esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Crack Growth from Notches:
## 1 Introduction
Based on the cyclic plasticity deformation obtained from the FE analysis, [PERSON] et al. [12] modeled the elastic-plastic behavior of 1070 steel and used a damage accumulation criterion to predict da/dN. When the R-ratio was positive, the crack growth was mainly influenced by the plasticity created by the notch. When the R-ratio was negative, the contact of the cracked surfaces during a part of a loading cycle reduced the cyclic plasticity of the material near the crack tip. In previous works of the authors, FCG in standard specimens was studied using the plastic crack tip opening displacement (CTOD). This approach assumes that crack tip plastic deformation is the driving force and that the plastic CTOD is able to quantify this deformation. Crack closure is included in a natural way, and small-scale and large-scale yielding are studied without distinction. This approach was used to study FCG in 6082-T6 [24], 7050-T6 [25], and 2050-T8 [26] aluminum alloys, in the 18 Ni300 steel [27] and in titanium [28], to predict the effect of stress ratio, stress state, and variable amplitude loading [29]; to predict fatigue threshold [30], and to predict the effect of material parameters [31]. The main objective here is to better understand the FCG from notches with the identification of the subjacent mechanisms. A numerical approach was followed to predict the FCG rates based on the plastic CTOD, assuming that this parameter is the crack driving force. In order to understand the notch effect, a comparison of cracks with and without notches is made. For the purpose of understanding the relevance of crack closure, models with and without contact of crack flanks were tested. These studies were made for different materials, notch radii and stress states, in order to understand the effect of these parameters in this context. Figure 1 is a schematic view of the paper, with the indication of the sections where the comparisons are made.
## 3 Results and Discussion
### Optimizing the Developed Solid-Phase Microextraction onto SSC-MOFs
These results indicate the sustainable nature of the fabricated SSC-MOFs, which serve to save materials via recycling and reduce the waste produced from the entire microextraction process. The sample volume of the primary arsenic(III) sample solution was investigated to assess the capacity of the developed microextraction procedure using SSC-MOFs. The results presented in Figure 6E indicate that up to 40 mL of the developed microextraction procedure exhibited a recovery% of about 98. In addition, the rate of adsorption of arsenic(III) onto the SSC-MOFs is investigated using the pseudo-first order and the pseudo-second order kinetic models (Figure 7). The data for the adsorption process tend to follow the pseudo-second order kinetic model with a correlation factor of 0.996, compared to 0.936 in case of the pseudo-first order model, indicating a fast adsorption process. Figure 6: The optimization of the developed solid-phase microextraction of the arsenic(III) onto the SSC-MOFs; (**A**) effect of pH, (**B**) shaking time investigation, (**C**) effect of eluent type, (**D**) recycling and reuse investigation and (**E**) effect of sample volume.
## 4 ReMouse Dataset Analysis
### Sessions Generated by The Same User
**Observation 1.2**: Even though the repeat sessions generated by each particular user became progressively 'closer' (as illustrated in Figure 6), no user is able to produce two entirely identical consecutive mouse trajectories when repeating the same online task. This observation is illustrated in Table 2, which shows the ids of the two closest consecutive sessions generated by each respective user in the ReMouse dataset when measured using the minimum normalized cumulative DTW distance. Moreover, since the overall cumulative DTW distances will be greater when the sessions are longer--cumulating over time--we normalized the DTW distance values by the time taken to complete each pair of sessions (i.e., the trajectory time-wise length). That way, the time component does not affect the results, and the minimum DTW distances show the actual trajectories' closeness. A closer inspection of the values in Table 2 reveals that user 74 produced the most similar consecutive trajectories in the ReMouse dataset (corresponding to sessions number 39 and 40), with a normalized cumulative DTW distance of 64.23521268 (note that two identical sessions would produce a DTW distance of 0). The graph shown in Figure 8 plots the minimum normalized cumulative DTW distance values from Table 2, confirming Observation 1.2. Figure 9 provides a closer look at the trajectories of sessions 39 and 40 of user 74, as well as their respective normalized cumulative DTW. **Observation 1.3**: Through the analysis of ReMouse dataset, we further observed that in the initial sessions the users acted generally more confused, i.e., their cursors exhibited more 'erratic' behavior until the users finally figured out what exactly they were expected to do. However, even in these initial sessions, the mouse speed was not considerably slower than in the later session, which is indicated through a relatively small positive slope value obtained from the 'trend line analysis'. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Users**} & \multirow{2}{*}{**Sessions**} & \multicolumn{2}{c}{**Min DTW Normalized**} & \multirow{2}{*}{**Users**} & \multirow{2}{*}{**Sessions**} & \multicolumn{2}{c}{**Min DTW Normalized**} \\  & & & & & & **Cumulative Distance** \\ \hline
0 & 7,8 & 591.6516 & 50 & 2,3 & 303.9826 \\
1 & 5,6 & 295.2985 & 51 & 4,5 & 291.6989 \\
2 & 35,36 & 147.0755 & 52 & 7,8 & 272.5094 \\
3 & 13,14 & 192.1207 & 53 & 13,14  
Figure 8: Minimum DTW normalized cumulative distances across sessions of each individual user.
