esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing  
The entire countermovement jump in this study was separated into three parts, including push-off, flight, and landing phases. Marker trajectories were processed by Visual 3-D software (version 3.26, C-Motion Inc., Germantown, MD, USA) and were smoothed using a low-pass filter with a 10 Hz cutoff frequency. The joint angles were calculated using an inverse kinematics algorithm, which was conducted in Visual 3D. The calculation of joint moments was based on the calculated joint angles and collected GRF, and an inverse dynamics algorithm was used to acquire the joint moments. The kinematics and kinetics of the lower limbs in the push-off and landing phases were analyzed, respectively, and the data for each phase were normalized to 100 frames. The vertical GRF were normalized to body weight (BW). Kinematic variables of interest included peak joint angles as well as the range of motion (ROM) of the ankle, knee, hip joints in three planes. Kinetic variables included peak values for vertical GRF and peak joint moments of the ankle, knee, and hip joints, which were normalized to the body mass of each participant. As for the flight phase, jump height was calculated by the flight time to evaluate the jump performance according to Equation (1) [34; 35].  
\[jump\ height(m)\ =\ \frac{9.80m\cdot s^{-2}\times flight\ time(s)^{2}}{8} \tag{1}\]  
Figure 3: The placement of reflective markers and rigid clusters.
## 2 Thermodynamic Theory and Modeling
### Thermodynamic Model
A minimum heat capacity threshold of 2 J g\({}^{-1}\)K\({}^{-1}\) was established (C), whereas thermal conductivity was considered acceptable for all thermal oils due to its low variability. Furthermore, the operational temperature range needed to be between 0 and 300 \({}^{\circ}\)C (\(D\)). Table 1 summarizes the elimination criteria. The three commercial thermal oils that pass all criteria are Therminol LT (\(\overline{M}=134\) g/mol) [45], Paratherm HR (\(\overline{M}=240\) g/mol) [51], and Paratherm NF (\(\overline{M}=340\) g/mol) [52]. The isobaric heat capacity of the selected thermal oils had been fitted to experimental data [45; 51; 52] by a second-order polynomial as  
Figure 1:1  
Table 2 shows the constants, \(\alpha_{i}\) for the thermal oils heat capacity in the temperature range between 313.15 to 493.15 K. Table 3 presents the assumptions that delimit this study's scope, including using an ORC turbine with a displaced volume of 120 cm\({}^{3}\) per revolution. A steam turbine is probably the most flexible driver available to the industry. With the advent of modern precision gears, turbine speeds are seldom below 1200 rpm and may be as high as 25,000 rpm [61]. This study is considered 2900 rpm for the operation of the ORC turbine. The condenser has a fixed temperature of 303.15 K. Moreover, the driving equipment is perfectly isentropic, i.e., their efficiency is 100%. In any event, the efficiency of the turbomachinery does not influence the exergetic evaluation of the isobaric stages. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Commercial Name**} & **Heat Capacity** & **Low.** & **High** & **Density** & **Viscosity** & \multirow{2}{*}{**Criteria**} & \multirow{2}{*}{**Ref.**} \\  & & **J g\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)** & & **Tem.** & & **Point** & **40 \({}^{\circ}\)C** & \\  & & **J g\({}^{-1}\)\({}^{\circ}\)C\({}^{-1}\)** & & \({}^{\circ}\)C & \({}^{\circ}\)C & **kg m\({}^{-3}\)** & **cSt** & & \\ \hline
1 & HTF-FDA HT1 NSF & 2.10 & \(-\)10 & 325 & 873 & 37.1 & \(B\) & [34] \\  
R & 2.22 & \(-\)27 & 288 & 840 & 15.0 & \(D\) & [60] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analyzed commercial thermal oils including their critical thermophysical data and selection criteria. Combustion exhaust gas is used as the thermal source. The gas is modeled using the standard air assumptions and data [62]. The air temperature entering the ORC is fixed at 493.15 K. In contrast, the outlet temperature is fixed at 373.15 K. Consequently, the mass flow ratio of the thermal source varies depending on the energy the working fluid needs to reach the saturated vapor state. As shown in Table 3, three values for the pitch point temperature are considered in typical operation ranges. The enthalpy and entropy of the air are calculated using a second-order polynomial [63]. Furthermore, to simplify the analysis, the system is considered to have steady-state conditions in all components, and the heat and friction losses in the system are neglected.
