esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results  
Grass N ranged between 0.45 and 1.71% throughout the park (Table 3). The red-edge bands were selected, optimally explaining grass N. In the bands-only dataset, red edge 4 (BSA) was the select band variable for optimally explaining grass N (RMSE = 0.22, \(R^{2}\) = 0.47). TVIR4 was the selected band variable optimally explaining grass in the red-edge-based vegetation indices dataset. Similarly, TVIR4 was also the selected band optimally explaining grass N in the dataset combining bands and vegetation indices (Table 4).  
The variable that contributed the most toward explaining was B8A, according to the random forest variable of importance score (Figure 3) in the bands-only dataset. In the vegetation indices-only dataset, TCARIR4 and MCARIR4 had the highest score of variable importance. All red-edge bands featured as contributing the most toward predicting grass N in a dataset combining bands and vegetation indices; however, the latter contributed the least (Figure 3).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Balestestest Variables** & **RMSE** & **R-Squared** & **MAE** & **Selected** \\ \hline \hline \multicolumn{5}{l}{Bands only} \\ \hline Red & 0.2272 & 0.4223 & 0.1727 & \\ Red Edge 4 & 0.2154 & 0.4794 & 0.1672 & * \\ SWIR & 0.2249 & 0.4274 & 0.1721 & \\ \hline \hline \multicolumn{5}{l}{Red Edge 4 Indices} \\ \hline NDVIR4 & 0.3184 & 0.00025 & 0.2347 & \\ TVIR4 & 0.3156 & 0.0000482 & 0.2284 & * \\ \hline \multicolumn{5}{l}{Band + Vegetation Indices} \\ \hline Red & 0.2655 & 0.02986 & 0.205 & \\ Red Edge 4 & 0.2473 & 0.10306 & 0.1879 & \\ SR4 & 0.2396 & 0.14878 & 0.1842 & \\ TVIR4 & 0.2387 & 0.15517 & 0.1813 & * \\ \hline \hline \end{tabular}
\end{table}
Table 4: The optimal variable(s) selected for estimation of grass N.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Datasets** & **N** & **Min** & **Max** & **Mean** & **SD** & **CV (\%)** \\ \hline Training & 35 & 0.58 & 1.638 & 0.867 & 0.219 & 25 \\ Test & 15 & 0.44 & 1.71 & 0.93 & 0.34 & 36 \\ All combined & 50 & 0.45 & 1.71 & 0.89 & 0.25 & 29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics: grass nitrogen (%).  
The random forest model explained 80% of the grass N in the bands-only model. For the vegetation indices model, a prediction of 81% was achieved. The highest prediction, however, was performed in the model combining both S2 and vegetation indices (Figure 4).  
Figure 4: Grass N estimation performance of grass for various modeling scenarios: (**top left**): bands (80%), (**top right**): vegetation indices (81%), and (**bottom left**) (85%).  
Figure 3: Measuring variables of importance for estimating grass N; (**top left**) (bands), (**top right**) (vegetation indices), and (**bottom left**) (a combination of bands and vegetation indices). The higher the variable of importance score the more important the variable is for estimating grass N.
## 2 Methodology
### Parameter Initialization and Updating Algorithm
According to Equations (13) and (14), we have \(\lambda_{j}\big{|}\lambda_{j-1}\sim N\big{(}\lambda_{j-1},\kappa\big{)}\) and \(y_{j}\big{|}\lambda_{j-1}\sim N\Big{(}y_{i-1}+\lambda_{i-1}\big{\uparrow}_{i -1}^{i_{j}}\,\varphi(\tau;\theta)\alpha\tau,m\lambda_{i-1}\big{(}t_{j}-t_{j-1} \big{)}+2\sigma_{m}^{2}\Big{)}\). When ignoring the constant item, the log-likelihood function can be formulated by  
\[ \begin{array}{ll}\ln L_{i}\big{(}\widetilde{\Theta}\big{)}&=-\Big{(}\ln \sigma_{\lambda,0}^{2}+(\lambda_{0}-\mu_{\lambda,0})^{2}/\sigma_{\lambda,0}^{ 2}+\sum_{j=1}^{i}\Big{[}\ln\kappa+\big{(}\lambda_{j}-\lambda_{j-1}\big{)}^{2} /\kappa\Big{]}\\ &+\sum_{j=1}^{i}\Big{[}\ln\big{(}m\lambda_{j-1}\big{(}t_{j}-t_{j-1}\big{)}+2 \sigma_{m}^{2}\big{)}+\Big{(}y_{j}-y_{j-1}-\lambda_{j-1}\big{\uparrow}_{i -1}^{i_{j}}\,\varphi(\tau;\theta)\alpha\tau\Big{)}^{2}\big{/}\big{(}m\lambda_{ j-1}\big{(}t_{j}-t_{j-1}\big{)}+2\sigma_{m}^{2}\big{)}\Big{]}\Big{\}}/2.\end{array}  \tag{18}\]  
