esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Therefore, the maximum length of the net distribution extracted from the VMS data of each vessel could be used to check whether the number of nets held by each vessel meets the TAC regulations and then standardize the fishing behavior of each fishing vessel. In addition,the distribution of fishing intensity for _A. chinensis_ was determined based on the fishing efforts of all net positions. This is vital for delimiting the fishing area for the following year. In the future, by appropriately increasing the number of sample vessels and recording the actual production of each netting of the sample vessels, which can be set as two parts of the training and the validation sets, models predicting catch volumes could be established. First, the fishing effort and corresponding catches of the fishing vessels in the training set at different temporal and spatial scales can be calculated to explore the resource abundances of _A. chinensis_. Then, some predicted productions of the fishing vessels in the validation set can be estimated by calculating these resource abundances and their fishing efforts at different temporal and spatial scales. Finally, these predicted productions from the validation set can be compared with the actual productions, and the prediction model with the smallest overall error will be selected. This idea is an important method for rectifying the reported catch of each vessel and controlling the total fishing quota for that year. It also provides a scientific basis for setting the number of vessels allowed to fish, the upper limit of the annual catch of _A. chinensis_, and the quotas given to each vessel for the next year. Based on the spatiotemporal distribution of all the vessel productions, the relationship between the distribution of _A. chinensis_ resources and marine environmental elements (including water temperature, salinity, and chlorophyll-a concentration) could be explored for accurately forecasting the fishing grounds and periods of _A. chinensis_ in further studies.
## 4 Experimental Validation  
### Experimental Apparatus  
The configuration of the drop device, the type of sensors, the DAQ device, and the model names used in the experiment are shown in Figure 6. The sprung and un-sprung masses of the constructed drop device are 230 kg and 15 kg, respectively. Experiments are categorized as follows: off-state pressure measurement with impact energy and pressure measurement corresponding to the input current when impact energy is fixed. The impact energy (\(IE\)) can be calculated through the sink speed at the moment of the touchdown of the un-sprung mass and the distance to the static equilibrium state:  
\[IE=(m_{s}+m_{u})(g\cdot z_{1,eq}+0.5\dot{z}_{1})-m_{u}\cdot g\cdot s_{eq} \tag{39}\]  
where \(m_{s}\) and \(m_{u}\) are sprung and un-sprung masses, respectively; \(g\) is the gravity acceleration and is set to 9.806; \(z_{1,eq}\) and \(s_{eq}\) are sprung mass and piston stroke displacements from the moment of touchdown to the static equilibrium state; \(\dot{z}_{1}\) is the sink speed of the landing gear system at the moment of touchdown. The sink speed is estimated through the displacement and the acceleration of the sprung mass, measured with a laser sensor and an acceleration sensor attached to the sprung mass. In other words, the impact energy is converted into the form of the energy dissipated by the pressure loss in the MR shock absorber after impact, the energy dissipated by the friction between the MR shock absorber and the guide shaft of the drop device, and the elastic potential energy stored in the gas chamber and the tire as a spring energy form. This work compared the dissipated energy due to the pressure loss in the MR shock absorber estimated experimentally and the pressure drop model. \(\Delta P_{total}\) and \(\Delta P_{maj}\) are calculated for the experimentally estimated piston stroke velocities to validate the mathematically modeled pressure drop model. The piston stroke velocity is estimated through stroke displacement, and acceleration is measured via the wire sensor and accelerometers mounted at the sprung and un-sprung masses. \(\Delta P_{total}\) and \(\Delta P_{maj}\) can be calculated by Equations (31) and (33). In this paper, the pressure drop model, calculated by estimated piston stroke velocity, is called the \(\Delta P-\hat{s}\) model. The pressure drop model is constructed in MATLAB, a numerical analysis program. Because the main content of this paper is a comparison of pressure drop in conformity with the presence or absence of the minor loss, the simulation on the landing efficiency using the landing gear model is omitted.
