esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Substrate Profile and Kinetic Parameters  
To investigate the substrate specificity of reFAEs, a comprehensive set of substrates was used for the hydrolytic activity assay. MFA, MCA, MSA and MpCA were selected as substrates besides CGA. The reFAEs showed totally different substrate preferences (Table 2).  
Based on the substrate specificity of the reFAEs and hydrolysis ability of MFA, MCA, MSA and MpCA, it was preliminarily speculated that both reFAE1 and reFAE2 belonged to type C FAE [26, 27]. The ability of the reFAEs to hydrolyze CGA was similar with that of MpCA, which could be attributed to the carbon chain length of the substrate molecule [20], the size of the substituent [28] and the loop structure of the enzyme molecule [29].  
The value of Km can reflect the binding ability of the enzyme and substrate. The smaller the Km value, the stronger the affinity between enzyme and substrate. With CGA as substrate, the Km and Vmax of reFAE1 were 79.85 umol and 6.19 mmol/mg:min, respectively; and the Km and Vmax of reFAE2 were 105.98 umol and 6.53 mmol/mg:min, respectively. The Km of the recombinases was, in both cases, lower than other reported enzymes, which showed better affinity for CGA than others. For example, the Km values of B-type FAE, AoFaeB produced by _Neurospora_ and two FAEB from _Aspergillus niger_ for CGA were 180, 160 and 630 umol, respectively [12, 13, 20].
## 3 Results and Discussion
### Mechanical Properties of NiTiNb Prepared by LSF  
Six tensile specimens prepared in the heat treatment part of specimen L7 were subjected to tensile tests at 0 \({}^{\circ}\)C, the loading direction was perpendicular to the building direction, and the temperature was between the austenite transformation start temperature (\(A_{\text{s}}\)) and the austenite transformation finish temperature (\(A_{\text{f}}\)), the specimens were in the coexistence of martensite and austenite. All six specimens suffered a brittle fracture, and only one specimen shows the stress-induced martensitic transformation plateau [46].  
The tensile properties of samples with different building heights are significantly different. The application of NiTi alloy puts forward high requirements for good mechanical and functional properties, especially excellent tensile properties because most NiTi devices work under tensile or partial tensile conditions [58]. Studies have found that NiTi alloys made by SLM exhibit good mechanical properties in compression mode [32]. However, NiTi alloys made by SLM are brittle under tension and the situation of total elongation <8% is often reported [68]. It can be seen from Figure 13 that the maximum elongation of LSF NiTiNb reaches 8% but the minimum elongation is only 0.8%. Due to the complex thermal history, microstructure with uneven grain size distribution is usually observed in parts manufactured by SLM [45]. The inhomogeneity of the microstructure will significantly affect the mechanical properties of NiTi made by SLM [44]. From the previous analysis, the samples prepared by LSF are not uniform in the area parallel and perpendicular to the building direction, so the tensile properties are not ideal and have no repeatability. Therefore, it may be necessary to improve the tensile properties and increase the repeatability from the aspects of powder uniformity, combination of process parameters, and subsequent heat treatment or processing.
## 3 Acoustic Individual Identification in Birds
### Automatic Individual Verification Algorithm  
#### 3.1.1 Automatic Identification of Bird Vocalization  
It is very common the audio records of bird vocalizations have periods of silence and other background sounds. For this reason, the segments of the signal corresponding to bird vocalization need to be extracted (see Figure 2). Let \(f(n)\) be an audio record of a bird vocalization. Based on a voice activity detection (VAD) algorithm [30], the bird vocalization region (\(SF\)) is extracted as follows: (i) Compute the energy average of the whole signal using \(E=\frac{1}{N}\sum_{n=0}^{N-1}|f(n)|^{2}\). (ii) Divide \(f(n)\) into \(M\) frames (\(fr_{j}(i)\)) of \(m\) equal samples according to sampling frequency. (iii) Compute the energy average of each \(j\)th frame as \(e(j)=\frac{1}{m}\sum_{i=1}^{m}|fr_{j}(i)|^{2}\). (iv) Determine a suitable whole energy threshold value (\(thr_{E}\)) depending on the signal to noise ratio (SNR). (v) Identify the bird vocalization frames by using Equations (6) and (7):  
\[SF_{s}=first(\{fr_{j}(i)|e(j)\geq thr_{E},1\leq j\leq M,1\leq i\leq m\}), \tag{6}\]  
\[SF_{f}=last(\{fr_{j}(i)|e(j)\geq thr_{E},1\leq j\leq M,1\leq i\leq m\}), \tag{7}\]  
where \(SF_{s}\) and \(SF_{f}\) are the first and last detected bird vocalization frames in \(f(n)\). (vi) Define the bird vocalization region according to:  
\[SF=[SF_{s},\ldots,SF_{f}]. \tag{8}\]  
Figure 1: Proposed acoustic individual identification algorithm. Each arrow in the diagram represents a signal processed.  
