esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Concerns about and Encounters with Underwater Wireless Sensor Network
### Underwater Acoustic Communications Characteristics
#### 4.3.3 Noise  
Numerous publications have presented representations for a submerged auditory connection that takes into account hooked-on interpretation variables, similar to salinity: hotness, complexity, and meddling from the environment, to name a few. Other physical ocean factors, such as medium noise, thermal noise, wind, turmoil, and ship noise, are also taken into consideration in these calculations, depending on the frequency and the following variables [42; 43; 44]:  
\[10\log\mathrm{N_{t}}\ (\mathrm{f})=18-31\mathrm{logf} \tag{5}\]  
\[10\log\mathrm{N_{s}}\ (\mathrm{f})=50-30(\mathrm{s}-0.11)+36\mathrm{logf} \tag{6}\]  
\[10\log\mathrm{N_{w}}(\mathrm{f})=60+7.5\ \mathrm{w}+30\mathrm{logf}(\mathrm{s}-0.11)+36 \mathrm{logf}-50\mathrm{log}(\mathrm{f}+0.5) \tag{7}\]  
\[10\log\mathrm{N_{s}}\ (\mathrm{f})=21+26\mathrm{logf} \tag{8}\]  
where \(\mathrm{N_{t}}\) is turbulence-related noise, \(\mathrm{N_{s}}\) is shipping-related noise, \(\mathrm{N_{w}}\) is wind-related noise, and \(\mathrm{N_{th}}\) is thermal noise.  
The complete noise PSD for a specified freq. f is formerly:  
\[\mathrm{n}\ (\mathrm{f})=\mathrm{n_{t}}\ (\mathrm{f})+\mathrm{n_{s}}\ (\mathrm{f})+ \mathrm{n_{w}}\ (\mathrm{f})+\mathrm{n_{th}}\ (\mathrm{f}) \tag{9}\]  
A cross-layer design method is the most significant way to increase network efficiency, especially in critical conditions, even if conventional layered approaches have historically been employed in underwater networking research [45]. As a result, we describe the difficulties underwater sensor networks confront with the conventional layered method. Cross-layer design ideas are essential for the undersea environment. To make the best use possible of the limited resources at hand. Even though a cross-layer design is encouraged to improve the network speed and prevent a function duplication, employing a modular design approach is crucial when keeping design simplicity in mind. This situation allows for upgrading and improving certain functionalities without completely reinventing the communication infrastructure [46].
## 4 Numerical Results
### Final Prioritization by Composite Scoring Function  
In this section, the efficiency and effectiveness score are aggregated by Equations (49)-(51). As seen in Figure 6, the value amplitude of the ultimate score is relatively large. In other words, the results of assessing the ability for moving high-tech production lines to Southeast Asia show the differences in the suitability of countries. With high maturity in technology, India is at the top of the list. Meanwhile, the rapidly growing economy, supportive policies, and the young population structure are significant advantages for Thailand, Vietnam, Malaysia, and Indonesia.
