esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Analysis and Discussion
### Average Latency  
The DSR and AODV protocols only locate a route upon request when it is requested. The route discovery may delay the transmission of packets by queuing them in the buffer. Therefore, the latency exceeds that of RRM, which adopts a proactive scheme in clusters. In RRM, the packets can be delivered from one cluster to next cluster rapidly by intra-cluster routing information. Furthermore, each border node caches multi-path information. The RRM can rapidly change routing path when a path or sensor node fails. Figure 18 shows the average latency of the end-to-end communication. The source and destination pair, which measures the delay time of a packet between sent by the source and received by the destination, is selected randomly. The cluster track can be obtained on demand using unicast rather than broadcast, thus reducing the energy consumption.  
Figure 17: Control overhead.  
Figure 18: Average latency.
## 2 Proposed Method
### Evaluation Criteria  
The recognition performance was evaluated using four metrics: (1) Cumulative Match Characteristics (CMC) curve; (2) rank-1 and rank-5 identification rates; (3) the Receiver Operating Characteristic (ROC) curve of False Acceptance Rates (FAR) and False Rejection Rates (FRR); and (4) Equal Error Rates (EERs). CMC curve, and rank-1/rank-5 identification rates were used for the identification task while ROC curve and EERs were used for the verification task.
# Unraveling Patient Safety Culture Trends in U.S. Hospital Settings: A Yearly Retrospective Analysis
## 3 Results
### Tracking Trends in Patient Safety Culture Across U.S. Hospitals  
Many hospitals demonstrated their commitment to regularly conducting the hospital survey to monitor and evaluate changes in patient safety over time. While the previous analysis reflects the overall results for all participating hospitals, whether they participated once or multiple times, some hospitals conducted the survey repeatedly, enabling comparisons across different time periods. An analysis of the data revealed that 331 hospitals  
Figure 9: Area of strength and weakness across PSC dimensions over time. Note: the red dotted line represents the threshold for areas of weakness (composite scores \(<\) 50%), while the green dotted line represents the threshold for areas of strength (composite scores \(\geq\) 75%). The symbols highlight dimensions in these areas: (\(\blacktriangle\)) indicates a dimension in the weakness area, and (\(\blacklozenge\)) indicates a dimension in the strength area. PSC dimensions: D1: Overall Perceptions of Patient Safety; D2: Frequency of Events Reported; D3: Supervisor/Manager Expectations and Actions Promoting Patient Safety; D4: Organizational Learning; D5: Teamwork within Units; D6: Communication Openness; D7: Feedback and Communication about Error; sD8: Nonpunitive Response to Error; D9: Staffing; D10: Management Support for Patient Safety; D11: Teamwork across Units; and D12: Handoffs and Transitions.  
submitted survey results twice, and 78 hospitals submitted results three times between 2013 and 2020. These findings provide valuable insights into how dimensions of PSC evolved over time, highlighting improvements of at least five percentage points in key areas, as well as instances of stagnation or decline. The analysis of PSC trends in these hospitals is summarized in Table 5.  
Figure 10 shows the number of hospitals that improved by 5%, declined, or showed no change or similarly small changes in PSC dimensions in part A, overall safety grades, and event reporting rates, as shown in part B. Based on Table 5 and Figure 10, most hospitals showed no changes in PSC dimensions, safety grades, or event reporting, with  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Two-Years**} & \multicolumn{10}{c}{**Three-Years**} \\ \cline{2-13}
**PSC** & **Change** & **SD  
variations of approximately \(\pm\)5% compared to previous survey results. This suggests a degree of stability in these measures across hospitals. However, some hospitals exhibited notable improvements, particularly in dimensions D2, D3, D6, D7, D8, D11, and D12. The \"Excellent\" safety grade category recorded the most significant increase, reflecting a higher number of hospitals achieving this rating. Conversely, the percentage of hospitals reporting no events decreased, while those reporting three to five events experienced a slight improvement. This change suggests that hospitals reported more events within this range, which may reflect changes in event reporting practices.