The whole energy threshold value used in the experiments was \(thr_{E}=0.001*E\).
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 5 Results and Analysis
### Metadata  
After the values of the fields in the metadata archiving model are obtained, they are first stored in the MongoDB database to facilitate other experiments. In order to study the influence on cluster performance under different shard key strategies, we choose different shard key strategies. Considering that the query terms with remote sensing data mainly concentrated on a spatial range including latitude and longitude information or an imaging time range in practical application, the \"TopLeftLatitude\" field is chosen as a shard key because it carries spatial information. In the meantime, the hashed values of \"_id\" are calculated, where the shard key named \"_id_hashed\" is established. Finally, we use \"_id_hashed\" and \"TopLeftLatitude\" as shard keys, and both the storage experiments and access experiments are conducted under these two circumstances.
# The Architecture of the Access Protocols of the Global Infocommunication Resources +
## 2 Materials and Methods  
In practice, network operators often use different solutions based on a decentralized approach to select the appropriate last-mile access radio network. In this case, the selection task is solved by each terminal device separately. This study suggests another alternative approach to selecting a last mile access radio network-a centralized approach based on the implementation of a heterogeneous access network architecture of the AD-access network aggregator (see Figure 1). An AD is an auxiliary network centralized hardware and software device that is an intermediate element that connects terminal devices with the base stations of access radio networks.  
The access network aggregator is designed for:  
Figure 1: Centralized approach to selecting the last mile access radio network.  
The procedure for selecting the most appropriate access radio network for providing the service is not considered in this study. It is assumed that this procedure can be carried out based on the analysis of collected and accumulated historical data characteristics established in a heterogeneous broadcast network of connections, as well as characteristics related to the terminal device and transmission quality requirements due to the type of service requested. For example, different groups of metrics can be analyzed that characterize:  
* Quality of data transmission in the access radio network;
* Terminal devices;
* Resource requirements that depend on the type of telecommunications service;
* Subscriber profile;
* Requirements for safety and reliability.  
Evaluation of the effectiveness of information and communication systems can be carried out using traditional indicators, which are the essence of the concept of Quality of Service, QoS. These are, for example: network performance characteristics: bandwidth of the communication channel, probabilistic and temporary characteristics of the information transfer process, information transfer speed [8], reliability and security characteristics: fault tolerance, availability coefficient [9; 10].  
The list of traditional and long-used indicators used for monitoring the effectiveness of information and communication systems can be expanded with alternative indicators. For example, in [11; 12], probabilistic and energy indicators that depend on the technical characteristics of terminal devices and on the spatial parameters of access radio networks are considered.
## 3 Simulation Results and Discussion
### Performance Analysis
#### 3.2.4 UERE Test  
As aforementioned, the UERE consists of SISURE and UEE, where the SISURE comprises errors related to the satellite orbit and clock and the group delays, and the UEE includes the unmodeled atmospheric error, the multipath error, and the receiver noise.  
Among others, UEE contributions may differ widely among individual receivers and environments. The unmodeled atmospheric error is assumed to be negligible in this study. This is because the ionospheric delay error, a dominant component of UEE, can be compensated by using a dual-frequency receiver. Additionally, the multipath-free environment is assumed due to the characteristics of the surrounding environment of GEO satellites in space. Therefore, the remaining component of the UEE is the receiver noise that differs significantly depending on \(C/N_{0}\), the loop bandwidth, and the correlator spacing, as shown in Equation (11).  
In this study, we assumed a static receiver for the GEO mission in the SSV environment. As shown in Table 1, the receiver setting had a 1 Hz of bandwidth with a 0.1 chip of correlator spacing in this simulation, which reflects a highly sensitive signal tracking loop for weak signals in static mode. The value for SISURE was assumed to be 1.8 m on average in this simulation [23; 25].  
