esa-sceva/satcom-chunk-collection huggingface dataset:
# A Benchmark for Morphological Segmentation in Uyghur and Kazakh
## 2 Related Work
### Results
#### 4.5.3 Ablation Studies  
To evaluate the impact of different feature extraction modules on morphological segmentation, we conducted a set of ablation studies in this section. The experimental results are presented in Table 6.  
The experimental results in Table 6 indicate that the masked attention mechanism (MAM) used in MMSeg effectively captures relevant features of n-gram characters, thereby improving the model's performance on Uyghur and Kazakh test datasets. When the CRF layer is used in the FEMSeg-CRF model, it adds constraints to the predicted labels  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Lang.**} & \multicolumn{8}{c}{**Uyghur**} & \multicolumn{8}{c}{**Kazakh**} \\ \cline{2-13}  & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Test**} & \multicolumn{3}{c}{**Validation**} & \multicolumn{3}{c}{**Test**} \\ \cline{2-13}  & \multirow{2}{*}{**ACC**} & **Over** & **Under** & **ACC** & **Over** & **Under** & **ACC** & **Over** & **Under** & **ACC** & **Over** & **Under** \\  & **Stem.** & **Stem.** & **Acc** & **Stem.** & **Stem.** & **Acc** & **Stem.** & **Stem.** & **Stem.** & **Acc** & **Stem.** & **Stem.** \\ \hline BiLSTM & 90.17 & 4.77 & 5.06 & 89.15 & 5.45 & 5.40 & 87.65 & 7.65 & 4.70 & 88.80 & 6.75 & 4.45 \\ BiGRU & 88.68 & 8.12 & **3.20** & 89.46 & 7.78 & **2.77** & 86.85 & 7.40 & 5.75 & 86.80 & 7.70 & 5.50 \\ FEMSeg & 90.21 & 6.40 & 3.39 & 91.08 & 5.87 & 3.05 & 89.75 & 5.00 & 5.25 & 90.35 & **4.80** & 4.85 \\ \hline HMM & 29.67 & 57.19 & 13.14 & 31.20 & 55.15 & 13.65 & 26.25 & 62.05 & 11.70 & 27.10 & 62.80 & 10.10 \\ CRF & 79.17 & 11.28 & 9.56 & 86.40 & 7.25 & 6.35 & 74.10 & 15.50 & 10.40 & 74.05 & 15.75 & 10.20 \\ BiLSTM-CRF & 89.82 & **4.59** & 5.59 & 90.36 & **3.82** & 5.82 & 87.75 & 8.30 & **3.95** & 89.00 & 7.90 & **3.10** \\ BiLSTM-ATT-CRF & 90.35 & 5.26 & 4.40 & 90.08 & 5.49 & 4.44 & 87.95 & 7.35 & 4.70 & 88.55 & 7.00 & 4.45 \\ FEMSeg-CRF & **90.92** & 5.64 & 3.44 & **91.51** & 5.20 & 3.29 & **90.15** & **4.95** & 4.90 & **90.70** & 5.00 & 4.30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of stemming (%). Bold represents the best result in the same group of experiments.  
to ensure their validity, whereas using softmax (FEMSeg) alone does not capture the transition relationships between labels. Additionally, when using a Transformer encoder, the model can capture as much inter-sequence correlation in different spaces as possible, thus mitigating the uncertainty caused by phonetic variations and enhancing the model's ability to recognize morpheme boundaries.
## 3 Applications of Game Theory in WSN
### Game Theory in Applications of WSN
#### 3.4.3 Packet Forwarding  
In WSNs, selfish nodes refuse to forward packets for other nodes in order to save energy which causes the network to enter into a faulty state. At the same time, some nodes may be malicious, whose aims are to damage the network. [PERSON] and [PERSON] [67] analyzed the cooperation stimulation and security in self-organized wireless networks under a game theoretic framework. The fault tolerance and security problem is modeled as a non-cooperative game in which each player maximizes its own utility function. The results showed that players can obtain the biggest payoff if they tell the truth and obey the cooperation strategy. [PERSON] _et al_. [41] adopted the viewpoints and methods in game theory to solve the packet forwarding problem in WSNs and proposed an algorithm named Pareto Optimal Utility based Packet Forwarding (POUPF) which is able to set a NE and to obtain the Pareto optimal utility for each node. [PERSON] _et al_. [187] formulated the interaction between sensors and monitoring nodes as a dynamic game with incomplete information, which provides a platform for designing reputation based data fusion. [PERSON] and [PERSON] [188] studied the tradeoff between data transmission delay and overall network energy efficiency, and developed a game-theoretic model of real-time reliable aggregation mechanism for WSNs. [PERSON] _et al_. [128] focused on the packet forwarding problem in WSNs and provided a solution with the method of game theory.  