## 3 Results  
The mean, SD, and intra-session reliability data for the shot metrics can be seen in Table 2. When using a driver, the ICC data showed that all metrics exhibited excellent reliability (\(\geq\)0.94) and good reliability for smash factor (0.80). All the CV data were considered acceptable (\(\leq\)3.54%). When using a 6-iron, the ICC data showed that the CHS and ball speed exhibited excellent reliability (\(\geq\)0.90), carry distance exhibited good reliability (0.87), and smash factor had moderate reliability (0.65). All the CV data were considered acceptable (\(\leq\)4.65%). The mean, SD, and intra-session reliability data for the physical performance tests can be seen in Table 3. All the physical assessment data showed excellent ICC values, with the exception of force at 100 ms in both the IMTP and isometric bench press, which exhibited good ICC values of 0.85. When considering the CV data, force at 100 ms in both isometric tests was also considerably elevated (19.76-25.78%), thus exhibiting the greatest variance of all the metrics and tests. In addition, force at 200 ms was also marginally elevated (10.85%) during the IMTP, but all the other CV data were considered acceptable (\(\leq\)7.69%). Given the volume of correlations reported herein, a heatmap has been presented to showcase the associations between the golf shot metrics and physical performance data (Figure 3). For CHS, the correlations with anthropometry were deemed small to large (\(r=0.12\) to 0.68), small to large with IMTP variables (\(r=0.26\) to 0.61), large to very large with isometric bench press variables (\(r=0.51\) to 0.76), moderate to very large with CMJ variables (\(r=0.46\) to 0.73), large with medicine ball throw distance (\(r=0.58\) to 0.62), and moderate to large with thoracic spine rotation (\(r=0.35\) to 0.52). The correlations between ball speed and physical characteristics with anthropometry were deemed trivial to large (\(r=-0.07\) to 0.65), moderate to large with IMTP variables (\(r=0.44\) to 0.65), moderate to
# A New Mobility Model for Multi-UAVs Reconnaissance Based on Partitioned Zone
## 3 Proposed Model
### Random Destination with Pheromone Zone Mobility Model
#### 3.2.2 Selecting Next Zone and Waypoint  
As explained in Algorithm 1, a UAV does not fly to the destination border zone directly, but tries to pass by zones with lower visited waypoints. Thus, we need a mechanism to select the next zone. First, we decide the candidate set of next zones from the current zone. A neighbor zone is called a candidate zone if its Manhattan distance or L1 distance to the destination border zone is shorter than that of the current zone. For example, let us assume that a UAV with the destination border zone \(z_{15}\) is now in \(z_{1}\), as shown in Figure 6a. Then, the candidate next zones are \(z_{2}\), \(z_{5}\), and \(z_{6}\). Similarly, Figure 6b,c shows the candidate next zones at each zone while the UAV moves.  
Next, we decide the next zone from the candidate next zones based on the number of waypoints visited previously. Similar to the equation of deciding the destination border zone, the probability of selecting the next zone is inversely proportional to the number of waypoints visited in the zone. Thus, for a given set of candidate next zones \(C\), the probability of selecting the next zone \(z\) is given by Equation (2).  
\[P_{z}=\left\{\begin{array}{cl}1&\text{if }|C|=1\\ \dfrac{N_{total}-N_{z}}{(|C|-1)\times N_{total}}&\text{if }|C|>1\text{ and }N_{total}\
eq 0\\ \dfrac{1}{|C|}&\text{if }|C|>1\text{ and }N_{total}=0\end{array}\right. \tag{2}\]  
In Figure 6a, let us assume that the numbers of waypoints in the three candidate next zones (\(z_{2}\), \(z_{5}\), and \(z_{6}\)) are known as 5, 3, and 1, respectively. Since the \(N_{total}\) (= 9) is not zero, we calculate the probability of selecting the next zone by using Equation (2). As shown in Table 3, each probability of moving to \(z_{5}\) is 6/18, \(z_{6}\) is 8/18, and \(z_{2}\) is 5/18. That is, the \(z_{6}\) is more likely to be selected because it has fewer visits than the other two zones. Figure 6d presents a newly added waypoint in each zone and a moving path by a UAV. As a UAV is passed through zones \(z_{6}\), \(z_{10}\), and \(z_{15}\), each zone has a new waypoint by the UAV. Thus, \(z_{6}\) has two waypoints and the other zones, \(z_{10}\) and \(z_{15}\), have three waypoints.  
Table 3 shows the probabilities of selecting the next zone in each step assuming that the UAV selects \(z_{6}\) and \(z_{10}\). If the UAV chooses \(z_{14}\) or \(z_{11}\) in Step 3 as the next zone, the zone to be chosen in the next step will be the destination zone because \(|C|\) is 1.  
Figure 5: An example of waypoints in the border zone of \(z_{2}\).  
Once the next zone is determined, the UAV decides the waypoint in the next zone so that it flies to the waypoint. One constraint for the waypoint is the minimum distance from the current position is more than the rotation radius in order to prevent a circuitous flight.
## 4 Proposed MIMU/GNSS UTC Integration Architecture  
The results in the previous section indicate that the large frequency tracking error causes the estimated \(C\)/\(N_{0}\) to rapidly decrease. Furthermore, the code discriminator estimation error caused by the large frequency error is greater than the error without frequency error. If the large frequency tracking error cannot be eliminated in time, especially in highly dynamic environments, it will seriously impair the performance of the navigation system.  
