esa-sceva/satcom-chunk-collection huggingface dataset:
# Pinion Failure Analysis of a Helical Reduction Gearbox in a Kraft Process
## 5 Conclusions
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Steps** & **Case-I Gearbox Operated at 3 MW** & **Case-II Gearbox Operated at 3.75 MW** \\ \hline
18 & Corrected bending-fatigue strength: & Corrected bending-fatigue strength: \\  & \(S_{fh}^{\prime}=6,235+174 HB-0.126 HB^{2}\) & \(S_{fh}^{\prime}=6235+174 HB-0.126 HB^{2}\) \\  & \(6235+174\times 600-0.126\times 600^{2}\) & \(6235+174\times 600-0.126\times 600^{2}\) \\  & \(65,\!275\times 6890=450\) MPa & \(65,\!275\times 6890=450\) MPa \\  & Service life \(20\) years, continuous run, & Service life \(-20\) years, continuous run, \\  & Operating temperature \(=70^{\circ}\) & Operating temperature \(=70^{\circ}\) \\  & No. of cycles during service & No. of cycles during service \\  & \(=8350\times 20\times 365\times 24\times 60=8.78\times 10^{10}\) & \(=8350\times 20\times 365\times 24\times 60=8.78\times 10^{10}\) \\  & Life factor \(K_{L}=1.3558(8.78\times 10^{10})^{-0.0178}=0.86\) & Life factor \(K_{L}=1.3558(8.78\times 10^{10})^{-0.0178}=0.86\) \\  & Temperature factor \(K_{T}=1.0\) & Temperature factor \(K_{T}=1.0\) \\  & Reliability factor \(K_{R}=1.0\) & Reliability factor \(K_{R}=1.0\) \\  & Corrected bending fatigue strength, & \(S_{fh}=(K_{L}\times S_{fh})/(K_{T}\times K_{R})\) \\  & \(=(0.86\times 450)/(1\times 1)=387\) MPa & \(=(0.86\times 450)/(1\times 1)=387\) MPa \\ \hline
19 & Corrected surface-fatigue strength: & Corrected surface-fatigue strength: \\  & \(S_{fc}^{\prime}=27,\!000+364\times\!HB\) & \(S_{fc}^{\prime}=27,\!000+364\times\!HB\) \\  & \(=27,\!000+364\times\!600\) & \(=27,\!000+364\times\!600\) \\  & \(=245,\!400\times\!6890\) & \(=245,\!400\times\!6890\) \\  & \(=1691\) MPa & \(=1691\) MPa \\  & \(C_{L}=1.4488(8.77\times 10^{10})^{-0.023}=0.81\) & \(C_{L}=1.4488(8.77\times 10^{10})^{-0.023}=0.81\) \\  & \(C_{T}=1.0,C_{R}=1.0,C_{H}=1.0\) & \(C_{T}=1.0,C_{R}=1.0,C_{H}=1.0\) \\  & \(S_{fc}=C_{L}\times C_{H}\times S_{fc}^{\prime}/(C_{T}\times C_{R})\) & \(S_{fc}=C_{L}\times C_{H}\times S_{fc}^{\prime}/(C_{T}\times C_{R})\) \\  & \(=0.81\times 1691=1370\) MPa & \(=0.81\times 1691=1370\) MPa \\ \hline
20 & Safety factor against bending failure: & Safety factor against bending failure: \\  & \(N_{spinion}=387/335.95=1.15\) (Safe) & \(N_{spinion}=387/419.92=0.91\) (**Unsafe**) \\  & \(N_{bgar}=387/235.71\) (Safe) & \(N_{bgar}=387/294.64=1.31\) (Safe) \\ \hline
21 & Safety factor against surface failure: & Safety factor against surface failure: \\  & \(N_{spinion}=(1370/1090.5)^{2}=1.57\) (Safe) & \(N_{spinion}=(1370/1219)^{2}=1.26\) (Safe) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cont. Ministry of Trade, Industry & Energy, Republic of Korea (no. 20164030201230). In addition, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2019R1I1A3A01058576). **Conflicts of Interest:** The authors declare that there is no conflict of interest with respect to authorship and publication of this article.
