esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Implementation of The Proposed System
### Data Storage
#### 3.1.2 Storage Model and Retrieval Method  
(1) Storage Model  
In this study, we use a hybrid data storage model for online applications, as shown in Figure 3. Plant electrophysiological data and their metadata are separated. Semi-structured and unstructured plant electrophysiological data are stored in HDFS and HBase. Key features of raw data will also integrate with raw data in HBase, and plant electrophysiological metadata is stored in MySQL. The main file formats in the current system are shown in Table 4. The intracellular and extracellular recording and the MEA recording data have different binary file formats according to different data acquisition software. To facilitate the experimental analysis, the raw plant electrical signals data is converted to a commonly readable CSV format for reading and analysis. CSV files will be stored in HDFS or HBase in a compressed format. Spark or MapReduce can read these CSV files in an efficient way for computing. For optical recording, the raw TIFF format images need to be converted to JPEG or PNG files for visualization in a browser.  
When user creates a dataset, system will save the experimental metadata of dataset into the MySQL, which will auto-generate a unique ID for the dataset. Next, when user uploads files to the dataset, the unique ID combines with the filename to generate the unique RowKey for each file in the HBase. Then, save the file into HBase. The unique ID can be used to search all the files belong to the special dataset in HBase.  
Large files are defined as data size larger than 64 MB, such as MEA files, are stored directly in HDFS. However, for the plant electrical signals recording, there are a large number of small files (the file size is less than the size of the HDFS data block). Therefore, HBase is used to store small files. In the addition, the extracted features are also stored in the HBase for the batch analysis tasks [49].  
(2) Retrieve  
When the dataset is created, users can retrieve it by the dataset metadata, e.g., the dataset name, creator, creation time, and even whether dataset is open to the public.  
Retrieval function is implemented through the back-end of system, and fuzzy query is performed by calling MySQL database. For the dataset query, the main SQL statements are as follows:  
Select datestid, name from Dataset where name like \"%var_1$% and Author like \"%var_2$%\" and creattetime like \"%$var_3$%' and open like \"%$var_4$%\" and species like \"%$var_5$%\" and recordMethod like \"%$var_6$%' and stimulation like \"%$var_7$%\" and sigtype like \"%$var_4$%'.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experimental Type** & **Binary File** & **Text File** & **Storage System** \\ \hline Intracellular and Extracellular Information & lsd, dat & csv, txt & HBase \\ MEA & modal & csv & HDFS \\ Optical mapping & TIFF, JPEG & & HBase \\ \hline \hline \end{tabular}
\end{table}
Table 4: The formats of file. MEA: multi-electrode arrays; HDFS: Hadoop distributed file system.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 3 Si-Based Photodetectors
### Ge-Introduced PD  
Another approach involves the introduction of Ge which has a smaller bandgap energy than Si. By appropriately combining Si with Ge, the detectable wavelength can be extended from 1100 nm, with Si alone, to longer than 1550 nm, covering all the telecommunication wavelengths. Furthermore,  
Figure 19: Schematic view of silicon resonator-enhanced PD [60].  
Figure 18: Cross-sectional view of waveguide PD [58].  
Ge exhibits higher mobility of electrons and holes, resulting in a faster detection. Therefore, Ge has long been considered as an ideal candidate to replace the conventional III-V detectors. However, the adoption of Ge is severely restricted due to the mismatch of the lattice constants between Si and Ge. The lattice mismatch poses a constraint on the maximum Ge thickness that can be grown on Si without introducing crystal defects [63]. Consequently, Ge thickness is limited for maintaining the dark current level as low as possible, resulting in a low quantum efficiency. Hence, the epitaxial growth of a thick Ge layer on Si should be the key to the realization of the SiGe PD and, more importantly, it should be achieved in a CMOS-friendly manner.  
