esa-sceva/satcom-chunk-collection huggingface dataset:
# Technical Performance of an Inflatable Solar Dryer for Drying Amaranth Leaves in Kenya
## 1 Introduction  
In most developing countries, the long drying times for agricultural commodities have been a major issue, especially for small- and medium-scale food processors [1]. Drying is an energy-intensive process, and solar drying is therefore a preferred method in developing countries with limited resources [2; 3; 4]. Cabinet dryers are well known to dry small quantities of vegetables, including amaranth leaves [2; 5]. The use of a solar dryer has been found to accelerate drying by 30% to 40% compared to conventional sun drying depending on the crop [6; 7]. In convection dryers, airflow distribution is one of the crucial factors due to its strong impact on the drying temperature [8; 9; 10]. An energy-efficient dryer that produces a high-quality product is still not easily accessible among farmers and processors in most developing countries [3]. Recent studies have focused on the development of solar collector and concentrators in a solar dryer [7; 9; 11]. Therefore, a solar tunnel dryer was developed and continuously improved at the University of Hohenheim [6; 12]. In this study, the latest version, which is an inflatable solar dryer (ISD), was used. The performance of the ISD has been investigated in a previous work for drying of paddy rice in the Philippines [13]. Due to the positive results, the ISD should now also be applied for high-value crops, such as amaranth leaves (_Amaranthus_ spp.) Amaranth leaves are considered as one of the most nutritive leafy vegetables in Kenya, and the crop is commonly cultivatedin an intercropping system [14]. The crop grows under semiarid conditions, and high temperatures are necessary to ensure a normal growth of the crop [15]. In Kenya, it has been recognized as a drought-tolerant crop with high nutritional values, which can grow rapidly in tropical areas [16]. As a pretreatment to dry leafy vegetables, blanching is commonly conducted to preserve the texture and color and to inactivate particular enzymes [17], such as peroxidase [18; 19].  
The objective of this research was to investigate the performance of the ISD for drying amaranth leaves in comparison to conventional sun drying.
## 3 Case Study
### The Built EAHX and Monitoring Equipment Used  
A photograph of the EAHX during its construction is presented in Figure 8.  
This photograph shows a trench as viewed from the EAHX air intake and two parallel pipes leaving this air intake towards the air handling unit (not yet built). The pipes (concrete) have 1 m diameter, are placed 4 m apart (distance between axes), are 70 m long (with the construction completed), and are buried 5.5 m deep (average depth, since pipes are sloped towards the air intake for water drainage).  
Simultaneous with the EAHX construction, a Vaisala weather station [41] was installed onsite to record outdoor data. Dataloggers [42; 43] and sensors were also installed to measure: undisturbed soil temperature at 0.1 m depth [44]; air temperature and air relative humidity at the AHU air inlet; and electric current at the AHU switchboard to monitor fan electricity consumption and ventilation rate. Fifteen-minute measurement intervals were selected, except for the weather station, which used one-minute intervals.
## 5 Discussion and Results  
Using the WRF model, vertical profiles of wind speed and air temperature over BAO were obtained under clear sky conditions. Based on the vertical profiles of these characteristics, we reconstructed the vertical profiles of the optical turbulence (\(C_{n}^{2}\)). It is shown that turbulence in the lower atmospheric layers is suppressed in comparison with the turbulence strength predicted via the Hufnagel-Valley model. A faster decrease in the optical turbulence strength with height is confirmed via experimental measurements of turbulent fluctuations over the cold water area of Lake Baikal under conditions of stable thermal stratification of the lower layers of the atmosphere. The correctness of the simulation results is, firstly, determined via the optimal parametrization schemes of atmospheric processes. Secondly, the simulation results are significantly affected by atmospheric conditions, which determine the characteristic ranges of changes in atmospheric characteristics, including the narrow limits of changes in the intensity of turbulent fluctuations and the turbulence coefficient under clear sky.  
A model of the outer scale of turbulence for the Baikal Astrophysical Observatory is proposed. The coefficients found in the dependence of the outer scale on the vertical wind speed gradients at the site of BAO differ from the coefficients proposed by Dewan. We obtained the coefficients taking into account the minimization of the standard deviation and the mean absolute error between the measured and calculated values of \(C_{n}^{2}\). We attribute these differences to the formation of mesoscale eddy structures above the observatory during the daytime. An analysis of vertical turbulence profiles shows that turbulence is suppressed in the lower atmosphere by mesoscale air movements that structure the atmospheric turbulence.  