# Influence of Aging Conditions on the Dynamic Stiffness of EPDM and EVA Rail Pads
## 5 Discussion
Our conclusions agree with those previously obtained by [PERSON] et al. [20]. In the case of exposure to hydrocarbons and UV radiation, the observed differences in effects between rail pads and exposure levels could reasonably be attributed to the substantial differences in how the polymer chains constituting EPDM and EVA respond to degradation processes. Thus, previous studies have already indicated the difference in the deterioration caused by environmental agents between different polymers [7]. Specifically, the distinct chemical structure of each material influences its degradation mechanisms, resulting in different behaviors under identical environmental conditions. In this way, the variations in dynamic stiffness observed in this study reflect the underlying chemical and structural characteristics unique to each polymer [13, 14, 15, 16, 17], affecting their performance and durability in real operating scenarios. Finally, although this study provides insights into the degradation mechanisms and their effects on the dynamic stiffness of rail pads made of EPDM and EVA, there are several important limitations that must be acknowledged. Firstly, while the experimental tests consider representative conditions of the railway environment, the effects of mechanical aging, ultraviolet radiation, and hydrocarbon exposure were evaluated separately without analyzing the possible interactions or simultaneous combinations of these factors, which may occur under real service conditions. Additionally, the study focuses exclusively on two specific materials with specific geometries, implying that the results obtained within this research may not be totally generalizable to other different components.
## 2 Literature Review
### Communication Technologies for IoT Healthcare
#### 2.3.4 Energy Harvesting Techniques  
One possible solution to address energy limits is to incorporate energy harvesting techniques into IoT devices. Battery power can be substituted or supplemented by methods including solar, thermal, and kinetic energy harvesting, allowing devices to run for extended periods of time between charges [42; 43; 44; 45].  
A variety of strategies, such as energy harvesting methods, sophisticated low-power communication protocols, and power-efficient sensing, are used to address energy limits in remote health monitoring.
## 1 Introduction and Previous Achievements
Those systems require short exposure times and have a desktop footprint. The source employed can generate the plasma efficiently, without debris production. The microscopes are easy to operate and have compact construction. Moreover, the EUV/SXR microscopes, based on those sources [34], do not require sample preparation such as gold coating for SEM microscopes or staining and marking like in STED. In such systems, the gas puff target is produced by the injection of a small amount of high-Z gas--working gas, into a stream of low-Z gas--outer gas, by a fast electromagnetic double valve system, as depicted in Figure 1a. The gas puff target is then irradiated by focused laser pulses from a Nd:YAG laser. The interaction of a laser pulses with gaseous target allows generation of the EUV and SXR radiation. Both full-field microscopes use reflective optics to focus the short wavelength radiation onto a sample and FZPs to obtain magnified images of the sample with high spatial resolution. The contact microscope uses the spectrally filtered plasma radiation directly, without pre-focusing, to form an imprint of the sample in the photoresist layer. These microscopes may represent an important alternative to performing experiments in small academic laboratories on a much larger scale than with the employment of synchrotron facilities and could have a significant effect on the nanotechnology development in the near future. The goal of developing these microscopes is to show feasibility to achieve high resolution imaging, low exposure times, together with a compact footprint, which may in the future open a possibility for commercialization.
