esa-sceva/satcom-chunk-collection huggingface dataset:
# LCA as a Tool for the Environmental Management of Car Tire Manufacturing
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Impact Category**} & \multicolumn{2}{c}{**Production**} & \multicolumn{2}{c}{**USE**} & \multicolumn{2}{c}{**End of Life**} \\ \cline{2-7}  & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** \\ \hline Carcinogens & \(1.51\times 10^{0}\) & \(1.18\times 10^{0}\) & \(9.43\times 10^{-4}\) & \(9.33\times 10^{-4}\) & \(-4.04\times 10^{-3}\) & \(3.06\times 10^{-3}\) \\ \hline Respiratory organics & \(2.34\times 10^{-3}\) & \(1.50\times 10^{-3}\) & \(1.72\times 10^{-2}\) & \(1.63\times 10^{-2}\) & \(-3.44\times 10^{-4}\) & \(-5.78\times 10^{-4}\) \\ \hline Respiratory inorganics & \(4.96\times 10^{0}\) & \(3.87\times 10^{0}\) & \(2.84\times 10^{1}\) & \(2.69\times 10^{1}\) & \(-2.22\times 10^{-2}\) & \(-3.88\times 10^{-2}\) \\ \hline Climate change & \(1.81\times 10^{0}\) & \(1.41\times 10^{0}\) & \(1.24\times 10^{1}\) & \(1.17\times 10^{1}\) & \(-1.15\times 10^{-2}\) & \(-1.38\times 10^{-3}\) \\ \hline Radiation & \(2.72\times 10^{-3}\) & \(2.08\times 10^{-3}\) & \(1.78\times 10^{-7}\) & \(1.68\times 10^{-7}\) & \(0.00\times 10^{0}\) & \(0.00\times 10^{0}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of grouping and weighting of environmental damage at individual stages of the traditional and ecological tire life cycle, including impact categories [unit: Pt]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Substance**} & \multicolumn{2}{c}{**Production**} & \multicolumn{2}{c}{**USE**} & \multicolumn{2}{c}{**End of Life**} \\ \cline{2-7}  & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** & **Traditional Tire** & **Eco Tire** \\ \hline Arsenic, ions & \(1.35\times 10^{9}\) & \(1.05\times 10^{9}\) & \(7.92\times 10^{-4}\) & \(7.79\times 10^{-4}\) & \(-4.62\times 10^{-3}\) & \(1.03\times 10^{-3}\) \\ \hline Cadmium, ions & \(1.15\times 10^{-1}\) & \(8.93\times 10^{-2}\) & \(9.28\times 10^{-5}\) & \(9.28\times 10^{-5}\) & \(-1.35\times 10^{-4}\) & \(2.87\times 10^{-5}\) \\ \hline Fine particles \(<2.5\,\mathrm{\SIUnitSymbolMicro m}\) & \(2.03\times 10^{-2}\) & \(1.59\times 10^{-2}\) & \(6.06\times 10^{-5}\) & \(6.06\times 10^{-5}\) & X & - \\ \hline Cadmium & \(1.61\times 10^{-2}\) & \(1.24\times 10^{-2}\) & \(2.16\times 10^{-7}\) & \(2.16\times 10^{-7}\) & \(-3.05\times 10^{-4}\) & \(1.04\times 10^{-4}\) \\ \hline Arsenic & \(1.55\times 10^{-2}\) & \(1.21\times 10^{-2}\) & \(7.59\times 10^{-8}\) & \(7.59\times 10^{-8}\) & - & - \\ \hline Unspecified metals & \(3.18\times 10^{-7}\) & - & - & - & \(1.34\times 10^{-3}\) & \(2.04\times 10^{-3}\) \\ \hline Doxins & - & \(1.01\times 10^{-3}\) & \(2.71\times 10^{-7}\) & \(2.71\times 10^{-7}\) & - & - \\ \hline \end{tabular}
\end{table} 
Table 4: Results of grouping and weighting of environmental damage from carcinogens at individual stages of the traditional and ecological tire life cycle [unit: Pt].
