esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Writing Ability
#### 3.2.3 Muscle Activity  
Table 6 shows the results of muscle activity in the upper extremity on the PS and NPS when writing Korean consonants and vowels using two types of writing aids. For the FCR, when SW was used, muscle activity on the PS was significantly higher by 4.6% for Korean consonants and 5.9% for vowels compared to AG (\(p<0.05\)). When comparing the difference in muscle activity between the PS and NPS, although not statistically significant, activation on the PS was higher only when writing Korean consonants and vowels with SW. For the ECRL, there was no significant difference in muscle activity based on the use of the two writing aids for both Korean consonants and vowels. Similarly, when comparing the difference in muscle activity between the PS and NPS, no statistical significance was observed. However, muscle activation on the PS was lower when writing Korean consonants and vowels using both AG and SW. For the BB, there was no significant difference in muscle activity depending on the types of writing aids for either Korean consonants or vowels. However, when writing Korean consonants, muscle activity on the PS was significantly higher by 18.0% with AG and 17.5% with SW compared to the NPS (\(p<0.05\)). For Korean vowels, muscle activity on the PS also increased compared to the NPS, but the difference was not statistically significant. For the TB, there was no difference in muscle activity depending on the type of writing aid, except that muscle activity was 5.8% lower when writing Korean vowels using AG on the PS compared to SW (\(p<0.05\)). When comparing the difference in muscle activity between the PS and NPS, overall muscle activity on the PS was lower than on the NPS. However, when writing Korean vowels using AG, muscle activity on the PS was significantly lower by 15.0% compared to the NPS (\(p<0.05\)). For the UT, there was no significant difference in muscle activity between the two types of writing aids, except that muscle activity was significantly lower by 3.5% when writing Korean consonants with AG on the PS compared to using SW (\(p<0.05\)). When using both types of writing aids, overall muscle activity on the PS was higher than on the NPS. Significant differences in muscle activity were observed when the activity on the PS was higher, specifically by 19.0% with AG and 13.5% with SW for Korean consonants, and 19.2% with AG for Korean vowels (\(p<0.05\)).
# Improving Spatial Reuse of Wireless LAN Uplink Using BSS Color and Proximity Information
## 3 Proposed Method
### Idea Overview
* \(S_{1}\): received power of the signal from node A to AP\({}_{1}\). * \(I_{1}\): received power of the interference from node B to AP\({}_{1}\). * \(S_{2}\): received power of the signal from node B to AP\({}_{2}\). * \(I_{2}\): received power of the interference from node A to AP\({}_{2}\). Among the four values, \(S_{2}\) and \(I_{1}\) are directly known to node B, because node B is receiving beacons from AP\({}_{2}\) and AP\({}_{1}\). \(S_{1}\) is also known to node B, because node A includes  
Figure 4: A scenario where node B transmits in parallel with node A according to PSR, but node B’s packet is lost due to low SNR at AP\({}_{2}\). the proximity information in the preamble which indicates the received power of the signal from AP\({}_{1}\). In order to estimate \(I_{2}\), we use the triangle inequality to estimate the \"distance\" between node A and AP\({}_{2}\). Note that the concept of distance we use here is a notion of signal attenuation, rather than the exact physical distance. If there is an obstacle between the transmitter and the receiver, the distance between the two points will be estimated longer than the actual physical distance. When maintaining RSSI obtained from beacons, weighted moving average is used to smooth out variations caused from multi-path fading. According to triangle inequality, the following inequalities hold:  
\[d_{2A}+d_{AB}\geq d_{2B} \tag{4}\]  
\[d_{2A}+d_{2B}\geq d_{AB} \tag{5}\]  
\[d_{2A}+d_{12}\geq d_{1A} \tag{6}\]  
Figure 3.0  
Combining these equations, we get the minimum distance between node A and AP\({}_{2}\), which can be used to estimate \(I_{2}\). Figure 3.1  
\(d_{AB}\), \(d_{2B}\), \(d_{12}\), and \(d_{1A}\) are all obtainable through BSS color, proximity information, and neighbor tables maintained at APs and STAs. Next, we describe the details on how the proposed method is designed and implemented.
# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 3 Materials and Methods
### Control and Monitoring Phase  
The second phase plans the timely determination of whether the planted crops are grown, their water and similar needs, and the realization of other purposes. At this stage, it is expected that the products will be monitored to increase productivity. This process can be a very costly and error-prone structure when performed by the farmer. However, being able to manage this whole process with technological solutions, and, in this context, offering farmers a user-friendly integrated application increases efficiency in every aspect.  
