esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Challenges and Considerations for Farmers
### Providing Meteorological Services to the Farmers  
Bringing weather and climate knowledge into agricultural decision making has contributed to an increase in agricultural production in many countries. However, in developing countries where several small farms are rain-fed, it is difficult to get access to weather and climate information to help direct decision making. Government officials should consider this critical knowledge which is useful to farmers and others dealing with poverty and hunger. This situation continues because national meteorological and hydrological services in developing countries do not receive adequate funding to cover the expense of delivering these services to the farmers. The government lacks substantial information about the significant role of meteorological services which contribute to the socioeconomicdevelopment of the nation. This state of obliviousness prevails even though governments confront multiple challenging issues in the realm of agriculture, water resources management, health, and disaster risk mitigation, all of which are heavily affected by weather and environment.  
Despite repeated periods of drought and crop failure, as well as occasional famines, flooding often leads to losses of human life and major property destruction. In addition, it also causes wide-ranging impacts on socioeconomic growth. Therefore, it is only with the advent of recent development that there has been substantial study into quantifying the benefits of weather and climate knowledge-related services. It is deemed necessary to take small steps by undertaking pilot projects to research the socioeconomic gains of using weather and climate information-related services in agriculture.
# Freeze-Thaw Pre-Treatment of Cassava Tubers to Improve Efficiency of Mechanical Peeling
## 1 Introduction  
Cassava, one of the staple foods in tropical and sub-tropical lands, has gained attention due to its capability to provide food security in recent decades [1; 2; 3]. Fresh cassava tubers do not have a long shelf-life due to their high moisture content [4]. Therefore, cassava tubers are usually processed to obtain various relatively shelf-stable products [5]. Peeling is an essential step in cassava processing, which includes removing the corky periderm and cortex from the tubers.  
Peeling of cassava tubers, either for industrial or domestic use, is still a major challenge in cassava processing. Currently, it is traditionally done by hand or by mechanical, thermal, or chemical methods [6]. Practically, each method of peeling presents its own advantages and disadvantages. Traditional manual peeling is slow and labor intensive. Mechanical peeling of cassava tubers done in small and large-scale industries creates high losses and has an inefficient peeling rate. The peeling efficiency decreased in mechanical peeling [6] by increasing the rotational speed of the peeling tools (brushes or drums) and the peeling time. The technical data on the properties of cassava tubers to design an appropriate peeling machine is insufficient. Furthermore, irregular shape, size, age, and different varieties of cassava tubers affect the peeling process [7; 8; 9].  
Despite several attempts to develop a mechanized peeling machine in Nigeria, Brazil, and China, no efficient cassava peeling machine is currently available on the market [10; 11; 9; 12]. This can be explained by the broad variations in size, weight, and peel thickness of cassava tubers as well as the irregularity in their shape [7; 13; 14]. Moreover, environmental factors such as relative humidity, temperature, rainfall,type of soil, moisture content of soil, acidity of soil, fertility of soil, and vegetation on the farm may affect the characteristics of cassava tubers that impact the peeling process [15]. Considering all the problems, [PERSON] et al. [10] stated that an effective cassava peeling machine should remove the cortex of the tuber efficiently without considerable loss of flesh.  
Previous studies have shown that the freeze-thaw method could improve the peeling process as achieved in tomato processing [16; 17]. It was found that 20 to 30 s of chilling process in cooled brine continued by further thawing in hot water could loosen the peels for easy removal [18].  
However, to the best of our knowledge so far no studies were published on the application of the freeze-thaw pre-treatment for the peeling process of cassava tubers. Accordingly, the main objectives of this study were: (1) to investigate the impact of freeze-thawing on the peeling of cassava tubers, (2) to optimize the freeze-thaw pre-treatment to improve the peeling process, and (3) to investigate the effect of freeze-thaw pre-treatment on the quality of peeled cassava tubers.
