esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Infrared Thermography in E-Bike Battery Pack Detail Analysis--Case Study
## 1 Introduction  
The work provides a postmortem analysis of an e-bike battery pack in the form of a detailed factorial review that surveys the battery pack end of service and lifetime properties after seven years of its age, five years of use, and hundreds of charge cycles in combination with 2543 km traveled in total. The analysis was performed on an e-bicycle, PEDELEC (pedal electric cycle), Kalkhoff Groove 28\" made in 2011, equipped with a front hub motor and Li-ion battery pack of nominal voltage 36 V, capacity 9 Ah (324 Wh). Experiences of other users with the same bike indicate a battery pack lifetime of four years regardless of the mileage but in the same period of time, as well as under similar terrain and climatic conditions [1]. The environmental temperatures that prevailed at the time of the use significantly contribute to battery life shortening [2], and age [3] contributed to an annual capacity drop of 20%. That is supported by an extremely small number of charging cycles compared with the usual practice of a cycle analysis [4]. The bicycles were being used on flat terrain from 2014 to 2018, without significant vibrational stresses that could reduce the battery life [5]. The average wind speed at the site was 2.1 m/s, and riding speed of the e-bike was in the range of 20 to 22.8 km/h. In comparison, at the same location, the average car speed was from 38.4 to 39.7 km/h and the classic bicycle from 12.5 to 14.6 km/h [6].  
An end-of-life battery problem directly impacts energy policy and transportation energy efficiency improvements, as well as the overall e-bike lifecycle. There are variouspossible causes of battery end of life [7]. Therefore, the article provides a brief history of the development of electric bicycles, a link between bicycle riding parameters and corresponding energy needs, and a description of the usual daily route to evoke the typical dynamics of battery usage. The core of the article deals with the usability case and comparative analysis of infrared thermograms, with the results of the analysis of the individual cells of the real-life e-bicycle battery pack based on a CAD modeling and spatio-thermal cells diversification. In order to preserve and prolong the functionality of the real-life exemplary e-bicycle, an additional analysis and modus operandi of a possible battery lifetime extension is proposed. It is based on the concept of the battery revival, in this case replacing 20 cells (half of the total number) with cells from other systems (laptops/notebooks). A condition assessment of scrapped cells and their placing optimization within the battery pack structure was carried out, thus allowing further unrestricted use of the exemplary real-life electric bike and giving back its functionality.
# Control of _Francisella tulareis_ Virulence at Gene Level: Network of Transcription Factors
## 7 Response Regulators Involved in Two-Component Systems Work as Transcription Factors  
Several important DNA-binding proteins participating in Francisella pathogenicity are also response regulators and parts of two-component systems (TCSs). TCS is usually composed of a membrane-bound sensor histidine kinase and a DNA-binding response regulator. TCS is an important part of the transmission of an external signal to a bacterial cell and the subsequent appropriate response to changing environmental conditions through altering the level of gene expression [128; 129]. If the membrane sensor kinase detects an external signal, its phosphorylation occurs. After the transfer of the phosphate group to the response regulator, it is activated and acts as a transcription factor. The response regulator is composed of two domains, a receiver, which is responsible for the phosphate group acceptation, and a DNA-binding domain [130]. So far, only three response regulators have been identified in Francisella, the response regulator PmrA (or its homolog QseB), the response regulator KdpE, and biofilm-regulating Francisella protein regulator BfpR (or homolog in _F. tularensis_ FTT_1543) [24]. The occurrence of TCS is different in the _F. tularensis_ species and subspecies. Two complete (FTN_1452/FTN_1453 and KdpD/E) and one orphan TCSs (PmrA/QseC) are present in _F. novicida_, whereas no complete TCS was found in _F. tularensis_ Schu S4. The sensor kinase FTT_1544 and response regulator KdpE are pseudogenes and FTT_1543, the sensor histidine kinase KdpD, PmrA, and the sensor histidine kinase QseC are orphan TCS components in the Schu S4 strain. _F. tularensis_ subsp. _halarctica_ FSC200 and LVS have only one response regulator PmrA and one orphan sensor kinase QseC [129; 131].