In this phase, IoT devices were used to easily track each product and obtain query or in-demand-based data flow. The features of these devices may vary according to the purpose and expectations in the field. In this study, the tomato case study is focused on as an example. Tomatoes have commercial importance as one of the most-grown vegetables in the world [51]. Today, tomatoes are grown in open fields and greenhouses. The method of growing tomatoes in the open field is considered one of the traditional methods and we did not consider it. The growing period of tomatoes may be different depending on the parameters of light, water, minerals, and temperature [53]. In greenhouses, parameters such as temperature, humidity, amount of light, amount of CO\({}_{2}\), and amount of water can be controlled more easily, and therefore tomato cultivation in a greenhouse can be preferred [52; 54].  
For this purpose, KIANI-WSN kit nodes were used as IoT nodes [55] as shown in Figure 2. These nodes consist of sensing, communication, processing, and power units. Since the communication unit is equipped with a Wi-Fi card, it also fully meets the need for an IoT. These nodes are generally of two types. One of them is the CC1101 transmitter chip and the other is the nodes equipped with both CC1101 and CC1190 chips. The first node types were used in this study. The CC1101 is a low-cost sub-1 GHz transceiver that can be used in very-low-power wireless-based applications [56]. It also supports packet processing, data buffering, burst transmissions, clear channel evaluation, link quality indication, and wake-on-radio. The CC1101's main operating parameters and 64-byte send/receive FIFOs can be controlled via the serial peripheral interface (SPI). These nodes are equipped with sensors such as humidity, temperature, and light. The ad-hoc feature of this device provided another advantage because it could transfer the required data to our system even when there was no internet. Thanks to this system, productivity in harvesting increased and resources such as water and manpower were used efficiently.
## 3 Results and Discussions
### Workability
#### 3.1.1 Influence of Slag Content  
Figure 2 illustrates the slump values of various GPC mixes with different proportions of slag. It can be noted that the slump values are influenced by varying the amount of slag in the mixes. The slump values show similar trends for different slag content in all the mix groups. The workability of the freshly mixed GPC was reduced gradually by increasing the slag replacement levels (10% to 50%) in the mixes.  
Figure 8: The testing setup for modulus of elasticity test.  
Figure 7: The testing setup for flexural strength test.  
This decreasing trend of slump values against increasing slag content is also consistent with the previous studies [28; 31; 34]. This is due to the angular shaped particles of slag as compared to spherical shaped particles of FA that makes the concrete more flowable. However, this effect was not the same for different quantities of slag. In group A, the mix A1-S10 having 10% slag content gives highest slump value of 130 mm as compared to other mixes with higher slag content in this group. The slump values were decreased by 8%, 23%, and 31% for mixes A2-S20, A3-S30, and A4-S50, with 20%, 30%, and 50% slag content respectively, when compared to A1-S10 (10% slag content).  
In group B, the mixes were prepared with two different molarities of NaOH, i.e., 12 M, and 14 M. The decrease in slump value for 12 M mixes is 44% by increasing the slag content from 20% (B1-S20) to 30% (B2-S30), whereas for 14 M mixes, the slump value was decreased by 50% by increasing the slag content from 20% (B3-S20) to 30% (B4-S30). It can be concluded, therefore, that higher molarity will further lower the workability of the FS-GPC mix.  
In group C, the slump of the mixes C2-S20, C3-S30, and C4-S50 was reduced by 15%, 31%, and 39% respectively as compared to mix C1-S10. For group D mixes, the slump value of the mix D2-S30 (SS/SH = 2.0) was reduced by 23% as compared to D1-S20 (SS/SH = 2.0). In the same group, the slump value of mix D4-S30 was 20% less than D3-S20 mix, both having SS/SH ratio of 3.0. In the last group E, where extra water was added to all the mixes, the similar trend was observed for slump values. The slump values of mixes E2-S20, E3-S30, and E4-S50 were reduced by 10%, 23%, and 33% respectively as compared to mix E1-S10.  
The influence of slag on slump values for mixes with 30% replacement levels seemed more prominent than 10%, 20%, or 50% levels. Furthermore, it was observed that slag content has substantial influence on slump values at higher molarity of NaOH solution (i.e., M = 14), lower AAS content (35%), and lower SS/SH ratio (2.0).