An intuitive way to obtain a thick Ge layer with minimal dislocations is by using a graded buffer layer [64]. Based on this technique, the work in [64] achieves an extremely low dark-current level which is comparable to the theoretical reverse saturation current. Even though it completely addresses the problem of lattice mismatch, it usually results in a tall PD structure; consequently, this technique is not compatible with CMOS back-end processes and waveguide coupling. Alternatively, a direct growth of Ge on Si with the aid of a thin Ge buffer layer can be more effective [65]. Starting from a thin Ge buffer layer formed at a low temperature of 350 \({}^{\circ}\)C, the Ge layer can be grown to be sufficiently thick for the absorption of infrared light at a relatively high temperature of 600 \({}^{\circ}\)C, thus avoiding the problem of crystal defects. Thus, the detection of a wavelength of 1300 nm can be successfully achieved with a responsivity of 240 mA/W. Although not reported in this work, the dark current level is estimated to be high because the defects are not clearly eliminated. In [66], it is demonstrated that the dislocation density can be remarkably reduced by applying cyclic thermal annealing at a high temperature of 900 \({}^{\circ}\)C. Further extending the work in [65] by employing a cyclic thermal annealing process, the work in [67] achieves a high responsivity with a low dark-current level and the frequency response is suitable for gigabit operation. However, high-temperature annealing may not be compatible with the standard CMOS process, necessitating a different defect-handling technique for the seamless integration of the EPIC. The work in [68] demonstrates that the selective growth of Ge through multiple hydrogen annealing is also a good approach to reduce the dislocation density.
## 3 Proposed Zone-Based Secure Multicast Techniques  
This chapter describes the zone-based secure multicast (ZSM) technique, which ensures secure data transmission from the source node to the member nodes in the presenceof malicious nodes in a MANET multicast network, while providing routing efficiency, such as packet delivery rate.
## 5 Results and Discussion
The tracking error \(\delta P=\sqrt{\delta P_{x}^{2}+\delta P_{y}^{2}}\) is basically less than 0.2 m.  
The simulation results show that the maximum tracking errors \(\delta P\) of the track-following servo subsystem using EMPC controllers are almost less than 0.2 m. According to Equations (39) and (40), it can obtain the influence factor of the tracking error on the CLRV motion in the horizontal direction:  
\[\varepsilon_{h}<0.125 \tag{41}\]  
In addition, the error impact factor in the vertical direction is as follows:  
\[\varepsilon_{p}<0.00045 \tag{42}\]  
\[\Delta F_{v}=F_{C}\bigg{[}1-cos\bigg{(}arctan\frac{\delta P}{10}\bigg{)} \bigg{]} \tag{36}\]0 The results of tracking desired path three by the crane and trolley are presented. The figures illustrate the following outcomes: a comparison between the trajectory of the upper lifting point and the expected trajectory under three different controllers, the tracking error \(\delta P=\sqrt{\delta P_{x}^{2}+\delta P_{y}^{2}}\) of the track-following servo subsystem, and the movement speed of both the crane and trolley. The simulations yield some key findings: the EMPC demonstrates a notably impressive performance for the system, with explicit MPC delivering the objectively best results. While the PI and LQI controllers show comparatively inferior results, they remain reliable options with their own strengths. The PI controller is easy to apply, while the LQI controller exhibits excellent robustness and stability. However, their application is limited for multi-constraint control systems. Although the EMPC shows good performance under different expected trajectories, it also has some limitations in terms of stability, especially in dealing with large disturbances in the \(X\) direction (i.e., when the CLRV speed changes rapidly). In such cases, the crane may lose control. To address this issue, future work will focus on conducting relevant methods to further analyze the stability of the proposed EMPC controller. In conclusion, EMPC is a controller that can provide the smallest possible dynamic tracking error and steady-state error under the condition of dealing with multiple constraints, and it has better control performance. Moreover, frequent acceleration and deceleration should be avoided in the \(X\) direction when driving the CLRV, to reduce the impact of the large inertia of the crane on the controller's tracking performance. Based on the simulation results, we compare the tracking performance of the track-following servo subsystem of the suspension gravity compensation system to that of the system designed by [PERSON] et al. [2]. Although the maximum tracking error of [PERSON] et al. 's system is approximately 0.1 m, its crane employs open-loop control, making it only suitable for tracking a slow-moving unmanned lunar rover. In contrast, the research presented in this paper provides a solution to the theoretical research gap of the track-following servo subsystem suitable for CLRV experiments.
