esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Materials, Manufacturing, and Characterization Methodologies  
#### 2.2.1 Raw Materials  
The raw materials used in the production of flexible fiber hydraulic lime concrete (FFLC) are outlined as follows. Table 1 includes the compositions of the mix designs.  
* Natural hydraulic lime NHL-3.5, \(\ell\) (\(\rho_{\ell}\) = 2580 kg/m\({}^{3}\)).
* Local tap water, \(w\) (\(\rho_{w}\) = 1000 kg/m\({}^{3}\)).
* Poli-aril-ether type-based superplasticizer, MasterEase 5025 \(\copyright\), \(SP\) (\(\rho_{SP}\) = 1058 kg/m\({}^{3}\), dry residue = 20%).
* Rounded siliceous fine aggregate from a fluvial source \(FA\) (\(d_{90}\) = 2 mm, \(d_{50}\) = 0.87 mm, \(d_{10}\) = 0.25 mm, \(\rho_{FA}\) = 2490 kg/m\({}^{3}\)).
* Crushed limestone coarse aggregate \(CA\) (\(d_{90}\) = 8 mm, \(d_{50}\) = 5.91 mm, \(d_{10}\) = 4 mm, \(\rho_{CA}\) = 2630 kg/m\({}^{3}\)).
* Polyvinyl alcohol fiber, MasterFiber 400 \(\copyright\), \(f\) (\(\ell_{f}\) = 18 mm, \(d_{f}\) = 0.20 mm, \(\lambda\) = 90, \(\rho_{f}\) = 1300 kg/m\({}^{3}\), water absorption = 17.5%).  
The mix design compositions of the hydraulic lime concrete (with and without PVA fibers) are presented in Table 1. Figure 0(a) plots the particle size distribution of aggregates and Figure 0(b) shows the morphology of the polyvinyl alcohol fibers. The use of a superplasticizer admixture is due to the interest in designing concretes (with and with out fiber reinforcement) within a soft consistency typology S2 (Abram's cone slump of 50-90 mm \(\pm\) 10 mm) suitable for good workability with the inclusion of flexible PVA fibers. Specifically, this poly-aryl-ether type-based superplasticizer was used based on rheological tests conducted on lime pastes to determine the compatibility of the lime-superplasticizer system and the appropriate dosage ranges to achieve the desired consistency [28].
## 5 Security and Privacy Threat Modelling
### Threat Modelling of Single User Single AMRSys  
In a multimodal robotic system, communication is done via multiple channels and multiple platforms. Therefore, within the robotic sensing space, information can be leaked easily unless the right channel or the right platform is adopted during an interaction with the user, depending on the situation and the scenarios, e.g., presence of children, strangers, or intruders, etc. The robotic system must learn and know when and how to react, depending on who is present within its sensing space. Otherwise, information can easily be leaked to unintended users, inappropriate users, or an intruder, as shown in Figure 3. When the robotic system is allowed to monitor and connect remotely, more security measures should be put in place, so that a man-in-middle cannot hijack the channel, DoS and DDoS attacks do not prevail, and the remote client application interacts only if the robot approves the authenticity of the users. If an appropriate continuous authentication mechanism is not adopted for the remote users, then unauthorised users can gain easy access, e.g., if one-time authentication is used, then once the user is authenticated then anyone can access what the authenticated user can access in the presence or absence of the authenticated remote user. Therefore, an innovative continuous authentication client application must be designed to avoid data leakage to any third-party during local or remote interaction with the robot. It has been reported by IBM that, over the years, compromised credentials caused the most data breaches [66], so a more secure method of authentication and authorisation needs to be designed during local or remote connection to the robot. In order to maintain better coordination and discipline, both the user and the robot may have to support each other and signal each other during the communication, to preserve the user's privacy and protect their data. In addition, a secure channel should be used in the presence of any third-party individuals, unless the user approves it, e.g., not communicating using verbal communication in the presence of any other individuals if related to private information (maybe use a text form of communication in the presence of inappropriate or unintended persons, or a potential intruder). Therefore, it is important to detect the presence of others, recognize the individuals, assess the situation, and then take the appropriate and necessary action, otherwise user's privacy could be easily compromised.
