esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Trace Element Composition  
According to the analysis results, the gold-colored and silver-colored glass beads were relatively similar in their main composition, excluding the abundance of chemical elements correlated to the colorants of the glass, and the inner and outer glass surrounding the metal foil was identified as being made of the same components. The flux, stabilizer, characteristics of the raw materials, and colorants were referred to in the composition classification of glass excavated in south Korea [13, 14, 15]. Regarding flux characteristics, the Na\({}_{2}\)O content ranged from 16.6 to 18.5% on average, indicating that the beads were made of soda glass. The CaO content was high, in the range of 5.2 to 7.2% for the stabilizer component, which was identified as an HCLA (CaO \(>\) 5%, Al\({}_{2}\)O\({}_{3}\) \(<\) 5%). It was measured to be 2.60-3.26% MgO and 2.96-4.14% K2O. Therefore, it is identified as HMK type, plant ash glasses. Gold- and silver-colored beads were further classified depending on the colorant, such as TiO2, MnO, Fe\({}_{2}\)O\({}_{3}\), and CuO, which are the main color pigments of glass. The gold-colored beads had a high Fe\({}_{2}\)O\({}_{3}\) content, ranging from 4.9 to 5.8%, which indicates that the silver foil beads appeared amber in color. Thus, the gold-colored beads were silver foil beads, as their inner metal foil was identified as silver through the composition analysis, whereas their outer and inner glasses were fabricated to be amber in color by using Fe\({}_{2}\)O\({}_{3}\) to give them the appearance of gold foil beads. The silver foil beads' low Fe\({}_{2}\)O\({}_{3}\) content of 0.2% was manufactured so that the inner silver foil was visible, because their outer and inner glass were colorless (Figure 4A). Although gold-colored silver foil glass beads and silver-colored silver foil glass beads were identified as a glass of soda-plant ash, the principal component statistical analysis was performed to determine whether the glass was made using a similar material. The silver foil glass beads of King Muryeong were classified into three types (Figure 4B). Type I was gold-colored silver foil beads (mr-13, 15) that had an amber glass color. Type II corresponded to silver-colored silver foil beads (mr-16, 17) that were colorless glass. Type III consisted of a gold-colored (mr-14) and a silver-colored (mr-18) silver foil glass bead. Therefore, it was confirmed that the silver foil glass beads of King Muryeong were of the same flux and stabilizer, but the gold-colored and silver-colored silver foil beads were manufactured using different raw materials.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 3 Model
### User Distribution Model [23]  
Depending on Satellite Image of Urban Night Lights, we generate the following traffic distribution to launch the simulation. In the Figure 6, the bluer the color, the higher the number of users. The red and blue dashed lines represent the virtual cell boundaries over land and sea, respectively.  
We have determined the distribution of users based on the above graph, but we still need to determine the traffic per user. In this article, we set user broadband services packets to follow a standard normal distribution with \(\mu=15\) and \(\sigma=5\), on the other hand, we set user delay services packets to follow a uniform distribution with \(\mu=1\).
## 2 Damage in Sewage Concrete Structure Due to Bio-Chemical Deteriorations
During the process of reducing sulfate ions, S2O32\({}^{-}\) and sulfide (S) are generated as intermediates, acting as an energy source for sulfur-oxidizing bacteria [40]. The third stage of the bio-chemical deterioration of concrete by acidophilic sulfur oxidizing bacteria is the formation of sulfuric acid through the supply of oxygen and water. Sulfur-oxidizing bacteria generate sulfuric acid in the process of acquiring energy for proliferation and growth, and the generated sulfuric acid causes circulating deterioration through a chemical reaction with cement hydrates on the surface of the sewer pipe in contact with the microbial colony. Through the above stages, when the concrete surface is exposed to a sulfate-dominant environment, sulfuric acid reacts with cement hydrates to produce dihydrate gypsum (CaSO42H2O) and anhydrous gypsum (CaSO4), as shown in Equations (2) and (3) [34]. Dihydrate gypsum is a water-soluble material, and as it easily dissolves inside the cement matrix, the structure becomes porous, accelerating the degradation of concrete. In addition, as shown in Equation (4), anhydrous gypsum reacts with aluminate (C3A, 3 CaO-Al2O3) in cement to form ettringe (3 CaO-Al2O3-3 CaSO4.32H2O), leading to volume expansion and threatening dimensional stability, and thus resulting in cracks [41]. Indeed, these cracks accelerate the intrusion of external deteriorating agents and have a critical effect on the corrosion of internal reinforcing bars [42]:  