## 3 Results and Discussion
### Analysis of Physicochemical and Technological Parameters of Model Meat Batters  
Significant differences in the apparent viscosity of the obtained model meat batters were found (Table 3). The model meat batter prepared with an interesterified mixture of milkfat and rapeseed oil (MB_iMF:RSO) was characterized by a significantly (\(p<0.05\)) lower apparent viscosity in comparison to the apparent viscosity of the control product (MB_C) and the meat batter with noninteresterified fat (MB_MF:RSO). Similar relationships were found by [PERSON] et al. [13]. In these studies, lard, a mixture of lard and rapeseed oil and an interesterified mixture of lard and rapeseed oil were used for the production of chicken meat batters. It was found that modifications of the fats significantly affected the apparent viscosity of the produced meat batters. The significantly lowest viscosity was observed in the case of meat batter in which an interesterified mixture of lard and rapeseed oil was used. According to [PERSON] et al. [33] and [PERSON] et al. [37], there is a relationship between emulsion viscosity and its stability. Emulsions with low viscosity are easily broken. In turn, viscoelastic parameters of meat batters depend on the fat content and its structure. For example, the viscosity of the meat batters produced with higher and harder fat contents will be increased. However, this relationship is dependent on the temperature. Lower viscosity of the meat batters will result in a more efficient filling of the casings. Additionally, in the studies of [PERSON] et al. [13], no significant differences in the thermal drip of produced chicken model meat batters with animal fat (lard), a mixture of lard and rapeseed oil or an interesterified mixture of lard and rapeseed oil were observed. However, the replacement of lard with a mixture of lard and rapeseed oil or interesterified mixture of lard and rapeseed oil significantly affected the texture of the meat batters. The meat batters produced with lard were characterized by significantly higher values of penetration force.  
According to [PERSON] [38] and [PERSON] et al. [39], the replacement of the animal fatty raw material (e.g., pork jowl) with vegetable oil may have unfavorable effects on the technological quality of the meat product and, among others, can cause the increase of thermal loss. In our studies, there were no significant (\(p\) > 0.05) differences in the thermal drip and texture of the produced model meat batters (Table 3).  
According to the literature [40; 41], the substitution of animal fat with vegetable oil may change the color of meat products--for example, increased yellowness. Changes in meat batter colors were also observed in our studies (Table 3). Meat batters produced with noninteresetrified or interesterified mixtures of milkfat and rapeseed oil (MB_MF:RSO and MB_iMF:RSO) were characterized by a significantly higher (\(p\) < 0.05) lightness (L* color component) and yellowness (b* color component) and significantly (\(p\) < 0.05) lower redness (a* color component).
# Phase Centre Corrections of GNSS Antennas and Their Consistency with ATX Catalogues
###### Abstract  
Changes of the antenna models on permanent global navigation satellite system (GNSS) stations can lead to jumps and discontinuities in the coordinate time series. In this paper, the results of research on the adequacy of the antenna phase centre corrections (PCC) variations are presented by analysing its component--the antennas' phase centre offset (PCO). For this purpose, height differences were determined using different and independent methods: EUREF Permanent Network (EPN) combined solutions, Precise Point Positioning (PPP), and the single baseline solution. The results of GNSS processing were referenced to direct geometric levelling outputs. The research was conducted only within the global positioning system (GPS) system due to the compatibility of one of the receivers, and the experiment was based on a comparison of the height differences between four GNSS antennas located on the roof of a building: two permanent station antennas and two auxiliary points. The antennas were located at similar heights; precise height differences were determined by geometric levelling, both at the beginning and the end of the session. Post-processing was conducted with the use of the GPS system, precise ephemeris, the adopted antenna correction model, and a zero-elevation mask. For one of the antennas, a change of the antenna characteristic model from IGS08 to IGS14 leads to an 8-mm difference in height. Older antennas used in the national (or transnational) permanent network need individual PCC.  
PCC; GNSS; GPS; antenna; calibration; EPN
## 3 Experimental Results
### Experimental Setup  
In this study, the results of BP were estimated by using root mean square error (RMSE) and mean absolute percentage error (MAPE). Moreover, the somewhat less stringent Association for the Advancement of Medical Instrumentation (AAMI) standard [22], which recommends a mean error of less than 5 mmHg and a standard deviation of error of not more than 8 mmHg.  
The model was trained in a fully supervised manner, and the weights and biases of each layer were initialized by randomly selected values. The cross-entropy is selected as the loss function, which was used to estimate the error between the true value and the predicted value. Adam, which is a stochastic optimization algorithm, was selected as the optimizer. The detailed parameters of the proposed neural network are shown in Table 1.