The seeing values derived from the WRF model are low in comparison with the measurement data. These low values are related to the fact that the WRF model underestimates the surface values of the structural constant of air refractive index fluctuations in comparison with the mast measurements.  
Investigation, visualization, writing--review and editing: A.Y.S. and P.G.K.; methodology: P.G.K., V.A.O., V.S.G., P.O.Z., M.A.K., A.Y.S. and A.A.L.; formal analysis, investigation, visualization: A.V.K., M.Y.S. and K.E.K.; writing--review and editing: A.Y.S., M.B.D. and P.G.K.; software, visualization: A.V.K., I.V.R. and M.Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the RSF grant No 22-29-01137.  
Not applicable.  
Data Availability Statement: Data used are available on request from the corresponding author.  
Measurements were carried out using the unique research facility large solar vacuum telescope, [[http://ckp-rf.ru/usu/200615/](http://ckp-rf.ru/usu/200615/)]([http://ckp-rf.ru/usu/200615/](http://ckp-rf.ru/usu/200615/)) (accessed on 1 March 2023).  
The authors declare no conflict of interest.
## 3 Earth Surface Modelling Advances and Links with EO Datasets
### River Discharge and Hydrological Forecasting  
Continental and global scale information on river discharge, and upcoming floods and droughts can be used in many applications including disaster risk reduction initiatives, particularly in preparing for severe events and providing early awareness where local flood models and warning services may not exist [241]. The European and Global Flood Awareness Systems (EFAS and GloFAS) are part of the European Commission's Copernicus Emergency Management Service (CEMS-flood) and provide complementary flood forecast information to relevant stakeholders supporting flood risk management at national, regional and global level at several different timescales ([242; 243; 244; 245]; [[http://www.globalfloods.eu](http://www.globalfloods.eu)]([http://www.globalfloods.eu](http://www.globalfloods.eu))). CEMS-flood is based on an Earth system modelling approach, and the forecasts are derived using in situ and satellite data as well as hydro-meteorological and Earth system models. It is only very recently that improvements in the resolution, precipitation processes, and the improvements in land surface representations discussed in the above subsections have meant that the forecasts of hydrological variables such as precipitation, soil moisture and more recently also runoff, can be considered good enough to make effective forecasts of river flow using Earth System models [241; 246]. GloFAS uses the ECMWF Ensemble Prediction System, the operational ensemble forecasting product of ECMWF which consists of a 51 member ensemble of global forecasts. The ECMWF land surface scheme, H-TESSEL, then calculates land surface response to atmospheric forcing, and estimates the surface water and energy fluxes and the evolution of snowpack, soil temperature and soil moisture. Operational ensemble forecasts of surface and sub-surface runoff are resampled to 0.1 degree resolution to be used as input by the river routing model (for more information see [242; 245]). River flow climatologies, against which forecasts are compared, have been created using corrected reanalysis products such as ERA-Interim Land [247] which includes precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). Similar corrected climatologies are planned for the next generation reanalysis product ERA5 (i.e., ERA5-Land). Future needs for hydrological forecasting in terms of data assimilation, model validation/evolution and parameterisation are better information on river flow rates and flood extent (particularly in real time), groundwater contributions, and high resolution river catchment and river channel topography from EO data [248; 249; 250].
