esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Irrigation with Desalinated Water on Lettuce Grown under Greenhouse in South Korea
## 3 Results and Discussion
### Lettuce Yield  
In the experiment with saline irrigation water, significant differences (\(p<0.05\)) in total and shoot fresh weights of lettuce were observed after both the first and second phases of the experiment (Figure 5). In the first phase, there were no significant differences in total and shoot fresh weights between the control and the two treatments with less saline water, i.e., BS#02 and BS#03, and between the control and the treatment #04. In the second phase, however, both fresh weights significantly decreased in the treatment BS#04 with the salinity level above 3.4 dS m\({}^{-1}\). This result indicates that the yield of lettuce is affected by continued use of saline irrigation water and that the threshold salinity level for lettuce yield could be about 3.4 dS m\({}^{-1}\). [PERSON] et al. [11] reported that there were no significant differences in fresh and marketable lettuce (_cv. Fully_) yield at low irrigation water salinity (1.2 and 2.1 dS m\({}^{-1}\)), whereas the significant decrease was observed above 3.7 dS m\({}^{-1}\). The reduction of lettuce yields might be attributed to disturbances in physiological and biochemical activities including water and mineral uptake [15; 22]. [PERSON] et al. [23] reported that the average of lettuce (_Lactuca sativa_ var. _crispa_) fresh weights ranged from 74.6 to 144.8 g with irrigation salinity levels ranging from 0.75 to 3.5 dS m\({}^{-1}\) in the greenhouse. In this study, the average of fresh weights ranged from 81.0 to 219.2 g with irrigation salinity levels ranging from 0.9 to 3.4 dS m\({}^{-1}\) (Figure 5).  
Figure 4: Effect of desalinated irrigation water on (**a**) number of leaves, (**b**) leaf width, and (**c**) leaf length. Bars represent the mean \(\pm\) standard deviation, \(n=20\). Different letters indicate significant differences among treatments by [PERSON]’s honest significant difference test at \(p<0.05\). AS: after water treatment using the BWD system, i.e., desalinated water irrigation.  
In the experiment with desalinated water, no significant differences in total and shoot fresh weights of lettuce were detected among the treatments (\(p>0.05\)) (Figure 6). This result indicates that irrigation with desalinated water did not negatively affect lettuce yield under the conditions of the current study.  
Figure 5: Effect of saline irrigation water on lettuce shoot and total fresh weights after the (**a**) first and (**b**) second phases of the experiment. Bars represent the mean \(\pm\) standard deviation, \(n=20\). Different letters indicate significant differences among treatments by [PERSON]’s honest significant difference test at \(p<0.05\). BS: before water treatment using the BWD system, i.e., saline water irrigation.
# A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and Images
## 3 Methodology  
### Problem Definition  
The overall framework of the proposed CAMRec model is illustrated in Figure 2. The CAMRec model consists of User-Item Interaction Network, Feature Extraction Network, and Preference Prediction Network. Specifically, the User-Item Interaction Network captures the interactions between users and items. The Feature Extraction Network extracts textual and visual information from online reviews that can represent users' preferences and then fuse them. The Preference Prediction Network concatenates the overall features to predict the final rating a user will leave for a specific item.  
Using information from both texts and images is a stepping stone to understanding users' preferences and behaviors for items in a comprehensive manner. Nevertheless, most studies have overlooked the informational value of images and have focused solely on texts. Meanwhile, although the fusion method is crucial for using multimodal data, previous studies are limited in capturing the dependencies and complementarity between modalities. Therefore, this study proposes the CAMRec model, which integrates the complementarity of textual and visual representations into the recommendations. The CAMRec model uses the RoBERTa and VGG-16 models to extract features from review texts and images, respectively. These models have been extensively validated and have been used to extract textual and visual features from review texts and images in previous studies [8,10]. Therefore, they ensure the reliability of our feature extraction process to support the proposed CAMRec model effectively. Moreover, the proposed CAMRec models the complementarity between texts and images using the co-attention mechanism, which is critical in this study. The co-attention mechanism addresses the task of integrating textual and visual features by dynamically aligning and fusing. It ensures that relevant features from each modality enhance the understanding of information from the other, thereby capturing their interdependencies. Finally, these joint representations of review texts and images enrich the features of the users and the items and are integrated with user-item interactions to perform the recommendation tasks.  
