esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Single-Particle Imaging with X-ray Lasers
### Light Source and Instruments
Ge, Si, Si\({}_{3}\)N\({}_{4}\) and B\({}_{4}\)C apertures are typically used depending on the instrument and photon energy. Background levels from apertures of different materials have been investigated with varying performance depending on the material and thickness. For example, cleaved and etched Ge was found to be a good choice for hard X-rays around 7 keV. The typical LCLS pulse energy for SPI is 2-5 mJ, equals to 7.80 \(\times\) 10\({}^{12}\)-1.95 \(\times\) 10\({}^{13}\) photons/[EMAIL_ADDRESS] keV and 1.78-4.46 \(\times\) 10\({}^{12}\) photons/[EMAIL_ADDRESS] keV. The calculation of photon number per pulse was based on the 100% efficiency assumption of optical system. Actually, the efficiency is closer to 50%. More photons typically mean higher SNR with stability being desirable. A single diffraction pattern is just one slice from the 3D diffraction volume. The number of photons interacting with the sample is different due to the intensity fluctuations shot-to-shot. The side effects of intensity fluctuations to the SPI 3D diffraction volume also need further study. Figure 6 shows the pulse intensity fluctuation with shots (time), the common fluctuation of intensity is ~10%. Also, the focused beam position fluctuates. The relative positions between X-ray laser beam and the sample are also different shot to shot. This leads to different incident photons onto samples and means that for SPI, is not so easy to normalize every frame. The pulse duration is another main factor for XFEL experiments. Short pulse duration is always required to outrun radiation damage. However, since shorter pulses usually come with a reduced number of photons, a balance is needed between pulse duration and pulse energy, a balance between damage and signal. For biological samples and molecules, Auger decay is predominant in carbon, nitrogen and oxygen [158]. The photoelectric effects will lead to a removal of two electrons from these elements. The energy and lifetime of these two electrons are different [159; 160; 161]. To figure out the exact Auger emission time scale and non-elastic scattering for specified samples is complex. However, the classic simulation and crystalline experiments have demonstrated that the radiation damage could be reduced or eliminated by using shorter pulses, such as \(<\)10 fs [100]. For SPI, to guarantee enough photons per pulse, the commonly used pulse duration is 30-70 fs. At the current limited resolution, these pulse durations do not lead to noticeable damage. For higher spatial resolution, radiation damage will be a limit for the development of SPI [25; 162; 163].
## 6 Key Issues and Challenges
### Data Transmission Range  
One of the current issues relating to the solar PV system is an increase in the size of utility-scale solar PV plants. These large-scale solar PV plants cannot be monitored by low-range data transmission modules such as Bluetooth, Wi-Fi, and ZigBee. The range of Bluetooth modules is around 100 m while for Wi-Fi and ZigBee the range is around 150 m and 300 m, respectively, which makes these modules unable to monitor the data from a solar power plant where the distance between the two farthest PV panels may be in km. Moreover, the implementation of GSM is not possible for the solar power plants located in distant places due to the issue of network coverage. LoRa is being considered an important platform in transmitting data over long ranges but it suffers from a low transfer rate and a high PER as the distance is increased.
## 2 Materials and Methods
### Operating Principle  
In this section, the single node sequential operation is examined. We discuss, in particular, the sequence of operations performed by each slave node, from the activation to the stable operation state.  
Firstly, after the node is powered for the first time, internal peripherals are set according to the latest parameters sent by the master unit, such as sampling frequency and mission time. After this initial state, the node passes to the idle state, where it waits for a command to be received from the master unit: commands include basic operations involving data sampling, internal memory control, and mission execution. A single mission is the entire time window where data are sampled and processed. Therefore, if a command containing a mission starting request is received by the node, it sets the internal accelerometers with incoming parameters, which are the actual sampling frequency and mission length, and checks for the GPS signals: if a GPS fix is detected, the node links to the satellite and obtains GPS data, such as its geological position (latitude and longitude) and temporal data (year, month, day, hour), and then uses the PPS signal to accurately synchronize the start of the acquisition with the other elements in the network; on the contrary, if no GPS fix is detected, node position remains undetermined for the master system during the execution of the command, and the synchronization loses some accuracy. A graphical representation of this workflow is reported in Figure 3.  