## 6 Results
### Usability Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**IBM CSUQ** & **Question** & **\(\mu\)** & \(\sigma\)** & \(M\)** \\ \hline \multirow{8}{*}{SYSUSE} & 1. Overall, I am satisfied with how easy it is to use this system. & 5.96 & 0.99 & 6 \\  & 2. It is simple to use this system. & 6.03 & 0.98 & 6 \\  & 3. I can effectively complete my work using this system. & 5.88 & 1.09 & 6 \\  & 4. I am able to complete my work quickly using this system. & 5.89 & 1.08 & 6 \\  & 5. I am able to efficiently complete my work using this system. & 5.94 & 1.06 & 6 \\  & 6. I feel comfortable using this system. & 5.96 & 1.13 & 6 \\  & 7. It was easy to learn to use this system. & 5.96 & 1.13 & 6 \\  & 8. I believe I became productive quickly using this system. & 5.85 & 1.08 & 6 \\ \hline \multirow{8}{*}{INFOQUIAL} & 9. The system gives error messages that clearly tell me how to fix problems. & 5.56 & 1.31 & 6 \\  & 10. Whenever I make a mistake using the system, I recover easily and quickly. & 5.73 & 1.18 & 6 \\  & 11. The information (on-screen messages and guidance or other & 5.80 & 1.02 & 6 \\  & documentation) provided with this system is clear. & 6.03 & 0.98 & 6 \\  & 12. It is easy to find the information I need. & 5.89 & 0.99 & 6 \\  & 13. The information provided with the system is easy to understand. & 5.90 & 1.00 & 6 \\  & 14. The information is effective in helping me complete my work. & 5.90 & 1.00 & 6 \\  & 15. The organization of information on the system screens is clear. & 6.00 & 0.96 & 6 \\ \hline \multirow{3}{*}{INTERQUIAL} & 16. The interface of this system is pleasant. & 5.90 & 1.12 & 6 \\  & 17. I like using the interface of this system. & 5.99 & 1.18 & 6 \\  & 18. This system has all the functions and capabilities I expect it to have. & 5.94 & 1.01 & 6 \\ \hline OVERALL & 19. Overall, I am satisfied with this system & 5.91 & 1.09 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean, standard deviation, and median per question. Figure 5: **(Left panel)**: IBM CSUQ question graph. **(Right panel)**: IBM CSUQ metrics. have the highest values, reflecting the fact that overall satisfaction with the application is strongly related to the information and ease of use of the system. In general, the usability of the system was well received and it can be considered that the participants were comfortable with the structure of the system, the content, and its interactive functions.
## 2 Triplet Ordinal Cross Entropy Hashing
### Triplet Ordinal Quantization Loss  
Generally, the sign function is adopted to map the real-valued data output by the last layer of deep neural network into binary codes. However, it generates discrete values and makes the objective function non-deterministic polynomial (NP) hard for optimization [20; 36]. To fix this problem, the continuous \(\tanh(\cdot)\) function is utilized instead of the sign(\(\cdot)\) function in this paper. Furthermore, to minimize the quantization loss caused by the continuous relaxation procedure, we expect the output of the \(\tanh(\cdot)\) function to be close to \(\pm 1\). Here, we utilize the triplet ordinal cross entropy to formulate the quantization loss. We define the binary code obtained by the \(\tanh(\cdot)\) function as \(B^{i}_{tab}\). \(B_{ref}\) is the reference binary code. The ideal encoding result is \(\mathbf{1}\). Thus, we formulate the quantization loss \(Q\) as in Equation (14).  
\[\begin{array}{ll}Q&=\sum\limits_{i=1}^{N}-\log P(1|(||B^{i}_{tab}||,1,||B_{ ref}||))\\ &=\sum\limits_{i=1}^{N}-\log\phi(-d_{h}(||B^{i}_{tab}||,1)+\delta)\\ &=\sum\limits_{i=1}^{N}\log(1+e^{-\alpha(-d_{h}(||B^{i}_{tab}||,1)+\delta)}) \end{array} \tag{14}\]  
In Equation (14), the triplet ordinal relation among \((\ ||B^{i}_{tab}\ ||,\ \mathbf{1}\) and \(\ ||B_{ref}\ ||)\) is defined as \(1\) and it indicates that the data pair \((\ ||B^{i}_{tab}\ ||,\ \mathbf{1})\) is more similar than  
Figure 3: The characteristic of the function \((\cdot)\).  
the data pair (**1**, \(\mid\mid B_{ref}\mid\mid\)). Therefore, to minimize the quantization loss, the Hamming distance of the data pair (\(\mid\mid B_{t\text{th}}\mid\mid\), **1**) should be smaller than the Hamming distance \(\delta=d_{h}(\mid\mid B_{ref}\mid\mid,\textbf{1})\). During the training procedure, we tune the value of \(\delta\) to balance the optimization complexity and the approximation performance. A small \(\delta\) value let the encoding results be close to the output of sign function and the training process will become hard. In contrast, a large \(\delta\) value creates low optimization complexity, but it leads to poor approximation results.  
After applying the continuous relaxation mechanism, we compute the Hamming distance of one data pair by Equation (15). \(\otimes\) computes the sum of bitwise production value. \(f_{\text{S}}(\cdot)\) represents the output of the deep neural network's last layer.  
\[d_{h}(B_{i},B_{j})=\frac{1}{2}(M-\text{tanh}(f_{\text{S}}(x_{i}))\otimes\text{ tanh}(f_{\text{S}}(x_{j}))) \tag{15}\]  
Finally, we utilize the back propagation mechanism to optimize the variables of the deep neural network by simultaneously minimizing the triplet ordinal relation cross entropy loss in Equation (13) and the quantization loss in Equation (14).