Note that Equation (18) involves hidden variables \(\mathbf{\lambda_{i}}=\{\lambda_{0},\lambda_{1},\cdots,\lambda_{i-1}\}\), so estimating unknown model parameters by maximizing this function is intractable. To solve the problem, we considered EM algorithm that provides a possible framework for estimating the unknown parameters involving hidden variables. A basic assumption of EM algorithm is that the hidden variables can be estimated by the monitoring data. Our problem meets the assumption since \(p\big{(}\lambda_{i}|\mathbf{y}_{i}\big{)}\) was obtained by Equation (16). A detail of the EM-step for our problem is given as follows (Algorithm 3). To calculate the conditional expectation \(\text{E}_{\lambda_{i}|\mathbf{y}_{i}\xrightarrow{\Delta(k)}}\big{[}\ln L_{i} \big{(}\widetilde{\Theta}\big{)}\big{]}\), we need to obtain the values of \(\text{E}_{\lambda_{i}|\mathbf{y}_{i}\xrightarrow{\Delta(k)}}\big{(}\lambda_{j} \big{)}\), \(\text{E}_{\lambda_{i}|\mathbf{y}_{i}\xrightarrow{\Delta(k)}}\Big{(}\lambda_{j}^{2} \Big{)}\), \(\text{E}_{\lambda_{i}|\mathbf{y}_{i}\xrightarrow{\Delta(k)}}\Big{(}\lambda_{j}^{2} \Big{)}\) and \(\text{E}_{\lambda_{i}|\mathbf{y}_{i}\xrightarrow{\Delta(k)}}\Big{(}\lambda_{j}^{-1} \Big{)}\). In this paper, these conditional expectations are estimated based on the properties of variance-covariance and Rauch-Tung-Striebel (RTS) smoothing algorithm [32]. Finally, the PDF of the updated RUL at the current monitoring time point \(t_{i}\) can be obtained by using the law of total probability, i.e.,  
\[f_{\mathbf{k}|y_{i}}\big{(}\,r_{i}|\mathbf{y_{i}}\big{)}=\int_{-\infty}^{+\infty}f_\mathbf{{k}|\lambda,y_{i}}(\,r_{i}|\lambda,y_{i})p\big{(}\,\lambda|\mathbf{y_{i}}\big{)}d \lambda=\text{E}_{\lambda|\mathbf{y}_{i}}\big{[}f_{\mathbf{k}|\lambda,y_{i}}\big{(}\,r_ {i}|\lambda,\mathbf{y_{i}}\big{)}\big{]}, \tag{19}\]  
where the model parameters have been derived above.
## 3 Results for the Research Questions
### RQ4: What Are the Methods That Have Been Used for Expert Finding Systems?
#### 3.4.3 Voting Models  
These models are based on methods from the field of data fusion [118]. Data fusion methods are concepts of integrating evidence from heterogeneous sources, rankers, or representations and have a long history in information retrieval [119]. The use of data fusion methods in expert finding is built on the intuition that the documents associated with candidate experts ranked with regard to a query can be seen as 'voting' for candidate experts. The voting methods for expert search aggregate scores from the same system across expert candidates instead of combining different systems' scores on a single document as in metasearch [1]. The authors of reference [118] proposed the simplest method in voting models. Their method assumes that documents provide binary votes given that they appear among the ranked documents returned by a document search engine. Candidate experts are ranked by the number of retrieved documents associated with them. Reciprocal Rank (RR) and CombSUM are examples of data fusion methods used for expert flinging systems. The authors of [69] used CombSUM and CombMNZ to combine different sources of expertise derived from the textual contents, the citation patterns for the community of experts, and an expert's profile. Also, the authors of reference [60] and reference [26] developed their models based on the weight that extracts the candidate's expertise from their documents. The authors of reference [60] calculated the total weight as the summation of publication, citation, and editor weight, while the authors of reference [26] focused on the distance weight. It is about the proximity between query terms and candidate names in the related documents, and it will be higher whenever the candidate name appears closer to the query term.
## 2 Materials and Methods
The concept of the designed and studied system is presented in Figure 1.  
