esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Slow-Release Fertilizer on the Growth of Garlic Sports and the Soil Environment
## 2 Materials and Methods
### The Experimental Design  
The pot experiment was conducted at Chongqing University (Chongqing, China) from 11 December 2024 to 19 January 2025.  
Four fertilization treatments were designed, as outlined in Table 1: no fertilization (CK), the application of potassium magnesium sulfate fertilizer (T1), the application of an equal amount of slow-release fertilizer (T2), and the application of a slow-release fertilizer with an equivalent nutrient content (T3). For each treatment, five garlic bulbs of a uniform size and with a healthy appearance were selected and planted into plastic pots, with five bulbs per pot (21 cm in height and 23 cm in diameter). A total of 12 pots were prepared, each containing 2 kg of homogenized soil, and the pots were grouped into sets of five.  
Figure 1: Preparation process of slow-release fertilizer.  
All treatments were conducted using a one-time fertilization approach. A predetermined amount of fertilizer was uniformly applied to the flowerpots by placing it at a depth of approximately 6 cm, followed by coverage with a layer of soil. A cross-shaped mark was drawn on the soil's surface to divide each pot into four equal sections. The garlic sprouts were then planted at the intersection point of the cross and at the midpoints of each quadrant, ensuring uniform spacing among all plants. This planting layout was designed to maintain an appropriate distance between each crop and the center of the pot, thereby facilitating consistent sampling and ease in the growth observations. The initial soil moisture content was controlled at 60%, and water was added every three days to maintain soil moisture without causing water accumulation. Approximately 200 mL of water was applied each time.  
At harvest, the plant height, stem diameter, and yield were recorded for each treatment. One garlic sprout per pot was reserved and refrigerated for a subsequent quality analysis. Plant height was measured using a ruler as the distance from the soil surface to the tip of the tallest leaf when fully extended. The diameter of the pseudostem was determined using a vernier caliper, corresponding to the maximum basal diameter of the plant. The relative chlorophyll content (SPAD) was measured using a TYS-A chlorophyll meter. Garlic sprout yield was determined by weighing them with an electronic balance. The contents of soluble sugars, soluble proteins, free amino acids, vitamin C, and alicin in the garlic sprouts were determined using the anthrone colorimetric method [36], the Coomassieie Brilliant Blue G-250 colorimetric method [37], the indene trione colorimetric method [38], UV spectrophotometry [39], and the phenylhydrazone method [40], respectively. Each parameter was measured in triplicate, and the average values were reported.
## 5 Discussion
### Discussion on Noisy/Incomplete Skeleton Data  
In real-world applications, it is common to capture noisy/incomplete skeletons. Handling occluded pose data is a challenge in the field of computer vision, especially in tasks such as human pose estimation and skeleton-based action recognition. We believe this issue can be addressed from two aspects. On one hand, the focus is on how to obtain more accurate and complete skeleton data, through strategies such as data augmentation, model design [37], pose correction [22], contextual information, and fusion of multi-modal data [44]. On the other hand, the research emphasis is on improving the robustness of action recognition models on noisy/incomplete data [10; 13; 20; 21]. [PERSON] et al. [13] have established an occlusion dataset for human actions by setting the occluded joints to zero and have achieved good performance by exploring more discriminative features of the skeleton data.  
In this paper, we can obtain skeleton data even in the presence of occlusions through a depth camera or existing pose estimation algorithms. Although these data may not be precise enough, and some joint data may be occluded (set to zero), we can still adopt some strategies to enhance the robustness of action recognition based on noisy/incomplete data. By preprocessing the skeleton data and using motion velocity and bone features as input streams to the network, we explore sufficient discriminative features to overcome the noise/inaccuracy of the skeleton. With the hierarchical structure, an EHC-GCN can handle features at different levels. After the network focuses on local joint features, it integrates these local features to understand the global structure. If joints are inaccurately located or incomplete at the local level, global information and more discriminative features may help to correct these errors, as it can understand the relationships between joints from a broader perspective. Inspired by this, the potential of this paper in handling noisy/incomplete skeleton data can be seen.
