate effect size is \(\eta^{2}~{}=~{}0.1\) for a small effect, \(\eta^{2}~{}=~{}0.25\) for a medium effect, and \(\eta^{2}~{}=~{}0.4\) for a large effect. As this is a novel study investigating the effect of dynamic speech level shifting performed by a dialog system, no established precedent exists to serve as a standard for interpreting the effect magnitude. However, please refer to the effect size in each analysis result to roughly interpret the strength of the effect. The Tukey method was used for the sub-test when a main effect was observed in the ANOVA. [PERSON]'s \(d\) was used as the effect size of the Tukey method. The approximate effect sizes are small \(d~{}=~{}0.2\), medium \(d~{}=~{}0.5\), and large \(d~{}=~{}0.8\).
# AI-Driven Optimization of Blockchain Scalability, Security, and Privacy Protection
## 2 Introduction to Related Technologies
### Blockchain
#### 2.1.1 Block Structure  
A blockchain block is composed of two primary elements: the Block Header and the Block Body. Its cryptographic design, combined with a layered architecture, establishes the fundamental technical basis for data immutability [36]. This structure ensures secure data storage through the following three layers:  
1. The information encapsulation layer of the block header  
The block header serves as the core verification unit of the blockchain, encapsulating six key fields: version number, timestamp, previous block hash, Merkle root, nonce, and target hash. Among them, the Merkle root--computed via a layered binary hash tree--ensures transaction integrity by uniquely summarizing all transactions within the block [37].
2. The data storage layer of the block structure  
The block body organizes verified transactions using a Merkle tree structure, where leaf nodes represent hashed transactions and non-leaf nodes result from recursive hash combinations. This design enables efficient O(log n) transaction verification and supports lightweight node validation. All transactions must pass digital signature and input-output checks before inclusion [38].
3. The collaborative layer of the security mechanism  
The block header and body are cryptographically linked, ensuring immutability. Any modification in transaction data alters the Merkle root, invalidating subsequent blocks. Combined with timestamps and hash chaining, this layered architecture preserves data integrity while enabling scalable storage and efficient validation.The structure of a block is illustrated in Figure 2.  
Figure 2: Block structure diagram.
## 5 Experimental Setup
### Nodes' Mobility  
To simulate node mobility, we used the following well-known real mobility traces:  
* **Cambridge**[8]--In the Cambridge trace, the devices were distributed to students from the University of Cambridge, mainly to students attending the Computer Laboratory, specifically Ph.D. and Master students and undergraduates.
* **Reality**[9]--In Reality, mobile phones were distributed to 97 students from the MIT and used over the course of six months.
* **NCCU**[10]--By using an Android application installed on smart handheld devices, it represents 115 students' moving on a typical school day at the National Chengchi University over a period of 15 days.
* **Sassy**[11]--In this experiment, the authors distributed 27 devices to students at the University of St. Andrews over a period of 79 days.  
Table 1 lists some parameters describing the traces used in our simulation experiments.
# Development and Preliminary Evaluation of a Lower Body Exosuit to Support Ankle Dorsiflexion
## 2 Exosuit Functionality and Design
### Exosuit Design  
The soft and active exosuit does not use any rigid frame structures and may be worn under or over clothing. All components are simply adaptable to different users and are independent from different footwear. Due to the primarily used soft textiles, there are no constraints on the natural passive joint motion. It assists the wearer in plantarflexion at the push-off phase and dorsiflexion during the swing phase of either the left or right foot, which is realized by a pneumatic actuator. The force flows from the actuator, which is located in a hip belt, to the foot by a Bowden tube system. The exosuit contains four main parts: (1) the supporting and force-transmitting structure; and (2) the actuation, (3) the sensory and (4) the control part. The system is illustrated in Figure 2. All main elements are explained in the following sections.
# Three-Dimension Resolution Enhanced Microscopy Based on Parallel Detection
## 4 Discussion  
The 3D-PRFED method has proven excellent to improve the transverse and axial resolution and reduce the background noise compared with conventional CLSM, which is an optional means to observe multiple kinds of subcellular organelles and other fine structures. Based on SLM, some advantages of 3D-PRISM should be mentioned. At first, 3D-PRFED succeeds all benefits of CLSM such as flexibility with almost all fluorescent dyes. Secondly, different from the traditional 3D vortex focus generation method based on phase plate [20], through Zernike correction, 3D-PRFED based on SLM can correct the aberration of the system and greatly optimize the excitation PSF, which is beneficial to make sure the quality of 3D negative focus. Additionally, the detector array should be well collimated with the Gaussian center of emission light focus which is related to the excitation light focus. Utilizing the modulation advantage of SLM, the alignment problem would be easier to solve.  
However, it is noted that the 3D negative focus should be close to the theoretical model because the quality of the 3D negative focus would greatly affect the ultimate resolution and the shape of the structure presented on the image, which is limited by the theory of 3D-PRFED. Moreover, it should be pointed out that this method would cost double time than CLSM because two different images should be gathered, positive confocal images and negative confocal images. Additionally, this method is not suitable for living cell researches because the movement of cells would change the place of second images scanned by a 3D negative focus and increase the possibility of a ghost image.  
In the future, besides CLSM, other imaging technologies, e.g., two-photons microscopy [25], can be also upgraded with 3D-PRFED to further enhance their resolution for the use of SLM.