# Visual and Haptic Guidance for Enhancing Target Search Performance in Dual-Task Settings
## 3 Study 1: Visual Guidance Design and Evaluation
### Results
#### 3.2.3 User Preference  
After the test, the participants needed to complete a questionnaire about their preference and comments. A one-way repeated-measures ANOVA across a combination of Arrow Type and Visual Guidance Continuity found a significant effect on user preference \((F_{1,61,17.68}=7.86,p<0.01)\). Post hoc Bonferroni pairwise comparisons showed that the preference of a Dynamic Persistent Arrow (7.58) was significantly higher than for other techniques \((p<0.05)\). And one-way repeated-measure ANOVA revealed no significant main effects for the TLX, Mental, Physical, Frustration, Performance, Effort, and Temporal factors in the analysis of the workload using the NASA TLX, as shown in Figure 4b.  
Figure 3: Mean reaction time, selection time, head rotation degree, and head movement distance results of Secondary Task in Study 1.  
Feedback from participants aligned with our preference findings. They reported that the Static Arrow was less noticeable than the Dynamic Arrow. The Dynamic Arrow's visual effect was reportedly encouraging, prompting users to expedite the target location task, thereby inducing a sense of temporal pressure. This, in turn, motivated users to locate the target more swiftly. Consequently, the Dynamic Arrow was deemed more noticeable, and its guidance more efficient and fluent. However, the Dynamic Arrow's visual effect could also induce anxiety if users were unable to locate the target swiftly, and the continuous movement could potentially lead to visual fatigue.  
Participants had diverse opinions about the Transient and Persistent Arrow. Some reported that the absence of a persistent guide required more effort and time to accurately locate the target. These users found locating the target easier with the Persistent Arrow. Despite providing initial guidance, some participants felt the Persistent Arrow offered no additional help in locating the target. When users were highly focused on the task, the Transient Arrow was deemed more efficient as it allowed users to concentrate on the target location independently, often resulting in faster results than following the arrow. However, for tired users or those seeking a more relaxed experience, the Persistent Arrow was recommended for its guiding ability. On the downside, the Persistent Arrow's drifting nature could be distracting during target search and selection. In contrast, the Transient Arrow only provided directional guidance without hovering around the target, which was deemed sufficient.
## 1 Introduction
### Motivations of the Study  
This study evaluates indoor environmental conditions in childcare centers, with a focus on air quality and lighting, two essential factors in children's health and development. Key indoor parameters, including air temperature (T\({}_{\text{a}}\)), relative humidity (RH), and CO\({}_{2}\) levels, are monitored to assess both ventilation efficiency and minimize airborne disease transmission. CO\({}_{2}\) is used as a reliable indicator of both ventilation performance and the risk of airborne contagion, supporting the development of targeted ventilation strategies that promote safer and healthier classroom environments. Additionally, the study evaluates the adequacy of existing lighting conditions in supporting children's circadian rhythms, which are critical for regulating sleep patterns and promoting overall well-being.
# Enhancement of Conventional Beat Tracking System Using Teager-Kaiser Energy Operator
## 2 Dataset and Methods
### Application  
Beat tracking systems are used in the music analysis software for the complex tempo, timbre, dynamic or other music analysis. Example of such freeware software is Sonic Visualiser [27]. Figure 10 shows an example of tempo analysis of the string quartet music from the second tested database. The first pane is the visualisation of the audio wave, the second one is the spectrogram and the last one is a layer of manually corrected beat positions. Beat positions were calculated automatically by the beat tracking system called BeatRoot [28] (Vamp plugin) and then corrected by trained ears. The green line shows how tempo evolves in time--if the audio track is locally slowing down or the tempo increases. The method which is proposed in this paper has not been developed as a Vamp plugin for Sonic Visualiser.  
Figure 10: Possible application of the beat tracking system.  
