esa-sceva/satcom-chunk-collection huggingface dataset:
# Industry 4.0 towards Forestry 4.0: Fire Detection Use Case +
## 5 Methodology
### Data Stream Processing Pipeline
#### 5.2.2 Query Window-Based Streaming Data  
The join query is a common and well-understood operation in batch and stream data processing to connect the rows of two relations and/or streams. Fink provides two main join types; regular and time-windowed. Regarding this work, a time-windowed join is considered which joins two input streams within specific time constraints, i.e., a time window. The join window operation matches the elements of two streams that share a common key within the same window. According to the time-streaming context, Fink provides three types of windows to join operations on finite size of time-stamped data including, tumbling, sliding and session. A tumbling window is a series of fixed-sized, non-overlapping and contiguous time intervals. Sliding window assigns elements to windows of fixed length; hence, sliding windows can be overlapping if the slide is smaller than the window size. Session window groups events with session activity gap.  
Without loss of generality, Figure 9 describes the translated Fink job of join query which contains data source and window operator. The Fink tumbling window operator splits the stream into buckets of finite size based on the configured window size, 60,000 s and then triggers the output when the time passes the pre-defined watermark.
## 2 Flash Storage Framework
### Data Storage Architecture and Controller Behavior  
Flash-based SSDs typically include 4 to 16 NAND chips [9], each composed of multiple hierarchical components: **Die**: A chip contains multiple NAND memory dies. **Plane**: Each die consists of 1-4 planes. **Block**: Each plane is divided into thousands of flash blocks. **Page/Wordline**: Blocks are composed of hundreds to thousands of pages (rows or wordlines). **String/Bitline**: Blocks also include hundreds to thousands of strings (columns or bitlines). **Cell**: Each page or string holds thousands of flash cells. In 2D NAND architecture, flash blocks are arrays of wordlines and bitlines. Pages, the smallest unit for reading and writing, typically store 2 KB to 16 KB of data, while blocks, the smallest erasable unit, range from 256 KB to 4 MB. Controller firmware--such as wear-leveling, error correction codes (ECC), and thermal throttling--plays a crucial role in overall SSD resilience. With advancements like TLC and beyond, 2D NAND transitioned to 3D NAND architecture, addressing limitations in lithography by stacking cells vertically [10].  
3D NAND architecture introduces layers to increase density and capacity, where the number of layers is typically a multiple of four. For example, QLC uses 3D NAND in its block-level design. Common configurations include 32, 36, 48, 56, 96, and 128 layers, with higher layer counts enabling greater storage density. Each layer is determined by the vertical arrangement of control gates, which expands storage capacity while maintaining efficiency. A flash storage device has two main storage areas: the main area and the spare area [11]. The main area stores user data like files and applications. The spare area is reserved for system purposes, such as storing bad block markers, error-correcting codes (ECC), and other important system information. This spare area is not available to users and is used exclusively by the system to ensure proper operation and data reliability.  
The controller is the part of the device that manages how data is stored and retrieved. It writes data one page at a time, avoids using damaged blocks, and spreads out write and erase operations to extend the lifespan of the storage. The controller also improves performance by splitting large files across multiple chips and ensures data reliability by storing extra error-correction information in a separate chip. These processes help keep the device reliable and accurate over time.
## 6 Conclusions  
According to the communication requirements of the semi-trailer ECPB in-loop testing system, the data flow of the system was designed and a reliable and effective communication interaction method was proposed, and the correspondence between the hub motor and break pressure of the air chamber in ECPBS was realized. A compensation controller was designed to mitigate data discontinuity caused by a communication delay during data interconnection and interaction between the twin test vehicle and the ECPB hardware test bench. The simulation results showed that the controller can ensure the stability of the data interaction with a goodness-of-fit of 0.826 under a communication delay of 0.7 s. In the data interaction of the in-loop test, the twin test vehicle responded to the input signals within 0.6 s, which meets the testing requirements. The feasibility of the interconnection method was verified through a series of low-speed straight-line driving tests, and it has proven to be feasible in the concept validation stage of semi-trailer vehicles' ECPB development. The above work has prepared and improved the test site for the ECPBS and provided a better test method and tool for the further study of the dynamics of articulated vehicles.  
