esa-sceva/satcom-chunk-collection huggingface dataset:
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 2 System Model and Problem Statement
### System Model  
In the proposed FL environment, two main entities constitute the basic parts of the whole system: users and the cloud server. The major objective of the proposed approach is to minimise the communication cost and to secure the privacy of individual users from the honest-but-curious adversaries during the training process. In particular, the cloud server honestly executes all the data aggregation process but it is also curious to infer private data from the inputs of users. Therefore, the proposed approach is designed in a way that it can prevent the collusion between the users and the cloud server. For this, we demand that the cloud server receives only the encrypted aggregated result from the local gradients in order to avoid the harmful use of private information. To this regard, in this model, we assume that all the users agree on the same leaning task with the same objectives and parameters as shown in Figure 2. In specific, these users are required to compute the local gradients from their private training datasets and then upload it to the cloud server. Afterwards, these users receive the aggregated global gradient from the cloud server. To ensure privacy, each local gradient is encrypted before being uploaded to the cloud server. Meanwhile, the cloud server is assigned the primary task, that is to compute the global gradient based-on the encrypted local gradients. After computing the global gradient, the cloud server broadcasts this global gradient to all the users, and then the training begins on the proposed model. Finally, the proposed approach works by following this iterative collaboration between the cloud server and the users.
# Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems
## 3 Research Methodology
### Foliage Color Detection Model Development
Likewise, (\(\overline{x}_{R,l}\)), (\(\overline{x}_{G,l}\)), and (\(\overline{x}_{B,l}\)) are the averages of three channel values in the (\(y\)) category and (\(R_{l}\)), (\(G_{l}\)), and (\(B_{l}\)), are the values of three channels in the (\(y\)) category. After determining the reference or threshold values, the color distance model was used to compute the foliage color difference from the threshold values. The Euclidean distance (ED) model was used in this study, and its general equation is presented below [4]. \[\mu_{y,\,j}=\big{[}\mu_{R,j},\mu_{G,j},\mu_{B,j}\big{]} \tag{8}\]6  
where \(\Delta R=R_{2}-R_{1}\), \(\Delta G=G_{2}-G_{1}\), and \(\Delta B=B_{2}-B_{1}\). Based on the ED model, two distances (\(d_{1}\)) and (\(d_{2}\)), were computed using two threshold values: (\(g_{ref}\)) and (\(y_{ref}\)), respectively. (\(d_{1}\)) determines the distance from the green color threshold, whereas (\(d_{2}\)) determines the distance from the yellow color threshold. For single foliage, both (\(d_{1}\)) and (\(d_{2}\)) are determined. A lower value of (\(d_{1}\)) and a higher value of (\(d_{2}\)) suggests that the color patterns of foliage are closer to (\(g_{ref}\)) or, in other words, green tones. Conversely, a lower value of (\(d_{2}\)) and a higher value of (\(d_{1}\)) suggests that color patterns of foliage are closer to (\(y_{ref}\)) or, in other words, yellow tones. The governing equations for (\(d_{1}\)) and (\(d_{2}\)) are given below. \[\mu_{y,\,j}=\big{[}\mu_{R,j},\mu_{G,j},\mu_{B,j}\big{]} \tag{8}\]7  
\[\mu_{y,\,j}=\big{[}\mu_{R,j},\mu_{G,j},\mu_{B,j}\big{]} \tag{8}\]8  
where (\(x_{R}\)), (\(x_{G}\)), and (\(x_{B}\)) are the mean values of three channels (R, G, B) of the foreground in the segmented image of the test samples. Lastly, the quality indicator (\(Q\)) is defined as a function of (\(d_{1}\)) and (\(d_{2}\)) for evaluating the plants' quality based on their foliage color. In this context, when green foliage with no leaf depigmentation is detected, the value of (\(Q\)) is equal to 1, which implies that the crop is healthy. On the other hand, when yellow foliage with leaf depigmentation is detected,the value of \((Q)\) is equal to 0, suggesting that the crop is unhealthy. \((Q)\) is represented as below:  
\[Q=f(d_{1},d_{2})= \begin{cases}1\;if\;d_{1}<d_{2}\\ 0\;if\;d_{2}<d_{1}\end{cases}  \tag{26}\]
## 2 Materials and Methods  
Materials. Milli-Q water (resistivity 18.2 M\(\Omega\cdot\)cm) produced by a Millipore Milli-Q unit, pretreated by a Millipore reverse osmosis system, was used in all experiments. Hydrogen tetrachloroaurate trihydrate (HAuCl4\(\cdot\)3H\({}_{2}\)O, \(\geq\)99.9%), sodium citrate tribasic dihydrate (\(\geq\)98%), sodium chloride (\(\geq\)99.5%), and nitric acid (70%) were purchased from Sigma-Aldrich and used as received. Hydrochloric acid solution (37%) was purchased from Panreac and used as received. All glassware was washed with aqua regia, rinsed three times with Milli-Q water, and dried under a gentle stream of N\({}_{2}\) before use.  
