esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Specifically, let \(m\geq 1\); for all \(i=1,\ldots,m\), let \(F_{i}:\mathbb{R}^{n}\rightarrow\mathbb{R}\) be a convex function and let \(\mathcal{X}\subset\mathbb{R}^{n}\) be a nonempty closed and convex set. The additive convex optimization problem is defined as follows:  
\[ \begin{array}{ll}\text{minimize}&\sum\limits_{i=1}^{m}F_{i}(\mathbf{u})\\ \text{subject to}&\mathbf{u}\in\mathcal{X}.\end{array} \]  
In 2001, [PERSON] and [PERSON] proposed the so-called incremental subgradient methods [21; 22] to address this problem. At each iteration, the solution is updated based on the subgradient of a single component of the objective function. The method is essentially structured as follows: \(\mathbf{u}[k]\in\mathcal{X}\) is the vector obtained after \(k\) cycles, and the vector \(\mathbf{u}[k+1]\in\mathcal{X}\) is updated by initializing with \(\mathbf{u}_{1}[k]:=\mathbf{u}[k]\), where we compute  
\[\mathbf{u}_{i+1}[k]:=P_{\mathcal{X}}\Big{(}\mathbf{u}_{i}[k]-\gamma_{k} \widetilde{\
abla}F_{i}(\mathbf{u}_{i}[k])\Big{)},\]  
and generate \(\mathbf{u}[k+1]\in\mathcal{X}\) after one more cycle of \(m\) steps as  
\[\mathbf{u}[k+1]:=\mathbf{u}_{m+1}[k],\]  
where \(\widetilde{\
abla}F_{i}(\mathbf{u}_{i}[k])\) is a subgradient of \(F_{i}\) at \(\mathbf{u}_{i}[k]\), and \(\gamma_{k}\) is a positive step size. These components can be selected either cyclically or randomly at each step. These methods are well suited for large-scale problems, as they reduce the memory and computational requirements compared with batch methods. Furthermore, this approach eliminates the need for a central user to collect all the data from each component. We notice that the delay technique has not yet been applied to the incremental subgradient method for solving problem (1). In this work, we propose the incremental delayed subgradient method for solving the decentralized nonsmooth convex-concave minimax problem (1). This approach updates the next iterates incrementally by incorporating delayed subgradients from past iterates of their respective variables while using the current iterates of the other variable. We provide a detailed characterization of the fundamental relations required to analyze the behavior of the generated sequences. Furthermore, we establish convergence results for both constant and diminishing step-size sequences. Finally, we present numerical examples to demonstrate the effectiveness of the proposed method. The following sections are structured as follows: In Section 2, we review essential concepts and important results related to convex-concave coupling functions and the min-max optimization problem. Section 3 introduces the incremental delayed subgradient method, outlines the assumptions, and provides relevant discussions. In addition, this section includes an analysis of the convergence properties of the proposed method. Section 4 presents numerical experiments on simple convex-concave functions to demonstrate the effectiveness of the proposed method. Finally, the concluding section summarizes our findings and provides closing remarks.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Qualitative Analysis of Multi-Object Tracking Performance  
#### 3.6.1 Performance Analysis of Handling Specific Target Occlusion Problems  
To more intuitively validate the performance of our multi-object tracking method in handling occlusion issues during target tracking, we have applied this method to videos from the MOT15 and MOT17 datasets.  
In Figure 5 we used the popular BYTEtrack method as well as our IOF-Tracker method to test the tracking performance on two videos from the MOT15 and MOT17 datasets where occlusion is more pronounced. The figure shows that when using the BYTEtrack method, the pedestrian with ID 19 in the MOT15 dataset and the pedestrian with ID 3 in the MOT17 dataset are assigned new IDs of 39 and 34, respectively, after being occluded. However, when using our IOF-Tracker method, the pedestrian with ID 19 in the MOT15 dataset and the pedestrian with ID 3 in the MOT17 dataset retain their original IDs of 19 and 3, respectively, even after being occluded. Therefore, our method demonstrates superior performance in handling occlusion issues during multi-object tracking.