Figure 9: Comparison of input and output speeds.  
**Author Contributions:** Conceptualization, J.X.; methodology, H.W. and R.Z.; software, H.W. and R.Z.; data curation, G.L. and R.Z.; writing--original draft preparation, H.W.; writing--review and editing, J.X. and G.L.; visualization, J.X. and H.W.; project administration, G.L.; funding acquisition, J.X. and G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Research and Development Program of Hubei Province of China, grant numbers YFXM2022000405 and 2022 BEC014.  
Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Heterogeneity of Ex Vivo and In Vivo Properties along the Length of the Abdominal Aortic Aneurysm
## 5 Conclusions  
The workflow and methodology described herein allowed for the characterization of the aortic tissue with a comprehensive location-specific analysis and provided a distinctive insight on the assessment of AAAs. The effect of position on ex vivo and in vivo properties was investigated and showed the significant heterogeneity present in AAA tissue along both the length and the circumference of the aorta, with the most striking and consistent results being found with respect to the position along the length of the artery.  
Present findings highlight the heterogeneity of AAAs and the essential role of regional characterization in the context of aortic assessment for disease management purposes. More work should be done towards the implementation of novel approaches accounting for the heterogeneity of the aneurysm--through correlation of the heterogeneity at the tissue level with non-invasive measurements--in order to improve risk stratification and clinical outcomes for individual AAA patients.  
Conceptualization, E.S.D.M.; Data curation, A.F.; Formal analysis, A.F., M.N., T.S. and L.N.; Funding acquisition, E.S.D.M.; Investigation, A.F., A.I. and A.B.; Methodology, A.F., T.S. and E.S.D.M.; Project administration, E.S.D.M.; Resources, R.D.M. and E.S.D.M.; Supervision, E.S.D.M.; Validation, A.F.; Visualization, M.N. and T.S.; Writing--original draft, A.F., M.N., T.S. and L.N.; Writing--review and editing, A.F., M.N., A.I., T.S., L.N., A.B., R.D.M. and E.S.D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Werner Graupe International Fellowship in Engineering and the Libin Cardiovascular Institute of Alberta scholarship, with contributions from the NaturalScience and Engineering Research Council of Canada Discovery Grant RGPIN/04043-2014, and RGPIN/07178-2019, and the Heart and Stroke Foundation Grant in Aid G 170019141.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the University of Calgary Conjoint Health Research Ethics Board (CHREB) (Ethics ID: REB15-0777).  
Informed Consent Statement: Informed written consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to acknowledge the work done by [PERSON], [PERSON], and [PERSON] in support of this research study.  
[PERSON], [PERSON], and [PERSON] are co-founders and shareholders of the start-up company ViTAA.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 4 Experiments
### Implementation Details
#### 4.1.2 Training FusionTrack  
For faster training and better performance, the model is trained on a weighted joint dataset containing CrowdHuman [31], and DanceTrack [13]. We define a key image's difficulty by calculating the max overlap IoU of objects and the number of objects. Difficult samples with more occlusion and more objects are given more weights. For DanceTrack, 4 frames from a 10-frame interval are sampled randomly around the keyframe. Random resizing, cropping, hue and flipping augmentations are applied to each frame. For CrowdHuman, pseudo clips are generated by applying different augmentations on static images following MOTR [11].  
We adopt a multi-stage training strategy for faster convergence. For all experiments, we start with a COCO [32] pretrained DAB-Deformable-DETR [12]. We first train the base detector on the unweighted joint dataset for 4 epochs with batch size of 4 and learning rate of \(2\times 10^{-5}\). After that, we initialize the whole model by copying the weights from the detection branch to the tracking branch and randomly initialize the query propagation and query fusion modules. We then freeze the detection branch and train newly initialized modules for 4 epochs with batch size of 16, learning rate of \(2\times 10^{-4}\) and clip length of 2 on the unweighted dataset to avoid new parameters from disturbing the pretrained ones. This gives a base pretrained model for all following experiments.  
On top of the two-stage pretrained model, the model that gives the main results is trained using another two-stage finetuning process. It is first trained for 400,000 iterations with a learning rate of \(2\times 10^{-5}\) on the weighted joint dataset, which is approximately equivalent to 6 epochs of the DanceTrack dataset. It is then fine tuned on the DanceTrack dataset only for 2 epochs with a learning rate of \(1\times 10^{-5}\) to obtain the final model.  
