esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative System Usability in Spaceflight Analog Environments through Remote Observations
## 5 Discussion
Instead, it appears that timeline-related discussion between crew members was often a necessary ingredient for completing the self-scheduling task. These interactions consisted of crew members seeking and offering recommendations, which is a keystone behavior of team coordination. NASA has acknowledged positive team dynamics as vital for mission success [26]. We found a negative correlation between off-topic conversations and workload. There are three possible interpretations of this finding. First, crews may engage in more casual conversation during self-scheduling when the task incurs low workload. Second, the workload questionnaire may be measuring general workload within the analog rather than the specifics of the self-scheduling task, explaining the crew's chatting behavior. The third interpretation is that the NASA-TLX accurately reflected workload during the task, and that off-topic chatting did not increase workload. The first interpretation suggests that tracking conversations could serve as a viable unobtrusive proxy for workload in remote environments, which is particularly valuable in a spaceflight analog where participant survey compliance may be challenging. It aligns with literature that suggests that low workload indicates spare cognitive resources [20], which can then be directed to task-unrelated behaviors like mind-wandering (e.g., [27]) or, as presently observed, chatting. These preliminary results require exploration in subsequent analogs with larger astronaut-like samples. After using the voice logs to establish that HERA is a collaborative, social environment during self-scheduling, we analyzed the descriptive statistics of post-mission usability survey responses to infer UX in a spaceflight analog. Our findings indicate that Playbook in C6 scored extremely well relative to a benchmark provided by the developers of the UEQ [25] and demonstrated comparable scores to those from prior testing in our controlled in-lab experiments [18]. While it remains uncertain to what extent general improvements and new features in Playbook [17] contributed to shifts in UX between lab and analog testing, our results suggest that Playbook is a usable system for actual autonomous crews in space missions. Conversation analysis proved to be a useful technique for characterizing the nuances of self-scheduling with Playbook. We found that using this method to unobtrusively study group problem-solving was invaluable for exploring how our collaborative software was used in a realistic setting. The present work aligns with a recent trend in usability science that encourages scholars to address the broader context in which computer systems are used [5]. By using voice data to enrich our understanding of user interactions with spaceflight technology, we have paved the way for subsequent context-sensitive approaches in usability science as the technologies of the future are designed, developed, and deployed.
# Detecting Disinformation in Croatian Social Media Comments
## 2 Related Work
### Disinformation Research in Slavic Languages  
The detection of disinformation in Slavic languages is an emerging field of research that has gained attention due to the increasing prevalence of disinformation campaigns, particularly in the context of geopolitical conflicts. Despite the growing importance of automated disinformation detection, the literature on Slavic languages remains underrepresented compared to English and other widely spoken languages.  
The morphological complexity, rich inflectional systems, and syntactic variability of Slavic languages pose additional challenges, making the direct adaptation of English-centric approaches ineffective. Furthermore, the scarcity of annotated disinformation datasets, limited language resources, and the geopolitical significance of disinformation campaigns in Slavic-speaking regions further widen the research gap. Addressing this underrepresentation is crucial for developing effective multilingual fact-checking systems and combating the spread of misinformation across diverse linguistic and cultural contexts.  
While some progress has been made for Russian [24], Ukrainian [25; 26], Czech [27], Polish [28], and Slovak [29], many Slavic languages, including Croatian, lack dedicated datasets and robust computational models.  
The study by [PERSON] et al. [30] addresses the challenge of automated fact-checking in West Slavic languages--specifically Czech, Polish, and Slovak. The authors compiled datasets for each language by extracting claims from fact-checking websites and employed standard machine learning techniques to establish baseline performance metrics for these datasets. They utilized features such as bag-of-words and TF-IDF representations, and applied classifiers including Support Vector Machines (SVMs) and Logistic Regression. The results highlighted the complexity of automated fact-checking in these languages, with performance varying across languages and categories.
## 9 Discussion
### Finding 3: Perceived Satisfaction with Multi-Modalities  
In terms of research question 3, most users believed that multi-modalities were effective in helping them to complete tasks, and all the existing modalities in the current applications satisfied most of the users' needs in assisting their learning performance, except for game-based learning applications. This finding is surprisingly different from many researchers' who aimed to assess the effectiveness of using digital game-based applications to improve learning performance, such as the studies of [48; 49]. Their research findings all agreed that the learning motivation of game-based application users improved, and their learning performance was also assisted to some extent. Therefore, the reason behind this phenomenon among Chinese university students leaves a research gap. Under these circumstances, it represents the slow development of game-based learning applications and the under-use or ineffectiveness of the gamification of learning among college students in China. Additionally, most learners agreed that multi-modalities in learning applications can improve their learning performance with well-organized content and a pleasant interface. Overall, a high satisfaction with multi-modalities was expressed.  