## 3 Results and Discussion
### Model Characteristics
* The enzyme 1.1.99.2, L-2-hydroxyglutarate dehydrogenase, is a metabolite repair enzyme responsible for the conversion of (S)-2-hydroxyglutarate into 2-oxoglutarate. In other organisms such as plants [47] or humans [48], the inactivation of this enzyme leads to the accumulation of the toxic (S)-2-hydroxyglutarate. * The enzyme 2.7.1.59, _N_-acetylglucosamine kinase, responsible for the conversion of _N_-acetyl-D-glucosamine into _N_-acetyl-D-glucosamine 6-phosphate. Many yeast species, including _S. cerevisiae_ have lost their ability to utilize _N_-acetyl-D-glucosamine as carbon source, however, genetically altered yeasts are able to use it, based on expression of _C. albicans_ genes [49]. In fact, this enzyme allows _C. albicans_ to utilize this carbon source, a feature that is particularly important for its survival inside the phagosomes [50]. * The enzyme 3.5.1.25, _N_-acetylglucosamine-6-phosphate deacetylase, responsible for the conversion of _N_-acetyl-D-glucosamine 6-phosphate into D-glucosamine 6-phosphate. Like 2.7.1.59, this enzyme is also involved in _N_-acetyl-D-glucosamine metabolism. * The enzyme 1.4.3.3, D-amino-acid oxidase, responsible for the conversion of a D-amino acid into a 2-oxo carboxylate and ammonia, is the first enzyme involved in the catabolism of D-amino acids and may allow the utilization D-amino acids as a source of carbon or nitrogen in some yeasts [51]. It may be an interesting feature to be explored in _C. albicans_.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 6 Evaluation
### Performance Metric  
We use the following performance factors to measure the performance of Escalator and existing scheduling schemes.  
Figure 9: Testbed and simulation topologies.  
* The packet delivery ratio (PDR) is the percentage of packets successfully delivered to the sink. The PDR is measured as the ratio of the number of packets received by the sink to the total number of packets transmitted by nodes.
* The end-to-end delay means the number of timeslots that a packet takes to reach from a source node to the sink. We measure the ASN when the packet leaves from the source node and the ASN when the packet arrives at the sink. The difference between these two ASNs is used to calculate the end-to-end delay. We evaluated the performance by measuring the average of the end-to-end delay by the number of hops of the packet.
* The duty cycle means the ratio of the radio usage time to the total operation time of nodes. The duty cycle is measured only in the simulation, and CPU usage time and radio transmit and receive times are measured while transmitting 2000 packets for each node. We evaluated the performance by measuring the duty cycle by the number of routes in the node.
# A Survey of Deep Learning for Electronic Health Records
## 5 Deep Learning Applications in EHR
### Representation Learning
#### 5.2.1 Vector-Based Patient Representation  
The goal of vector-based patient feature representation is to turn each patient's data into a mathematical vector that can be made up of completely linked neural networks, CNN, AE, or Word2 Vec.  
At an early point, [PERSON] et al. [57] attempted patient representation learning using fully connected neural networks, and most contemporary investigations used the framework proposed by [PERSON] et al. [58] as a baseline model. This represented technique sorts the complete EHR data by time, and the data is sorted both by the patient and by time. To label the data in each attribute, this model employs three neural network methods (LSTM, TANN, and a neural network with boosted time-based decision stumps). This data structure is much more straightforward. Although the produced data can be utilized for any prediction challenge, the created features may or may not perform better on certain tasks. The CNN network developed the Text-CNN network, which is specifically suited to handle text data. It obtains patient characterization in several convolution-processing forms, with more detailed patient attributes. In addition, attempts have been made to include temporal information outside the convolution layer to imitate patient longitudinal information in CNN's more complex variation networks [59].  
Above, the Auto-Encoder(AE) was briefly described. Denoising AE [84], Stacked Denoising AE [85], Variational AE [86], and Compressed AE [87] are all variations of the vanilla Auto-Encoder. [PERSON] et al. [1] used AE for patient representation learning for the first time, based on its great performance in representation learning. An unsupervised three-layer stacked automated denoising encoder is the model architecture proposed in this paper. This model can be used to create a detailed and versatile patient portrayal.  
Word2 Vec is a popular approach for large-scale natural language word embeddings. In Word2 Vec, there are primarily two algorithms: CBOW and skip-gram. Both methods have their merits, but only one hidden layer is present in each. As a result, they are known as shallow neural networks. Variants have recently been applied to patient representation learning problems, such as the technique proposed by [PERSON] et al. [60] that used three types of embeddings: MCEMJ, MCEMC, and MCECN. The link between coding sequences is also among the features obtained.