## 2 Materials and Methods
### Ecosystem Service Assessment Methods
#### 2.3.4 Soil Conservation  
In this study, soil erosion control services within the ecosystem services of Liaoning Province are represented by soil retention quantity. This study quantifies soil conservation in ecosystem services using the RULSE equation, where soil conservation is assessed by the difference between potential soil erosion and actual soil erosion. Soil conservation is the actual soil erosion per unit area. By calculating the difference between potential soil erosion (potential erosion) and actual soil erosion (actual erosion), the amount of soil conservation (asoil conservation) can be obtained. The calculation of soil retention employs the Revised Universal Soil Loss Equation (RUSLE), which is widely used and scientifically validated by most researchers [45]. This approach quantifies soil erosion control services by calculating the difference between the potential soil erosion (assuming no vegetation cover, i.e., vegetation cover factor \(C\) = 1 and soil conservation factor \(P\) = 1) and the actual soil erosion. The difference is considered the soil retention quantity for the area. The formula for this calculation is as follows [46; 47; 48]:  
\[SC=R\times K\times LS\times(1-C\times P) \tag{5}\]  
In the equation: \(SC\) represents the soil conservation amount; \(R\) is the rainfall erosivity factor [49]; \(K\) is the soil erodibility factor [50]; \(C\) is the vegetation cover factor (dimensionless) [51]; \(P\) is the conservation practice factor (dimensionless) [51]; and \(LS\) is the slope length and steepness factor (dimensionless) [52]. The detailed calculation formulas for these factors can be found in the referenced literature.  
The empirical formula for calculating the rainfall erosivity factor \(R\) using various rainfall amounts is as follows:  
\[R=\sum_{i=1}^{12}1.735\times 10^{(1.5\times[g\frac{p^{2}}{p})-0.8188} \tag{6}\]  
In the equation: \(R\) represents the rainfall erosivity factor for a grid cell; \(i\) denotes the month; \(pi\) is the rainfall amount for a specific month; and \(p\) is the annual rainfall amount.  
The soil erodibility factor \(K\) is calculated using the formula developed in the EPIC model.  
\[\begin{array}{c}K=0.2+0.3 exp\left[-0.0256 Sa\left(1-\frac{Si}{100}\right) \right]\times\left(\frac{Si}{Cl+Si}\right)^{0.3}\\ \times\left[1-\frac{0.255 SOC}{SOC+exp(3.22-95 SOC}\right]\\ \times\left[1-\frac{0.75n}{5n+exp(-5.51+22.95n)}\right]\end{array} \tag{7}\]  
In this formula, \(K\) represents the soil erodibility factor; \(Sn\) is defined as 1 s the soil sand content (\(Sa\)) divided by 100; \(Sa\) denotes the soil sand content; \(Cl\) is the soil clay content; and \(SOC\) stands for the soil organic carbon content.
## 4 Results and Discussion
### The Optimum Architecture of the ELM Model  
Similar to the optimization procedure of the ANN model, the architecture of the ELM model is here optimized in terms of (i) the number of hidden neurons, \(H_{h}\), and (ii) the hidden neuron activation function, \(G()\). We follow an exhaustive searching procedure by considering possible values of 90, 150,  
Figure 4: The influence of the number of hidden neurons on the prediction performance of the ANN model.  
300, 500, 700, 900, 1000, 1200, and 1500 for the number of hidden neurons, and five activation functions for the hidden neuron activation function. The five activation functions considered in this work are _\"Triangular Basis\"_, _\"Sine\"_, _\"Sigmoid\"_, _\"Hard Limit\"_, and _\"Radial Basis\"_ functions (interested readers can refer to [47] for more details on the ELM hidden neuron activation functions).  
The performance of each ANN candidate architecture is evaluated by computing the standard performance metrics (Section 3.4) on the obtained predictions of the validation set of data by using 100 cross-validations procedure.  
The best ELM candidate architecture is found at \(H_{h}=700\) hidden neurons using the _\"Triangular Basis\"_ neuron activation function. For clarification purposes, Figure 5 shows the influence of the hidden neurons on the four performance metrics when the _\"Triangular Basis\"_ activation function is used. One can notice that the prediction performance of the ELM model increases as long as the number of hidden neurons increases; e.g., \(MSE\) values decrease with the number of hidden neurons. However, for large numbers of hidden neurons, i.e., \(H_{h}\geq 700\), it seems that the ELM performance starts to saturate and, thus, increasing the number of hidden neurons might not be necessary to avoid the computational effort of the ELM operation. In this regard, the optimum ELM architecture is defined by a hidden layer composed of 700 neurons using a _\"Triangular Basis\"_ activation function, for which the average performance metrics values on the validation set of data are \(MSE=5.92\%\), \(Accuracy=98.61\%\), \(R^{2}=87.84\%\), and \(R^{2}_{adjusted}=87.2\%\).