Finding 4: The Relationship Between Multi-Modalities and Learners' Assisted Learning Performance Through More Capable Others  
The findings for research question 4 revealed a strong positive relationship between the multi-modality sub-scale and learners' assisted learning performance through more capable (knowledgeable) others with the use of mobile technology (applications). There were seven dimensions under the multi-modality sub-scale: perspectives on multi-modalities, perspectives on the visual modality, perspectives on the aural modality, perspectives on theverbal modality, perspectives on the reading modality, perspectives on the writing modality, and the perceived satisfaction with multi-modalities. Among them, the significant and strong relationship between perspectives on multi-modalities and the dependent variable was the highest (r = 0.714, \(p\) < 0.05), while the perspectives on the writing modality was the lowest (r = 0.569, \(p\) < 0.05). The results showed that positive attitudes or perspectives toward different modalities can significantly influence the effect of learning performance, which is similar to the conclusion of research [50], which showed that, when students hold positive attitudes, they are more likely to feel more motivated and engage more in the learning process, which is an important predictor of successful learning. On the other hand, the participants in this study held the view that, for assisted learning performance with mobile applications, the writing mode is less vital compared with the other modes. This is understandable because the writing mode was barely mentioned in relation to effective learning or learning improvement in the principles of multimedia learning summarized by [47].
## 4 Results and Discussion
### Land Use and Land Cover Assessment and Change Detection
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\ \cline{3-14}  & \multicolumn{1}{c}{**LULC Types**} & \multicolumn{1}{c}{**Ur**} & \multicolumn{1}{c}{**Pa**} & \multicolumn{1}{c}{**Fch**} & \multicolumn{1}{c}{**Po**} & \multicolumn{1}{c}{**Aq**} & \multicolumn{1}{c}{**Id**} & \multicolumn{1}{c}{**Ef**} & \multicolumn{1}{c}{**Mf**} & \multicolumn{1}{c}{**Sf**} & \multicolumn{1}{c}{**Wa**} & \multicolumn{1}{c}{**Mi**} & \multicolumn{1}{c}{**Total**} \\ \hline  & Urban and built-up area (Ur) & 125.21 & - & - & 0.02 & - & 0.09 & - & - & - & - & - & 125.32 \\  & \multicolumn{1}{c}{Paddy field (Pa)} & 0.18 & 0.15 & 0.39 & 0.06 & 0.02 & 1.32 & - & - & 0.03 & - & - & 215 \\  & \multicolumn{1}{c}{Field crop and horticulture (Fch)} & 0.28 & - & 0.91 & 0.04 & - & 0.13 & - & - & 0.10 & - & - & 1.46 \\  & \multicolumn{1}{c}{Perennial trees and orchards (Po)} & 7.29 & - & 1.80 & 176.44 & - & 8.86 & 0.63 & - & 1.50 & 0.05 & 0.06 & 196.63 \\  & \multicolumn{1}{c}{Aquaculture (Aq)} & 0.11 & - & - & 0.02 & 8.52 & 0.08 & - & - & - & 0.01 & - & 8.74 \\  & \multicolumn{1}{c}{Idle land (Id)} & 5.19 & - & 0.29 & 1.85 & - & 25.33 & 0.10 & 0.01 & 1.69 & 0.19 & 0.01 & 34.67 \\  & \multicolumn{1}{c}{Evergreen forest (Ef)} & 0.58 & - & - & 3.69 & - & 0.87 & 73.28 & - & 2.43 & - & - & 80.86 \\  & \multicolumn{1}{c}{Mangrove forest (Mf)} & 0.08 & - & - & 0.02 & - & 0.14 & - & 24.70 & 0.20 & - & 0.02 & 25.15 \\  & \multicolumn{1}{c}{Scrub forest (Sf)} & 1.53 & - & 0.03 & 2.16 & - & 2.22 & 0.15 & - & 20.94 & 0.02 & 0.02 & 27.08 \\  & \multicolumn{1}{c}{Water body (Wa)} & 0.14 & - & - & 0.04 & 0.01 & 0.14 & - & - & 0.02 & 14.22 & - & 14.56 \\  & \multicolumn{1}{c}{Miscellaneous land (Mi)} & 1.07 & - & - & 0.05 & - & 0.56 & - & - & 0.09 & 0.32 & 3.35 & 5.43 \\ \hline  & Total & 141.64 & 0.15 & 3.43 & 184.39 & 8.56 & 39.74 & 74.16 & 24.72 & 27.00 & 14.81 & 3.46 & 522.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: LULC change between 2014 and 2019 as a transition area matrix. which stated that most agricultural land and vegetation areas were transformed into urban and built-up areas to meet people's demands, leading to the LULC change.
