esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparing GRACE-FO KBR and LRI Ranging Data with Focus on Carrier Frequency Variations
## 6 Instrument Noises and Corrections  
In order to understand which noises or error sources are limiting the KBR-LRI residuals, we have plotted many of the known effects in KBR and LRI in Figure 4.  
We analyzed the light-time correction extensively in [45] and skip it, therefore, in our analysis given here, as we found no indication in our previous work that the accuracy of this correction could be limiting KBR-LRI residuals.
## 2 Physically Based Dynamic Models for Modeling Complex Landslides
### Entrainment Rate and Corresponding Production Source Terms  
Experiments and field observations have shown that erosion and subsequent entrainment can significantly alter the dynamic characteristics of geophysical mass flow [27; 51; 52]. During the entrainment process, part of the eroded material along the path will be accelerated and entrained into the avalanche. During this process, mass and momentum exchange between the avalanching body and the bed. The dynamic model represented by Equation (19) contains mass and momentum production (in terms of entrainment rate) caused by mass entrainment. The entrainment rate describes the quantity of mass that is entrained into (or detached from) the avalanching body per unit of time. Various entrainment rate formulas have been proposed for special types of avalanche events (e.g., [46; 53; 54]).  
An empirical entrainment model [55] will be used in this study, in which the entrainment rate \(E\) is determined by multiplying an empirical coefficient \(C_{E}\) with the bulk momentum \(M\) of the flow:  
\[E=\mathrm{C_{E}M} \tag{28}\]  
where the empirical coefficient \(C_{E}\) is usually determined concerning similar practical applications [56; 57].  
Since the entrainment rate represents the mass amount added to or removed from the flow per unit of time, it can be directly introduced into the mass conservation equation. The entrainment-induced momentum should be introduced into the momentum conservation equation based on rigorous physical principles. When erosion occurs, the eroded species on one side of the eroded interface often do not directly gain the same velocity as the flow on the other side of the eroded interface [46; 54]. The variable \(\lambda_{b}\) in Equation (25) is the erosion drift coefficient that connects the mean flow velocity \(\mathbf{u}\) of a landslide to the velocity of the eroded mass, \(\mathbf{u_{b}}=\lambda_{b}\mathbf{u}\). [PERSON] and [PERSON] [36] derived a full set of dynamic equations in which the erosion-induced change in inertia and the net momentum production (i.e., \(2\lambda_{b}Eu\)) have been included in the momentum balance equations. Following their work, the same entrainment source terms were used in Equation (25). Entrainment is treated as a complementary function in ravaflow, which will be executed at the end of each time step in a forward Euler manner. The complementary functions can be considered a reasonable first approximation of the entrainment [55].
###### Abstract  
This study investigated treatment methods for urban wastewater sludge, specifically examining natural drying over five years, accelerated freeze-thaw-drying cycles, and composting with and without a zeolite additive. The findings reveal that composting effectively stabilized the sludge while retaining essential nutrients crucial for agriculture. Notably, with the addition of 2% zeolite by total mass, approximately 40% of the total nitrogen was preserved. Adequate aeration during composting maintained acceptable levels of phosphorus compounds, with the phosphorus content expressed as P\({}_{2}\)O\({}_{5}\) showing significant retention compared with the natural drying methods. Composing also demonstrated a substantial reduction in petroleum hydrocarbon concentrations, which decreased from 30 mg/kg to 3 mg/kg, thereby showcasing its potential for processing contaminated sludge. The inclusion of zeolite enhanced the nitrogen retention by an additional 10-20% compared with the composting without zeolite, aligning with previous studies on its effectiveness. While composting and thermal treatments, like accelerated freeze-thaw cycles, influenced the physical properties of the sludge--such as reducing the moisture content and altering the volatile substance concentrations--they did not significantly affect the heavy metal levels. Natural drying over five years resulted in reduced metal quantities, which possibly reflected changes in the wastewater characteristics over time. Given that the heavy metal concentrations remained largely unchanged, additional treatment methods are recommended when the initial sludge contains high levels of these contaminants to ensure the safe use of the final product as fertilizer. This study underscored the significant role of biochemical and microbiological processes during composting and natural drying in transforming sludge properties. Future research should focus on establishing upper contamination thresholds and exploring microbiological safety measures to enhance the viability of sludge reuse in agriculture, balancing nutrient preservation with environmental safety.  
wastewater treatment; sludge treatment; composting; biofertilizers; natural drying; freeze-thaw-drying Article
## 2 Smartphone Global Navigation Satellite System (GNSS) Data Collection and Analysis
### Data Quality Analysis  
We analysed the dual-frequency GNSS observations collected in the experiment to obtain a reference for data processing during positioning. The statistical results reported below exclude data with elevations below 10\({}^{\circ}\).