\[\text{Ca(OH)}_{2}+\text{H2 SO}_{4}\rightarrow\text{CaSO}_{4}\cdot 2\text{H2O} \tag{2}\]  
Figure 2: Metabolic reaction process of sulfate-reducing bacteria. \[\mathrm{CaO}\cdot\mathrm{SiO}_{2}\cdot 2\mathrm{H}_{2}\mathrm{O}+ \mathrm{H}_{2}\mathrm{SO}_{4}\rightarrow\mathrm{CaSO}_{4}+\mathrm{Si}(\mathrm{ OH})_{4}+\mathrm{H}_{2}\mathrm{O} \tag{3}\] \[3\mathrm{CaO}\cdot\mathrm{Al}_{2}\mathrm{O}_{3}+3(\mathrm{CaSO}_{4} \cdot 2\mathrm{H}_{2}\mathrm{O})+26\mathrm{H}_{2}\mathrm{O}\rightarrow 3\mathrm{CaO}\cdot\mathrm{Al}_{2}\mathrm{O}_{3} \cdot 3\mathrm{CaSO}_{4}\cdot 32\mathrm{H}_{2}\mathrm{O} \tag{4}\]
## 6 Connection between Different Stability Notions  
For a given two-sided model with choice functions \(F\) and \(G\) for each side, we have defined four kinds of stability:  
1. Subset \(S\) of \(E\) is _three-stable_, if there exists subsets \(A\) and \(B\) of \(E\), such that \(F(A)=S=G(B)\) and \(A\cup B=E\), \(A\cap B=S\).
2. Subset \(S\) of \(E\) is _four-stable_, if there exists subsets \(A\) and \(B\) of \(E\), such that \(F(A)=S=G(B)\) and \(A\cap B=S\) and \(\mathcal{D}_{F}(A)=E\setminus B\) and \(\mathcal{D}_{G}(B)=E\setminus A\).
3. Subset \(S\) of \(E\) is _dominating stable_, if \(\mathcal{D}_{F}(S)\cup\mathcal{D}_{G}(S)=E\setminus S\).
4. If \(F\) is substitutable, \(G\) is substitutable and loser-free; we defined _generalized score-stability_ in Subsection 3.5  
**Theorem 15**.: _If \(F\) and \(G\) are substitutable and path independent, then \(S\) is three-stable \(\Leftrightarrow S\) is four-stable \(\Leftrightarrow S\) is dominating stable._  
**Theorem 16**.: _If \(F\) and \(G\) are substitutable choice functions, \(F\) is path independent, (but \(G\) may not be); then, every four-stable set is three-stable._  
Compared to other stability concepts, three-stability, four-stability and dominating stability are defined on every substitutable choice functions, \(F\) and \(G\), but for score stability, we need substitutability on one side and a substitutable, loser-free function on the other side.  
As we showed in Theorem 13, if choice function \(F\) is substitutable and path independent and \(G\) is substitutable loser-free, then every score-stable set is also four-stable.  
**Theorem 17**.: _If \(F\) is substitutable and path independent and \(G\) is substitutable and loser-free, then every score-stable solution is three-stable._  
Theorems 13, 15, 16 and 17 are summarized in Figure 5 below. In the notations, \(3\) stands for three-stable, \(4\) for four-stable, \(d\) for dominating stable and \(s\) for score-stable sets.  
If the graph is simple: If \(F\) and \(G\) are path independent, all four properties are equivalent; If \(F\) is path independent, four-stablility is equivalent to score-stability; If \(F\) and \(G\) are not path independent, similarly to the upper picture, only score-stable \(\Rightarrow\) three-stable is true.  
**Statement 7**.: _For all the other directions in Theorems 13, 15, 16 and 17, if one implication \"if \(S\) is \(x\)-stable, then it is \(y\)-stable\" does not appear in the upper diagram, then there exists a counterexample for it._
## 2 Background and the Related Work
### Software-Defined Networking (SDN)
#### 2.1.2 Control Layer  
The controller manages the flow of data in the network. The southbound APIs enable the controller to configure the forwarding devices, proceed traffic to the data plane in accordance with forwarding policies, and determine the best network path. In essence, the controller provides a centralized view of the network for better management and performance tuning, and it offers network abstraction for high-level network functionality. Some examples of controllers are ONOS, OpenDaylight, Floodlight, Beacon, Ryu, and POX etc. [14, 15]. Specifically, the southbound interface named OpenFlow allows for communication between the control layer and the network layer. [PERSON] et al. [16] explained the analysis on the SDN controller based on throughput, response time, etc. To analyze the performance depending on variables like RTT, jitter, delay, etc., authors employed a mininet emulator that includes a Ryu controller.