All stages utilize the AdamW optimizer with weight decay at \(1\times 10^{-2}\) to train the model. The learning rate varies depending on the training stage and decays in a cosine annealing pattern that eventually drops to 1/1000 of the starting learning rate.
## 4 Experiment Evaluation
### Experimental Results
#### 4.3.2 Effect of Aspect Number on Rating Prediction  
This experiment set and compared the different aspect numbers and parameters of the CNN to conduct the ADLRP method, as proposed in this study. Based on the experimental results, the best aspect number and best CNN parameters were selected. The aspect number was set to 1, 4, 6, 10, 15, 20, 25, 35, 45, etc. The parameters of the CNN include a filter (i.e., \(F\)), strides (i.e., \(S\)), the activation function of the convolutional layer (i.e., \(G_{conv}\)), activation function of fully connected layer (i.e., \(G_{fc}\)), and optimizer (i.e., \(O\)).  
At the beginning of this experiment, we set the parameters as \(\left(F=2,S=1,G_{conv}=\tan H,G_{fc}=ReLu,O=Adam\right)\) and fixed the convolutional kernel size of the CNN to 2 *. 2. The following four steps were used to determine the optimal parameter values of the CNN.  
1. Assume that the number of input aspects was \(n\). The number of filters \(F\) was adjusted from 2 to \(n\) and increased to the power of 2. Based on the prediction accuracy, the optimal number of filters was decided.
2. The moving pace \(S\) was set from 1 to 6 to determine the optimal moving pace.
3. For the activation functions of the convolutional layer and fully connected layer, three activation functions: _Sigmoid_, _tantH_, and _ReLu_, were set, respectively. The best activation function was decided based on the experimental results.
4. The optimizers were set as _Adadelta_, _Adagrad_, _SGD_, and _Adam_, respectively. The optimal optimizer was decided based on the experimental results.  
Based on the results of several experiments, the optimal parameters of the CNN in different aspects are shown in Table 1. The ADLRP method has the lowest MAE and RMSE when the aspect number is set as 15. The parameters of the CNN are \(F=2,\ S=4\), \(G_{conv}=\tan H,\ G_{fc}=ReLu,\ O=Adam\).
# Imaging Microstructure on Optically Rough Surfaces Using Spatially Resolved Acoustic Spectroscopy
## 4 Discussion and Conclusions
### Resolution  
As previously detailed by [PERSON] et al. [16], the spatial resolution of an SRAS measurement is approximately equal to half the diameter of the generation patch. Therefore, to increase spatial resolution, two approaches are available. First, the number of fringes can be reduced; however, this results in a broadband signal and a greater error in velocity measurement. The second approach is to use a smaller acoustic wavelength, giving a smaller patch size for the same number of fringes, but this introduces two separate challenges. Using smaller wavelengths requires the detection of higher frequencies, and so the bandwidth of the detector used in the measurement can become a limiting factor. As shown in Figure 2, whilst prepared surfaces can be measured in the hundreds of MHz by the KED, and indeed other solutions exist to measure SAWs in the GHz regime [46], rough surfaces are limited to 100 MHz, with many detectors having an upper limit below 50 MHz. Additionally, the attenuation of the SAW as it propagates must also be considered. The short propagation distances in SRAS help to minimise the influence of attenuation, and increasing the frequency to improve resolution will eventually lead to the attenuation dominating. As derived and observed experimentally by [PERSON] and [PERSON] [22], the attenuation of a SAW due to roughness varies with the frequency to the fourth power in the stochastic regime; further detail on the can be found in the recent work of [PERSON] et al. [47].  
The results presented in this work have an approximate spatial resolution of 400 \(\upmu\)m; the inherent flexibility of the SRAS instruments makes it possible to switch between optiming for spatial resolution and optimising for velocity resolution. The need to work at lower frequencies usually means spatial resolution is often sacrificed for samples with rough surfaces. The velocity resolution is determined by the number of fringes used in the generation pattern and the SNR achieved via averaging, and so a trade-off between spatial resolution and scan time is usually made.