## 3 Experimental Results and Discussions
### GCD for calcification Detection  
In the test of calcification detection, 155 digital mammograms (digitized by the \"LumiSys\" scanner, from the DDSM database) were analyzed by the proposed GCD algorithm. Those 155 mammographic images consist of CC views only, which include 80 normal images (from 80 healthy subjects, randomly selected one image from each subject) and 75 cancerous images (from 74 patients). To balance two sample numbers used in the training set, 80 normal images (rather than 159) were deployed here in contrast with 75 cancerous images. In DDSM database, a calcification was also depicted by its _type_ (Amorphous, Pleomorphic, Fine_Linear_Branching, _etc._) and _distribution_ (Clustered, Linear, Segmental, Regional, Diffusely_scattered), which was suggested by the ACR Bi-RADS \(\copyright\) lexicon.  
Similarly, a ten-fold cross validation method was used to sufficiently use the dataset (of 155 samples) and to conduct reliable results. The same experimental parameters were used as that used in mass detection (in Section 3.1). We achieved TP (Segmentation) = 94% at FPI (Segmentation) = 4.21; and TP (Classification) = 93% at FPI (Classification) = 1.19 in calcification detection with the GCD algorithm.  
Figure 9: GCD results of Case “3634_Right” (normal and healthy): (a) Original mammogram; (b) Preprocessed image without any annotation—No false alarms were annotated; (c) 10 alarm segments (\(P_{\mathrm{C}}\) = 0.44, 0.41, 0.35, 0.29, 0.28, 0.28, 0.28, 0.27, 0.25, 0.25).  
Figure 11: GCD results of Case“0068_Right” (two malignant calcifications with amorphous type and clustered distribution, assessment = 5, subtlety = 4): (a) Original mammogram; (b) Preprocessed image with one annotation (\(P_{\rm C}\) = 0.69) and two overlays—a good match since one annotation hit two overlays; (c) 6 alarm segments (\(P_{\rm C}\) = 0.69, 0.39, 0.33, 0.27, 0.24, 0.22) overlapped on two overlays.  
Figure 10: GCD results of Case “3039_Left” (one malignant calcification with pleomorphic type and segmental distribution, assessment = 4, subtlety = 3): (a) Original mammogram; (b) Preprocessed image with one annotation (\(P_{\rm C}\) = 0.71, detected by GCD, in heavy black outline) and one overlay (ground truth, in light black line)—an excellent match; (c) 9 alarm segments (\(P_{\rm C}\) = 0.71, 0.36, 0.35, 0.34, 0.34, 0.33, 0.33, 0.31, 0.31) overlapped on one overlay.  
Two cancerous cases of malignant calcifications are shown in Figures 10 and 11; whereas one healthy case is demonstrated in Figure 12 (where one false alarm is present after classification). The similar symbols and notions are used here. The case analyses are given in figure captions.
# A Real-Time Data Delivery for Mobile Sinks Group on Mobile Cyber-Physical Systems
## 2 The Proposed Mechanism
### Group Sink Modeling  
In the MCPS environment, a group of mobile sinks usually have a common goal such as maintenance or production work in a restrict region. However, they have different roles for individuals. Thus, we assume that the group of mobile sinks collectively moves, but each member sink in the group moves independently in a restrict region. Each sink receives data from the nearest sensor as an agent node. To forward data to a sink, the coordinates of the sink are needed. However, in the case of a mobile sink group, per-sink movement management causes excessive energy consumption. M-Geocasting [10] offers an effective data delivery to mobile sink groups. We can gather the geographical information of member sinks and report the group information by a leader sink of the group that is responsible for gathering the location of all member sinks and periodically advertising coordinates of center point and a radius of the area. With the information of the mobile sink group, a source node sends its data using the geographic routing toward the center point. Once the data enter the area, it is flooded within the  
Figure 1: Overview of the proposed scheme.  
area. Using the flooding, the protocols do not need to independently manage the locations of each members. The group has a ring-based movable area with the center point and the radius. Each member sink selects one of the sensor nodes as an agent to access the network since the mobile sinks in the group exist on an infrastructureless field.
