esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The current research on the application of plant protection UAVs mainly focuses on the effect of various parameters of aerial spraying on the droplet deposition distribution and drift distance. These spraying parameters mainly include flight height and velocity [15; 16; 17; 18; 19; 20; 21], spray volume and nozzle size [21; 22; 23; 24], meteorological conditions [17; 24; 25; 26; 27; 28], plant shape [29; 30], and spray system [31; 32]. The comparison of control efficiency against different insects and plant diseases between UAVs and conventional sprayers was also investigated [33; 34; 35; 36]. Furthermore, UAVs have been used to deliver biological control agents for pest control [37; 38]. Considering the advantages mentioned above, plant protection UAVs are undoubtedly a suitable choice for cowpea pest control, while effectively maintaining the control effect and remarkedly decreasing the occupational exposure to pesticides. However, the pesticide exposure assessment of operators during the spraying application with UAVs, especially the comparison with conventional ground sprayers, has not been well investigated. In the present study, the control against cowpea thrips _Megalurothrips usitatus_ (Bagnall) in Hainan Province was chosen as the model scenario. Spinetoram suspension concentrate (SC) was used as the insecticide to control cowpea thrips. Due to its distinctive properties, such as solubility in spray mixtures, low toxicity, quick quantification, and a bright color differentiating it from naturally occurring substances, we have reported that water-soluble food dye of Allura Red could be used as a pesticide surrogate for the exposure assessment of operators and spray deposition on target crops [39; 40]. In this work, Allura Red was applied for occupational exposure quantification to compare the exposure difference between UAVsand commonly used knapsack electric sprayers. Moreover, the control efficiency against cowpea thrips by aerial and ground spraying was also investigated and compared.
## 2 Measurement System and Data Collection Methodology
### Measurement System  
The measurement campaigns were performed using the Rohde & Schwarz (R&S) TSMA6 toolkit [37], which includes the R&S TSMA6 network scanner and a laptop running the ROMES software. ROMES was used for visual inspection of the ongoing measurement campaigns and for exporting the corresponding collected data. The R&S TSMA6 network scanner is an integrated system comprising a Radio Frequency (RF) omnidirectional antenna and a Global Positioning System (GPS) antenna. The RF antenna was used for passive monitoring of downlink control signals transmitted by 3 rd Generation Partnership Project (3 GPP) access technologies, i.e., 5G NR, Long-Term Evolution (LTE), and Narrowband Internet of Things (NB-IoT), working at different licensed frequency bands. The GPS antenna was used to identify the geographic information of the measurements being collected. As shown in Figure 1, the used measurement system included an R&S TSMA6 toolkit, RF and GPS antennas, ROMES software, and also a 5G-capable device running performance measurements. Note that performance-related data collected via the 5G-capable device are not included in the present dataset, which instead contains coverage-related data collected via TSMA6 scanner, as further detailed in Section 3.  
More in detail, the R&S TSMA6 scanner was used to measure 5G NR Synchronization Signal Blocks (SSBs) transmitted by the 5G BSs active in the area covered by the measurement campaigns. In particular, TSMA6 was used to (a) provide Automatic Channel Detection (ACD), in order to detect downlink signals by cellular systems operating at different channel frequencies, and (b) decode Physical Cell Identifiers (PCIs)/SSBs information, ultimately providing quantitative coverage measures for all the detected signals. TSMA6 was also used to provide estimated positions of the detected 5G BSs, which were identified by the corresponding PCIs.
# A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
## 3 Mobility-Aware Adaptive Duty Cycling Mechanism in Tunnel Excavation  
### Tracking Objects  
As the tunnel excavation progresses, the excavation face, i.e. blind end, gets farther away from the tunnel entrance. Accordingly, the equipment and construction workers have to move to the face to work; so their traveling distance gets longer. As previously mentioned in the introduction, a variety of equipment is required, depending upon the construction process. During the intermission between two consecutive processes, there is traffic to switch the equipment inside the tunnel being constructed, as well as construction workers. Even during a single construction process, traffic can also occur.  
We designed a ZigBee [23] based system because Radio-Frequency Identification (RFID), that is typically applied technology to the tracking system [24], needs numerous anchors that are used for estimating the location of mobile objects and can only transmit limited information. The proposed system can conduct tracking of mobile objects by cooperation between a small number of anchors (cluster heads) with sensor nodes attached to mobile objects. Also, ZigBee based approach can offer support for a wide range of services by applying several sensors located in the tunnel environment.  
