esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Small-Signal Modeling of the Distributed WEDES System
### Small-signal Modeling  
Based on the block diagram of the WEDES controller shown in Figure 2, the small-signal of the distributed WEDES system with controller is shown in Figure 3. The transfer functions and symbols in Figure 3 are summarized as follows. For simplicity, the \(r\)th battery module is used for illustration.  
\(L_{bus}(s)\): Bus voltage control loop gain;  
\(L_{MXr}(s)\): Battery module voltage control loop gain;  
\(L_{SOCr}(s)\): SOC balancing control loop gain;  
\(G_{pdf}(s)\): Duty cycle to DC-DC converter output voltage \(V_{\text{MXN}}\) transfer function;  
\(G_{idN}(s)\): Duty cycle to cell current transfer function;  
\(G_{socir}(s)\): Cell current to cell SOC transfer function;  
\(G_{iTR}(s)\): Gain of the input current of the half-bridge inverter to the output current of rectifier;  
\(G_{vTR}(s)\): Gain of the input voltage of the half-bridge inverter to the output voltage of rectifier;  
\(G_{PWM}\): PWM module gain;  
\(K_{divr}(s)\): Output voltage sensing gain (including voltage divider gain and ADC conversion gain);  
\(Delay_{wr}(s)\): Delay of wireless communication;  
\(Delay_{dr}(s)\): Delay of digital computation;  
\(ZOH_{vr}(s)\): Zero order hold model of voltage sampling;  
\(ZOH_{ir}(s)\): Zero order hold model of current sampling;  
\(ZOH_{SOCr}(s)\): Zero order hold model of SOC sampling.  
Figure 3: Small-signal of distributed the WEDES battery system with the WEDES controller.
## 5 Results and Discussion
### Performance Comparison Based on Aberration-Free OV Beams
The number over each bar indicates the number of the measurements whose AE values are within the corresponding interval. Figure 7: Examples showing the differences of the closed paths (the red circles) and the TC values determined by (**a**) the watershed transformation method (WTM), (**b**) the maximum average-intensity circle method (MAICM), (**c**) the WT-MAICM, (**d**) the fixed-center perfectly round circle method (FC-PRCM), and (**e**) the shifting-center (SC-PRCM). The theoretical TC values are 10 (top row), 15 (middle row), and 20 (bottom row). In each image, the background is the intensity sum map, and the red circle is the closed path determined by the individual methods. The corresponding measured TC value is labeled in each image. According to the previous discussions, we concluded that the FC-PRCM and SC-PRCM are not appropriate as closed path determination methods, and that generating a perfectly round circle is not a plausible nor practical closed path determination strategy. Therefore, we started to just involve the other three closed path determination methods in the following comparison.
# Managing Emergency Situations in VANET Through Heterogeneous Technologies Cooperation
## 4 Vehicle On-Board Equipment
### Extended On-Board Unit (E-OBU)  
The E-OBU permits to connect on-board sensors of vehicle to VANET services. In this paper, we introduced two types of sensors that, interfaced with the OBU, can increase the on-board safety of passengers. The sensors are the GPS module for geolocalization services and the proximity sensors, used to scan surrounding area of vehicles and raise alarms when an obstacle is found. This on-board device allows to enhance safety of the car and nearest vehicles too, by exchanging warning messages in case of dangerous events. An ad-hoc protocol has been designed to allow communication between nearest vehicles by tackling flooding and link congestions. The E-OBU may represent an improvement of VANET environment that can supply additional information for ITS tasks.  
In this work, the on-board sensors are used to scan the surrounding area of the vehicle to avoid undesired collisions commonly recognized as pile-up: they happen because of drivers distractions or in case of a driver with an altered state. When multiple collisions happen, the involved lane suddenly meets congestion due to the obstacles represented by vehicles. If vehicles are equipped with the E-OBU, the proposed idea provides two main actions, triggered by the device. As the first step, when the distance between vehicles is reducing too quickly, then the system acts on brakes in a preventive way to force car braking and, in this way, driver may react braking car by itself. The resultant effect is a reduction of reaction time. The second step is to notify a possible dangerous event to vehicles that are arriving. This case is shown in Figure 10.  