# Trade-Off Analysis for Array Configurations of Chipless RFID Sensor Tag Designs
###### Abstract  
The accurate detection and reliable performance of chipless radio frequency identification (RFID) tags and sensors present significant challenges due to their inherently low radar cross section (RCS) and pronounced mutual coupling effects. These limitations adversely influence the quality (Q) factor and overall detectability, complicating the optimisation of chipless RFID systems for practical applications. This study investigates the performance characteristics and trade-offs among RCS, Q-factor, and detectability in Pi-shaped array configurations of chipless RFID tags. A comprehensive analysis of various array configurations is conducted, supplemented by a link budget evaluation to elucidate how different array structures impact system performance. The simulation results reveal that planar arrays outperform linear arrays in both RCS and Q-factor, highlighting essential trade-offs between tag identification range and angular coverage, which are influenced by array size and electromagnetic coupling. The findings emphasise optimising resonance quality and scattering efficiency to tailor chipless RFID systems for specific application requirements. This research provides valuable insights into the design and operation of chipless RFID arrays, contributing to their advancement in practical applications.  
aray configurations; chipless radio frequency identification (RFID); detectability; detection performance; radar cross section (RCS)  
2025251653  
21 January 2025
## 4 Results and Discussions
### Effect of Impurities on the Anount of Condensation  
The proportion of condensation recorded for case 1 is plotted along the nozzle on the X axis in Figure 11 through the decrease in gas volume fraction (\(\alpha_{gas}\)). As can be seen from the numerical results in Figure 7, there is no condensation observed in the converging section of the nozzle. This is seen as a straight line in Figure 11 from the nozzle inlet to the throat, at X = 0. The presence of condensation is seen quantitatively after the throat in the diverging section of the nozzle. The highest \(\alpha_{liq}\) value for case 1 is of the order of 0.004, corresponding to the 3% impurity case, as shown in Figure 11. In fact, the greater the quantity of impurities present in the initial CO\({}_{2}\), the lower the condensation. This implies that, in the case of pure CO\({}_{2}\), a very high amount of condensation should have been obtained, as illustrated in Figure 12, for case 1 conditions at the fully supersonic regime. In this figure, only a few cells are filled with liquid in the divergent part of the nozzle. This may be due to the increased tensile strength of the fluid as the quantity of impurities decreases.  
Figure 11: Gas volume fraction profile with comparison based on % impurities for Case 1. As the amount of impurities increase, lower condensation is observed.  
Figure 10: Effect of various % impurities composition on (**a**) \(\gamma\) and (**b**) \(n_{s}\) profiles of the CO\({}_{2}\)-N\({}_{2}\) mixtures.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 2 Research Methodology (CNN-LSTM-SAM-Attention Model)
### Principles of Long and Short-Term Memory Networks (LSTM)  
Long Short-Term Memory Network (LSTM) is a special kind of Recurrent Neural Network (RNN). RNN has the advantage of being able to relate the information from past data to the current data; nevertheless, it faces the issue of gradient vanishing or gradient expanding when the data length exceeds a particular point. To efficiently capture the long-term dependencies in time series data, the LSTM regulates the information flow by introducing three gate control units (Input Gate, Oblivious Gate, and Output Gate). It also resolves the gradient vanishing and gradient explosion issues of RNN in lengthy sequences and preserves the hidden state or memory of previous inputs.  
The cell states \(C_{t}\) and hidden states \(h_{t}\) make up the LSTM's basic structure. Through three gates, each LSTM cell regulates the output and updating of data. In particular, the output gate determines the output information at present, the forgetting gate determines the extent to which the cell state has been forgotten, and the input gate determines the impact of the current input on the cell state. The following equation represents the LSTM's updating process:  
\[f_{t}=\sigma\left(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}\right) \tag{2}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{3}\]  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},x_{t}]+b_{C}) \tag{4}\]  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t} \tag{5}\]  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{6}\]  
\[h_{t}=o_{t}\odot tanh(C_{t}) \tag{7}\]  
where \(f_{t}\), \(i_{t}\), \(o_{t}\) are forgetting gate, input gate and output gate, respectively, \(C_{t}\) is cell state, \(h_{t}\) is hidden state, \(\sigma\) is sigmoid activation function and \(\odot\) denotes element by element multiplication.  
By efficiently transferring time information backward and capturing long-term temporal dependencies, the LSTM model's strong memory capacity allows it to handle time-series data in landslide prediction tasks. This helps the model comprehend the data change trend over time, increasing prediction accuracy.