# New Eldercare Robot with Path-Planning and Fall-Detection Capabilities
## 5 Discussions  
The developed robot has performed both path planning and fall detection successfully, but there are several observations that were found. For path planning implementation, it was found that the robot experiences some difficulty in reaching its destination when there are dynamic obstacles (other than the subject) in the map. Moreover, for fall detection to be optimum and achieve the highest accuracy, the robot must be situated in a distance between 185 and 220 cm from the subject. This result is shown in Figure 15a. Hence, 200 cm is chosen as the default distance and is continuously monitored by the Lidar sensor. This remark also explains the need for the path-planning feature in the new eldercare robot. Additionally, the robot velocity is also a contributing factor in increasing the detection accuracy. Several experiments were performed to determine the fall-detection accuracy with respect to the expected subject speed. The fall detection accuracy ranges from 96.3% to 95.1% when the fall is at a velocity of 0 to 1.1 m/s, respectively. Such a speed range is convenient to the expected speed of the elder subject age range (60 to 80 years old) [27]. Different robot velocities were compared regarding the fall-detection accuracy, as can be seen in Figure 15b.  
Figure 14: Real images for fall-detection classification.  
Figure 13: The confusion matrix for the fall-detection classification.  
Based on the literature of eldercare robots that was explained in Section 1, several remarks are observed as well: Firstly, it is difficult to find a robot with both indoor navigation and fall-detection capabilities. Secondly, most of the robots in the literature use pose estimation instead of object detection in fall detection, which consumes more processing power and decreases the prediction accuracy [28]. The developed robot and its features were compared to other eldercare robots in the literature, as can be seen in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Mechanical** & \multirow{2}{*}{**Sensors**} & **Indoor Navigation** & **Fall Detection** \\  & **Design** & & **Capability (Y/N)** & **Technique (Y/N)** \\ \hline \multirow{2}{*}{Matilda [7]} & \multirow{2}{*}{Differential} & Ultrasonic, & \multirow{2}{*}{\begin{tabular}{c} Y \\ (Obstacle avoidance) \\ \end{tabular} } & \multirow{2}{*}{N} \\  & & & & \\ \hline \multirow{2}{*}{Hobbit [8]} & \multirow{2}{*}{Differential} & \
\begin{tabular}{c} Bumper, \\ Temperature, \\ Sound, Camera \\ \end{tabular} & N &
\begin{tabular}{c} Y \\ (Pose estimation \\ and object \\ detection) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison between the new eldercare robot and other robots which were mentioned in the literature.  
Figure 15: Experimental comparison that shows the effect of a change in distance and robot velocity with the fall-detection accuracy (**a**) Experimental distance of the eldercare robot vs. fall-detection accuracy; (**b**) Experimental velocity of robot vs. fall-detection accuracy.
# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
## 3 Dataset and Methods
### Dataset
#### 3.1.2 Data Processing  
Data Format Transformation  
This study employs Python for the analysis and processing of raw JSON-formatted data. Due to the multi-layered nesting in the data, a step-by-step traversal approach is employed to extract content, followed by regularization to ensure uniformity in data format. Upon regularization, essential data dimensions are extracted from the JSON file and stored using DataFrame, a data storage format in the pandas library, with predefined dimension titles and quantified representations. Different feature dimensions necessitate varied processing approaches, such as statistical computation for total filing counts, keyword extraction and counting for plaintiff and defendant quantities, and summing amounts for executed funds. Some data dimensions contain extensive information, requiring further iterative traversal. Upon completion of company dimension data traversal, the data are stored in the DataFrame and subsequently saved as an Excel spreadsheet for further analysis.  
During data processing, challenges were encountered regarding missing data dimensions and format errors. To address format errors, an error-jumping approach was adopted, and zero-padding was applied for missing company dimensions. Following the statistical analysis of data dimensions, attention shifts to the analysis of enterprise news data. News data are sourced from two parts: website-packaged data and search engine-derived data. These are merged into a single list for subsequent iterative traversal operations.  
Data Normalization Process  
Before conducting model analysis, it is necessary to preprocess and normalize the original data to address significant data variations. The normalization methods used in this study are as follows:  
* MinMax Scaling This method scales features to a specified range, usually between 0 and 1. It is suitable when features have similar ranges or when a specific range is required.