## 5 Conclusions  
The rGMFM-66 is a time-saving alternative to the GMFM-66, demonstrating a high level of agreement with the original method. These findings are consistent with studies advocating for the use of AI-based tools to enhance efficiency and maintain validity in clinical practice. The rGMFM-66 can enable the feasibility of standardized gross motor testing in children with CP in clinical situations that were previously not possible due to limited time resources, e.g., in outpatient pediatric neurological care. Further multicenter studies are recommended to validate the rGMFM-66 across diverse clinical environments and populations and to confirm its robustness and applicability in broader settings.  
Conceptualization, S.S. and I.D.; methodology, I.D.; software, I.D.; validation, S.S., J.B. and I.D.; formal analysis, S.S.; investigation, S.S.; data curation, S.S.; writing--original draft preparation, S.S.; writing--review and editing, K.S., L.S., J.B., E.S. and K.L.; visualization, I.D.; supervision, K.L. and I.D.; project administration, I.D.; funding acquisition, I.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CEFAM University Hospital Cologne, Science Budget for the 2024.  
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of the University of Cologne (protocol code 22-1380/22. January 2023) for studies involving humans. A detailed description of the registry can be found at www.germanctr.de (DRKS00030419, [[https://drks.de/search/en/trial/DRKS00030419](https://drks.de/search/en/trial/DRKS00030419)]([https://drks.de/search/en/trial/DRKS00030419](https://drks.de/search/en/trial/DRKS00030419)), access date 1 June 2024).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patient(s) to publish this paper.  
The data presented in this study are available on request from the corresponding author due to privacy and ethical restrictions.  
We would like to express our gratitude to the participants who supported and made this study possible. Special thanks to our colleagues who facilitated the administration of the GMFM-66, and to all, who performed the calculation of the GMFM-66 score.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AI & Artificial Intelligence \\ CP & Cerebral Palsy \\ GMFM-66 & Gross Motor Functional Measurement-66 \\ ICC & Intraclass correlation coefficients \\ rGMFM-66 & reduced Gross Motor Functional Measurement-66 \\ WBV & Whole Body Vibration Training \\ \end{tabular}
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### How Can ChatGPT Assist throughout Each Stage of the Nutrition Care Process?
#### 3.2.2 Nutrition Diagnosis  
Following the assessment, dietitians and nutrition professionals proceed to identify and describe specific nutritional problems or issues. They formulate a clear and concise diagnosis, which serves as the foundation for developing an appropriate intervention plan for the subsequent phase. These diagnoses are formulated using standardized terminology (e.g., International Dietetics and Nutrition Terminology) or other widely recognized classification systems. The comprehensive nature of these systematic frameworks ensures that dietitians consider various factors when formulating diagnoses and intervention plans. Some examples of how ChatGPT can assist in this phase are as follows.  
* Provide sample evidence-based nutrition diagnoses for common health conditions.
* Assist in identifying nutrition-related problems based on the assessment findings.
* Explain the significance of nutrition diagnoses in developing intervention plans.
* Help identify potential comorbidities that may influence the nutrition diagnosis.
* Offer guidance on documenting nutrition diagnoses in a standardized format.
* Assist in identifying nutrition-related risk factors for specific populations.
## 3 Methods
### Gaussian Process Regression
#### 3.4.1 Gaussian Processes  
The Gaussian process regression (GPR) is a probabilistic non-parametric regression that can model non-linear systems. The regression function \(f(x)\) is determined by a Gaussian probability distribution of an infinite number of functions. Gaussian processes are defined by a mean function \(m\) and a covariance function \(k\):  
\[f(x)\sim GP(m(x),k(x,x^{\prime})). \tag{3}\]  
Common covariance functions are the Squared Exponential kernel, the Matern kernel, the Exponential kernel and the Rational Quadratic kernel. The covariance function and its hyperparameters characteristic length scale \(l\) and signal variance \(\sigma_{f}^{2}\) influence the smoothness and periodicity of the regression function. The hyperparameter noise variance \(\sigma_{n}^{2}\) estimates the level of noise in the data [22].  