# Investigations of the Windage Losses of a High-Speed Shrouded Gear via the Lattice Boltzmann Method
## 3 Results and Discussion
### Verification of CFD Model  
To further verify the effectiveness of the above CFD model exploiting the LBM method to estimate the windage losses, the numerical windage losses of the shrouded spur gear (\(\zeta_{r}=0.1009\), \(\zeta_{a}=1.0644\)) are compared with experiments of [PERSON] et al. [30] at a rotating speed ranging from 2500 rpm to 15,000 rpm, as shown in Figure 3. As shown in Figure 3, the changing trends of the numerical and experimental windage losses are the same; they are proportional to the cube of the rotating speed. Simulation results are equivalent to real experimental measurements with an average difference of less than 6.7% (ignoring the measured data for about 2600 rpm, whose uncertainties are greatest), which validates the validity of the CFD method. More than that, the dimensionless windage torque of the gear (\(C_{T}\)) is also presented in Figure 3. When the rotating speed is low (\(n_{g}<5900\) rpm, Ma < 0.3), the dimensionless value of the resisting torque can be regarded as a constant. The torque value evidently begins to fall, especially when the rotating speed exceeds 5900 rpm. At this point, the air becomes compressible instead of incompressible, as the Mach number is over 0.3. Additionally, it is noticeable that the windage power losses can reach about 10 kW for a single gear, even with a shroud inhibiting the flow motion and power loss generation. In a word, the numerical findings correlate qualitatively quite well with experiments, which confirms the precision and suitability of the CFD model based on the LBM method developed for shrouded spur gear.
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 5 Experimental Results
### Hardware Test  
As shown in Table 4, we tested the models both on a PC with a GTX 1080 Ti and Jetson TX2. The results certify that the detection performance of the two hardware environments are the same, which ensures that we can train the models on the PC and then copy the models to Jetson TX2. PC with a GTX 1080 Ti has a high speed for training and testing, thus we trained all the models on the PC.  
In order to test the feasibility of using Jetson TX2 on micro-nano satellites, we summarize some indexes of Jetson TX2 in laboratory. Table 5 provides the results and all the indexes are suitable for micro-nano satellites. The characteristics of cheap cost, small size and light weight mean that we can even layout several Jetson TX2 on micro-nano satellites. The standby power and peak power of Jetson TX2 are 3\(\sim\)5 W and 15 W, respectively, which is not a great burden for energy system in micro-nano satellites. Besides, the temperature of Jetson TX2 is usually less than 45 \({}^{\circ}\)C, which is not a challenge for thermal  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hardware Environment** & **Precision** & **Recall** & **Training Time/Samples** & **Processing Time (1 K \(\times\) 1 K)** \\ \hline
**PC (GTX 1080 Ti)** & 0.959 & 0.805 & 0.4 s & 0.08 s \\
**Jetson TX2** & 0.959 & 0.805 & 7.68 s & 1.25 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of different hardware environments.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Recall** & **Precision** & **Strict Recall** & **Strict Precision** \\ \hline
**NMS (0.3)** & 0.979 & 0.816 & 0.228 & 0.190 \\
**BL-NMS (0.3)** & 0.959 & 0.805 & 0.920 & 0.772 \\
**BL-NMS (0.5)** & 0.979 & 0.747 & 0.929 & 0.710 \\
**BL-NMS (0.7)** & 0.993 & 0.502 & 0.933 & 0.472 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparisons using different NMS methods and NMS thresholds.  
control system. The indexes above demonstrate that Jetson TX2 can become a part of computing platform in micro-nano satellites. The ship detection method based on deep learning can be operated on Jetson TX2 due to its computing capability of 1 TFLOPS. The final model size of our method is 108.03 MB and the average processing time of 1 K \(\times\) 1 K image patches is 1.25 s. These two indexes meet the requirements of on-board ship detection on micro-nano satellites for storage resource and processing time.
## 5 Results and Discussion
### Stress Characteristics of the Shaft Lining Model  
The strain measurements obtained during the loading process were converted to stress values using the experimental stress analysis method. On this basis, the load-stress curves of the specimens were plotted. Figures 9-12 show the load-hoop stress curves of the steel plates and concrete layers contained in specimens ZJ1, ZJ4, ZJ6, and ZJ8, respectively.  
Figure 10: Load–hoop stress curve of specimen ZJ4.  
Figure 9: Load–hoop stress curve of specimen ZJ1.  
