esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
Based on our observations we can argue that collisions (interference between different transmissions within the same network channel) have the most significant negative impact on PDR. We have explored numerous possibilities and we have seen conclusively that ToA is directly related to collisions. Specifically, the bigger the SF, the payload size and the distance, the longer a transmission would last. Further, we can say that the longer a spectrum channel is occupied, the higher the possibility of collisions. This means that we do not care about the ToA of a payload transmission itself but the sum of its repetitions, acknowledgments or retries of all EDs that occurred within the same SF. Thus, we can say that the most crucial factors that affect LoRaWAN performance are those that affect the channel occupancy exponentially. Based on these observations, we determine that the number of EDs that are deployed and the transmission interval are the most important factors. However, the more direct aspect of the problem is not the overall number of EDs but the number of them within each SF, especially as the SF gets higher. It is clear that SF7 can handle a tremendous number of EDs itself but cannot work on its own if we want wider coverage. Finally, we can claim that it is far better to use more GWs to avoid the usage of SF12 completely, which causes a huge number of problems for realistic data acquisition frequency requirements, especially when ACKs are active because in Europe most deployments use SF12 for RX2.
## 3 Results

Table 3: Distribution of different degrees of the position of MF according to teeth and dental apices divided according to sex and side (R: right; L: left). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & **Width** & **Height** & **Depth** & \multicolumn{3}{c}{**Distance from Inferior**} & \multicolumn{3}{c}{**Distance from Superior**} \\ \multicolumn{3}{c}{**Mandibular Edge**} \\ \hline \multirow{2}{*}{Males} & R & 3.3 (0.7) & 3.2 (0.9) & 7.3 (1.7) & 15.8 (1.8) & 13.2 (2.8) \\  & L & 3.2 (0.9) & 3.1 (0.9) & 7.2 (1.8) & 16.1 (1.9) & 13.0 (2.7) \\ \hline \multirow{2}{*}{Females} & R & 3.0 (0.9) & 2.7 (0.6) & 6.1 (1.7) & 14.5 (1.7) & 12.6 (2.8) \\  & L & 3.1 (0.7) & 2.7 (0.7) & 5.8 (1.4) & 14.7 (1.6) & 12.5 (2.9) \\ \hline \multirow{2}{*}{Total} & R & 3.2 (0.8) & 2.9 (0.8) & 6.7 (1.8) & 15.2 (1.9) & 12.9 (2.8) \\  & L & 3.2 (0.8) & 2.9 (0.8) & 6.5 (1.8) & 15.4 (1.9) & 12.7 (2.8) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average values reported for each metrical parameter divided according to sex and side (R: right side; L: left side): among round brackets, standard deviation (SD). [PERSON]'s test found a significant positive correlation between height and width, both in males and in females, independently from the side, with a correlation index ranging between 0.39 and 0.52 (\(p<0.01\)); moreover, in females, a significant positive correlation was found between the depth and distance from the inferior mandibular edge, with a correlation index of 0.44 and 0.40, respectively, on the right and left sides (\(p<0.01\), Tables 5 and 6). Finally, no significant correlation was found between the metrical parameters and the position of the MF according to the teeth and dental apices, independently of sex and side (\(p>0.01\), Table 7). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Females**} & \multirow{2}{*}{**Width**} & \multirow{2}{*}{**Height**} & \multirow{2}{*}{**Depth**} & \multicolumn{2}{c}{**Distance from Inferior**} & \multicolumn{2}{c}{**Distance from Superior**} \\  & & & & & **Mandiubular Edge** & **Mandiubular Edge** \\ \hline \multirow{2}{*}{Width} & \multirow{2}{*}{-} & 0.403 & \(-\)0.272 & \(-\)0.045 & 0.131 \\  & & (0.004) * & (0.056) & (0.757) & (0.365) \\ \hline \multirow{2}{*}{Height} & 0.388 & \multirow{2}{*}{-} & \(-\)0.220 & 0.185 & 0.133 \\  & & (0.005) * & (0.125) & (0.198) & (0.359) \\ \hline \multirow{2}{*}{Depth} & \(-\)0.186 & 0.061 & \multirow{2}{*}{-} & 0.440 & 0.084 \\  & & (0.196) & (0.673) & & (0.001) * & (0.560) \\ \hline \multirow{2}{*}{Distance from inferior} & \(-\)0.151 & 0.020 & 0.397 & - & 0.241 \\  & & (0.296) & (0.892) & (0.004) * & - & (0.092) \\ \hline \multirow{2}{*}{Distance from superior} & 0.117 & 0.088 & 0.192 & 0.145 & - \\ \cline{1-1}  & & (0.418) & (0.546) & (0.182) & (0.314) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Pearson’s correlation indices among different metrical parameters in females: the \(p\) value is reported among round brackets. *: statistically significant values (\(p<0.01\)).