The impact recorder is designed with a microcontroller that integrates real-time and Wi-Fi devices, as well as an electronic ink screen, a memory card, and inertial sensors. The mobile application with servers is designed as follows: Warehouse server--stands in each warehouse and knows its geographical position and name. A central server can consist of several computers, depending on the amount of data and requirements for system reliability. User device--a mobile phone with access to a central server to track the shipments recorded in the warehouse or the path of one shipment. General requirements for the execution of the process: In the shock recorder, the inertial sensor constantly measures the acceleration and transmits it to the microcontroller, which records it on the memory card with the time and date, and if the permissible limit has been exceeded, it also changes the background of the screen to red. Normally, the screen displays the recipient's information and tracking number. The microcontroller scans for wireless stations at regular intervals, and when it detects a wireless connection in the warehouse, it transmits its tracking number to the local server along with information about whether the allowable impact limit has been exceeded. The local server fills in the data on the time of receipt, the name of the warehouse, and its coordinates, and transmits it to the central server. With the mobile application, the user can, at any time, check the information available on the central server about the shipment of interest. To ensure the reliability of the measurement results, it is mandatory to use the manufacturer's components (e.g., accelerometers) that have been metrologically checked. In addition, it is very important that the shock detector inside the package is securely fixed (e.g., to the inner wall of the package). It is important that, in the event of an impact, the detector does not detach from the mounting location (it does not lose its adhesion to the mounting location).
## 3 Image processing and evaluation  
### Removing background disturbances  
As shown in Fig. 2, the bottom of the tub is not uniformly illuminated, and the painted grid lines and unexpected objects like the food source, dust and dead ants are imaged together with the investigated active moving ants. The ant image recordings must be cleaned before being evaluated to ensure reliable and accurate results. The method described in [14] can be used to effectively extract the image of the constant background disturbances and to remove it from each ant image recording. Assume an ant image recording group has \(N\) frames, and the gray value distribution of each frame is represented as _G\({}_{k}\)(x,y)_ (_k=1,2,3,...,N_). _(x,y)_ is the position of each pixels in the image frame. The first step is to average all the frames to obtain an averaged gray value distribution as  
\[G_{{}_{m}}(x,y)\!=\!\frac{1}{N}\sum_{k=1}^{w}G_{{}_{k}}(x,y) \tag{1}\]  
The second step is to subtract the averaged gray value distribution _G\({}_{m}\)(x,y)_ from each image frame, i.e.  
\[G_{{}_{k}}^{\prime}(x,y)\!=\!\left\{\!\!\begin{array}{ccc}G_{{}_{m}}(x,y)\!- \!G_{{}_{k}}(x,y)&\mbox{for}&G_{{}_{m}}(x,y)\!>\!G_{{}_{k}}(x,y)\\ 0&\mbox{for}&G_{{}_{m}}(x,y)\!\leq\!G_{{}_{k}}(x,y)\end{array}\right. \tag{2}\]  
Equation (2) is valid only when dark objects move in bright background, otherwise the images should be inverted. Fig.3 demonstrates the effect of the background removing determined by equation (1) and (2) for _N_=1228. Fig. 3a shows a part of a raw ant image recording of size of 256\(\times\)128 pixels. In the raw image sample the grid lines, food resource, dead ants, and dust pieces are included. In the averaged image, i.e. Fig. 3b, some of the ant images disappear because they are images of active moving ants whose contribution to the averaged gray value is divided by 1228. When the background disturbances are removed by using equation (2), only images of active moving ants stay in the processed image frame, see Fig. 3c. Note that the ant images in Fig. 3c are inverted.
## 1 Introduction
This was achieved by introducing algorithms for handling binary formats, thereby reducing the data file size, increasing write rates. These algorithms maximize the benefits of binary formats with the goal of storing large amounts of data rapidly and accurately without requiring expensive storage upgrades. This allows for AE measurements to be adopted in multiple channels to perform well when employing a conventional HDD at a considerably lower cost than when investing in more expensive storage solutions. Accordingly, the number of bytes required to store a value can be minimized by encoding data using a specialized binary format based on the measurement range and resolution of the employed DAQ device, resulting in a smaller file size. In addition, the algorithms exhibited a remarkable enhancement in write performance, allowing sensor readings to be stored at a rate of hundreds of mega-samples per second (MS/s), or even higher. This capability supports not only AE measurements but also Scanning Acoustic Microscopy (SAM) [28; 29] using hard disk drives (HDDs), which are acknowledged for their limited write performance. High sampling rates ensure that the SAM system can accurately reconstruct acoustic signals, producing detailed images of the specimen's internal structure or surface. When obtaining measurements, monitoring programs are often necessary to perform multiple tasks concurrently. These tasks include acquiring, analyzing, visualizing, encoding, and writing sensor readings in real time. Furthermore, in high-performance measurements, such as those required for acoustic emission applications, the workload is considerably higher. This requires advanced configurations to ensure the efficient functioning of all tasks, even when computer resources are limited. To address these challenges, the proposed algorithms were used to develop a condition-monitoring program. A series of measurements were conducted to verify its ability to adapt to high-performance measurements.