# Mobile User Connectivity in Relay-Assisted Visible Light Communications
## 3 System Model  
### VLC Channel  
We consider the Tx to be a monochromatic point source with a Lambertian radiation pattern. The LOS link gain is given by [21]:  
\[H=\begin{cases}\frac{(m+1)A_{r}}{2\pi d^{2}}\cos^{m}(\theta)\cos(\Psi)g(\Psi)T_{s }(\Psi)&,0\leq\Psi\leq\Psi_{FOV}\\ 0&,\Psi>\Psi_{FOV}\end{cases} \tag{1}\]  
where \(A_{r}\) is the effective area of the Rx photodiode, \(d\) represents the distance between the Tx and the Rx, \(\theta\) stands for the irradiance angle with respect to \(\mathbf{n_{s}}\), and \(\Psi\) is the incident angle with respect to \(\mathbf{n_{r}}\) (see Figure 1). \(T_{s}(\Psi)\) is the optical filter gain, \(g(\Psi)\) the optical concentrator gain, \(\Psi_{FOV}\) is the field of view (FOV) of the Rx and \(m\) represents the Lambertian emission, which is given by:  
\[m=\frac{-\ln(2)}{\ln(\cos(\theta_{1/2}))} \tag{2}\]  
where \(\theta_{1/2}\) is the half-power angle of the LED.  
By adopting Lambert-Phong method [9], the diffuse paths are assumed to be represented by scattered rays, re-radiated from the wall to the Rx, which are being attenuated (i.e., based on the surface reflection coefficient). We define the reflection scattering using a generalized Lambert radiation pattern as:  
\[P_{rWall}=\frac{P_{i}(u+1)}{2\pi}\rho\cos^{u}(\delta) \tag{3}\]  
where \(P_{i}\) is the incident normalized unit power at the wall, \(P_{rWall}\) is the reflection power from the reflected surface, \(u\) is the smoothness of the reflecting material, \(\rho\) is reflection coefficient, and \(\delta\) is the randomly uniformly distributed angle between reflected rays and the diffusely reflected ray. Note that, in this paper, we study a practical scenario of VLC system with mobile users being used as RNs. To be as much as illustrative, we have used the average reflectivity over the entire visible spectrum defined by [22] and the nonlinearity of LED sources is not considered. However, model presented in this work can be extended to include non-LOS configuration (NLOS) (i.e., reflections) as part of the future studies, by considering spectral dependency of reflective surfaces [26] and non-Lambertian reflections [27; 28].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Room size & - & \(5\times 5\times 3\) m \\ No. of rays & - & 100,000 \\ No. of reflection & - & 5 \\ Time resolution & \(\Delta t\) & 0.2 ns \\ Bit rate & - & 100 Mb/s \\ Reflectivity of walls & \(\rho_{wall}\) & 0.74 \\ Reflectivity of ceiling & \(\rho_{ceiling}\) & 0.38 \\ Reflectivity of floor & \(\rho_{floor}\) & 0.61 \\ Smoothness of the reflecting material & \(u\) & 1 \\ Tx position & - & \(2.5\times 2.5\times 2.8\) m \\ Tx power per LED & - & 20 mW \\ Size of the LED array & - & 60 \(\times\) 60 \\ Semiangle at half power & \(\theta_{1/2}\) & 60\({}^{\circ}\) \\ Tx elevation & - & \(-90^{\circ}\) \\ Tx azimuth & - & 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key System Parameters.
