esa-sceva/satcom-chunk-collection huggingface dataset:
# Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts
## 1 Introduction
Then, the machine tool numerical control (NC) programming language, known as G-code, is analyzed through an automated algorithm that identifies the position of the tool-workpiece contact point and updates the stock geometry, modifying the shell element's thickness. This information allows the system to generate an updated FE model of the workpiece that takes the material removal effect into account, enabling the accurate identification of its dynamic behavior. In the final step, the workpiece FRFs are calculated at the driving points (i.e., the tool-workpiece contact point) for every step of the machining process. This provides all the required information to optimize the milling process. To prove the effectiveness and accuracy of the proposed dynamics identification technique, two specimens of a test case thin-walled component were manufactured through the WAAM process. In this work, the WAAM process was selected because its high productivity has the drawback of low accuracy and the need for an extensive subtractive process to realize the final product. However, the proposed approach could be applied to other directed energy deposition processes. A five-axis milling cycle was defined and analyzed using the proposed technique, identifying the evolution of workpiece dynamics. Then, the machining parameters (i.e., spindle speed and feed rate) were adjusted following two different optimization techniques. The two specimens were machined, interrupting the process to perform modal analysis, required to verify the accuracy of the proposed modelling technique. At the end of the milling process, both specimens were analyzed through surface measurements.
## 2 State of the Art
### Data-Driven Methods for System Design  
From the point of view of mathematics and computer science, data-driven methods are indeed state of the art. For some years now, methods of data analysis have also been introduced in production engineering to gain different knowledge about the behavior of machines and tools and about the interactions in the manufacturing process. Many applications ranging from statistical methods to machine learning techniques, which will not be discussed further here. All applications face the same problem: the analysis results can only be as good, as valuable, as the quality of the incoming data.  
In the different areas of production engineering, different software systems are used for data analysis, such as R, Matlab/Simulink, Modelica, or SPSS. In the past, the focus was on mathematical algorithms. However, for the acceptance of data-based methods in production, automation of the methods in an end-to-end data management workflow is needed. Systems that support all phases from connection to sensor and control interfaces to data analysis and result feedback are therefore particularly important. Some examples are ProDaMi-Suite, SAS, SAP-Hana, Detact, or Time-View.  
Procedures have been developed for data-driven knowledge generation to develop a powerful, targeted analysis strategy through a systematic way of working and to introduce it into the application in the company. Knowledge Discovery in Databases (KDD) was introduced as a general process for extracting knowledge from data using data analysis algorithms [27]. KDD comprises the following phases: (1) selection of target data to focus on, (2) preprocessing and cleaning data to obtain consistent data, (3) transformation to reduce dimensions of data, (4) data mining to search for patterns of interest, (5) interpretation and evaluation of mined patterns. Based on KDD, there are two widely used methodological approaches [28; 29]. One is the SEMMA method, developed by SAS Institute for the use of their statistical and business intelligence software. IBM, on the other hand, developed the method CRISP-DM (revised in ASUM-DM [30]) for the use of the SPSS software. The application focus of both methods was originally in business analyses (business intelligence). Therefore, further developments were dedicated to the transfer to manufacturing tasks [31; 32]. With DMME [33; 34] (Data Mining Methodology for Engineering Applications), CRISP-DM was extended to integrate additional work steps for engineering-specific development tasks. The additional work steps support the development and testing of suitable measurement methods for the required condition parameters. DMME was justified by the fact that previous approaches assumed that the data are already available--in the engineering world, the data must usually first be generated by measurement procedures.
## 2 Methods
### Simulated Atrial Fibrillation and Exercise Conditions  
In this work, the effects of atrial fibrillation (AF), exercise, and right kidney failure on the efficacy of HD and TH were assessed.  
AF was simulated by clamping both atrial elastances to their diastolic values. The diastolic value of elastance in the left atrium is 0.5 mL/mmHg, and in the right atrium is 0.3 mL/mmHg. In addition, AF induced irregular heart rate was incorporated into the model simulated as described in the literature [37].  
Exercise was simulated by increasing the ventricular end-systolic elastances and the heart rate, while reducing compartment resistances and large vessel compliances [37]. Specifically, the ventricular systolic elastances were increased by 140%, and the intrinsic heart rate was increased by 28.6%. Simulated exercise also encompassed a reduction of all microvascular resistances by 33%, and a reduction of compliances either by 10% (all aortic segments) or by 32% (all venous segments) [37].  
