esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
#### 2.2.1 Experimental Design  
To distinguish the impacts of meteorological data and FPAR on MOD17A2H GPP, we conducted the following four groups of experiments to recalculate GPP under dissimilar circumstances: (1) GMAO meteorology data and MOD15A2H FPAR were chosen as meteorology inputs and FPAR inputs, respectively (MOD\({}_{\it GMAO}\) GPP) (the same as the standard MOD17A2H algorithm); (2) using site meteorological data as meteorology inputs and MOD15A2H FPAR as FPAR inputs (MOD\({}_{\it Tower}\) GPP);(3) GMAO meteorology data were used as meteorology inputs, and GLASS FPAR were introduced as FPAR inputs (GMAO_GLASS GPP); (4) using site meteorological data and GLASS FPAR to estimate GPP (Tower_GLASS GPP). At last, the four types of GPP estimates were compared with GPP values measured by EC flux tower sites (Flux_Tower GPP), respectively.
## 2 Background and Related Work
### Time-Aware Recommender Systems  
Time can influence user preferences in different ways and has been widely studied in the literature. Systems considering these temporal effects are commonly known as time-aware recommender systems (TARS). They have been classified according to the type of influence being considered, although the categories established by different authors are not the same. Most of these systems differentiate between the evolution of preferences over time and their change as a consequence of the temporal context (time of day, workday or weekend, season, etc.).  
Within CF approaches, models to address time effects can be classified in three groups [18]: recency-based models, periodic context-based models and models that explicitly use time as an independent variable. Recency-based models give more importance to recent ratings than to older ones in CF. Depending on the way the ratings are managed, this category can be subdivided in window-based and decay-based models. Periodic context-based models are context-aware approaches where the only contextual dimension is time, which is split into intervals corresponding to diverse temporal contexts (winter/spring/summer/autumn, weekend/workday, morning/evening, etc.). The third group includes more advanced methods for modeling user and item trends.  
In [33], three categories of time-aware recommender systems are defined: continuous time-aware, categorical time-aware, and time-adaptive methods. The first category is analogous to recency-based models that capture the evolution of preferences. It is usually done by means of functions that increase the weight of the ratings as they approach the target recommendation time. Categorical time-aware systems implement temporal context-aware methods. In time-adaptive systems, the more recent the ratings, the more important they are, which is similar to continuous time-aware methods, but without considering any target recommendation time.  
Recently, temporal dynamics have been addressed in some studies with regard to the repeat of consumption. These approaches are suitable for application domains where the same items are consumed more than once (purchasing a product, listening to a song, visiting a place, etc.). While some proposals are only focused on predicting when the users will repeat the consumption of items [34], others provide recommendations of items when a repetition is predicted [35]. In addition, the short-term and long-term time fluctuations of repeat consumption have also been modeled [30]. Although these approaches may be useful in the music field, the objectives are very different from those of our proposal. Our approach falls within the scope of CF, where new products that the user has not previously consumed are recommended. In [36], CF is also considered as a recommendation, but the temporal dynamics are restricted to consumption repetition. Preference evolution, which is one of the main concerns of our work, is not considered.
# Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
## Appendix B  
For the linear version of model (3), it is possible to use different diagonal variance matrices for different mixture components, as implemented by [16]. In the quadratic version, when we use different diagonal variance matrices for different mixture components, the only change is that \(\Sigma\) will become \(\Sigma_{k}\) in the log-likelihood (6). The estimators for \(\alpha\), \(\beta\), \(\eta\), and the cubic equation for \(z_{k}\) used in the M-step are the same as in Equations (7)-(10). The estimator for the diagonal elements of \(\Sigma_{k}\) used in the M-step takes the following form:  
\[\phi_{jk}^{2}=\frac{\sum_{i=1}^{n}w_{k}(x_{ij}-\alpha_{j}-\beta_{j}z_{k}-\eta z _{k}{}^{2})^{2}}{\sum_{i=1}^{n}w_{ik}}. \tag{12}\]  
When we apply the quadratic model with this type of variance parameterization to the first speed-flow dataset with both \(K=4\) and \(K=5\), we obtain similar clustering results as before, as shown in Figure 11, with \(K=4\) leading to the smallest BIC value. However, we observe that when \(K=5\), it leads to an undesired clustering result where Cluster 5 is surrounded by data points that belong to Cluster 4. This can cause problems for the interpretation of the ordered clustering along the fitted curve.