## 2 Module Design
### Design of the Thermal Control System
#### 2.2.1 Constitution of the Thermal Control System  
To achieve a better automatic cooling ability for the mid-infrared laser, the thermal control system was composed of an automatic temperature control circuit, TEMs, and an aluminum cabinet with cooling fins, axial fans and heat pipes. The heat sink was stuck to the cold side of TECs through grease and compressed tightly, allowing full contact and consequently, better heat transfer. The hot sides of the TECs were positioned close to the bottom of the cabinet. Thus, when the laser was working, the large amount of waste heat generated from the pump source module was absorbed by TECs and dissipated to the bottom of cabinet. The cooling fins were distributed outside the bottom of the cabinet to increase the heat transfer area. The axial fans were fixed on one side of the fins, increasing the heat convection coefficient through forced convection cooling. There were also two heat pipes fixed at the bottom of laser cabinet to improve heat transfer performance of the system.  
The TEM used here was MCTE1-12712L-S, manufactured by Multicomp (Warsaw, Poland) with parameters shown in Table 1. There were 16 pieces of TECs divided into 4 groups to cool the heat sink. Each group was made up of 4 TECs with two pieces electrically connected in series and then electrically connected in parallel together. As shown in Figures 4 and 5, two groups of TECs in a line were used to cool the LD modules and the remaining two groups were used to cool the fiber. This electrical connection and configuration mode improved the heat transfer efficiency by increasing the contact area and simplified the circuit structure through one output channel to drive 4 TECs. The axial fan used here was a pulse width modulation (PWM) speed controlled fan with 24 V direct current (DC) power supply. The max rated speed of this fan was 18,000 r/min and the maximum airflow was 0.011 m\({}^{3}\)/s. There were 7 fans parallel connected in a line to increase airflow.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Parameters**} \\ \hline Internal resistance & & 1 \(\Omega\)\(\pm\)10\% \\ \(I_{\text{max}}\) & & 12 A \\ \(V_{\text{max}}\) & & 15.4 V \\ - & \(T_{h}\) = 27 \({}^{\circ}\)C & \(T_{h}\) = 50 \({}^{\circ}\)C \\ \(Q_{\text{max}}\) & 110 W & 134 W \\ \(\Delta T_{\text{max}}\) & 68 \({}^{\circ}\)C & 75 \({}^{\circ}\)C \\ Solder melting point & & 138 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: TEC parameters.  
Figure 3: Schematic diagram of the laser and thermal control system.
## 2 Temperature Lidar Observations
The nightly mean profiles from 30 to 90 km have been smoothed over 3 km and interpolated every 1 km are used. The mean thermal profile of the mesosphere is characterized by a regular temperature decrease from the stratopause around 50 km up to the mesopause around 90 km. However, the temperature profiles obtained by Rayleigh lidar in the south of France frequently exhibit near 65 to 70 km a temperature inversion. Examples of nightly mean vertical temperature profiles from the OHP lidar since 1982 have been shown in Figure 1. These temperature profiles indicate temperature inversion layers with different vertical behaviors. The temperature uncertainties have been reduced by vertical and time integration and have been derived with 3 km effective vertical resolution and one sigma error. MILs in the mesosphere, are characterized by a minimum temperature around 60-70 km, with a strong positive gradient above, and a secondary maximum higher up.
# Software Design and Experimental Evaluation of a Reduced AES for IoT Applications
## 4 Results and Evaluation
### Frequency Test  
The frequency test is an important measure that assesses whether the number of zeros and ones in a sequence are equivalent, as would be expected for a truly random sequence [10]. This test computes a \(p\)-value for a series of bits to determine its randomness; if the computed \(p\)-value is less than 0.01, this means that the sequence is not random [14]. To compute the \(p\)-value for a binary sequence \(X\), where \(Xi\) represents the \(i\)th bit of the sequence, the bits are first converted into \(+1\) and \(-1\) values. This process is conducted by converting the zero binary digits to \(-1\) and the ones to \(+1\); then, Equation (6) is applied to find the sequence sum (\(S\)) that has a length of (\(N\)). _Sobs_ is then calculated to find the absolute value of the sum using Equation (7). Finally, the \(p\)-value is calculated using Equation (8), where erfc is a function as explained by [10]:  
\[S=\sum_{i=0}^{N}X_{i} \tag{6}\]  
\[S_{obs}=\frac{|S|}{\sqrt{N}} \tag{7}\]  
\[p-value=erfc\bigg{(}\frac{S_{obs}}{\sqrt{2}}\bigg{)} \tag{8}\]  
Figure 4 displays the rate of success for frequency testing obtained from the cipher output of LAES 128, Present 128, Clefia 128, and AES on various sizes of files. The x-axis identifies the success rate for each cryptographic algorithm on a specific size of the file, and the y-axis identifies the file size. It can be seen that very good randomness properties were exhibited by all of the algorithms, with success rates generally ranging between 80-100%. The trend lines for the LAES and its counterparts also show anticipated performance for cryptographic algorithms that are supposed to produce a random cipher (to avoid exposing confidential information), which could be useful for undesired cryptoanalysis. It can be seen that the percentage of success increased with a larger file size, which is attributable to the statistical features of the test, which is directly affected by the large number of sequences generated from larger file sizes (which usually have a higher volume of chaotic data).  