## 2 Construction of Productivity Prediction Method of Reservoir
### Construction of Productivity Coefficients
The efficiency and permeability indices obtained the Equation (10) are projected onto a plane formed by horizontal and vertical coordinates. Then, based on the productivity index, the high gas production area, middle gas production area and low gas production area are distinguished. As a result, the productivity assessment plan for the buried hill reservoir is devised. A quantitative comparison of well productivity across multiple wells was achieved by evaluating the discrepancies in in productivity (Figure 1). The research results indicate that when the productivity index of a gas reservoir is higher, the ratio of reservoir effectiveness and permeability index is lower. The disparities in effectiveness and permeability index vary significantly between areas of high, medium and low production. This indicates that these indices can proficiently differentiate varying productivity zones of reservoirs and prompt prediction of reservoir productivity (Table 1). \begin{table}
\begin{tabular}{c c c c} \hline  & **Productivity Coefficient** & **Effectiveness Index** & **Permeability Index** \\ \hline high-yield well & \textgreater{}100 & \textless{}0.5 & \textless{}0.4 \\ \hline Middle-yield well & 10–100 & 0.5–0.7 & 0.4–0.7 \\ \hline Low-yield well & \textless{}10 & \textgreater{}0.7 & \textgreater{}0.7 \\ \hline \end{tabular}
\end{table} 
Table 1: Productivity Evaluation of Metamorphic Buried Hill Reservoir. Figure 1: Well productivity evaluation chart based on the ratio of fracture availability to permeability.
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Applications in Agriculture
#### 3.1.2 Agricultural Soil Treatment and Pest Control
Figure 3: Prototype of the mobile ozone–mist spray system (MOMSS): **(a)** sketch of MOMSS; **(b)** photo of the prototype, where (1) DBD ozone generator, (2) HF inverter, (3) Li-ion battery, (4) O\({}_{2}\) vessel, (5) water pump, (6) water tank and (7) ozone–mist spray. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Plant** & **Insect** & **Percentage Sterilisation Rate/Treatment Parameters** \\ \hline
1 & Tobacco & Green-peach aphid & 80–90\%—O\({}_{3}\) concentration: 86 g/m\({}^{3}\); flow rate: 1 litre of O\({}_{2}\) \\  & & & per minute; O\({}_{3}\) solubility: 5 ppm; spraying time: 10 s \\
2 & Tobacco & Green caterpillar & 50\%—treatment parameters like in No. 1; spraying time: 20 s \\
3 & Canadagoldenrod & Red goldenrod aphid & 95–100\%—treatment parameters same as in No. 1 \\
4 & Orange & Black citrus aphid & 100\%—treatment parameters same as in No. 1 \\
5 & Green tea & Plant lice & 100\%— ozone gas; 90\%—ozone–mist spray: treatment \\  & & & parameters same as in No. 2 \\ \hline \hline \end{tabular}
*** Own study results based on [84; 134].  
\end{table} 
Table 3: Results of the inactivation of plant insects ***. The presented experiment on a real agricultural field proved the possibility of practical use of soil treatment with ozone, both as a pesticide protecting crops against fungi, insects, weeds, and as a fertiliser [134].
## 1 Introduction  
Masonry shear walls are commonly designed to resist the effect of gravity load and lateral load resulting from an earthquake excitation or wind loading. The design criteria depends on many factors, including the magnitude of axial loading, the amount of flexural and shear reinforcement, the wall aspect ratio (height to length) and the mechanical properties of masonry [1; 2].  
Generally, there are two types of failure mode which can be occur in masonry shear walls subjected to horizontal loading. The first type is a flexural failure, which is defined by the presence of crushing the masonry in the plastic hinge region or the tensile yielding of the vertical reinforcement [3; 4; 5; 6; 7; 8]. This type of failure is preferred due to its efficiency in energy dissipating and providing more ductility through yielding of flexural reinforcement and inelastic deformation of the masonry. The second type is a shear failure, which is defined by the presence of diagonal tensile cracking or sliding shear along the mortar bed joints. This type of failure should be avoided due to its relative brittleness and rapid strength degradation after it reaches its maximum strength [9; 10; 11; 12; 13; 14].  
Nowadays, many developed countries have applied the energy conservation concept in building technology to create alternative building materials which have positive impacts on the environment. Therefore, a newly-developed type of concrete masonry unit with self-insulating features has been used for residential buildings with low to medium heights in seismic area zones with severe ambient conditions [15].  
A new type of clay masonry system was recently developed in Europe to be used in the construction of single story houses. The developed system has a good thermal resistance, with capability to be used in earthquake zones. Also, this system contributes significantly in the design and construction of cost-effective buildings in seismic regions [16]. In this study, a new type of self-insulating concrete masonry shear wall has been proposed which can be used for both low and medium height residential buildings in seismic area zones. There are two advantages of this new system, firstly, it provides stronger bonds than the ordinary concrete masonry units by facilitating the grout between the units, both vertically and horizontally, and secondly, it reduces the thermal bridges by using continuous insulation materials [15]. Accordingly, there is a vital need for a comprehensive study to evaluate the parameters that affect the behavior of the new type of the self-insulating concrete masonry shear wall (SCMSW) under earthquake excitation.  
In this research, the behavior of six fully-grouted masonry shear walls which were built from a new type of self-insulating concrete masonry unit under in-plane loading have been evaluated and reported. Moreover, the relationship between the key design parameters such as the amount of axial loading, aspect ratio and the amount of reinforcement of self-insulating concrete masonry shear walls (SCMSWs) were defined to facilitate better understanding of the inelastic behavior under seismic loading.