Musicologists can then draw conclusions from the measurement results. An automated beat tracking system is able to reduce the time of analysis significantly. For example, if we measure the EAT of the first motif of the second database for each recording, we get interesting results. One of the general assumptions is that presently we usually play the same piece of classical music faster than we did before. Figure 11 shows that this assumption may not be correct. There is a trend (see the slope of the linear regression line based on the sum of squares)--older recordings are on average at a faster pace. We do not have enough audio recordings to declare it as a fact, but the tendency is there. However, when we plot the EAT of the entire first movement (Figure 12), the tempo decrease is not so evident. Each black dot represents one interpretation and the blue line is a trend line. The sample from the year 1928 was an outlier and therefore we did not consider it in the regression analysis.  
Figure 11: Results of the EAT calculation for the first motif of the string quartet database.  
Figure 12: Results of the EAT calculation for the entire first movement of the string quartet database.
# Resilience in the Context of Cyber Security: A Review of the Fundamental Concepts and Relevance
## 3 Cyber Resilience: Fundamental Concepts
### Concepts of Resilience in Its Various Contexts
#### 3.1.1 Organizational Resilience  
The International Organization for Standardization (ISO) defines organizational resilience as the ability to absorb and adapt to a changing environment to meet its goals. It also adds that more resilient organizations can anticipate and respond to threats and opportunities arising from sudden or gradual changes in their internal and external context, where increasing resilience can be a strategic organizational objective, the result of good business practices and effective risk management [19].  
The ability to develop organizational resilience is a key factor in the operations of organizations, as well as their ability to adapt and operate consistently over the long term [20]. Organizational resilience has a dynamic and contextual approach, since an organization's level of resilience can change over time depending on the importance the organization attaches to the topic (dynamic), as well as the specific needs that require organizations to take a tailor-made approach (contextual) [21].  
Organizational resilience is linked not only to the survival of organizations--i.e., their ability to produce quick and effective responses in times of adversity, facing their strategic challenges and ensuring business continuity--but also to being able to contribute to the evolution and growth of the organization based on the learning produced by the adaptations and proactivity required in times of crisis, enabling the organization to acquire a competitive advantage in complex and challenging business environments [22; 23; 24].
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 3 Result
### Fusion and Analysis of Detection Results  
Eliminating the scattered small area interference points, the final obtained building target detection results are shown in Figure 15.  
As can be seen from Figure 15, after separating the ocean area, the interference brought by various types of ships and trestles was significantly reduced, and the accuracy of building target detection was improved.  
The accuracy, precision, recall, and F1 of the rectangular approximation-based detection results, saliency-based detection results, two mask fusions, and detection results after interference removal were calculated for images I, II, III, and IV, as shown in Tables 3-6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Rectangular & 0.9012 & 0.7419 & 0.6445 & 0.6898 \\ approximation-based & 0.9328 & 0.9207 & 0.6627 & 0.7706 \\ Saliency-based & 0.9204 & 0.7456 & 0.8087 & 0.7759 \\ Fusion & 0.9310 & 0.8131 & 0.7730 & 0.7925 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Image I detection results.  
Figure 15: Final obtained building detection results. (**a**) Image I, (**b**) Image II, (**c**) Image III, (**d**) Image IV.  
From the statistics of the evaluation indicators of the above detection results, it can be seen that the proposed building target detection method, which combines rectangular approximation-based and saliency-based detection results, is able to improve in accuracy, recall, and F1 compared to the detection results of buildings with a single feature. Simultaneously, the overall evaluation index is further improved by removing the ocean area, eliminating the possibility of ships and trestles that cause interference being detected.
