esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Life Prediction of Radial Tire Bead Using a Maximum Strain Energy Density Range Method
## 7 Conclusions  
In this study, a new fatigue life prediction method has been presented to evaluate the durability of tire bead. Based on the steady-state rolling analysis of the tire and the fatigue life equation of the rubber compound (carcass), the fatigue life of tire bead is predicted. Conclusions are as follows:  
1. Based on the steady-state rolling analysis by a finite element method and the fatigue life equation of the rubber compound (carcass) in the bead region, a method that can flexibly predict the fatigue life (\(N_{j}\)) and the driving distance (\(D_{j}\)) to failure of the tire bead is established;
2. The maximum strain energy density range can flexibly reflect the strain level of the tire, and practice also indicates that it is suitable as a parameter for fatigue evaluation;
3. The carcass plays a key role in the fatigue life of tire bead, and targeted optimization of this compound may effectively improve the fatigue performance of the tire bead.  
[PERSON] and [PERSON] carried out experiments; [PERSON] wrote the manuscript with the assistance of [PERSON] and [PERSON]; [PERSON] was responsible for the supervision and review; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors appreciate the engineers of KENDA Rubber (China) Tire Co. Ltd. for kindly providing their constructive suggestions and helping during the process of the experiment. The authors declare no conflict of interest.
## 2 Materials and Methods  
Ceramic samples of Bi\({}_{1-y}\)Ca\({}_{y}\)Fe\({}_{1-x}\)Mn\({}_{x}\)O\({}_{3}\) (0.1 \(\leq y\leq\) 0.175, 0.35 \(\leq x\leq\) 0.45) ferrites were synthesized via a solid-state reaction. Stoichiometric amounts of Bi\({}_{2}\)O\({}_{3}\), CaCO\({}_{3}\), Fe\({}_{2}\)O\({}_{3}\), and Mn\({}_{2}\)O\({}_{3}\) powders (with a characteristic particle size of \(\sim\)10 \(\upmu\)m) were precisely weighed and thoroughly mixed in an agate mortar using a pestle. The resulting homogeneous powders were then pressed into pellets. These pellets were placed in sealed alumina crucibles and heated to 950 \({}^{\circ}\)C at a rate of 300 \({}^{\circ}\)C/h, followed by annealing at this temperature for 20 h. Subsequently, the samples were allowed to cool to room temperature at a rate of \(\sim\)100 \({}^{\circ}\)C/h. The processing parameters were selected based on a synthesis strategy that was previously successfully applied to the synthesis of Ca/Mn-substituted bismuth ferrites with similar chemical compositions [37]. Prior to magnetic characterization, the purity of the samples was assessed using X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. Room-temperature XRD patterns were obtained with a Bruker D8 Advance diffractometer (Karlsruhe, Germany) employing Cu K\(\alpha\) radiation (\(\lambda_{\text{K}\alpha 1}=1.54056\) A, \(\lambda_{\text{K}\alpha 2}=1.54439\) A). A Ni filter was used to reduce Cu K\(\beta\) radiation. The diffraction data were analyzed with the Rietveld method utilizing the FullProf software suite [38]. Secondary electron and backscattered electron microscopy images of fractured surfaces of gold-palladium-coated Bi\({}_{1-y}\)Ca\({}_{y}\)Fe\({}_{1-x}\)Mn\({}_{x}\)O\({}_{3}\) ceramics were captured using a VEGA-3 SB (TESCAN) scanning electron microscope (Brno, Czech Republic). Magnetization measurements were performed in magnetic fields up to \(\pm\)9 T over a temperature range of 2 to 400 K using a cryogen-free Physical Properties Measurement System (PPMS DynaCool, Quantum Design, San Diego, CA, USA).