Figure 4 illustrates the tunnel excavation and tracking objects. In terms of wireless communication, the equipment and construction workers have attached monitoring devices (sensor nodes) that are equipped with a processor, sensors, and radio module in order to report their state to a remote operator as shown. Also various sensors, including inertial sensors such as accelerometer or gyroscope, are used to estimate positions and velocity of moving objects as well as detect dangerous situations at a tunnel excavation site.  
It is impossible to directly send data of each construction unit to the remote operator, so that sensor nodes compose groups (clusters) and send their data to a cluster head of each cluster. The cluster head gathers data of all member nodes and sends the collected data to the gateway that is  
Figure 4: Illustration of tunnel excavation and tracking objects.  
installed at the entrance of the tunnel. Each cluster head connects to neighbor cluster heads, making up the backbone link, to send all kinds of monitoring data toward the gateway. The gateway can be connected to operator by wireless interfaces such as ZigBee, Bluetooth, WIFI, and Cellular (3G/LTE), which enables monitoring of further data.
# Minimum-Energy Scheduling of Flexible Job-Shop Through Optimization and Comprehensive Heuristic
## 4 Minimum-Energy Scheduling Heuristic with Due Times
### Illustration of the Assignment and Scheduling Process of the Proposed Heuristic  
To illustrate the core of the new assignment process represented by relation (24), Figure 5 represents a situation after seven machine assignments and scheduling of the operations of the jobs considered in Section 2.2. For the eighth iteration of the inner loop of the algorithm, the active rank of job 1 is 4, with candidate activity \(O_{15}\) for machine assignment and scheduling; for job 2, the active rank is also 4, with candidate activities \(O_{24}\) and \(O_{25}\); and finally, for job 3, the active rank is 3, with candidate activity \(O_{33}\).  
Here, only numerical values necessary to understand the selection process of Equation (24) are introduced. Table 2 gives the next free times for the seven machines; Table 3 gives the end time of already scheduled and assigned operations, and Table 4 gives the added delays and energies resulting from a machine assignment to each candidate operation in the active rank of each job.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Machine \(l\)** & **1** & **2** & **3** & **4** & **5** & **6** & **7** \\ \hline _nf_ (\(l\)) & 0+12 & 0+14 & 0+17 & 0 & 0 & 0+7 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Current next free times for the machines in the job shop.  
Figure 5: Current machine assignment after 7 iterations of the inner loop.  
Now, assuming that the sets \(MM_{ik}\) for the candidate operations are subdivided in two subsets according to the spent energy and are given by \(MM_{15}^{1}=\{m_{4}\}\), \(MM_{15}^{2}=\{m_{3}\}\), \(MM_{24}^{1}=\{m_{3},\ m_{5}\}\), \(MM_{24}^{2}=\{m_{4}\}\), \(MM_{33}^{1}=\{m_{6}\}\), and \(MM_{33}^{3}=\{m_{7}\}\), the earliest start time for \(O_{15}\) is \(max\{0,\ 17\ +10\}\); for \(O_{24}\), it is \(min\{max\{14,\ 12\ +8\},\ max\{14,\ 12\ +10\}\}\); and for \(O_{33}\), it is \(max\{0,\ 7+14\}\). Then, at this stage, relation (24) produces \(i^{*}=3\), \(k^{*}=3\), \(h^{*}=2\), and \(l^{*}=3\), i.e., at this stage, operation \(O_{33}\) is scheduled at \(t_{33}=20\), and machine \(m_{3}\) is assigned to process it.  
The local decision rule of this heuristic, which first addresses the temporal constraints by considering the most quickly available machines to perform an operation, before making a choice based on energy consumption, seems adapted to the situation of flexible workshops where different machines can perform the same operations.