The results of UERE analysis are shown in Figure 11 and summarized in Table 5. The estimated range errors using multi-GNSS were 2.63 m (main lobe only) and 3.21 m (main and side lobe) above Korea on average. The result of UERE had similar values regardless of whether the region is above Korea or not. If side lobe signals are used, it is possible to achieve available pseudo-ranges in more sections than using the main lobe signal only, but the value of UERE may be larger. Therefore, we should consider the GDOP simultaneously with the UERE when estimating the error in the navigation solution, as described in the next section.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Figure**} & \multirow{2}{*}{**Type**} & \multicolumn{6}{c}{**Main Lobe Signal Only**} & \multicolumn{6}{c}{**(Main + Side) Lobe Signal**} \\ \cline{2-11}  & & **GPS** & **GLO** & **GAL** & **BDS** & **Multi** & **GPS** & **GLO** & **GAL** & **BDS** & **Multi** \\ \hline Above & Min & 0 & 0 & 0 & 0 & 0 & 20.03 & 0 & 20.01 & 20.15 & 0 \\ Korea & Avg & 33.50 & 30.98 & 34.21 & 33.09 & 33.34 & 27.58 & 26.32 & 27.43 & 27.07 & 27.33 \\ (128\({}^{\circ}\)E) & Max & 39.58 & 36.58 & 39.83 & 39.72 & 39.83 & 39.58 & 36.57 & 39.83 & 39.72 & 39.83 \\ \hline Opposite & Min & 0 & 0 & 0 & 21.85 & 0 & 20.02 & 0 & 20.01 & 20.00 & 0 \\ Korea & Avg & 33.60 & 30.98 & 34.22 & 35.35 & 34.43 & 27.53 & 26.32 & 27.43 & 29.92 & 28.36 \\ (308\({}^{\circ}\)E) & Max & 39.63 & 36.58 & 39.83 & 39.84 & 39.63 & 36.58 & 39.83 & 39.84 & 39.84 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(C/N_{0}\) [dB-Hz] for 24 h in geostationary orbit.  
Figure 10: \(C/N_{0}\) for 24 h in geostationary orbit.
## 4 Principles and Solving Steps of the Multiplier Path Following Method
### Principles
#### 4.1.3 Solving Along the Direction of the Center Path  
The solution with the use of the path following method is as follows: first, arbitrarily choose a point \((\mathbf{T},\mathbf{y},\mathbf{w})\), where \(\mathbf{y}>0\) and \(\mathbf{w}>0\); then, determine the direction \((\Delta\mathbf{T},\Delta\mathbf{y},\Delta\mathbf{w})\) such that \((\mathbf{T}+\Delta\mathbf{T},\mathbf{y}+\Delta\mathbf{y},\mathbf{w}+\Delta\mathbf{w})\) satisfies Formula (18), i.e.,  
\[\left\{\begin{array}{l}\mathbf{Q}(\mathbf{T}+\Delta\mathbf{T})-\mathbf{A}^{\rm T}(\mathbf{y}+ \Delta\mathbf{y})+\mathbf{C}=0\\ \mathbf{A}(\mathbf{T}+\Delta\mathbf{T})-(\mathbf{w}+\Delta\mathbf{w})-\mathbf{b}=0\\ (\mathbf{Y}+\Delta\mathbf{Y})(\mathbf{W}+\Delta\mathbf{W})\mathbf{e}=\mu\mathbf{e}\end{array}\right. \tag{19}\]  
where \(\Delta\mathbf{Y}=\text{diag}(\Delta y_{1},\Delta y_{2},\cdots,\Delta y_{\rm m})\); and \(\Delta\mathbf{W}=\text{diag}(\Delta w_{1},\Delta w_{2},\cdots,\Delta w_{\rm m})\).  
