esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
The results presented in this section are obtained on the validation dataset and on the test dataset that consists of 1577 slices labeled as \"fracture\" and 6890 slices labeled as \"noFracture\", for a total of 30 patients. The partition of the dataset into training, validation, and test dataset was done randomly at level-patient, this means that all the slices for a single patient were considered in one of the three sets (training, validation, and test). Nevertheless, the validation and test datasets were not similar to each other. First, the CT scans were performed on different devices and, therefore, we have substantial differences among them, then, the fracture can affect any part of the splanchonceranium and, since the latter is a very large and complex region, the CT images can be very different from each other. The confusion matrix of the validation and test datasets is shown in Figure 6a,b, respectively; the AUC-ROC for both validation and test datasets is shown in Figure 6c,d, respectively.  
For the evaluation of the performance, we considered the following metrics:  
* \(\text{Accuracy}=\frac{TP+TN}{P+N}\)
* \(\text{Recall (or sensitivity)}=\frac{TP}{TP+TN}\)
* \(\text{Precision (or positive predictive value)}=\frac{TP}{TP+FP}\)  
The corresponding values for the validation and test datasets are shown in Table 1.  
Figure 6: Results in terms of the confusion matrix for the validation (**a**) and test (**b**) datasets and ROC curve for the validation (**c**) and test (**d**) datasets. The corresponding AUC for the validation dataset is 0.83 (0.82, 0.84), while for the test dataset is 0.82 (0.81, 0.83). The 95% confidence intervals for the values of the AUC were calculated with the analytic method of [PERSON] and McNeil [29], such as described in Ref. [30].  
The actual width of the confidence interval is the same for both recall and precision in both validation and test datasets, while it is much smaller for the accuracy in both datasets.  
In order to make a prediction in terms of a patient's injury rather than single slices, we performed an evaluation of the neural network. To this aim, the slices were grouped by referring to a single patient according to the following assumption: if two consecutive slices, belonging to the same patient, are classified as \"fracture\" by the CNN with a probability greater than 0.99, then classify the patient as a patient with a fracture. The confusion matrix we obtained for the test dataset is shown in Figure 7.  
The measures of diagnostic accuracy (accuracy, recall (sensitivity), and precision (positive predictive value)) with 95% confidence intervals for the test dataset in terms of patients are shown in Table 2.
## 3 Methodology
### Motivations and Deployment Considerations
#### 3.2.1 Third-Party CA for Traffic Monitoring  
Deploying a third-party CA in VANETs is crucial to monitoring urban traffic. The CA enhances vehicular communication's overall security and reliability by employing a dedicated authority to oversee and regulate the network. This monitoring capability identifies and mitigates potential threats or malicious activities, ensuring a safer and more efficient transportation system.
## 3 The Proposed Methodology
### G Component: Reforming LDA Topics as Topic Clusters  
Reconstructing topics as extracted by LDA with the remaining features in \(W_{F}\) may result in topics of unequal sizes (refer to Figure 4). This imbalance can pose challenges in the scoring component, potentially introducing bias. Smaller topics with a few highly important features might score higher than larger topics with more averaged importance values, potentially overshadowing larger topics in the model training process.  
To tackle this issue, faSTNT employs an extended approach called same-size k-means [40], which strives to create \(k\) compact clusters of equal size of \(b\), where \(b=f/k\). Here, \(k\) corresponds to the number of LDA topics, and \(f\) represents the total number of words in the final word list (\(W_{F}\)). It is important to note that the cluster size is less than the LDA topic size (\(b<m\)).  
In this aspect, faSTNT performs word clustering using word embedding (WE) to preserve the semantic relation between words in each cluster since the distances between word embedding vector representations can reflect the semantic similarities between words.  
In this approach, faSTNT leverages a pre-trained fastText model (refer to Figure 5), a semantic information source about features, to generate word embeddings for all words in \(W_{F}\). Specifically, fastText provides embeddings for n-grams, and the word embedding vector for a complete word is calculated as the sum of these n-gram embeddings. After transforming \(W_{F}\) into a word embedding matrix, it is used as input for the same-size k-means to create multiple preliminary clusters of words that share semantic similarities or relatedness.  