## 3 System Architecture and Service Process  
The agricultural environment monitoring server system proposed in this paper collects environmental information such as luminance, temperature, humidity, wind direction, wind speed, EC, pH, CO\({}_{2}\)_etc._ which affect growth of crops and soil information through the WSN environmental sensors and soil sensors installed at the outdoors, and collects image information on the outdoors and location information on the position where the server system is installed through CCTVs and GPS modules. The information collected in real time like this is converted into a database through the agricultural environment monitoring server, which provides suitable information to producers according to a variety of services. This section describes the structure and components of the proposed system and services provided by the system.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
### Research Objectives  
The present study pursues a threefold objective: (1) Identify and prioritize the most commonly used external factors affecting the two primary constructs, such as perceived usefulness (PU) and perceived ease of use (PEOU) of the Technology Acceptance Model (TAM) in e-learning, agricultural and virtual reality innovations. Those three sectors are selected based on the innovative tool's nature under analysis. (2) Identify the main research topics addressed in the literature of TAM in e-learning, agriculture and virtual reality. (3) Extend the application of the Quality Function Deployment (QFD) as an organizing framework for the literature review. The results obtained are useful for academics and practitioners conducting similar research to have a general idea and theoretical evidence about the external factors driving the acceptance of technologies. The paper starts with a brief description of the TAM and QFD models (Section 2) and the proposed research methodology (Section 3). Then, we proceeded to the case study (Section 4) and discussion (Section 5), to analyze thanks to QFD the most commonly used external variables in the fields of interest that affected perceived usefulness (PU) and perceived ease of use (PEOU) of the Technology Acceptance Model. Based on the most commonly used external variables, we proposed a TAM extension for further validation within the context of the FARMER 4.0. Finally, we reported our conclusions and future directions of investigation in Section 6.
# Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres
## 4 Case Application of a Machining Centre
### Comparison Analysis  
The proposed method in this paper is compared with the importance evaluation method proposed in Reference [49]. When evaluating the importance of machine tool system components, the method in Reference [49] only evaluated the importance of components from the perspective of the fault propagation mechanism and did not consider the structural characteristics of the model. Combining the application examples in this paper, when the model structure characteristics are not considered and only the fault propagation mechanism is considered, the fault propagation probability value of each directed edge at 1500 h can be calculated according to Equations (10)-(13). Combined with the fault propagation hierarchy structure model of the machining centre in Figure 6, the fault propagation hierarchy structure model of the machining centre based on the fault propagation probability is drawn as shown in Figure 14. The model only considered the fault propagation mechanism of the machining centre.  
Figure 13: Comparison diagram of the fault propagation intensity of each directed edge at different times.  
As expressed in Figure 14 at 1500 h, the fault source of the machining centre is component D. The fault will propagate along the components with a high probability of fault propagation. As \(P(v_{D},v_{B})=0.2397>P(v_{D},v_{W})=0.2365>P(v_{D},v_{NC})=0.2358\), the fault will preferentially propagate along D\(\rightarrow\)B and because \(P(v_{B},v_{F})=0.2866>P(v_{B},v_{T})=0.2830\), the fault is more likely to propagate along B\(\rightarrow\)F. The critical fault propagation path at this time is D\(\rightarrow\)B\(\rightarrow\)F and the critical nodes are D, B, and F. That is, at 1500 h, the fault is most likely to be transmitted as such: electrical system\(\rightarrow\)spindle system\(\rightarrow\)feeding system. However, according to the method proposed in this paper, the result of calculation and analysis is D\(\rightarrow\)NC\(\rightarrow\)B\(\rightarrow\)F. There is a certain difference in the critical fault propagation path obtained by the two methods that is mainly because the method based on the fault propagation probability does not consider the structural characteristics of the model. The component NC plays an important role in the fault propagation structure model; when it fails, it will have a greater impact on the entire system, thus requiring attention. The influence of the structural characteristics of the model on the propagation of faults cannot be ignored. Therefore, the method proposed in this paper is more conducive to the analysis of the fault propagation behaviour of a machining centre and the analysis result is more reasonable.
## 4 Implementation and Discussion of Results
### LSTM Deep Learning (Setup and Training)
#### 4.2.3 Prediction of Future Confirmed, Death, and Recovered Cases  
The time-series data provide information on COVID-19 disease development and investment resource management. Such information can be used to analyze new confirmed, recovered, and mortality cases that will occur. The LSTM model is used to fit COVID-19 cases from 1 April 2020 to 31 May 2021, based on the assumptions and by applying the feature set utilizing the current available time-series data. We report a 14-day forecast, 30-day forecast, and 60-day forecast of the COVID-19 pandemic for each of the three cases: confirmed (Figure 5), recovered (Figure 6), and mortality (Figure 7).  