###### Abstract  
IEEE 802.11 ad is the practical networking technology widely used as a 60 GHz millimeter-wave communication method ahead of Beyond 5G and 6G networks. However, compared with the existing Wi-Fi systems that use 2.4/5 GHz, it is difficult to manage the communication quality due to the high carrier frequency, e.g., the signal strength suddenly and significantly degrades due to various factors such as distance and obstacles. For IEEE 802.11 ad to be widely adopted, a simple and inexpensive measurement method is needed for non-specialists, including end users, to build and manage a stable IEEE 802.11 ad network. In addition, easy-to-understand performance indicators are required. This paper then proposes a multilayer measurement methodology with open-source tools, i.e., _ipref3_, _ping_, and _iw_, for IEEE 802.11 ad communication. Our methodology selects indicators necessary to evaluate IEEE 802.11 ad communication performance. The tools _ipref3_ and _ping_ mainly evaluate the IP layer indicators such as throughput, jitter, packet retries, packet loss, and RTT. On the other hand, the RSSI and Tx bitrates of the _iw_ results are used to investigate the wireless link quality. Through empirical results with our measurement methodology in real environments, we show that in addition to traditional indicators such as through-put, the Tx bitrate output by _iw_ can be a new indicator for understanding the IEEE 802.11 ad communication quality.  
IEEE 802.11 ad; mmWave; measurement; multilayer; Tx bitrate; Beyond 5G; 6G Article  
Multilayer Measurement Methodology with Open-Source Tools for Evaluating IEEE 802.11 ad Communication Quality. _Appl. Sci._**2023**, _13_, 5378. [[https://doi.org/10.3390/app13095378](https://doi.org/10.3390/app13095378)]([https://doi.org/10.3390/app13095378](https://doi.org/10.3390/app13095378))
## 4 Discussion
### Planktonic and Biofilm Cells Use Similar and Distinct Pathways for Increasing Resistance  
#### 4.4.1 TCA- and Urea-cycle Mediated Effects  
The tricarboxylic acid (TCA) cycle is a central metabolic pathway for generating energy (ATP) and precursors for the biosynthesis of macromolecules, such as oxaloacetate, 2-oxoglutarate, succinate, malate, fumarate, isocitrate for coordinating acetate and amino acid catabolism. Increased TCA cycle activity is advantageous for maintaining viability under nutrient- and oxygen-limited conditions in _S. aureus_ biofilms [60], while decreased TCA cycle activity has been linked with increased persister formation during the stationary phase [61]. Decreased TCA cycle activity reduces the available pool of NADH, which by preventing the formation of ROS leads to increased survival [62]. On the other hand, TCA cycle-mediated production of ROS can also trigger the activation of SOS mutagenesis increasing viability via beneficial genetic mutations [63]. In this instance, the abundances of many TCA cycle enzymes (AcnA, MDH, MQO, and 2-OGDH), ROS detoxification enzymes (SodA/M, AhpC/F, KatA, and TrxA), and Fe-S repair proteins (SufB/D/C) were increased on cell surfaces during both growth modes. This finding implies that the production of ROS could be induced and that 2-OGDH (producing NADH to provide electrons for the respiratory chain) and aconitas, AcnA (catalyzing the isomerization of citrate to iso-citrate), could have been affected under these conditions; both of these enzymes are highly vulnerable to ROS [62]. However, the increased abundances of these moonlighters on the cell surfaces may not necessarily correlate with TCA cycle activity inside the cells. A recent study showed that decreased TCA cycle activity results in decreased protein levels in the biofilm matrix [64]. In this instance, the predicted total protein abundances decreased only on planktonic cells and biofilm cells initiated with exponentially growing cells over time, while an increasing abundance trend was detected for biofilms initiated with stationary phase cells. Thus, it remains to be shown if the detected TCA cycle changes on the cell surfaces of the planktonic and biofilm cells are in line with the TCA cycle changes occurring in the cell cytoplasm of these cells.
