esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Non-Encapsulated and Microencapsulated Lactic Acid Bacteria
## 3 Results
### Probiotic Powder Storage  
Probiotic choice based on _in vitro_ tests involves a representative selection for the application of the encapsulation process. After spray drying, the powders stability of strains (_L. salivarius_ IBNA 33, _L. salivarius_ IBNA 41, and _L. acidophilus_ IBNA 64) was monitored at 0, 7, 14, 21, 27, 35, 42, 49, 56, and 62 days. During storage, the cell viability of all encapsulated _Lactobacillus_ strains weakly decreased. In the first month, both powders of _L. salivarius_ (IBNA 33 and IBNA 41) registered constant survivability (Figure 2), and exhibited a growth rate of up to 75% during the first to 21 days, followed by decreases after 42 days.  
Figure 1: The bacterial population of _L. salivarius_ (IBNA 33, IBNA 41) and _L. acidophilus_ (IBNA 64) before and after spray drying process. Data are represented as means \(\pm\) standard error of means (SEM) of three independent experiments. \({}^{**}\)\(p<0.0001\) and \({}^{***}\)\(p=0.0003\) compared with IBNA 64; \(\#\)\(p=0.0159\) compared with IBNA 41 and \(\#\#\)\(p<0.0001\) compared with IBNA 64.  
A 3 Log CFU g\({}^{-1}\) reduction of viability was reached after 2 months of storage, when strains resisted around 65%. In contrast, after spray drying, the viability of _L. acidophilus_ IBNA 64 was reduced gradually after 7 days of storage at room temperature. Moreover, after 30 days, _L. acidophilus_ IBNA 64 completely lost viability. We can affirm that the drying process displayed a decrease in viability, reaching a resistance around 87% for IBNA 33, 95% for IBNA 41, and 3% for IBNA 64 at 35 days of powder storage. These results showed that spray drying decreased the stability of _Lactobacillus_ powders during the entire period of storage. Comparatively, with _L. acidophilus_ IBNA 64 where a larger loss of viability was noted, the _L. salivarius_ (IBNA 33 and IBNA 41) involved better stability. We can conclude that powders of _L. salivarius_ (IBNA 33 and IBNA 41) were more stable than _L. acidophilus_ IBNA 64.
## 2 Materials and Methods
### 1d-Cnn
#### 2.4.2 1D-CNN Model and Methodology  
In this study, a 1D-CNN was used to perform numerical data regression analysis, evaluating its predictive performance for continuous values. The model was designed with multiple convolutional, pooling, and fully connected layers. The input data were processed in a one-dimensional time-series format, with each data point containing numerical data over a specific period. In the first stage of the model, a Conv1D layer was used to extract local features. The first convolutional layer applied 32 filters with a kernel size of 3. In  
Figure 3: 1D-convolutional neural network model structure.  
the second convolutional layer, 64 filters were used to learn more complex patterns, and L2 regularization was applied to each convolutional layer to prevent overfitting. After the second convolutional layer, a MaxPooling1D layer was applied to reduce the output dimension, retaining only the most essential features. An additional convolutional layer with 128 filters was then applied, and L2 regularization and dropout (40%) were used to reduce overfitting during training.  
After the convolution and pooling processes, the data were converted into a one-dimensional format using a Flattened layer, and the final regression results were predicted through fully connected (Dense) layers. Two Dense layers were used in this process, containing 128 and 64 neurons, respectively. Dropout (50%) was also added to randomly exclude some neurons during training to improve the model's generalization performance. The final output layer used a single neuron to predict continuous numerical values.  
During the model training process, the Adam optimization algorithm and Huber loss function were used to minimize prediction errors. Additionally, the R\({}^{2}\) score and MAE were monitored to evaluate performance during training. To achieve this, the R\({}^{2}\) score callback was defined to record the R\({}^{2}\) score and MAE for the training, validation, and test datasets, with detailed training parameters shown in Table 2.  