# Effect of Blood on Synovial Joint Tissues: Potential Role of Ferroptosis
## 4 In Vitro Models
### Synovium  
The synovium plays a critical role in maintaining the equilibrium between normal joint function and potential pathology. Joint trauma often leads to blood entering the joint space and mixing with synovial fluid (SF), thereby changing the rheological properties of SF and leading to increased iron deposition [77]. Also, synovitis and synovial angiogenesis are directly linked to OA progression and can exacerbate pain and joint damage [78]. Inflamed synovial tissue can often become hypoxic, which stimulates the production of hypoxia-inducible factor-1 (HIF-1) and vascular endothelial growth factor (VEGF), further promoting angiogenesis [89]. FLS also proliferate under inflammatory conditions, contributing to the growth and potentially invasiveness of new blood vessels. Researchers have also found that unspecified cells derived from human synovial tissue are able to synthesize ferritin, a storage form of iron, from the breakdown of erythrocytes and accumulation of iron deposits following hemarthrosis and the inflammation of synovial tissue [47]. This study highlights the degradative effects of erythrocytes found in whole blood but other blood constituents have also been shown to have restorative effects. To compare the efficacy of blood-derived products with other anti-inflammatory therapeutics, researchers treated unspecified cells derived from equine synovial fluid with platelet-rich plasma (PRP). While the addition of PRP did not increase ROS, it did increase concentrations of inflammatory cytokines up to 48 h after injection, leading them to conclude that PRP should be used with caution during the post-operative period [80].
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 7 Tradeoff Between Privacy and Utility
### Empirical Results  
In this section, we compare the communications throughput under each interference level.  
Figures 20 and 21 demonstrate the effect of interference on channel throughput. In Figure 20, the Wi-Fi throughput is plotted against the interference rates due to injected ping packets. A clear negative correlation between interference rate and Wi-Fi throughput is observed, as the linear fit has an RMSE of 0.4318 and R\({}^{2}\) of 0.952. Overall, an interference rate of 1000 Hz causes a large 25% reduction in throughput for the client-server pair. This once more motivates the use of more conservative interference injection to minimise throughput loss. The linearity of this trend is an interesting result, and can be compared against Figure 11. In Figure 11, the achieved CSI rate, which is proportional to throughput as per Equation (10), approaches an asymptote with the introduction of more devices. Due to hardware constraints on the Raspberry Pi 4B, we were unable to investigate whether the linear trend in Figure 20 approaches an asymptote with further increases in the transmission rate. We motivate future work to investigate this, when commodity devices are able to propagate more dense packet streams. To visualise the trade-off between the security and channel utility, Figure 21 plots the _PPP gain_ against the measured throughput loss.  
Figure 19: Security rate for each key as interference increases.  
Here, the _PPP gain_ is the percentage increase in the PPP over the baseline scenario with no interference. We observe a clear trade-off between the PPP gain and the available throughput. To achieve a PPP gain of 60%, we sacrificed nearly 25% of the available Wi-Fi throughput. Initially, we observe that a 20% gain in the PPP can be achieved with only 4% loss of throughput. This corresponds to the case of interference at a rate of 200 Hz. Referring back to Figure 18, this conservative interference rate is sufficient to reduce the keystroke sensing accuracy below 60%. The trade-off becomes more severe beyond this, as shown by the increasing slope of the curve in Figure 21. A further 20% gain in the PPP (from 20% to 40%) entails a much larger throughput loss of 11%. These results demonstrate the effectiveness of conservative interference injection in significantly mitigating sensing threats, while having minimal impact on Wi-Fi throughput.