## 5 Discussion
### Real-World Applications  
The ultrasound-based localization system offers significant advantages in public spaces with high pedestrian traffic. Unlike SLAM or LiDAR systems, it maintains accuracy in dynamic environments, relatively unaffected by moving individuals or varying crowd densities. This robustness, combined with its non-reliance on visual data, makes it effective in areas such as retail stores, hospitals, and museums, where consistent performance with people traffic is essential for robotic operation.  
The system is especially well suited for medical supply robots in hospital corridors, positioning interactive guide robots in museums, and other public spaces that offer a controlled environment where an installation can be made. In traditional robotics applications such as warehouse and factory automation, it can serve as a complementary system or cost-effective solution for precise absolute positioning of inventory management robots or automated guided vehicles (AGVs). Its capacity to provide accurate localization in the presence of moving objects or personnel makes it valuable in environments where human workers and automated systems coexist, offering a reliable alternative or backup to existing positioning methods.
## 4 Procedures and Results of Diagnosis
### Validation of Proposed Method via Diagnosis of Offset Scratch  
For validation and for checking the robustness of the developed method, the proposed CNN method was tested on an offset scratch. This scratch had a length of 10 mm, a width of 0.5 mm, and a depth of 0.5 mm and was generated away from the center (1.5 mm from the vicinity of the bearing). The experimental conditions were set to be like those of SS and FS, as described in Section 3.1, for precise comparison and validation. The feature distribution of the offset scratch is shown in Figure 11, and the rotation speed of the induction motor was not considered.  
The features of the offset scratch (30 and 90 Hz) were loaded into the CNN model, and the diagnosis was performed as in the previous tests. The rotation speed of the induction motor was not considered in the diagnosis. The datasets of the 30 and 90 Hz frequency components were divided randomly into two groups in a 70:30 ratio. The diagnostic results of the offset scratch are presented in Table 4. Even in this case, the accuracy rate of the  
Figure 11: Feature distribution without considering the rotating speed (offset scratch).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Bearing Conditions** & **Accuracy Rate (\%)** \\ \hline H–SS & 100 \\ H–C2S & 76.25 \\ H–C4S & 96.67 \\ SS–C2S–C4S & 90.14 \\ H–SS–C2S–C4S & 86.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Diagnosis results for concentrated scratch faults.  
diagnosis was high. However, as seen in Figure 11, overlapping occurred between the feature distributions of healthy and faulty motors. Nonetheless, a high accuracy rate was obtained by disregarding the presence of overlapping between the two data features. Thus, the diagnosis accuracy rate was not affected by a change in the fault position, and the robustness of the proposed diagnostic system remained high. This high accuracy rate ensured position diagnosis and fault traceability. This validated the proposed method and extended its application.
## 2 Materials and Methods
### Loss Function  
Typically, for MTL, the most straightforward way is to add the losses of the total \(K\) tasks directly to obtain the overall loss. Therefore, the loss function is depicted as:  
\[L_{total}=L_{1}+L_{2}+\ldots+L_{K} \tag{7}\]  
\[L_{k}=-\frac{1}{N}\sum_{i=1}^{N}y_{i}\text{log}(p(y_{i}))+(1-y_{i})\text{log}(1 -p(y_{i})) \tag{8}\]  
Here, \(K\) denotes the number of multitask learning tasks. For each individual task \(L_{k}\), we employ binary cross-entropy as the loss function, as indicated in Equation (8). To compute task-specific prediction probabilities, we applied the SoftMax function, denoted as \(p(y_{i})\) to the output of the prediction layer.  