# CERRT: A Mobile Robot Path Planning Algorithm Based on RRT in Complex Environments
## 4 Simulation and Experiment
### Path Planning Simulation
#### 4.1.3 Narrow Environment  
The RRT algorithm faced challenges in solving narrow passage problems, while the RRV algorithm was specifically designed to address this issue. Additionally, the Fast-RRT has also proposed solutions for narrow passages. In this study, we used a narrow environment to test the performance of the new algorithm and evaluate its environmental adaptability. Figure 11 shows the planning process of the four algorithms. It can be seen that RRT had a large number of vertices on the left side of the map, and the algorithm could not effectively detect the narrow passages on the walls. Fast-RRT expands multiple times near obstacles to find passages. RRV and CERRT were quickly able to discover and pass through the narrow passage.  
Table 3 showcases the performance of the algorithms in the narrow passage environment. The planning success rate of the RRT algorithm was 0.97, while the other algorithms were both 1.00, highlighting the shortcomings of RRT in narrow environments. The average running time of RRT was 27 times that of CERRT, the average running time of RRV was four times that of CERRT, and the average running time of Fast-RRT was slightly higher than that of CERRT. The new algorithm had a shorter planning time. The tree vertex numbers of CERRT and RRV were similar, and much lower than that of the RRT and Fast-RRT algorithm, indicating that both derived algorithms can solve narrow passage problems. RRT's time standard deviation was 135 times higher than CERRT, RRV's time standard deviation was 20 times higher than CERRT, and Fast-RRT's time standard deviation was 75 times higher than CERRT, suggesting that CERRT exhibited optimal stability.  
Based on the above analysis, we can conclude that the new CERRT algorithm outperformed the RRT, RRV and Fast-RRT algorithms in narrow environments. The main  
Figure 11: Performance comparison of four algorithms in narrow environment. (a) RRT; (b) RRV; (c) Fast-RRT; (d) CERRT.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Avg. Time (ms)** & **Min Time (ms)** & **Max Time (ms)** & **Std** & **Avg. Nodes** & **Success Rate** \\ \hline RRT & 455.13 & 7.52 & 5189.01 & 871.53 & 2931 & 0.97 \\ RRV & 65.72 & 25.04 & 1287.83 & 127.12 & 259 & 1.00 \\ Fast-RRT & 58.03 & 4.87 & 3779.04 & 487.47 & 869 & 1.00 \\ CERRT & 16.70 & 11.66 & 74.69 & 6.42 & 255 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results for planning in a narrow environment.  
reason for this is that RRV requires complex principal component analysis to determine the environment, while CERRT's environmental perception ability only requires simple sampling to determine the surrounding environment and select new expansion points without complex calculations, effectively reducing the environmental recognition cost.
# Design Considerations and Performance Evaluation of Gossi Routing in LoRa-Based Linear Networks
## 4 Experimental Setup
### Coverage Estimation and Placement of Relay Nodes  
It is a challenging task to estimate the exact coverage overlap. We tried to estimate the transmission coverage range by using two end nodes with almost zero packet loss at each station (packet loss increases with the increase of the physical distance between nodes as the RSSI and SNR values become low). To set up more relay nodes between the source and destination nodes, we used the lowest transmission power (14 dBm) and highest bandwidth (500 Hz). Using the highest bandwidth (500 kHz) significantly dropped the SNR ratio by 4 dB to 5 dB as compared to a lower bandwidth (125 kHz). The transmission coverage range also significantly depends on hardware imperfection. We discuss the impact of hardware imperfection on the RSSI and SNR in Section 6.  
However, based on a single pair of nodes, we identified where on our campus nodes can be placed to avoid overlap.