## 4 Discussion  
### Soil Chemical Properties  
Soil pH, in isolation, is the factor that most affects the availability of P in the soil, as values close to 6.5 promote the greatest availability of this nutrient in the soil solution. The presence of other ions in the soil can also interfere with P absorption. For example, Mg has a synergistic effect on P absorption, as it acts as a carrier for P by activating ATPase in membranes and generating ATP during photosynthesis and respiration [26].  
This pH effect helps interpret the results of the low P availability (an average of 8 mg dm\({}^{-3}\)) in the soil fertility analyses of the present study (Tables 1, 2, 13 and 17), as pH values were close to 5.2. Conversely, Mg levels were high (an average of 15 mmol dm\({}^{-3}\)), which may have increased P absorption by crops, temporarily immobilizing the element in plant biomass.  
However, the results previously presented in Figure 6 show an increase in soil P content with the rising application rates of the treatments, indicating that the ideal dose was 192.5 kg ha\({}^{-1}\) of P\({}_{2}\)O\({}_{5}\). This pattern reflects the tendency of phosphorus to become fixed in the soil matrix at higher application rates, resulting in decreased availability beyond an optimal threshold. These findings support the hypothesis that phosphorus dynamics are significantly influenced by long-term fertilization strategies in conservationist systems.  
In integrated production systems, organic matter input into the soil is higher than in conventional systems, which is closely related to the soil microbial population. Microorganisms play a crucial role in organic matter transformations, using crop residues as an energy and nutrient source for cell formation and development. This process leads to the temporary immobilization of carbon, nitrogen, calcium, magnesium, phosphorus, sulfur, and micronutrients, which are later released into the soil upon microbial death and become available to plants again [27]. This also supports the lower average levels of P, Ca, Mg, and, consequently, BS and V%, observed in inoculated areas of this study.  
During the decomposition and mineralization of soil organic matter, H+ ions, organic acids, and S are released, increasing soil acidity [28]. This explains the higher average potential acidity and sulfur content in inoculated areas. Additionally, dry matter production (crop residues) was higher in the inoculated area throughout the crop rotation period preceding this study [29]. The decomposition of this organic material increased soil acidity in these areas and, due to higher grain yield, nutrient export was also greater, which explains the lower nutrient levels in the soil of the areas inoculated with _A. brasilense._
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Description** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{N-gram} & Comparison of & \multirow{2}{*}{Very simple and fast computation} & Sensitive to word order and synonyms \\  & overlapping N-grams (typically using cosine similarity) & & \\  & Document-term & & \\ TF-IDF & vectorization + cosine similarity & & \\  & N-gram Jaccard + LSA & & \\ Hybrid & + LDA, weighted combination & & \\  & bert-base-multilingual- & & \\ BERT & cased with cosine similarity & & \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main advantages and disadvantages of the near-duplicate detection methods. Let us consider an example that illustrates the difference. In Kazakh, the sentence \"Stuain universentite kiaatim okylan\" is equivalent to the sentence \"The student read the book at the university\" in English. In Kazakh, case endings allow flexible word order \"Kiaatim chqain universentite okylan\" without altering the meaning. In English, word order is crucial; reordering the words as \"The book the student at the university read\" results in an ungrammatical sentence. Table 2 describes the features of the Kazakh language in comparison with English. The analysis of theoretical aspects and approaches to detecting plagiarism and near duplicates in Kazakh texts demonstrates that this issue holds significant importance for education and human resource management. Thus, the research highlights various approaches to text similarity analysis, including statistical, semantic, and their combinations. Integrating structural and semantic information helps to improve accuracy and adapt methods to the specific features of languages and tasks. Considering semantics and optimizing features, selection proves beneficial when working with texts in morphologically complex languages such as Kazakh. These methods can be applied to tasks such as detecting borrowings, text classification, and semantic analysis. The literature review indicates the need to address the scientific gap related to the lack of effective and linguistically adapted methods for detecting near duplicates in low-resource agglutinative languages, such as Kazakh, by developing and validating a hybrid model that combines statistical and semantic analysis. Thus, the goal of this study is to develop a hybrid model for near-duplicate detection and to conduct a comparative analysis of four approaches to identifying textual similarities: N-gram, TF-IDF, BERT, and a hybrid model combining statistical and semantic analysis. The objectives include describing and formalizing near-duplicate identification according to the hybrid model, creating a Kazakh language corpus for conducting experiments, and evaluating the effectiveness of the hybrid model for near-duplicate detection, as well as the N-gram, BERT, and TF-IDF methods, for the Kazakh language case study.