## 3 Data Fusion and Multimodal Integration
### Multimodal Data Representation
#### 3.2.2 Strategies for Handling Complex, Noisy, and High-Dimensional Data  
Navigating complex, noisy, and high-dimensional data presents one of the primary challenges in multimodal learning [43]. Here are several techniques to tackle these challenges effectively:  
* **Dimensionality reduction**: Techniques such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoders are commonly used to reduce the dimensionality of data. By decreasing the number of features, these methods simplify the fusion process and mitigate challenges like the curse of dimensionality, which can hinder model performance [40]. \[\mathbf{X}_{\text{reduced}}=\text{PCA}(\mathbf{X})\] (13) where \(\mathbf{X}\) represents the original high-dimensional data, and \(\mathbf{X}_{\text{reduced}}\) is the smaller, reduced feature set.
* **Data augmentation**: This includes techniques such as adding synthetic noise and generating new data points to enhance the robustness of the multimodal system. These methods enable the system to manage noise and variability in real-world data more effectively.
* **Noise filtering**: to enhance the quality of sensor data before it is fused, noise filtering methods such as Kalman filters, wavelet transforms, or deep learning-based denoising autoencoders can be utilized to clean the data.
# NTPP-MVSNet: Multi-View Stereo Network Based on Neighboring Tangent Plane Propagation
## 4 Experiments
### Evaluation Metrics  
In order to evaluate the reconstruction quality of our model, ascertaining the accuracy and completeness of the predicted point cloud when weighed against the ground truth point cloud is essential. The completeness and accuracy of the distance metric are crucial metrics utilized for effective point cloud evaluation. Completeness of the point cloud is identified as the shortest distance measure between every ground truth point and its corresponding anticipated point. Accuracy, on the other hand, pertains to the shortest distance between each estimated point and its corresponding point in the actual ground truth point cloud. The F1 score, an evaluation technique considering both completeness and accuracy parameters, contributes to providing a comprehensive overview of the point cloud's reconstruction quality.
## 2 Results and Discussion
### Chemical Oxygen Demand Analysis  
Decolorization of dye does not mean that there is complete removal of the organic carbons from the water samples. Mineralization, which is defined as the complete decomposition of organic compounds into CO\({}_{2}\) and H\({}_{2}\)O, should be the target of any photocatalytic processes. One of the results of mineralization is the lowering of the chemical oxygen demand (COD) values of the treated samples. In this study, the presence of organic substances or intermediates can be detected by using a COD test. The COD test is attributed to the degradation of MB dye, as well as its by-products during the photocatalytic reaction using the TP2 sample. There is a possible contamination from DSAT, and these contaminations were completely cleaned up during the washing process prior to the photodegradation of MB dye. Figure 10 presents the detected COD values for the mineralization of MB dye versus  
Figure 9: The recyclability graphs of TP2 under the photodegradation of MB dye.  
irradiation time. The COD values (mg\(\cdot\)L\({}^{-1}\)) detected were 0.81, 0.75, 0.60, 0.45, 0.30, 0.25, 0.10 and 0.05 and kept decreasing with time at 60, 120, 180, 240, 300, 360, 420 and 480 min, respectively. Hence, the complete mineralization of MB dye through the COD test was greatly caused by the improved diffusion of dye into photocatalyst layers, stemming from the porous surface of the TP2 photocatalyst sample.