## 3 Results
### Membrane Performance
The blending of CA with PAA led to drastic improvement of the performance of the membranes in Cd remediation exploiting the carboxylic group introduced by PAA easily ionisable in aqueous solutions [53; 54]. Thus, the rejection percentage increased proportionally with the PAA amount in the starting casting solution. Moreover, the maximum removal rate was observed for M15 with values of 80.5% and 83.1% at pH of 2.5 and 6.5, respectively, reduced from ca. 20% at a pH of 4.5 [55]. A similar trend was observed for membrane prepared using a PAA concentration in the dope solution of 8 wt. % (M8). Usually, the pH value has a strong influence on ion rejection by altering the net charge of the membrane surface affecting ultimately the interactions between the contaminants and the membrane [56]. At high pH, the carboxylic groups resulted in ionized form favouring the metal-ion complexation or ionic interactions (\(-\)O\(\cdots\)Cd\({}^{2+}\)\(\cdots\)O\(-\)), thus increasing the degree of Cd removal consequently. At a pH of 4.5, the carboxylic groups are neutralized (isoelectric point) and the membrane-Cd interactions are minimized. At low pH, the membrane shifts to positive charge generating repulsion interactions between the membrane surface and the cations of Cd\({}^{2+}\).
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 4 Numerical Results
### System Level Simulations
As expected, only for 1024-QAM there is an obvious difference in CDF results due to imperfect estimation compared to perfect estimation. For the other constellations, there are almost the same CDF results which is in agreement with the BLER results of Figure 6. The receiving throughput of all users exceeds 2.5 Gbps, 3.5 Gbps, 4.5 Gbps, 5.5 Gbps, and 6.5 Gbps for 10% of the users with 4-QAM, 16-QAM, 64-QAM, 256-QAM, and 1024-QAM (perfect estimation), respectively. For 50%  
Figure 10: Average throughput vs number of users for \(N_{sc}=256\), \(N_{tx}=17\) antennas/user and \(N_{rx}=16\) antennas/user with perfect channel estimation. of the users, the throughput received corresponds to the performance results presented on Figure 9. Only less than 10% of users receive a throughput level lower than 100 Mbps, with the exception of 1024-QAM users with imperfect estimation. Figure 11: Average throughput vs number of users for \(N_{sc}=256\), \(N_{tx}=17\) antennas/user, and \(N_{rx}=16\) antennas/user with imperfect channel estimation. Figure 12: CDF of throughput for 100% transmitted power for \(N_{a}=2\), \(N_{sc}=256\), \(N_{u}=15\), \(N_{tx}=17\) antennas/user, and \(N_{rx}=16\) antennas/user. Table 4 summarizes the average throughput per user with perfect and imperfect channel estimation for C-RAN with clusters 1C and 3C and the corresponding throughput gain. The maximum throughput gain is 1.55 and the minimum is 1.25.