_applied sciences_  
_Article_  
**Evaluation of the Suitability of Selecting a Faecal Microbiota Transplant: Bacterial Composition and Subsequent Long-Term Monitoring of the Viability of Its Frozen and lyophilised Forms**  
**[PERSON] 1, [PERSON] 1,** **  
the dysbiotic condition by increasing the overall diversity of the intestinal microbiota and restoring its functionality [1]. It is based on the reasoning that manipulation of the microbiome constitutes potential therapy of pathological conditions with altered composition of the microbiota [2], and its success is proof of the important role of microbiota in various diseases [3]. FMT has been increasingly recognised and accepted due to its approval as a standard therapy of recurrent C. _diffic_ (CDI, _Clostridioides difficile_ infection) infections, resulting in recovery rates as high as 90% [4] in patients suffering from recurrent C. _diffic_ diarrhoea. In addition to the confirmed instances of effective treatment, such as CDI, several studies described clinical conditions for which FMT can constitute a promising and perspective alternative to standard therapies. Published studies have revealed the potential role of FMT in the treatment of refractory ulcerative colitis [5], Crohn's disease [6], constipation [7], irritable bowel syndrome (IBS) [8], liver diseases [9], oncology [6], blood diseases [10], autism [11; 12], and epilepsy [13]. There were also reported experiments with the use of FMT as a supportive therapy in the treatment of diseases such as metabolic syndrome [14], diabetes [15], Parkinson's disease [16], multiple sclerosis [17], psoriasis, mental anorexia [3], or Alzheimer's disease [18].  
Although the preliminary investigations brought some promising results with respect to future therapeutic strategies, a number of questions need to be resolved in order to effectively translate microbiome-based findings into clinical practice. The extensive use of FMT as an effective therapeutic alternative is prevented by concerns related to the lack of adequate standardisation of the entire process. The ongoing research in this field is heterogeneous in all its phases: from the screening of donors, type of donors (blood-related vs. unrelated), simple stool sample vs. composite samples, processing of stool with optimum composition, its quantity, whole stool vs. sample enriched with viable bacterial products obtained by in vitro fermentation, up to storage and administration in raw vs. frozen vs. lyophilised form of stool, optimum number of FMT doses and way of administration of FMT to recipients. The increasing number of clinical indications points to the need to identify an ideal donor separately for each disease, or an individual patient who cannot be treated indiscriminately by the same faecal microbiota.  
The aim of this study was to confirm the suitability of FMT from a selected donor based on the optimal composition of his faecal microbiota, intended for its potential use in supportive therapy in patients with ulcerative colitis. Another aim was to evaluate the viability of microbiota in frozen and lyophilised forms of processed FMT under varying storage conditions.
# The _Ins_ Gene of _Escherichia coli_ Is Transcriptionally Down-Regulated by (p)ppGpp
###### Abstract  
Second messenger nucleotides, such as guanosine penta- or tetra-phosphate, commonly referred to as (p)ppGpp, are powerful signaling molecules, used by all bacteria to fine-tune cellular metabolism in response to nutrient availability. Indeed, under nutritional starvation, accumulation of (p)ppGpp reduces cell growth, inhibits stable RNAs synthesis, and selectively up- or down- regulates the expression of a large number of genes. Here, we show that the _E. coli__Ins_ promoter responds to intracellular level of (p)ppGpp. _Ins_ encodes the DNA binding protein H-NS, one of the major components of bacterial nucleoid. Currently, H-NS is viewed as a global regulator of transcription in an environment-dependent mode. Combining results from _reLA_ (ppGpp synthetase) and _spoT_ (ppGpp synthetase/hydrolase) null mutants with those from an inducible plasmid encoded ReLA system, we have found that _Ins_ expression is inversely correlated with the intracellular concentration of (p)ppGpp, particularly in exponential phase of growth. Furthermore, we have reproduced in an in vitro system the observed in vivo (p)ppGpp-mediated transcriptional repression of _Ins_ promoter. Electrophoretic mobility shift assays clearly demonstrated that this unusual nucleotide negatively affects the stability of RNA polymerase-_Ins_ promoter complex. Hence, these findings demonstrate that the _Ins_ promoter is subjected to an RNA polymerase-mediated down-regulation by increased intracellular levels of (p)ppGpp.  