Figure 4: Frequency test.
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
###### Abstract  
Ultrasonic power and data transfer through multilayered curved walls was investigated using numerical and experimental analysis. The acoustic channel used in this paper was formed by two concentric pipes filled with water, aiming for applications that involve powering and monitoring sensors installed behind the pipe walls. The analysis was carried out in the frequency and time domains using numerical and experimental models. Power and data were effectively simultaneously transferred through the channel. A remote temperature and pressure sensor was powered and interrogated throughout all the layers, and the power insertion loss was 10.72 dB with a data transmission rate of 1200 bps using an amplitude modulated scheme with Manchester coding. The efficiency of the channel was evaluated through an experimental analysis of the bit error rate (BER) with different values of signal-to-noise ratio (SNR), showing a decrease in the number of errors compared with detection without Manchester coding.  
Keywords:power and data transmission; ultrasonic communication; multiple layers acoustic transmission; passive sensor communication +
Footnote †: journal: Accepted: 18 September 2019; Published: 20 September 2019
## 1 Introduction  
Rural roads play an important social and economic role globally, especially in agricultural production, transportation, and rural-urban connectivity. However, rural roads often have irregular shapes, fuzzy boundaries, and complex environmental interference, which bring great challenges to the automatic recognition and semantic segmentation of road scenes. Existing semantic segmentation techniques mostly focus on structured urban road scenarios, while related research on unstructured rural roads is not sufficient.  
Rural road semantic segmentation has a wide range of applications not only in agriculture [1], such as supporting autonomous driving, navigation, and path planning of intelligent agricultural machinery, but also in several other fields [2; 3; 4; 5]. First, rural roads are an important infrastructure, connecting remote rural areas with cities, and their state directly affects the transportation of agricultural products, distribution of goods, and local economic development [6; 7]. Second, rural roads play a crucial role in emergency rescue after natural disasters, and accurate road segmentation can help rescuers quickly determine road conditions and improve rescue efficiency [8]. In addition, semantic segmentation of rural roads can support the application of autonomous driving technologies in non-urban environments and help intelligent transportation systems adapt to more complex and irregular road conditions [9]. Segmentation in such scenarios is significantly more complex and requires effective solutions tailored to the rural road environment.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 8 Challenges and Future Directions  
### Development of CML Data Acquisition Standards  
The feasibility of using CML to measure rainfall has been verified by works and projects, but the difficulty of obtaining available CML data in WCNs is obvious, thus limiting the adoption of this technology [19; 116]. Although some studies have published open-access datasets, as listed in Table 4, due to security considerations, the way to obtain CML sensing data is still greatly limited, especially the CML datasets from the Asia-Pacific region, most of which are not public available to the best of our knowledge. The original intention of obtaining CML data is to improve wireless communications business, and using CML data for sensing requires complicated signal analysis and processing, i.e., we need to transform the rain-induced attenuation data, which are noise for communication, into useful information for sensing. It is suggested that relevant standard protocols for microwave-based fronthaul/backhaul communications should be standardized to facilitate not onlycommunications but also sensing and attenuation data acquisition. By achieving this, the barriers between scientific research and commercial business can be overcome, and the CML-based rainfall measurement can be widely adopted to benefit the society as a whole.  
[[https://doi.org/10](https://doi.org/10)]([https://doi.org/10](https://doi.org/10)).
# Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods
## 1 Introduction
In another study [20], monthly soil temperature in Adana, Turkiye, was modeled using linear regression (LR), nonlinear regression (NLR), and ANN methods. Furthermore, several studies have conducted comparative analyses of fuzzy logic and neural network models for soil temperature estimation [21; 22; 23; 24; 25; 26; 27; 28]. For instance, [PERSON] et al. [29] compared radial basis function neural networks (RBFNNs), generalized regression neural networks (GRNNs), MLP, and MLR, revealing that RBFNN achieved the highest accuracy at shallow soil depths, whereas MLR and GRNN performed better at greater depths. [PERSON] et al. [30] evaluated the performance of both standalone models--ANN and CANFIS--as well as hybrid models that combine wavelet transformation with these algorithms (WANN and WCANFIS) for predicting soil temperature in Iran. Similarly, [PERSON] [31] employed the CANFIS model to estimate daily soil temperatures at depths ranging from 5 to 100 cm in arid and semi-arid regions of Iran, using meteorological variables, such as mean, maximum, and minimum air temperatures, relative humidity, sunshine duration, and solar radiation, as model inputs selected through correlation analysis. [PERSON] et al. [32] used two intelligent models--ANN and CANFIS--to estimate soil temperatures at six depths (5, 10, 20, 30, 50, and 100 cm), relying solely on mean air temperature as input. Recent research has investigated a range of machine learning techniques for soil temperature prediction, including random forest (RF) [33], support vector regression (SVR) [34, 35], extreme gradient boosting (XGB) [36], and k-nearest neighbors (KNN) [37], among several others [38, 39, 40, 41, 42]. Despite the progress that has been made in predicting soil temperature using machine learning and fuzzy logic techniques, there are still some gaps in the existing literature. Most previous studies focus on individual modeling approaches without performing a systematic comparison between fuzzy logic models and conventional regression techniques across multiple soil depths. Moreover, many models require extensive input data sets, which limits their practical application in data-scarce regions. To address these gaps, the present study aims to perform the following:  
* Conduct a comparative evaluation of fuzzy logic-based models (MFIS and ANFIS) and supervised machine learning algorithms (MLP, SVR, RF, XGB, KNN, and MLR) for predicting soil temperature at different depths (5, 10, 20, 50 and 100 cm). * Develop models with minimal and easily accessible input variables, such as average air temperature and soil depth, to enhance usability in regions with limited observational data. Through this integrated approach, this study contributes to advancing soil temperature modeling methods and supports the development of practical decision support tools, especially for environments with limited data availability.
## 5 Discussion  
We must take into account that such 3D (depth) cameras usually need to be calibrated according to different lighting conditions in order to provide precise depth estimation. In our application, there is also a possibility that the lighting conditions are different in daily life. So, we do not have the freedom to calibrate for every kind of lighting situation, which is not feasible and also time consuming. This is another reason for possible use of 2D (RGB) cameras in depth estimation.  
Based on the results shown in Figure 10, the depth estimation of Alhashim is not as consistent as the results of RealSense and MiDaS at different distances and lighting  
Figure 14: Worst case scenario that causes False Positive cases.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **RealSense** & **MiDaS** \\ \hline
**Precision** & 0.74 & 0.69 \\ \hline
**Recall** & 0.95 & 0.90 \\ \hline
**F1-Score** & 0.84 & 0.78 \\ \hline
**Sensitivity** & 0.95 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance in RealSense and MiDaS.  
conditions. Moreover, [PERSON]'s results (Section 4.2.1) show that we have a different low success rate in different lighting conditions, which is still lower than the other methods.  
Moreover, regarding the effect of light on depth estimation, we observed in Figure 11a that the colours of the display are not stable horizontally at distances less than 34 centimetres. This is because the Intel RealSense camera can only provide accurate depth measurements up to a certain value. However, we observe a colour gradient that shows consistency in both depth estimation and depth difference estimation. In Figure 11b, we can observe the change in light with distance in both vertical and horizontal directions, but of course not as clearly as with the Intel RealSense data (Figure 11a). The results of the Alhashim method, shown in Figure 11c, show a chaotic estimation and do not follow any pattern. It can be concluded that the Alhashim method is intolerant to light changes.
# Synthesis of High-Precision Sub-Micron CaCO\({}_{3}\) Anticancer Drug Carriers from Coral Remains
## 3 Result and Discussion
### Exploration of Optimal Parameters for the Synthesis of Vaterite
#### 3.3.3. EG Concentration  
This step examined the effect of different EG concentrations on the particle size and crystal growth of CaCO\({}_{3}\), with operational parameters detailed in Table 5. As demonstrated in Figure 5a,b, CaCO\({}_{3}\) samples with an EG concentration of less than 50% tended to form calcite-like crystals, which was attributed to an overreaction. Conversely, increasing the EG concentration above 50% gradually suppressed the growth rate of CaCO\({}_{3}\), favoring the formation of complete vaterite structures. This is evident in particle size and distribution observed at higher EG concentrations, which were more favorable compared to those observed at 50% or lower, as shown in Figure 5c,d. The mean particle sizes at 50% and 85% EG concentrations were 1038 nm and 475 nm, respectively. Therefore, the optimal EG concentration was determined to be 85%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Factors** & **Parameters** \\ \hline Adding Order & CaCl\({}_{2}\)-first \\ Stirring Speed (rpm) & 400, 600, 800, 1000, 1150 \\ Concentration of EG & 85\% \\ Reaction Time (min) & 60 \\ CaCl\({}_{2}\)/Na\({}_{2}\)CO\({}_{3}\) (M) & 1 M/0.1 M \\ Reaction Volume (mL) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for stirring speed experiment.  
Figure 4: (**a**) Average particle size as a function of different stirring speeds. (**b**) SEM image of CaCO\({}_{3}\) with 400 rpm stirring speed.