The consequences of right kidney dysfunction on clinical measurables were simulated. The presence of right kidney dysfunction was programmed as either a large increase of right kidney resistance, or a large simultaneous reduction of aortic and right kidney compliances. Further, right kidney dysfunction was simulated as either homogeneous or inhomogeneous conditions. Under homogenous conditions, all microvascular resistances in the right kidney were either augmented 10 fold, or compliances reduced by 90%, or both resistance and compliance alterations implemented simultaneously. Heterogenous right kidney dysfunction was simulated by manipulating three of the six right kidney microvascular beds' resistances (increased 10 fold) and compliances (reduced by 90%). In addition, in accordance with experimental information [38; 39], dysfunction of right kidney was also defined as either a 10 fold increase of the right kidney's resistance, a 90% reduction of compliance (i.e., increase of stiffness) of the aorta (systemic artery), or both. Results using the latter definition of right kidney dysfunction are presented.  
The HD sub-model simulates temporal changes of plasma volume under prescribed ultrafiltration targets. The efficacy of HD was assessed using the fraction change of plasma volume. The efficacy of HD was assessed under AF, exercise, and right kidney dysfunction conditions.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 3 Materials and Methods
### Key Performance Indicators
#### 3.4.2 Signal-to-Interference-Plus-Noise Ratio (SINR)  
The SINR evaluates the quality of the radio link between a base station and the UE. It is the ratio of the power of the intended received signal, \(P_{\rm signal}\), to the power of the interference signal, \(P_{\rm interference}\), plus the noise power, \(P_{\rm noise}\):  
\[\text{SINR}=\frac{P_{\rm signal}}{P_{\rm interference}+P_{\rm noise}} \tag{3}\]  
Figure 4: Double\(-\)sided exponential wave at 2.2 GHz.  
For OFDM-specific waveforms, the SINR at the \(k\) subcarrier is  
\[\text{SINR}_{k}=\frac{|H_{k}|^{2}P_{\text{Tx}}}{\sum_{j\
eq k}|I_{j}|^{2}P_{\text{ Tx}}+N_{0}B_{k}} \tag{4}\]  
where \(H_{k}\) is the channel frequency response for the \(k\) subcarrier, \(P_{\text{Tx}}\) is the power of the signal transmitted per subcarrier, \(|I_{j}|^{2}\) is the power of interference, and \(N_{0}\) and \(B_{k}\) are the spectral density of the noise power and the bandwidth of the \(k\) subcarrier, respectively.  
We consider the signal power received from the demodulated OFDM symbols. This metric is crucial for network operators, as it helps assess and optimize network resilience.
## 2 Results and Discussion
### Detection of (+)-Ectoine in Fermentation Broths of Salt-Tolerant Marine Streptomyces  
In our program of screening marine bacteria for bioactive compounds, numerous _Streptomyces_ sp. have been isolated from marine sediments and various organisms from the Adriatic Sea. Their taxonomic affiliation to the genus was verified by 16S rRNA gene sequencing (Table S2). Here, we used HPLC for the rapid detection of (+)-ectoine directly in the fermentation broths of 11 salt-tolerant marine _Streptomyces_ isolated from sponge (\(n\) = 1), tunicates (\(n\) = 3), mussel (\(n\) = 1), and sediments (\(n\) = 6). As previously reported, under osmotic stress, bacteria mainly retain the produced ectoine in cells and membranes to protect themselves from osmotic stress [33]. The presence of (+)-ectoine in samples was analyzed using Synergy Polar-RP HPLC column at 220 nm in gradient as described in Experimental. As shown in Table 2 the method used in this study successfully detected ectoine in the culture broth of all isolates and reference strain if grown in media supplemented with different concentrations of NaCl. The ectoine concentration in the samples was calculated from calibration curve.  
As shown in Table 2 and Figure 4, the HPLC method used in this study successfully detects microgram quantities of ectoine directly in the fermentation medium and can be used for rapid screening of microorganisms with increased potential for the synthesis of this osmolyte. Among the tested strains, BC81 (sample 6) and BC167 (sample 16) showed the highest potential for ectoine production and secretion into the medium, which indicates that these strains have the ability for large-scale fermentation and ectoine production. However, this possibility should be further investigated.  
Figure 3: Chromatograms of (+)-ectoine (green) and (\(\pm\))-ectoine (blue) using method 30.  
Figure 2: Overlay chromatograms of (\(\pm\))-ectoine: method 29 (blue); 30 (green); 31 (red); 32 (grey).  
Figure 4: \(Cont.\)
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes  
Fitts' law, which was first developed for physical devices, is today used as a foundational framework to comprehend how people interact with a wide range of computer input devices [41]. The law's use has broadened to include screens on electronic devices, including those used in vehicles used for transportation, such as airplanes [42]. The ideas of the law have also been applied to foot-operated and handheld devices, demonstrating the law's adaptability in capturing human behavior across many interfaces. Fitts' law has been adopted more recently in the research of VR device interaction design, indicating its continuous applicability and flexibility in assessing user experiences in developing technologies [43].