## 2 Improved Homotopy Analysis Method  
### Problem Description  
The bending equations of a uniform cross-section beam with large deformation are [5; 6; 7; 8]  
\[\frac{\mathrm{d}\theta(s)}{\mathrm{d}s}=\frac{F}{EI}[l-x(s)] \tag{1a}\] \[\frac{\mathrm{d}v(s)}{\mathrm{d}s}=\sin[\theta(s)]\] (1b) \[\frac{\mathrm{d}x(s)}{\mathrm{d}s}=\cos[\theta(s)] \tag{1c}\]  
subject to boundary conditions  
\[\theta(0)=0,\ \theta^{\prime}(L)=0 \tag{2a}\] \[v(0)=0\] (2b) \[x(0)=0 \tag{2c}\]  
where \(s\) is the arc-coordinate of the neutral axis of the beam; \(x\) denotes the horizontal coordinate from the fixed end; \(L\) represents the length of the beam; \(F\) stands for the point load at the free end; \(EI\) is the bending stiffness of the beam; \(\theta\) specifies the rotation angle of cross-section of the beam; and \(l\) is the horizontal distance of two ends and is unknown.  
Differentiating the equation with respect to \(s\) and then using the dimensionless variables \(\xi=s/L\), \(\alpha=FL^{2}/(EI)\), \(X=x/L\), \(V=v/L\), \(\Theta_{b}=\theta_{b}/(\pi/2)\), \(V_{b}=\delta_{v}/L\) and \(U_{b}=\delta_{h}/L\), the original equations become  
\[\theta^{\prime\prime}(\xi)+\alpha\cos[\theta(\xi)]=0 \tag{3a}\] \[V^{\prime}(\xi)=\sin[\theta(\xi)]\] (3b) \[X^{\prime}(\xi)=\cos[\theta(\xi)] \tag{3c}\]  
subject to boundary conditions  
\[\theta(0)=0,\ \theta^{\prime}(1)=0 \tag{4a}\] \[V(0)=0\] (4b) \[X(0)=0 \tag{4c}\]  
where the prime denotes the differentiation with respect to \(\xi\).  