# GPSMAP 66 sr: Assessment of Its GNSS Observations and Centimeter-Accurate Positioning
## 2 Observation Data and Data Processing  
We performed and recorded several 100 h of GNSS observations with GPSMAP 66 sr. Most of these datasets were collected in a roof-top environment without any signal obstructions and with low multipath levels (Sections 3.1-3.3 and 4.1). Further datasets originate from field measurements under adverse environmental conditions (Sections 4.1 and 4.2). All these observations were performed in static mode, mostly at sites with precisely known coordinates from earlier determinations with geodetic-grade GNSS equipment.  
All GPSMAP 66 sr observations analyzed and discussed in this paper were performed, with the latest firmware available when we bought the receiver in June 2021: Garmin software v3.30/GPS software v2.00.05.  
The observation data were recorded internally in RINEX 3.04 and transferred to a computer using the micro-USB (universal serial bus) connector. The data interval was fixed to 1 s. For many processing steps, we reduced the observation interval to 15 s after the data transfer. RINEX navigation files are not recorded by GPSMAP 66 sr. Thus, we had to obtain appropriate RINEX navigation files from other sources, e.g., from NASA's archive of space geodesy data CDDIS [8].  
The internal, rechargeable built-in lithium-ion battery lasted for at least 16 h in full GNSS mode (dual-frequency, all systems). For longer observation sessions, we used the external power supply via micro-USB.  
Centimeter-accurate positioning in precise point positioning (PPP) mode or in relative mode requires additional data, such as precise GNSS orbit and clock information or GNSS observations from reference stations. For analysis steps that required a local GNSS reference station, we used a Septentrio PolaRx5 receiver connected to a choke ring antenna of type Javad RingAnt-DM JVDM. Additional datasets are described, and their sources are listed in the following Sections 3 and 4.  
Figure 1: Garmin GPSMAP 66 sr with its quadrifilar helix antenna at the top left.  
All analysis steps presented in Section 3 were performed with software modules belonging to the WaSoft software package [9]. Our original intention was to produce positioning results using free processing services. However, we failed to obtain such results in the case of PPP. Therefore, we used the WaSoft module WaPPP instead. Centimeter-accurate baseline results with respect to a VRS could be obtained from the free processing service SAPOS GPPS-PrO. Further details are provided in Section 4.
# Simulation Analysis and Experiment of Piezoelectric Pump with Tapered Cross-Section Vibrator
## 5 Simulation Analysis of Pumping Performance Based on Fluid-Solid Coupling
### Flow-Field Analysis of the Piezoelectric Pump
During the simulation analysis, the outlet flow rate was calculated. After the post-processing, the simulated flow rate of the piezoelectric pump was obtained. Theoretically, when the vibrator works at the second-order mode, it has good driving performance. Flow rates were simulated and calculated at different driving frequencies with the tapered cross-section vibrator. The driving frequencies were selected around the second-order mode frequency (588 Hz) of the vibrator, with a frequency range of 540-660 Hz. The pumping flow rates at different frequencies were obtained, as shown in Figure 12. Simulation results indicated that the pump reached its largest pumping flow rate near the driving frequency of 588 Hz. Figure 11: Fluid velocity distribution at the vibrator tail region during period T. **(a-1)** Amplitude curve of point P. **(a-2)** Vibrator tail position at the beginning. **(a-3)** Fluid velocity distribution at the beginning. **(b-1)** Amplitude curve of point P. **(b-2)** Vibrator tail position at T/8. **(b-3)** Fluid velocity distribution at T/8. **(c-1)** Amplitude curve of point P. **(c-2)** Vibrator tail position at T/4. **(c-3)** Fluid velocity distribution at T/4. **(d-1)** Amplitude curve of point P. **(d-2)** Vibrator tail position at 3T/8. **(d-3)** Fluid velocity distribution at 3T/8. **(e-1)** Amplitude curve of point P. **(e-2)** Vibrator tail position at T/2. **(e-3)** Fluid velocity distribution at T/2. **(f-1)** Amplitude curve of point P. **(f-2)** Vibrator tail position at 5T/8. **(f-3)** Fluid velocity distribution at 5T/8. **(g-1)** Amplitude curve of point P. **(g-2)** Vibrator tail position at 3T/4. **(b-1)** Amplitude curve of point P. **(h-2)** Vibrator tail position at 7T/8. **(h-3)** Fluid velocity distribution at 7T/8. **(i-1)** Amplitude curve of point P. **(i-2)** Vibrator tail position at T. **(i-3)** Fluid velocity distribution at T.  
The vibrator has the advantages of simple structure, good process performance, closer to the shape of biological fish body, and improved pumping performance. In the future, it will be widely used in agriculture drip irrigation, fruit tree spraying management, cooling and lubrication of precision equipment, and so on.