## 2 Results
### Functional Annotation of the Unigenes  
We annotated 40,850 unigenes using seven databases, with the NR database having the highest number of annotations at 25667, accounting for 62.83% of all genes. This was followed by Swissprot (57.05%), NT (43.78%), KO (26.78%), Pfam (53.61%), GO (53.61%), and KOG (23.56%) (Figure 2B). In addition, 72.89% of the genes were annotated in at least one database, and 9.73% of the genes were annotated in all databases.  
In the NR database, it was found that _A. paniculata_ had the highest similarity with _Sesammum indicium_ at 36.5%, followed by _Handroanthus impetiginosus_ (17.3%), _Erythranthe guttata_ (6.4%), _Quercus super_ (4.3%), _Salvia splendens_ (3.4%), and others (32.1%) (Figure S1A).  
The KOG database allowed us to predict and classify the functions of unigenes, with 9628 unigenes being annotated and classified into 25 groups (Figure S1B; Table S3). The largest category was Post-translational modification, protein turnover, and chaperones with 1284 unigenes, followed by General function prediction with only 1131 unigenes, and Translation, ribosomal structure, and biogenesis with 1006 unigenes.  
The KEGG metabolic pathways in _A. paniculata_ transcriptome could be divided into four categories: environmental information processing, cellular processes, metabolism, and genetic information processing, including 24 branches and 302 metabolic pathways (Figure 3A; Table S4). The top ten pathways in annotation were Ribosome (1043), Carbon metabolism (832), Spliceosome (792), Protein processing in endoplasmic reticulum (722), Biosynthesis of amino acids (647), RNA transport (632), Endocytosis (545), Purine metabolism (467), Plant pathogen interaction (454), and Oxidative phosphorylation (447).  
In the GO database, 21,901 unigenes were annotated, accounting for 53.61% of the total. According to the GO function, these were divided into three categories: molecular function, cellular component, and biological process (Figure 3B; Table S5). In the annotation results, all three types of functions were involved and were annotated in 43 second-level branches. In the molecular function category, the main branches were Binding proteins with 12,175 unigenes and Catalytic activities with 10,401 unigenes. In the cellular component category, the main branches were Cellular anatomical entity with 9997 unigenes, Intracellular with 6028 unigenes, and Protein-containing complex with 4620 unigenes. In the biological process category, the main branches were Cellular processes with 13,321 unigenes and Metabolic processes with 12,339 unigenes.  
Figure 2: Transcription splicing and database annotation. (**A**) Transcript and unigene length distribution. (**B**) Number of gene annotations in the database.
## 3 Simulation and Results
### Area-A
#### 3.4.2 Area Characterization  
The area can be characterized by identifying the relationship between the CQI and SINR values. Figure 7 shows the CQI versus SINR curve for terrestrial APs considering the CDL profiles for all SNs. It can be observed that the LoS data set is clustered at higher SINR values than the NLoS data. For simplicity, each data set is modeled as a linear equation, as follows:  
\[CQI=a_{1}\ SINR+b_{1}, \tag{9}\]  
Figure 5: Variation in CQI between customized and standard CDL channel profiles, Area-A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Terrestrial APs** & **UAV-Assisted Network** & **Improvement** \\ \hline RSS mean (dBm) & \(-94.32\) & \(-63.87\) & \(32\%\) \\ SINR mean (dB) & \(-0.55\) & \(20.69\) & \(3862\%\) \\ LoS channels (\%) & \(38.2\) & \(100\) & \(162\%\) \\ ASE (bps/Hz) & \(3.417\) & \(6.973\) & \(104\%\) \\ Average CQI & 6 & \(10\) & \(67\%\) \\ Average Network DL & \(7.44\) & \(10.7\) & \(44\%\) \\ throughput (Mbps) & \(7.44\) & \(10.7\) & \(44\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Key performance indicators for area-A.  