As demonstrated in Figures 9-12, when the load was small, the shaft lining structure displayed well-behaved linear elastic characteristics. The stress on the steel plate and the concrete layer both increased nearly linearly as the load increased. As the external load increased, the stress increased at an increasing rate, deviating from the linear pattern. Thus, the shaft lining structure entered the plastic stage. As the specimen entered the plastic deformation stage, the inner steel plate did not buckle and fail because of the anchoring action of the anchor clamps. The inner steel plate, together with the outer steel plate, strongly constrained the concrete layer between them and prevented this layer from undergoing deformation or crack initiation. As a result, the compressive strength of the concrete increased significantly, and this material displayed notable plastic properties [30]. Compared with the test results of the other shaft lining models, a similar change rule of stress was shown.  
By analyzing Figures 9-12, we observed that the maximum hoop stress on the shaft lining concrete in the specimens ZJ1, ZJ4, ZJ6, and ZJ8 reached 97.3 MPa, 108.0 MPa, 122.6 MPa, and 132.7 MPa, respectively, during the loading process until failure, much higher than the compressive strength determined from cube tests performed on the concrete used to fabricate the specimens (61.7 MPa, 60.2 MPa, 70.6 MPa, and 65.8 MPa, respectively). This finding suggests that the concrete in this type of shaft lining structure is under triaxial compressive stress and, consequently, that its actual compressive strength is significantly higher than its uniaxial strength. This type of composite shaft lining structure is enhanced by this mechanism.  
Figure 11: Load–hoop stress curve of specimen ZJ6.  
Figure 12: Load–hoop stress curve of specimen ZJ8.
# Parameter Optimization for Ship Antiroll Gyros
## 5 Comparative Analysis of Parameter Optimization Results
### Results and Analysis of the Highest Roll Reduction Rate  
By taking the ship and its supporting antiroll gyro provided in Section 3 as an example, this Section conducted a simulation analysis on the parameter optimization. Given the ship's size, the parameter optimization model with the highest roll reduction rate as the objective could be written as follows:  
Figure 11: Schematic diagram of the bacteria foraging optimization algorithm.  
Figure 10: Schematic diagram of the pattern search optimization algorithm.  
\[\left\{\begin{array}{l}\text{min}Z_{1}=1-TT\\ \left\{\begin{array}{c}0.8\leq D_{1}\leq 1.2,\;1\leq D_{2}\leq 1.4\\ 0.15\leq D_{3}\leq 0.22,\;0.5\leq H_{1}\leq 0.9\\ 0.03\leq H_{3}\leq 0.06,1100\leq\omega_{m}\leq 1300\\ 10,000\leq C\leq 40,000,Ms\leq 2000\\ 0.75\leq TT,D_{2}-D_{1}\geq 0.06\end{array}\right.\end{array}\right. \tag{25}\]  
The relationship between the roll reduction rate and the iterations of the PSOA and BFOA is illustrated in Figure 12, and the solution time and results of the two algorithms are shown in Figure 13. Figures 11 and 12 indicate that the convergence speed of the BFOA was faster, and its roll reduction rate was higher than that of the PSOA. With the same mass of 2000 kg, the roll reduction rate obtained through the BFOA reached 79.6%, higher than that of the PSOA (78.5%).  
The comparison between the parameters of the gyro before and after the BFOA's optimization of the roll reduction rate is presented in Table 3. The gyro's damping coefficient \(C\) increased by 13.2%, which was within the optimal interval in Figure 6. The inner diameter \(D_{1}\) became larger, and the outer diameter \(D_{2}\) became slightly smaller, which caused the rotational momentum moment of the gyro to decrease. The rotor thickness \(H_{1}\) increased, which caused the precession momentum moment of the gyro to increase. It is consistent with Figure 6 that the increase of rotational momentum moment and the decrease of precession momentum moment contributed to the increase of roll reduction rate. Although the mass only decreased by 20.6%, the optimization of these parameters made the roll reduction rate increase to 79.6%.  
Figure 12: The iterative curve of roll reduction rate.  
Figure 13: Comparison of the results between the two algorithms.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 3 The DRL Algorithm
### The Double Rate Scheme
#### 3.1.2 The Double Rate Scheme in DRL Algorithm  
Based on the relationship between \(T_{s}\) and the transmission range, in this paper, we propose a Double Rate Scheme that separates the localization process into ranging procedure and localization procedure. Each of them uses different transmission mode by choosing proper \(T_{s}\).  