###### Abstract  
This study aims to evaluate the effects of mandarin peel (_Citrus reticulata_ L.), oregano (_Origanum vulgare_), and common lambsquarter (_Chenopodium album_) powders as natural additives on the shelf lives of artisanal Bologna-type sausages by comparing them with a chemically preserved control formulation. In this regard, four mortadella formulations (MTS1, MTS2, MTS3, and MTC) were produced and analyzed for physicochemical, microbiological, rheological, and sensory properties at days 1 (T0), 15 (T1), 25 (T2), and 30 (T3) after vacuum packaging. The results highlighted greater performances in the experimental samples MTS2 (made with common lambsquarterts) and MTS3 (made with oregano), particularly in microbiological stability and antioxidant activity, which were similar to those of the control sample (MTC), with TBAR values extremely low, even at the end of the storage for both MTS2 (0.65 mg MDA/kg) and MTS3 (1.11 mg MDA/kg), reflecting effective lipid oxidation control. The sensory analysis further revealed oregano-containing mortadella (MTS3) as the most preferred sample for appearance and taste. These findings suggest that natural additives, like oregano or lambsquarter powders, could replace or complement nitrites in Bologna-type sausages, ensuring product quality, safety, and shelf life while meeting consumer demand for clean-label and chemical-additive-free products. Further research could optimize these formulations to support commercial applications.  
artisanal Bologna-type sausages; oregano powder; mandarin peel powder; common lambsquarter powder; natural additives +
Footnote †: journal: Applied sciences
## 2 Methods
### Mechanism of Gait Functionality  
The MGTR design was of an end-effector type, implementing the gait trajectory through the foot. It generates movement at the ankle by arranging it on a five-bar link, using it to induce flexion and extension around the knee and hip joints [20]. A flywheel was used to ensure the stable and continuous operation of the link structure, designed to enable symmetrical movement on the left and right sides. An electric stepper motor was connected to the center of rotation of the flywheel and driven. The foot step comprised of several rollers so that when the foot fixing unit came in contact with them, rollers at both ends of the step could rotate; this assisted the patient in walking. To ensure safety, the foot anchor used was of the binding-type (Figure 2).  
In general, the walking speed increased with step length [21]. Variables such as speed and step length were reflected in the VR character's movements, synchronizing the movements of the user and VR character so that the patient could focus on the rehabilitation training.  
The operation diagram of the whole system is shown in Figure 3. When AC power of 220 volts is applied, power is divided from an AC switch and input into computer, switching mode power supply, monitor and VR head mount display device. Switching mode power supply is input to the main PCB, DC motor, and BLDC motor as 24 volts DC. The main PCB communicates the data of the rotary encoder, and the DC motor driver controls the step length and magnetic brake. The BLDC motor driver transmits mechanical power to the lower limb crank axis and links.  
Figure 1: MGTR design.  
Figure 2: Mechanism of gait functionality.  
To enable step-length adjustment with walking speed, a system was constructed to detect the walking speed. Using an LED photo-interrupter, the linked arm measured the speed at which the emitted light was interrupted during the spinning of the flywheel. This measurement was made through a continuously detected delay--if the calculated walking speed increased from the initial value, the length of the link (in the form of a screw) was adjusted in real time to decrease the step length; if the speed decreased, on the other hand, step length was increased by adjusting the length of the link. This was used to enhance the immersive component of the exercise in a VR environment by adjusting the step length according to the speed. The flow chart of the system and the driving algorithm for step control are shown in Figure 4.  
Figure 4: Step length control system algorithm.  
Figure 3: Block diagram of MGTR system operation.