## 4 Results and Discussion
### Results on Throughput  
The throughput experiment in this paper aims to evaluate the processing capacity of the proposed user-centric data privacy protection framework across different models. Throughput is a critical metric that measures the number of tasks a system can handle per unit of time, which is essential for ensuring efficiency in practical applications. This is particularly important in scenarios involving large-scale data processing, such as financial computing and facial recognition, where maintaining high throughput while protecting privacy is crucial for assessing the practical value of a data privacy protection method.  
As shown Table 7, the baseline model, without any privacy protection, has a throughput of 1, representing its maximum processing capacity. When our method is applied, the throughput slightly decreases to 0.93 but remains at a high level. This indicates that our method has minimal impact on system throughput while providing privacy protection, enabling efficient data processing. In contrast, the fully homomorphic encryption (FHE) method shows a significantly low throughput of only 0.02. This is due to the extensive complex mathematical operations involved in FHE, which heavily consume computational resources and drastically reduce processing efficiency. The throughput for federated learning (FL) is 0.83, which is acceptable but lower than our method. This is because FL requires substantial communication during multiple model parameter transmissions and aggregations, which, despite enhancing data privacy protection, increases the system's communication overhead. The throughput for differential privacy (DP) is 0.90, which is close to our method's performance. This is because DP primarily achieves privacy protection by adding noise to data or model parameters, which has relatively low computational overhead but is still slightly less efficient than our method. The method combining DP and FL (DP + FL) has a throughput of 0.71, performing poorly. This is because it requires simultaneous noise addition for differential privacy and multiple communications for federated learning, leading to significant overhead and reduced processing capacity. In summary, by comparing the throughput of different privacy protection methods in models, it is evident that our method provides effective privacy protection while maintaining high processing capability. This not only validates the effectiveness of our method but also demonstrates its broad applicability and superiority in practical applications. By introducing the user attention mechanism and the unified loss function, our method optimizes the computational process, minimizing computational overhead while protecting privacy, ensuring efficient system operation. These results indicate that the proposed user-centric data privacy protection framework holds significant practical value in real-world applications.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
###### Abstract  
Software-Defined Networking (SDN) enhances network control but faces Distributed Denial of Service (DDoS) attacks due to centralized control and flow-table constraints in network devices. To overcome this limitation, we introduce a multi-path routing algorithm for SDN called Trust-Based Proximal Policy Optimization (TBPPO). TBPPO incorporates a Kullback-Leibler divergence (KL divergence) trust value and a node diversity mechanism as the security assessment criterion, aiming to mitigate issues such as network fluctuations, low robustness, and congestion, with a particular emphasis on countering DDoS attacks. To avoid routing loops, differently from conventional 'Next Hop' routing decision methodology, we implemented an enhanced Depth-First Search (DFS) approach involving the pre-computation of path sets, from which we select the best path. To optimize the routing efficiency, we introduced an improved Proximal Policy Optimization (PPO) algorithm based on deep reinforcement learning. This enhanced PPO algorithm focuses on optimizing multi-path routing, considering security, network delay, and variations in multi-path delays. The TBPPO outperforms traditional methods in the Germany-50 evaluation, reducing average delay by 20%, cutting delay variation by 50%, and leading in trust value by 0.5, improving security and routing efficiency in SDN. TBPPO provides a practical and effective solution to enhance SDN security and routing efficiency.  