## 4 Implementation
### BER Estimation for Optical OOK Modulation  
The calculation of the electrical amplitude of the received signal r(\(t\)) on the receiver side of the communication system is determined as follows:  
Figure 2: Implementation results of the 2D MIMO scheme for an \(8\times 8\) LED matrix.  
\[\mathbf{r}(t)=I(t)+\sum_{i=-\infty}^{+\infty}I(t)a_{i}g\left(t-iT_{symbol}\right)+n(t) \tag{3}\]  
where \(a_{i}\) represents the magnitude of the \(i\)-th symbol, with \(a_{i}\) taking values of either \(-1\) or \(1\). \(t\) represents the time process. \(I(t)\) represents the amplitude of the signal. The probabilities for bit 0 and bit 1 are denoted as \(P_{0}\) and \(P_{1}\), respectively. The function g(t) corresponds to a rectangular function, and the symbol duration is denoted by \(T_{symbol}\). Equation (4) provides an expression for BER in the case where the channel model contains only additive white Gaussian noise (AWGN). AWGN is a basic noise model used in information theory to mimic the effect of many random processes that occur in nature. erfc represents the complementary error function.  
\[P_{e}=\frac{1}{2}erfc\left(\sqrt{\frac{E_{b}}{2\sigma_{n}^{2}}}\right) \tag{4}\]  
Because of the exclusive existence of the AWGN channel, the representation of the OOK signal for both bits 0 and 1 is as follows:  
\[r(t)=\left\{\begin{array}{c}n(t),a_{i}=-1\\ 2.I(t)+n(t),a_{i}=1\end{array}\right. \tag{5}\]
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 2 GNSS Methodologies
### Conversion of Tropospheric Delays to Water Vapor
The most precise approach is to use measurements of temperature andpressure at the station location, but sometimes this is not possible or not fast enough for some applications. In addition, it is important to apply the necessary corrections when the meteorological station and the GNSS receiver are not at the same height. Moreover, the use of models can be very helpful for some applications or to retrieve data from receivers that are far away from meteorological stations or to extend the time series of stations that have a meteorological station nearby in the present but not in the past.
## 3 Discussion of the Proposed ZVS ITLDC
### Design of the Proposed ACAC
#### 3.1.2 The Auxiliary Inductor \(L_{\rm A}\)  
When the auxiliary current \(i_{\rm A}\) is positive, the capacitor \(C_{\rm A}\) is discharged, and when the current \(i_{\rm A}\) is negative, the capacitor \(C_{\rm A}\) is discharged. When the ITLDC reaches steady state, the voltage of \(C_{\rm A}\) remains unchanged during one period. Thus, the charge variation of \(C_{\rm A}\) in one period should be zero. According to Figure 4, during the time interval \(t_{0}\)-\(t_{1}\), the auxiliary current \(i_{\rm A1}\) decreases from \(i_{\rm A\_peak}\) to \(-i_{\rm A\_peak}\), the voltage (\(V_{\rm CA}-V_{\rm in}/2\)) is applied to the auxiliary inductance \(L_{\rm A1}\) and the time is \(DT_{\rm S}\); thus, the voltage of the auxiliary capacitor \(C_{\rm A}\) can be expressed as (7).  