###### Abstract  
Intrabody communication (IBC) establishes a wireless connection between devices in a Wireless Body Area Network (WBAN) by utilizing the human body as a transmission medium. The characteristics of the IBC channel are significantly influenced by the geometric and biological features of the human body and tissues. This paper analyzes a dataset with experimental real subjects' data on signal loss in a galvanic IBC channel, models IBC identification using the K-Nearest Neighbors (KNN) algorithm, and proposes a novel IBC WBAN architecture incorporating an identification function. The analysis of the dataset revealed that the IBC channel gain exhibits a wide range of variations depending on individual human body characteristics such as height, weight, body mass index, and body composition. Consequently, biometric identification can be leveraged within the IBC WBAN paradigm. Through modeling IBC identification on cleaned and labeled data, we demonstrated an identification accuracy of 99.9% based on the results of our modeling. The proposed IBC WBAN architecture with an integrated identification function is anticipated to enhance the application scope and accelerate the development of IBC WBANs.  
channel gain; galvanic coupling; IBC identification; NB-IoT; KNN algorithm; wireless body area network (WBAN) Article
# Microbiota \"Fingerprint\" of Greek Feta Cheese through Ripening
## 4 Discussion
The increase of ripening time significantly affected the total solids, proteins, and pH values of the cheese. The decrease of the protein content was contributed to the proteolytic system of LABs which takes place during the ripening process. The catabolism of amino acids through microorganisms (LAB), is producing aromatic compounds [41]. The rest examined chemical parameters (moisture, fat, salt) were not significantly affected by the ripening periods. In the microbiological status of Feta cheese, appear several differentiations in species level compared to the examined ripening periods, demonstrating its core microbiota, which plays the crucial role for the technological properties and the sensory characteristics of Feta cheese (Figure 5). Major pathogenic bacteria (_Listeria monocytogenes_, _Salmonella spp_, _Enterobacteriaceae_, and _Coagulase-Positive Staphylococcus_) were absent in all tested samples, thus all batches were considered safe according to the European Union regulatory criteria. Therefore, the examined Feta cheese representative microbiota profile was not influenced by pathogenic factors.
# Non-Destructive Testing Applications for Steel Bridges
## 2 Need for Inspection of Steel Bridges  
Steel bridges constitute a substantial portion of national wealth. Unchecked damages and the potential failure of these critical infrastructure links could not only have a significant economic impact, but also threatens public safety. As such, they should be inspected between short intervals to prevent the exacerbation of potential damages and failure. Typical damages of in-service steel bridges include corrosion and the loss of cross-section, delamination, cracks, wear, and surface blemishes (Figure 1). These damages, after detection, should be repaired or appropriately rehabilitated to prevent future catastrophic failures. It goes without saying that detecting the damages is the first step in this process.  
Figure 1: Most common deficiencies in steel bridges: (**a**) Corrosion [5, 6, 7]; (**b**) Delamination [8]; (**c**) Cracks [9]; (**d**) Top chord plate wearing groove into a top chord pin [10]; (**e**) Surface blemishes (welding arc strike on surface of steel plate) [10].  