In order to enhance the performance of the UTC navigation system under large frequency errors, it is necessary to improve the CUTC integration architecture shown in Figure 1. In this section, we propose a new UTC integration architecture. Figure 4 summarizes the structure of the proposed MIMU/GNSS UTC architecture.  
Figure 4: A block diagram of the proposed UTC architecture.  
Figure 3: RMS errors of the code discriminator.  
The architecture is referred to as the enhanced UTC (EUTC) integration in the rest of the paper. A large frequency error detector and a serial refined frequency processor based on retuned frequency are added to each tracking loop to reduce the impact of large frequency tracking errors on the coherent integration results. In addition, an adaptive prefilter is used to replace the conventional channel prefilter to enhance the estimation performance of abrupt frequency errors. Next, we introduce the principles of these three modules in detail.
## 2 Related Work
### Academic Solutions
#### 2.2.1 Home Automation Systems for Heterogeneous Networks
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Home Controller Hardware** & **Communication Temperatures** & **Communication Topology** & **Sensors and Actuates** & **Node Hardware** \\ \hline \hline ZWN & Rayberry P M Model B & WHI, Zglee & Mesh & Temperature (MSMS, TAPE, DHT11), humidity & NodeMCU (ISFN66), Node Series 2 \\  & & & & (HILS600, DHT11), humidity (EDRs), mobility & \\  & & & & (Parallel P/Rev. A) and ambient sensors (OSST2) & \\ \hline
[1] & PC & Zglee, WHI and CSM/GFR & Star & Temperature (M-3/3D2) and relays & WHI-USB Zglee module \\ \hline
[23] & 32-bit ARM microcontroller & XH, Serial, EBglee, Bluetooth, DDHG, CAN and & Star & Multiple I/O pins for attaching sensors & Proprietary board based on a 32-bit ARM microcontroller \\  & & GSM/GFR/(TMMS) & & and actuators & ARM microcontroller \\ \hline
[21] & Android tablet and Arduino MEGA & X10, Zigbee & Tree & Light and switch modules & Arduino \\  & with an Ethernet shield & & & & \\ \hline
[22] & 32-bit ARM-Cerite & Zigbee & Tree & The upper only suggests different sensors and actuators for the flash, but it is actually not implemented & CCS30 \\ \hline
[23] & - & EIR/XX, WHI, Bluetooth & Star & Environment (door/voltage opening, light, and/or), longitude (version), longitude (version), longitude (version), longitude (version), longitude (version) and actuators & VasyMote Platform \\  & & & & & body size, wrist blood pressure, error (frommer) and actinery (movement detection), load/disdis & \\  & & & & & presence, lighting control, value and electricity & \\  & & & & & next) sensor and actuators & \\ \hline
[24] & Rayberry P 2 & WHI, Zglee, iADA, Ethernet & Star & Smart plug (P/sens) & Raspberry P 2 (the controller also acts as sensor model) \\ \hline
[25] & Cubetersk board (ARM-Corite A7) & WHI, Zglee & Star & Temperature, light and current sensors (ACST2), & INFN66, None \\  & & & & & behaviors and diameters & \\ \hline
[41] & PC & Zigbee & Star & Smart plugs & ATneg382 microcontroller \\ \hline
[43] & - & IEEE WHI,15.4 & Tree & & MC\(\times\) modes \\ \hline
[44] & - & WHI,R & Star & Dust sensor & Commercial UAVEWH module \\ \hline
[45] & Rayberry P 2 model B & WHI & Star & - & FPS36 \\ \hline
[46] & PC & WHI & Star & Luminosity sensor (LDP, LED and barzer & FPS36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the main features of the most relevant academic HAS and the proposed system (part 2).