A key challenge in this multi-task setup lies in the imbalance of loss scales across different tasks. Due to variations in task difficulty, data distribution, and convergence rates, directly summing the loss values can lead to task dominance, where one task disproportionately influences the overall optimization process. This can result in biased learning and diminished performance on other tasks. To mitigate this issue, we adopted a technique inspired by Dynamic Task Prioritization [37], which introduces adaptive weighting of task-specific losses. Instead of statically combining losses, this method assigns a floating weight parameter to each task's loss term, dynamically adjusting their contribution to the overall objective during training. To maintain computational efficiency and avoid frequent recalculation of evaluation metrics outside the training loop, we implemented a simplified version of this strategy, as described below:  
\[L_{total}=W_{L1}L_{1}+W_{L2}L_{2}+\cdots+W_{LK}L_{K} \tag{9}\]  
To address the imbalance in task-specific contributions during training, we applied a squared loss weighting strategy, i.e., we set \(W_{Lk}=L_{k}\). This dynamic weighting ensures that no single task dominates the training process, thereby preventing the model from biasing its performance toward any one outcome at the expense of others. The rationale behind this approach is grounded in the intuition that tasks with higher losses should receive greater emphasis, while well-fitted tasks should be assigned lower priority. By squaring the loss for each task, we naturally amplify the influence of underperforming tasks and reduce that of tasks already performing well. This simple yet effective strategy enables the model to dynamically balance learning across tasks, and our experimental results confirmed its ability to stabilize training and improve overall performance in the multi-task learning setting.
## 2 Structure, Theory, and Numerical Model
### Model Structure and Mesh
Each SOFC unit has a total of 680,716 mesh cells, and the meshes at the electrodes are encrypted. The meshes for the inlet and outlet manifolds are twice as large as those of the channels because these areas have only pure fluid flow and mesh encryption is not necessary. There is no difference in the meshes between the anode, cathode, and electrolyte layers; and they are uniformly set to the same size meshes. From the model's overall structural mesh diagram and locally enlarged diagram, the SOFC unit has a very high mesh quality, and hexahedral meshes are used. The meshes do not have a large length and width aspect ratio, which guarantees the correctness and efficiency of the calculation.
# A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets
## 3 Methodology
### xy-Pair-Set: Problem Transformation  
The proposed transformation method _xy_-pair-set transforms a multi-label classification dataset \(D\) into a supervised binary dataset \(\hat{D}\) as follows:  
\[\begin{split}\forall x_{i}\in X,y\in Y\text{ and }(x_{i},\hat{Y}_{i})\in D,\exists!((x_{i},y),\phi)\in\hat{D}\\ \text{ where }\phi=\begin{cases}1&\text{if }y\in\hat{Y}_{i} \\ 0&\text{otherwise}\end{cases}\end{split} \tag{2}\]  
Algorithm 1 explains the implementation of the proposed transformation method. It takes as inputs a multi-label dataset \(D\) (Equation (1)) and a set of labels \(Y\), and it returns a transformed binary dataset. We show next an illustrative example.  
```
Input: Input: a multi-label classification dataset \(D\) and a set of labels \(Y\) Output: Output: a binary classification dataset \(\hat{D}\)
1\(\hat{D}=\{\}\);
2 foreach\((x_{i},\hat{Y}_{i})\in D\)do
3 foreach\(y\in Y\)do
4\(\hat{x}_{i}=(x_{i},y)\) ; \(\triangleright\) a tuple of \(x_{i}\) and \(y\).
5 if\(y\in\hat{Y}_{i}\)then
6\(\hat{D}=\hat{D}\bigcup(\hat{x}_{i},1)\) ;
7
8 else
9\(\hat{D}=\hat{D}\bigcup(\hat{x}_{i},0)\) ;
10
11 end if
12
13 end for return\(\hat{D}\)
```  
**Algorithm 1**_xy_-pair-set algorithm.  
Let \(X=\{x1,x2\}\), \(Y=\{a,b,c\}\) and \(D=\{(x1,\{a,c\}),(x2,\{b\})\}\). The output of the binary relevance transformation method is a set of three independent binary datasets, one for each label. That is, \(D_{a}=\{(x1,1),(x2,0)\}\), \(D_{b}=\{(x1,0),(x2,1)\}\), and \(D_{c}=\{(x1,1),(x2,0)\}\). In contrast, the output of our transformation method is a single binary dataset \(\hat{D}=\{((x1,a),1),((x1,b),0),((x1,c),1),((x2,a),0),((x2,b),1),((x2,c),0)\}\).  
The task in this case, unlike the traditional supervised binary classification algorithms, is to develop a learning algorithm to learn a function \(g:X\times Y\rightarrow\{0,1\}\). The success of such an algorithm is based on three requirements: (1) an encoding method to represent an instance \(x\in X\) as a high-dimensionalvector \(V_{x}\), (2) a method to encode a label \(y\in Y\) as a vector \(V_{y}\), and (3) a method to represent the relation between the instance \(x\) and the label \(y\). These three conditions make \(g\) able to capture the relationships inputs-to-labels and labels-to-labels. In this work, we take advantage of the successes of deep learning models to fulfill the three requirements listed above. We empirically show the success of our system with respect to these conditions as reported in Sections 4.6 and 4.7.