_Ins_; guanosine penta-tetra-phosphate; stringent response; transcriptional regulation +
Footnote †: journal: Microorganisms
# A Review of Time-Scale Modification of Music Signals +
## 7 Applications
### Websources  
Various free implementations of TSM procedures are available in different programming languages. MATLAB implementations of OLA, WSOLA, PV-TSM, and TSM based on HPS, as well as additional test and demo material can be found in the TSM Toolbox [43; 44]. A further MATLAB implementation of PV-TSM can be found at [45]. PV-TSM implemented in Python is included in LibROSA [46]. Furthermore, there are open source C/C++ audio processing libraries that offer TSM functionalities. For example, the Rubber Band Library [47] includes a transient preserving PV-TSM and the SoundTouch Audio Processing Library [48] offers a WSOLA-like TSM procedure. Finally, there also exist proprietary commercial implementations such as the elastique algorithm by zplane [49].
## 2 Materials and Methods
### Preprocessing PPG Signal and Generation of Complex Networks  
This study initially performed preprocessing operations on the MW-PPG signals. During preprocessing, the obtained one-minute segments of MW-PPG signals were screened and cleaned. A second-order Butterworth bandpass filter was applied to filter the MW-PPG signals [32]. Considering that the primary frequency band of PPG signals lies within 0.1-10 Hz, and the major noise component in the captured signals is concentrated around 9 Hz, the filter's bandwidth was selected to be between 0.5-8 Hz. The signals before and after filtering are illustrated in Figure 6A. Figure 6: (**A**) shows the data preprocessing process and (**B**) shows the PPG signal morphological features used in generating multi-sample complex networks, where A1 A2 is the area of the PPG signal cycle in the figure divided by the boundary line. When generating multi-sample complex networks, this study selected 26 PPG features that could describe the morphology and shape of the PPG signal as comprehensively as possible. These features include the height of the peak point (Iso), the height of the dicroie peak (Ido), the height of the dicroie notch (Ino), the total conduction time of the PPG signal (To2o), the time from the start of the PPG signal to its peak (Iso), and so on. A detailed list of all extracted features is presented in Table 1, and their schematic representation is shown in Figure 6B. After extracting the features from one-minute PPG signals, the multiple PPG signal features within the same sample were averaged to obtain a 1 \(\times\) 26 feature vector for each of the four samples. Subsequently, each 1 \(\times\) 26 feature vector, representing a wavelength within each sample, was treated as a node, and the Euclidean distance [33] between these feature vectors was calculated. Euclidean distance, a common method for defining the distance between two distinct points, is defined as follows: For two points A = \((a1,a2,\ldots,an)\) and B = \((b1,b2,\ldots,bn)\) in an n-dimensional space, their Euclidean distance \(d\) is given by:  
\[d(\text{A},\text{B})=\sqrt{\sum_{i=1}^{n}(ai-bi)} \tag{7}\]
## 6 Conclusions and Perspectives  
### Achievements  
In this paper, we studied recent advances in the field of AI techniques for developing assistive technologies for BVIP. This survey was made by assessing recent case studies in the field with a well-defined research methodology. Several aspects of the proposed systems were analyzed, including their human-machine interface (acquisition and processing hardware, feedback types and conveyors), the AI techniques chosen, and the ways they have been assessed.  
Helping disabled people, such as BVIP, has been a major research topic for a long time, and the burst of technologies in everyday life has dramatically accelerated the development of the field. Although lacking functionality, several smartphone apps are already available for BVIP and are being adopted by many to ease their everyday activities. Some examples are:  
* _Lookout_ (Android) and _SeeingAI_ (iOS) to identify objects or people;
* _TapTapSee_ and _VizWiz_ (iOS/ Android) to identify elements in pictures taken by the user;
* GPS apps _Ariadne GPS, Microsoft Soundscape_ and _Blindsquare_ (iOS) to provide customized environment descriptions;
* _Evelity_ (iOS/Android) to navigate in equipped buildings using a specific GPS system,
* _MyMoveo_ (iOS/Android) to activate connected crosswalks and spot important elements (such as entrance doors).  