For infinitesimal deformation, the below linear equations are sufficiently accurate  
\[\theta^{\prime\prime}(\xi)+\alpha=0 \tag{5a}\] \[V^{\prime}(\xi)=\theta(\xi)\] (5b) \[X^{\prime}(\xi)=1 \tag{5c}\]  
subject to boundary conditions  
\[\theta(0)=0,\ \theta^{\prime}(1)=0 \tag{6a}\] \[V(0)=0\] (6b) \[X(0)=0 \tag{6c}\]  
Accordingly, the solutions are  
\[\theta(\xi)=\frac{\alpha}{2}(2-\xi)\xi \tag{7a}\]\[V(\xi)=\frac{\alpha}{6}\xi^{2}(3-\xi) \tag{7b}\] \[X(\xi)=\xi \tag{7c}\]  
The rotation angle, vertical and horizontal displacement of cross-section plane at end as denoted by \(\theta_{b}=\theta(1)\), \(V_{b}=V(1)\), \(U_{b}=1-X(1)\), are calculated to be  
\[\theta_{b}=\frac{\alpha}{2} \tag{8a}\] \[V_{b}=\frac{\alpha}{3}\] (8b) \[U_{b}=0 \tag{8c}\]  
In our previous paper, \"Explicit Solution to Large Deformation Cantilever Beam by Improved Homotopy Analysis Method I: ROTATION ANGLE\", the improved homotopy analysis method was proposed to solve Equations (3a) and (4a), and a series of solutions to the rotation angle are derived as [27]:  
\[\theta(\xi)=\theta_{0}(\xi)+\sum_{n=1}^{+\infty}\theta_{n}(\xi) \tag{9}\]  
where  
\[\theta_{n}(\xi)=\sum_{k=1}^{+\infty}a_{k}\xi^{k},n=1,2,\cdots \tag{10}\]  
In this paper, we apply the improved homotopy analysis method to derive the explicit expressions of deflection and horizontal displacement using Equations (3b), (4b), (3c), and (4c) on the basis of the derived explicit expressions of rotation angle in Equations (9) and (10).
# Quantum Leap from Gold and Silver to Aluminum Nanoplasmonics for Enhanced Biomedical Applications
## 6 Summary  
In summary, several biomedical applications of technologies that could be improved by using aluminum plasmonics were highlighted. The toxicity of various NPs was discussed and, while other metals are more prevalent in NP research, aluminum is less toxic and therefore requires less material to achieve effective treatment.  
Aluminum plasmonics in SERS and SEF have the important advantage of SPR tunability in the UV range. While Au and Ag NPs are of great importance in biosensing, Al NPs provide similar results with higher tunability and lower cost. In plasmonic medicine, Au and Ag NPs have been investigated in PTT and SERS imaging. While NP-based PDT has limitations, Al NPs are promising candidates to overcome the issues due to their broad spectral response and unique chemical properties. Several enhancements to aluminum plasmonic effects such as alloys, chemical enhancement, and quantum effects were discussed.  
In conclusion, this review underlines the opportunities of the aluminum plasmonics research in biomedical applications including biosensing, imaging, and phototherapy. Al NSs are promising candidates for these improvements with the advantages of low toxicity and cost. It is our hope that future research will focus more heavily on aluminum plasmonics and on the great potential for developing the field of quantum medicine.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### In Vitro Digestibility
#### 3.5.2 Protein Digestibility  
The protein hydrolysis of the steamed breads is shown in Figure 2. The protein hydrolysis rates of samples showed a rapid upward trend during the process of pepsin digestion, but the difference among the samples was not significant. During the process of trypsin digestion, the protein hydrolysis rate continued to rise; there was a slight difference within 30 min and thereafter an increasing gap appeared between the groups. At the end of the two phases of protease digestion (I120), the protein hydrolysis rates achieved by WF and WF&MF were 75.85% and 69.63%, respectively. Meanwhile, some food treatments might cause a reduction due to the formation of protein aggregation or S-S  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **RDS** & **SDS** & **RS** \\ \hline WF & 49.80 \(\pm\) 2.01 a & 22.59 \(\pm\) 0.57 a & 0.44 \(\pm\) 0.08 c \\ WF& 44.94 \(\pm\) 1.39 b & 18.04 \(\pm\) 1.03 b & 0.49 \(\pm\) 0.05 c \\
2\% DF & 44.21 \(\pm\) 1.77 bc & 17.91 \(\pm\) 1.22 b & 0.53 \(\pm\) 0.10 bc \\
4\% DF & 43.73 \(\pm\) 0.65 bc & 16.56 \(\pm\) 0.86 bc & 0.60 \(\pm\) 0.07 bc \\
6\% DF & 43.24 \(\pm\) 2.43 bc & 15.33 \(\pm\) 0.94 c & 0.58 \(\pm\) 0.08 bc \\
8\% DF & 42.39 \(\pm\) 1.52 cd & 15.57 \(\pm\) 1.44 c & 0.70 \(\pm\) 0.04 b \\
10\% DF & 41.07 \(\pm\) 1.14 d & 14.45 \(\pm\) 0.78 c & 0.89 \(\pm\) 0.05 a \\ \hline \hline \end{tabular} Values are represented as means \(\pm\) SD (standard deviation). Values in a column with different lowercase letters are significantly different (\(p<0.05\)). Sample WF was the control.  