A common aggregation model was proposed in [23], which is independent of the specific application environments, but based on the evolutionary game theory called evolutionary game-based data aggregation model (EGDAM) in WSNs. EGDAM consists of formal definition, functional model and a general process is defined to map the competition and cooperation in aggregation procedure into games, and well-avoid perfect rationality. Guided by the model, an evolutionary game-based adaptive weighting algorithm named EGWDA was provided for the pixel-level data aggregation with homogeneous sensors. Reasonable sensors weight distribution can be achieved during the aggregation process in WSNs. [PERSON] _et al_. [189] proposed a scheme to reach shorter multicast delay, better energy utilizing efficiency and higher efficiency of data transferring for Sensor Grid. [PERSON] _et al_. [190] proposed an efficient bottleneck throughput maximizing routing framework for correlated data gathering in WSN. In [144], [82] and [191], the estimation reliability in the mean square error (MSE) criterion was defined, and it is between a source node in the sensor field and its estimation at a sink node. [PERSON] _et al_. [4] considered the cooperation of nodes in data transmission in terms of a group, since the major consumer of power is the data transmission process. A mobile node may move to a location, in which it is desirable for the node to join a group. They proposed an algorithm for nodes to choose the best group in their signal range, using coalitional game theory to determine what is beneficial in terms of power consumption.
# Special Issue on Ensemble Learning and/or Explainability
## 1 Introduction  
This article will summarize the works published in a Special Issue of _Algorithms_, entitled \"_Ensemble Learning and/or Explainability_\"([[https://www.mdpi.com/journal/algorithms/special_issues/Ensemble_Algorithms_Explainability](https://www.mdpi.com/journal/algorithms/special_issues/Ensemble_Algorithms_Explainability)]([https://www.mdpi.com/journal/algorithms/special_issues/Ensemble_Algorithms_Explainability](https://www.mdpi.com/journal/algorithms/special_issues/Ensemble_Algorithms_Explainability)), accessed on 24 November 2020). The main aim of this Special Issue is to present the recent advances related to a wide range of ensemble learning algorithms and methodologies and investigate the impact of their application in a diversity of real-world problems. At the same time, the need to research the explainability issues involved in theory and practice is of paramount importance for all kinds of daily and industrial applications. A total of eight papers were accepted, after undergoing a rigorous peer-review process based on their scientific merit and other quality criteria. All accepted papers possess considerable elements of novelty and cover a diverse range of application domains, providing the reader with a glimpse of the state-of-the-art research in the machine learning (ML) area. This Special Issue can be considered the sequel (second volume) of \"_Ensemble learning and their applications_\" [1].  
During the last few decades, in the area of machine learning and data mining, ensemble methods constitute a state-of-the-art option for the development of powerful and robust prediction models. These models exploit the individual predictions of a variety of constituent learning algorithms to obtain better prediction performance, which has been proved both theoretically and experimentally. Thus, many ensemble learning algorithms have been proposed in the literature and have found applications in various real-word problems ranging from face and emotion recognition through text classification and medical diagnosis to financial forecasting, among others.  
Quite recently, the European Union General Data Protection Regulation (GDPR) demanded a \"_right to explanation_\" about decisions made by automated and artificial intelligent algorithmic systems. This demand, combined with the recognized need to Interpret or Explain and justify the decisions/predictions of a classifier or ensemble, led to the development of \"_Interpretable/Explainable Machine Learning and Artificial Intelligence_\", which has earned great attention from the scientific community.  
Therefore, the needs of this data-driven era, as well as the challenging necessities of the industrial sector, resulted in the development of robust, accurate, and explainable prediction models based on novel ensemble-learning methods and explainability techniques.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Development of Adjustment Factor Estimation Criteria
#### 3.4.4 The Adjustment Factor (\(\mathrm{f_{AD}}\))  
Pertaining to the Number of Access Density  
The number of access points (\(\mathrm{V_{AD}}\)) and the adjustment factor are constant for both the x- and y-axes. The larger the number of access points is, the lower the speed is. The slope estimated by the regression equation (\(\alpha_{\mathrm{AD}}\)) was \(-0.36\), corresponding to a constant of 58.63.  