Synthesis of Nanoparticles. Gold nanoparticle solution comprised an aqueous colloid (average diameter 9 \(\pm\) 2 nm) that was prepared by adding 5 mL of 1 wt % sodium citrate solution to 95 mL of boiling 0.5 mM HAuCl\({}_{4}\) under vigorous stirring. After 15 min of boiling, the solution was cooled down to room temperature and then stored at 4 \({}^{\circ}\)C for long-term storage.  
Characterization. TEM images were collected with a JEOL JEM-1400 PLUS transmission electron microscope operating at 120 kV, using carbon-coated 400 square mesh copper grids. UV-visible spectra of the gold nanoparticles as-synthesized and under different values of ionic strength were recorded in a 1 cm path length disposable cuvette using a Cary 100 Bio UV-visible spectrophotometer.  
Photothermal heating experiments. The samples were irradiated using a Xe900 Xenon Arc Lamp (450 W ozone-free xenon arc lamp) from Edinburgh Photonics. The spot of the incoming beam was 1.1 cm, with a circular shape. The irradiation was performed using a constant power density of 1.64 mW/cm\({}^{2}\). The temperature of the bulk solution were monitored along the heating process using an electronic thermometer. The probe of the thermometer was immersed in the bulk dispersion of the nanoparticles. The increase of temperature was found to be very reproducible. The standard deviation between measurements was in all cases equal to or lower than 5%.
## 3 Results and Discussion
### Soil Texture
The relative proportions of these fractions determine the soil's texture, which in turn influences its water-holding capacity, drainage, and aeration. Additionally, the textural fractions of soil can impact the adsorption and retention of contaminants, such as heavy metals, affecting their mobility and bioaccessibility (Table 5). For example, clay particles have a higher surface area and greater capacity to adsorb heavy metals than sand particles [13]. The way that plants absorb heavy metals can be altered by clay particles, which also raises the possibility of environmental pollution. Understanding the relationship between the textural fractions of soil is essential for evaluating the quality of the soil, as well as plant growth and health. The relationship between cadmium and lead (total and bioaccessible forms) and the soil's sand, silt, and clay fractions is crucial for understanding the potential impact of cadmium/lead contamination on meadow plants and the overall ecosystem. The textural soil fractions, including sand, silt, and clay, significantly influence plants' mobility, availability, and the potential uptake of cadmium and lead. The distribution of cadmium/lead within different soil fractions, such as sand, silt, and clay, can impact their bioaccessibility to meadow plants as environmental contaminants in soil [13]. For example, clay particles' adsorption and retention of cadmium/lead can influence their mobility and bioaccessibility, potentially affecting plant uptake. Additionally, the textural soil characteristics may affect the movement and distribution of lead and cadmium, potentially impacting their availability to meadow plants. Assessing the possible dangers associated with cadmium/lead soil pollution and its impact on meadow plants require an understanding of the link between cadmium/lead and the textural fractions of the soil.