Given \(T=(u,r,m,i,y)\) as a tuple composed of user \(u\), item \(i\), user's review text \(r\), user's review image \(m\), and rating \(y\). The proposed model aims to train the prediction model \(A\) formalized as follows:  
\[A(u,r,m,i,y;\theta)\rightarrow\hat{y}, \tag{1}\]  
where \(\theta\) and \(\hat{y}\) indicate bias and predicted preference rating. During the training process, the model is trained to learn predicted preference rating \(\hat{y}_{u,i}\) based on the actual user preference rating \(y_{u,i}\). The detailed description of each network of the CAMRec model follows in Section 3.2.
## 4 Discussion  
### Laboratory Experiments  
Results of this study demonstrate that exposure of treated mine soils to different rainfall intensities influenced the amount of soil loss. In particular, increasing rainfall intensities from 0.25 to 4 L/m2 led to decreasing soil losses (Figure 4). Possible explanations for this reduced wind erosion might be that greater amounts of applied water led to greater particle cohesion in treated mine soils, or that the infiltrated water had not completely evaporated. As shown in several studies (e.g., [21; 22; 23; 24; 25]), higher moisture content improves soil cohesion and thus the resistance to wind erosion. The increase in soil cohesion can be attributed to adsorptive and capillary forces [25; 26]. Regardless, it appears that more water retained in the soil samples improved the inter-particle bonding. This effect might be intensified by the food processing by-products as they consist of carbohydrates and proteins, which can augment the water retention capacity of soils and retard moisture loss from soils [27; 28].  
By contrast, exposure of treated samples to repeated wet-dry cycles resulted in increased soil losses and thus reduced the effectiveness of tested food processing by-products to suppress dust emissions (Figure 5). It appears that water applied at frequent intervals leached the water-soluble amendments from the substrates to such an extent that it reduced the cohesion between soil particles. This created soil surfaces that were less resistant to aerodynamic lift forces and saltation bombardment, resulting in higher soil losses. In fact, saltating sand grains are mainly responsible for aeolian dust entrainment [29; 30; 31]. Further analysis of the soil loss data (Table 1, Appendix A) reveals differences between the food processing by-products in their susceptibility to leaching. While chicory vinarses showed the highest soil losses, palatinose molasses, corn steep liquor and decantation syrup were less prone to leaching. Such different responses to wetting and rainfall intensities may be explained by the composition of the tested additives. The chosen biomaterials comprise mixtures of carbohydrates and proteins, which have different affinities to water molecules. Mono- and disaccharides provide multiple hydroxyl groups that form hydrogen bonds with water [32]. Structural features such as branching structure, charged groups and, molecular weight affect the solubility of polysaccharides, which can promote or hinder intra- and intermolecular association [33]. Protein solubility is primarily influenced by the hydrophobic and hydrophilic amino acid residues exposed to water [34; 35]. However, it is not possible to explain why some biomaterials are more water-soluble than others because the exact material compositions are not accessible.
## 5 Dynamic Analysis
### Simulation Results
#### 5.3.6 Test Case 6--Generic Burr Profile  
A set of generic burr profiles have been generated using a random variation of burr height in order to test the mechanism in more realistic scenarios. The random burr profiles are then used as an input for the dynamic simulations. Random profiles have been generated starting from the power spectral density (PSD) of the random process [33]. The three most common shapes for the noise have been assumed, namely white noise (flat PSD), flicker-noise (PSD with slope -1 in a log-log diagram), and random-walk noise (PSD with slope-2). The latter is usually adopted when it come to the roughness of road surfaces (ISO 8608:2016). The shape of the PSD is related to the frequency distribution of the profile, while the area under the PSD is related to the RMS (squared) of the random profile. It has been assumed that the profiles, which are generated with a frequency of 1000 Hz, have RMS between 1 and 4 mm.  