As described earlier, the constant (\(\mathrm{y_{AD}}\)) for the x-axis is 162.9. If the estimated regression line is projected to be 80 km, the constant (\(\delta_{\mathrm{AD}}\)) corresponding to the x-axis is. Thus, when the total number of access points per km is approximately, the travel speed, in theory, becomes zero:  
\[85\mathrm{th}\ \mathrm{percentile\ speed}=58.63-0.36\times\mathrm{V_{AD}},\]  
\[\delta_{\mathrm{AS}}=80/\alpha_{\mathrm{AD}}=80/0.36=222.2.\]  
Next, we obtain the standardization value corresponding to the observed number of access roads:  
\[\mathrm{SV_{ADj}}=\mathrm{V_{ADj}}/\delta_{\mathrm{AD}},\mathrm{f_{ADj}}=1.0- \mathrm{SV_{ADj}}=1.0-(\mathrm{V_{ADj}}/\delta_{\mathrm{AD}}),\]  
where \(\mathrm{SV_{ADj}}\) denotes the standardized number of access points for the jth section, \(\mathrm{V_{ADj}}\) denotes the number of access points for the jth section, and \(\delta_{\mathrm{AD}}\) is the x-axis constant corresponding to the 80 km projected line.  
Using the above equation, the adjustment factor according to the number of access points in the jth section is calculated as:  
\[\mathrm{f_{ADj}}=1.0-(\mathrm{V_{ADj}}\times\alpha_{\mathrm{AD}})/80=1.0-( \mathrm{V_{ADj}}/222.2). \tag{8}\]  
The estimation process is expressed in a graph, as shown in Figure 16.
## 3 Plant-Based Biopolymers
### Cellulose-Based VMAs
For example, the cement paste made with PMS and cellulose VMA showed a higher mechanical strength than the cement paste prepared with PNS and cellulose [29]. On the other hand, [PERSON] et al. showed that some incompatibilities may appear and a pronounced reduction in fluidity may result when the HPMC is combined with a PNS HRWR. Thus, it has been acknowledged that PMS is more compatible with cellulose-based VMAs [11; 30]. In addition, cellulose has been used in other forms, including cellulose nanofibers (CFFs) and cellulose filaments (CFs), which constitute a new class of nanocellulose materials. Figure 7: The chemical structure of cellulose [from PubChem Database]. It was agreed that CFs (95% cellulose and 5% hemicellulose) improve the rheological properties particularly the yield stress, the plastic viscosity, and the stability of SCC [31; 32]. [PERSON] et al. claim that the rheological and the stability improvements are due to the hydrophilicity, the high aspect ratio, and the flexibility of CFs [32]. Furthermore, CFs increase the heat of hydration without causing a delay of the hydration. CFs also lead to performances comparable to Welan gum at a dosage of 0.05% in terms of yield stress and plastic viscosity. Moreover, it was added, in terms of CF dosages, that they should be less than 0.12% to ensure adequate dispersion and stability [31]. As for CFs, which were used as admixtures for self-leveling mortars, they reduced the spreading diameter and increased the flow time. Identically to CFs, they increase the yield stress and the plastic viscosity. Finally, it was mentioned that using 0.03% of CFFs, the performances of concrete were favorable for projected concrete applications [33]. On the other hand, in a recent study, the impact of paper pulp as a VMA in cement-based materials was assessed [34]. Paper pulp, primarily composed of cellulose, differs from conventional VMAs biopolymers due to its insolubility in water, attributed to the dimensions of the fibers in terms of diameter and length. Two variants of paper pulp were examined: low-energy and high-energy, depending on the mechanical grinding process. The findings indicated that these variants significantly influenced the rheological properties of the mixtures, serving as versatile VMAs comparable to conventional admixtures. Low-energy variants increased dynamic yield stress and plastic viscosity, outperforming MasterMatrix (MM) at equivalent dosages, while high-energy variants exhibited similarities to Diutan gum, offering even more substantial enhancements. This study highlighted the environmentally friendly and effective role of paper pulp as a VMA, modifying the rheology of cement mixtures through a combination of mechanisms, including the flocculation and water affinity of pulp fibers.