# A Review of High-Sensitivity Tracking Techniques for Satellite Navigation Signals  
[PERSON]  
1 College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China; [EMAIL_ADDRESS] (Z.G.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (Z.L.); [EMAIL_ADDRESS] (L.H.); [EMAIL_ADDRESS] (G.O.)12 National Key Laboratory for Positioning, Navigation and Timing Technology, Changsha 410073, China
## 4 Experimental Results and Analysis
### Structured Pruning Analysis
From this table, we can gauge the trade-offs between efficiency gains and potential accuracy improvements, providing a holistic view of model performance after compression. Table 4 focuses on the top-performing pruned models for each dataset. It details the best results obtained in terms of parameter reduction, MAC reduction, accuracy improvement, and final accuracy. By highlighting the best model candidates, this table allows readers to quickly identify the most successful compression strategies and compare their relative performance across datasets. Table 5 presents insights into the architectural characteristics of the best-performing models. This includes the average number of convolutional layers (_Conv Layers_), the average kernel size (_Kernel Size_), and the usage rates of LSTM (_LSTM_) and Transformer (_Transformer_) components. Additionally, it reports the final accuracy achieved by these architectures. Examining this table helps clarify how different architectural decisions--such as network depth, kernel dimensions, and sequence modeling techniques--contribute to the overall accuracy and robustness of the compressed models. Ultimately, these architectural insights provide guidance for designing efficient and high-performing models tailored to each dataset. The comprehensive analysis across datasets reveals several key insights:  
* **Pruning Tolerance**: NTU-Fi demonstrates exceptional pruning resilience, maintaining high accuracy (>80%) even with significant parameter reduction. This suggests robust feature learning in basic activity recognition tasks. * **Task Specificity**: Widar's gesture recognition models show higher sensitivity to pruning, indicating that fine-grained motion detection requires more precise parameter preservation. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Model** & **Param. Red. * ** & **MAC Red.** & **Acc. Change** & **Final Acc.** \\ \hline NTU-Fi & Model 2 & 55.71\% & 75.17\% & +51.52 & 91.67\% \\ UT & Model 1 & 78.49\% & 81.53\% & +45.58 & 69.88\% \\ Widar & Model 4 & 75.74\% & 65.19\% & +33.34 & 44.74\% \\ NTU-Fi-HumanID & Model 3 & 0.00\% & 0.00\% & +36.73 & 55.10\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Top pruning results by dataset. * **Efficiency Gains**: UT-HAR achieves impressive efficiency improvements (78.38% MAC reduction) while maintaining acceptable accuracy, suggesting significant initial architectural redundancy. * **Architecture Impact**: Models with higher transformer usage (NTU-Fi-HumanID) show different pruning patterns compared to CNN-dominant architectures, indicating the importance of architecture-aware pruning strategies. These results validate our combined NAS and structured pruning approach, demonstrating effective architecture optimization across diverse WiFi sensing tasks while maintaining task-specific performance requirements.
# New Step Size Control Algorithm for Semi-Implicit Composition ODE Solvers
## 2 Materials and Methods
### Semi-Implicit CD Method  
The semi-implicit CD method [12; 13; 14], which is a generalization of the Stormer-Verlet method for non-Hamiltonian systems, was chosen as a basic symmetric method for this study. We will show how its semi-explicit and semi-implicit variants can be used to construct an efficient error estimator. The CD method is of accuracy order 2 and exists for ODE systems of dimension 2 and higher. Let us consider the following initial value problem (IVP):  
\[\begin{cases}\frac{dx}{dt}=f(x,y,t)\\ \frac{dy}{dt}=g(x,y,t)\end{cases},\]  
with initial conditions \(x(t_{0})=x_{0},\ y(t_{0})=y_{0}\). We denote the vector of state variables as \((x\ y)^{\top}\). Then, a symmetric semi-implicit second-order method [12] can be described as a composition of two adjoint methods:  
\[\Phi(\mathbf{x}_{n+1},h)=\Phi_{1}\bigg{(}\mathbf{x}_{n},\frac{h}{2}\bigg{)} \circ\Phi_{2}\bigg{(}\mathbf{x}_{n+\frac{1}{2}},\frac{h}{2}\bigg{)},\Phi( \mathbf{x}_{n},h)=\Phi_{2}\bigg{(}\Phi_{1}\bigg{(}\mathbf{x}_{n},\frac{h}{2} \bigg{)},\frac{h}{2}\bigg{)},\]  