# Single Cell Chemotactic Responses of
## 3 Results and Discussion
### Impact of Urea on H. pylori Cellular Velocity  
Three independent tests were conducted and 20-30 cells in each test were randomly selected and tracked for at least 10 s to analyze individual bacterial swimming behavior. Cellular velocity is shown in Figure 1. At baseline (no urea added), the cells exhibited a low velocity. The relative limited motility of _H. pylori_ in chemotaxis buffer may be attributed to the lack of an adequate internal energy store to power the flagella [28]; chemotaxis buffer is nutrient poor. The relatively large standard deviation for cellular velocity reflects the instability in _H. pylori_ motility, which might be partially the result of a frameshift mutation-prone repetitive sequence in HiP, the flagellar biosynthetic gene [29]. A decrease in the cellular velocity was observed (compared to baseline) when concentration gradients of urea were generated in the microenvironment. However, there were no significant differences in velocity between cells exposed to 10 _vs._ 100 mM urea.
# Food and Drinking Water as Sources of Pathogenic Protozoans: An Update
## 14 _Dientamoeba fragilis_  
_Dientamoeba fragilis_ belongs to the clade Metamonada, phylum Parabasalia, family Dientamoebidae [2] and has been reported in humans with a worldwide distribution. Two described genotypes of _D. fragilis_, 1 and 2, can be distinguished by restriction fragment length polymorphisms of the SSU rRNA gene [39].  
Most studies on this protozoan parasite were conducted in industrialized countries and prevalence values ranged between 0.3% and 82.9%. The few studies available for developing countries reported prevalence values between 0% and 60.6% [39; 284]. Spreading by the fecal-oral route is most likely though zoonotic transmission was also suggested since _D. fragilis_ was detected in pigs, cats, and dogs [21].  
The life cycle and clinical significance of this parasite are not well defined. Recently, a cyst stage was discovered and its transmission and association of the protozoan with symptomatic disease was shown in a rat model [285]. Acquisition together with pinworms (_Enterobins vermicularis_) was also suggested [284]. The advent of PCR methods to replace microscopy methods which do not allow a definitive diagnosis determined an apparent increase in _D. fragilis_ prevalence and the proportion of asymptomatic carriage, ranging between 11% and 39%, raised doubts on its pathogenicity [21].  
However, symptoms such as abdominal pain and diarrhea or loose stools and a chronic course lasting up to several years have been reported for 2-32% of patients. In a retrospective study in Finland, 85% of patients were infected with _D. fragilis_, and 54% were also infected with _B. hominis_. These patients reported loose stool, abdominal pain, constipation, and fecal urgency for a median duration of 180 days [21]. Of 438 patients with diarrhea, 11.9% were infected with _D. fragilis_ in Turkey [39].
## 4 Experiments and Analysis
### Hyperparameter Setting  
Our model implementation is based on PyTorch and is trained on a single NVIDIA RTX 4060 Ti GPU with 8 GB of memory capacity. During the training process, we used Adam as the optimizer and set the weight decay to 0.0005. Meanwhile, the initial learning rate was set to 0.001, and a warm-up strategy was adopted in the first few epochs of training to avoid excessive learning rates in the early stages, which could help the model converge more smoothly, reducing instability. In addition, considering the size of the dataset and computing resources, we set the number of epochs for training to 100 and the batch size to 4. In particular, we found that when the learning rate was set below 0.0003, the model's convergence speed significantly slowed down, and it was prone to getting stuck in local minima. Additionally, when the number of epochs was set to 50, the model's performance was poor, with its IoU being around 3% lower compared to the 100-epoch setting. This was likely due to underfitting caused by insufficient training.