In Figures 5-7, the orange line extending to June 2021 represents the actual daily confirmed, recovered, and mortality cases, and is followed by the forecasts for 3 periods: 0-2 weeks (yellow line), 2 weeks-1 month (black line), and 1-2 months (green line).  
Figure 5 indicates that the number of daily new confirmed cases (based on 60-day ahead forecasts) fluctuates between approximately 900 and 1600. It is seen that the number of daily new confirmed cases maintains a consistent fluctuation without any clear sign of a downtrend for the next 60 days, assuming existing illness preventive measures, the social environment, and medical service investment remain unchanged. In addition, the 60-day ahead forecast does not show periodic growth.  
Figure 4: A scatter plot of the mortality cases by the LSTM on the testing dataset.  
Figure 5: Prediction over a 60-day forecast for the confirmed cases using the LSTM model.  
Figure 6 indicates that the number of daily new recovered cases (based on 60-day ahead forecasts) fluctuates between approximately 600 and 2600, in a somewhat wider range than the confirmed cases. The forecast curve, though inflating after the first two weeks, does not reveal any increasing or decreasing trends. Thus, based purely on the LSTM model, we maintain a similar conclusion about the recovered cases, given all other conditions are unchanged.  
A forecast of the mortality cases reveals an interesting picture. There is an increasing trend in the forecasted mortality cases during the first two weeks, until it falls initially during the interim period of 2 weeks to 1 month before spiking suddenly, and finally showing a clear downtrend during the final phase of our 60-day forecast. The model of the mortality cases appears to be reflecting the trend that prevailed in the actual daily mortality cases from the start of our records until February 2021. We now present our forecasts using the three models for each of the three cases: confirmed, recovered, and mortality.
## 6 System Evaluation and Model Prediction Performance
### Models Prediction Performance
The outcomes of the experiment validate the efficacy of integrating EKF and DL technologies into the FELL platform, enabling the effective prediction of electric vehicle battery health and offering diverse short-, medium-, and long-term prediction results. Figure 14: Overall comparison of models predicting the SoH of EV batteries.
## 2 INS/GNSS Concepts
### Extended Kalman Filter (EKF)
#### 2.4.2 Indirect Mode (Error State Model)
\begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{0}_{3\times 3}\\ -\mathbf{R}_{b}^{n}&\mathbf{0}_{3\times 3}\\ \mathbf{0}_{3\times 3}&\mathbf{R}_{b}^{n}\end{bmatrix} ,\] (23) The observations of the algorithm consist of GNSS positions and velocities, using the same matrix as in Equation (10). Also, define the following: \[\mathbf{Q}_{k-1} =\mathbb{E}[\mathbf{w}_{k-1}\mathbf{w}_{k-1}^{T}],\] \[\mathbf{R}_{k} =\mathbb{E}[\mathbf{v}_{k}\mathbf{v}_{k}^{T}],\] \[\mathbf{P}_{k-1}^{+} =\mathbb{E}[(\delta\mathbf{x}_{-1}^{+}-\delta\mathbf{x}_{k-1})( \delta\mathbf{x}_{k-1}^{+}-\delta\mathbf{x}_{k-1})^{T}],\] (24) \[\mathbf{P}_{k}^{-} =\mathbb{E}[(\delta\mathbf{x}_{k}^{-}-\delta\mathbf{x}_{k})( \delta\mathbf{x}_{k}^{-}-\delta\mathbf{x}_{k})^{T}],\] \[\mathbf{P}_{k}^{+} =\mathbb{E}[(\delta\mathbf{x}_{k}^{+}-\delta\mathbf{x}_{k})( \delta\mathbf{x}_{k}^{+}-\delta\mathbf{x}_{k})^{T}],\] Once the process and measurement models are defined by the previous equations, the IEFK procedure can be summarized as follows: 1. Prediction--Estimates the states for the next time step using the accelerations and current angular velocity combined with the states produced by the IEKF for the previous time step: \[\delta\mathbf{x}_{k}^{-}=\mathbf{\Phi}_{k-1}\delta\mathbf{x}_{k-1},\] (25) \[\mathbf{P}_{k}^{-}=\mathbf{\Phi}_{k-1}\mathbf{P}_{k-1}^{+}\mathbf{\Phi}_{k-1 }^{T}+\mathbf{G}_{k-1}\mathbf{Q}_{k-1}\mathbf{G}_{k-1}^{T}.\] (26) 2. Kalman Gain--Finds the Kalman gain \(\mathbf{K}\) based on the estimation produced in the Prediction step: \[\mathbf{K}_{k}=\mathbf{P}_{k}^{-}\mathbf{H}_{k}^{T}(\mathbf{H}_{k}\mathbf{P}_ {k}^{-}\mathbf{H}_{k}^{T}+\mathbf{R}_{k})^{-1}.\] (27) 3. Update--Uses measurements at time step \(k\) and the Kalman gain to correct the estimation produced in the Prediction step: \[\delta\mathbf{x}_{k}^{+}=\delta\mathbf{x}_{k}^{-}+\mathbf{K}_{k}( \delta\mathbf{z}_{k}-\mathbf{H}_{k}\delta\mathbf{x}_{k}^{-}),\] (28) \[\mathbf{P}_{k}^{+}=(\mathbf{I}-\mathbf{K}_{k}\mathbf{H}_{k})\mathbf{P}_{k}^{ -}.\] (29) where \(\delta\mathbf{z}_{k}\) represents the difference between the positions and velocities estimated by the INS and those measured by the GNSS.
