esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Evaluation of Dichotomous Estimates/Forecasts
#### 5.4.2 Evaluation of Dichotomous Estimates/Forecasts for Precipitation above 15 mm  
In contrast, for precipitation above 15 mm/day, IMERG indicates better results for POD and CSI in all case studies while ERA-Interim indicates a very weak value (Figure 18a-c and Table 18) that could be due to KuPR (Ku-band (13.6 GHz) precipitation radar) instrument that can detect heavy precipitation.  
Notice that almost in all study areas the value of FAR for precipitation above 15 mm/day from all products was rather high which could be due to observed liquid water in the atmosphere profiles which did not fall as precipitation due to their small size or evaporation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{2}{c}{**POD**} & \multicolumn{2}{c}{**FAR**} & \multicolumn{2}{c}{**CS1**} \\ \hline  &
\begin{tabular}{c} **Number of** \\ **Precipitation** \\ **Days +15 mm** \\ \end{tabular} & **IMERG** & **3B42** & **ERA** & **IMERG** & **3B42** & **ERA** & **IMERG** & **3B42** & **ERA** \\ \hline
**Guilan (G8)** & **85** & 0.35 & 0.32 & **0.72** & 0.65 & 0.71 & **0.51** & 0.21 & 0.18 & **0.41** \\ \hline
**Bushehr (G4)** & **17** & **0.59** & 0.45 & 0.39 & 0.64 & 0.62 & **0.61** & **0.29** & 0.26 & 0.25 \\ \hline
**Kemanshah (G5)** & **51** & 0.57 & 0.44 & **0.63** & **0.50** & 0.61 & **0.50** & 0.37 & 0.25 & **0.39** \\ \hline
**Tehran (G2)** & **57** & **0.52** & 0.47 & 0.51 & **0.48** & 0.72 & 0.60 & **0.34** & 0.21 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Spatially averaged POD, FAR and CSI metrics for precipitation below 15 mm/day.  
Figure 18: (**a–c**) POD, FAR and CSI in four study regions for precipitation above 15 mm/day.
## 3 Method  
Combining the advantages of LSTM for anomaly detection of time series data and SincNet's powerful feature extraction ability for one-dimensional signals, a control torque gyroscope anomaly detection method based on condition classification and transfer learning is proposed. Firstl the Sinc-LSTM network is built, and dataset from the Mars Reconnaissance Orbiter (MRO), the the Soil Moisture Active Passive (SMAP) satellite, and the Mars Science Laboratory (MSL) rover, Curiosity, are used for the two-stage pre-training. Then the telemetry data of the whole life cycle of the control torque gyroscope of the orbiting spacecraft are obtained, and the working conditions are classified. The telemetry data of the control torque gyroscope after condition classification were input into the pre-trained Sinc-LSTM network for training, and the model that could realize the fitting and reconstruction of the control torque gyroscope telemetry data was developed. Based on the fitting and reconstruction model of the telemetry data of the control torque gyroscope, the control torque gyroscope that needs to be detected is detected. The reconstructed data and the corresponding original telemetry data are differentiated, and the threshold judgment is used to complete the fault detection.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 6 Discussion
### Limitations and Recommendations for Future Research  
Despite the study's success in validating structural safety, several limitations must be acknowledged:  
1. Simplified Model Assumptions: The numerical model did not fully account for complex factors such as rock joints, weak layers, and heterogeneous materials, which could influence vibration behavior. Future models could incorporate these variables for greater accuracy.
2. Lack of Long-Term Monitoring: While the study focused on short-term vibration effects, the long-term impacts of repeated vibrations on structural integrity remainunexplored. Extending the monitoring period could reveal whether cumulative stress affects the durability of concrete elements.
3. Exploration of Alternative Construction Techniques: Although the cover-and-cut method proved effective in this case, other construction methods, such as tunnel boring machines (TBM) or NATM (New Austrian Tunneling Method), could be compared to identify optimal techniques for different geological conditions.
4. While this study focuses on the immediate impacts of blasting vibrations, we recognize the importance of examining the long-term cumulative effects of repeated vibrations on structural integrity. Over time, these vibrations may induce micro-cracks or fatigue in structural materials, potentially compromising durability. Future research should incorporate extended monitoring periods and explore advanced predictive models to assess long-term risks. Implementing real-time vibration monitoring systems in construction projects can further enhance safety by identifying and mitigating potential issues before they escalate.