## 3 Methodology
### Bandpass Filtering  
A critical filtering operation, namely bandpass filtering, was performed on the raw seismic data, to which we applied a Butterworth filter [34]. This filtering step is important because it enhances relevant low-frequency seismic noise to probe subsurface characteristics. Through its precise frequency attenuation, the filtering process allows the data to reflect subtle seismic oscillations, which are integral to the HVSR analysis, while filtering out the unwanted higher-frequency noise.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 1 Introduction
These innovations have potential applications in various areas of computer vision and pattern recognition, where multimodal data fusion is essential, contributing to the advancement of state-of-the-art approaches in image understanding and analysis. The key contributions of this paper include the following:  
* Advanced fusion: We introduce more fusion techniques at both the feature and score levels, further enhancing Re-ID accuracy by leveraging the complementary strengths of appearance and gait data and analyzing their effectiveness across different dataset characteristics. * In-depth theoretical analysis: We provide a deeper exploration of the foundational concepts of our approach, offering a comprehensive analysis of the models and fusion strategies, as well as their impacts on the Re-ID process. * Comprehensive dual-dataset evaluation: We provide an in-depth analysis of GAF-Net's performance on both the iLIDS-VID and MARS datasets, offering insights into its efficacy in various Re-ID scenarios. * Enhanced performance: The improved GAF-Net achieves state-of-the-art results on the iLIDS-VID dataset and competitive performance on the MARS dataset. This paper is organized as follows: Section 2 reviews related works, Section 3 introduces the proposed method, Sections 4 and 5 present experimental results and analyses, and Section 6 concludes the study.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## Appendix A Appendix A Appendix A Appendix A Appendix A.1  
```
Input: ESP32-CAM sensors, ESP-NOW network, MQTT broker, YOLOv5 Output: Fire alerts and anomaly detection
1 Initialize ESP32-CAM, ESP-NOW, MQTT, and YOLOv5.
2 Set up ESP-NOW multi-hop network and assign nodes.
3 Establish an MQTT broker for communication with a central monitoring server.
4 Load pre-trained YOLOv5 for fire detection
5 Set up ESP-NOW multi-hop network between sensor nodes
6 Establish an MQTT broker and set the central monitoring server as the subscriber
7 while (the system is active) do:
8 Data Collection
9 for each ESP32-CAM sensor in the network do:
10 Capture environmental data (e.g., fire)
11 if (data exceeds predefined threshold levels) then:
11 i. Capture image using ESP32-CAM
12 ii. Transmit image data to Raspberry Pi (or central processing unit)
13 end if
14 end for
15 Anomaly Detection
16 for each received image from ESP32-CAM sensors do:
17
18 a. Process image using YOLOv5
19 b. if (YOLOv5 detects fire in the image) then:

19 i. Mark anomaly as \"FIRE DETECTED\"
20 ii. Generate alert
21 else:
22 i. Mark anomaly as \"NO FIRE DETECTED\"
23 end if
24 end for
```  
**Algorithm 1**Fire Detection System Using ESP32-CAM and YOLOv5
## 8 Case Study
### Evaluation of Two Segmentations  
We have two segmentations. The first is a segmentation found from the voxel's marginal distribution function. The second outcome is a segmentation found from the multivalued 3D (hence 4D) wavelet transform.  
Now we will assess T1_segm_marg6 versus T1_segm_kmean6. If we use BIC, using the T1 image and first one and then the second of these segmented images, we find essentially the same BIC value. (The BIC values of the two segmentations differ in about the 12 th decimal place.) Note though that the model used by BIC is the same as that used for the marginal segmentation; but it is not the same as that used for k-means. Therefore it is not fair to use BIC to assess across models, as opposed to its use within a family of the same model.  
Figure 5: Segmented frame number 15 from an MRI brain image.  
Using Renyi quadratic entropy, in the \"Segmentation\" pull-down menu, we find 4.4671 for the marginal result, and 1.7559 for the k-means result.  
Given that parsimony is associated with small entropy here, this result points to the benefits of segmentation in the wavelet domain, i.e. the second of our two modeling outcomes.
# Path Tracking of Agricultural Vehicles Based on 4 WIS-4 WID Structure and Fuzzy Control
## 2 Materials and Methods
### Experimental Design  
To further verify the algorithm's effectiveness in this paper, the authors conducted a linear path-tracking experiment in a greenhouse at the Guan Tang farm of Zhejiang Agriculture and Forestry University from November to December 2021. The greenhouse has a size (L \(\times\) W \(\times\) H) of 25 m \(\times\) 18 m \(\times\) 3 m. It mainly grows small tomatoes and peppers, and has a path of about 3.5 m width between each cultivation bed for the test, as shown in Figure 11. The height of each UWB base station and the mobile tag from the ground is 1.6 m. The mobile tag projection point was located at the center of the 4 WIS-4 WID structured prototype vehicle, and the sampling frequency was 5 Hz. The location and orientation of the prototype vehicle are obtained from the gyroscope installed in the vehicle body. The prototype vehicle travels at a speed of 0.5 ms\({}^{-1}\), and the upper computer continuously records the platform's position information in the travel process.