* Z-Score Normalization This method adjusts data to be centered at zero with a standard deviation of one. It is useful for models sensitive to input scales, allowing each feature to have equal influence in distance calculations.
* Log Scaling Log transformation reduces data skewness and mitigates the effects of outliers, making the data distribution more symmetric. It is particularly effective for data spanning multiple orders of magnitude.
# The Progress on Lung Computed Tomography Imaging Signs: A Review
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Author** & **Year** & **Dataset** & **Sensitivity \%** & **Specificity \%** & **Accuracy \%** & **Others** & **AUC** & **Task** & **Method Type** \\ \hline [PERSON] et al. & 2016 & LBS & **100** & 33.13 & - & - & - & - & GOO segment & Threshold \\ [PERSON] et al. & 2018 & LBS & **100** & - & - & - & - & GOO segment & Threshold \\ [PERSON] et al. & 2019 & LBS & 56.37 & **92.29** & 89.89 & - & - & - & Califoldeion segment & Morphology \\ [PERSON] et al. & 2015 & LBS & 70 & - & - & F1-score 0.80 & - & - & LBS classification & Deep learning \\ [PERSON] et al. & 2016 & LIDS-Others & 70 & - & F1-score 0.74 & - & lymph node and LIDS classification & Deep learning \\ [PERSON] et al. & 2018 & LIDS-IDRI & **96.64** & 71.43 & 82.51 & F1-score 0.83 & - & - & CAO classification & Deep learning \\ [PERSON] et al. & 2019 & LIDS-IDRI & 85 & 99 & - & - & - & Cavity classification & Deep learning \\ [PERSON] et al. & 2012 & Private & **91.8** & 98.5 & 98 & - & - & Nine Class classification & Machine learning \\ [PERSON] et al. & 2014 & LIDS & 70.2 & **97.2** & 80.2e & - & - & Nine Class classification & Machine learning \\ [PERSON] et al. & 2015 & Private & 70.4 & **96.54** & 76.79 & - & - & Nine Class classification & Machine learning \\ [PERSON] et al. & 2016 & Private & **97.65** & **90.3** & 91.96 & - & - & Nine Class classification & Machine learning \\ [PERSON] et al. & 2019 & LIDS & **92.73** & **99** & **91.83** & - & - & Nine Class classification & Deep learning \\ [PERSON] et al. & 2019 & LIDS-IDRI & - & - & - & - & Five Class classification & Deep learning \\ [PERSON] et al. & 2019 & LIDS-IDRI & 8.2 & - & - & - & Mean AUC.09184 & - & Five Class classification & Deep learning \\ [PERSON] et al. & 2017 & LIDS & - & - & - & mAcc. 77.49\% & 0.4854 & - & Nine Class retrieval & Machine learning \\ [PERSON] et al. & 2017 & LIDS-IDRI & - & - & **93.32** & - & - & - & - \\ [PERSON] et al. & 2020 & LIDS & - & - & - & mAcc. 70\% & 0.58 & - & Nine Class retrieval & Machine learning \\ [PERSON] et al. & 2020 & Private & - & - & 80 & - & - & Nine Class retrieval & Machine learning \\ [PERSON] et al. & 2021 & LIDS-IDRI & Private & - & **94.83** & - & - & Nine Class retrieval & Deep learning \\ [PERSON] et al. & 2021 & LIDS & - & - & mAcc. 98\% & - & - & Nine Class retrieval & Deep learning \\ \hline \end{tabular} Note: The table contents are sorted first by the task column and secondly by year, mAcc - mean accuracy.  
\end{table} 
Table 3: The performance comparison of detection methods for CISs.
# LightR: A Fault-Tolerant Wavelength-Routed Optical Networks-on-Chip Topology +
###### Abstract  
Optical networks-on-chip (NoCs) have emerged as a next-generation solution to overcome the limitations of electrical NoCs. In particular, wavelength-routed optical networks-on-chip (WRONoCs) are well known for their high bandwidth and ultra-low signal delay. Despite these advantages, WRONoCs are challenged by reliability concerns, because the main components in WRONoCs, i.e., microring resonators (MRRs), are susceptible to fabrication inaccuracies. When an MRR along a signal path is defective, the signal transmitted on that path will fail to reach its designated destination, which leads to transmission errors and data loss. In this work, we propose a fault-tolerant WRONoC topology, LightR, which provides two independent signal paths for each master-slave pair to tolerate defective MRRs. Moreover, we minimize the MRR usage to enhance the reliability of the WRONoCs. The experimental results show that LightR is able to provide a higher reliability with a modest MRR usage, insertion loss, and crosstalk noise. As the fault rate or the network size grows, the advantages of LightR in terms of the fault tolerance become even more significant. For example, when considering the 3% fault rate of MRRs and a 64-master \(\times\) 64-slave network, LightR decreases the number of error signals by 85-90% compared to the typical state-of-the-art WRONoC topologies.  