To evaluate the model's performance, prediction performance for the training, validation, and test data was monitored at each epoch, and the ModelCheckpoint callback was used to save the model weights that demonstrated the best performance. Finally, the R\({}^{2}\) score and MAE were calculated for the test data, and a post-processing step was performed to adjust the predicted values within the range of actual values, improving the model's predictive performance.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 3 A New Risk Measurement Method Considering Multiple Driving Behaviors
### Proposal of a New SSM
#### 3.1.1 Longitudinal Risk Assessment (Car-Following Behavior)  
The longitudinal risk assessment considers the spacing difference and speed difference between the vehicle and the preceding vehicle: The calculation of \(R_{\text{lon}}\left(t\right)\) is given by Equations (6) and (7).  
\[R_{\text{lon}}\left(t\right)=\delta\frac{\max(S_{n}(t)-\Delta x_{n}(t),0)}{S_{n }(t)}+(1-\delta)\frac{\max(v_{n}(t)-v_{n-1}(t),0)}{v_{n}(t)+\epsilon} \tag{6}\]  
\[S_{n}(t)=s_{0}+Tv_{n}(t). \tag{7}\]  
where \(s_{0}\) is the minimum distance (a constant), and \(T\) is the safe time headway.  
\(\Delta x_{n}(t)=x_{n-1}(t)-x_{n}(t)-L_{n-1}\): Actual spacing, considering the length of the preceding vehicle.  
\(x_{n}(t),x_{n-1}(t)\): The longitudinal positions of the subject vehicle \(n\) and the preceding vehicle \(n-1\) at time \(t\).  
\(L_{n-1}\): The length of the preceding vehicle.  
\(v_{n}(t),v_{n-1}(t)\): The speeds of the subject vehicle and the preceding vehicle at time \(t\).  
\(\delta\): Weight parameter ranging from \([0,1]\), balancing the influence of spacing difference and speed difference.  
\(\epsilon\): A very small value to prevent division by zero, usually set to \(\epsilon=0.01\).  
Spacing Risk Term: When the actual spacing \(\Delta x_{n}(t)\) is less than the ideal safe distance \(S_{n}(t)\), there is a risk of rear-end collision.
## 2 Benefits Assessment of ESS Introduction in the F/R Market
### Economic Assessment of ESS in F/R the Market, with Respect to UBs  
Following the SMP estimation, we determined UBs as a function of introducing F/R ESS. The UBs consist of benefits in the energy and A/S markets. Simulations were conducted for each of case A, B, and C, using two ESS capacity scenarios that consider existing installed capacity (i.e., 52 MW) and planned future capacity (i.e., 500 MW). To calculate UBs in each scenario, the value of UBs is derived by using the following settings: \(Availability_{ESS}=48\%\), \(W_{ESS}=1.1\), \(W_{Drop}=1.05\), \(W_{Deadmund}=1.05\) and \(U_{FR}=2.53\,\$/\)kWh. Furthermore, the daytime, nighttime, and annual average SMPs are applied to each equation in a regular sequence. Results pertaining to UBs during the 2019-2028 period are presented in Table 7.  
In case A, over the 10-year period, for a 52-MW ESS (500-MW ESS), we could anticipate ASP benefits of about $3 million ($31 million), EP benefits of about $33 million ($318 million), and UBs of about $36 million ($348 million). When considering other cases based on case A, we can see that case B (case C) is 26% (155%) the scale of case A. In cases A, B, and C, the difference between the two capacity scenarios was approximately $31, $8, and $48 million, respectively--demonstrating an 862% increase for an ESS capacity increase from 52 MW to 500 MW. This finding demonstrates that UBs are directly proportional to the size of the ESS. The benefit scale for these cases differs very much from the simulation results; this divergence derives from the fact that SMP change depending on the time involved and the times in which ESS benefits are generated. Consequently, changes made to the electricity industry--such as changes to energy policy, fuel costs, and demand among others--will decide the benefit level.