# Detection of Safety Signs Using Computer Vision Based on Deep Learning
## 3 Deep Neural Network  
### YOLOV4-Tiny Network  
The deep neural network in this work was based on the YOLOV4-tiny model [46], designed based on the YOLOV4 network [41]. The overall structure of the network is shown in Figure 1. It employs the CSPDarknet53-tiny network as the backbone feature extraction network with fewer network parameters and faster detection speed than the CSPDarknet53 network in the YOLOV4 model. To make the calculation process faster, YOLOV4-tiny uses the Leaky ReLU function as the activation function instead of the Mish activation function in YOLOV4. The purpose of the Leaky ReLU activation function is to calculate the gradient and thus avoid neural deactivation. As can be seen in Figure 1, YOLOV4-tiny also extracts multi-scale features for detection, with two effective feature layers of different sizes. For the input of 416 \(\times\) 416 pixels, the scales of these two feature layers are 26 \(\times\) 26 and 13 \(\times\) 13. Subsequently, the shallow effective feature layer is stacked with the deep effective feature layer after convolution and upsampling operations (see Figure 1). Finally, the fused features are sent to the YOLO Head for classification and position regression, and then the final prediction result of the model is obtained by non-maximum suppression. Its accuracy on the COCO dataset is also higher (mAP = 40.2%) than other versions (Light-Head R-CNN's mAP is 37.7%, YOLOV3-tiny's mAP is 16.6%.) of the lightweight model. In terms of speed and accuracy, YOLOV4-tiny is more appropriate for safety signs detection.
# Method of Calculating Desynchronization of DVB-T Transmitters Working in SFN for PCL Applications
## 9 Experiment Results  
A series of measurements were conducted with the described system. After initial signal processing, cross-correlation was calculated for each pair of receivers (example for stations 1-2 pair in Figure 8). As predicted, in every cross-correlation result, four distinct maxima were observed.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Tx name** & **Latitude \({}^{\star}\)** & **Longitude \({}^{\star}\)** & **Channel** & **Power** \\ \hline RTCN Raszyn & 52.0733 N & 20.8855 E & 48 (690 MHz) & 100 kW \\ \hline RTCN PKiN & 52.2317 N & 21.0064 E & 48 (690 MHz) & 3 kW \\ \hline \end{tabular} \({}^{\star}\) source: Wikipedia.org.  
\end{table}
Table 1: Transmitter information.  
\begin{table}
\begin{tabular}{c c c} \hline
**Source** & **Latitude of PKiN** & **Longitude of PKiN** \\ \hline FMSCAN.org & 52.23166 N & 21.00611 E \\ \hline DVBTmap.eu & 52.23172 N & 21.00675 E \\ \hline Google Earth & 52.23184 N & 21.00599 E \\ \hline Wikipedia.org & 52.23167 N & 21.00639 E \\ \hline \end{tabular}
\end{table}
Table 2: Transmitter localization differences.  
Figure 8: Example of cross-correlation results calculated for stations 1-2 pair. Four distinct areas of interest with correlation peaks can be seen.  
Next, the data were processed to extract local maxima corresponding to TDoA peaks and ghost detection peaks. For every measured peak, a comparison to the calculated value was made. For TDoA peaks, the mean difference between the calculated and measured was equal to 30m, with the maximum values never exceeding \(\pm\)150 m (Table 4). This difference can be explained as the sum of two sources of error: receiver synchronization error (15 m RMS 1-sigma), and the positioning error of receivers and transmitters.  
Meanwhile, the differences were significantly higher for ghost peaks and oscillated around \(\pm\)1100 m (Table 5). There is clearly another source of error.  
This disparity can be explained by the time delay in transmission between two transmitters. To find the value of this delay, the algorithm described in Chapter 7 was used. Using the measured TDoA peaks, the Cartesian positions of the transmitters were found. Based on the Cartesian positions of the transmitters, the assumed positions of the ghost peaks were calculated. Lastly, by subtracting this value from the measured ghost peak positions, a desynchronization between the emitters was found. In this case, based on multiple short measurements spanning within 1 h, it can be approximated to be 3.63 \(\upmu\)s, meaning that the Tx2 broadcast was lagging behind Tx1. This made a lot of sense, since this value minimized the SFN multipath effect for DVB-T recipients in the city of Warsaw. Further tests have shown that, for the two transmitters chosen, this value is relatively stable over a few hours but can vary over a few days, thus a time delay calculation should be made every time in a PCL system before using DVB-T transmitters as illuminators of opportunity.