Most of these damages in their advanced stage can be detected easily with a visual inspection. However, the detection of small and subsurface cracks and damages in deficient welded connections require inspection and testing techniques with capabilities above and beyond visual inspection. To address this, various NDT methods have been practiced over recent decades. The type of NDT method required for the inspection should incorporate the most practical and cost-effective technique to successfully assess the condition of the structural components and to determine the need for maintenance and repair action. In addition to a familiarity with, and skill in, the use of these various available methods, the inspector needs to be aware of the environment in which the inspection takes place, as well as the bridge section that is under survey, to determine the proper procedure.
## 4 Acoustic Finite Element Analysis (AFEA) of Acoustic Wave Propagation in a Straight Pipe
### Analysis Procedure and Boundary Conditions  
In creating the intact pipe model using the solid elements AC3D8 and AC3D20, the 3-D modelling space of the Abaqus/CAE part module was used to create a single solid shape deformable cylinder that represents the model of the air volume that occupied the pipe cavity. In the case of the intact pipe model using the axisymmetric elements ACAX4 and ACAX8, a longitudinal sectional plane of the 3-D modelling space was used to part-define the model from which the full solid shape deformable cylinder was derived internally by revolving the plane about its axis. In both cases, only the fluid (air) content of the pipe was modelled without the inclusion of the pipe in the model. Since the specific acoustic impedance of the air medium is much smaller than the specific impedance of the wall of a metallic or plastic pipe, this assumption is reasonable. This eliminates contact interaction of the solid pipe wall and the fluid, and as a consequence, reduces computational time and resources.  
The acoustic properties of air used for the intact models are density \(\rho=1.225\) kg m\({}^{-3}\) and bulk modulus \(\mathcal{B}=142,000\) N m\({}^{-2}\) which give an acoustic velocity of \(340.47\) m s\({}^{-1}\). Volumetric drag of the air medium was assumed to have negligible effect on the analysis and was ignored. The next  
Figure 2: Reference cross-section and axisymmetric solid elements: (**a**) 4-nodes (**b**) 8-nodes [19].  
step in creating the model involves defining and assigning material and section properties to the deformable domain.  
In defining the analysis step for the simulation, the step module of the Abaqus/CAE was selected. The air properties were evaluated to define a general dynamic implicit step with a primary period of \(1.469\times 10^{-3}\) s for the initial step, and with an increment size set at \(1.469\times 10^{-6}\) s. Also, a fixed maximum number of time increments of \(10,000\) was defined to guarantee convergence in the time increments. The solution technique used for the equation solver was the Full Newton, while the default analysis product that converts severe discontinuity iterations to propagate from the previous step was selected. The extrapolation of prior state of each increment was chosen as linear, while Alpha time integrator parameter was specified as \(-0.333\).  
An acoustic impulse of a half sine wave, as shown in Figure 3, was created. The pulse had a maximum amplitude of 1 Pa, and was applied for a duration of \(10^{-4}\) s at the inlet. Similarly, at the pipe outlet region, a zero acoustic pressure was defined to create a profile of pressure damping from maximum to atmospheric at the pipe exit boundary.
# An Embedded Sensor Node Microcontroller with Crypto-Processors
## 3 Microcontroller Design
### Dco  
The TNODE5 design integrates a digitally controlled oscillator (DCO). It supports 64 different frequencies ranging from 5.8 MHz up to 13.9 MHz. The DCO has a size of 0.024 mm\({}^{2}\) and consumes in active mode around 265 \(\upmu\)W. When disabled, its standby power is around 2.5 \(\upmu\)W. The oscillator is programmable via a 64-bit 1-hot decoder that improves DCO frequency stability. DCO generates clock signals with a duty cycle of nearly 50% and has a settling time of 15 \(\upmu\)s. More details on the implemented DCO are given in [61].
## 5 Validation of RPAL SCAOF in Simulations
### Simulation Setup and Designated Scenarios  
In this section, the configuration of the COOJA simulator, MSPsim emulator and wireless channel model will be presented. Then, the validation of SCAOF in RPAL model will be carried out for different network topologies and different scenarios. Furthermore, the SCAOF algorithm is tested under enhanced simulation scenario scripts which enable runtime adjustments of radio medium, node state and behavior. To the best of our knowledge, this is the first work to evaluate the RPL-based protocol with dynamic simulation and runtime fault injection.