For each rider type, we trained an overall GPR model, including all sections, and a GPR model for the steep uphill section, referred to as the uphill GPR model.
## 3 Results
### Information Quality
#### 3.2.2 Digital Information  
AST and GreenScenario present digital results on climate adaptation performance. Both software tools provide calculations of the impact of climate adaptation measures on climate by estimating rainwater management, evapotranspiration, heat reduction, and costs. GreenScenario has an advantage over AST as it presents information viaxially attractive dashboard (see Figure 6). Moreover, itscalculations present the impact of the whole design, while AST gives an overview of the impact of each climate adaptation measure (see Table 5). Due to different calculation objects, values, and the low precision of the AST tool, these results are hardly comparable: GreenScenario calculates 125 e/m\({}^{2}\) for construction and 12.3 e/m\({}^{2*}\)y for maintenance, while AST calculates 27.5 e/m\({}^{2}\) and 2 e/m\({}^{2*}\)y, respectively. However, AST deals with a high level of inaccuracy in the drawings. Therefore, GreenScenario can produce more accurate assessments, allowing different climate adaptation goals to be compared in a visually attractive manner.  
Figure 5: Comparison of UTCI simulations: (**a**) ENVI-met; (**b**) Ladybug (source: authors).  
Figure 6: Final climate adaptation plan using GreenScenario (source: authors).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Measure** & **Surface** & \begin{tabular}{c} **Storage** \\ **Capacity (m\({}^{\star}\))** \\ \end{tabular} & \begin{tabular}{c} **Return Time** \\ **Factor** \\ **(\(\star\))** \\ \end{tabular} & \begin{tabular}{c} **Genonwater** \\ **Recharge** \\ **(\(\star\))** \\ \end{tabular} & \begin{tabular}{c} **Eavpotrans-** \\ **Portion** \\ **(mm/yr)** \\ \end{tabular} & \begin{tabular}{c} **Heat Reduction** \\ **(\(\star\))** \\ \end{tabular} & \
\begin{tabular}{c} **Coal Areas** \\ **(\(\star\))** \\ \end{tabular} &
\begin{tabular}{c} **Construction** \\ **Maintenance** \\ \end{tabular} \\ \hline
**Adding trees to** & 14,004.09 & 700 & 3.52 & 67 & 60 & 0.31 & 18 & 159,647 & 54 \\
**Bioswale (with** & & & & & & & & & \\
**drainage)** & 962.11 & 337 & 3.09 & 27 & 0 & 0.02 & 0 & 72,158 & 722 \\ \hline Green roofs & 19,806 & 198 & 1.51 & 0 & 13 & 0.44 & 0 & 2,376,720 & 118,836 \\
**Urbas forest** & 10,985.09 & 2179 & 12.6 & 27 & 30 & 0.24 & 2 & 10,895 & 545 \\
**Pernoale** & & & & & & & & & \\
**pavement systems** & 7384.41 & 738 & 6.9 & 16 & 22 & 0 & 0 & 738,441 & 3101 \\
**(inflation)** & & & & & & & & & \\ \hline
**Rainwater** & & & & & & & & & & \\
**dretention pond** & 435.08 & 131 & 3.08 & 13 & 0 & 0.01 & 0 & 17,403 & 87 \\
**(wet pond)** & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact on climate and costs calculated with AST (source: authors).