where \(a_{1}\) and \(b_{1}\) are constants that depend on the environmental characteristics and _SINR_ is (in dB). Certain exclusion rules can be imposed to increase goodness of fit. For example, the _CQI_ values are 1 whenever the _SINR_ is less than or equal to \(-25\) dB for the case of _NLoS_ or less than or equal to 0 dB for the case of _LoS_. On the other hand, the _CQI_ values are 15 when the _SINR_ values exceed 20 dB in the _NLoS_ scenario, and if they exceed 45 in the case of LoS. The results of modeling the _CQI_ vs. _SINR_ curves will be a piecewise linear equation illustrated in (10) and (11), where _CQI\({}_{NLoS}\)_ is for the case of _NLoS_ and _CQI\({}_{LoS}\)_ is for the case of LoS.  
\[CQI_{NLoS}=\begin{cases}1&if\ SINR\leq-25\\ 0.3975\ SINR+7.918\ if-25<SINR\leq 20\\ 15&if\ SINR>20.\end{cases} \tag{10}\]  
\[CQI_{LoS}=\begin{cases}1&if\ SINR\leq 0\\ 0.3822\ SINR+1.742\ if\ 0<SINR\leq 45\\ 15&if\ SINR>45\end{cases} \tag{11}\]  
The goodness of fit is evaluated in terms of R-squared and root mean square error (RMSE), which are found to be 0.6 and 3.1, respectively, for _NLoS_ and 0.6 and 3.5 for _LoS_. It is worth noting that acceptable ranges for R-squared and RMSE  
Figure 6: Variation in CQI between standard TDL and CDL channel profiles, Area-A.  
Figure 7: Relation between SINR values and resulting CQI, Area-A.
# External Load \"Periodization\" during Pre-Season and In-Season in a High-Level Futusal Team
## 3 Results  
### Training Microcycles  
Table 2 shows the external load for the analyzed microcycles in each training period. In the pre-season, TD was significantly lower in PS-M1 and PS-M3 than in PS-M2 and PS-M4 (\(p\) < 0.01 and \(p\) < 0.05). Regarding Drel, PS-M1 presented lower values compared to PS-M2 and PS-M3 (\(p\) < 0.05). For HSR, players achieved lower values in PS-M1 than in PS-M2 (\(p\) < 0.05), PS-M3 (\(p\) < 0.01), and PS-M4 (\(p\) < 0.01). Additionally, HSR was lower in PS-M3 compared to PS-M4 (\(p\) < 0.05). The SPR values in PS-M1 and PS-M3 were also lower compared to those in PS-M4 (\(p\) < 0.05). A lower number of ACC was recorded during PS-M1 compared with PS-M2 and PS-M4 (\(p\) < 0.05). Additionally, ACC was lower in PS-M1 compared to PS-M2 and PS-M4, and in PS-M3 compared to PS-M4 (\(p\) < 0.05). Meanwhile, DEC was significantly lower (\(p\) < 0.01) in PS-M1 compared to the other three pre-season microcycles, and PS-M3 showed lower DEC values compared to PS-M4 (\(p\) < 0.05).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Pre-Season**} \\ \cline{2-4}  & **PS-M1** & **PS-M2** & **PS-M3** & **PS-M4** \\ \hline TD (m) & 18,719 \(\pm\) 1921 **\(\ast^{-}\)** & 24,076 \(\pm\) 2702 \(\#\) & 20,954 \(\pm\) 2045 \(\char 1300\) & 24,789 \(\pm\) 2048 \\ Drel (m/min) & 39.20 \(\pm\) 3.97 **\(\ast\)** & 46.28 \(\pm\) 5.25 & 46.80 \(\pm\) 5.05 & 41.70 \(\pm\) 3.34 \\ HRS (m) & 1464 \(\pm\) 530 **\(\#\)** & 2502 \(\pm\) 981 & 2215 \(\pm\) 496 \(\char 1300\) & 3060 \(\pm\) 884 \\ SPR (m) & 386.38 \(\pm\) 248.31 \(\char 1300\) & 593.00 \(\pm\) 346.31 & 519.75 \(\pm\) 209.23 \(\char 1300\) & 968.38 \(\pm\) 514.46 \\ ACC (n) & 51.25 \(\pm\) 21.62 \(\ast^{-}\) & 85.50 \(\pm\) 35.86 & 76.38 \(\pm\) 24.80 \(\char 1300\) & 117.63 \(\pm\) 43.83 \\ DEC (n) & 179.50 \(\pm\) 42.24 \(\ast^{-}\) & 294.63 \(\pm\) 67.21 & 270.88 \(\pm\) 46.31 \(\char 1300\) & 341.25 \(\pm\) 77.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: External load of the microcycles during pre-season and in-season.  