## 2 Concepts and Architecture of Multi-Sensor Signal Processing
### Local Optimization  
The procedure of local optimization follows the _divide-et-impera_ principle and dismantles the design problem into a set of local, smaller design problems with reduced search space, e.g., select or combine methods and to optimize their parameters, or structure gradually in the chain of sensor signal processing. However, the individual design steps depend on each other. This dependence can be honored, by letting the subproblems exchange information with their neighboring blocks. The simplest way to do this is to sequentially evolve the system. The direction of local optimization can be '_bottom-up_' or '_top-down_' optimization, as suggested by Figure 3. In this Figure, the top row repeats the general block strucuture of the intelligent multi-sensor system under design composed of multi-sensor/sensor array (including signal conditioning), signal pre-processing and enhancement, feature computation, dimensionality reduction, and classification (including the hierarchical classifiers). The second row illustrates the distributed optimization of the design tool, which in each local optimization block contains a potentially extensible collection of proven methods and algorithms of sensor and recognition systems and the local optimization tool, including single or multiple assessment methods from Section 3.5, for searching and finding of the best structure and parameters in each block. In addition to the the named assessment functions, which quantify the achieved decision making ability on the regarded level, further constraints related to, e.g., computational complexity and/or energy consumption, could be included as multiple objectives in the optimization approach on each level. As most the optimization tasks pursued here, rely on the concept of _learning-from-examples_, data and supervised information must be made available in an appropriate form on that level. This includes also statistically meaningful validation techniques employed in the design hierarchy.  
In the third row, the knowledge base or memory block is depicted. Its main task is to avoid starting from scratch and to reduce the search space complexity by collecting and employing design knowledge  
Figure 3: The concept of intelligent multi-sensor system design based on local optimization.  
from previous applications. The initial design (expert driven) can be obtained from the knowledge of expert designer or the previous best solution. The knowledge about the methods or parameters from a specific application of sensor systems can be applied to rapidly design for similar application, but usually result in sub-optimum. Those design decisions and parameters of each block in the intelligent systems commonly still need a refinement or adjustment by the optimization procedures. The issue of determining a similar problem in the database gives rise to interesting recognition tasks on a meta-level [46].
# Gridshells in Recent Research--A Systematic Mapping Study
## 3 Gridshells in Recent Research  
The objective of this study has been to get an overview of the field of gridshell research regarding the scholarly fields (who), the timeline of publications (when), the publication channels (where), and, maybe most important, grasp the content and focus of the research (what). The questions address these objectives, and the results are presented in the following subsections.  
What Is the Amount of Peer-Reviewed Journal Papers on Gridshells Published Yearly Since 2011, How Has This Developed over Time, and What Are the Most Common Keywords?  
Following the query in the database search and the definitions used in the screening, this study has identified a total of 327 peer-reviewed journal papers concerning gridshells in the last ten years. As seen in Figure 4, the articles from the mapping increased from ten publications in 2011 to 58 in 2020.  
A brief overview of the content and focus of the gridshell research can be found in Figure 5. Here are the keywords from all articles mapped according to the concurrence, with a minimum of ten co-occurring keywords. This mapping resulted in a total of 54 keywords. The most occurring words can say something about research focus. The five most frequently occurring keywords were shells (structures) (133), structural engineering (44), buckling (42), finite element analysis (32) and structural design (32).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Gridshell type** & \\ \hline \multirow{2}{*}{Bending active} & Gridshells where the initially flat members are bent into shape as defined in Section 1.1, like Mannheim Multihalle \\  & Gridshells constructed from pre-curved members, typically \\  & steel or glue-laminated timber like centre Pompidou Metz \\  & Gridshells where straight members are connected at nodes, \\  & defined in Section 1.1, like British Museum Great Court and \\  & the Pods Sports Academy \\  & Gridshell type is not mentioned or determined \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._  
Figure 4: Publications per year of the mapped articles. The search was conducted in September 2021 and is therefore not covering the total of 2021.