## 5 Methodology and Development
### Machine Learning Dataset Preprocessing
#### 5.3.2 Decision Tree Processing  
A decision tree is a flowchart tree structure. It is characterized by internal nodes, one for each variable that makes up the input dataset. From them, new branches appear, each of which represents a decision rule. At the end of the tree, each leaf-node represents an outcome, the result of the calculation.  
In addition, unlike neural networks, which are black boxes, in a decision tree, the calculations being made are easy to understand because it is totally transparent [51].  
Decision trees follow some specific steps to build the most effective tree to get the best results in each case. That is, it looks for the best feature of the dataset using an algorithm called \"attribute selection measures\" to split the dataset into different parts, i.e., smaller subsets. This process is repeated recursively for each new child, trying to meet one of the desired conditions. The possible stop conditions are the lack of attributes, the lack of subsets, or the leaves that last belonging to the same attribute [51]. As can be seen, the attribute selection measure is the cornerstone of the decision trees. The main idea behind it is to provide each feature with a rank by explaining the input dataset. The one with the best rank is selected as the splitting feature.  
There are different algorithms to determine the rank. The most common one is the \"information gain\" which calculates the entropy of the features that shape the dataset in order to measure the \"randomness\" of the set. Furthermore, the entropy and \"Gini index\" are used to measure a weighted sum of the impurity of each partition of the selected features [52]. Different tests were carried out in order to reach different results. Some other parameters that can be chosen in a decision tree are the depth of the tree itself, to make simpler trees that are not overfitted, or the criteria to split the dataset itself (not the feature), by choosing the best split calculated or by randomizing the process.
## 2 Data and Methods
### Data
#### 2.2.3 Presence/Absence Data  
Presence/absence data were collected in the field using Garmin eTrex GPS with an error below two meters, concurrently with the capture of PS imagery in February 2022. A stratified random sampling, based on land use/land cover types, was employed. A total of 557 points were collected, with the distribution proportional to the extent of each land use/land cover type in the district. Of these, 195 points (35%) represented presence data in Acacia _decurrens_ planted areas, while the remaining 362 points (65%) were collected in croplands, grasslands, natural forests, and settlement areas. The proportions were determined based on previous studies conducted in the district by [PERSON] and [PERSON] [62] and [PERSON] et al. [63]. The minimum distance between two consecutive points was two hundred meters, and the Spatially Rarefy Tool in the SDM ToolBox v2.10 under ArcGIS 10.8 was used to reduce the spatial autocorrelation between points.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 2 Materials and Methods
### Coupled PV and Air Conditioning  
Most common PV efficiencies are around 15 to 20%, while some high-quality PV panels can go above 22% efficiency. The efficiency of PV technology has increased and the cost has dropped in the past few years due to large scale installations and the learning  
Figure 2: Photovoltaic (PV) technology layout over the German Jordanian University (GJU) campus.  
curve. The c-Si PV module has around a 75% share of the PV technology market. PV systems' prices are expected to decrease by 60% in the coming decade [48].  
In this research, two electricity sources were used to power the air conditioners: the main source was photovoltaic, while the utility grid acted as a backup for periods without sunlight, where the system automatically switched between sources, ensuring that the power supply was not disrupted. They could be utilised in air conditioning mode in addition to heating or dehumidification modes. Both the photovoltaic panels and 220 V grid power supply were directly connected to an external unit, as shown in Figure 3. It included a dual MPPT tracker to optimise the usage of available solar energy and to reduce the extent to which the electricity grid was used.  
The seasonal refrigeration efficiency (SEER), which denotes the air-conditioner's cooling output over the electricity costs, can be as high as 24 BTU/W. When the sun is shining, hybrid air conditioners function completely on the photovoltaic source, and if energy class A++, can offer 3500 W (12,000 BTU) or 5000 W (18,000 BTU) of air conditioning and heating.