###### Abstract  
Underwater acoustic communications are challenging because channels are complex, and acoustic waves when propagating in the ocean are subjected to a variety of interferences, such as noise, reflections, scattering and so on. Spread spectrum technique thus has been widely used in underwater acoustic communications for its strong anti-interference ability and good confidentiality. Underwater acoustic channels are typical coherent multipath channels, in which the inter-symbol interference seriously affects the performance of underwater acoustic communications. Time-reversal mirror technique utilizes this physical characteristic of underwater acoustic channels to restrain the inter-symbol interference by reconstructing multipath signals and reduce the influence of channel fading by spatial focusing. This paper presents an M-ary cyclic shift keying spread spectrum underwater acoustic communication scheme based on the virtual time-reversal mirror. Compared to the traditional spread spectrum techniques, this method is more robust, for it uses the M-ary cyclic shift keying spread spectrum to improve the communication rate and uses the virtual time-reversal mirror to ensure a low bit error rate. The performance of this method is verified by simulations and pool experiments.  
spread spectrum communication; time-reversal mirror; underwater acoustic communication 2019 12 3577 14 August 2019; Published: 16 August 2019
## 5 Power Optimization and Optimized Scheduling Results  
The optimized electrical power is shown in Figure 8, with the resulting electrical power generally lower than the reference value.  
Due to the system's time step being 360 min, the time span is relatively long, and since the drone is in cruising mode from 19 to 345 min, the energy scheduling shows periodic variations. Therefore, three time periods are selected to explore the energy scheduling situation: 0-18 min, 19-36 min, and 346-360 min. As shown in Figure 9, the cumulative rewards for energy scheduling during the three time periods are represented as DDPG-1, DDPG-2, and DDPG-3, respectively. DDPG-M-1, DDPG-M-2, and DDPG-M-3 are the average cumulative rewards over every five episodes. The cumulative rewards for all three time periods converge. As shown in Figures 10-12, the hydrogen fuel cell and SOC scheduling results for the three time periods are all within a reasonable range, achieving efficient utilization of electrical energy.  
Figure 8: Comparison between MPC predicted power and reference value.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **Value** & **Parameters** & **Value** & **Parameters** & **Value** \\ \hline \(\alpha^{p}\) & 0.001 & _Memory_ & 4000 & \(\sigma_{\max}\) & 0.35 \\ \(\alpha^{Q}\) & 0.001 & _Batch size_ & 96 & \(T_{Nd}\) & 4800 \\ \(\tau\) & 0.001 & \(\mu\) & 0 & \(k_{1}\) & 100 \\ _episode_ & 200 & \(\theta\) & 0.15 & \(k_{2}\) & 50 \\ \(\gamma\) & 0.99 & \(\sigma_{\min}\) & 0.001 & \(l_{1}\) & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values of the DDPG algorithm.  
Figure 10: Hydrogen fuel cell and SOC scheduling during 0–19 min.  
Figure 9: DDPG training cumulative reward.
## 3 Test and Results
### Training Time  
Training time is particularly relevant in deep learning models as it directly impacts energy consumption, resource utilization, and the ability to iterate rapidly during model development. In this study, we focus on training time as a key metric to evaluate the computational efficiency of SDKU-Net compared to other methods. To evaluate the proposed SDKU-Net, training time measurements were conducted using the same hardware and batch size across all models. Table 2 presents the training time required for each model.  
As observed in Table 2, SDDNet and MSADNet require significantly higher training times. This is primarily due to the inclusion of data augmentation during training, as well as the increased architectural complexity, both of which demand more computational resources. U-Net++ and U-Net, on the other hand, have the shortest training times, reflecting  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Time (s)** & **Epochs** \\ \hline U-Net ++ & 1880.58 & 75 \\ \hline Unet & 1507.87 & 75 \\ \hline SDDNET & 50,216.2 & 100 \\ \hline MSADNet & 47,771.5 & 100 \\ \hline CADDN & 2424.34 & 100 \\ \hline SDKU-Net & 1980.07 & 75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training time comparison.  
their simpler architectures and lack of advanced features. CADDN demonstrates an efficient training time despite its 100-epoch setup, making it a strong competitor in this aspect. The proposed SDKU-Net, however, achieves a favorable balance between performance and efficiency. By requiring only 75 epochs to converge, SDKU-Net reduces training time while maintaining comparable or superior accuracy to more complex models. Notably, despite incorporating additional layers, operations, and residual connections, SDKU-Net maintains a training time close to U-Net++, while significantly improving results across all evaluated metrics. This highlights its potential for energy-efficient training in scenarios where computational resources are limited or when rapid deployment is needed. The reduced training time in SDKU-Net can be attributed to its dynamic kernel adjustment mechanism, which optimizes feature extraction throughout training. However, future studies could explore whether fewer epochs might impact generalization on more diverse datasets.