## 5 Applications of BSNs
### Trends and Challenges
With the development of smartphones, the capabilities of processors are becoming increasingly powerful, and so are smartphone operating systems. With the help of mobile phones, more complex, diverse and humanized functions will be achieved, which is a significant trend of BSNs in the short run. In recent years, BSN theory has undergone significant development, but there are still many challenges in practical applications. For a lot of BSN products it is difficult to meet design requirements. In the conversion process from technology to product, defects of usability and accuracy limit BSN technology used in some important fields such as the military and serious medical field. The usability of BSNs consists of many aspects and needs to be considered appropriately. Robustness is the base of usability. BSN systems should be able to diagnose faults and achieve failback by themselves [2]. If the network topology of a BSN changes, it should be able to correctly handle problems of node connectivity. Users' privacy issue is also very important. The application must ensure that privacy data can only be retrieved by authorized users. The data should be encrypted during transmission and storage [25]. In addition, much interdisciplinary knowledge is needed in the studies of wearability, lifetime, and service intelligence _etc._, which is difficult. The accuracy of a BSN system depends on the process of signal acquisition, denoising, feature extraction, fusion calculation, data compression, transmission, and other follow-up analysis. However, it is difficult to make sure that all the processes are accurate. Although current BSN applications can achieve some of the basic functions, the accuracy is still not good enough to fully meet application requirements. For example, the fall-prediction system proposed in Reference [141] may mistake some special movements for falls and pop the air bag unexpectedly, which will seriously affect usual activities. However, if overemphasizing accuracy while designing a system, it would be inevitable to achieve other target such as low computational cost and low power consumption [152]. Large amounts of work need to be done to balance the relationship between computational cost and accuracy of a system. The introduction of simpler mathematical models to reduce the computational cost and power consumption in the future is an effective way to accomplish all this.
## 2 Materials and Methods  
### Experiment Setup  
Figure 1 shows the previous outdoor experiment to observe the Joule heating effect by experimenting with PVT-mode and T-mode alternately [13]. From the experimental setup in Figure 1a, the PVT collector faced south from the Kanagawa Institute of Technology campus, Atsugi-Japan, at a 50\({}^{\circ}\) tilt. For constant temperature setting and single flow rates (\(\dot{m}=6.7\times 10\) m\({}^{3}\)/s \(\approx 4\) L/min), the water circulation system used a temperature-controlled water chamber connected to a pump and flow meter. The heat exchanger system took heat from the outlet water collector before entering the water circulation system. The experiments took place during sunny days in winter, with four inlet water temperatures \(T_{i}=12\); 15; 20; and 25 \({}^{\circ}\)C. The data of irradiation \(I\) from pyranometer, wind speed \(V_{w}\) from anemometer, inlet water temperature \(T_{i}\), outlet water temperature \(T_{o}\), ambient air temperature \(T_{a}\), voltage \(V_{mpp}\), and current \(I_{mpp}\) were collected by a data logger every 30 s. Data collection was focused during 12:00-13:00 to ensure stable irradiation and ambient air temperature. The experiments were run before and after that period to avoid the hysteresis effect on the PVT collector. Figure 1b shows the PVT array 2 \(\times\) 2 connected in series for higher power output to provide a higher Joule heating effect. The experiment used polycrystalline silicon (p-Si) PV modules. Each PV module covered the top of the thermal collector. The numbers indicated the water flow from 1 (as inlet)--2--3--4 (as outlet). Figure 1c shows the construction of the PVT collector. Each PVT collector is 790 mm \(\times\) 820 mm, or 0.60 m\({}^{2}\). Each PV module area \(A_{pv}\) is 780 mm \(\times\) 770 mm or 0.58 m\({}^{2}\). The total area of the PVT collector \(A_{c}\) is 2.40 m\({}^{2}\). A more detailed setup was given by [13].