\end{table}
Table 6: The rapidly digested starch (RDS), slowly digested starch (SDS), and resistant starch (RS) content of steamed breads (dry basis %).  
Figure 1: Effects of various samples on starch digestibility in vitro. WB represents the reference food (white bread) in this section.  
bonds [27]. Then, the digestion rate decreased with the addition of DF, and the 10% DF group achieved a rate of 35.60% at the end of digestion. Protein digestibility might be related to the process and composition of food. Research has also showed that relatively high protein digestibility is relevant to non-fiber carbohydrates, and the protein digestibility decreases with increasing fiber content [28]. As a consequence, we may draw a conclusion that too much dietary fiber is unfavorable for protein digestion of steamed bread.
## 3 Results and Discussion
### Physico-Chemical Parameters
stations) and high ecological status (34.4% of stations). The highest nitrate concentrations were determined at sites 27 (Corbu) (1.871 mg/L) and 26 (Gura Portitei) (1.870 mg/L) and the minimum (0.716 mg/L) corresponded to site 31 (Mangalia). Total Nitrogen has a similar trend with nitrate, with good ecological status (53.1% of monitoring points) and high ecological status (46.9% of sites). The Total Nitrogen concentrations ranged from 1.10 mg/L (site 32 (Vama veche)) to 3.17 mg/L (site 8 (Siret R. upstream)). Orthophosphates, expressed in dissolved phosphorus, are 100% bioavailable to plants. All dissolved phosphorus concentrations are above the maximum acceptable limit of 0.1 mg/L (high ecological class). The maximum value (0.053 mg/L) was measured in site 2 (Ostrov, Danube old branch) and the minimum (0.005 mg/L) in 3 sites on the Black Sea coast: 30 (Costinesti), 31 (Mangalia), and 28 (Mamaia). Total phosphorus represents the sum of dissolved phosphorus and particulate phosphorus (a long-term source for algae and plants). The concentrations obtained for this parameter revealed that surface water in the monitored area corresponds to high ecological class, except the sites 8 (Siret R. upstream), 11 (Galati shipyard downstream), 15 (Isaccea downstream), 21 (Sf. Gheorghe upstream), and 23 (Sulina mouth), that correspond to good ecological class. The relative contribution of dissolved phosphorus to total phosphorus varied between 12.260% (site 11 (Galati shipyard downstream) and 58.47% (site 2 (Ostrov, Danube old branch)). The values determined for chlorophyll \"a\" ranged from 1.4 ug/L (site 26, Gura Portitei) to 17.3 ug/L (site 31, Mangalia), indicating a high ecological class (quality standard of 25 ug/L) in all 32 sites. In general, the examined surface waters were in the alkaline pH range. Only seven surface waters had pH values within the 6.5\(-\)8.5 range recommended by the Romanian legislation. The minimum value (7.77 pH units) was obtained in Site 3 (Fetesti) and maximum (9.23 pH units) in site 31 (Mangalia). A comparison of nutrient levels in Danube water can be made with the findings published in the JDS4 report of the ICPDR for the 2019 survey [31]. The report includes three sites in the monitored sector of lower Danube and Delta in the SE part of Romania, starting with km 375--JDS4-48 (Chiciu-Silistra), JDS4-50 (Reni), and JDS4-51 (Vylkovo, RO-UA, Chilia branch)--which are similar with the sites of our monitoring network nos. 1 (Ostrov ferry), 14 (Reni downstream), and the combination of sites 20 (Chilia veche downstream) and 22 (Musura bay mouth), respectively. Our results indicate higher concentrations for nitrates and nitrites in all selected sites, orthophosphate in site 1, total nitrogen in sites 1 and 14, and comparable levels of ammonium and total phosphorous across all the sites. For orthophosphate, the concentrations at sites 14 and 20/22 are lower than the ones reported by JDS4 for this sector.