# Retained Placenta as a Potential Source of Mastitis Pathogens in Dairy Cows
###### Abstract  
(1) Background: Retained placenta (RP) and mastitistis are relevant diseases in dairy cows. This study mainly aimed to evaluate the contamination of uterine fluid by mastitisti's pathogens in dairy cows presenting RP. (2) Methods: Uterine fluids were sampled at RP diagnosis (89 \(\pm\) 15 h after calving) from 5 primiparous and 10 (parity: 2-5) multiparous cows. The real-time PCR methodology was used to identify 15 mastitisti and uterine pathogens. Results were analyzed using multivariate logistic regression, including the factors' and parity. (3) Results: The prevalence of _Escherichia coli_ was 93.3% (95% CI: 70.2-98.9%); _Staphylococcus_ spp. (93.3%; 95% CI: 70.2-98.9%), yeasts (92.9%; 95% CI: 68.5-98.7%), _Truependella pyogenes/Pettonihihs indicicus_ (80.0%; 95% CI: 54.8-93.0%), _Streptococcus_ _uberis_ (78.6%; 95% CI: 52.4-92.4%) and _Streptococcus dysgalactic_ (57.1%; 95% CI: 32.6-78.6%) comprised the largest proportions of pathogens in uterine contamination. _Strep. uberis_ was related to the presence of fever (relative risk: 1.6; 95% CI: 1.0-2.8; \(p\) = 0.05). (4) Conclusions: Dairy cows with RP can be a relevant source of mastitisti pathogens in farms. Only _Strep. uberis_ was linked to clinical signs of infection. A high proportion of yeasts was observed in uterine fluids. Further research is needed to evaluate the real impact of RP on mastitis prevalence in dairy herds.  
retained fetal membranes; mastitisti; dairy bovine farms; bacterial contamination; _Streptococcus uberis_
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 2 Materials and Methods
### Preprocessing Client-Declared Sample Sizes
#### 2.2.3 Truncation Given a Partial View of N  
When \(K\) is very large, we may want to sample only \(k\ll K\) elements IID from \(\mathbf{N}\). In this case, we will need to test that the inequality in Equation (4) holds with high probability. Such a test is useful in practice as the number of clients and their dataset sizes is not always tractable or pre-known, especially in the cross-device federated learning scenarios.  
We consider \(k\) discrete random variables taken IID from \(\mathbf{N}\) after truncation by \(U\), that is, taken from a distribution over \(\{0,\dots,U\}\). We mark these random variables as \(X_{1},\dots,X_{k}\), and their order statistic as \(X_{(1)},\dots,X_{(k)}\) where \(X_{(1)}\leq\dots\leq X_{(k)}\).  
**Theorem 2**.: _Given parameter \(\delta>0\) and \(\epsilon_{1}=\sqrt{\frac{\ln\left(3/\delta\right)}{2k}},\ \epsilon_{2}=U\sqrt{\frac{\ln\left(3/\delta \right)}{2k(\delta-\epsilon_{1})+1}}\), \(\epsilon_{3}=U\sqrt{\frac{\ln\ln\left(3/\delta\right)}{2k}}\), we have that \(\operatorname{mwp}(\operatorname{trunc}(\mathbf{N},U),\alpha)\leq\alpha^{*}\) is true with \(1-\delta\) confidence if the following holds:_  
\[\frac{\alpha\big{(}\frac{\sum_{k\in[k]}^{k}\left[\left(1-(\alpha-\epsilon_{1}) \right)k\right]X_{(0)}}{k-\left[\left(1-(\alpha-\epsilon_{1})\right)k\right]+1 }+\epsilon_{2}\big{)}}{\left(\frac{1}{k}\sum_{i\in[k]}X_{i}-\epsilon_{3} \right)}\leq\alpha^{*}. \tag{6}\]  
Proof.: See Appendix A.2.
# Geometric Benchmarking of Metal Material Extrusion Technology: A Preliminary Study
## 4 Conclusions  
Metal-MEX represents a cheaper alternative to powder bed fusion and direct energy deposition for the additive manufacturing of metals. However, the quality of the components still needs to be assessed in terms of geometrical performances to understand how the debinding and sintering treatments influence the achievable geometric characteristics. This work presents a preliminary geometrical characterization of 316L stainless steel geometric benchmark test artifacts produced using metal-MEX technology. The analyzed features were chosen to investigate geometric tolerances after both the printing process and the debinding and sintering treatments required to obtain dense metal parts.  