## 4 Implement and Discussion of Intelligent Dynamic Offloading Model
### Problem of Offloading Based on Edge Computing  
Both offloading are centralized to the edge node at the same time. Figure 4 shows two significant problems when tasks distributed without considering data offloading and task offloading at the same time--Inefficient partitioning results in a tradeoff.  
storing/computing tasks is focused only on specific nodes as shown in the figure. Therefore, inefficient splitting of storing/computing offloading occurs when an overload occurs only at certain edge nodes and some nodes are relaxed. As a result, overload occurs only at certain edge nodes, and some nodes save too much space, resulting in inefficient partitioning of storing/computing offload.  
Trade-off between storage/computing offloading: Inefficient partitioning problems also lead to issues with a trade-off. If the data storage functions offloaded to a particular node are large, there is not enough space for computing functions. On the contrary, when more computing functions become available, there is not enough space for data storage functions. This causes delays due to the inability to do some work and cannot guarantee the real-time advantage of edge computing.  
Therefore, this paper proposed an intelligent collaboration model with a focus on solving two problems. The model solves problems and distributes results in real-time by dispersing functions through the proposed collaboration algorithm.
# The Long and Winding Road to Cardiac Regeneration
## 7 Future Directions
### Organs-on-Chips
One such example is a study published by [PERSON] et al. (2018). The researchers developed a human-induced pluripotent stem cell (hiPSC)-derived cardiac microtissue on a chip, which recapitulated key features of human heart tissue. The microtissue consisted of hiPSC-derived cardiac cells arranged in a three-dimensional structure that mimicked the native organization of heart tissue. The chip allowed for the monitoring of contractile function, electrical activity, and drug responses, enabling investigations into cardiac diseases, such as hypertrophic cardiomyopathy, and the evaluation of potential therapeutic interventions [350]. Another notable publication by [PERSON] et al. (2022) showed the development of a heart-lung micromodel on a chip. This organ-on-chip platform integrated cardiac and pulmonary functionalities to mimic the physiological interaction between the heart and lungs. The chip contained compartments representing the heart, lung, and vasculature, allowing for the study of cardiopulmonary interactions, oxygen transfer, and drug responses. This model demonstrated the potential to investigate cardiopulmonary diseases, such as pulmonary hypertension, and evaluate the effects of drugs or interventions targeting both the heart and lungs [351]. Furthermore, a study published by [PERSON] et al. (2017) presented a multi-organ-on-chip platform, where a cardiac microtissue was combined with liver and lung models. This multi-organ chip allowed for the investigation of inter-organ interactions, such as the effects of drug metabolism in the liver on cardiac function. The integrated platform provided a comprehensive in vitro system to study drug toxicity, drug-drug interactions, and the systemic effects of drugs on various organs [352]. These examples highlight the diverse applications and capabilities of cardiac organ-on-chip models. By replicating the structure and function of the human heart, these chips enable researchers to study cardiac physiology and pathophysiology, evaluate drug responses, and investigate disease mechanisms. They also offer the opportunity to test potential therapeutic interventions and evaluate their effectiveness in a highly controlled and realistic environment. Continued research and development in the field of cardiac organ-on-chip technology are expected to yield further advancements and contribute to our understanding and treatment of cardiovascular diseases. Furthermore, numerous studies have focused on refining and optimizing cardiac organ-on-chip models by incorporating additional features. For instance, researchers have integrated sensors to monitor contractile forces and electrical activity in real-time, enabling precise measurements of cardiac function and drug responses [353]. Other studies have explored the inclusion of vascular networks within the chip to mimic the intricate blood supply to the heart, allowing for investigations into vascular dynamics and the impact of blood flow on cardiac function [354].