## 3 Results
### Circumferential Root Cutting Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multirow{2}{*}{**Effects**} & \multirow{2}{*}{_df_} & \multicolumn{3}{c}{**Small Plant**} & \multicolumn{2}{c}{**Large Plant**} \\ \cline{3-6}  & & & \(F\) & \(p\) & \(F\) & \(p\) \\ \hline \multirow{3}{*}{NRS} & RCD & 3 & 6.227 & **0.001** & 2.321 & 0.087 \\  & RCR & 2 & 0.247 & 0.782 & 9.543 & **<0.001** \\  & RCD \(\times\) RCR & 6 & 1.091 & 0.381 & 1.156 & 0.345 \\ \hline \multirow{3}{*}{NRS\({}_{2020}\)} & RCD & 3 & 0.983 & 0.415 & 0.239 & 0.869 \\  & RCR & 2 & 0.825 & 0.448 & 0.664 & 0.521 \\  & RCD \(\times\) RCR & 6 & 0.280 & 0.942 & 0.296 & 0.935 \\ \hline \multirow{3}{*}{RSH\({}_{2020}\)} & RCD & 3 & 0.266 & 0.849 & 2.092 & 0.127 \\  & RCR & 2 & 0.263 & 0.771 & 1.384 & 0.269 \\ \cline{1-1}  & RCD \(\times\) RCR & 6 & 6.842 & **<0.001** & 0.779 & 0.594 \\ \hline \multirow{3}{*}{RSD\({}_{2020}\)} & RCD & 3 & 1.197 & 0.336 & 1.236 & 0.317 \\  & RCR & 2 & 0.349 & 0.710 & 0.124 & 0.884 \\ \cline{1-1}  & RCD \(\times\) RCR & 6 & 6.151 & **0.001** & 0.590 & 0.735 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Two-way ANOVA test for the effects of root-cutting distance (RCD) and root-cutting ratio (RCR) on root sprouting ability of _Aralia elata_. Significant effects (\(p\) < 0.05) are bolded. NRS: number of root sprouts in 2018; NRS\({}_{2020}\), RSH\({}_{2020}\) and RSD\({}_{2020}\): number, height and basal diameter of root sprout in 2020, respectively.
# Aggregation Dispatch and Control Strategies for Multi-Type Loads in Industrial Parks
## 3 Aggregated Control Strategy for Multi-Type Loads in Industrial Parks
### Day-Ahead Optimal Dispatch of Aggregated Multi-Type Loads in Industrial Parks
#### 3.1.2 Optimization Objective of Day-Ahead Dispatch  
The industrial park, with the aggregated multiple types of loads, participates in the day-ahead electricity spot market as a virtual power plant. The day-ahead clearing curve is formed, which serves as the day-ahead pre-dispatch plan curve for the industrial park. If there is a deviation between the actual execution curve and the day-ahead pre-dispatch curve, the industrial park will bear the deviation penalty. Considering the deviation penalty, the optimization objective for day-ahead is to minimize the overall operation cost:  
\[minC_{Dh}=\sum_{t=1}^{T_{day}}C_{Grid,t}+C_{Dr,t} \tag{6}\]  
\[C_{Grid,t}=P_{Grid,t}R_{t} \tag{7}\]  
\[C_{Dr,t}=\max(0,P_{Grid,t}-P_{base,t})R_{dr,t}^{u}+\max(0,P_{base,t}-P_{Grid,t })R_{dr,t}^{l} \tag{8}\]  
Figure 2: Aggregated control framework for multi-type loads in industrial parks.
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 2 Analysis of the Influence of the Dynamics of the PTS on the Operation of the VTS
### Impact of Traffic Flow Dynamics on the Performance of ML Models  
In the context of the VTS intelligent algorithms, changes in the probability distribution of data from the PTS can be seen as data drift [97]. This drift can be categorized into three types when PTS is viewed through a statistical model with input data X and output processing data Y. The first type (or drift of the first kind) is the drift of input data X, which reflects changes in traffic flow density due to daily, weekly, or seasonal cycles [98] and other time-related factors. The second type (the drift of the second kind) is the drift of output data Y, which could manifest as a shift in the simulation model's trace graph relative to the PTS due to external factors. The third type (the drift of the third kind) is simultaneous changes in both input and output data X and Y, indicating the combined influence of global and local factors.  
Data drift from PTS dynamics directly impacts the accuracy of simulation modeling, especially when using ML models, as the distribution of input data used for analysis and training becomes outdated [11]. Therefore, calibrating the SMTF is essential to adapt to PTS data drift.  