###### Abstract  
The aim of this study was to investigate the effects of maleic acid (MA), salicylic acid (SA), and citric acid (CA) on alleviating the drought stress of a lettuce (_Lactuca sativa_ L) hydropromic culture. The effect of these organic acids was tested under stress conditions induced by polychene glycol (PEG 6000) at 5% and 7.5% concentrations. Drought stress reduced the fresh and dry matter yields of plants. The acid treatment caused increasing tendencies in the fresh weight yield:control (SA, MA), PEG 7.5% (SA, MA, CA)) and dry weight yield (control (SA, MA), PEG 5% (MA), PEG 7.5% (SA, MA)). The acid treatment also enhanced the nutrient uptake of stressed plants: SA: N (PEG 7.5%), K (PEG 5 and 7.5%); MA: N, P, K, Ca (PEG 5 and 7.5%). This work found that chlorophyll al and b amounts did not change under applied experimental conditions. Most parameters of chlorophyll fluorescence did not depend on either the level of applied water stress (PEG level) or the type of spraying. Drought stress increased leaf superoxide anion (O2*-) and malondialdehydedehyde (MDA) levels but decreased H2O2. Proline (Pro) and phenolic compounds (TFC), including flavonoids (Fla), accumulated more in stressed plants. Drought stress also affected the chlorophyll fluorescence. Our results suggest that acids can improve plant tolerance to drought stress by boosting the antioxidant defence system and reducing the oxidative damage caused by reactive oxygen species.  
drought stress; polychene glycol; organic acids; photosynthetic parameters; chlorophyll fluorescence; reactive oxygen species; antioxidant properties +
Footnote †: journal: Applied sciences
## 2 Distributed Generation Technology
### Distributed Generation, Grid Interconnection Pros and Cons  
There are many advantages for the distribution generation interconnection with the grid. First of all, economic advantages can be summarized by: the cost of DG electricity varies because of a number of factors. Therefore, the cost fluctuates below and above grid electricity cost. A good monitoring of DG electricity prices can lead to massive price reductions. The DG can be a means of voltage regulation in buses that lie in the distribution level. The reliability of the system is increased, this comes from the fact of having multiple sources in the network. On the other hand, the disadvantages of DG, grid interconnection are: Grid operators will request money in exchange of the interconnection action. The owner of the DG will be expected to pay for the electricity supplied by the grid and a fee proportional to the maximum power supplied. Apparatus and maintenance fees are required. The addition of control, metering, and protection devices are very essential to separate the DG system from the grid; this is very critical to ensure that the complexity of the system does not reflect terribly on the operation of the overall system. To enhance the reliability of the overall grid the aforementioned maintenance must be satisfied regularly. Problems that occur far from the DG units can still harm them. Therefore, more complex and numerous protective devices must be installed to avoid critical situations [7; 8; 40; 41].  
In order to provide the optimal solution that maximizes the benefits, minimizes the costs, and overcome the technical challenges of DG installation; the planning of the DG-Integrated distribution systems requires the characterization of several factors, such as: the total number and capacity of the distributed generators, their optimal location in the network, the kind of connection with the grid, etc. [3; 4; 5; 6; 7].
## 4 Comparative Results
### Steganalysis Test
The network employs a softmax layer as the penultimate step, which maps the output of the fully connected layers onto a probability distribution over predicted output classes. This is essential for tasks such as classification, where the output needs to represent a probability that the input sequence belongs to a particular class. Finally, the architecture concludes with a classification output layer, which formalizes the network's predictions based on the probabilities calculated by the softmax layer. This layer effectively determines the most likely class for the given input based on the learned temporal dependencies and patterns. The proposed technique has achieved high hiding capacity ratios, making it more susceptible to detection by steganalysis tools. However, as shown in Figure 10, The RNN was unable to detect the secret data within the carrier signals produced by the proposed Octree technique. The accuracy rate of the proposed hiding strategy stands at 50%, suggesting that the RNN could not effectively differentiate between the host and carrier signals. An additional steganalysis test was performed using the OpenPuff v4.01 tool to evaluate the robustness of the proposed Octree technique. Figure 11 shows the analysis results for all stego video segments generated by the technique. The tool failed to detect any hidden secret data in all the files analyzed. For every file, the \"Mean Integrity\" was reported as 0.00%, and the \"Mark\" status was consistently identified as \"not a string\", indicating the absence of detectable embedded markers or hidden content. Additionally, the \"Overall Integrity\" of 100.00% across all files confirms that the proposed Octree technique preserves the visual and structural integrity of the stego video segments. These findings demonstrate the effectiveness of the proposed method in securely concealing secret data, ensuring it remains undetectable to one of the most widely used steganalysis tools, further validating the robustness and stealthiness of the technique. The enhanced security of the proposed Octree technique is largely attributed to the spatiotemporal hiding strategy employed within the method. Unlike previous methods, such as those outlined in [8], which utilize 1D vectors for hiding, the proposed Octree technique implements a 3D segmentation technique that operates across three-dimensional regions. This approach allows for the distribution of noise along the edges of these regions, which are high-frequency areas of the image. The literature indicates that the human visual system is less sensitive to disturbances in these high-frequency zones, thereby reducing the visibility of any abrupt visual discrepancies between regions [36]. Figure 11: Results of the OpenPuff steganalysis test on stego video segments produced by the proposed Octree technique, showing no detectable hidden data and 100% overall integrity across all files. Figure 10: Steganalysis test: Attempting to distinguish between a host signal and an Octree carrier signal. As it is clear, the RNN failed to classify the images.