# Bridge Health Monitoring Using Proper Orthogonal Decomposition and Transfer Learning
## 2 TL-Based Damage Identification  
Previous research has shown that a TL approach can generalize trained damage detection knowledge from one structure to a group of similar structures [23; 32]. To use ML methods to train a model for the damage classification of a structure, training and testing data distributions are assumed to be the same. This assumption might be violated for a population of structures with different features. This is the main motivation behind using a TL-based approach. To transfer the knowledge from one structure to another using this approach, the data distributions are mapped from a source domain to a targeted domain, thereby extensively reducing the computational effort needed for retraining. It is understood that the type of transferred knowledge and source to target structure mapping method relies on the level of similarities between the structures in the two mapped domains [32]. This research focused on a set of simulated experiments from a modeled bridge that are nominally identical. Therefore, the mapping used for this TL problem incorporates consistency in both the feature and label spaces.
## 3 Comparison among Different AC Signal Analysis Methods
### Processing Time
#### 3.1.2 Possible Hardware Acceleration  
Although hardware acceleration is possible for all AC signal analysis methods, only some hardware acceleration methods could be relevant due to the computational overhead required before parallelization, such as filling arrays, etc. Other methods require more time to set up the context per each thread or transfer data to the dedicated units, negating the efficiency of the parallelism.  
In Table 2, we summarize the optimal acceleration methods for the AC signal analysis, whether data-level or instruction-level parallelism is relevant to the considered method.  
First, and for FFT (Radix-2, or Radix-4, or Radix-8), block calculation can be accelerated by either using data-level or instruction-level parallelism or both. Data-level parallelization provides a more efficient way to compute the twiddle coefficients. It can also be used to evaluate the expressions within each radix block calculation or by concatenating them into a single vector. Alternatively, each radix block can be evaluated within one thread. Second, Both of Goertzel filter and DTFT computation can be parallelized at either data or instruction level to parallelize the calculation of each frequency component. When possible, concatenating all calculations in single or multiple vectors allows for more efficient parallelization. Third, Cross-correlation computation can be accelerated at the data level by concatenating all computation in a single vector or multiple vectors. Alternatively, parallelization can be performed at the instruction level for each frequency component. Fourth, Sine fitting can exploit data-level parallelism to calculate the novel signal. However, due to the sequence of its steps, parallelizing instructions is not efficient.
## 3 Results and Discussion
### Test Conditions and Test Scheme
#### 3.1.2 Test Scheme  
Three stages of the tests carried out in the field are described as follows:  
The first test stage: The seed fertilizer drill machine controlled the application rate according to the calibrated relationship model between the mass flow rate and rotation speed. After each test, the application rate calibration system fitted the relationship model between the mass flow rate and rotation speed based on the latest test data but did not update the system control parameters.  
The second test stage: To significantly and quickly change the calibrated model parameters, the gap between the fluted rollers and the top of the brush was artificially shortened by 1 mm by trimming the brush length of the seed-fertilized drill machine. This would increase the mass flow rate and change the relationship model between the mass flow rate and rotation speed. The seed-fertilized drill machine applied the original calibrated relationship model between the mass flow rate and rotation speed to complete the same test as in the first stage.  
The third test stage: Using the latest test data from the application rate calibration system, the relationship model parameters of the control system were updated to control the application rate more accurately.  
After each test stage, the performance of the application rate calibration system was evaluated by comparing the difference between the relationship model parameters and the relative deviation of application rates. To evaluate the control effect of the actual application rate, bins were placed under each outlet to collect the seeds or compound fertilizers. After the completion of each test, the collected materials were weighed.