wavelength-routed optical networks-on-chip; fault-tolerant topology; reliability +
Footnote †: journal: Applied Sciences
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
###### Abstract  
Significant advances have been made in emotional speech synthesis technology; however, existing models still face challenges in achieving fine-grained emotion style control and simple yet precise emotion intensity regulation. To address these issues, we propose Easy-Control Emotion Text-to-Speech (ECE-TTS), a zero-shot TTS model built upon the F5-TTS architecture, simplifying emotion modeling while maintaining accurate control. ECE-TTS leverages pretrained emotion recognizers to extract Valence, Arousal, and Dominance (VAD) values, transforming them into Emotion-Adaptive Spherical Vectors (EASV) for precise emotion style representation. Emotion intensity modulation is efficiently realized via simple arithmetic operations on emotion vectors without introducing additional complex modules or training extra regression networks. Emotion style control experiments demonstrate that ECE-TTS achieves a Word Error Rate (WER) of 13.91%, an Aro-Val-Domin SIM of 0.679, and an Emo SIM of 0.594, surpassing GenerSpeech (WER = 16.34%, Aro-Val-Domin SIM = 0.627, Emo SIM = 0.563) and EmoSphere++ (WER = 15.08%, Aro-Val-Domin SIM = 0.656, Emo SIM = 0.578). Subjective Mean Opinion Score (MOS) evaluations (1-5 scale) further confirm improvements in speaker similarity (3.93), naturalness (3.98), and emotional expressiveness (3.94). Additionally, emotion intensity control experiments demonstrate smooth and precise modulation across varying emotional strengths. These results validate ECE-TTS as a highly effective and practical solution for high-quality, emotion-controllable speech synthesis.  
ECE-TTS; emotional speech synthesis; zero-shot text-to-speech Article
## 5 Discussion
### Limitations of This Study  
Although the presented study is based on a quite large sample size, and pre- and post-intervention surveys increase the reliability of the research, this study presents a limited generalisability of the findings as it was performed only in a particular scenario in Peru. Further experiments with senior populations in other settings around the world and the use of control groups would allow the results obtained to be generalised.
## 2 Literature Review
[PERSON] et al. [31] dealt with task allocation and worker assignment in a UALBP, in which the processing time of each task depended on the workers. Then, an enhanced migrating birds optimization algorithm (EMBO) was used to minimize the cycle time. The numerical results demonstrate that the proposed algorithm outperforms other algorithms. [PERSON] et al. [24] formulated a UALBP with worker assignment to simultaneously minimize cycle times and ergonomic risks. In the proposed problem, operators can perform all tasks with potentially different processing times, and each task is executed with a type of posture (position and movement of the upper limbs of an operator). A Restarted Iterated Pareto Greedy algorithm was designed to optimize both objectives. The proposed multi-objective algorithm outperforms existing methods on a large number of benchmark instances. In all the above literature, walking time is not considered. Studies that deal with walking time in UALBPs are rare. [PERSON] and [PERSON] [15] dealt with a U-shaped production line and considered processing, operation and walking times. They formulated the optimal worker allocation problem, which minimized the overall cycle time and showed the advantages of the U-shaped layout over the linear layout. [PERSON] and [PERSON] [32] considered the optimal worker allocation problem for a U-shaped production line. They proposed an algorithm to find the optimal allocation of workers to machines that minimizes the cycle time for the minimum number of workers. [PERSON] [33] addressed the worker allocation problem for lean U-shaped production lines. A mathematical model that takes into account walking time, with workers following circular paths and walking around other workers, was developed. A heuristic algorithm was developed to optimize the allocation of workers. The above literature related to U-shaped production lines deals with walking time, but all the studies focus on the worker allocation problem in which the locations of machines (or tasks) are fixed. Therefore, they ignore the alternative locations of tasks that follow precedence relationships. The present research deals with a U-shaped production line balancing problem in which the precedence relationship between tasks and walking times between the locations of tasks are simultaneously taken into consideration. The problem that the present study addresses is not found in the literature. This is the main contribution of this research.