# Drone-Based Parcel Delivery Using the Rooftops of City Buildings: Model and Solution
## 7 Future Research  
For future research, various uncertainties, such as the weather conditions (e.g., wind direction and speed, temperature), need to be further considered when formulating a planning model. More realistic data should be collected and used for model parameters when the real validation work of the proposed delivery system starts later. Because the location of the drone operation center might affect drone operation planning, the location problem needs to be assessed. Additionally, multiple drone operation centers can be considered for drone-based parcel delivery. Under multiple drone operation center environments, drones could freely move to each center for battery charging, which might improve the utilization rates of the drones. As for the performance of the proposed heuristic, it needs to be compared with that of metaheuristics such as genetic algorithms.  
Finally, we will be able to apply the proposed heuristic to other application areas, such as electric car recharging and operating problems.  
Conceptualization, J.K. and H.J.; methodology, H.M. and H.J.; software, H.M. and J.K.; validation, J.K., H.M., and H.J.; formal analysis, J.K. and H.J.; writing--original draft preparation, J.K.; writing--review and editing, H.J.; supervision, H.J.; funding acquisition, H.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Education (NRF-2017R1D1A1B03032560). This work was also supported by the Inha University Research Grant (60630-1).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Appendix A**  
**Algorithm A1:** Pseudo Codes of the Proposed Heuristic.  
1: Enumerate all demand and distance information of each destination. Calculate delivery time, charging time, and battery consumption after delivering a parcel to each destination. Create a destination block for each building, which consists of delivery and charging blocks.  
2: Enumerate drones and consider each drone to be a container to stack blocks.
## 3 Results and Discussions
### Ipmv  
This study used a modified version of option C in the IPMVP to calculate the energy saving for air conditioning in the classroom.  
The VGS lowers the temperature by 6.721%. The real room temperature with no VGS is increased by 6.721% of the amount of energy consumed by the air conditioner. The drop in temperature is considered in calculating the amount of energy saved by the VGS.  
The steps for calculation of energy saving are:  
Step 1: Determine the temperature T of classroom during class time without AC while with VGS:  
\[\mathrm{T_{withVGS}=T\;of\;empty\;classroom+Anthropogenic\;interference\;1.598 \({}^{\circ}\)C} \tag{3}\]  
Step 2: Obtain the T of classroom during class time without AC and without VGS:  
\[\mathrm{TwithoutVGS=TwithVGS+T\;reduction\;by\;VGS} \tag{4}\]  
Step 3:  
\[\mathrm{Air\;conditioning\;load=T_{withoutVGS}-T_{classroom\;with\;AC}} \tag{5}\]  
Step 4: Calculate the air conditioning load reduced by VGS:  
\[\mathrm{T_{AC\;load\;reducing}=T_{withoutVGS}-T_{withVGS}} \tag{6}\]  
Step 5:  
\[\mathrm{Energy\;saving=T_{AC\;load\;reducing}*Air\;conditioner\;energy\;saving\; coefficient} \tag{7}\]  
(In this study, the energy saving coefficient of the air conditioner is 4.6% per \({}^{\circ}\)C)  
*T = temperature Our calculation shows a reduction of approximately 8.308% in the energy consumed by the air conditioner in a day and a maximum of reduction in air conditioner energy consumption of 8.844%. The average reduction is 8.483%, as shown in Table 4.
## 3 Results and Discussion
### Dsc  
The thermograms of raw PLGA, POLOX and the produced MPs are summarized in Figure 3. As shown in Figure 3A, the thermogram of PLGA shows a glass transition temperature (Tg) of around 48.5 \({}^{\circ}\)C, while F68 and F127 POLOX exhibited an endothermic melting peak at approximately 54 and 56 \({}^{\circ}\)C (Figure 3B,C). DSC traces of HA showed no thermodynamic events in the test conditions (data not shown). Similarly, MPs did not show the endothermic peaks of POLOX, thereby indicating that they were molecularly mixed with PLGA.  