A random burr profile using a PSD slope of \(-2\) and a RMS of 2.5 mm and the related simulation results are presented in Figure 15. It can be noticed that the proposed mechanism performs well also for a generic burr profile. In particular, the compliance of the mechanism is exploited to reduce the cutting forces and, as a consequence, a second cutting cycle is necessary to completely remove the burr (see Figure 16). Similarly to the Test case 5 (Section 5.3.5) small bounces on the mechanical stop are visible in the middle plot of both Figure 15; Figure 16 (for example after \(t\) = 0.55 s, after \(t\) = 1.9 s, etc.). From the analysis of Figure 16, it can be noticed that after the second cutting cycle a very high precision is ensured: most of the burr profile is at 2.85 * 10\({}^{-4}\) mm that is the equilibrium position of the mechanical stop, and very small variations (<1 * 10\({}^{-5}\) m) with respect to this value are present. Indeed, in the second cutting cycle the grinding wheel support is always in contact with the mechanical stop, and the small variations in the burr profile are due to the (low) compliance of the mechanical stop. In this case the compliance of the mechanism is not exploited, since the maximum burr height is below 2.5 mm, which is the minimum burr height that is necessary to make the mechanism work in the compliant mode, as explained in Section 5.3. Very similar conclusions can be drawn when other values of PSD slope and RMS are used (as above specified).  
Figure 14: Three-steps burr profile (“high–low–high” (**top**)); position (**middle**) and velocity (**bottom**) of the grinding wheel support.
# Automatic Detection of Tomato Diseases Using Deep Transfer Learning
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Study** & **Aim** & **Data Source** & **No. of Images** & **Training Subset** & **Method** & **Performance** \\ \hline [PERSON] et al. [10] & Classification (five disease + core healthy) & Planet/Milge [23] & 6000 tomato leaf images. & 80\%. & CNN. & Accuracy = 96.5\%. \\ [PERSON] and Zaw [11] & Classification (five disease + core healthy) & Planet/Milge [23] & 3074 tomato leaf images. & 90\%. & Feature extraction = 97\%. & Accuracy = 84.7\%. \\ [PERSON] and [PERSON] [12] & Classification (five disease + core healthy) & Planet/Milge [23] & 14050 tomato leaf images. & 90\%. & LeNet, VGG, ResNet and Accuracy = 92.5\%. \\ [PERSON] et al. [13] & Classification (five disease + core healthy) & Planet/Milge [23] & 18,140 leaf images. & 13,300 images. & Alexei, Logistic, and Accuracy = 94.9\%. \\ Amobiral and Classification (five disease + core healthy) & Planet/Milge [23] & 2375 tomato leaf images. & 70\%. & Segmented, feature extraction = random forest. & Accuracy = 94.1\%. \\ Amobiral and Classification (five disease + core healthy) & Planet/Milge [23] & 1240 leaf images. & 1400 images per class & Modified VGG+ instance. & Accuracy = 98.8\%. \\ [PERSON] et al. [13] & Classification (five disease + core healthy) & 14,160 leaf images. & 1400 images per class & VGG, VGG & F1 score = 92.7\%. \\ [PERSON] and [PERSON] [17] & Detection of early leaf pose & Locally collected. & 2288 plant images. & 80\%. & VGG, VGG & F1 score = 92.7\%. \\ [PERSON] et al. [7] & Classification (five disease + core healthy) & Planet/Milge [23] & Classification (five disease + core healthy). & Inception/3.2\%. & Highest accuracy = 99.8\%. & ResNet \\ [PERSON] et al. [20] & Identification of disease type & & & & & & \\ [PERSON] et al. [20] & Classification (five disease + core healthy) & Cellosed from Internet. & 2288 plant images. & 60\%. & Faster R\(\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{\mathrm{ \mathrm{\mathrm{\mathrm{ \mathrm{  \mathrm{ \mathrm{ \mathrm{ }}}}}}}}}{}}{{}}{{}{}{}{}{}{}{}{}{{}{}{}{{}{}{}{{}{}{}{}{{}{}{}{}{}{{}{}{}{}{}{}{}{}{}{}{}{}{}{}{}{{}{}{}{}{}{}{}{}{{}{}{}{}{}{}{{}{}{}{}{}{{}{}{}{}{}{{}{}{}{}{}{}{{}{}{}{}{}{The present study has some limitations. First, tomato has two major leaf shapes (regular and potato leaf) and multiple other variations relating to leaf dimensions, color, and shades of green. However, the dataset does not include varieties of tomato leaf shapes. This will narrow the applicability and performance of any tomato disease identification system to the specific tomato variant in the dataset. Second, all the images in the dataset have a unified background. It would be worthwhile to investigate leaf images with different backgrounds taken in a non-unified manner. Third, tomatoes are susceptible to other diseases or pests (e.g., Tuta absoluta) that are not part of the dataset. Fourth, the dataset is imbalanced with varying numbers of images in each class.