Figure 1: Structure of each training period studied. PS-M1 = microcycle one of the pre-season; PS-M2 = microcycle two of the pre-season; PS-M3 = microcycle three of the pre-season; PS-M4 = microcycle four of the pre-season; IS-M1 = microcycle one of the in-season; IS-M2 = microcycle two of the in-season; IS-M3 = microcycle three of the in-season; IS-M4 = microcycle four of the in-season; TT = total time; MD+1 = match day plus 1; MD-3 = match day minus 3; MD-2 = match day minus 2; MD-1 = match day minus 1.  
Furthermore, during the in-season training period (Table 2), Drel was lower in IS-M1 compared to IS-M3 (\(p<0.05\)). Conversely, the HSR values obtained in IS-M1 and IS-M3 were higher than those in IS-M4 (\(p<0.05\) and \(p<0.01\), respectively). Additionally, a higher (\(p<0.05\)) number of ACC was recorded during IS-M3 compared to IS-M4.
## 2 Material and Methods  
### Samples and Chemicals  
FKBT and 1Y-KBT (produced in 2020 and 2019, respectively) were acquired from Anhui Guorun Tea Industrial Co., Ltd. (Chizhou City, Anhui, China). The 1Y-KBT sample was stored in a cabin that could keep a relatively stable storage environment. After their acquisition, the samples were stored in a clean, odorless environment at \(-20\)\({}^{\circ}\)C. The distilled water used in the experiments was purchased from Watsons Water Company, Inc. (Guangzhou, China). Chromatography pure methyl tert-butyl ether (MTBE) and pentane were purchased from Aladdin (Shanghai, China) and redistilled before use; 2-octanol and ethyl decanoate (99.5% and 99% pure) were also obtained from Aladdin. \(n\)-Alkanes (C5-C40; Sigma-Aldrich, St. Louis, MO, USA) were employed to assist in identifying volatiles by enabling the calculation of retention indices. A solid-phase extraction (SPE) cartridge (LC-Si, 500 mg/6 mL) and a solid-phase microextraction (SPME) needle (50/30 \(\upmu\)m DVB/Carboxen/PDMS) were purchased from Supelco (St. Louis, MO, USA). All reference aroma compounds were chromatographically pure and commercially obtained, and the actual purities were confirmed through GC-MS.
# Distributed Learning for Dynamic Channel Access in Underwater Sensor Networks
## 5 Proposed Algorithm  
In this section, we propose a multi-agent DQN-based dynamic channel access algorithm for distributed UASNs, as illustrated in Algorithm 1. As mentioned before, it is impractical to coordinate or exchange messages between underwater sensors to manage channel access in UASNs. Thus, we aim at developing a distributed algorithm that does not require coordination among underwater sensors. Algorithm 1 presents the proposed algorithm, which is implemented in each underwater sensor.  