## 3 Results  
### Hyperparameter Tuning Results for LSTM  
In this study, we built four different LSTM models to see which LSTMs performed best for our data as follows: (1) One LSTM layer with 64 hidden units (LSTM1), (2) Two LSTM layers and 128 hidden units (LSTM2), (3) Two LSTM layers with 64 hidden units (LSTM3), and (4) Two LSTM layers with 256 hidden units (LSTM4). To achieve the best results in terms of prediction accuracy and error, we performed hyperparameter tuning steps for our models. We trained our LSTM models with 100 epochs and the best hyperparameters were tuned by using a function GridSearchCV [33] which is an automated tuning method in the Scikit-learn library in Python. Figure 4, an example of the parameter tuning procedure in the LSTMs, illustrates that loss and MAE for training and validation were identified through 100 epochs. Table 3 shows the best hidden units by the hyperparameter tuning results based on the smallest MAPEs for each LSTM model. The other parameters, such as batch size, epoch size, and the number of layers were also selected in the same way. Table 4 summarizes the list of the best selected hyperparameters for all four models based on our empirical study. Table 5 shows the results of the best loss and MAE for training and validation among 100 epochs.  
Figure 4: Hyperparameter tuning process for each LSTM model. Loss and MAE values of training and validation for: (**a**) LSTM1, (**b**) LSTM2, (**c**) LSTM3, and (**d**) LSTM4.
# Traditional Grain-Based vs. Commercial Milk Refirs, How Different Are They?
## 3 Results and Discussion
### Targeted Metabolites Analysis  
In addition to the VOCs, relevant metabolites in the kefir beverage were quantified after 23 \(\pm\) 1 and 47 \(\pm\) 1 h of fermentation of traditional grain-based kefirs as well as commercial kefirs (Table 1). These metabolites were lactose, the only carbon source in milk, and its mono-saccharide compounds glucose and galactose. Furthermore, we determined the concentration of lactate and acetate as the typical organic acid side products of bacterial fermentation, and glycerol was measured in kefir samples as a growth-related compound of yeast. As it is seen in Table 1, extensive hydrolysis of lactose is the matter of long fermentation [39]. The lactose concentration of milk used in our study was 47.2 \(\pm\) 0.3 g\(\cdot\)L\({}^{-1}\). In kefir PN1, which showed the fastest fermentation, only around one quarter of lactose hydrolyzed after 23 \(\pm\) 1 h. This concentration decreased further to 9.2 \(\pm\) 5.4 g\(\cdot\)L\({}^{-1}\) after 47 \(\pm\) 1 h of fermentation, which was significantly lower than the concentration in commercial kefirs (\(p<0.05\)). The remaining lactose in traditional kefir FN was still high (37.6 \(\pm\) 5.3 g\(\cdot\)L\({}^{-1}\)) after 47 \(\pm\) 1 h, with no significant difference from lactose concentrations in commercial kefirs (\(p<0.05\)). Based on these results, one can conclude that the lactose hydrolysis rate in grain-based traditional kefirs is related to the diversity of microbial composition. [PERSON] et al. (2001) [39] showed the different lactose hydrolyzation activity in milk fermented by single or mixed-cultures of _L. lactis_ and _Candida kefyr_, where the lactose digestion in mixed-culture fermentation is improved. Lactose hydrolyzing yeasts seems to have an important role, as it was seen that the kefir samples PN1-PN3 had the lowest lactose concentration while containing _Kl. marxianus_. Glucose is considered as the main energy source for many microorganisms, especially also for those yeasts in kefirs that are not able to consume lactose, like _Kazachstania_ spp. Glucose decreased significantly in all traditional kefirs from 23 \(\pm\) 1 h to 47 \(\pm\) 1 h (\(p>0.05\)), which led to increased lactate and acetate concentrations at the same time. Like glucose, the galactose content decreased during the prolonged fermentation. The galactose content was also significantly lower (\(p<0.05\)) than that of commercial kefirs even after shorter fermentation periods, i.e., 23 \(\pm\) 1 h. A lower concentration of galactose in traditional kefirs is due to the consumption of galactose for polysaccharide synthesis [40]. Galactose accumulation in commercial dairy products has been linked to the application of conventional starter cultures, e.g., _L. lactis_, _S. thermophilus_, and _L. bulgaricus_[41]. A higher galactose concentration can also be due to the presence of only one (_L. lactis_) or very few species in commercial kefirs.