## 3 Results
### Statistical Tests
#### 3.4.4. Corrected 10-Fold Cross-Validation Paired \(t\)-Test  
The summarized results for the corrected version of the 10-fold cross-validation method, as proposed by [24; 35], are presented in Table 9, while the complete statistical data for all pairwise comparisons and metrics are provided in Appendix A (Table A3). The corrected 10-fold CV test effectively reduces bias and Type I error, leading to a lower number of model pairs exhibiting statistically significant differences compared to the standard 10-fold CV test. However, the overall pattern remains consistent, with the logistic regression (LR) model demonstrating clear inferiority across all evaluated metrics. RF occupied a mostly inferior position across all metrics. It showed notable superiority over LR in all metrics, for example, on accuracy (t = 6.967, \(p\) < 0.001) and precision (t = 2.787, \(p\) = 0.005), while no statistically significant differences were detected against ANN in most comparisons except on ROC-AUC (t = \(-\)4.140, \(p\) < 0.001), where it underperforms. However, CB consistently surpassed RF on almost all metrics (except recall) on accuracy (t = \(-\)4.783, \(p\) < 0.001) and precision (t = \(-\)4.461, \(p\) < 0.001), and XGB likewise outperformed RF in precision (t = \(-\)2.797, \(p\) = 0.005) and ROC-AUC (t = \(-\)4.552, \(p\) < 0.001). RF on most metrics statistically does not differ from SVM but has better performance on ROC-AUC (t = 3.923, \(p\) < 0.001). Overall, RF was neither conclusively the best nor the worst, frequently ranking in the mid-lower tier. ANN demonstrated strong performance across multiple metrics, outperforming SVM (t = 2.888, \(p\) = 0.004) and LGBM (t = 2.020, \(p\) < 0.043) on precision, as well as surpassing SVM on ROC-AUC. However, it underperformed relative to SVM in recall (t = \(-\)12.188, \(p\) < 0.001). ANN significantly outperformed LR across all metrics, including ACC (t = 7.090, \(p\) < 0.001) and PREC (t = 4.338, \(p\) < 0.001). Comparisons with CB and XGB revealed no statistically significant differences (e.g., ACC: ANN vs. CB, t = \(-\)0.696, \(p\) = 0.486), indicating that while ANN ranks among the stronger classifiers, it does not decisively outperform the top models. LGBM demonstrated statistically significant inferior performance in the vast majority of cases where differences were identified, with the exception of RF and SVM on the ROC-AUC metric and LR across all metrics. CB outperformed LGBM on four out of five metrics, while XGB showed superiority on three out of five. ANN achieved a statistically significant advantage only in precision (t = \(-\)2.020, \(p\) = 0.043), whereas no statistically significant differences were observed in other pairwise comparisons involving these models. SVM demonstrated notable strengths in recall, outperforming all models except RF. It significantly surpassed leading classifiers such as CB (t = 3.329, \(p\) = 0.001) and XGB (t = 3.786, \(p\) < 0.001) and, in certain instances, RF, although this difference did not reach statistical significance (\(p\) = 0.107). Across all other metrics, SVM either underperformed or yielded
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 5 Materials and Methods
### Traffic Load  
The goal of the studied simulations is to analyze how the new scheduler behaves compared to Orchestra RBS in a situation where there is a sudden increase in traffic. All 20 nodes send messages toward the root after the RPL tree is formed and the network is considered to be stable. Initially, each node issues messages at a low rate (1 packet per minute and 2 packets per minute). Jitter is added in between messages to mimic the behavior of a real network. After 250 messages, the rate is increased (5, 10, 15 packets per minute). At these high rates, each node sends 500 messages. As a final step, the traffic load is decreased again to 1 or 2 packets per minute and 250 messages are sent by each node, with the exception of the sink. The figure in the Table 3 shows a visual representation of the traffic in the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Radio medium & UDGM: Distance Loss \\  & Random Seed & New random seed on reload \\
**Cooja Simulator** & \
\begin{tabular}{c} Note Type \\ Grid Dimensions \\ Transmission Range \\ Interference Range \\ \end{tabular} &
\begin{tabular}{c} Cooja mote \\ 20 \(\times\) 20 (200 m \(\times\) 200 m) \\ 50 m \\ \end{tabular} \\ \hline
**Operating System** & Contiki-NG & Version 4.7 \\ \hline RPL & Mode of Operation & Storing mode \\ \hline \multirow{4}{*}{**Orchestra**} & EB slotframe & 397 slots \\  & Broadcast slotframe & 31 slots \\  & Unicast slotframe & 17 slots \\  & Mode & Receiver-based Shared \\ \hline \multirow{4}{*}{**New Scheduler**} & EB slotframe & 397 slots \\  & Broadcast slotframe & 31 slots \\  & Unicast slotframe & 17 slots \\ \cline{1-1}  & \begin{tabular}{c} activation\_threshold \\ \end{tabular} & 5 \\ \cline{1-1}  & \
\begin{tabular}{c} deactivation\_threshold \\ subtree\_lower\_threshold \\ \end{tabular} & 2 \\ \cline{1-1}  &
\begin{tabular}{c} subtree\_upper\_threshold \\ \end{tabular} & 5 \\ \cline{1-1}  & M & 2 \\ \cline{1-1}  & N & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values for the parameters of the evaluation environment.  