## 2 Materials and Methods
### Data Collection and Processing  
Participants were recruited through email invitations and announcements during departmental seminars. Data were collected using printed and electronic formats of the questionnaire. Participation was voluntary, and all responses were anonymized. Out of approximately 125 clinicians invited, 87 responded, resulting in a response rate of 69.6%. All statistical analyses were performed using IBM SPSS (Statistical Package for the Social Sciences) version 29. Before analysis, data were cleaned and checked for normality, outliers, and missing values to ensure the reliability of statistical results. The data was analyzed using descriptive and inferential statistical methods to identify significant trends and associations. Descriptive statistics, including means (M), standard deviations (SD), frequencies (N), and percentages (%), were calculated to summarize the demographic characteristics of the sample and the distribution of responses for each selection criterion. Categorical variables were transformed into dummy variables and Spearman correlation coefficients were used to determine the strength and direction of associations between professional characteristics (such as gender, age, clinical experience, postgraduate education, and em ployment setting) and factors influencing resin material selection. Statistical significance was set at \(p\) < 0.05, with stronger relationships considered at \(p\) < 0.01. Additionally, non-parametric Mann-Whitney tests were conducted to compare distributions between groups, particularly for variables such as gender (male vs. female) and employment setting (private practice vs. other). These tests allowed for the identification of statistically significant differences in material selection preferences between different professional subgroups. Generally, all our analyses were univariate, and no multivariate models were applied to control for potential confounding variables. Future studies with larger samples should consider multivariate approaches to account for possible interactions among variables.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 3 Evaluation Metrics
### Encryption Quality  
The quality of an image encryption algorithm is analyzed using various metrics, by comparing the pixel values of plaintext images and their respective ciphers. The encryption algorithm is considered to have good quality when there is a high change in pixel values between plaintext images and their respective ciphers. To quantify the encryption quality, image encryption researchers use a set of metrics, namely, MSE, MAE, and PSNR. Each of these metrics is discussed further in the following.
## 5 Particle Detectors
### Mass Spectrometry
#### 5.2.3 IRMS
Depending on the type of sample to be analyzed and the isotopic ratio to be measured, a different device is connected to the spectrometer, so that the sample can arrive to the instrument in the gaseous state:  
* For \(\delta^{15}\)N and \(\delta^{13}\)C analysis, an Elemental Analyzer is used (CN Flash EA 1112 Series, Thermo Scientific, Finnigan), where a flash combustion occurs at 1020 \({}^{\circ}\)C, in oxygen presence. Solid samples are combusted and subject to red-ox reactions to produce CO\({}_{2}\) and N\({}_{2}\), chromatography separated. * For \(\delta^{18}\)O analysis, a High Temperature Conversion/Elemental Analyzer (TC/EA, Thermo Scientific, Finnigan) is used, where a pyrolysis occurs at 1350-1450 \({}^{\circ}\)C. The reaction takes place on glassy carbon substrates, in complete absence of oxygen. * For compound-specific \(\delta^{15}\)N and \(\delta^{13}\)C analysis, gas chromatography (GC Isolink II, Thermo Scientific, Finnigan) is adopted, which enables the measurements of isotopic ratio on specific molecules, isolated and separated upstream from organic material (i.e., fatty acids). All of the peripherals are connected to the spectrometer via a ConFlo IV (Continuous-Flow Interface, Thermo Scientific, Finnigan), which modulates sample- and standard-gas flows, enabling autodilution of the different flows and an easy switching between applications. After ionization, the ions are accelerated by an electric field and deflected by a magnetic field, where the different masses are separated, in order to be then collected in the Faraday cups of the IRMS detecting system. A multi-collector detection system enables continuous and simultaneous measurements of multiple isotopes in a single run. Faraday cups measure the current intensity of the ion beams. Our IRMS spectrometer is equipped with a triple collector (3 Faraday cups) for N\({}_{2}\), CO, NO, O\({}_{2}\), CO\({}_{2}\), N\({}_{2}\)O and SO\({}_{2}\), able to detect masses 46 and 30 (cup 2), 45 and 29 (cup3), 44 and 28 (cup 4). Every cup is connected to its own amplifier, each of which has a gain such that ion ratios at natural abundance levels produce similar signals. Signals from each amplifier are recorded simultaneously, typically every tenth of a second, digitized and recorded by the IRMS data system. This creates a \"chromatogram\" for ions of given M/Z, where the peak areas are proportional to the number of ions detected and the cups signals are represented as voltage signals in a range from 0 to 50 V, with typical optimal values of approximately 20-30 V. This instrument has a high sensitivity, which enables measurement on very small mass samples, down to the order of 50 \(\upmu\)g. Typical standard deviations, at iCONa lab, are: 0.1% for \(\delta^{13}\)C, 0.2% for \(\delta^{15}\)N and 0.4% for \(\delta^{18}\)O analysis [280].