## 4 Materials and Methods
### Transferase Activity, Fructoligosaccharides Production, and HPLC Analysis  
The transferase activity analysis was performed in 600 g/L sucrose and 100 mM sodium acetate pH 5.5 containing 5 U/mL of enzymatic activity. Reactions were incubated at 60 \({}^{\circ}\)C in orbital shaker (Vortemp 56, Labnet International, Woodbridge, NJ, USA) at 600 rpm. Aliquots of 50 \(\mu\)L were withdrawn at different times, incubated for 8 min at 100 \({}^{\circ}\)C, diluted 30 times in water, and filtered through nylon membranes of 0.45 \(\mu\)m (Scharlab, Barcelona, Spain). Samples were analyzed by HPLC with a quaternary pump (Delta 600, Waters, Milford, CT, USA) coupled to a Liquid Purple amino column (4.6 \(\times\) 250 mm, from Analisis Vinicos, Tomelloso, Spain) and a precolumn-NH2 (Phenomenex, Torrance, CA, USA). Detection was performed using an evaporative light scattering detector (ELSD; mod. 1000, Polymer Laboratories Ltd., Church Stretton, UK) equilibrated at 90 \({}^{\circ}\)C along with an automatic injector (mod. 717 Plus, Waters, Milford, CT, USA). An acetonitrile/water mixture, degassed with an in-line vacuum generator (ser. 200, Perkin-Elmer, Eden Prairie, MN, USA), was used as mobile phase at 1.0 mL/min during 45 min (first 10 min acetonitrile:water 85:15, changing to 75:25 over 2 min, and this proportion maintained until the end of the analysis). Temperature was 28 \({}^{\circ}\)C and volume injection 10 \(\mu\)L. Data were analyzed using the Empower software (v.1.0; Waters). Compounds were quantified on the base of peak areas using the most closely related standard: glucose, fructose, sucrose, 1-kestose, nystose, neokestose, and neonystose, the last two produced from sucrose using \(\beta\)-fructofuranosidase from _X. dendorhous_[34] and blastose and 6-kestose using the _Sch. occidentalis_ enzyme [21,45].  
Transfrucsfosylation of potential acceptors by RdINV was assessed using 1 mL reactions containing 100 g/L acceptors and 100 g/L sucrose in 100 mM sodium acetate pH 5.5 for up to 180 min. Monosaccharides (fructose, glucose, galactose, xylose, L-arabinose, mannose), disaccharides (trehalose, isomaltulose, lactose, lactulose, leucrose, maltose, melibiose), and alditols (erythritol, galactitol, sorbitol, mannitol, ribitol, xylitol), all from Sigma-Aldrich Corp. (St. Louis, MO, USA), were used. Negative controls included reactions without enzyme, sucrose, or alternative acceptors. Conditions and sample preparation were carried out as mentioned above. For HPLC-analysis of monosaccharides and sugar alcohols, a gradient phase was used with 80:20 acetonitrile:water for 18 min, followed by an increase of the water proportion to 30 for 30 min, and then return to the initial composition mixture (total analysis time: 35 min). For disaccharides, 80:20 acetonitrile:water was employed for 50 min.