## 2 Motor Topology and Demagnetization Analysis
### Motor Topology  
In this study, the model proposed in reference [29] is selected as the basic model for further investigation. This model belongs to the category of PMA-SynRMs, which have unique structural features, such as an asymmetric-rotor with 8 poles and 48 slots and a three-layer U-shaped flux barrier structure. The stator-rotor topology of the basic model can be clearly observed in Figure 1. The asymmetric-rotor PMA-SynRM exhibits a special effect of magnetic axis shift (MAS) compared to the conventional PMA-SynRM. By reducing the phase angle between the permanent magnet torque and the reluctance torque in the PMA-SynRM, a higher total output torque can be effectively achieved, thus improving the torque performance of the motor. Due to the use of multi-layer and thin permanent magnets in the basic model, this makes it less resistant to demagnetization. In this paper, the anti-demagnetization performance of the basic model is improved by changing the rotor topology, and the torque ripple is also reduced, which significantly improves the stability and reliability of the asymmetric-rotor PMA-SynRM. Table 1 presents the basic parameters of the basic model. The software used for FEM simulation and analysis in this paper is JMAG (v.21.0), which contains simulation types such as demagnetization simulation, no-load, and load simulation.  
The simplified analytical model of the basic model neglecting core saturation and cross coupling is shown in Figure 2. Since the rotor of the basic model is an asymmetric structure, there is a magnetic flux linkage with a certain angle to the _d-axis_. The constant  
Figure 1: Basic model stator-rotor topology. (**a**) Stator and winding structure. (**b**) Rotor structure.  
coordinate transformation based on the magnetic potential yields the torque formula for the basic model, as shown in Equation (1).  
\[\begin{array}{ll}T_{E}&=\frac{3}{2}p\psi_{pm}i_{s}cos(\beta-\alpha)+\frac{3}{4}p (L_{q}-L_{d})i_{s}^{2}sin(2\beta)\\ &=T_{PM}+T_{RE}\end{array} \tag{1}\]  
where \(T_{E}\), \(T_{PM}\), and \(T_{RE}\) are the total torque, permanent magnet torque, and reluctance torque of the basic model, respectively; \(\psi_{pm}\) is the permanent magnet flux linkage; \(L_{d}\) and \(L_{q}\) are the _d-axis_ inductance and _q-axis_ inductance, respectively; and \(p\) is the number of pole pairs.