According to Figure 4, in the time interval \(t_{\rm S}\)-\(t_{9}\), the auxiliary current is built up. The voltage \(V_{\rm CA}\) is applied to the auxiliary inductance \(L_{\rm A1}\). At \(\upbeta\), the auxiliary current reaches the peak value \(i_{\rm A\_peak}\); thus, the time to build up the auxiliary current can be expressed as (8). According to (7), the voltage \(V_{\rm CA}\) changes with the duty cycle and the minimum voltage of \(V_{\rm CA}\) will occur at the minimum duty cycle \(D_{\rm min}\). The maximum value of the time \(t_{\rm A\_build}\) should be smaller than \((1-D_{\rm min})T_{\rm S}\), i.e., \(t_{\rm A\_build}<(1-D_{\rm min})T_{\rm S}\). Considering that \(t_{\rm A\_build}\) can be expressed as (8) and the voltage \(V_{\rm CA}\) can be expressed as (7), the maximum value of the auxiliary inductor \(L_{\rm A}\) can be calculated as (9). Large \(L_{\rm A}\) will result in large conduction losses and large ripple voltage of the auxiliary capacitor \(C_{\rm A}\); thus, the auxiliary inductor \(L_{\rm A}\) can be selected with a small value but much greater than the leakage inductance \(L_{\rm r}\).  
\[V_{\rm CA}=\frac{1}{2}V_{\rm in}-\frac{2i_{\rm A\_peak}L_{\rm A}}{DT_{\rm S}} \tag{7}\]  
\[t_{\rm A\_build}=\frac{L_{\rm A}i_{\rm A\_peak}}{V_{\rm CA}} \tag{8}\]  
\[L_{\rm A}<\frac{D_{\rm min}(1-D_{\rm min})V_{\rm in}T_{\rm S}}{2(2-D_{\rm min} )i_{\rm A\_peak}} \tag{9}\]
## 4 Discussion
### Cell Viability 24 h and 48 h after LIPUS  
Further, the effect of increased treatment duration was evaluated for a fixed intensity of 0.4 W/cm\({}^{2}\) at 20% DF. This intensity was chosen since not visibly changes were observed at 1 min treatment at any DF. Figure 2c shows no effects 24 h after increasing treatment times (1, 3, 5, 7 and 10 min). Again, we were unable to detect proliferative nor detrimental effects on MIO-M1 viability.  
In order to better characterize the LIPUS effects on MIO-M1 viability, we diluted (1:10) the cell population by trypsinization and sub-culturing after LIPUS treatment. This way we would be able to distinguish if cells maintained their ability to proliferate and if they presented delayed LIPUS effects for longer incubation periods after exposure. By means of this protocol, we observed (Figure 2d) a significant decline in cell viability at 3 min and 5 min treatment after 48 h treatment and passage. In fact, that trend was already visible after 24 h. These results are consistent with [8]. They observed that at 0.3 W/cm\({}^{2}\) and 10% DF, there is minimal instant cell lysis, but cell death starts increasing 6-24 h post-treatment. The values they observed of total cell death at 24 h incubation post-treatment were highly variable, as we also observed (Figure 2b), and they proposed that cell mortality was the product of secondary cell necrosis and early apoptosis. We suggest, therefore, that the decrease in cell viability in our model at 48 h is a product of the long-term effect of LIPUS to induce secondary cell necrosis.
# Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping
## 2 Materials and Methods
### Alignment  
The second step is to estimate a global continuous alignment path between the two sequences of features. This process consists of three sub-steps, which are described in the next three paragraphs.  
First, we compute a pairwise cost matrix \(C\in\mathbb{R}^{L_{1}\times L_{2}}\) between the two feature sequences. For speech audio, we use Euclidean distance as a dissimilarity metric, and we use cosine distance for music audio. Therefore, \(C[i,j]\) indicates the dissimilarity between the feature \(x_{i}\) in the first sequence and the feature \(y_{j}\) in the second sequence.  