where  
\[\Phi_{1}\bigg{(}\mathbf{x}_{n},\frac{h}{2}\bigg{)}:\begin{cases}x_{n+0.5}=x_{ n}+\frac{h}{2}f(x_{n},y_{n},t_{n})\\ y_{n+0.5}=y_{n}+\frac{h}{2}g\big{(}x_{n+0.5},y_{n},t_{n}\big{)}\end{cases}, \tag{5}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\gamma_{1}=\gamma_{7}\) & \(0.78451361047755726382\) \\ \(\gamma_{2}=\gamma_{6}\) & \(0.233557321335935813368\) \\ \(\gamma_{3}=\gamma_{5}\) & \(-1.1776799841788710069\) \\ \(\gamma_{4}\) & \(1.3151863206839112189\) \\ \hline \hline \multicolumn{3}{c}{s17 ord8} \\ \hline \(\gamma_{1}=\gamma_{17}\) & \(0.13020248308889008088\) \\ \(\gamma_{2}=\gamma_{16}\) & \(0.56116298177510838456\) \\ \(\gamma_{3}=\gamma_{15}\) & \(-0.389474962644847228641\) \\ \(\gamma_{4}=\gamma_{14}\) & \(0.15884190655515560090\) \\ \(\gamma_{5}=\gamma_{13}\) & \(-0.39590389413323757734\) \\ \(\gamma_{6}=\gamma_{712}\) & \(0.18453964097831570709\) \\ \(\gamma_{7}=\gamma_{11}\) & \(0.25837438768632204729\) \\ \(\gamma_{8}=\gamma_{10}\) & \(0.29501172360931029887\) \\ \(\gamma_{9}\) & \(-0.60550853383003451170\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._\[\Phi_{2}\bigg{(}\mathbf{x}_{n+0.5},\frac{h}{2}\bigg{)}:\begin{cases}y_{n+1}=y_{n+0.5 }+\frac{h}{2}g(x_{n+0.5},y_{n+1},t_{n+1})\\ x_{n+1}=x_{n+0.5}+\frac{h}{2}f(x_{n+1},y_{n+1},t_{n+1})\end{cases}. \tag{6}\]  
The obtained methods Equations (5) and (6) are adjoint, with \(\Phi_{1}\) being semi-explicit and \(\Phi_{2}\) semi-implicit. The implicitness can be resolved through the Newton iterations, simple iterations, or analytically (where possible).
## 3 Results and Discussion  
### UAV Prices Analysis  
A preliminary price analysis was carried out considering the raw image price per hectare, without fixed, elaboration, and processing costs. This analysis involved prices in 2017 and 2020 for UAV data, while for satellite and plane data the comparison was performed only for 2020.  
Figure 2 reports the frequency distribution of all prices detected in 2017 and 2020 for UAV-derived NDVI images for arable and specialized crops. The average price of an NDVI image for arable crops was 72.5 EUR/ha in 2017, while in 2020 it was 43.36 EUR/ha, showing a price decrease of 40.2%. Similarly, the cost of NDVI images for specialized crops was 95.00 EUR/ha in 2017 and 79.00 EUR/ha in 2020, showing a decrease of 16.8% (Figure 2A,B). On the one hand, UAV prices for arable crops in 2020 showed a lower standard deviation (std = 11.60) compared to data collected in 2017 (std = 25.45). On the other hand, differences in the standard deviation of price distribution between 2017 and 2020 were not significant for specialized crops. The price of NDVI images for arable crops in 2017 was 23.7% lower than the one for specialized crops. In 2020 the price difference between images for arable and specialized crops increased: the cost for arable crops was 45.1% lower than that for specialized crops. (Figure 2C,D).  
The price reduction between 2017 and 2020 can be justified by the high number of companies that were on the market in 2017. Some of the companies with less competitive prices quit their business by 2020 (five companies from a sample of 22). The smaller price reduction for specialized crops over the same period can be justified by the specific skills (e.g., different camera orientation, computer vision techniques, more ground sampling points) required for UAV flights over specialized crops (e.g., vineyards and orchards). Moreover, to achieve the high-spatial resolution necessary to manage the inputs in these crops, additional time is required. Experienced know-how is demanded to provide NDVI images for specialized crops, thus leading to less competition on the market. Differences between average prices for NDVI images of arable (Figure 2C) and specialized (Figure 2D) crops increased in the 2020 survey, showing a segmentation of the market which better matched arable (low income per hectare) and specialized (high income per hectare) crop needs.  
Although results were obtained considering only the Italian UAV market, they suggested that UAV acquired images are more suitable for variable rate application where high spatial and spectral resolution is required and fully exploited (e.g., precision weed control). Moreover, the UAV imaging cost analysis showed that prices decreased between 2017 and 2020 by 28.5%, having two different prices for arable crops (e.g., wheat, corn, soybeans) and specialized ones (e.g., vineyards, vegetables, nursery crops).