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 3 Materials and Methods
### Trend Analysis  
The next step was to compute the spatiotemporal LST distribution after comparing the data's quality. An examination of the trends using statistics was then carried out. Many statistical trend analysis techniques, including linear trend analysis, polynomial trend analysis, harmonic trend analysis, and Mann-Kendall (MK) trend analysis, have already been employed in related investigations [39]. In order to evaluate the statistical significance of the trends and to precisely estimate the amplitude of the changes in LST, the Sen's slope estimator and the non-parametric MK methods were utilized in this study. Numerous research projects have used the non-parametric MK test because it does not rely on any assumptions about the distribution of the data or the linearity of the trend [40]. The alternative hypothesis indicates that there is a monotonic decreasing or increasing trend, whereas the null hypothesis in the calculation of this test asserts that there is no trend in LST data over time. The equations to use the MK test are as follows:  
\[S=\sum_{k=1}^{n-1}\sum_{j=k+1}^{n}sgn\left(X_{j}-X_{k}\right) \tag{20}\]  
\[sgn\left(X_{j}-X_{k}\right)=\left\{\begin{array}{ll}1,\text{ {if}}\ X_{j}>X_{k}\\ 0,\text{ {if}}\ X_{j}=X_{k}\\ -1,\text{ {if}}\ X_{j}<X_{k}\end{array}\right. \tag{21}\]  
\[Var(S_{mk})=\frac{n(n-1)(2n+5)-\sum_{p=1}^{q}t_{p}\left(t_{p}-1\right)\left(2t _{p}+5\right)}{18} \tag{22}\]\(X_{j}\) and \(X_{k}\) are two consecutive data values in an \(n\)-dimensional data set. The term \"\(sgn\)\" represent sign function. For a normal distribution, E(\(S\)) = 0 with the variance, and the \(S\) statistic behaves roughly the same. Here, \(q\) is the total number of affiliated groups in the data set, and \(t_{p}\) is the number of input values contained within the \(p\)-th affiliated group. By utilizing above equations, \(Z_{mk}\) is calculated as follows:  
\[Z_{mk}=\begin{cases}\frac{S-1}{\sqrt{Var(S_{mk})}},\;if\;S>0\\ 0,\;if\;S=0\\ \frac{S+1}{\sqrt{Var(S_{mk})}},\;if\;S<0\end{cases} \tag{23}\]  
A statistically reliable trend identification tool is \(Z_{mk}\). A \(Z_{mk}\) positive sign indicates an upward trend, while a \(Z_{mk}\) negative sign indicates a downward trend. The null hypothesis is rejected if the absolute value of \(Z_{mk}>Z1/2\), where Z1/2 is obtained from the normal cumulative distribution tables.  
The estimator of Sen [41], which is defined by Equations (24) and (25), was used to calculate the real slope of an existing trend.  
\[Q_{i}=\frac{X_{j-}X_{k}}{j-k}\;for\;all\;j>k \tag{24}\]  
There will be \(N\) = \(n\) (\(n-1\))/2 slope \(Q_{i}\) estimations if the time series has \(n\)\(X_{j}\) values. The median of these \(N\) values of \(Q_{i}\) is Sen's slope estimator which calculated as follows:  
\[Q_{med}=\begin{cases}Q_{\lfloor\frac{N+1}{2}\rfloor},\;if\;N\;is\;odd\\ \frac{1}{2}\Big{(}Q_{\lfloor\frac{N}{2}\rfloor}+Q_{\lfloor\frac{N+1}{2}\rfloor }\Big{)},\;if\;N\;is\;even\end{cases} \tag{25}\]
## 3 Results
### Characterization of The Synthesized AgNPs
#### 3.3.3 Transmission Electron Microscopy (TEM) Analysis  
The morphology, shape and size of AgNPs were determined using TEM, the recorded images being shown in Figure 12.  
Figure 10: The influence of stirring time (0, 15\({}^{\prime}\), 30\({}^{\prime}\), 45\({}^{\prime}\), 60\({}^{\prime}\), and 75\({}^{\prime}\)) on AgNPs-EtOH formation.  
TEM images show well-individual nanoparticles of almost spherical shape, more pronounced in the case of AgNPs-EtOH. AgNPs-H\({}_{2}\)O presented an average diameter of approximately 15 nm, while AgNPs-EtOH of around 10 nm.  
The average size of AgNPs synthesized using _T. erecta_ flowers under the previously mentioned conditions was smaller than those presented in other papers. For example, [PERSON] et al. described AgNPs obtained using an aqueous extract of _T. erecta_ leaves as spherical and polydisperse, with an average diameter of 30 nm [24]. Similarly, for an aqueous extract of _T. erecta_ leaves, [PERSON] et al. obtained mostly spherical AgNPs with an average size of 22 nm [37].