After simplifying the preceding equation and neglecting the second-order incremental terms, the following expression is obtained:  
\[-\mathbf{Q}\Delta\mathbf{T}+\mathbf{A}^{\rm T}\Delta\mathbf{y}=\mathbf{C}+\mathbf{Q}\mathbf{T}-\mathbf{A}^{ \rm T}\mathbf{y} \tag{20}\]  
\[\mathbf{A}\Delta\mathbf{T}-\Delta\mathbf{w}=\mathbf{b}-\mathbf{A}\mathbf{T}+\mathbf{w} \tag{21}\]  
\[\mathbf{W}\Delta\mathbf{y}+\mathbf{Y}\Delta\mathbf{w}=\mu\mathbf{e}-\mathbf{Y}\mathbf{W}\mathbf{e} \tag{22}\]  
It follows from Formula (22) that  
\[\Delta\mathbf{w}=\mathbf{Y}^{-1}(\mu\mathbf{e}-\mathbf{Y}\mathbf{W}\mathbf{e}-\mathbf{W}\Delta\mathbf{y}) \tag{23}\]  
The substitution of Formula (23) into Formula (21), followed by its representation in matrix form, results in the formula below:  
\[\begin{bmatrix}-\mathbf{Q}&\mathbf{A}^{\rm T}\\ \mathbf{A}&\mathbf{Y}^{-1}\mathbf{W}\end{bmatrix}\begin{bmatrix}\Delta\mathbf{T}\\ \Delta\mathbf{y}\end{bmatrix}=\begin{bmatrix}\mathbf{C}+\mathbf{Q}\mathbf{T}-\mathbf{A}^{\rm T}\bm {y}\\ \mathbf{b}-\mathbf{A}\mathbf{T}+\mu\mathbf{Y}^{-1}\mathbf{e}\end{bmatrix} \tag{24}\]  
The solution to Formulas (23) and (24) provides the search direction. A one-dimensional search is then conducted to determine the new solution and the corresponding optimal step length. This iterative procedure is repeated until convergence to a solution meeting the predefined accuracy criterion is achieved.
## 4 Methods for Improved Reliability and Robustness
### Automatic Correction
#### 4.2.4 Domain Shift  
Domain adaptation is the umbrella term used to refer to the techniques addressing the domain shift problem [103; 104]. Within our work, we consider domain adaptation as an MI technique that aims at improving robustness to domain-shifted inputs. It consists of combining labelled source domain data, i.e., data from the original training distribution, with target domain one, i.e., the domain shifted data, typically in an unsupervised manner that avoids labelling the target domain, where in principle no annotated data are available.  
Different alternatives have been explored to improve the generalization capacity of CMR segmentation models to an unseen domain, where the unseen domain can be a different image modality, such as computed tomography [105; 106; 107], a different magnetic resonance sequence, such as late gadolinium enhancement [108], or the same modality with varying statistical properties (e.g., different vendors and/or centers) [99]. [PERSON] et al. [105; 106] present an unsupervised domain adaptation framework, named SIFA. This framework adapts a segmentation network to an unlabeled domain by aligning source and target domains from both image and feature perspectives. Adversarial learning is enforced at multiple levels in the pipeline, guiding the two adaptive perspectives through a shared feature encoder to exploit their mutual benefits. [PERSON] et al. [107] introduce an unsupervised domain adaptation method specifically designed to compensate for the drawback of domain adversarial training when only a small number of target samples is available. This result is achieved by introducing prior regularization on a shared domain-invariant latent space of the source and target domain images, which is exploited during segmentation. [PERSON] et al. [108] tackle the problem of domain adaptation by using a common feature generator to fuse the feature spaces of source and target data into a combined feature domain. This new space is kept domain-invariant via indirect double-sided adversarial learning.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 2 Influential Factors on the Aging and Reliability of EMD Components
### Non-Electromagnetic Stress
#### 2.2.3 Environmental Stress  
The ambient working conditions of EMD are not always the same as they change over time. Therefore, the EMD is exposed to various fluctuating conditions that put stress on its components and consequently affect its reliability and useful life [54]. For the purposes of this paper, the classification of environmental stress is shown in Figure 4.  
Temperature and humidity are factors that continuously change with the climate. Nowadays, the dynamics of temperature and humidity are much faster, resulting in constant changes in the demands placed on control systems. In this context, temperature stress is related to the effect of ambient temperature, rather than the absolute temperature stress on components. In [55], the authors discuss the negative influence of humidity due to the occurrence of corrosion, material damage, and a decrease in mechanical properties within the EMD. Furthermore, the presence of fine particles, such as dust or abrasive materials, can lead to mechanical wear and clogging of ventilation systems, further compromising the functionality of components [56]. Long-term exposure to such conditions increases the risk of failures and reduces system reliability by degrading insulation, accelerating oxidation, and hindering thermal dissipation. This leads to higher temperatures, increased resistance, and a greater likelihood of short circuits or dielectric breakdowns.  
