esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Satisfaction Related to the Treatment (Parents' Ratings)  
Results about the satisfaction related to the treatment as rated by the parents are reported in Table 2.  
Regarding treatment satisfaction, a significant difference between the two groups was observed concerning the possibility of repeating the same treatment with the given appliance (Q1, \(p=0.013\)). In particular, although the vast majority of the respondents in both groups reported their willingness to repeat the treatment, four parents in the EA group stated they would not. In the EA group, the number of mothers who would absolutely recommend the treatment to other people was twice as many as the fathers (42.86% vs. 21.43%, \(p=0.022\)). Almost half of the parents considered the whole duration of the treatment with CAs shorter than expected (Q4, \(p\)= 0.003), while the majority of the parents from the EA group rated the treatment duration as long as they expected. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Total** & **EAs** & **Cas** & \\  & **N = 56** & **n (\%)** & **n (\%)** & **_p-Value_** \\  & & **28 (50.00)** & **28 (50.00)** & \\ \hline Sex, n (\%) & & & & \\ Female & 19 (33.93) & 7 (25.00) & 12 (42.86) & 0.158 * \\ Male & 37 (66.07) & 21 (75.00) & 16 (57.14) & \\ Age, median (IQR) & 10 (9–11) & 11 (10–12) & 9 (9–10) & **<0.001 ** \\ Questionnaire compiled by, n (\%) & & & & **0.003 * \\ Mother & 39 (69.64) & 25 (89.29) & 14 (50.00) & \\ Father & 17 (30.36) & 3 (10.71) & 14 (50.00) & \\ \hline \end{tabular}  
* \(\chi^{2}\) test. ** Two-sample Wilcoxon rank sum (Mann–Whitney) test. Bold indicates statistically significant difference.  
\end{table} 
Table 1: Sociodemographic characteristics of respondents; EAs (Elastodontic Appliances), CAs (Clear Aligners). The majority of the parents of the EA group considered their child's treatment moderately to very painful (Q5, \(p\) = 0.003), while a small percentage considered the treatment with aligners painful. Similarly, most of the parents from the EA group reported the device as moderately to very uncomfortable (Q6, \(p\)= 0.001) and difficult to wear (Q7, \(p\) < 0.001). Parents from the CA group reported that their child was significantly happier wearing their appliance, as compared to the EA group (75% vs 35%, Q8, \(p\) = 0.003). No major changes were observed with regard to social and school lives with EA therapy, while in the CA group, the school life and social life of the children were reported to be improved (Table 2). Regarding functions, in the EA group, half of the parents observed that the treatment led to an improvement in breathing. On the other hand, in the CA group, improvements in grinding were more frequently reported (Table 2).
# The Role of Environmental Assumptions in Shaping Requirements Technical Debt
## 5 Discussion and Implications
This introduces _outdated assumption debt_, where obsolete assumptions persist in the specification, requiring changes such as updating the requirement to include periodic sensor recalibration or fallback mechanisms in case of degraded accuracy. These examples show how even well-defined, safety-critical requirements can accumulate technical debt when environmental assumptions are not explicitly documented, validated, or adjusted over time. The proposed classification categories capture fundamental patterns of misalignment between requirements and real-world conditions, regardless of the specific application. As such, this framework offers a generalizable lens for identifying and managing RTD in diverse engineering contexts. A key practical insight from this study is that documenting and continuously validating environmental assumptions is not just a best practice; it is essential for sustainable requirements engineering. Several well-known system failures illustrate the risks of over-looking environmental assumptions during system development. The Ariane 5 rocket explosion [12] occurred due to the reuse of software from Ariane 4, which assumed lower horizontal velocity values; this led to a fatal overflow error [43]. The Uber self-driving car incident in 2018 [44] involved a fatal crash caused by a combination of system design flaws and incorrect environmental assumptions. The system failed to accurately and consistently classify a pedestrian walking a bicycle across the road, leading to delayed recognition of the need to brake. It also relied on a safety driver for emergency intervention, based on the flawed assumption that the driver would be fully attentive. In the case of the Therac-25 radiation therapy machine [45], several flawed and unvalidated assumptions contributed to fatal overdoses. These included the belief that hardware-safety interlocks were present and functional, that reused software would behave reliably in the new system, and that operators could correctly interpret and respond to ambiguous system error messages. These incidents underscore how unverified, outdated, or implicit environmental assumptions can contribute to system failures and lead to technical debt that originates as early as the requirements engineering phase. When such assumptions are not explicitly captured, validated, or updated, they can persist throughout the development lifecycle, creating long-term risks and hidden costs. Overall, our findings suggest that environmental assumptions, if left undocumented or invalidated, can significantly contribute to the accumulation of RTD. This study contributes to the sustainability of software-intensive systems by exposing how unvalidated or evolving environmental assumptions introduce hidden forms of RTD, ultimately affecting maintainability and adaptability in safety-critical domains. By understanding these dynamics, requirements engineers can better anticipate long-term consequences and build more resilient and future-proof systems.