# Application of Label Correlation in Multi-Label Classification: A Survey
## 1 Introduction
These challenges often include imbalanced label distribution, difficulties in feature extraction, and substantial computational resource consumption. Therefore, effectively exploring and utilizing label correlations has become a crucial research question. The comprehensive exploration and effective utilization of label correlations have been proven to be a key approach for improving the performance and generalization ability of MLC [10], and have become a significant research focus in this field. The application of label correlations is extensive, and their potential value should not be underestimated. With the development of deep learning technologies, Graph Convolutional Networks (GCNs) have been proven to be an effective approach for studying label correlations in Multi-Label Image Classification (MLIC) [11]. Graph-based methods have also found wide application in the field of MLC, as they can model label correlations [12]. Modeling label correlations can not only significantly improve MLC performance but also contribute to various domains such as recommender systems [13], information retrieval [14; 15], and social networks [16]. For instance, in recommender systems where the interest information of new users is sparse, label correlations can be used to expand user interests, thereby enhancing the diversity of recommendation results. When retrieving resources using tags, similar tags can be displayed to discover more diverse retrieval results. In social networks, label correlations can be used to analyze user relationships, improving the quality of user recommendations and social connections. By analyzing users' tagging behaviors, the system can recommend users who have similar tags in interests or activities, thus fostering more meaningful social relationships. Currently, numerous researchers have proposed various strategies to learn and utilize label correlations. However, there is still a lack of review articles systematically summarizing the research progress on label correlations. In view of this, this paper focuses on label correlations in MLC, providing a comprehensive and systematic review of related work. The literature was primarily retrieved using academic search tools such as Google Scholar, Web of Science, and ScienceDirect, with keywords such as \"Multi-Label Classification\" and \"Label Correlation\". A total of 88 articles, published in the last decade and highly relevant to the application of label correlations in multi-label tasks, were selected for analysis. The contributions of this paper include investigating and analyzing Multi-Label Learning (MLL) methods that incorporate label correlations, presenting different types of label correlations, methods for mining label correlations, and summarizing the applications of label correlations in MLC and its subproblems. The aim is to provide useful references and inspiration for future researchers.
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 2 Methods
### OISL-OSU Service Slicing  
#### 2.2.1 Mapping and Multiplexing OISL-OSU Signals into an OISL-OPU  
The OISL-OSU protocol stacks are illustrated in Figure 2. Figure 1(a) depicts the frame encapsulation processes. Specifically, Gigabit Ethernet (GE) and 10 Gigabit Ethernet (XGE) interfaces connect to the Ethernet service interface through the physical layer (PHY) and the medium access control (MAC) layer. This Ethernet service interface is responsible for transmitting and receiving Ethernet frames. After processing within the MAC layer, the Ethernet frames are directed into the first-in-first-out (FIFO) queues associated with the virtual local area network (VLAN) and Internet Protocol (IP) layers. The 10 Gigabit Media Independent Interface (XGMII) serves as the standard interface for XGE, enabling  
Figure 1: Comparison of CCSDS AOS standard frame and OISL-MPLS extended frame.  
the connection between the MAC layer and the PHY layer. Following conversion into the XGMII format, the Ethernet frames undergo 64-bit/66b-bit encoding and are subsequently transformed into 257-bit data blocks. The primary purpose of the 64-bit/66-bit and 66-bit/257-bit encoding schemes is to ensure compatibility with the higher-layer framing structure. Finally, the 257-bit data is encapsulated into a 192-byte OSU frame.  
Moreover, the OISL-OSU adheres to the hierarchical International Telecommunication Union--Telecommunication Standardization Sector (ITU-T) G.709 compliant architecture, as presented in Figure 2b [21]. On the transmitter side, the processing involves optical channel payload unit (OPU) mapping, optical channel data unit (ODU) multiplexing, optical channel transport unit (OTU) encapsulation, forward error correction (FEC) coding, and scrambling for synchronization and robustness. Conversely, on the receiver side, the processing includes descrambling, frame alignment, FEC decoding, ODU demultiplexing, and OPU payload extraction.
## 3 Results and Analysis
### Influence of TIL and Airflow Parameters on Temperature and Thermal Stress Evolution
#### 3.3.3 Influence of Airflow Parameters  
Figure 10 displays the variations of the temperature and thermal stress with changing heat convective coefficient (\(h\)) and inlet airflow temperature (\(T_{in}\)). Contrary to the influence of TIL factors, varying the airflow parameters has an unnoticeable effect on the temperature and stress distribution of the tunnel rock. Both the temperature and thermal stress remain nearly unchanged as the heat convection coefficient \(h\) increases from 50 W/(m\({}^{2}\cdot\)K) to 200 W/(m\({}^{2}\cdot\)K). Similarly, the inlet airflow temperature plays an unobvious role in the rock temperature and stress. The rock surface temperature \(T_{r}\) increases by 12.46 \({}^{\circ}\)C as the inlet airflow temperature \(T_{in}\) rises from \(-\)20 \({}^{\circ}\)C to 20 \({}^{\circ}\)C, while the associated thermal stress \(\sigma_{T}\) decreases by 5.50 MPa. This indicates that the heat convective coefficient and inlet airflow temperature have little influence on the temperature and thermal stress evolution in a ventilated high-temperature tunnel.  