# A Novel Index to Detect Vegetation in Urban Areas Using UAV-Based Multispectral Images
## 2 Materials
### UAV Image Processing
When vegetation was classified using NDVI, GNDVI, BNDVI, RGBVI, GRVI, and SAVI, there were locations in which the index values of long-run steel roofing, waterproof coated roofs, and urethane-coated areas were slightly higher than that of grass. Therefore, it was necessary to develop a novel vegetation index suitable for urban areas to effectively address this issue. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Vegetation** & **Min** & **Max** & **Mean** & **StD.** \\
**Index** & & & & \\ \hline NDVI & \(-\)0.356 & 0.806 & 0.253 & 0.226 \\ GNDVI & \(-\)0.410 & 0.755 & 0.232 & 0.173 \\ BNDVI & \(-\)0.350 & 0.812 & 0.301 & 0.216 \\ RGBVI & \(-\)0.810 & 0.744 & 0.117 & 0.184 \\ GRVI & \(-\)0.667 & 0.455 & 0.034 & 0.126 \\ SAVI & \(-\)0.535 & 1.210 & 0.380 & 0.339 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Characteristics of existing vegetation index maps. Figure 7: Examples of various types of land cover. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Land Cover**} & \multicolumn{2}{c}{**NDVI**} & \multicolumn{2}{c}{**GNDVI**} & \multicolumn{2}{c}{**BNDVI**} & \multicolumn{2}{c}{**RGSVI**} & \multicolumn{2}{c}{**GRVI**} & \multicolumn{2}{c}{**SAVI**} \\ \cline{2-13}  & & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** & **Value** & **Mean** \\ \hline
1 & Long-run steel roofing & 0.48 & 0.38 & & & & 0.04 & & \(-\)0.24 & & 0.12 & 0.72 & & 0.75 \\ \cline{2-13}
2 & (Blue color) & 0.50 & 0.50 & 0.41 & 0.41 & & 0.07 & 0.08 & \(-\)0.25 & \(-\)0.24 & 0.11 & 0.11 & 0.75 & 0.75 \\ \cline{2-13}
3 & (Blue color) & 0.53 & & 0.44 & & 0.12 & & \(-\)0.23 & & 0.11 & & 0.79 & \\ \hline
4 & Artificial grass playground & 0.12 & & & 0.09 & & 0.11 & & 0.05 & & 0.03 & & 0.19 & \\ \cline{2-13}
6 & (Green color) & 0.15 & 0.14 & & 0.12 & & 0.11 & 0.11 & & 0.01 & & 0.04 & 0.02 & 0.04 & 0.22 & 0.21 \\ \cline{2-13}
7 & & & & & 0.09 & & 0.11 & & 0.07 & & 0.06 & & 0.22 & 0.22 & \\ \cline{2-13}
8 & Playground track & \(-\)0.04 & & & 0.22 & & 0.23 & & \(-\)0.24 & & \(-\)0.26 & & \(-\)0.06 & & \(-\)0.06 \\ \cline{2-13}
9 & (Red color) & \(-\)0.04 & & & 0.22 & & 0.23 & & \(-\)0.24 & & \(-\)0.26 & & \(-\)0.06 & & \(-\)0.06 \\ \cline{2-13}
10 &
## 3 Results
### Raman Spectrum Denoising Module Training Results  
Although we have been able to generate a large amount of spectral data similar to the original data by using the RSEM, careful observation of the generated images reveals that there is still some noise in the data, and the model seems to have encountered a bottle-neck.  
Figure 10: (**a**) Without transfer learning; (**b**) With transfer learning. VP refers to _Vibrio para-haenolyticus_, _E. coli_ refers to _Escherichia coli_, Ah refers to _Aeromonas hydrophilla_, and _A. veronii_ refers to _Aeromonas veronii_.  
We attempted to increase the number of RSEM iterations, such as by 1000, 2000, or even 5000, but the noise still existed. Therefore, the RSDM was introduced in the experiment to denoise the data generated by the RSEM. To verify the denoising effect of our proposed denoising module, we evaluated the RSDM module using three indicators, MSE, SNR, and PSNR, which are commonly used indicators in spectral denoising tasks. MSE is used to calculate the difference between the denoised data to be evaluated and the original data. SNR stands for signal-to-noise ratio. The higher the signal-to-noise ratio, the smaller the noise mixed in the signal, and the higher the quality of the denoised data. PSNR stands for peak signal-to-noise ratio, with the denominator being the energy difference between the evaluated data and the original data, which is also equivalent to noise. The smaller the noise, the better, so the larger the PSNR, the better. We added a certain amount of noise to the original spectral data, and then used our improved module for noise reduction. The specific results are shown in Table 3.  
As can be seen, we added 15 dB, 25 dB, and 50 dB of noise to the original data, and our improved model achieved the best results. To improve the quality of the generated data, we take the generated data from RSEM as the input of the model, and set the optimizer of the noise reduction model to the Adam optimizer, the learning rate to 0.0001, and the loss function to the RMSE (Root Mean Square Error) function. Comparing the original data, generated data, and denoised data, as shown in Figure 11, it can be seen that although the green lines (generated by RSEM) have a distribution similar to that of the original data (orange lines), there is clearly a substantial amount of noise present. After 1000 iterations, the model not only removed noise from the data, but also fully retained the original features of the generated data, making the denoised data (blue lines) more in line with the real data.