Benefiting from the recent innovations of AI, assistive technologies have developed possibilities while increasing robustness and efficiency. In the very near future, it will be possible to propose safe and reliable wayfinding systems for BVIP, as well as detailed scene description and customized object recognition. Devices are also becoming cheaper and more comfortable to wear, especially smartphones, which are now equipped with many sensors, such as cameras, gyroscopes, and accelerometers, and endowed with enough computing capacity to run deep- or machine-learning algorithms.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 1 Introduction
Section 2 describes the proposed spatial-temporal saliency network architecture for space debris detection. The centroid computational method is introduced in Section 3. Section 4 presents the experimental setup. Section 5 presents experimental studies for verifying the proposed space debris detection method, including the comparison results with the current space object detection algorithms. Finally, we conclude our research in Section 6.
## 2 Model Development
### Metabolism Rate Assessment during Walking/logging  
The described experiment [3] was comprised of two steps. After wearing shoes and 10 min of rest, participants walked for 30 min. So, at the end of the first 10 min, it can be expected that the foot and shoe have reached a thermal equilibrium (Figure 4). In the absence of any mechanical work due to walking, and for this steady-state condition, the governing equation (Equation (1)) is reduced to Equation (11) with the following boundary condition.  
\[k_{s}\frac{\dot{d}^{2}T}{dT^{2}}=0 \tag{11}\] \[T(x=0)=T_{f0}\] \[T(x=l)=T_{G}\]  
where \(k_{s}\) is the shoe sole thermal conductivity and \(T\) is temperature. \(l\) is the shoe sole thickness. \(T_{f0}\) and \(T_{G}\) are the average foot sole temperature and ground temperature, respectively. Equation (11) is an ordinary differential equation with a solution in the form of:  
\[T(x)=\left(T_{\infty}-T_{f0}\right)\frac{x}{l}+T_{f0},\ldots 0\leq x\leq l \tag{12}\]  
Figure 3: Shoe contact area and contact force for V = 3.0 km/h modified from [2].  
This would be the initial condition at the begging of exercise.  
Because of the thermal equilibrium and steady-state condition, the dissipated heat flux from the foot sole is equal to (Figure 4):  
\[q^{{}^{\prime\prime}}_{met0}=k_{s}\frac{T_{f0}-T_{G}}{l} \tag{13}\]  
Starting from the rest condition to walking, the metabolism rate increases sharply by factor \(F\):  
\[q^{{}^{\prime\prime}}_{met}(\tau)=F.q^{{}^{\prime\prime}}_{met0} \tag{14}\]  
The metabolism intensification factor (\(F\)) is a function of the gait speed and is adopted from [PERSON]'s experimental correlation [34].  
\[F(V)=1.3\frac{0.009V^{2}+0.002V+0.082}{0.009V^{2}_{ref}+0.002V_{ref}+0.082} \tag{15}\]  
\(V_{ref}\) is 0.833 m/s (3 km/h).
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
###### Abstract  
We took the mining counter-rotating fan FBD No.8.0 as the research object, used orthogonal test and numerical simulation to study the influence of wing angle blade on fan performance, and simulated and analyzed its aerodynamic noise. The results show that the pressure distribution of the optimal blade angle blade fan on the pressure surface of the secondary blade is stronger than that of the prototype blade, and the maximum pressure at the blade height of 25%, 50%, and 75% is increased by 2.3%, 9.3%, and 8.1%, respectively, than original blade. Compared with the prototype blade; wing angle blades can effectively reduce the generation of shedding vortices at the trailing edge of the blade, and reduce the strength of shedding vortices, so that the entropy production of the optimal wing angle blade fan is 1.55% lower than that of the prototype fan. Compared with the prototype fan, the full pressure and efficiency of the angle blade fan under the rated flow have increased by 7.24% and 1.76%, and the average increase of 11.32% and 3.88%, respectively, under the full flow condition. Compared with the prototype fan, the maximum sound power of the wing blade fan in the first and second blade trailing edge regions is reduced by 0.17% and 1.62%, respectively.  
counter-rotating axial fan; orthogonal experiment; numerical simulation; wing angle blade +
Footnote †: journal: Applied sciences
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 3 System Overview and Problem Formulation
### Mathematical Model  
Table 1 shows the model of our system. The cluster is represented by a set of \(n\) nodes denoted by \(V\). The set of actual network state are denoted by \(S_{n}\). The state of each node and each link are represented by \(s_{n}\) and \(s_{l}\), respectively. Each node manages a table of member states represented by the function \(fs\). Nodes use Gossip protocol to make member states globally consistent. To select a leader, a node first selects a set of candidates basing on \(r_{c}\), namely candidate rule. And it uses the function of \(h(v)\) and \(l(v)\) to determine a leader, where \(h(v)\) is used to get a unique identity of \(v\) and \(l(v)\) is used to return who is the leader. Leader removes a faulty node basing on the function of \(r_{d}\), namely downing rule.  