## 5 WBAN and the Communications Protocol Stack  
### Introduction  
This section outlines a number of protocols operating at different layers. Many of the schemes have been outlined in the literature as WSN-specific, but are included here as either they form an important part in the understanding of sensor networks generally, or have formed the basis of WBAN specific protocols.  
The unique attributes of a WBAN and its close proximity to the human body (in the context of low energy efficient communications) mean there are specific implications at each layer of the protocol stack [16]. Protocols working at each of the layers need to contribute to low packet transmission delay,  
Figure 5: IEEE 802.15.6 high level architecture.  
minimum jitter, extremely low levels of congestion, low energy usage, whilst fulfilling specific QoS demands.
# Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling
## 3 Machine Learning Model Prediction Results and Discussion
### Effect of Hyperparameter Optimization on Machine Learning Model Performance  
The machine learning training-validation process with hyperparameter optimization was carried out after the dimensionality reduction process. Figure 12 shows an example of actual value against predicted value plot for predicted percent finer than 0.075 mm sieve value, which visualize the effect of hyperparameter tuning and number of PCs optimization of the machine learning models. It can be interpreted from the figure that the distribution of data points is closer to the center diagonal line in Figure 12b compared to Figure 12a. Figure 12b is the prediction result from the optimized model and Figure 12a is that from an unoptimized model. In addition, better performance indicated by RMSE and MAE is also observed as the score decreased from 3.0359 to 2.1242 for RMSE, and from 1.0939 to 0.56282 for MAE. This result proves that appropriate hyperparameter optimization is able to improve the machine learning model performance significantly.  
Figure 11: The number of features and training completion time of **(a)** GWL, **(b)** GWL, **(c)** SPTN, **(d)** FINER, and **(e)** SC models.  
Figure 12: Visualization of EDF model optimization effect by tuning the GPR algorithm hyperparameters for target class percent finer than 0.075 mm sieve (FINER model); **(a)** predicted vs. actual plot for unoptimized GPR algorithm; **(b)** predicted vs. actual plot for optimized GPR.
## 5 Machine Learning Model Designs
### Regression and Classification Problems
#### 5.1.2 Logistic Regression  
Like linear regression, logistic regression is the simplest machine-learning model for classification problems. Logistic regression is an example of supervised machine learning and works when the labels are available during the training process. The logistic regression model calculates the weighted sum of the input characteristics (plus a bias term) and then produces the logistic of this result rather than the actual result itself [30]. The input variable is the crucial element in this process. Predicting a binary result is one of the most common applications of logistic regression. When there is a possibility of more than two outcomes coming from a condition, multinomial logistic regression is a useful tool to employ to predict the output class [31]. The logistic function is represented as in Equation (9):  
\[f(x)=\frac{1}{1+e^{-x}} \tag{9}\]  
The output can be defined as \(y_{pred}=\begin{cases}0&\hat{p}<0.5\\ 1&\hat{p}>0.5\end{cases}\).