## 3 Proposed Hybrid Framework
### Framework Description
In Figure 5, a DID Document should include the Context, ID, Controller, Public-Key, Service, and Authentication fields. The content of Context is the version information of the DID protocol and some explanations about the DID, such as data size, data type, data organization form, usage method, etc. ID is the number of the DID, and a hash value is generated according to the content of the DID Document to represent it. Controller is the owner of the DID Document, and this field can have multiple users. Public-Key is a collection of public keys used to verify a user's ownership of the DID. Service is the relevant service information provided by the issuer of the DID, and the content is a URL. Authentication is the organization that issues the DID, and this field can be a collection. In the process of data flow, the fields of a DID Document corresponding to a set of data should be changed at any time. The Controller and Public-Key fields need to be added or modified at any time. The changes are saved on the blockchain, and each change needs to generate a data digest pointing to the historical change record of the DID, so that we can perform relevant traceability operations. Figure 5: Data flow process based on DIDs. The change of data ownership is accomplished by modifying the ‘controller’ field in the Decentralized Identifier (DID) associated with a specific data segment. This modification process is recorded on the blockchain, ensuring that the entire life-cycle of the data is traceable. The red part indicates the ownership and public key of the DID, which is an important attribute of the DID.
# Improving the Retrieval of Crop Canopy Chlorophyll Content Using Vegetation Index Combinations
## 3 Results  
### Sensitivities of Spectra and MTCI to LAI and CCC  
Figure 4 shows that all of the MERIS bands reached saturation due to the increase in LAI, particularly in the visible region, with bands 7 and 8 being the most severe, but the effect was slightly mitigated in the near-infrared region. The relationship plot for MTCI and CCC suggests that the saturation during CCC retrieval was largely caused by the LAI (Figure 5a). Figure 5a provides an overview of the saturation effect observed for the MTCI when the LAI is >2 at different LCCs. In summary, these results suggest that higher LAIs largely contributed to the abnormal saturation of the spectra and MTCI. The soil background and average leaf angle also affected the retrieval of the CCC based on the MTCI. Figure 5b suggests that the soil background had little impact on the MTCI and CCC estimation, especially at high LAIs. This was because the canopy reflectance contained less background reflectance due to the increase in vegetation coverage. By contrast, the effect of the average leaf angle increased with the LAI during CCC estimation, which was due to the influence of the complex canopy structure on the transmission of solar radiation (Figure 5c). Figure 5b,c also show that the LAI was the main cause of saturation for the MTCI.
## 8 Machine Learning for Metocean Data Integration
### ML for Metocean Conditions during Hurricanes  
Metocean conditions in hurricanes have historically damaged the environment, which can occur by multiple parameters such as significant surge height and wave height that can vary in time and cross-correlation [23]. However, Metocean systems are remote complexes which lead to hazards such as hurricanes that affect platforms [84]. For example, in 2014, significant damage to the Taylor energy oil platform was caused by Hurricane Ivan after being installed for 20 years [92]. ML techniques can be applied for hurricane predictions. [PERSON] et al. [93] proposed an ML model for the prediction of Atlantic hurricane activity based on a convolutional neural network (CNN) approach.
# Integrating IP Mobility Management Protocols and MANET: A Survey
###### Abstract  
The Mobile ad hoc Network (MANET) is a collection of mobile devices that forms a self-created, self-administered, and self-organized network. It is an infrastructureless network that does not require an existing infrastructure to operate. MANET suits scenarios where a temporary network is needed, such as emergency rescue, the military field, and disaster areas. MANET is an isolated network, and communication is restricted to the participating nodes' transmission coverage. In order to increase its connectivity and its application scope, however, MANET requires integration with other networks, forming a hybrid MANET. The integration of MANET and IP networks raises many challenges and issues. Mobility management is one of the main challenges. Traditional mobility management protocols provide seamless mobility in a single hop infrastructure network. Consequently, mobile nodes can maintain their global connectivity without interrupting the ongoing sessions. Mobility management becomes more challenging in a network that relies on multi-hop communication, such as MANET. This paper presents a survey of the use of mobility management systems when integrating MANET with the internet, with the objective of serving as a handy reference in this field of research. It presents, analyzes, and discusses existing mobility management solutions for integrated MANET networks. It also investigates their shortcomings and provides a comparative study of the surveyed proposals.  