By explicitly leveraging semantic information through external knowledge sources (e.g., word embeddings pre-trained over millions of documents), faSTNT aims to enhance the semantic cohesion of LDA topics. This enhancement can be logically explained since LDA does not explicitly model semantic relationships between words; it indirectly captures semantic information by grouping words that frequently appear together in similar contexts over the dataset based on statistical patterns.  
We refer to the produced clusters as topic clusters (_TC_) to differentiate them from the original topics (_T_) extracted by LDA. The _TC_ will form an input for the S component.  
Figure 5: The working mechanism of the G component.
# Covert Channel Based on Quasi-Orthogonal Coding
## 2 Radio Physical Layer Steganography
### Channel Estimation Error  
In a real-world situation, the receiver does not have full information about the wireless channel (2), but only estimates the parameter \(h\) with a certain error value \(\varepsilon\) (\(\varepsilon\sim CN\big{(}0,\sigma_{\varepsilon}^{2}\big{)}\)):  
\[\hat{h}=h+\varepsilon, \tag{4}\]  
where \(\hat{h}\) is a channel gain estimation.  
Taking into account the channel estimation error (4), the recovered covert signal \(\hat{x}_{2}(t)\) is distorted by the residual cover signal according to the following formula (non-ideal SIC):  
\[\hat{x}_{2}(t)=h\sqrt{P_{2}}x_{2}(t)+\varepsilon\sqrt{P_{1}}\hat{x}_{1}(t)+w(t) \tag{5}\]  
The error introduced by the SIC propagates and affects the demodulation of the covert signal, i.e., the interference that arose during the recovery of the cover signal (stronger signal) propagates during the recovery of the covert signal (weaker signal) [17; 18; 19]. Errors arising during SIC mostly depend on the channel coefficients and the power allocation coefficient [20].  
According to Formula (5), the channel estimation error variance value \(\sigma_{\varepsilon}^{2}\) has a fundamental effect in the recovery of the covert signal. In this paper, we consider a situation where \(\sigma_{\varepsilon}^{2}\
eq 0\) as a non-ideal SIC. In such a case, the best way to distinguish signals \(x_{1}(t)\) from \(x_{2}(t)\) is to ensure their mutual orthogonality or (if this is not possible) quasi-orthogonality.  
Figure 1: Transmission and reception of a covert signal as a superposition of cover and covert signals.
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 4 Experimentation and Evaluation Results
### Water Consumption Results  
From the experimental results, Figure 7 depicts irrigation time in minutes per day for dates from 2 March 2017 to 31 July 2017 for the majority algorithm. It is obvious that as we move towards the summer the irrigation time per day constantly increases, with the exception of heavy rain days (30 June and 11-12 July).  
The total irrigation time for the first scenario using the MVA algorithm was 883.9 h for a period of 150 days, which results to 5.89 h of irrigation time per day. The watering time for the second scenario was 821.36 h, which corresponds to 5.48 h of irrigation time per day. The FITRA algorithm saved 62.54 irrigation hours, which results in 7% less water consumption for the same and even better confirmed tomato plantation results. Tomato crops per field gave an average of 7.8 kg tomatoes per plant per month for the MVA field and 8.9 kg tomatoes per plant per month for the field using the FITRA algorithm (production increase of 14%).
# Value of Information Based Data Retrieval in UWSNs
## 5 Performance Measures, Simulation Setup and Results
### Results
#### 5.3.3 Comparative Analysis  
In this section, we do a thorough comparative analysis to have a better understanding of the performance dynamics of the various path planners. The parameters we explore for this purpose are Y\({}_{ACC}\), Y\({}_{L}\), T\({}_{Tour}\) & \(\Omega^{n}\). We vary the number of hot-spots \(N_{HS}\) from 0 to 10 and see the effect on the path planners' performance.  