## 3 Observations  
The storm under investigation, also referred to as the Gannon Superstorm [45], commenced at 17:05 UT (sudden storm commencement-SSC time) on 10 May 2024 [46]. Following a brief initial phase with positive Dst, the main phase began at 18:04 UT and lasted until Dst reached its minimum (\(-412\) nT) at 02:00 UT on 11 May. During the subsequent recovery phase, Dst gradually returned to pre-storm values. Figure 1 displays the Bz and By component of the Interplanetary magnetic field (IMF), the IU, IL indices and the Dst, Kp indices during the evolution of the storm. This study considers the 10-12 May interval, including a pre-storm period, the initial, main, and the early recovery phases of the event.  
Figure 1: The Bz (panel (a)) and By (panel (b)) component of the interplanetary magnetic field, the IU and IL indices (panel (c)) for the characterization of auroral electrojet (over Europe) and the geomagnetic indices (Kp and Dst; panel (d)) are portrayed during the Mother’s Day storm. Red and blue vertical dashed lines represent the SSC time and the time of the Dst minimum, respectively. Light yellow bars (I, II, III) are indicating the three analyzed F-layer uplift phases (see the detailed description of these phases below in the Section 3.1).  
The temporal evolution of the Kp index is shown in Figure 1d. Following the SSC, the Kp value jumped close to 8 from the pre-storm 4', and then oscillated between 8-9 until the evening of 11 May.
## 2 Methods
### Measuring Classification Success
The area under the ROC curve (AUC) gives an aggregate measure of the classification performance. The ROC was originally developed for a binary classifier and was later generalized for a multi-class classification system [36]. The test set labels are binarized by employing either the one-vs-one or the one-vs-rest configuration. We employed the one-vs-one configuration for our task. In more detail, different sound samples are ranked by their probabilities, and then false positive and true positive rates are computed by choosing different probability cut-offs \(\rho\) to generate the ROC curve. The AUC is computed as the area under the ROC curve. In the end, an average across species is computed to get one AUC value for the entire data set, i.e., \[AUC=\frac{1}{n}\sum_{\rho}\sum_{j=1}^{n}r_{tp}(j,\rho).\] (11)
* _Mean Average Precision (mAP):_ The evaluation metric gives us a way of characterizing the performance of a classifier by monitoring how precision changes with varying the classification probability threshold that the model uses to make a decision if a bird sound sample belongs to a class \(j\). A good classifier will maintain a high precision as recall increases, while a poor classifier will take a hit on precision as recall increases with changes in threshold. In more detail, to compute Average Precision for a species \(j\), a list of probabilities is generated in which the discrimination probabilities our model has assigned to all test samples for class \(j\) are stored. The list is then sorted by decreasing probabilities, and each element is assigned a rank \(k\). By varying the rank \(k\) (by gradually lowering the probability threshold), a list of true positives and false positives is generated. Note that, as the classification threshold is lowered, the model labels increasingly more samples as positive. This will lead to an increase in false positives. The list is consequently employed to produce a list of precision values at different ranks \(p(k)\). Considering all the \(K\) cases in the list where the sound sample belongs to class \(j\), the average precision is computed as:  
\[P_{A}(j):=\frac{\sum_{k=1}^{K}p(k)\mathbf{1}(k)}{c_{tp}(j)}, \tag{12}\]  
where \(\mathbf{1}(k)\) is an indicator function that equals unity if the sample at threshold \(k\) is a true positive. The mean average precision \(P_{mA}\) is then computed by averaging over all classes (species) [15]. Figure 2:0