The two transmission modes are defined as low-rate transmission mode (LM) and high-rate transmission mode (HM), which are used in the ranging and localization procedure respectively.  
1. Low-rate Mode(LM): The signal of the low-rate mode has a large code-width which could transmit a great distance based on Equation (3). Hence, the LM signal can be used in the ranging procedure by choosing an appropriate code-width so that the signal could transmit across the network to increase the ranging accuracy in multi-hop networks.
2. High-rate Mode(HM): In contrast, the signal of the high-rate mode has a small code-width which could only transmit a one hop distance. However, signal of HM could be used in the localization procedure to transmit a mass of data at a high rate.  
According to the analysis above, the proposed Double Rate Scheme could improve the ranging accuracy in multi-hop networks as well as maintaining the data transmission rate.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 1 Introduction
[23] proposed a multi-objective deep belief networks ensemble (MODBNE) method for RUL prediction, which employs a multi-objective evolutionary algorithm integrated with the traditional deep belief networks (DBN) training technique to evolve multiple DBNs simultaneously subject to accuracy and diversity as two conflicting objectives. Within the deep learning architecture, convolutional neural network (CNN) is further applied in this study because of its excellent feature extraction ability in the face of variable and complex signals [27]. [PERSON] et al. [28] firstly applied CNNs to the field of RUL prediction and incorporated automated feature learning from the raw sensor signals in a systematic way. [PERSON] et al. [27] established a deep CNN model and conducted a preliminary exploration of the depth of the networks on RUL prediction. While CNNs have shown great potential in prognostics, there are still few applications of CNN in RUL prediction. In this paper, a novel CNN is presented. The kernel module composed of multiple convolutional kernels was proposed for feature extraction. This module was derived from the inception network proposed by [PERSON] et al. [25]. Compared with the module used in the inception network, the feature extraction of the convolution kernel was in the time dimension, which reduced the parameters of the  
Figure 1: Remaining useful life (RUL) prediction: (**a**) Based on health indicator (HI); (**b**) based on time window. module and transfers the application in the image field to prognostics. The relationship between the raw signals and the RUL could be established through the learning process. The RUL was calculated according to the new vector of input signals. In this study, we used the C-MAPSS dataset provided by NASA for the RUL prediction of aircraft engines [29]. The engine is the key component of the aircraft and there is always a pressing need to develop new approaches to better evaluate the engine performance degradation and estimate the remaining useful life [30]. The proposed network showed its superiority compared with the state-of-art methods on the same dataset. Our attempt was a useful exploration for implementing the RUL prediction on the unexpected events and provides the possibility to transfer the proposed method in a simpler scenario. This remainder of the paper is organized as follows. The proposed CNN is given in Section 2, which illustrates the proposed architecture and the main scheme of the kernel module used in the convolution layers. The experimental results of the proposed model compared with different depths, time windows, optimization algorithms and other state-of-the-art methods are given in Section 3. Section 4 summarizes the conclusions of this work.
## 3 Results
### Rietveld Refinement of Sample A10  
The Rietveld refinement of sample A10 was performed to determine the purity of the sample. The results after fitting the pXRD pattern are shown in Figure 4a. When the experimental and calculated patterns are evaluated for an overlap between the two, a good correlation is observed. The data could be fitted with \(\chi^{2}\) = 0.14. The cell parameters are calculated as: \(a\) = \(b\) = \(c\) = 8.4721(5) A; \(\alpha\) = \(\beta\) = \(\gamma\) = 90\({}^{\circ}\). The volume of the unit cell is calculated as \(V\) = 608.0975(7) A\({}^{3}\). When the cell parameters are compared with the standard data, a small change in the lattice parameters is noticed, \(\Delta a\), \(\Delta b\), \(\Delta c\), \(\Delta V<1\%\), which suggests that the ZnO concentration-optimized sample is of high purity.  
Figure 3: (a) pXRD pattern of three samples with ZnO:TiO\({}_{2}\) concentration of 1.80:1.00, 1.60:1.00 and 1.40:1.00, annealed at a temperature 800 \({}^{\circ}\)C (2 h); (b) zoom-in of new reflections of ZnO peaking at 32.75 \(2\theta\) degrees.  