## 3 Target Platforms  
Our implemented design serves as a hardware accelerator for Elliptic Curve Scalar Multiplication (ECSM), specifically for the NIST Elliptic Curve B-233. The operation it performs is also denoted as \(kP\), where the scalar \(k\) is an up-to-233-bit binary number and \(P=(x,y)\) is a point on the EC B-233. The coordinates \(x\) and \(y\) are elements of the extended binary Galois field \(GF\) (\(2^{233}\)) with the irreducible polynomial \(f(t)=t^{233}+t^{74}+1\). They are also represented as up-to-233-bit-long binary numbers. The accelerator is based on the Montgomery \(kP\) algorithm using Lopez-Dahab projective coordinates [48]. The algorithm processes each bit of the scalar \(k\) iteratively from left to right (from its Most Significant Bit (MSB) to the Least Significant Bit (LSB)) using field operations. One ECSM iteration consists of six field multiplications, five field squarings, three field additions, and write-to-register operations and takes only 54 clock cycles. The processing sequence of the calculations, i.e., comprising additions, multiplications, and squaring operations of finite field elements, is independent of the bit value of the processed scalar \(k\), which was the reason in the past to denote Montgomery ladders as sources of resistance against timing attacks and simple power analysis attacks. Please note that the resistance of the Montgomery ladder against simple SCA attacks is based on the important assumption that the addressing of different registers comprises indistinguishable operations regarding their energy consumption [49]. Nowadays, equipment allows one to measure even long power and electromagnetic traces with a high time resolution applying a high sampling rate. This allows one to observe the differences in the measured power shapes caused by the key-dependent addressing of the registers. Dependent on the target platform, both the options used for design synthesis as well as the frequency of the shapes corresponding to the processing of the key bit value '1' can be distinguishable from the shapes corresponding to the processing of the key bit value '0' [50]. The general structure of the design is depicted in Figure 2.
# Erasure Codes for Cold Data in Distributed Storage Systems
## 2 Related Works
### Decentralized Architecture  
The typical decentralized structure is peer-to-peer (P2P) storage technology, which refers to the technology of establishing storage networks in a functionally equivalent manner between storage nodes. In peer-to-peer technology, many server nodes compose  
Figure 1: Google cloud storage system architecture.  
the peer-to-peer storage system, which can be divided into a closed system and an open system, according to the environment to which they adapt [17].  
The nodes are relatively stable in the closed system, which has strict central authentication, auditing, and management functions to ensure the continuous operation of the nodes. Although there may also be temporary errors in the closed system, it will be repaired in time. The nodes will not quit the system at any time, and they will cooperate very well.  
Amazon's Dynamo [18] is a classic example. Amazon has established dozens of large data centers around the world, routing requests from the local to the corresponding nodes through the Gossip protocol. They use a modified consistency hash algorithm to determine the range of nodes where the data are stored and assign this hash to each node on the ring. A value in the range represents its position on the ring, and then the data key is hashed to determine the node it stores. The decentralized storage system has good scalability and will not cause the whole system to collapse due to the failure of a single server. In addition, Microsoft uses a location service [19] to solve the problem of routing in multiple data centers.  
In an open system, nodes can join or quit at any time without guaranteeing continuous online service delivery, which leads to temporary errors and permanent errors more frequently than closed systems. The degree of collaboration between nodes is low, and there can be many rational users. A typical representative of such a system is a P2P storage system composed of personal computers.
## 3 Results and Discussion
### The Impact of Different Influencing Factors on Carbon Emissions in Wastewater Treatment
#### 3.4.1 The Impact of Treatment Processes on Carbon Emissions  
A depiction was conducted on the total carbon emissions and emission intensity of different processes in 49 wastewater treatment plants in Shandong Province in 2022 (Figure 5). The AAO process contributes the most to carbon emissions. Among the 49 wastewater treatment plants, 36 adopted the AAO process, but the average emission intensity of the AAO process was 0.425 kgCO\({}_{2}\)-eq/m\({}^{3}\), which was lower than that of other processes. The emission intensity of the oxidation ditch process was the highest, boasting an average value of 0.697 kgCO\({}_{2}\)-eq/m\({}^{3}\). This figure was markedly greater than that of the AAO process and nearly approached the 0.79 kgCO\({}_{2}\)-eq/m\({}^{3}\) value reported for the MBR process in Shenzhen's WWTPs [36]. The higher carbon emissions from the oxidation ditch process are attributed to the lack of a clear boundary between the aerobic and anoxic zones within the oxidation ditch and the fact that the oxidation ditch operates in a completely mixed flow pattern. The anoxic conditions required for denitrification are not sufficient, leading to incomplete denitrification and the production of more N\({}_{2}\)O. Additionally, poor control of anaerobic conditions may affect the phosphorus-releasing effect of polyphosphate-accumulating organisms, increasing the dosage of phosphorus-removing agents.  
Figure 5: Carbon emissions and carbon emission intensity of different treatment processes.