###### Abstract  
The adoption of Precision Farming (PF) practices involving ubiquitous computing advancements and conceptual innovations of \"smart\" agricultural production toward Agriculture 4.0 is a significant factor for the benefit of sustainable growth. In this context, the dynamic integration of PF facility systems into the Internet of Things (IoT) represents an excessive challenge considering the large amount of heterogeneous raw data acquired in agricultural environments by Wireless Sensor and Actuator Networks (WSANs). This paper focuses on the issue of facilitating the management, process, and exchange of the numerous and diverse data points generated in multiple PF environments by introducing a framework of a cloud-based context-aware middleware solution as part of a responsive, adaptive, and service-oriented IoT integrated system. More particularly, the paper presents in detail a layered hierarchical structure according to which all functional elements of the system cope with context, while the context awareness operation is accomplished into a cloud-based distributed middleware component that is the core of the entire system acting as a Decision Support System (DSS). Furthermore, as proof of concept, the functionality of the proposed system is studied in real conditions where some evaluation results regarding its performance are quoted.  
Keywords:agriculture 4.0; precision agriculture; sustainability; farm management; internet of things; middleware; context awareness; cloud; wireless sensors actuators network +
Footnote †: journal: Applied
## 2 Scientific Novelty and Importance of This Research  
The stochastic nature of wind and its complex interaction with the WT results in the variation of the power curve and significant uncertainty in its determination. This highlights the importance of uncertainty analysis associated with the power curve to assist turbine operators in the interpretation of performance validations. In forecasting, many articles such as [39; 40; 43], are presented to quantify the uncertainty for wind speed, wind power and smart grids purposes. [PERSON] et al. [44] proposed numerous techniques for calculating confidence intervals (CIs) for Least Squares Support Vector Regression. However, CI-based uncertainty assessment for SVM-based power curve models research is limited. Accurate estimation of uncertainty ensures early detection of anomalies caused by expected failures at an early stage and supports O&M decision management as highlighted by [45]. This research extends the work of [44] for SVM power curve construction in which two techniques, namely, simultaneous and pointwise, will be used as their CIs are close to the standard bootstrap method, compared to others. This paper fills this gap by proposing and comparing these CIs' techniques and suggesting which one is suitable for SVM-based power curve modelling.  
The rest of the paper is organised as follows: Section 3 focuses on turbine power curve monitoring and its characteristics: air density correction and its importance. The WT SCADA data are described in Section 4, together with the data filtering undertaken. Section 5 has many subsections that cover the methodologies for constructing the SVM power curve and techniques to estimate the uncertainty associated with it via calculating CIs. Section 6 discusses the results, followed by comparative studies of the proposed uncertainty estimation techniques. Finally, with closing remarks and potential future work, the paper ends with Section 7.