Keywords:software-defined network; deep reinforcement learning; multiple routing algorithm; network security +
Footnote †: journal: applied  
_Article_
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 4 Case Study: Stabilization of a Smart Grid  
In this section, we illustrate how Algorithm 1 can be used for adding stabilization to a controller program of a smart grid. We consider an abstract version of the smart grid described in [9] (see Figure 4). In this example, the system consists of a generator \(G\) and two loads \(Z_{1}\) and \(Z_{2}\). There are three sensors in the system. Sensor G shows the power generated by the generator and sensors 1 and 2 show the demand of load \(Z_{1}\) and \(Z_{2}\), respectively. The goal is to ensure that proper load shading is used if the load is too high (respectively, generating capacity is too low).  
The control center is shown by a dashed circle in Figure 4. It can read the values of the sensors and turn on/off switches connected to the loads. The program of the control center has to control switches in a manner that all the conditions below are satisfied:  
1. Both switches must be turned on if the overall sensed load is less than or equal to the generation capacity.
2. If sensor values reveal that neither load can individually be served by G then both are shed.
3. If only one load can be served then the smaller load is shed assuming the larger load can be served by G.
4. If only one can be served and the larger load exceeds the generation capacity, the smaller load is served.
# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 4 Experimental Results
### Results and Analysis
#### 4.5.2 Overall Segmentation Performance  
We performed the training stage on the BraTS2018 training set and then evaluated the segmentation results on the validation set. The segmentation performances of our proposed model were 81.82%, 89.75%, and 84.05% for the enhancing tumor, tumor core, and whole tumor, respectively. We also set up a table comparing our performance with other segmentation methods such as 3D U-Net [42], 3D-ESPNet [50], Kao et al. [51], DMFNet [43], Reversible U-Net [48], or comparing with winners of the competition such as No New-Net [49] and NVDLMED [54] (single model). As shown in Table 3, our method achieved the best performance on enhancing tumor segmentation compared to other models. Enhanced tumors are directly related to the early development of brain cancer and they are the most important one in early diagnosis of cancers. However, in most cases, the number of small tumors in the dataset could be relatively small. Therefore, the performance improvement of small tumors may not contribute much to the overall performance in some cases. For the whole tumor and the tumor core, our model achieved comparable results. Note that the No New-Net ranked second in the BraTS2018 challenge and its performance on the whole tumor is better than others. However, it is worth knowing that they trained their models with external datasets from other institutes. The NVDLMED [54] method has a very complex model, which takes up almost 32 GB.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ET** & **WT** & **TC** \\ \hline
3D U-Net [42] & 0.7559 & 0.8883 & 0.7177 \\ DMFNet [43] & 0.8012 & 0.9062 & 0.8454 \\ Reversible U-Net [48] & 0.8056 & 0.9061 & 0.8571 \\ No New-Net [49] & 0.8101 & 0.9083 & 0.8544 \\
3D-ESPNet [50] & 0.7370 & 0.8830 & 0.8140 \\ [PERSON] et al. [51] & 0.7875 & 0.9047 & 0.8135 \\ S3D-Unet [52] & 0.7493 & 0.8935 & 0.8309 \\ NVDLMED [54] & 0.8173 & 0.9068 & 0.8602 \\ Ours & 0.8182 & 0.8975 & 0.8405 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance comparison of the dice score of different models on the BraTS2018 validation set.  
Figure 6: Dice similarity coefficient (DSC) stratified by tumor size.