Figure 8: Possible placements of nodes. The green area represents the transmission and interference ranges, both equal to 50 m. The size of the grid is 200 m \(\times\) 200 m. (**a**) Placement 1: the sink node is situated at the top of the grid. (**b**) Placement 2: the sink node is situated in the middle of the grid.  
In Table 3, all the parameters of the evaluation environment and the placements of nodes are given. Simulations are run 10 times for each scenario. Confidence intervals are computed and are indicated with the measurements. The confidence level is 95%.
## 4 Discussion
This proof-of-principle study had some limitations: firstly, the small sample size that could not allow adequate external generalizability; secondly, the possibility that sEMG measurement could be influenced by muscle crosstalk, albeit we tried to minimize it by considering and carefully selecting the appropriate electrode size, inter-electrode distance, and the locations of electrode placement; finally, it was not possible to define which section of the neuromuscular warm-up proposed had obtained the greatest influence in preventing ACL injuries.
## 4 Conclusions  
This paper investigates four different packaging materials and structures that could be used to improve the handling and transportation of pear fruits in post-harvest transportation using FEA methods. A computational explicit dynamics software using Ansys Academic Mechanical, Release 2020R1 was used to perform drop height simulations at 0.25 m, 0.5 m, and 1.0 m on two packaging sizes (0.22\({}^{\prime\prime}\) and 2.1\({}^{\prime\prime}\)) for vulcanized rubber, corrugated cardboard, and 3D-printed ABS. The key findings from the study revealed that:  
* Conventional corrugated cardboard packaging is very prone to plastic and permanent structural for any fruit drop height;
* A double-layer corrugated structure can reduce the maximum pear stress by about 33%, but subsequent layers do not greatly improve the protection against fall damage;
* Vulcanized rubber has a critical height performance limit for cushioning protection; there does not seem to be any benefit of using 0.22\({}^{\prime\prime}\) thickness rubber packaging;
* For 2.1\({}^{\prime\prime}\) packaging size, vulcanized rubber performed the best at all heights with minimal bruising susceptibility, while 0.22\({}^{\prime\prime}\) size corrugated cardboard performed the best at 0.25 m;
* 3D-printed material could potentially lead to multi-use protective packaging that does not get damaged, but the rigidity of ABS materials is currently not suitable for fruit drop protection;
* FEA-simulated bruise susceptibility values were compared with RSM-predicted values where relative error did not exceed 24%, which supports the adoption of the theoretical model as an effective tool for decision-making in agriculture and the post-harvest sector. Overall, the reported findings suggest that packaging performance that falls between rubber and corrugated at 0.22\({}^{\prime\prime}\) packaging size is most effective for current post-harvest transport technology. Future work on minimizing bruising susceptibility should focus on a mixture of design and material technology. Investigation of reusable or recycled plastic-based packaging and cushioning designs would enhance and boost post-harvest logistics. Overall better protection for the fruits can help improve the sustainability of post-harvest technologies and improve food security. Conceptualization, F.T. and P.B.P. ; Data curation, M.H. and A.M.; Formal analysis, M.H. and A.M.; Funding acquisition, F.T. and P.B.P. ; Methodology, all authors; Project administration, F.T. and P.B.P. ; Software, M.H. and A.M.; Supervision, F.T. and P.B.P. ; Writing--original draft, M.H. and A.M.; Writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.