## 6 Potential Future Technological Developments  
### Deopening the Application of Intelligent Monitoring Technology  
The future is likely to see further refinement of smart sensor technology, which will play an integral role in enhancing the precision of monitoring key performance indicators of vertical cut-off walls. Of particular note is the anticipated development of micro-sensors designed for real-time, in situ monitoring of the permeability coefficient of bentonite vertical cut-off walls. It is expected that these advanced sensors will feature significantly improved sensitivity and stability [151]. They can be strategically embedded at various depths and locations within the cut-off wall structure. This strategic placement will facilitate continuous data collection, with the data being transmitted to a cloud-based platform for storage and preliminary processing. Subsequently, through the application of sophisticated big data analytics and machine learning algorithms, researchers and engineers will be able to conduct in-depth mining and analysis of the vast dataset. This data-driven approach will not only enable a more accurate assessment of the current barrier efficacy of the cut-off wall but also allow for the prediction of potential performance decline trends. Such insights will provide a robust scientific basis for the timely implementation of maintenance measures, ensuring the long-term service performance and effectiveness of the cut-off wall in containing contamination.
## 4 Evaluation
### Results  
Table 2 shows the results for five datasets (i.e., HTML, XML, EML, RTF and VBA): each part of the table shows the average accuracy, precision, and recall of all five folds for every combination of a feature extraction method and a classification model. The boldfaced results indicates the best performance for each file type.  
In the case of HTML files, the best accuracy and precision were obtained with the combination of DNN with Word2 Vec\({}_{\text{Avg}}\). SVM with BoW showed the best recall, but lower accuracy and precision than DNN with Word2 Vec\({}_{\text{Avg}}\). For XML files, DNN with BoW achieved 100% accuracy as shown in Table 2. SVM with BoW also showed very high accuracy. In the case of the EML files, DNN with BoW showed the best accuracy and recall. DNN with Word2 Vec\({}_{\text{Avg}}\) showed 100% precision, but provided unreasonably low recall. In the case of RTF files, BoW presented remarkably high accuracy with both the SVM and the DNN model. Results for VBA files showed reasonably high accuracy both DNN with Word2 Vec\({}_{\text{Avg}}\) and SVM with BoW.  
Results showed mostly reasonably high accuracy for all file types and with most combinations of a feature extraction method and a classification model. As shown in Table 2, lower accuracy was obtained with EML than that with other datasets except for DNN with BoW. A possible reason is that some EML files did not include enough plaintext data for the classification. Word2 Vec, a more sophisticated method for vectorization, was not more accurate than simple BoW and TF-IDF. This could have been due to the use of a naive aggregation strategy. Overall, DNN with BoW showed the highest accuracy. Experimental results validated that the proposed framework effectively detects malicious documents in a practical sense.
## 4 Experimental Data  
The vibrations transmitted by the engine running at 850 rpm were measured by placing 17 unidirectional accelerometers in correspondence with the attachment points of the chain cover to the rest of the engine (Figure 3), and placing a unidirectional accelerometer (for this kind of application, piezoelectric accelerometers perform pretty well) on top of the cover (Figure 4). Moreover, a microphone was placed in proximity of the cover (near-field microphone) (Figure 5). A microphone array was avoided because, due to its extension, it would be sensible to noise coming from parts other than the chain cover.  
The standard ISO 3745 recommends the use of one microphone at the distribution side, one at the exhaust, one at the inlet, one for the timing of valves, and another one for gears. Being only interested in the (near-field) distribution emission, only the corresponding microphone was arranged.  
Figure 4: Accelerometer placed on the chain cover.  
Figure 3: Accelerometers placed in proximity to the connecting pins between the chain cover and the rest of the engine.  
The accelerometers placed on the fasteners detect the excitations acting on the cover in terms of local imposed accelerations; the latter are subsequently integrated by the code for vibro-acoustic assessment in order to obtain a profile of vibrational velocities (boundary conditions for the acoustic analysis). The accelerometer placed on the cover surface detects the vibration response of the chain cover, to be compared with the numerical vibration response. The near-field microphone--due to its short distance from the cover--essentially measures the sound pressure generated by the cover vibration phenomenon.