Second, we compute a cumulative cost tensor \(D\in\mathbb{R}^{2\times L_{1}\times L_{2}}\) (and corresponding backtrace tensor \(B\in\mathbb{N}^{2\times L_{1}\times L_{2}}\)) using the following dynamic programming rules:  
\[D_{h}[i,j]=\begin{cases}\frac{\gamma+\alpha}{2}&i=0,j=0\\ \min\begin{cases}D_{h}[i,j-1]+\gamma\\ D_{h}[i-1,j]+\alpha\\ D_{v}[i-1,j-1]+\gamma+\alpha\end{cases}&\text{otherwise}\\ D_{v}[i,j]=\begin{cases}C[0,0]\\ \min\begin{cases}D_{v}[i-1,j-1]+2\cdot C[i,j]\\ D_{v}[i-2,j-1]+3\cdot C[i,j]\\ D_{v}[i-1,j-2]+3\cdot C[i,j]\\ D_{h}[i,j]+\beta\end{cases}&\text{otherwise}\end{cases}\end{cases}\]  
where \(D_{h}\) and \(D_{v}\) correspond to the hidden and visible planes, respectively. In the multi-case expressions for both \(D_{h}[i,j]\) and \(D_{v}[i,j]\), the first case handles the initialization of the cumulative cost tensor, and the second case specifies the dynamic programming rules. There are three types of transitions that end in the hidden plane: (i) a horizontal transition to the right in the hidden plane with a penalty of \(\gamma\), (ii) a vertical transition upwards in the hidden plane with a penalty of \(\alpha\), and (iii) a diagonal transition from position \((i-1,j-1)\) in the visible plane to position \((i,j)\) in the hidden plane with penalty \(\gamma+\alpha\). Note that these three cases all incur fixed penalties that are independent of the pairwise cost \(C[i,j]\). There are four types of transitions that end in the visible plane: three regular transitions \(\{(1,1),(1,2),(2,1)\}\) in the visible plane with corresponding transition weights \(\{2,3,3\}\), as well as a transition from position \((i,j)\) in the hidden plane to position \((i,j)\) in the visible plane with a penalty of \(\beta\). As we compute each element of \(D_{h}[i,j]\) and \(D_{v}[i,j]\), we also update the corresponding backtrace tensor entries \(B_{h}[i,j]\) and \(B_{v}[i,j]\) to record the optimal transition type at each position.  
Third, we determine the optimal global continuous alignment path through \(D\) using backtracking. We start with the element in the last row and column of \(D_{v}\) or \(D_{h}\) (whichever has a lower cumulative path score) and then follow the back pointers in \(B\) to determine each step of the optimal path. We stop backtracking once we have reached either \(D_{v}[0,0]\) or \(D_{h}[0,0]\).
# Analysis of Fine Crack Images Using Image Processing Technique and High-Resolution Camera
## 4 Measurement Results and Analysis
### Pixel Values According to Working Distance
#### 4.1.2 Measured Pixel Values According to Changes in Focal Length  
In order to identify the measured pixel values according to changes in the focal length, 100 and 135 mm were applied to the SC equipment as focal lengths, and the results are shown in Figure 9 below. With regard to SC100-0.1, a pixel value of 7 was obtained through measurement at the minimum working distance of 470 mm, and pixel values of 21 and 36 were identified for SC100-0.3 and SC100-0.5, respectively. In the case of SC135-0.1, 0.3, and 0.5, pixel values of 8, 25, and 41, respectively, were obtained when measured at the minimum working distance of 565 mm. Relative comparisons with the results for SC50 were carried out. Since the minimum working distance varied depending on the physical characteristics of the lens, the comparisons were carried out at the same working distance of 600 mm. Compared to SC50-0.1, a 2.5 times higher value was obtained for SC100-0.1, and a four times higher value was obtained for SC135-0.1. In the case of a 0.3 mm virtual image, approximately 2.29 times and 3.57 times higher values were obtained, respectively,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Equivalent Focal Length**} \\ \cline{2-3}  & \multicolumn{2}{c}{**Number of Pixels (Each)**} \\ \cline{2-3}  & **CC50** & **SC50** \\ \hline
0.1 mm & 2 & 4 \\
0.3 mm & 8 & 11 \\
0.5 mm & 11 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The number of pixels measured by resolution differences at 400 mm working distance.  
Figure 9: Results of pixel measurement applying different focal lengths using SC.  
and with regard to a 0.5 mm virtual image, approximately 2.16 times and 3.16 times higher values were obtained, as can be seen in Table 4.