## 3 Application Sectors and Valuable Products from Microalgae Biomass
The analysis of publication trends and research activity in global databases clearly shows the growing interest of the scientific and industrial communities in issues related to the intensification of microalgae production, process automation, and the implementation of tools for monitoring physicochemical and biological parameters. At the same time, the diverse applications of microalgae in the energy, food, pharmaceutical, cosmetics, and environmental sectors, as well as the growing number of valuable substances that can be extracted from their biomass, emphasise the enormous economic potential of these organisms. However, for this potential to be effectively exploited on an industrial scale, advanced strategies for optimising cultivation conditions and precise, multi-parametric monitoring systems must be developed and implemented. In this context, the remaining parts of the thesis focus on discussing the key factors that influence the efficiency and composition of microalgae biomass, as well as advanced technologies that enable their control and regulation. The aim is to show how integrated monitoring and optimisation strategies can help to increase the efficiency, sustainability, and cost-effectiveness of microalgae-based biotechnologies. \begin{table}
\begin{tabular}{c c c c} \hline
**Application Area** & **Process** & **Final Product (Outcome)** & **Species** & **References** \\ \hline \multirow{5}{*}{\
\begin{tabular}{c} Pharmacy, Cosmetology, \\ Functional Foods, Dietary \\ Supplements, \\ Nutraceuticalids, Biomaterials \\ \end{tabular} } & Pigments (phycocyanin, chlorophyls, lutein, astaxanthin) & _Spirulina platensis_, _Dunaliella salina_, _Haematococcus pluvialis_ & [82; 83; 84] \\  & & & _Shizolytrium_ sp., _Nanochloropysis sp., _Isochrysis galbuna_ & [85; 86; 87] \\  & & & _Shi_c_ & _Shi_c_ \\ \cline{2-4}  & & & _Chlorella vulgaris, _Spirulina platensis_ & [88; 89] \\ \cline{2-4}  & & & _Chlorella vulgaris, _Arthospira platensis_, _Tetraselmis chui_ & [90; 91; 92] \\ \cline{2-4}  & & & _Shi_c_ & _Shi_c_ \\ \cline{2-4}  & & & _Shi_c_ & _Shi_c_ \\ \cline{2-4}  & & & _Shi_c_ & _Shi_c_ \\ \cline{2-4}  & & & _Shi_c_ & _Shi_c_ \\ \hline \multirow{5}{*}{
\begin{tabular}{c} Polyakov and \\ EPS extraction \\ \end{tabular} } & Currageenan, alginates, agar, EPS (exopolysaccharide) & _Porphyridium cruentum_, _Chlorella sp., _Chlorella_ sp., _Phaeodactylum tricrontum_ & [93; 94; 95] \\  & & & _Tetraselmis succia_, _Phaeodactylum tricrontum_, _Phaeodactylum tricrontum_, _Scenedesmus sp._ & [96; 97; 98] \\ \cline{1-1} \cline{2-4}  & & & _Shi_c_ \\ \cline{1-1} \cline{2-4}  & & & _Shi_c_ & _Shi_c_ \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of selected economically valuable final products obtained from microalgae biomass.
## 3 Methodology
### RF-EMF Prediction  
In order to determine the effects of weekly changes in RF-EMF levels within daily RF-EMF levels, an ANN model with ten sigmoid hidden neurons was created using the Neural Network Fitting tool in MATLAB. Figure 10 shows the structure diagram of the proposed ANN model.  
The optimal number of hidden neurons in this model was determined by randomly partitioning the input data and evaluating the test MSE for various numbers of neurons (max. 25 neurons) using the same partitioned data. The number of neurons that resulted in the minimum test MSE was chosen. The change in test MSE versus the number of hidden neurons used are presented in Figure 11.  
Figure 10: Proposed artificial neural network model.  
Figure 9: Daily cross correlation coefficients of RF-EMF levels for each week.  
The post-processed RF-EMF data for the same days of the first four weeks were randomly partitioned into 70% training, 15% validation, and 15% testing. The first four week's measurement data were used as input, while the fifth week's data were used as target. The training stopped when either the number of epochs reached 1000 or the validation criterion was met. Because each training had different initial weights and biases, the network was trained fifty times with the LM and the BR algorithm, and the averaged results were given.
## 2 System Description  
Cold storage running on a VCR cycle essentially requires a power supply that is reliable and consistent. The cold storage unit, with specifications listed in Table 1, was designed using a vapor compression refrigeration system running on grid electricity. The vapor compression system comprises four major components, namely, the evaporator, compressor, condenser, and expansion device. Of these, the evaporator and expansion device, which is the capillary tube, are placed inside the cold room, while the compressor and the condensing unit are situated on the outer side. A humidifier is also placed outside, which generates steam through an electric heater arrangement in order to maintain the humidity within the range in the cold room, which is essential for the preservation of perishable food items.  
Figure 1a,b displays the two views (front and side) of the 3.4 m\({}^{3}\) cold storage setup used for this experimental study. The setup comprises the VCR system, the humidifier, and its control panel. The cold room door for product loading/unloading is also shown. The different tests, namely, the no-load test, the water sample test, and the PCM sample test, were conducted on this setup.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Dimensions & \(1.83\) m \(\times\) 1.22 m \(\times\) 1.83 m \\  & (6 feet \(\times\) 4 feet \(\times\) 6 feet) \\ Storage volume & \(3.4\) m\({}^{3}\) (122 ft\({}^{3}\)) \\ Storage capacity & \(0.5\) MT \\ Temperature range & \(-15\)\({}^{\circ}\)C to \(10\)\({}^{\circ}\)C \\ Humidity range & \(80\)–\(95\)\% \\ Cooling capacity & \(0.63\) TR \\ Refrigerant used & R134a \\ Compressor type & Hermetically sealed rotary-type \\ Compressor rating & \(1.5\) kW \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the cold storage unit.  