The data calibration process includes four stages [97]:  
1. Data extraction: Collecting data fragments from streams to create a current sample of the studied parameter;
2. Extracting critical characteristics from the collected data;
3. Calculating test statistics to evaluate the static deviation degree of the extracted data from the original or current VTS distribution;
4. Determining the statistical significance of the deviation to initiate system adaptation to the new distribution.  
These steps can be implemented as a monitoring system for the macroscopic model generated to determine when calibration is necessary.  
Figure 2: Statistical indicators of deviation of speed distribution characteristics obtained from SMTF and PTS: mean, standard deviation, minimum, 25 th percentile, 50 th percentile, 75 th percentile, and maximum.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 3 Preliminaries
### Fragmentation and Reassembly  
Through fragmentation and reassembly, defined in RFC 4944, IPv6 packets are split into smaller frames to meet MTU requirements and are reassembled at the destination to reconstruct the original packet. Each fragment is assigned a number that helps in reordering and reassembling the fragments.
### Major Contributions  
The major contribution of this work lies in the development of a novel ConvMixer and AdaBoost-based face recognition system that effectively addresses face occlusion challenges and outperforms existing deep learning algorithms. Its potential for transfer learning and real-world applicability make it a valuable solution for enhancing face recognition accuracy and reliability in critical surveillance applications. The proposed FAR-Conv approach differs from previous methodologies in the following four aspects.  
1. A new face recognition system (AFR) method to handle issues of face occlusion based on a residual connection and ConvMixer with AdaBoost is developed in this study to addresses data limitations, computational cost, and the lack of a lightweight and efficient feature descriptor.
2. We address the challenges of AFR in two different scenarios: utilizing masked faces to train to recognize faces without a mask, and using faces without mask to train to detect masked faces.
3. The AFR-Conv algorithm integrated into the ConvMixer model is a novel approach for handling face occlusion. By assigning priority-based weights to different face patches and using residual connections, the algorithm can effectively focus on relevant facial regions, even when faces are partially occluded, leading to improved recognition accuracy in challenging real-world scenarios.
4. The introduction of the ConvMixer architecture specifically tailored for face recognition tasks is a significant contribution. ConvMixer's ability to capture complex spatial patterns in face images efficiently makes it a powerful feature extractor, enhancing the model's discrimination and recognition capabilities.
5. The ConvMixer and AdaBoost approach offers lightweight and efficient feature descriptors. This characteristic is vital for real-time processing in video surveillance systems, where computational complexity is a significant concern.
6. The experimental results demonstrate that the proposed ConvMixer and AdaBoost-based face recognition system outperforms advanced methods for face classification. This superiority showcases the system's competitiveness and effectiveness compared with other existing deep learning algorithms.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 2 Steam Trap Study
### Steam Trap Operation  
For an efficient and secure steam process, steam traps play a vital role. They remove condensate to prevent water hammer, ensuring the safe operation of steam systems. By eliminating condensate, they allow dry, superheated steam to flow freely, improving heat transfer and reducing energy consumption. Additionally, steam traps protect pipes and equipment from corrosion and operational issues while maintaining a stable steam flow, which is essential for optimizing equipment performance and minimizing process interruptions.  
Related to this, water hammer is a dangerous phenomenon caused by the sudden stopping or redirection of steam or condensate flow, generating high-pressure waves that can severely damage pipes, valves, and critical components such as heat exchangers and pumps. This issue is particularly severe in steam systems due to the high pressures and temperatures involved. Beyond physical damage, water hammer poses significant safety risks, as pipe failures can lead to high-pressure steam leaks, endangering workers and facilities. Additionally, it can disrupt continuous steam flow, causing equipment shutdowns, reduced production efficiency, and considerable economic losses. Frequent repairs due to water hammers also result in increased maintenance costs, including labor and replacement parts. Moreover, when a steam trap leaks, the resulting annual energy and cost losses can be substantial, depending on the volume of steam lost, operating pressure, and duration.