In Table 2, the list of the most common effects of stress factors is presented. Combining these stress factors with dynamic events provides a comprehensive mission profile that models the real-time working conditions of EMD, suitable for system-level optimization, lifespan extension, and reliability improvement.
## 3 Results and Discussion
### Computational Study
#### 3.3.4 Quantum Theory of Atoms in Molecules (QTAIM)
In addition, the interaction on N-H\(\cdots\) is more efficient and intensive because of the high values of \(\rho\)(r).
###### Abstract  
In this work, a thin film transistor (TFT) with Zr-doped aluminum-zinc-tin oxide (Zr-AZTO) semiconductor as active layer was investigated. The Zr-AZTO thin films were co-sputtered by ZrO\({}_{2}\) and AZTO targets (RF-Sputter) in Ar, and annealed at 350 \({}^{\circ}\)C in air atmosphere. With the discharge power of AZTO increasing from 100 W to 120 W, the content of Zr element decreases from \(0.63\pm 0.01\) at.% to \(0.34\pm 0.01\) at.%, and the oxygen vacancy decreases from \((19.0\pm 0.1)\)% to \((17.3\pm 0.8)\)%. The results of Zr-AZTO thin film show that the main factor is the co-sputter power of ZrO\({}_{2}\) target. With the co-sputter power of ZrO\({}_{2}\) increasing from 15 W to 45 W, the content of Zr element increases from \(0.63\pm 0.01\) at.% to \(2.79\pm 0.01\) at.%, the content of oxygen vacancy decreases from \((19.0\pm 0.1)\)% to \((14.1\pm 0.1)\)%, Eg increases from 2.76 eV to 2.86 eV, and the root mean square (RMS) roughness firstly decreases from 0.402 nm to 0.387 nm and then increases to 0.490 nm. The Micro Wave Photo Conductivity Decay (\(\mu\)-PCD, LTA-1620 SP) was used to measure the quality of Zr-AZTO thin film and the mean peak and D value decreases from 139.3 mV to 80.9 mV and from 1.54 to 0.77 as the co-sputter power of ZrO\({}_{2}\) increases from 15 W to 45 W, which means it has highest localized states and defects in high ZrO\({}_{2}\) co-sputter power. The devices prepared at 15 W (ZrO\({}_{2}\))/100 W (AZTO) co-sputter show a best performance, with a \(\mu_{\text{sat}}\) of \(8.0\pm 0.6\) cm\({}^{2}\)/(V\(\cdot\)S), an Ion/Ioff of \((2.01\pm 0.34)\times 10^{6}\), and a SS of \(0.18\pm 0.03\) V/dec. The device of Sample B has a 0.5 V negative shift under \(-20\) V NBS and 9.6 V positive shift under 20 V PBS.  
Keywords:thin film transistor; oxide semiconductor; Zr-AZTO; co-sputter +
Footnote †: journal: Applied Physics Letters
# Energy Aware Cluster-Based Routing in Flying Ad-Hoc Networks
## 3 EALC Methodology  
EALC is a FANET communication model that attempts to minimize the computational and communicational overhead. The computational overhead can be reduced by keeping the clustering mechanism simpler and communicational overhead can be minimized by increasing the lifetime of the cluster. We also attempt to select optimal transmission range of UAVs according to the network requirements. Here, the network requirement means the need of minimum transmission range for a node to communicate efficiently.  
At the start of EALC, nodes are grouped into clusters using K-Means Sorted Fitness algorithm and communication is started through the CHs. Nodes also periodically broadcast their energy and position to CHs. During the lifecycle, if the fitness of a CH falls below some threshold (e.g., 20% below of any CM), all nodes of that cluster will be considered as unclustered nodes. If 20% of all nodes are unclustered, then clustering will be recalled. A flow chart for EALC using a variant of K-Means Density as a clustering technique is presented in Figure 1 and Algorithm 1. The key steps exhibited are explained below:  
Figure 1: Cluster-based routing protocol for FANETs.