```
1: Establish a trained DQN with weights \(\theta\) and a target DQN with weights \(\theta^{-}\)
2: Initialize \(\theta\) and set \(\theta=\theta^{-}\)
3: In time slot \(t\leq Z\), the agent randomly selects an action \(a\) and executes the action, and then observes the reward \(r\) and new state \(s^{\prime}\)
4: Store \(\left(s,\mathbf{a},\mathbf{r},\mathbf{s}^{\prime}\right)\) in reply buffer \(\mathcal{D}\)
5:Repeat:
6:for\(t\geq Z\) to \(T\)do
7: In each time slot \(t\), the agent chooses action \(a\left(t\right)\) by following the below distribution described in Equation (11)
8: Execute \(a\left(t\right)\) and observe reward \(r\left(t\right)\), feedback information \(o\left(t\right)\) and new state \(s^{\prime}\left(t+1\right)\)
9: Store \(\left(s\left(t\right),a\left(t\right),r\left(t\right),s^{\prime}\left(t+1 \right)\right)\) in reply buffer \(\mathcal{D}\)
10: Update the estimation of corresponding to the chosen action using (2) with feedback information \(o\left(t\right)\)
11: The agent randomly samples a minibatch with \(Z\) experiences from reply buffer \(\mathcal{D}\), and then updates weights \(\theta\) for the trained DQN
12: In every predetermined time slot, the agent updates the weights for the target DQN with \(\theta^{-}=\theta\)
13:endfor
```  
**Algorithm 1** DQN-based dynamic channel access algorithm for each underwater sensor  
In Algorithm 1, at each time \(t\), the agent chooses \(a\left(t\right)\) according to the following distribution,  
\[\Pr\left(a\left(t\right)=a\right)=\frac{\left(1-\gamma\right)e^{\beta Q\left( a\right)}}{\sum\limits_{a\in\mathbf{A}\left(t\right)}e^{\beta Q\left(a\right)}}+ \frac{\gamma}{K+1}, \tag{11}\]  
where \(\beta\) is the temperature and \(\gamma\in\left[0,1\right]\) is the weighting factor for determining an action. It should be noted that (11) balances between the softmax and \(\varepsilon\)-greedy methods, which is known as the Exp3 scheme [62]. Specifically, if \(\gamma\) is set to 1, the action is selected randomly, by following a uniform distribution. As \(\gamma\) goes to 0, the algorithm becomes greedier with time, with respect to selecting actions with high estimated Q-values.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 3 Proposed Methods  
In this section, we propose a pseudo-phoneme label (PPL) loss for the TI-SR task. It integrates content cluster loss at the frame level and speaker recognition loss at the segment level into a unified network by multi-task learning, without additional data requirement and exhausting computation. We also tried different PPL loss implementation methods.
## 5 Discussion
### Potential Future Developments of IoES in Emergency Management and Response  
The Internet of Emergency Services (IoES) has the potential to transform emergency management and response in many ways, and it is likely that there will be significant developments in this area in the future. Some potential future developments of IoES in emergency management and response include:  
* Increased integration with other technologies: IoES will likely become more integrated with other emerging technologies, such as 5G networks, edge computing, and artificial intelligence. This integration will enable emergency responders to access and analyze data more quickly and accurately, improving their ability to respond to emergencies.
* Use of drones and robots: Drones and robots can be used to gather data and provide assistance in emergency situations. In the future, these technologies could become more advanced and more widely used in emergency management and response.
* Wearable technology for responders: Wearable technology, such as smart helmets and body cameras, can provide responders with real-time data and situational awareness in emergency situations. As this technology improves, it could become a more integral part of emergency response.
* Improved communication and collaboration: IoES can enable more effective communication and collaboration among emergency responders, agencies, and the public. In the future, this could be further improved with the use of advanced communication technologies, such as virtual reality and augmented reality.
* Greater focus on data analytics: IoES generates a large amount of data, which can be used to inform emergency response and management decisions. In the future, there will likely be a greater focus on using data analytics and machine learning algorithms to analyze this data and identify patterns and trends that can inform emergency response strategies.
* Increased emphasis on resilience: As the frequency and severity of natural disasters and other emergencies continue to increase, there will likely be a greater emphasis on building resilience into emergency management and response systems. This could involve the use of IoES to better prepare for and respond to emergencies, as well as the development of new technologies and strategies to reduce the risk of disasters and mitigate their impacts.  
The future of IoES in emergency management and response is likely to be characterized by increased integration, collaboration, and data analysis, as well as the development of new technologies and strategies to enhance resilience and reduce the impact of disasters. However, there are also likely to be challenges and limitations associated with the implementation of IoES in emergency management and response, and these will need to be carefully addressed in order to ensure that the potential benefits of IoES are realized.