###### Abstract  
The violent and persistent wildfires that broke out along the southeast coast of Australia in 2019 caused a large number of pollutant emissions, which seriously affected air quality and the global climate. The existing two methods for estimating combustion emissions based on burned area and fire radiative power mainly use a medium resolution imaging spectrometer (MODIS) on the Aqua and Terra satellites. However, the low temporal resolution of MODIS and insensitivity to small fires lead to deviation in the estimation of fire emissions. In order to solve this problem, the Visible Infrared Imaging Radiometer Suite (VIIRS) with better performance is adopted in this paper, combined with the fire diurnal cycle information obtained by geostationary satellite Himawari-8, to explore the spatio-temporal model of biomass combustion emissions. Using this, a high-spatial- and -temporal-resolution fire emission inventory was generated for southeastern Australia from November 2019 to January 2020, which aims to fully consider the highly dynamic nature of fires and small fires (low FRP) that are much lower than the MODIS burned area or active fire detection limit, with emphasis on dry matter burned (DMB). We found that during the study period, the fire gradually moved from north to south, and the diurnal cycle of the fire in the study area changed greatly. The peak time of the fire gradually delayed as the fire moved south. Our inventory shows that the DMB in southeast Australia during the study period was about 146 Tg, with major burned regions distributed along the Great Dividing Range, with December 2019 being the main burning period. The total DMB we calculated is 0.5-3.1 times that reported by the GFAS (Global Fire Assimilation System) and 1.5 to 4 times lower than that obtained using the traditional \"Burned Area Based Method (FINN)\". We believe that the GFAS may underestimate the results by ignoring a large number of small fires, and that the excessive combustion rate used in the FINN may be a source of overestimation. Therefore, we conclude that the combination of high-temporal-resolution and high-spatial-resolution satellites can improve FRE estimation and may also allow further verification of biomass combustion estimates from different inventories, which are far better approaches for fire emission estimation.  
biomass combustion emissions; fire radiative power (FRP); geostationary satellite Himawari-8; fire diurnal cycle; southeast Australia +
Footnote †: journal: remote sensing
## 2 Material and Methods  
### Low-Noise Acquisition Electronic  
_Electrode_ consists of a floating electrode (FIB, model F2080) connected to a transimpedance, current-voltage converter amplifier, designed and realized by the authors of this paper. The currents collected by the electrode were of the order of nano-Ampere or a fraction of femto-Ampere, and the transimpedance gain was R = V\({}_{out}\)/I\({}_{in}\) = 10\({}^{9}\)\(\Omega\). The amplified voltage signals were then sampled by a commercial high speed (1 MS/s) \(\pm\) 10 V digital scope (model NI 9223, National Instruments). The acquisition software, able to monitor and register the currents and perform a preliminary analysis of the acquired signals, was also developed by the authors.  
The trans-impedance amplifier contains four principal elements:  
1. A current-voltage converter (I-V), based on an OPA128 (Texas Instruments) operational amplifier, coupled with a high-resistance feedback resistor (1 G\(\Omega\)) and a 100-pF capacitor, in parallel to the resistance. This configuration limits the maximum cutting frequency to 1.6 Hz; moreover, it allows the control of the autoinduced oscillations in the converter and avoids overshoot phenomena. The I-V converter response is linear in the output range of \(-\)10 V \(\leq\) V\({}_{out}\)\(\leq\) +10 V. The conversion range will hence be \(-\)10 nA \(\leq\) I\({}_{in}\)\(\leq\) +10 nA.
2. A low-pass filter made of a Resistive-Capacitance (RC) network, with a cut frequency of 0.9 Hz and an attenuation of \(-\)68 dB at 50 Hz. The filter has two main roles: it protects the OPA128 operational amplifier from possible electrostatic discharges (ESD) induced both by patient and/or medical personnel during the pretreatment phases; it also weakens the 50-Hz noise currents, induced in the patient by the environment's electromagnetic fields, by a factor 2.500.
3. A couple of Junction gate Field-Effect Transistors (JEFT), which protect the OPA128 whenever the I-V converter reaches saturation. If the I-V converter works inside its operational range, the two JFETs are not operative and the leakage currents they introduce are negligible (order of 100 fA).
4. An active, inverting, second-order, low-pass filter. Its main aim is to cut (with a slope of 40 dB per decade) the residual noise at the output of the I-V converter. The inverting characteristic of this filter was designed to compensate the negative voltage sign at the output of the I-V converter. With this solution, the final output voltage will be positive, i.e., of the same sign as the input current.