# Aerial Grasping of an Object in the Strong Wind: Robust Control of an Aerial Manipulator
## 3 System Control
### Manipulator Control  
The manipulator controller structure is shown in Figure 3. The position controller of the end-effector is a PID (Proportion Integral Derivative) controller, so that we can get the desired velocity of the end-effector in the frame \(\Sigma_{I}\), and they are denoted by \(v_{e,d}\) and \(\omega_{e,d}\), respectively. The velocity control of the end-effector is realized in the frame \(\Sigma_{B}\). The desired velocities of the end-effector in the frame \(\Sigma_{B}\) are denoted by \({}^{B}v_{be,d}\) and \({}^{B}\omega_{be,d}\), respectively. To get \({}^{B}v_{be,d}\) and \({}^{B}\omega_{be,d}\), inverse velocity kinematics of the aerial manipulator system is used in the manipulator controller. Through inversing Equation (4), \({}^{B}v_{be,d}\) and \({}^{B}\omega_{be,d}\) can be found as follows:  
\[\left\{\begin{array}{ll}{}^{B}v_{be,d}={}^{I}R_{B}^{-1}(v_{e,d}-v_{b})-{}^{B} \omega_{b}\times{}^{B}p_{be}\\ {}^{B}\omega_{be,d}={}^{I}R_{B}^{-1}\omega_{e,d}-{}^{B}\omega_{b}\end{array}\right. \tag{30}\]  
The theme of velocity control of a manipulator is to find the right joint velocity that can generate the desired velocity of the manipulator end-effector. After getting the desired joint velocity, denoted by \(\dot{q}_{d}\), the actuators have a low level controller, which can track the desired joint velocity with enough accuracy. The velocity control of the redundant manipulator has a framework that is introduced in a previous work [35], which can minimize \(\frac{1}{2}q^{T}W\dot{q}+\alpha\dot{f}(q)\), where \(W\) is a symmetric positive-definite weighting matrix and \(\alpha\) is a scalar. This frame can be described as follow:  
\[\dot{q}_{d}=\left[\frac{B_{be}(q)}{N_{J}^{T}W}\right]^{-1}\left[\frac{V_{d}}{ -\alpha N_{J}^{T}\Delta f}\right] \tag{31}\]  
where \(V_{d}=\left[\begin{array}{cc}{}^{B}v_{be,d}&{}^{B}\omega_{be,d}\\ \end{array}\right]^{T}\). \(N_{J}\) is a matrix whose columns are a spanning set of the null space of \({}^{B}J_{be}(q)\). That is \({}^{B}J_{be}(q)N_{J}=0\). \(\Delta f\) is the gradient of \(f(q)\), which is a function of the joint angle vector.
## 5 Discussion  
In this section, we discuss and make comparisons between the actual strength and predicted strength of concrete. The actual strength was estimated from test cores that were extracted during experiments, and the predicted strength of concrete was predicted based on data analyzed using fuzzy logic. Figure 9a-c shows the comparison between the actual and predicted strengths of concrete samples. The actual strength values are represented by bars and correspond to the 1 st, 3 rd, 7 th, 14 th, and 28 th days.  
In Figure 9a, specimen 1 shows matching actual and predicted compressive strength results on the first day; both of the values were 3.4 MPa. After that, the actual strength on the third day (38.2 MPa) was higher than the predicted strength (37.1 MPa); this difference may be due to signal fluctuation. Similarly, the predicted and actual strengths on the seventh day were very similar; after slight variation, they were observed to almost match at the end of the 28 th day. This plot shows 92% similarity between the strength development patterns of the actual and forecasted compressive strengths of concrete.  
Figure 9: _Cont._The data for specimen 2, in Figure 9b, show similar strength development for the actual strength and estimated strength until the second day of curing. After that point, the predicted strength values were observed to be higher than the actual values until the 20 th day of concrete aging. This may be due to signal fluctuations and temperature effects. After 28 days, the results are quite similar, with only a slight difference of 0.39 MPa; the actual strength was 67.26 MPa and the predicted result was 67.65 MPa.  
Figure 9c shows a smoother plot. The data from specimen 3 indicate that the predicted and actual strengths of concrete do not match closely at the first day. This is due to the fact that the specimen was placed at a high temperature of 40 \({}^{\circ}\)C. Thus, the strength development started quickly. The actual strength after 24 h was observed to be 6 MPa, while the predicted strength was 11.09 MPa. After curing for seven days, the results are much more closely matched; this similarity was maintained through the 28 th day, where the observed core strength was 59.23 MPa and the strength predicted based on the fuzzy logic simulated data was 58.70 MPa.