MANET; integration; gateways; mobility management; mobile IP +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 150; doi:10.3390/fi12090150
## 3 Materials and Methods  
This paper is inspired by a data-augmentation technique called back-translation, which was used to construct a parallel corpus from a monolingual corpus of formal written language. The constructed parallel corpus was then used to train two state-of-the-art end-to-end translation models, Seq2 Seq+attention and Transformer. The trained models were evaluated on a separate validation set to assess their performance. To ensure a fair and objective evaluation, in this paper, we employed two evaluation metrics: BLEU score [15] and manual evaluation. The final experimental results show that the parallel corpus constructed by our proposed method can be used for the task of de-colloquialization and that an effective de-colloquialization model can be trained using the end-to-end machine translation approach. The training process diagram of the proposed method is shown in Figure 1.  
Furthermore, the model trained by our proposed method also possesses some error correction capabilities, such as correcting punctuation and spelling errors. Table 1 illustrates some examples of the model's error correction abilities.  
In the example given above, not only was the colloquial language converted to formal written language, but the model also corrected errors in punctuation. Specifically, the corrected sentence contains a complete set of quotation marks, which were missing in the original sentence. The semicolon after was changed to a comma, and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Language** & **Original Sentence** & **De-Colloquialization** \\ \hline \multirow{3}{*}{Chinese} & \(\tikzfig{1}\) & \(\tikzfig{1}\) \\  & \(\tikzfig{1}\) & \(\tikzfig{1}\) \\  & \(\tikzfig{1}\) & \(\tikzfig{1}\) \\  & \(\tikzfig{1}\) & \(\tikzfig{1}\) \\ \hline \multirow{3}{*}{English} & After graduation, I need a curve to save the country; This & After graduation, I need a curve to save the country. \\  & goal is likely to gradually materialize. But I still have & This goal may gradually be achieved, but I still have \\  & confidence in the entire financial system, “[PERSON] said: & confidence in the entire financial system, “[PERSON] said. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Example of model error correction capability.  
period after \"\" \" \" \" \" was changed to a comma. Moreover, the colon at the end of the original sentence was changed to a period. In addition to these punctuation corrections, the model also corrected the spelling error \" \" to \" \".
## 3 Results and Discussion
### Assessment of Soil Contamination
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sample** & **Depth** & **EF\({}_{Cd}\)** & **EF\({}_{Co}\)** & **EF\({}_{Cr}\)** & **EF\({}_{Co}\)** & **EF\({}_{Ni}\)** & **EF\({}_{Pb}\)** & **EF\({}_{Zn}\)** \\ \hline T-1a & 0–5 & 0.60 & 0.49 & 32.46 & 5.59 & 4.73 & 1.62 & 3.47 \\ T-1b & 5–30 & 0.53 & 1.15 & 11.66 & 4.43 & 4.51 & 2.34 & 2.88 \\ T-2a & 0–5 & 0.26 & 0.98 & 8.24 & 3.17 & 5.34 & 0.43 & 2.65 \\ T-2b & 5–30 & 0.30 & 0.95 & 7.30 & 3.30 & 4.72 & 0.78 & 2.58 \\ T-3a & 0–5 & 0.16 & 1.44 & 8.99 & 2.71 & 5.80 & 0.64 & 1.90 \\ T-3b & 5–30 & 0.15 & 1.63 & 10.56 & 2.92 & 6.27 & 0.74 & 1.96 \\ T-4a & 0–5 & 0.29 & 1.21 & 12.24 & 2.92 & 6.01 & 0.55 & 2.06 \\ T-4b & 5–30 & 0.31 & 1.24 & 12.83 & 3.08 & 6.33 & 0.67 & 2.31 \\ T-5a & 0–5 & 0.16 & 1.37 & 9.26 & 2.38 & 5.47 & 0.74 & 1.89 \\ T-5b & 5–30 & 0.15 & 1.50 & 10.39 & 2.43 & 4.96 & 0.96 & 1.90 \\ T-6a & 0–5 & 0.37 & 0.85 & 8.04 & 4.73 & 2.76 & 0.69 & 1.21 \\ T-6b & 5–30 & 0.44 & 0.96 & 9.83 & 5.72 & 2.93 & 0.54 & 1.28 \\ S-1a & 0–5 & 0.42 & 1.40 & 15.12 & 2.25 & 4.94 & 1.79 & 1.65 \\ S-1b & 5–30 & 0.34 & 1.46 & 15.24 & 2.37 & 5.16 & 1.84 & 1.58 \\ S-2a & 0–5 & 0.36 & 1.47 & 17.95 & 2.87 & 4.44 & 1.70 & 