This kind of PADC is collectively nominated as a channel-interleaved PADC (CI-PADC), including the time interleaved PADC [16; 17; 18] and time-wavelength interleaved PADC [19; 20; 21; 22; 23]. The influence of channel mismatch on the performance of CI-PADCs has been widely studied, including clock timing mismatch, amplitude mismatch, pulse shape mismatch, photodetection bandwidth mismatch, and electronic aperture jitter mismatch [16; 17; 18; 20; 21; 22; 23; 24]. And many mismatch correction methods and compensation algorithms, such as the spectral analysis and compensation method and fractional Fourier domain filtering, were proposed to eliminate spurious components and improve signal ENOBs. However, there are few research reports on the impact of a channel sampling timing misalignment caused by a fiber length misalignment or an electrical phase misalignment of optoelectronic or electronic devices such as a photoelectric detector (PD), EADC, RF lines, etc., on channel synchronization. Herein, we established a two-channel, time-wavelength interleaved PADC system with a sampling rate of 10.4 GSa/s, and a concise method for measuring and correcting the channel sampling timing misalignment of PADCs in signal recovery was demonstrated. Two detection RF signals, \(f_{1}=100\) MHz and \(f_{2}=200\) MHz, were selected to measure the misalignment by finding the minimum root mean square error (MRMSE). When the data from channel 2 were shifted forward 12 positions, the MRMSEs of the two detection signals were found with the values of 44.4174 and 28.9738, respectively. Additionally, the peak limit method (PLM) and normalization processing were adopted to reduce the impacts of signal peak jitter and power inconsistency between the two channels. The signals collected from the two channels, as well as the corresponding direct synthesis signals and shift synthesis signals, were analyzed and discussed in both time and frequency domains. The ENOB of the signal synthesized directly from two channels of 100 MHz data was only \(-0.1051\) bits, while it was improved up to 4.0136 bits after correcting the channel sampling timing misalignment. Further, the ENOB reached 4.5668 bits when the PLM and normalization were added. Finally, the established two-channel PADC system was used to detect the 21.1 GHz signal. The ENOB had increased from the initial \(-0.9181\) to 4.1913 bits by the joint corrections of the PLM, normalization, and shift synthesis. Our proposed measurement and correction method can help achieve channel synchronization by controlling the fiber length of each channel in the hardware, as well as effectively recover the test signals in software data processing.
## 2 Materials and Methods
### Meshes Generation for FEM Analysis and 3D Printing  
Geometries of the femur, tibia and cartilage were imported on the CAD software 3-Matic 17.0 (Materialise, Belgium) to generate the needed superficial and volumetric mesh for FEM and 3D-printing steps. Since the focus of the analysis is on cartilage, the bones were meshed coarsely to reduce the computational requirements, while a finer mesh was used for the femoral cartilage. Specifically, a 2D triangular mesh with a size of 4 mm was used for most of the bone surface, while a 2 mm triangular mesh was used for the cartilage. To achieve a better load distribution at the transition from bone to cartilage, called the chondral zone, the bone mesh was refined towards the contact zones with the cartilage, ultimately reaching 2 mm. Moreover, a 1 mm mesh was employed to finely mesh the cartilage regions containing geometric singularities (holes caused by chondral defects).  
The volume mesh was generated using quadratic tetrahedral elements (TET10) for femoral cartilage, while for the femur and tibia, as the bone components were of the least focus, were used linear tetrahedral elements (TET4) [21]. In the Control model, the average 3D element mesh size measured 0.81 mm, whereas in the Degenerative model, it was 0.79 mm.  
Anatomical supports for mechanical testing were also designed to realize a physical model of the knee joint to compress cartilages with standard universal testing machines. A 80 \(\times\) 80 mm square was created with the intent of providing a surface area sizable enough to guarantee adequate compression force distribution. A 25 \(\times\) 25 \(\times\) 10 mm rectangle was created to enable accurate closure between the testing machine's grips and suitable positioning of the square support on the end of the grips. The superficial and volume meshes generated for the anatomical supports were the same as mentioned above.