Particularly, we model the link in our system to be undirected [19; 23], i.e., \(s_{l}(v_{i},v_{j})\) is equal to \(s_{l}(v_{j},v_{i})\). In addition, we only consider the scenario where the majority of the nodes in the cluster work,  
\[V_{h}:=\{v:v\in V,s_{n}(v)=Healthy\},\ |V_{h}|>\frac{|V|}{2} \tag{1}\]  
We next talk about an important property of connectivity between nodes, i.e., transitivity. With the property of transitivity, no partial connectivity is appeared in the topology. This is to say two healthily connected end-nodes have the same link states to any other end-nodes. In traditional network this property is applicable because the endpoints do not have the ability to forward messages to others and the route protocol will eventually take care of the partially connecting issue. However, in case of topology like ad-hoc networking where endpoints are responsible for forwarding data or in case that unfair QoS is adopted, this property may not be applicable then. Therefore, we classify our algorithm into two cases divided by the applicability of this property. The transitivity can be described as the following formula:  
\[\exists v_{i},v_{j}\in V,v_{i}\
eq v_{j},s_{l}(v_{i},v_{j})=Healthy,\Rightarrow \forall v_{k}\ s_{l}(v_{i},v_{k})=s_{l}(v_{j},v_{k}) \tag{2}\]
# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
## 2 Background
### Drone Attack Models
#### 2.1.2 Hijacking  
Disruption of drone operations through modification of software can also be staged at the adversarial machine learning level. The drone quadrotor can be programmed to disrupt drone flight when it is subject to fictitious objects that can be observed through its sensors during flight, causing it to trigger a hazard avoidance routine and thus leading to a variation in flight trajectory. This can also be defined as a hijacking attack.  
Quadrotors typically have a return to home (RTH) state, that may be induced by the adversary so as to cause abandonment of the next waypoint. Typically, a drone that runs low on battery or is disconnected from the ground controller would have the RTH state activated. However, the authors in [12] have identified four threat models, namely, jamming of the communication channel, obscuring the main sensor/mirror, duplicating the target image and disrupting the object tracker (hide or change object makeup). Subject to these threats, the drone can not simply be forced to change to an RTH state, but can also be misconfigured and forced to reach an incorrect waypoint. Deliberate attempt to present to the drone's vision sensors images, reflections or other attack vectors (visual) can be adopted by the adversary to cause such disruptions [12].
###### Abstract  
The timely detection and recognizing of distress on an airport pavement is crucial for safe air traffic. For this purpose, a physical inspection of the airport maneuvering areas is regularly carried out, which might be time-consuming due to its size. One of the modern approaches to speeding up this process is unmanned aerial vehicle imagery followed by an automatic evaluation. This study explores the automatic detection of the transverse crack, its dimension measurement, and position determination within the slab on the concrete runway. The aerial image data were obtained from flights at the given altitude above the runway and processed using commercial multi-view reconstruction software to create a dataset for the training, verification, and testing of a YOLOv2 object detector. Once the crack was detected, the main features were obtained by image segmentation and morphological operations. The YOLOv2 detector was tuned with 3279 images until the detection metrics (average precision AP = 0.89) reached sufficient value for real deployment. The detected cracks were further processed to determine their position within the concrete slab, and their dimensions, i.e., length and width, were measured. The automated crack detection and evaluation system developed in this study was successfully verified on the experimental section of the runway as an example of practical application. It was proven that unmanned aerial vehicle imagery is efficient over broad areas and produces impressive results with the combination of artificial intelligence.  
unmanned aerial vehicle; airport; runway; crack detection; convolutional neural network; deep learning; morphological operations +
Footnote †: journal: Applied Sciences