# Coupling and Trapping of Light in Thin-Film Solar Cells Using Modulated Interface Textures
## Results
### Good Coupling and Scattering Using Double Textured ZnO:Al
The combined top and bottom cell current increased by 1.1 mA/cm\({}^{2}\) on the double textured ZnO:Al film as compared to the single texture ZnO:Al film. Using the double texture, the best tandem solar cell yielded an initial efficiency of 12.5% (FF: 76.5%, V\({}_{\text{oc}}\): 1.39 V and J\({}_{\text{sc}}\) = 11.7 mA/cm\({}^{2}\)) in the remarkably thin total silicon absorber thickness of only 1.64 \(\upmu\)m (measured on the particular sample). The two absorber layers and all the doped silicon layers had been deposited in a single chamber process. As reference the best tandem solar cell on the single-textured sample yielded an initial efficiency of 12.1% (FF: 75.7%, V\({}_{\text{oc}}\): 1.40 V and J\({}_{\text{sc}}\) = 11.4 mA/cm\({}^{2}\)). Thus, the concept of a double texture to increase the short-circuit current density has proven to be very efficient without deterioration of electrical performance. The double-textured (DT) ZnO:Al was additionally included in a comparative study of front contact materials with other  
Figure 7: SEM surface images of single-textured (ST) and double-textured (DT) surfaces: single-textured after HCl etch only (**a**), double-textured with HCl etch + subsequent HF-dip (**b**). state-of-the-art TCOs showing superior optical performance in a-Si:H/\(\mu\)c-Si:H thin-film solar cells with an accumulated short-circuit current density calculated using EQE measurements of 29.5 mA/cm\({}^{2}\)[71]. The described effects on light-coupling and light-trapping are based on the interface textures at the front and rear interfaces. In most cases, one of these interfaces is engineered and the other one just evolves during absorber layer growth [21; 59]. Since light management recently has been adopted by other thin film technologies, the analysis of light-coupling and -trapping is of general interest and should be considered in future solar cell development [13; 14; 15].
## 3 Results and Discussion
### Scenario Analysis  
ATOLL software suggested that a minimum of five BSs are needed to provide good service coverage throughout the campus. However, we wanted to assess from the point of view of energy consumption how the number of BS will affect energy consumption. This parameter is not considered by the Wi-Fi network software optimizer.  
In order to minimize energy consumption and thus GHG emissions, we developed different scenarios in which we changed the number of access points. This will help link the quality of coverage with energy consumption within the Wi-Fi network. Clearly, reducing the number of access points will reduce energy demand. Figure 8 summarizes the percentage of the total campus area covered with different signal levels for different numbers of access points.  
Figure 8: Histogram based on the percentage of areas covered by (**a**) 3, (**b**) 4, (**c**) 5 and 6 (**d**) access points.  
Figure 7: Coverage evolution and signal quality as a function of iterations.  
To estimate the quantity of CO\({}_{2}\) produced, we have used the following formula:  
\[\mathrm{Q_{CO_{2}}}=\mathrm{E_{ap}}*\mathrm{EI}*\mathrm{U_{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \mathrm{ }}}}}}}}}}}}}}} \tag{9}\]  
where  
\(\mathrm{Q_{CO_{2}}}\) : amount of CO\({}_{2}\) (g);  
\(\mathrm{E_{ap}}\): power consumption at each access point (kW);  
EI: electricity intensity (g/kWh);  
\(\mathrm{U_{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{ \mathrm{ \mathrm{     }}}}}}}}}}}}}}}\) duration of AP utilization (h).  
Below, CO\({}_{2}\) emissions during the use phase are estimated for each scenario.
## 3 Materials and Methodology
### Automatic Grading  
Based on previous research, several strategies have been implemented to obtain a good ASAG performance. However, the best results from the research pf [PERSON] et al. (2018) used the T5 model to achieve a correlation value of 92.5%. The T5 model has a large model size, as mentioned in Table 1, and is also computationally expensive. This research tries to find the right combination of models and hyperparameters to get better results with lower computational costs. Figure 4 depicts the recommended ASAG process.  
This research will utilize eight SentenceTransformers models which have a relatively small size and fine-tune the models using some hyperparameters. Based on Table 1, we recommend several SentenceTransformers models, including paraphrase-albert-small-v2, all-MiniLM-L6-v2, bert-base-uncased, all-MiniLM-L12-v2, multi-qa-distilbert-cos-v1, all-distilroberta-v1, stsb-distilbert-base, and multi-qa-mpnet-base-dot-v1. All of these models are less than 500 MB in size.  
In this study, we will fine-tune each model by exploring different combinations of hyperparameters. The parameters used include the size of the training-test data split, the number of epochs, learning rate, pre-processing steps, batch size, and the utilization of gradient checkpointing. Gradient checkpointing serves as a technique aimed at mitigating the memory requirements during deep neural network training, at the cost of having a small increase in computation time ([PERSON] et al., 2015). The system will be evaluated using various evaluation metrics, such as accuracy, F1-score, and Pearson correlation. All details about the experimental setup will be explained further in the next sub-section.