Figure 2: Final node scheme (**a**) and photo (**b**).
## 3 Results
### Optimization of Lipase Immobilization Conditions  
To increase the lipase loading and immobilization efficiency of CALB@MCM-41-C\({}_{8}\), the key factors including the lipase solution and pH value of the phosphate buffer were investigated. As illustrated in Figure 3a, with the protein concentration continually increasing from 2 mg/mL to 10 mg/mL, the loading amount of lipase CALB was also increased from 42.3 mg/g to 112.2 mg/g, as increased protein concentration provided more lipase molecules to interact with MCM-41-C\({}_{8}\), thus enhancing the lipase immobilization. Nevertheless, the maximum immobilization efficiency of 56.5% was noticed with the protein concentration reaching 8 mg/mL, while the further increase in protein concentration led to the significant reduction in immobilization efficiency to 13%. This can be explained by the saturation of lipase CALB on MCM-41-C\({}_{8}\), as the increase in lipase loading amount became insignificant when the protein concentration reached 8 mg/mL while the further increase in protein concentration could not improve lipase loading but inversely decreased its immobilization rate [55]. Hence, the optimal protein concentration of 8 mg/mL is selected and used in the following study.  
The influences of solution pH on lipase immobilization are illustrated in Figure 3b. The lipase loading was observed to slightly increase from 113.2 mg/g to 124.3 mg/g with the pH value increased from 5.0 to 6.0, followed by an unremitting decrease to 87.3 mg/g at pH 9.0. Similar results were also identified for the immobilization efficiency, which achieved a peak point of 68.2% at pH 6. The optimal value of pH 6 is mainly associated with the weakly acidic carrier of MCM-41, which requires the weak acid condition to keep its structural stability [56]. Hence, the optimum pH value of pH 6 is identified for the following lipase immobilization.  
Figure 2: (**a**) SEM morphologies and (**b**) TEM image of MCM-41. (**c**) N\({}_{2}\) adsorption–desorption isotherms and (**d**) pore size distribution curves, (**e**) FT-IR spectroscopy, and (**f**) XPS images of MCM-41, MCM-41-C\({}_{8}\), CALB@MCM-41-C\({}_{8}\).
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Signal Processing Methods
#### 3.2.1 Frequency Domain Methods
That is because when a local mechanical failure occurs in the motor, it comes into contact with another part of the machine and produces a shock pulse in the vibration [65]. For faults in different parts of the bearing, the corresponding characteristic frequency is as follows [19]:  
\[f_{IRF}=\frac{N_{B}}{2}f_{R}(1+\frac{D_{B}\cos\theta}{D_{P}}) \tag{5}\]  
\[f_{ORF}=\frac{N_{B}}{2}f_{R}(1-\frac{D_{B}\cos\theta}{D_{P}}) \tag{6}\]  
\[f_{BF}=\frac{D_{P}}{2D_{B}}f_{R}(1-\frac{D_{B}^{2}\cos^{2}\theta}{D_{P}^{2}}) \tag{7}\]  
\[f_{CF}=\frac{1}{2}f_{R}(1-\frac{D_{B}\cos\theta}{D_{P}}) \tag{8}\]  
where \(f_{R}\) represents the rotor frequency, \(f_{IRF}\) represents inner raceway fault frequency, \(f_{ORF}\) represents outer raceway fault frequency, \(f_{BF}\) represents ball fault frequency, \(f_{CF}\) represents the cage fault frequency, \(D_{B}\) represents ball diameter, \(D_{P}\) represents pitch diameter, \(N_{B}\) represents number of rolling elements, and \(\theta\) represents ball contact angle.
# An Outdoor Navigation System for Blind Pedestrians Using GPS and Tactile-Foot Feedback
## 3 System Description
### Localization and Guidance  
The operation principle and main components of the proposed system are shown in Figure 2a.  
Modern smartphones containing GPS, a digital compass, accelerometers, inertial sensors, and connectivity capabilities have become ideal candidates for portable computing applications [40]. Nowadays, they are the simplest and most common solutions for GPS receiver modules. In this current prototype, a smartphone (Samsung Galaxy S5 running Android 4.4.2) was used for user GPS coordinate and orientation acquisition.  