## 2 Materials and Methods
### Statistical Analysis  
For the first objective to identify poor balance from the movement characteristics at the person's center of mass, we employed a logistic regression of the dichotomized BBS scores on the parameters derived from the lower back IMU data. The logistic regression model was formalized as shown in Equation (11).  
\[logit(P)=\log\biggl{(}\frac{P}{1-P}\biggr{)}=\beta_{0}+\beta_{1}X_{1}+\dots+ \beta_{k}X_{k} \tag{11}\]  
where \(P\) is the probability of losing balance and \(\beta_{0},\beta_{1},\dots,\beta_{k}\) are coefficients of independent variables \(X_{0},X_{1},\dots,X_{k}\) representing movement characteristics derived from lower back IMU data. Each coefficient was evaluated at the significance level of 0.95.  
Figure 3: The 3D visualization of accelerations, movement trajectory, and convex hull volume; example for task 10.  
To address multicollinearity and prevent overfitting, we examined the correlations among independent variables. The variables showing strong correlations with other variables were systematically eliminated from the logistic regression model, while we retained those strongly correlated with the binary outcome of poor balance.  
For the second research objective to explore how sensor placement and task type affect the balance assessment, we analyzed data from different IMUs (head, torso, and lower back) separately as well as categorized the tasks as static or dynamic based on their nature. We then applied logistic regression to each combination of sensor and task type to identify predictive characteristics specific to each sensor location and task type.
## 3 Numerical Simulation
### Governing Equations  
The ANSYS Fluent solves the conservation equations of mass and momentum for all analyzed flows. The general form of the mass conservation equation is given by Equation (4), while the equation for the conservation of momentum is given by Equation (5). An additional energy conservation Equation (6) is solved for flows account for heat transfer or the compressibility of the fluid [26].  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\rho\overset{\rightarrow}{v}=0 \tag{4}\]  
\[\frac{\partial}{\partial t}\Big{(}\rho\overset{\rightarrow}{v}\Big{)}+ \overset{\rightarrow}{v}\cdot\
abla\Big{(}\rho\overset{\rightarrow}{v} \Big{)}=-\
abla p+\
abla\cdot\overset{\rightarrow}{\overline{\tau}}+ \overset{\rightarrow}{F^{\prime}}_{b} \tag{5}\]  
\[\frac{\partial}{\partial t}(\rho e_{t})+\
abla\cdot\Big{[}\overset{ \rightarrow}{v}(\rho e_{t}+p)\Big{]}=\
abla\cdot\Big{[}k\
abla\mathrm{T}+ \Big{(}\overset{\rightarrow}{\overline{\tau}}\cdot\overset{\rightarrow}{v} \Big{)}\Big{]}+S_{g} \tag{6}\]  
where: \(\rho\) is the fluid density, Nabla operator \(\
abla\) stands for the partial derivative with respect to all directions in the coordinate system. \(\overset{\rightarrow}{v}\) is the velocity vector, \(p\) stands for static pressure, \(\overset{\rightarrow}{\overline{\tau}}\) is the stress tensor, \(\overset{\rightarrow}{F^{\prime}}_{b}\) is for the body force per unit volume, \(e_{t}\) is total energy in the system, \(k\) is the thermal conductivity, \(T\) stands for temperature, and \(S_{g}\) include the heat of the chemical reaction and any defined volumetric heat sources.
## 3 Methodology
### Data Collection and Pre-Processing
#### 3.2.1 Dataset and Data Collection  
The synthetic dataset that we evaluated in our system is a release from the Insider Threat Test Dataset collection, generated from Carnegie Mellon University Division. This dataset is \"free of privacy and restriction limitations\" [12] to allow researchers to experiment with it and evaluate algorithms. There are various releases of datasets to choose from, with most of them having one instance of each scenario, depending on the creation time. We chose the release r4.2, the \"dense needle\" dataset, since it includes many instances of each scenario, with multiple users involved in each scenario. The r4.2 dataset is split up in seven (7) different parts, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**File** & **Description** \\ \hline Device.csv & Log of user’s activity regarding connecting and disconnecting a thumb drive \\ Email.csv & Log of user’s e-mail communication \\ File.csv & Log of user’s activity regarding copying files to removable media devices \\ http.csv & Log of user’s internet browsing history \\ Logon.csv & Log of user’s workstation logon and logoff activity \\ psychometric.csv (1) & Users’ psychometric scores based on big five personality traits \\ Ldap (1) & A set of eighteen (18) files regarding the organizations’ users and their roles \\ \hline \hline \end{tabular}
\end{table}
Table 1: Insider threat dataset r4.2 files’ description.  