## 4 Factors that Affect the Adsorption Process
### Adsorption Mechanism  
Some of the methods used to remove harmful metals from the environment include surface adsorption, ion exchange, electrostatic forces, precipitation, and chelation [130]. Metal is diffused onto the adsorbent's pores via the physisorption process known as surface adsorption. The adsorbent and the charge on the metal ions (the adsorbate) interact electrostatically to cause sorption. Moreover, the complex formation generally involves the interaction between electron donor ligands and metal ions, which is responsible for extraction. The following studies are briefly discussed:  
[PERSON] et al. 2014 explored the properties of polyacrylamide/oxime/nano iron oxide as an adsorbent for the extraction of fluoride ions from aqueous solution (Figure 14) [131].  
Here, the adsorption of fluoride ions occurred through hydrogen bonding, complex formation, as well by surface adsorption.  
Magnificent adsorption capacity of poly(acrylonitrile-2-amino-2-thiazoline) resin was demonstrated by [PERSON] and [PERSON] (2003). The adsorption values of poly(acrylonitrile-2-amino-2-thiazoline) resin for Rh\({}^{3+}\), Ru\({}^{4+}\), Ir\({}^{4+}\), and Pd\({}^{2+}\) were 72.07 mg/g, 137.6 mg/g, 147.1 mg/g, and 230.7 mg/g resin, respectively (Figure 15) [132].  
Figure 14: Adsorption mechanism of fluoride ions by polyacrylamide-oxime-iron oxide nano-particles.  
Figure 15: Possible sorption mechanism of different metal ions by poly(acrylonitrile-2-amino-2-thiazoline) resin. The adsorption by modified adsorbent was more selective for Pd\({}^{2+}\) and Rh\({}^{3+}\) in acidic conditions.  
[PERSON] and [PERSON] (2019) reported maghemite- (\(\gamma\)- Fe\({}_{2}\)O\({}_{3}\)) and graphene-oxide -incorporated polyacrylamideonitrile nanofibers synthesized through an electrospinning method and used them to remove As\({}^{5+}\) ions from aqueous solution [124]. The removal efficiency was influenced by factors such as adsorbent dose (0.01-0.09 g), pH (3-8), exposure period (30-180 min), and initial As\({}^{5+}\) ion concentration (1-50 ppm). As shown in Figure 16, the adsorption of arsenic by the GO/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)-modified polyacrylamideonitrile was increased due to ionic interaction as well as complex formation by the ions.
# Recent Progress in Pressure Sensors for Wearable Electronics: From Design to Applications
## 2 Pressure Transduction Mechanism and Design Principles
### Piezoelectric Type  
The working principle of a piezoelectric type pressure sensor is that dipole moments in the active material result in the production of electrical potential (Figure 1c). The piezoelectric pressure sensor shows a high resolution, fast response, and high sensitivity. Due to a relatively high piezoelectric characteristic (piezoelectric voltage coefficient, \(g_{33}\)), low impedance, and good flexibility, polyvinylidene fluoride (PVDF) and its copolymers were widely used [77]. Composite materials are often used inthe production of piezoelectric pressure sensors, as in the two types mentioned above. In particular, BaTiO\({}_{3}\) has often been used as a combined material to enhance the electrical performance of piezoelectric pressure sensors [40; 78; 79; 80]. However, PVDF has a relatively low piezoelectric charge coefficient (\(d_{33}\)), resulting in a relatively low pressure sensitivity. On the other hand, inorganic materials, such as ZnO, GaN, InN, ZnS, and CdS has a high \(d_{33}\) and are thus more frequently applied in pressure sensors [81; 82; 83].  
The above-mentioned structured active layers for resistive and capacitive pressure sensors were also actively introduced to enhance the sensitivity of the piezoelectric pressure sensor [84; 85; 86; 87; 88]. Since these structured layers experience more deformation under external pressure, more charges are generated from the devices. Meanwhile, the electrospinning technique is widely used as a fabrication method for a piezoelectric pressure sensor. In particular, composite electrospun NFs have been extensively introduced because their combined particles are oriented along with the NFs due to the strong electric field force during electrospinningning [89; 90; 91; 92; 93; 94]. In addition, piezoelectric pressure sensors can be easily applied as self-powered sensors because they generate current and voltage by moving electrons when the sensor is placed under pressure. Self-powered pressure sensors are very attractive in the field of wearable electronics because they do not require an additional power source or energy storage device, such as a battery. However, since the materials used for piezoelectric pressure sensor have poor stretchability, their applications in wearable electronics have been limited.