For this section we have used stacked bar-plots (Figures 5 and 6). The results have not been normalized to give a more clear picture of the actual values obtained from the simulation. Each reading for a [Planner, Hot-Spot(s)] tuple is averaged over 100 different VoI profiles and is represented by a block in the stacked bar-plot. The exact value of a block is given in a corresponding cell in the table below. The changing color gradient of the blocks matches to the index column \(N_{HS}\) in the table. The changing color tones of the table cells correspond to a change in intensity of values. The better the performance, the darker the color tone is in the table. Color tones for comparative performance can only be compared across a row, i.e., values can be only compared across a particular \(N_{HS}\) value.  
Vol Accumulated and VoI Lost  
This discussion refers to graphs in Figure 5. The bar graph in Figure 5a is for VoI accumulated Y\({}_{ACC}\) by various path planners, while Figure 5b pertains to VoI lost Y\({}_{L}\). They have the same conclusions in terms of performance but the performance results for Y\({}_{L}\) are more pronounced as compared to Y\({}_{ACC}\) and this is because, as stated earlier, they remove the bias due to loss before \(t=\tau_{Start}\). LPP performs best when there are no hot-spots. In this case, the performance of HPP and HIPP is same as LPP. With \(N_{HS}>0\) the greedy and hybrid approaches start to perform better. The hybrid algorithms always perform better than their greedy counterparts. Also, GIPP always performs better than GPP. An interesting thing to note is that initially HPP has better performance than GIPP but after \(N_{HS}=4\) the GIPP path planner starts outperforming HPP; which is due to the fact that the AUV using GIPP might start encountering lesser valued hot-spots more frequently while on its way towards a higher valued hot-spot; a benefit in hindsight of using intermediate node visitation. The cumulative performance rank for \(N_{HS}=[0{:}10]\) is: \(HIPP>GIPP>HPPP>GPP>LPP>RPP\).  
Figure 5: Comparative analysis for Y\({}_{ACC}\) & Y\({}_{L}\): (**a**) The top stacked bar graph is for VoI accumulated Y\({}_{ACC}\); (**b**) The bottom stacked bar graph is for Vol lost Y\({}_{L}\).  
Figure 6: Comparative analysis for \(T_{tour}\) & \(\Omega\): (a) The top stacked bar graph is for time to complete the tour \(T_{tour}\); (b) The bottom stacked bar graph is for the efficiency measure \(\Omega\).
# Effectiveness and Design of PBL-Based Project Approach for Non-Major University Computing Courses
## 3 Methodology
### Data Collection and Analysis Methods  
To assess the effectiveness of the problem-based project design, both quantitative and qualitative data were collected and analyzed. The project-based evaluation results from the first semester of 2024 were compared to the programming test results from the second semester of 2023, which primarily assessed individual coding and algorithmic skills. The focus of the analysis was to determine the impact of the evaluation method on students' computational thinking, algorithmic understanding, and collaboration skills.  
The project course was evaluated based on students' contributions throughout four phases: problem creation, problem extension, peer feedback, and presentation. Key evaluation criteria included creativity, originality, code accuracy, efficiency, and the quality of feedback provided by students. These metrics were compared with the results of the programming test conducted in 2023.  
To further assess the overall effectiveness and satisfaction of the course, an official university-administered survey was utilized. The survey included multiple-choice and open-ended questions, addressing topics such as course preparation adequacy, teaching effectiveness, and the appropriateness of assignments and feedback. The survey comprised both multiple-choice and open-ended questions. While the multiple-choice responses were analyzed using Likert scales for quantitative analysis, the open-ended responseswere analyzed using thematic coding to identify recurring themes and provide qualitative insights. Although the open-ended responses were not included in the formal evaluation scores, they were reviewed to assess the reliability and consistency of student feedback. A summary of the survey items is presented in Table 2.  
The course evaluation survey consisted of two categories of questions: general questions applicable to all courses and specific questions tailored to practice-based sessions. The general questions assessed the overall satisfaction of the students with the course, the adequacy of the content and preparation, the effectiveness of the teaching methods, the relevance of the lecture plan, and the appropriateness of feedback on assignments and tests. In addition, specific questions for practice-based sessions evaluated whether hands-on activities were well organized, enhanced student understanding, and encouraged active participation with constructive feedback.  