Using the smartphone's internet connectivity, the user's geospatial information is transmitted to a cloud server as a data sentence. Upon reception, the server constructs a text file containing longitude, latitude, and orientation and discards other information, such as time, altitude, number of satellites, etc. The smartphone updates the server at 5 Hz, which extensively covers any walking speed.  
A remote computer accesses the server's text file via internet and locates the user in a locally running spatial database. The GIS used is OpenStreetMap [41]. A dedicated script links to the YOURS application programming interface (API) [42], an open source route planner, to compute the shortest pedestrian route to a previously chosen destination. The route waypoints are returned from YOURS, and they are placed and stored in the GIS. Directions to the destination are processed locally. The GIS no longer needs to communicate with the route planner API unless a route recalculation is necessary.  
Directions are translated to actuator commands, which are transmitted via radiofrequency (RF) to an electronic module that the user carries with him/her. Finally, signals are interpreted by a microcontroller, which sets the actuators in the tactile display.  
Figure 2: The assistive navigation system: (**a**) main components and operation principle; (**b**) Depiction of a blind pedestrian using the device. White cane or guide dog can be used in conjunction with the device (RF: radiofrequency).  
Figure 2b illustrates how a blind pedestrian would use the device. Though it would be possible to implement the full navigation software as an Android application and have it all running in a smartphone, the cloud server and the remote station provide a more flexible option at this moment for participant monitoring, system debugging, and concept validation.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 4 Soil Classification Using Random Forest Method
### Out-of-Bag (OOB) Predictor Importance  
In this study, the Out-of-Bag (OOB) feature of the RF model is utilized to assess the importance of seven factors in soil classification. Through the OOB method, we examine the RF model's training process and utilize the OOB samples to estimate the decrease in prediction accuracy when specific feature arrangements are utilized.  
The OOB predictor importance serves as a technique for assessing the relevance of features within the RF model. It consists of multiple decision trees, each constructed using distinct training and feature subsets. As these trees are built using different random samples and features, certain data points may never be utilized during training, known as OOB data. The OOB predictor importance evaluates each feature's contribution to the model based on its performance in the OOB testing. This importance is determined by the frequency of feature usage across all trees and the average reduction in testing error observed when the feature is employed in each tree. The OOB predictor importance offers an intuitive means to gauge features' impact on the RF model's predictive performance and aids in selecting the most crucial features for modeling purposes. By integrating the importance of the OOB predictors, influential predictors are identified within the RF model utilized.  
The analysis findings regarding variable importance using the OOB predictor, as shown in Figure 4, reveal intriguing insights. Factors 1 (Cc) and 2 (Cu) exhibit negative importance values, indicating their diminished relevance in the classification process. The OOB analysis results of this study align well with the facts. Specifically, the coefficients Cc and Cu are not employed for classifying all soil types. These coefficients are primarily used to describe the grain size distribution of coarse-grained soils. They are not applicable to fine-grained soils (such as silt and clay), which lack a significant range of particle sizes, and are also not used for coarse soils with fines greater than 12% or for organic soils. Conversely, Factors 5 (LL) and 7 (percentage passing No. 200 sieve) demonstrate the highest importance values, highlighting their pivotal roles in soil classification. This observation implies that LL and the percentage passing No. 200 sieve are not only critical but also integral factors that significantly influence the outcome of the classification process, emphasizing the need for careful consideration in soil analysis and interpretation.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 3 Results
### Comparative Analysis: Precipitation vs Groundwater Storage (2003-2023)
#### 3.2.3 Southern Zone  
* _Precipitation_: Averages between 680 and 740 mm/year with a negative trend (\(-\)0.09 mm/year).
* _Interpretation_: Over 20 years, precipitation increased by just 1.69 mm, which is also statistically negligible.
* There is a slight positive trend of +0.08 mm per year, although it is not significant.
* The extremely low R2 indicates that interannual variability far exceeds any linear trend.
* _GWS_: Most significant drop (\(\sim\)70% to \(\sim\)32%).