Figure 3 shows the representative DSC traces of the pure materials and of the as-produced 20-100, 20-90 and 20-70 MPs. HA did not show any thermal event in the utilized temperature range (data not shown). PLGA showed only a glass transition at around 48.5 \({}^{\circ}\)C (Figure 3A), with no crystallinity or fusion due to its amorphous nature, while F68 and F 127 POLOX evidenced a relatively sharp endothermic peak attributed to polymer melting (Figure 3B,C, respectively). Interestingly, the thermograms of MPs (Figure 3D-F) displayed a glass transition event at temperatures similar to the Tg of pure PLGA. In all cases, anyhow, the glass transition event was overlapped with an endothermic peak attributable to POLOX. It must be underlined that 20-100 MPs were produced with POLOX in the external water phase; therefore, the small endothermic peak observed is probably due to the POLOX adhering to the external MP surface. As for the 20-90 and 20-70 formulations, the endothermic peak is more relevant due to the increasing presence of POLOX in the organic phase of the emulsion. These outcomes hint at the formation of a partially phase-separated matrix in the MPs. The same outcomes were found for the 24-100, 24-90 and 24-70 formulations.  
Figure 4 depicts the time trend of Tg for all formulations, until day 28. The glass transition temperature was found to follow a slowly decreasing time trend, substantially superimposable for all MP formulations. Tg steadily decreased until day 28, dropping from approximately 49 to 42 \({}^{\circ}\)C, due to the drop in the PLGA's molecular weight during release.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 4 Results
### Possible Practical Applications  
Compensated Chebyshev decimation filters, a class of digital filters designed to provide accurate signal decimation while compensating for the effects of Chebyshev filters, such as passband droop, are particularly valuable in applications where maintaining high signal fidelity during decimation is of utmost importance. Here are some possible practical implementations:  
* _Communication Systems_. Providing efficient decimation since the signals are often decimated to match a target sampling rate. In radio frequency (RF) signal processing, these filters can significantly improve the performance of intermediate frequency (IF) decimation stages by reducing signal distortion and improving signal-to-noise ratio.
* _Biomedical Signal Processing_. These filters can be used to decimate high-frequency sampled biomedical signals to preserve signal information by accurately retaining the key features of the original signal while reducing the data rate.
* _Audio Signal Processing_. To maintain high sound quality during resampling or multi-rate processing, minimize aliasing and maintain audio integrity.
* _Scientific Instrumentation_. Preservation signal quality in high-resolution imaging or spectroscopy applications. Accurate decimation of geophysical signals aids in the real-time monitoring and analysis of seismic data.
# Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction
## 2 Methods
### Computational Complexity Analysis  
The computational cost of GLAD-naive consists of three parts: the building of BFS tree rooted at one candidate source \(v\in V\backslash O\), the calculation of path intersection matrix \(\Lambda_{ps}\), and the estimation of diffusion parameters \(t_{0},\sigma\) and objective function. Suppose that the numbers of nodes and edges in the network \(G(V,E)\) are \(N\) and \(M\) and we have \(K\) observers. Generally, when the topology of the network is known, building a BFS tree rooted at one node \(v\) will cost \(O(M)\). In addition, in the meantime, the paths from \(v\) to each nodes on the tree can be gained naturally along with the tree building scheme. As each element in the path intersection matrix \([\Lambda_{ps}]_{i,j}\) denotes the length of the common path from \(v\) to observers \(i\) and \(j\), \(O(N)\) should be the cost to check how many edges on the tree lay on the common path \(\mathcal{P}(s,o_{i})\cap\mathcal{P}(s,o_{j})\). Thus, the computational cost of this step is \(O(NK^{2})\). The main costs in the third step of parameter estimation and objective function calculation were the computation of \(\Lambda_{ps}^{-1}\) and \(|\Lambda_{ps}|\), which both cost \(O(K^{3})\) by typical algorithms in linear algebra. In total, the whole computational complexity of GLAD-naive is \(O(NM+N^{2}K^{2}+NK^{3}))\).  
The calculation time could be further reduced in GLAD-modified because the path intersection matrix \(\Lambda_{ps}\) did not need to be explicitly constructed, since we only cared about its diagonal elements \(d_{sk}\). This process could be performed in a batch mode with only \(O(KM)\) in which we started the BFS process for each observer \(o_{k}\) and then recorded the distance \(d_{sk}\) from it to all candidate sources \(s\in V\backslash O\). According to Equations (15)-(18), the parameter estimation and objective function calculation can be finished in a linear time \(O(K)\) for each candidate source. Consequently, the whole computational complexity of GLAD-modified is linear, i.e., \(O(KM+KN)=O(KM)\), which enables its wide application in large-scale networks.