## Materials and Methods
### Principal Component Analysis and Classifier Training  
PCA performs an orthogonal transformation on the original data and determines new orthogonal basis vectors, which describe the data set in a lower-dimensional space [42]. Therefore, PCA was applied to present OCT data and highly dimensional spectral RS data with its principal components (PC). The chosen PCs described a variance of 95% of the dataset. PCA cannot only be used for reduction of dimensions of a given dataset, but also as a first estimation of the performance of more sophisticated classifiers [43; 44]. We implemented a supervised kNN algorithm to classify OCT data and the spectral data of RS. Due to our limited data sets and dimension of data we used the kNN. The fine kNN classifier was set to 1 neighbor, using Euclidian distances, equal distance weights and data were standardized. The validation included a 5-fold cross-validation to protect for overfitting. The dataset was partitioned 5-fold and accuracy was estimated on each fold.
# Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures
## 3 Shear Formulations and Experimental Database for SFRC Beams without Shear Reinforcements
### Shear Resistance Formulation
#### 3.1.3 [PERSON] et al. [27] Mechanical Model  
The proposed model for estimating the mean shear strength of SFRC beam without stirrups, according to [PERSON] et al. [27], is expressed as Equation (5).  
\[V_{Bernat\ et\ al.}=\bigg{[}\zeta\bigg{(}0.84-0.10\frac{\sigma_{tu}}{f_{ctm}} \bigg{)}\frac{x}{d}+0.08+1.10\frac{\sigma_{tu}}{f_{ctm}}\bigg{]}f_{ctm}b_{w}d \tag{5}\]  
The average residual tensile stress \(\sigma_{tu}\) is obtained using Equation (6)  
\[\frac{\sigma_{tu}}{f_{ct}}=2\eta_{0}\eta_{1}F \tag{6}\]  
where \(x\) is the neutral axis of plain concrete. \(\zeta\) is the size and slenderness effect factor. \(\eta_{0}\) is the fiber orientation factor taken as 0.405. \(\eta_{1}\) is the length efficiency factor for fiber
# The Impact of the 4.0 Paradigm in the Italian Agricultural Sector: A Descriptive Survey
## 5 Discussion and Conclusions
### Limitations and Future Research Directions  
As with any other research, the current study also comes with limitations. First, the sample investigated in the present study is not perfectly aligned with the current Italian agricultural context in terms of revenues and land size. Indeed, the sample differs from the Italian landscape, which is smaller in terms of the size of arable land and turnover [47]. Thus, there is still extensive room for improvement. Moreover, the current study focused only on Italian agricultural companies, which may limit the generalization of the results. Despite this, however, it is necessary to specify that the Italian agricultural sector is one of the best performers in the Italian economy, with one of the highest added value to the gross domestic product (GDP) in Europe.  
A possible limitation lies in comparing the benefits and barriers from two \"parallel\" clusters, potentially creating bias. To overcome this problem, it would be interesting to perform a longitudinal study as a follow-up. Here, the current survey could be repeated in a few years, comparing the cluster that is not currently adopting any solution and evaluating their evolution over time; this can be done mainly to compare the evolution from the point of view of adoption and analyze what the new users see as the benefits and barriers compared with initial expectations.  
Another interesting future direction of work could be to compare the level of awareness and adoption with other countries, both with a similar sector structure (such as Spain or France) and others that are among the early adopters of Agri 4.0 and precision farming (such as the Netherlands), to carry out specific benchmark analyses. Further areas of research can be derived from adopting the same research also in companies from another sector, such as breeders of livestock for meat production and meat, eventually comparing the differences between the internal branches of the agricultural sector.  
Conceptualization, F.A.M., M.A. and A.B., methodology, M.A. and F.A.M.; validation, F.A.M., M.A. and A.B.; formal analysis, F.A.M.; investigation, F.A.M., A.B.; data curation, F.A.M.; writing--original draft preparation, F.A.M.; writing--review and editing, F.A.M., M.A. and A.B.; supervision, A.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 4 Performance Verification and Analysis  
This section focuses on the performance analysis of a low-altitude network sensing model based on ISAC. A series of rigorous tests were conducted thoroughly to evaluate the performance of our model.  
This series of tests, from ideal environments to real-world scenarios and from single base station to networked systems, comprehensively verified the performance of the sensing model, providing a solid foundation for the model application and optimization.