Figure 9: Temperature \(T_{i}\) and \(T_{r}\) and stress \(\sigma_{T}\) variations with varying TIL thermal conductivity.
## 6 Discussion
#### 6.1.3 Complex Gas Signal  
Table 3 lists the relationships of \(M\) with the reconstruction error and iteration number (i.e., the computation complexity) during the reconstruction of gas data using the TVS-MH algorithm, and Figure 8 illustrates the effect of the number of measuring points (\(M\)) on the reconstruction performance of a complex gas signal. It can be observed that since the gas signal varies intensively, the reconstruction accuracy is lower than that under the gas mutation state at the same compressive rate. When the compression ratio is equal to or greater than 0.4, the reconstruction error is smaller than 4% and the iteration number is smaller than 100.
## 3 Parameter Analyses
### Parameter Study  
The initial values of each design parameter and corresponding non-dimensional variable are kept the same as listed in Tables 2 and 3. The ranges of these non-dimensional control variables are consistent with the range in Table 3.  
To investigate the relationship of these variables with the lower-limit \(\beta_{\rm min}\) and the stability parameter \(S\), univariate curves of each non-dimensional parameter are drawn in Figure 5 based on Equations (3) and (4) while the rest of the variables are constants listed in Table 3.  
In Figure 5, it is obvious that only the variable \(b\) corresponding to longitudinal length has a negative correlation with the lower-limit \(\beta_{\rm min}\), and the other variables are positively correlated with the lower-limit \(\beta_{\rm min}\). By selecting reasonable values of these parameters, the lower-limit \(\beta_{\rm min}\) can be controlled below \(50^{\circ}\) so that the rotation range is about \(0^{\circ}\) to \(40^{\circ}\). Comparing the change in the lower-limit \(\beta_{\rm min}\) in Figure 5, it can be seen that shell thickness \(a\), the ratio of the trolley mass to FCRA mass \(c\), and back frame density \(n\) have great influence on the lower-limit \(\beta_{\rm min}\) with the differences as high as \(40^{\circ}\), while the influence range of the others is somewhat small. Figure 5a,e represent that the effect of shell thickness \(a\) on the lower-limit \(\beta_{\rm min}\) is greater than track thickness \(a_{1}\) when \(a\) and \(a_{1}\) are in the same range. Longitudinal length \(b\) has a greater influence on the lower-limit \(\beta_{\rm min}\) than total track width \(b_{1}\) as illustrated in Figure 5b,f. Additionally, the lower-limit \(\beta_{\rm min}\) becomes better with the increase in longitudinal length \(b\) while the lower-limit \(\beta_{\rm min}\) becomes worse with the increase in total track width \(b_{1}\).  
There is positive correlation between these parameters and stability parameter \(S\) except the ratio of the trolley mass to FCRA mass \(c\) in Figure 5. There is transition in the curve of \(c\) and \(S\) as illustrated in Figure 5c. The corresponding value of \(S\) in the transition phase is below zero, which means that the value of \(c\) cannot meet the stability requirements (refer to Equation (4)). In addition, the value of \(c\) in the ascending phase is invalid as the value of \(\beta_{\rm min}\) cannot exceed \(90^{\circ}\). In Figure 5, longitudinal length \(b\) and back frame density \(n\) have great influence on structural stability, followed by shell thickness \(a\), the ratio of trolley mass to FCRA mass \(c\), and total track width \(b_{1}\), and track thickness \(a_{1}\) has the least influence on structural stability.  
In summary, on the basis of ensuring the stability of the FCRA, the optimal parameter combination for obtaining the maximum rotation capacity of the FCRA is \(a\) = 0.02, \(b\) = 2, \(c\) = 0.3, \(n\) =1, \(a_{1}\) = 0.02, and \(b_{1}\) = 0.4.