# Photo-Stimulated Luminescence Approach for Effective Identification of Irradiated Fruit
## 2 Materials and Methods
### PSL Method  
The analyses were performed following the protocol described in the CEN EN 13751 Standard. The PSL reader used for the measurements was the model with a single infrared stimulation of the SUERC (Scotland, UK) specially designed for irradiated food analyses. The measurement, taking only 60 s, was performed on fragments of peel and shell of fresh fruit and nuts, respectively, and on the entire dried fruit (raisins, dried prunes, dried dates and figs). The material to analyze (fruit/peel/shell) was placed inside a disposable Petri dish (50 mm of diameter) with the external surface facing upwards in order to expose the mineral contaminants to the infrared stimulation. Whenever possible, parts of the stem (kiwi, mango, orange) or leaves (pineapple) were also tested. Two or four aliquots were analyzed for each sample depending on the test: two in the preliminary tests and four in the inter-laboratory test. Before measurement, the samples were dried at about 45 \({}^{\circ}\)C for about 1 h. During the measurement, the minerals in the sample were exposed to 60 pulses of infrared radiation (890 nm) to stimulate the emptying of the traps and restore the original assessment of the crystalline structure. The light emitted during the subsequent re-arrangement processes was converted into electrical charge by the phototube (Figure 1b). The total quantity of charge collected, given in (total) counts, was compared with two reference thresholds, T\({}_{1}\) (700 counts) and T\({}_{2}\) (5000 counts), as indicated by the EN 13751 Standard, and the final response was obtained on the basis of the decision-making scheme reported in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Total Count** & **Response** & **Classification** \\ \hline total counts \(<\) T\({}_{1}\) & negative & not irradiated \\ T\({}_{1}\) \(<\) total counts \(<\) T\({}_{2}\) & intermediate & indeterminate \\ total counts \(>\) T\({}_{2}\) & positive & probably irradiated \\ \hline \end{tabular}
\end{table}
Table 1: Decision-making scheme for the classification of the sample with the EN 13751 method.  
According to the scheme, the response is negative, and the sample is considered non-irradiated, if the total counts is less than T\({}_{1}\). If the total number of counts is greater than T\({}_{2}\) threshold, the response is positive, indicating that the sample is probably irradiated and needs a further confirmatory analysis. In the event that the total counts are intermediate between the two thresholds (due to low mineral content in the irradiated matrices or very high mineral content in the non-irradiated ones), it is not possible to classify the sample. This sample is indicated as indeterminate, and it must be re-examined by using a confirmatory method.
## 5 Results and Discussion
### Discussion
#### 5.2.3 Limitations of the Proposed Method  
While generating the topics from the DBSCAN clusters, the topic \"generations\" was identified. It represented the generations of the product, and even in the summaries, some comparisons between the generations were found. This topic was dropped since it represents generational differences or development. It would enable filtering the user needs that were fulfilled with a later version. While evaluating the resulting topics and corresponding feature requests, we noticed that some reviews mentioned Google Now--a service that Google Assistant replaced in 2016 [64]. Enabling a software development team to filter out out outdated requests, including the posting date in a future export, would be helpful. Having a date and information on the product that was reviewed would help deliver the correct requests to the right product teams. The puck-shaped echo dot has other challenges than the sphere-shaped fifth-generation Echo regarding sound quality, which currently cannot be addressed in the analysis process. Moreover, considering the date could show the importance and satisfaction of the topics over time. Due to new apps, technology, or skills, some user needs might get higher opportunities.  
The aggregated summaries delivered requests that were not specific enough to enable a developer to derive an actual solution. Additional engineering activities are necessary, such as requirements specification, planning, and validation. While our method is suitable for requirements elicitation of online reviews, for a more holistic approach to requirements engineering, online reviews should not be the only source of requirements. Furthermore, a transformer model that delivers reasonable requirements summaries could be trained. A model that directly delivers specific requirements from the summaries is desirable. To generate training data, the presented UI could be enhanced. The user could be able to mark the summaries from which a requirement was derived and enter the requirement as text. The training dataset would then consist of two columns: the entered requirement as the label and all combined sentences used for the summary as the text. Another helpful enhancement of the presented UI would be the functionality to drill down into the summaries. It would enable users to see from which summaries the highest aggregated summary was created, and a drill-down to sentences would be possible. It helps in understanding the sentiment of the summaries.  
Generally, transformer models have shown significant improvement in the past and are expected to continue doing so in the future. This progress may result in the chosen models performing less effectively than newer models. Nevertheless, our proposed methodology would remain valid, and the results would likely improve using more advanced models.