1.99 \\ S-2b & 5–30 & 0.32 & 1.53 & 16.56 & 2.40 & 4.38 & 1.61 & 1.73 \\ V-1a & 0–5 & 0.50 & 1.13 & 12.51 & 3.74 & 5.02 & 1.81 & 2.34 \\ V-1b & 5–30 & 0.48 & 1.01 & 12.81 & 3.82 & 4.70 & 1.52 & 2.29 \\ V-2a & 0–5 & 0.31 & 1.71 & 21.24 & 3.40 & 5.31 & 1.64 & 2.11 \\ V-2b & 5–30 & 0.29 & 1.35 & 23.44 & 3.44 & 5.60 & 1.34 & 2.09 \\ V-3a & 0–5 & 0.25 & 0.90 & 14.81 & 309 & 4.31 & 1.02 & 2.01 \\ V-3b & 5–30 & 0.23 & 0.81 & 15.50 & 2.68 & 4.58 & 0.67 & 1.92 \\ \hline \multicolumn{2}{c}{_Legend_} & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: EF\({}_{i}\) index quantification for the agricultural layers of soil in the Lower Danube Basin. T-4b, T-5a, T-5b, T-6a, T-6b, S-1a, S-1b, S-2a, S-2b, V-1a, V-1b, V-2a, V-3b), low contaminated (Class 2) with Cu (sites T-1b, T-2a, T-2b, T-3a, S-2a, V-1a, V-1b, V-2a, V-3a, V-3b), Ni (T-1a, T-1b, T-2b, T-4a, T-4b, T-5b, T-6a, T-6b, S-2a, S-2b, V-1a) and Zn (sites T-1a, T-1b, T-2a, V-2b, V-3a), moderately contaminated (Class 3) with Cr (site T-2b), Cu (site T-1a), and Ni (sites T-2a, T-3a, T-3b, T-5a, S-1a, S-1b, V-2a, V-2b, V-3a, V-3b), highly contaminated (Class 4) with Cr (sites T-1b, T-2a, T-3a, T-3b, T-4a, T-4b, T-5a, T-5b, V-1a, V-1b) and Cu (sites T-6a, T-6b), and very highly contaminated (Class 5) with Cr (sites T-1a, T-6a, T-6b, S-1a, S-1b, S-2a, S-2b, V-2a, V-2b). All soil samples from TUL, SEN, and VAD areas have a low risk of soil enrichment based on PERI evaluation. (Table 9).
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 3 Probiotic Encapsulation Techniques
### Layer-by-Layer Method (Multilayer Technique)  
For better performance, encapsulated probiotics are coated with more than one layer, using different polymers for each layer. The layer-by-layer method (multilayer technique) was proven to increase the survivability of probiotics against the conditions of processing, storage, and along the gastrointestinal tract [50; 67]. For instance, [PERSON] et al. [68] revealed that microencapsulation of _L. casei_ using a combination of layer (canola oil)-by-layer (sodium caseinate) double emulsion and ionic gelation technique could enhance the thermal stability and cell viability of _L. casei_ during storage and digestion. The functional characteristics of _L. casei_ C24 were also retained through microencapsulation using the layer (aglinate)-by-layer (chitosan) double emulsion technique [68]. In another study, layer (carboxymethyl cellulose)-by-layer (zein protein) encapsulating _L. plantarum_ 299v was applied to apple slices [12]. The two-layer coating was able to protect the probiotics both under storage and during simulated gastrointestinal conditions.  
The layer-by-layer technique involves the alternative adsorption of positively and negatively charged materials on surfaces through the chemical electrostatic deposition technique. This technique produced a protective outer layer on a microencapsulated probiotic by immersing the capsule in a biopolymer solution. This layer coating process can be repeated several times until the desired number of layers or thickness is obtained. The strength of the multilayer-coated capsule can be enhanced by increasing the interaction intensity between the charged materials. This is made possible by modification of the pH, concentration, and ionic strength of the polymer solution [31; 45; 60].  
This technique does not involve high operational costs, as only mild conditions, aqueous solutions, and naturally charged materials are used in the coating process. The thickness, permeability, strength, and morphology of the layers can be tailored depending on the desired application. However, the adhesion times of each layer are between 1 and 60 min, which is not instantaneous. This leads to a certain degree of aggregation of the capsules during the adhesion of the subsequent layer, reducing the available surface area for consecutive layer adhesions [53; 69].