The section that follows explains the methodology involving the detailed procedure undertaken to carry out the current study.
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches
#### 3.2.4 \(A^{3}\)  
The scheduler proposed in [12], \(A^{3}\), is fully autonomous and adapts to the traffic load. The sender predicts the traffic load by counting the remaining number of packets in its queue. This number is compared to a threshold, and cells are allocated consequently. The receiver estimates the incoming traffic by sampling the channel status without any additional information from the sender. The scheduler is implemented in Contiki and is evaluated on an open-source testbed, from which it is concluded that it improves PDR and delay compared to RBS Orchestra and ALICE for high traffic loads.
## 3 Application and Results
(2) During coring operations, due to the slow increase in depth, the temperature and pressure basically did not change. But due to the rotation of the  
Figure 5: X wave and P wave scanning picture of several typical runs. Figure 6: The temperature and pressure curve of the WPCS in run 13 (the critical line is the phase equilibrium curve of gas hydrate). 1 Lowered through the drill string to latch in the BHA; 2) coring; 3) retrieved; 4) pressure core transferred. drill string, the temperature and pressure had a certain pulsation change. (3) Upon retrieval, initial stages maintained stable temperature and pressure levels. However, delayed closure of the ball valve during later retrieval stages caused a reduction in confining pressure, resulting in temperature elevation and subsequent hydrate destabilization. This led to increased pressure within the hydrate core in the pressure chamber. (4) During pressure core transfer operations, both temperature and pressure remained relatively constant. According to the design requirements of WPCS, the sequence involves raising the inner pipe assembly above the ball valve, followed by the activation of the ball valve drive sub and drive spring to facilitate its overturning and subsequent sealing. However, during three specific runs (run 8, run 9, run 13) detailed in the data, the depth of turn-over of the ball valve was recorded, respectively, at 198.92 m, 496.85 m, and 1580.03 m, as indicated in Figure 7. Delays in the closure of the ball valve during the retrieval of the drilling tool resulted in a reduction in confining pressure within the pressurized core. This unintended consequence led to an increase in temperature, thereby compromising the stability of the hydrate and resulting in its decomposition. Concurrently, analysis of pressure data, depicted in Figure 7, indicates that the hydrate continued to decompose after the ball valve closure, resulting in a continuous increase in the pressure of the hydrate core within the pressure chamber. Figure 7: The temperature and pressure curve of the WPCS recorded over time during the whole coring process: (a) run 8; (b) run 9; (c) run 13. 1 Lowered through the drill string to latch in the BHA; 2 coring; 3 retrieved; 4 pressure core transferred.
# A Review of Plant Disease Detection Systems for Farming Applications
## 2 Literature Review: Precision Agriculture Research Developments
### Plant Disease/Pest/Weed Detection System Basic Principles  
Detection of diseases, pests, or weeds is achieved by utilizing machine learning (ML) [3; 5; 6; 11; 12]. [PERSON] [3] defined ML as an intelligent technique in which a machine is capacitated to recognize a pattern, recall historical information, and train itself without being commanded to do so. Both supervised and unsupervised training strategies can be utilized for machine training [8]. While there are distinct training and assessment datasets for supervised training, there is no such distinction for unsupervised training datasets [12]. [PERSON] [12] further stated that since ML is an evolving procedure, the machine's performance becomes better with time. As soon as the machine has finished learning or training, it may classify the data, make predictions, and even generate fresh test  
Figure 2: The general structure of this review paper.  
data from which its re-trains itself, and the process goes on and on [8]. [PERSON] [4] defined ML as a decision-making tool capable of visualizing the potentially complicated inter-relationships between important parameters on a farm and making educated predictions and/or decisions.  
The author further provided an illustration of an ML application in precision agriculture as seen in Figure 3. In the three-level precision agricultural layout shown, the first level, which is the physical layer, represents all the field equipment such as sensors, trackers, actuators, and probes that are in physical contact with the farm environment and are collecting data for further processing [4]. In the second level, the edge layer is where the processing of the data collected in Level 1 is taking place to convert the raw data into useful information that is used to inform the decision making. The decision making takes place at this level through computational tools such as computers, microcontrollers, microprocessors, and similar ones [4]. In the third level (the cloud layer), the storage of data for iterative training of the machine takes place [4]. Therefore, the plant disease detection system is made up of two main subsystems, viz. the image-processing system and the classification system. The image processing is further subdivided into four steps. The four most cited different classification protocols are summarized in Table 1.  
The latest studies of phenomics and high-throughput picture-data gathering are available; however, most of the research on image interpretation and processing can be found in textbooks that dive into extensive detail into the methodologies [14]. Figure 4 summarizes the techniques for image acquisition and processing generally utilized for plant disease detection systems.  
Figure 3: Format of precision agriculture system [4].