# Autostereoscopic 3D Display System for 3D Medical Images
## 4 Conclusions  
In conclusion, we proposed a novel 3D heart CT navigator system based on a 3D autostereoscopy method based on an eye tracking algorithm which does not require wearing special 3D glasses. The proposed system enables enhanced medical image diagnosis via 3D autostereoscopy and facilitates rapid and accurate visualization of complex 3D anatomical structures. Our proposed light field 3D subpixel rendering method combined with the machine learning based eye tracking algorithm provided higher 3D resolution with lower 3D crosstalk (\(\leq\)10% 3D crosstalk) compared to previous glasses-free medical 3D display approaches. Importantly, the proposed 3D autostereoscopic system is extremely versatile and can be applied to any 3D volumetric medical images for not only clinical practice usage but also medical education and a shared decision between medical doctors and patients.  
Conceptualization, D.K.; methodology, D.K., J.-H.C. and H.H.; software, D.K. and H.H.; validation, D.K. and J.-H.C. and H.H.; formal analysis, D.K. and H.H.; investigation, D.K. and H.H.; resources, D.K. and J.-H.C.; data curation, D.K. and J.-H.C.; writing--original draft preparation, D.K.; writing--review and editing, D.K., J.-H.C. and H.H.; visualization, D.K. and H.H.; supervision, D.K. and H.H.; project administration, D.K. and H.H.; funding acquisition, D.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by 2022 Hongkik University Research Fund; The National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021K2A9A1A01102122).  
Not applicable.  
Figure 15: Examples of the proposed 3D display system being applied to other modalities; namely, abdomen CT and head CT. The inset camera images show the real-time eye tracking results for the proposed autostereoscopic 3D display.
## 4 Limitations, and the Future Studies  
We must acknowledge there are several limitations in this study. Firstly, the CMF design of other important interior components such as doors and top decoration were not examined due to the complexity of the experimental design, and future studies should include these components. Secondly, only four design themes were provided under each style in the design practice step, and future studies should produce more design themes to help select the best design. Thirdly, only a small number of young University student majoring in design methodology were employed in the Kansei evaluation step, and future studies should employ a larger sample of subjects from various disciplines and also include different age ranges. Fourthly, this study only examined human visual attention and  
Figure 11: The preference of the subjects to the experimental samples.  
preference for to select the optimum design, and future studies will be performed from the perspective of marketing behaviors (such as decision making), and the relationship among the CMF designs, human visual attention, and their marketing behaviors to improve the design of automotive interiors.
## 6 Results
### Single Transmission
#### 6.3.1 Delay and Throughput Stability  
The transmission rate and delay stability significantly impact network performance and user experience. We set the bottleneck link bandwidth to 40 Mbps and the total transmission delay to 20 ms. Four schemes were tested for instantaneous transmission rate and time-delay changes, as shown in Figure 7.  
Figure 7a shows the variation in the transmission delay over time, and Figure 7b shows the transmission throughput over time. The graph shows that the transmission delay and transmission throughput of the three comparison schemes showed periodic fluctuations, with the NewReno scheme having the largest delay fluctuation and the BBR scheme having the largest rate fluctuation. The Nodctl scheme had a relatively stable transmission delay and transmission throughput.  
Table 5 shows the fluctuation differences in the transmission delay and transmission throughput among the four schemes, and it can be seen that the Nodctl scheme had better delay stability and throughput stability than the other three schemes. Among the three comparison schemes, BBR had the smallest delay fluctuation but the highest throughput fluctuation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link Delay** & **NewReno** & **Cubic** & **Bbr** & **Nodctl** \\ \hline
30 ms & 37.5\% & 37.5\% & 35.0\% & 77.5\% \\
35 ms & 32.5\% & 32.5\% & 30.0\% & 75.0\% \\
40 ms & 30.0\% & 30.0\% & 26.2\% & 72.1\% \\
45 ms & 26.2\% & 26.2\% & 23.7\% & 70.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bandwidth utilization of 3 MB file transfer.  
Figure 6: Throughput.  
Figure 7: Delay and throughput performance.