* _Relation_: Lower precipitation correlates with deeper groundwater decline, aggravated by the arid climate and limited natural recharge.
## 3 Results and Discussion
### CH-CNC-CD Mixture Characteristics  
In order to study the combined effect of CNC and CD, samples of CH/CNC and CH/CD, both in proportion of 50/50, were mixed at three levels (75/25, 50/50 and 25/75). Therefore, the final proportions of the CH-CNC-CD samples were 50-37.5-12.5, 50-25-25 and 50-12.5-37.5 respectively. The following properties were studied: viscosity, surface tension, thickness, oxygen permeability, water vapor permeability, color difference, elastic modulus and breaking stress (\(\sigma\)). Viscosity and surface tension were evaluated in the primary solutions and the rest in the final formed edible films. It was observed that most properties (viscosity, oxygen permeability, water vapor permeability, color difference, elastic modulus and breaking stress) underwent statistically significant changes (\(p<0.05\)) in the composite films. Samples with CH-CNC-CD in the proportion of 50-37.5-12.5 displayed the best combination of viscosity and surface tension (Table 5) for coating (low viscosity and high surface tension) and better mechanical properties. As far as the barrier properties (Table 5) are concerned, samples with CH-CNC-CD in the proportions of 50-25-25 and 50-12.5-37.5 showed better results compared to samples with CH-CNC-CD in the proportion of 50-37.5-12.5. As expected, the color difference (Table 6) decreased as the CD proportion increased, due to the higher transparency of the CH/CD solution. Therefore, samples with CH-CNC-CD in the proportion of 50-12.5-37.5 excelled in their optical properties. Correspondingly, films with increased CNC proportions, presented higher mechanical properties (Table 6), as nanocrystal compounds create more stable structures. Apparently, there is no ideal ratio of CH-CNC-CD to achieve the best results in all properties. Thus, the choice of the most suitable combination depends on the priorities set in each case. More specifically, the 50-25-25 mixture is considered optimal when it comes to barrier properties. The 50-37.5-12.5 mixture provided enhanced coating characteristics and high mechanical properties. Finally, regarding optical properties, the 50-12.5-37.5 mixture offers the most appropriate solution. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CH–CNC–CD** & **AE (-)** & **Elastic Modulus (N/mm)** & **\(\sigma\) (MPa)** \\ \hline
50–37.5–12.5 & 47.59 \(\pm\) 2.86 \({}^{\text{a}}\) & 5.76 \(\pm\) 0.47 \({}^{\text{a}}\) & 0.98 \(\pm\) 0.06 \({}^{\text{a}}\) \\
50–25–25 & 39.17 \(\pm\) 3.27 \({}^{\text{b}}\) & 4.49 \(\pm\) 0.34 \({}^{\text{b}}\) & 0.78 \(\pm\) 0.13 \({}^{\text{b}}\) \\
50–12.5–37.5 & 33.56 \(\pm\) 2.38 \({}^{\text{c}}\) & 3.90 \(\pm\) 0.57 \({}^{\text{c}}\) & 0.65 \(\pm\) 0.24 \({}^{\text{c}}\) \\ \hline \multicolumn{3}{c}{**Desirability**} & **min** & **max** & **max** \\ \hline \hline \end{tabular} Values are given as mean \(\pm\) standard deviation. Different letters in the same column indicate significant differences (\(p<0.05\)) when analyzed by the Duncan’s Test. Different superscripts indicate differences based on the proportions of the materials.  
\end{table} 
Table 6: Properties of CH–CNC–CD final formed edible films.