Findings limited to the analyzed range of nominal characteristic dimensions revealed that the residual stresses induced by debinding and sintering ultimately affected the investigated geometrical tolerances. In general, degradations in perpendicularity and parallelism tolerances were observed in the white state. Notably, parallelism exhibited an exponential increase with the length of the beam in the white state. On the contrary, improvements were observed in the measures of coaxiality acquired through hollow cylindrical features. The results of this study identified that the metal-MEX technology can be used to fabricate 316L stainless steel complex parts composed of primitive features. It was also determined that the dimensional ratio of the features influences the feasibility of the process: over specific values, the white parts cannot be obtained, due to the collapse or distortion resulting from post-printing treatments. However, discrepancies with the guidelines provided by the producer of the filament were observed, as collapse and distortion were encountered both above and below the height-to-thickness ratios suggested for both thin walls and pillars. A general overview of the aspect ratio behavior was obtained and described by maps, where it was possible to derive the assumption that employing smaller nozzles, compared to the printed feature, might noticeably reduce the risk of structural collapse and enable the fabrication of geometrical reproducations that are more accurate in terms of geometrical tolerances. This represents a starting point for further research that should be dedicated to a deeper comprehension of how layer density, infill strategy, and residual stresses affect part distortion throughout the entire manufacturing process.  
Conceptualization, G.L., M.Q. and G.D.; methodology, G.L. and M.Q.; formal analysis, G.L.; investigation, G.L.; data curation, G.L. and M.Q.; writing--original draft preparation, G.L. and M.Q.; writing--review and editing, G.D. and C.G.; supervision, G.D. and C.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors declare no conflicts of interest.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 8 Results and Discussion
### Key Performance Indicators
#### 8.1.2 Latency  
Latency is the end-to-end delay in data transfer. In large and complex networks with many nodes, latency can increase by many folds. Low data transfer rates hinder all the communications and choke the network. MQTT is a protocol with a very small overhead, which is ideal for use in circumstances where the payload is a small string of numbers, as required in the proposed case. A comparison of the latency of MQTT and HTTP is given in Table 2. It shows the data collected by sending 1000 messages via HTTP and MQTT from distant telemetry devices, where the packets were collected by sniffing tools. The system was concurrently sending and receiving packets from multiple publishers, and the data show the packets sent by one of the publishers. The test was repeated multiple times  
Figure 12: Comparison of the system efficiency before and after the implementation of the prototype.  
during different times of the day with variable network traffic to have average values. The results showed significantly faster data transmission rates from MQTT with low latency. This result was more prominent in the case of multiple nodes, as MQTT reuses the same connection, whereas HTTP/s requires a three-way handshake before every re-connection. The results may vary by changing the size of the payload, which was the same in this particular case as the data consisted of the ID of the cartons.
# Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving
## 5 Results and Discussion
Our proposed method improves the contrast between trees and the night sky and clarifies the details between layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. ** & **Metrics** & **Target** & **Predicted** & **Deviation** \\ \hline \multirow{8}{*}{Sample 1} & CPP & 94.4899 & 94.0496 & 0.4403 \\  & MPC & 11.4896 & 11.0152 & 0.4744 \\  & EME & 30.5660 & 28.4836 & 2.0824 \\  & IC & 9.7094 & 9.7622 & \(-\)  
Table 2 shows the objective evaluation index values for sample 5 and sample 6 in Figure 4. We can see in sample 5 that the CPP and MPC values of the image enhanced using the proposed method are larger than those of images enhanced using the other methods. The EME values of the images enhanced using our method are lower than those of images enhanced using the HE method, but higher than those using the SSR and MSR methods. In terms of image sharpness, the IC values of the images enhanced using the proposed method are lower than those of images enhanced using the HE and MSR methods. For sample 6, the CPP and MPC values of the images enhanced using the proposed method are significantly higher than those of images enhanced using the other methods, and the EME values of the enhanced images are also higher than those of images enhanced using the other methods. In terms of image sharpness, the IC values of the images enhanced using the HE and MSR methods in samples 6 and 7. In addition, a comparison of the mean values of 300 images in test dataset shows that our designed algorithm has a clear advantage in CPP, MPC, and EME evaluations, but is slightly inferior to MSR in IC comparisons. Therefore, improving IC will be the main direction of the subsequent optimization of our algorithm. The above subjective evaluation and objective index evaluation show that the proposed method can not only enhance the contrast of the infrared image, but also highlight the image details, which can effectively improve the quality of infrared images. Figure 4: Results of different infrared image enhancement algorithms. To study the computational speed of different algorithms, 10 images of size 200 \(\times\) 200 were tested using an i5 CPU. Table 3 shows the average time required for enhancing the ten images using MSR, LEPF [18], PSO [33], and the proposed method. The convolutional neural network method proposed in this study requires the shortest average time of 2.02 s, and the MSR algorithm is the next shortest. In contrast, the LEPF algorithm takes the longest average time of 302.3 s. This shows the unique advantage of the short time required for the convolutional neural network method to enhance the image.
# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 3 Methodology  
### Artificial Neural Networks  
As shown above in Figure 1, which illustrates the models' performance in EHC prediction that was achieved during the first experiments [30; 31], the ANN algorithms produced the most effective overall results. Accordingly, only ANNs were trained for EHC prediction in this new attempt.  
It should be noted that ANNs are among the most effective DM algorithms for resolving challenging issues across a variety of knowledge domains [37; 38; 39], including in civil engineering [40; 41; 42; 43]. In slope stability problems, several applications of ANNs can be found in the literature [13; 44; 45]. Another important point of evidence underlying the huge potential of ANNs is their recent developments under the deep learning field, which have significantly advanced the state of the art in speech recognition, visual object recognition, object detection, and numerous other fields, including drug discovery and genomics [46; 47; 48].  
In spite of these important advances, in this work, we adopt the \"traditional\" neural networks. In a nutshell, ANNs are learning systems that originally were driven by how human brains work [49]. They process information iteratively by multiple neurons. ANNs are reliable when exploring data with noise and are capable of modeling intricate non-linear mappings. This research adopted the following parameters for the ANN:  
* A multilayer perceptron with only feedforward connections;
* One hidden layer containing \(H\) processing units;
* A grid search of \(\{0,2,4,6,8\}\) to determine the optimal value of \(H\);
* A logistic function applied to the neural function of the hidden nodes \(1/(1+e^{-x})\);
* The BFGS technique [50] as an optimizer for the ANN.  
The BFGS technique is a quasi-Newton method (also referred to as a variable metric algorithm) that was published simultaneously in 1970 by [PERSON], [PERSON], [PERSON], and [PERSON] [50]. It uses function values and gradients to construct a representation of the optimized surface [51].
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Methods
#### 3.2.4 ML Model Training  
An ML model was trained using the selected features from each selection technique and the set of all created features. A low-complexity regression tree model was chosen due to its utility in systematically classifying outputs based on low input parameters that enable the reduction of parameter range based on objective criteria and aid in establishing relationships from input data to the end results [50].  
Regression trees, a variant of decision tree models, are adept at processing continuous data by partitioning the dataset into homogeneous groups at specific nodes, each representing a decision point based on an independent variable, and the final partitions condense the outcome as a continuous value [48]. Regression trees can also discern variables' importance within a dataset. Still, this local optimization can prioritize background variables with significant indirect effects over those directly influencing the response [49], hence the usage and feature selection techniques to ensure proper feature assortment.  
The model training phase utilized seven distinct feature sets, each representing the output of various feature selection processes. Five of these sets were derived from separate individual selection processes, while, alongside, an encompassing set was created incorporating all the variables that were produced. A control set was also established, containing solely the original dataset wind variables--wind speed, direction, and gust, at a 2-min timestep. Each of these sets corresponds to the features chosen through their respective selection methods. Consequently, seven corresponding models were developed, each representing the unique characteristics of its respective feature set.  
All models were trained using the mean squared error (MSE) as the evaluation metric. For the XGBoost model, the default hyperparameters from scikit-learn version 1.3.0 were employed. The parameters encompassed a learning rate set to 0.3, a number of estimators set to 100, a maximum depth set to 6, a subsample value set to 1, a column sample by tree set to 1, and the objective set as'reg:squarederror'.
## 3 Results and Discussion
### Calibration Procedure  
All standards at nine different concentrations that were used to set up calibration curves were prepared with blank urine (0.5, 1, 2, 5, 10, 20, 50, 100, and 150 \(\mathrm{\SIUnitSymbolMicro g}\)/L), and the calibration curves were linear for all 13 pesticide metabolites and one IS. The coefficients of determination (i.e., \(R^{2}\) values) for calibration curves were higher than 0.995, indicating excellent linear relationships within the detection range.