Figure 1: Diagram of proposed method and data flow inside the model.  
We are not going to employee the data of psychometric.csv in our system, since it contains personality data and we want to avoid any privacy or legal issues regarding this in a real-life scenario. Psychometric.csv provides personality scores for each user, based on the big five personality traits. Since the used dataset is a synthetic one and there are no privacy or legal constraints in using this dataset's personality scores, we could add these features as well to experiment with. However, the scope of this work is intended to be used by organizations which might not have this kind of data. In addition to this, psychometric data are usually recorded by Human Resources (HR) and it will be difficult or even impossible to include scores for all internal actors, such as consultants, contractors, partners or even personnel from subsidiaries. Furthermore, we followed the [PERSON] et al. [14] approach and chose features that can be used to model user behavior across several different domains.  
The Http.csv file contains data that can be used to trace employees' visits to employment websites and report indications for unsatisfied or even disgruntled employees that are planning to leave the company. While these data can be useful to predict an employee intention for leaving the company, privacy concerns arise. Furthermore, an additional overhead is added to the model, which eventually may not be effective in a real-world scenario where an employee can use their smartphone to access this kind of website.
# Correcting Ski Resort Trajectories Extracted from Video
## 2 Perspective Correction
### Corrected Trajectories for Improved Corrections  
As we have determined the correction coefficient function, we can now apply the two regression models to our trajectory data, processing the raw data we obtained. This processed data allows us to visualize and analyze the adjusted trajectory effectively.  
As shown in Figure 10, there is a clear difference between the two correction methods. The linear correction exhibits a smaller amplitude, noticeably expanding the movement range in the \(x\) direction. In the higher \(y\) region, the linear correction closely aligns with  
Figure 8: Linear regression analysis for the dependence of \(\alpha\) on the position using the data from Table 3.  
Figure 9: Nonlinear regression analysis using the same points as in Figure 8.  
the original trajectory, whereas the nonlinear correction is shifted further to the left of the original trajectory. Additionally, in the lower y region, the nonlinear correction (green dotted line) consistently shows the corrected trajectory lower than the original one (blue solid line) in the \(y\) direction. Conversely, the linear corrected trajectory (red dotted line) starts lower than the original trajectory and then surpasses it as it progresses.  
Due to the lack of direct ground truth data, we adopted an internal consistency approach to evaluate our methods. The deviations were quantified using RMSE and MAE, with the five-reference-line nonlinear regression method serving as the refined reference standard. Table 4 summarizes these results, which highlight the superior performance of this approach.  
In this section, we applied basic perspective correction to the original trajectory and then refined the correction coefficient. We implemented both linear and nonlinear corrections based on two regression formulas and compared their effects on the trajectory. The results suggest that further refinement of the correction methods is necessary to achieve a more accurate representation of the skier's movement. It may be essential to explore alternative models or incorporate additional factors into the correction process to better align the visualized trajectory with the actual dynamics of the skier. Some alternative correction schemes have been explored in [26].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **RMSE (Pixels)** & **MAE (Pixels)** &
\begin{tabular}{c} **Correlation** \\ **Coefficient (R)** \\ \end{tabular} \\ \hline Two Reference & 2.84 & \(1.67\) & 0.91 \\ Lines & 2.12 & \(1.12\) & 0.94 \\ Linear Regression & 2.12 & \(1.12\) & 0.94 \\ (5 Lines) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of alternative methods relative to the five-reference-line nonlinear regression (baseline).  
Figure 10: Comparison of original and corrected skier movement trajectories for the linear (**left**) and non-linear (**right**) correction.