## 6 Case Studies
### Analysis of the Robust Model on Planning Result  
Robust programming model can increase the adaptability of the system to uncertainty. The specific analysis is as follows.  
(1) Comparative analysis of Case 1 and Case 2  
In Case 1, the total installed capacity of \(DG\) is 1600 kW, the penetration rate of clean energy is 40.0%. The annual average comprehensive cost is USD 7.4136 million, of which the network loss cost is USD 250,300, the \(DG\) comprehensive cost is USD 1.8628 million, and the power purchase cost from the superior power grid is USD 5.305 million.  
In Case 2, the robust planning model is used to plan the network without considering the network reconfiguration, and only the upper model is solved. In this planning scheme, the robust planning model is adopted. The planning scheme is more conservative, and the total installed capacity of \(DG\) increases. As shown in Table 1, the total installed capacity of \(DG\) is 1800 kW (penetration rate is 45.0%), increasing by 200 kW. \(DG\)'s comprehensive expenses increased by USD 184,200. However, the utilization rate of \(DG\) in the system has been improved, so the cost of purchasing power from the large power grid has been reduced by USD 770,900. At the same time, due to the improved utilization of \(DG\), the source load distance is greatly shortened, reducing the network loss by 20.18%, which is of great significance for saving electric energy and improving the utilization of clean energy. The annual average comprehensive cost of case 2 is USD 637,200 lower than that of Case 1.  
(2) Comparative analysis of Case 3 and Case 4  
The network reconfiguration is considered, but a deterministic planning model is adopted in Case 3, while network reconfiguration is considered and a robust programming model is adopted in Case 4. Table 1 and Figure 6 show that when considering network reconfiguration, the impact of the robust optimization model on the planning results is similar to the comparative analysis of Case 1 and Case 2. Due to the adoption of robust programming model, compared with Case 3, the total installed capacity of \(DG\) in Case 4 is increased by 300 kW, thus the comprehensive cost of \(DG\) is increased by USD 280,400. However, the power purchase cost and network loss decreased by USD 497,800 and USD 101,100, respectively. The reconstruction cost was reduced by USD 19,200. The comprehensive cost decreased by USD 337,700. In particular, the network loss cost of Scenario 4 is only USD 53,200, which is significantly reduced compared with other scenarios. This is precisely because Case 4 considers both network reconfiguration and a robust optimization model. Thus, the power flow is optimized, the \(DG\) is absorbed locally, the transmission power on the line is reduced, and the network loss is reduced.  
To sum up, using a _DG_ robust planning model considering network reconfiguration to plan ADN can not only reduce network loss and delay _DG_ investment, but also improve _DG_ utilization, reduce power purchase cost from large power grid, and improve the economy of distribution network.
# Oxidation-Reduction of Ti-6 Al-4V in Direct Energy Deposition Subject to Minimum Argon Consumption
## 5 Discussion  
### High Initial Activation Flow Rate  
Analyzing the results above, it is evident that reaching an optimum shielding effect takes 40 s for the gas flow. In an additive manufacturing process, 40 s is too lengthy to turn on the attachments and create a safe environment for the deposition process, as Ti64 is a reactive material and can oxidize within that time frame. Deposition paths that require a quick transition of attachments might risk oxidation of Ti64 due to extended activation time.  
A closer examination of the graphs reveals that high flow rates reduce the time required to achieve shielding. Hence, an initial high flow rate of the shield gas through the attachments will reduce the oxygen concentration in the surroundings and make a suitable environment for deposition. As shown in Figure 14, it takes 5 and 7 s at flow rates of 50 and 60 SCFH, respectively, to bring down the oxygen concentration to 0 PPM levels. The slope of the curve indicates that the oxygen concentration decreases rapidly after the attachments are activated, followed by a slow decline. Therefore, to reduce the activation time of these attachments, maintaining a baseline flow of 5 SCFH in each nozzle at all times might help lower the ambient oxygen levels, enabling a quicker response from the attachments.  
Figure 13: Numerical simulation results showing the top view of a plane located 10 mm below the base of the attachment. The figure illustrates the argon concentration percentages for flow rates of 20, 25, 30, and 35 SCFH within the attachment.