## 3 ML Model Development
### Convolutional Neural Networks (CNN)  
Convolutional neural networks (CNN) is another variation of ANN, most applied to visual imagery analysis [48]. The main advantage of using CNN is that CNN is able to capture the spatial dependencies in an image by applying filters. Furthermore, CNN extracts and reduces images without removing critical features. CNN has four different types of layers, input layer, convolution layer, pooling layer, and fully connected layer, respectively. In the input layer, CNN takes an input variable as an image, with a shape of image width \(\times\) image height \(\times\) channels. In this study, this corresponds to surrounding data width (5) \(\times\) surrounding data height (5) \(\times\) predictor variables (23). The convolution layer extracts the high-importance features from the image with a spatial filer, which is decided to be a \(3\times 3\times 1\) filter in this study. The pooling layer extracts the dominant features from the convolved data. In this study, max pooling is used to return the maximum value from the image covered by the filter. Finally, the model goes through a fully connected layer to make a decision. CNN has been applied to various problems in satellite imagery such as determining land type classification [49] and cloud classification [50]. CNN with one convolution layer with \(2\times 2\) kernel size and ReLu activation function is used. After the convolution layer, we used two dense layers with ReLu activation function, and one dense layer with sigmoid function for classification. Batch size is set to be 512, with 2000 epochs. Batch size and the number of epochs are selected with a gridsearch algorithm. Five different structures with different number and size of hidden layer were tested, and the best performing structure was selected.
# In Silico Modeling Study of Curcumin Diffusion and Cellular Growth
## 3 Results and Discussion
### Curcumin Diffusion Model
A similar dose response of curcumin on the proteasome function has been demonstrated in other cell types, such as fibroblasts. Induction of the activity of the Na/K ATPase pumps in normal human epidermal keratinocytes treated with curcumin was evaluated. Lower doses stimulated the pump activity; meanwhile, a higher dose was inhibitory [68]. The present diffusion model shows the biphasic behavior developed using the experimental data in [17]. This behavior is of great importance in experimental settings, whether in vitro or in vivo, where curcumin is released in a controlled manner. Since curcumin has low solubility, low bioavailability, and fast metabolism [19], its release must be controlled to benefit the organism. In experimental settings, this can be achieved by encapsulating the active agent in a polymeric matrix, allowing for controlled dosage and release, thus extending the time that the therapeutic dosage is effectively present [18]. However, this model was fitted to a specific experiment, where the polymeric matrix comprises PHB microparticles. The release of curcumin can be better modeled if more experimental data are used with similar diffusion mechanisms.
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design
### Collision Avoidance and Multiple Mobile Tags  
One of the major drawbacks to the use of UWB radios is their limited capability to detect the presence of other UWB transmissions. This lack of carrier sensing results in increased collisions and high packet loss when multiple mobile tags need to be localized [27]. However, our approach uses a sub-GHz beacon to synchronize the nearby anchor nodes to the mobile tag TDMA scheme prior to any UWB transmissions. As such, this sub-GHz beacon can be used as 'virtual' carrier sense by other mobile tags. Mobile tags overhear the synchronization beacon from other nodes, and hence know to defer the start of their own superframe until after the other superframe is finished, thereby avoiding UWB packet collisions. As such, superframes from multiple mobile drones will not overlap.
## 4 Results and Analysis
### Finite Element Model Updating  
The optimization problem, as shown in Equation (16), was solved in the parameter space using the PSO algorithm. The PSO parameter settings were as follows: the population size was set to 50, the maximum number of iterations was 50, and both the global learning rate and the local learning rate were set to 1.49. After 41 iterations, the objective function converged to a stable minimum value of 0.03657. The updated parameters are summarized in Table 8, and the convergence curve of the objective function is presented in Figure 18.  
The optimization results indicate that the cable initial tension in the updated model is 0.866 times the design value, and the metal frame mass is 2 kg. A possible reason is that the cable was not tensioned to the design value during construction, or a tension loss of 13.4% occurred during service. In addition, the mass of the metal frame should be accounted for in the modeling process, and its value is 2 kg.  
By incorporating the updated \(M\) and \(T\) into the FE model, the first four modal frequencies were calculated, as shown in Table 9. Results indicates that the relative errors between the updated model and the measured results for the first, second, and fourth modal frequencies have significantly decreased, from 13.78%, 24.22%, and 20.31% to 0.47%, 1.52%, and 0.28%, respectively. However, the relative error for the third modal frequency in the updated model increased from 3.38% to 8.46%.  
Figure 17: Response surface fitting results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Updating Parameter** & **Initial** & **Optimization** \\ \hline \(M\) (kg) & 0 & 2.00 \\ \(T\) (kN) & 60 & 51.96 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimization results of updating parameters.