By combining multiple data sources and evaluation methods, this study ensures a robust and multifaceted assessment of the educational impact of the problem-based project design. Quantitative measures, such as test scores and Likert-scale responses, were complemented by qualitative insights from open-ended survey questions, offering a holistic understanding of the course's strengths and areas for improvement.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 3 Materials and Methods
### ADCS System
This process is pivotal for determining the satellite's lifetime, as it approximates the integration of slowly evolving orbit elements over time. The methodology involves integrating across a single orbit to establish the rate of change for each variable. Subsequently, this rate is assumed to remain constant for a specific number of orbits during each calculation. To further elevate the accuracy of this procedure, the integration process for a single orbit is subdivided into six sub-arcs, each maintaining a constant angular measure (in true anomaly, not time). Within each of these sub-arcs, a \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Initial Position** & **Final Position** & **Maneuver** & **CubeSat Format** \\ \hline A & B & AB & 2U, 6U \\ B & A & AB & 2U, 6U \\ A & C & AC & 3U, 6U \\ C & A & AC & 3U, 6U \\ B & C & BC & 6U \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Type of maneuver for the cases studied. nine-point Gaussian quadrature is employed, signifying the presence of nine sample points for precise integration.
## 3 Results  
### Ocular Parameters  
Forty-three (21 males and 22 females) consecutive LASIK patients aged between 25 and 59 years (mean age: \(40.34\pm 8.09\)) were recruited. Seven were classified into the post-LASIK DED group, 16 into the post-LASIK DED-pain group and 20 composed the control group. No recruited patient suffered from diabetes. The clinical tests are summarized in Table 1. There were significant (\(p\leq 0.03\)) differences between control and both post-LASIK groups for OSDI scores. In addition, the post-LASIK DED-pain group showed significantly (\(p<0.0001\)) higher NRS pain scores in comparison with the post-LASIK DED and the control group. There were no significant differences (\(p>0.05\)) among groups for the other ocular parameters and age.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \multirow{2}{*}{**Clinical Test**} & \multirow{2}{*}{\begin{tabular}{c} **Control (C)** \\ **(\(n=20\))** \\ \end{tabular} } & \begin{tabular}{c} **Post-LASIK** \\ **DED (D)** \\ **(\(n=7\))** \\ \end{tabular} & \begin{tabular}{c} **Post-LASIK** \\ **DED-Pain (P)** \\ **(\(n=16\))** \\ \end{tabular} & \multirow{2}{*}{\
\begin{tabular}{c} **ANOVA** \\ **Global \(p\)-Value** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Post-Hoc Comparisons** \\ **C vs. D** \\ \end{tabular} } \\ \hline Age & \(42.55\pm 6.11\) & \(41.29\pm 8.99\) & \(37.25\pm 7.53\) & 0.0940 & 0.6894 & 0.0987 & 0.3303 \\ OSDI & \(8.33\pm 3.16\) & \(36.61\pm 23.99\) & \(55.47\pm 25.21\) & \(<\) 0.0001 & 0.0308 & 0.0001 & 0.1132 \\  & & & & & & & \\  &
# Analysis of Conflict Distribution Characteristics between Motor Vehicles and E-Bikes at Roundabouts
## 1 Introduction
### Safety Problems of E-Bikes  
Current research on e-bikes' safety mainly focuses on the mechanism and factors that result in accidents. [PERSON] conducted a survey on e-bike cyclists who were hospitalized due to traffic accidents in one year, based on the analysis of the surveyed cyclists' personal characteristics, the cause of the accident, and the degree of seriousness of the injury; it was found that 76% of the injured were men, and the average age of the injured was 47.5 years old. Traffic accidents were mainly caused by high-speed driving, improper operation, and lack of concentration. Due to Switzerland's more strict implementation of the helmet-wearing policy and smaller number of e-bikes than China's, the cyclists' degree of injury in China is higher [19]. [PERSON] used the Structural Equation Model to identify and quantify the factors that influence the risky riding behaviors of e-bikes riders. In this research, the four scales of riding confidence, safety attitude, risk perception, and risky riding behavior were extracted to