## 1 Introduction
To answer RQ1, an evaluation and analysis of the gathered data were performed for one version of each included software system, focusing on the differences between the Index variants. To answer RQ2, the results were analyzed from two perspectives: between versions of the same software system, where three software systems with their versions were considered, and between different software systems, where the last version of each included software system was considered. In summary, the key contributions of this paper are: (1) a review of Maintainability Index variants utilized for maintainability measurement in object-oriented software; (2) an empirical investigation on how Maintainability Index variants perform when utilized for the maintainability measurement of a single object-oriented system; (3) an empirical investigation on how Maintainability Index variants perform when utilized for the maintainability measurement of an object-oriented software system in the context of other systems, i.e., other versions of the same software system and completely different software systems. The rest of the paper is structured as follows. Section 2 provides the background on object-oriented software maintainability measurements needed to follow this paper. Related work is reviewed and discussed in Section 3. Section 4 presents and explains the experiment's procedure. The experiment results are reported in Section 5. The paper continues by analyzing and discussing the obtained results in Section 6. Finally, Section 7 provides conclusions and outlines possible related open research topics and areas.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 3 Experiments and Results
### Comparison with Baseline Models  
To provide clearer context for the obtained results, the performance of the deep learning models was compared with the official baseline performance reported in the ImageCLEFmed 2020 leaderboard. The comparison includes precision, recall, F1-score, and standard deviation across the models. The results are summarized in Table 13.  
As shown in Table 13, the proposed DenseNet121 model using transfer learning achieved a significant improvement compared to the official ImageCLEF baseline, particularly in terms of F1-score (89.0% vs. 37.0%). Moreover, precision and recall values were consistently higher across all evaluated models. In contrast, the VGG19 model trained without transfer learning on the full set of 3047 concepts achieved lower performance, with an F1-score of 27.0%. The relatively low standard deviations confirm the stability and robustness of the training process.
## 3 Results and Discussion
### Proliferation Assay  
The proliferative activity of GNPs in the keratinocytes cell line was studied with the lowest two non-toxic concentrations (0.005 and 0.01 ug/mL) observed at 24 h (Figure 4) to avoid any increase of toxicity by increasing the treatment time to 72 or 96 h (Figure 5).  
Although the GNPs showed lower proliferative activity than the EGF growth factor, both non-cytotoxic concentrations of GNPs showed a significant increase of proliferation with respect to control at 72 and 96 h of exposure. It is important to remark that the required exposure time (72 h) to induce proliferation in human keratinocyte HaCaT cells for the GNPs was lower than the 96-h necessary for other nanomaterials such as carbon nanofibers or silver nanoparticles [13]. Proliferative capacity was demonstrated also in GNPs/calcium silicate composites, showing a synergic effect of both components after different periods of exposition to human osteoblast cells [29]. However, the proliferative activity of GNPs in pure state against HaCaT cells is reported here for the first time.
## 2 Related Work
### Reinforcement-Learning-Based Approaches  
[PERSON] et al. [21] developed an adaptive bitrate reinforcement learning (ABRL) system for Facebook's web-based video platform. The ABRL system generates RL-based ABR policies and implements them in the production environment. They established a simulator to train the ABR agent using RL techniques, and then deployed the translated ABRL policies to the user interface. Similarly, [PERSON] et al. [22] proposed an enhanced ABR approach known as 360 SRL (sequential reinforcement learning) to reduce the decision space and improve QoE. The 360 SRL method refines the ABR policy by optimizing the QoE value or reward, which they define as a weighted sum of video quality, rebuffering, and spatial and temporal video quality variance. Throughout their training process, they used the Deep Q-Network (DQN) method.  
These studies highlight the utility of reinforcement learning techniques, such as RL and DQN, in designing and enhancing ABR systems for web-based video platforms. Their primary objective is to optimize ABR policies to improve user QoE, taking into account factors like video quality, rebuffering, and temporal/spatial video quality variance. Nonetheless, it is worth noting that there may be additional factors to consider that have not been addressed in these studies.