## 1 Introduction
After a large amount of research, it can be found that in the open-source data set of batteries, there are few test data involving SOC range changes, and the range of test changes is also very limited, not to mention the multi-range SOC test data for the retired batteries. Because the battery SOC range needs to be changed repeatedly to test its capacity attenuation, the detection time is long and the detection cost is high; so, the quite limited test data became a major constraint in this field of research. Given the shortcomings of the existing research and related constraints, this paper focuses on how to use the limited SOC range of retired battery experimental data, fit a high-precision battery capacity degradation model that can cover different SOC ranges, and fully consider various operating conditions of batteries. Combining the methods of the empirical model and data-driven model, three semi-empirical modeling methods are proposed to obtain the law of retired battery capacity degradation. Firstly, the mathematical concept of interval number similarity is introduced into the battery capacity degradation model considering the SOC range. By making full use of the mathematical characteristics of known test data, the unknown SOC data can be obtained from the limited SOC data set. Two semi-empirical models of retired battery capacity degradation based on model interval number similarity and parameter interval number similarity are proposed. Secondly, based on the curve fitting method of the nonlinear least squares fitting, an improved semi-empirical model of retired battery capacity degradation is proposed. In this method, the SOC range of battery operation is involved in the semi-empirical capacity degradation model in terms of parameters, and a more perfect and realistic capacity degradation model of retired batteries can be established. Finally, the accuracy of these three methods is compared together, and the applicable scenarios are clarified.
## 4 Results
### Parallel Corpus Selection and Retranslation Results  
Data cleaning methods, such as long sentence splitting, de-duplication, and line length ratio control, were applied to the dataset. The original corpus and the slice-filtered parallel corpus were used for training, respectively, and the results are shown in Table 2. The experiments show that the use of the processed parallel corpus, validated on ASPEC-JC, the BLEU score of the Chinese-to-Japanese neural machine translation model improves by 0.92, and the BLEU of the Japanese-to-Chinese neural machine translation model improves by 0.83.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Encoder Blocks** & \
\begin{tabular}{c} **Encoder** \\ **Dims** \\ \end{tabular} & **Attention Heads** &
\begin{tabular}{c} **Group** \\ **Size** \\ \end{tabular} & **Params** \\ \hline Conformer-S & 16 & 176 & 4 & - & 13.0 \\ SAformerNet-S & 15 & 120,168,240 & 4 & 3 & 13.4 \\ Conformer-M & 18 & 256 & 4 & - & 30.6 \\ SAformerNet-M & 16 & 180,256,360 & 4 & 3 & 33.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed architectural configuration of SAformerNet and Comformer.  
From the model's BLEU score on OPUS JA-ZH, the model has good robustness. However, the Japanese-to-Chinese model, which was trained using the processed data, did not obtain better translation results, indicating that enhancing the quality of the parallel corpus may not necessarily improve the model's generalization ability. The generated pseudo-parallel sentence pairs contain more noise and may provide little or no improvement to the final translation. To enhance the generalization ability of the model, the initial Transformer model is first trained using all the parallel corpus in ASPEC-JC, resulting in two NMT models. The subsequent training employs only the screened real parallel corpus except for the pseudo-parallel corpus. As can be seen from Table 3, the generalization ability of the model is enhanced, improving the effectiveness of NMT. The neural machine translation model from Chinese to Japanese, which incorporates a filtering mechanism, enhances the BLEU score on ASPEC-JC by 1.54. Likewise, the model that translates from Japanese to Chinese enhances the BLEU score by 2.8 compared to the cleaned data.