explore the intrinsic causal relationships among the variables that affect risky e-bike riding behavior. Safety attitude was found to be significantly associated with riskyriding behaviors [20]. [PERSON] analyzed the lateral distance parameters of vehicles when overtaking an e-bike and found that there are statistically significant differences in the lateral distance when overtaking an e-bike regarding helmet use, vehicle categories, and speeds of the e-bike. A faster speed of an e-bike led to a shorter lateral distance when overtaking it, and SUV drivers had the shortest lateral distance when overtaking e-bikes [21]. Compared with traditional bicycles, the higher speed and weight of an e-bike was more likely to result in an accident, and it was proposed that electric bikes attract plenty of people who lack riding experience, which has further intensified the traffic safety problems. Based on the data analysis of three local intersections in Norway, it was found that women were at a higher risk of electric bike accidents than men [22]. It was also found that the traffic violation rate of e-bikes is higher than that of traditional bicycles [23]. The average conflict rate for e-bikes was 2.5 times as that for bicycles [24]. The bike type, dividing strip type, bike lane width, temporary on-street parking, and whether or not it is a working day all have a significant effect on cyclists' behaviors of occupying motor vehicle lanes [25].
## 4 Results and Discussion  
### ASR Results  
Table 2 presents the results of the different ASR systems using 54 sessions for the HMM-GMM, TDNN, transfer learning and cross-validation.  
As presented in Table 2, we initially started by building a HMM-GMM model using only the GSHTS dataset and got an initial WER of 67.18%. Using instead an TDNN model yielded a WER of 47.18%. The following step was to train the TDNN model using the complete Librispeech database and test the model on the GSHTS dataset without any adaptation to investigate the use of a more common, and much larger, database in the ASR field, which resulted in a WER of 70.30%. This high WER can be attributed to the mismatch between the two datasets. In particular, the presence of more accented English in the GSHTS dataset rather than the read English speech of audiobooks in Librispeech [55]. After applying transfer learning using the acoustic model trained on the LibriSpeech corpus and adapted to the GSHTS dataset, the results gained were a WER of 35.86%, which showed better results than the initial system's results. These initial models were all trained using a fixed partitioning of the dataset into a training and a test part. The typical procedure when using sparse domain datasets, such as GSHTS, is to apply the cross-validation techniques. For our data the results were WERs with an average absolute lower value of 1.85% with WERs of 65.49%, 45.29% and 33.88% on the HMM-GMM, TDNN and transfer learning systems, respectively. This reflects that, when using cross-validation techniques, more data can be set aside for the training in each fold, resulting in more powerful models. After performing transfer learning, the final results fall in the range of what has been achieved in the literature. As discussed in Section 1, the range of the WER achieved after applying ASR  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Number of Sessions** & **Train** & **Test** & **Cross-Validation** & **Adapt/Transfer** & \%**WER** \\ \hline HMM-GMM & 54 & GSHTS & GSHTS & & & 67.18 \\ TDNN & 54 & GSHTS & GSHTS & & & 47.18 \\ TDNN & 54 & LibriSpeech & GSHTS & & & 70.30 \\ TDNN & 54 & GSHTS & GSHTS & & & LibriSpeech model + \\  & & & & & GSHTS dataset & 35.86 \\ \hline HMM-GMM & 54 & GSHTS & GSHTS & 6 folds & & 65.49 \\ TDNN & 54 & GSHTS & GSHTS & 6 folds & & 45.29 \\ TDNN & 54 & GSHTS & GSHTS & 6 folds & & LibriSpeech model + \\  & & & & & GSHTS dataset & 33.88 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ASR system results using transfer learning and cross-validation techniques; for each model the data used for training, testing and transfer learning has been specified.  
systems in therapeutic domains [32; 33; 34] is usually between 30% and 40% which is aligned with the results we gained on the guided self-help session recordings dataset.