# Validation of Lumbar Compressive Force Simulation in Forward Flexion Condition
## 2 Method
### CF Estimation
#### 2.3.3 Model of Potvin  
[PERSON] developed a static human model by directly using mechanical equilibrium and regression analyses. In this model, the magnitude of the CF, \(CF_{p}\), can be expressed as follows:  
\[CF_{p}\equiv\|\mathbf{CF}_{p}\|=(0.54 BW\times g\times WB+L\times g\times HB)/0.06+\sin( \gamma)\times(0.54 BW+L)\times g, \tag{5}\]  
where \(BW\) is the body weight (kg) (the upper body weight is 54% of the \(BW\) and acts at the upper CoM); \(WB\) represents the moment arm (m) of the upper body's CoM; \(L\) is the external load (kg); \(HB\) is the moment arm (m) of the external load about L5/S1, and \(\gamma\) is the angle between the trunk (C7-S1) and the horizontal direction (rad).  
\(WB\) and \(HB\) are obtained by two linear regression models:  
\[WB=-0.032+0.104 LA-0.004V+0.000356\gamma+0.354\text{cos}(\gamma), \tag{6}\]  
\[HB=-0.144+0.991 LA-0.031V+0.000193\gamma+0.281\text{cos}(\gamma), \tag{7}\]  
where \(LA\) is the horizontal distance between \(L\) and the ankles, and \(V\) is the vertical distance between the ground and \(L\). On the right side of Equation (5), the first component represents the amount of muscular force exerted by the upper body weight and the external load, the moment arm of the back muscle (erector spinae) was determined to be 0.06 m, and the second component represents the amount of the contact force between L5 and S1 [15].  
The characteristics of the three models (i.e., the models by [PERSON] et al., [PERSON] et al., and the CF simulator proposed in this study) are compared in Table 1. From Figure 1 and Table 1, it can be seen that the angles of the three models are different, and it is not convenient for us to find the relationship between the CF and flexion posture. Thus, only the angle \(\beta\) and \(\gamma\) are used for calculating the CF. To display the relationship between the flexion level and the CF, we use the flexion angle \(\theta\).  
The CFs at the L5/S1 level estimated by [PERSON] et al. and [PERSON] et al. were compared with those estimated by the CF simulator [15; 16].  
There are other models that can be used to estimate CF, such as the statistical method, EMG method, and optimization method [27; 28; 29]. However, these models may yield some errors in motions. For example, in the statistical method, an error larger than 20% can occur as the torque is smaller than 10 N\(\cdot\)m [27]. In the EMG-assisted method, a part of muscular force can be provided by passive tissues which cannot be monitored by EMG device (e.g., flexion-relaxation phenomena). Thus, muscular force and the compressive force can be underestimated. The accuracy of the optimization criteria on presenting muscular forces has not been validated. Hence, we select the models of [PERSON] and Merryweather because they are potentially applied to more realistic conditions than other models.
# Integrated Digital-Twin-Based Decision Support System for
## 5 Integrated DT-Based Decision Support System for Relocatable Module Allocation Plan
### Develop Algorithm for Module Selection
The process begins with access to the sources of existing module units. An empty list (M) is initialized, and the required module types are obtained from the project requirements (P). The algorithm then iterates through all the available modules, checking if each module meets the criteria for inclusion in the list. The criteria include whether the module is reusable, its type matches the required types, and its availability. If a module satisfies these conditions, it is added to the list M. This ensures that only suitable and available modules are considered for the project. * Algorithm on (M) to find result (R): This flow illustrates the algorithm that operates on the available module list (M) to find the final result (R) based on the project requirements (P). The process starts by initializing an empty result list (R) and obtaining the list of required types from the project requirements (P). It also retrieves the number of required modules for each type from the project requirements (P). The algorithm then iterates through all the module types in the available module list (M). For each type, it picks the necessary number of modules, sorts them by proximity to the project site, and selects them to add to the result list (R). This proximity is determined using the geographic coordinates (longitude and latitude) of each module's location on the GIS map. After iterating through all types, the selected modules (m1) are added to the result list (R), which is then output as the final result.