Zn\({}_{2}\)TiO\({}_{4}\) has a cubic spinel structure with the space group Fd-3m (#227). In the structure, the Zn\({}^{2+}\) cations occupy the 8a and 16d sites, Ti\({}^{4+}\) cations occupy the 16d sites, and O\({}^{2-}\) occupies the 32e site. The representative crystal structure is shown in Figure 4b. In the crystal structure of Zn\({}_{2}\)TiO\({}_{4}\), Zn(1) occupies the tetrahedral site, while Zn(2)-Ti together occupy the octahedral site. The present host can also be written as Zn[ZnTi]O\({}_{4}\)[15]. We wish to mention here that despite a change in the expected concentration ratio of ZnO:TiO\({}_{2}\) from 2.00:1.00 to 1.40:1.00, no major changes in the crystallographic reflections of Zn\({}_{2}\)TiO\({}_{4}\) are observed, and only reflections due to unreacted ZnO diminish.
###### Abstract  
Understanding the order of limiting amino acids (AA) in reduced-protein (RP) diets for laying hens will facilitate precise feed formulation and ensure that AA requirements are met cost-effectively. The order of the first three limiting AAs--lysine (Lys), methionine (Met), and threonine (Thr)--has been well established in RP laying hen diets. This study aimed to determine the priority order of eight additional limiting AAs (critically important AAs) when formulating wheat-sorghum-based RP diets for laying hens: tryptophan (Trp), valine (Val), isoleucine (Ile), arginine (Arg), leucine (Leu), histidine (His), phenylalanine (Phe), and glycineequivalent (Gly). A total of 330 Hy-Line Brown laying hens were randomly assigned to 11 dietary treatments (30 replicates of individual birds per treatment) from 20 to 39 weeks of age (WOA). Treatments were a standard-protein (17.24% CP) diet as the control (SP); a reduced-protein (15.00% CP) diet with sufficient levels of Lys, Met, and Thr but insufficient levels of the eight experimental essential AA (RP); a reduced-protein diet with sufficient levels of all essential AAs (RP-EAA); and eight subsequent dietary treatments of the RP-EAA diet with one of the experimental essential AAs removed: Trp (RP-EAA-Trp), Val (RP-EAA-Val), Ile (RP-EAA-Ile), Arg (RP-EAA-Arg), Leu (RP-EAA-Leu), His (RP-EAA-His), Phe (RP-EAA-Phe), and Gly (RP-EAA-Gly). Eggs were collected and weighed daily, and feed intake and feed conversion ratio (FCR) were calculated weekly. External and internal egg quality was measured at 29 and 39 WOA. Nutrient digestibility, serum uric acid concentration, caecal microbiota composition, and tibia parameters were measured at 40 WOA. Overall, hens fed the RP-EAA-Val, RP-EAA-Ile, and RP diets presented significantly lower egg mass compared to hens fed the SP, RP-EAA-His, and RP-EAA-Gly diets (\(p<0.001\)). Hens fed the RP diet and RP-EAA-Val diet had a higher FCR compared to those offered the RP-EAA, RP-EAA-Leu, RP-EAA-Phe, and RP-EAA-Gly diets (\(p=0.046\)). Lower protein intake and excretion were observed in hens offered the RP diets compared to hens fed the SP diet (\(p=0.001\) and \(0.018\), respectively). Based on the egg mass, Ile may be considered the fourth and Val the fifth limiting AA, after Lys, Met, and Thr, in laying hens fed wheat-sorghum-based RP diets during peak lay. However, if ranked based on FCR, Val may be considered the fourth limiting AA, followed by Trp, Ile, Arg, and His as the co-fifth limiting AAs. Leu, Phe, and Gly may be considered as non-essential AAs for laying hens fed RP diets.  
amino acid; egg production; egg quality; low protein; microbiota; nutrition; poultry 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 2 Materials and Methods
### Characterization of ZnO NPs
#### 2.4.2 XRD  
The crystalline structure of the powdered ZnO NPs was examined using a _Bruker_ D8 Advance diffractometer (Billerica, MA, USA) with CuK\(\alpha\)_radiation_ (\(\lambda\) = 1.5418 A).