Moreover, on-board sensors may help to advice drivers and other vehicles in case of people that are crossing the roads when a car is arriving. Several studies have pointed out this kind of event by raising up attention on safety of pedestrians. An example is shown in Figure 11, where a pedestrian is starting to cross the lane while a car is passing through. The E-OBU recognizes the pedestrian and performs two actions. The first one is alerting the driver about the pedestrian with on-board signaling  
Figure 10: Example of warning event recognized by E-OBUs. Warning message dissemination between neighbor vehicles.  
system and the second one is to send a protocol warning message to the nearest vehicles. This should act on the collision probability by reducing drivers response time.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 4 Experiment
### Experiment Setups  
Our experiment setting is consistent with the baseline to guarantee that our model is comparable to it.  
**Network Architecture.** We use ResNet12 as the backbone. It mainly has four blocks, which include one residual block. The last feature of the backbone is processed by global average pooling, then goes through an FC layer with batch normalization (BN) to obtain a 128-dimensional object embedding as the final output of the backbone network.  
**Implementation Details.** We perform data augmentation prior to training, e.g., horizontal flipping, random cropping, and color jitter (brightness, contrast, and saturation), as mentioned in [36; 37]. We set the number of episodes to 28 in each epoch. In our experiments, we use the Adam optimizer with an initial learning rate of \(10^{-3}\). The decay of the learning rate is set to 0.1 per 15,000 iterations and the weight decay to \(10^{-5}\).  
**Evaluation Metrics.** We evaluated MDPN in 5-way 1-shot and 5-way 5-shot settings on CUB-200-2011. Following the evaluation process of previous methods [25; 37; 38], we randomly sampled 10,000 tasks to calculate the mean accuracy with 95% confidence intervals as the final result. Our experiments are implemented in PyTorch.  
**Evaluation Metrics.** The number of iterations in GMDP is chosen to be 6 as a trade-off between convergence time and accuracy.
# Centralized Database Access: Transformer Framework and LLM/Chatbot Integration-Based Hybrid Model
## 3 Modeling a Hybrid Model
### Model Architecture
#### 3.2.2 Masked Multi-Self Attention  
This sophisticated mechanism ensures that the model focuses on the relevant parts of the input text for each token and uses masking to avoid the influence of future information on the current token. In addition, the model can adapt its information processing to the current context, which increases the accuracy of the text analysis.  
In mathematical terms, for attention in the \(i\)-th step and \(h\)-th attention head:  
\[Attention_{hi}=Softmax\Bigg{(}\frac{W_{ghi}x(EmbedFinal+PositionEmbedding)}{ \sqrt{dk}}\Bigg{)}\cdot(W_{bhi}\cdot(EmbedFinal+PositionEmbedding)) \tag{3}\]  
where \(dk\) is the dimension of the key, and \(Softmax\) is the function that calculates the probabilities.  
In matrix form, this can be written as:  
\[Attention_{hi}=Softmax\Bigg{(}\frac{W_{ghi}\cdot\begin{bmatrix}EmbedFinal\\ PositionEmbedding\end{bmatrix}}{\sqrt{dk}}\Bigg{)}\cdot\bigg{(}W_{bhi}\begin{bmatrix}EmbedFinal\\ PositionEmbedding\end{bmatrix}\bigg{)} \tag{4}\]  
The outputs from all transformations are combined and a linear transformation is added:  
\[Combined_{h}=Concat(Attention_{h1},Attention_{h2},\dots,Attention_{hi}) \tag{5}\]  
in matrix form as:  
\[Combined_{h}=Concat\left(\begin{bmatrix}Attention_{h1}\\ Attention_{h2}\\ \vdots\\ Attention_{hi}\end{bmatrix}\right) \tag{6}\]  
Furthermore, the following is:  
\[FinalEmbedding_{h}=LayerNorm(U_{hi}\cdot Combined_{h}+\gamma_{hi}\cdot Combined _{h}+\beta_{hi}) \tag{7}\]  
in matrix form:  
\[FinalEmbedding_{h}=LayerNorm\big{(}\begin{bmatrix}U_{hi}&\gamma_{hi}&\beta_{hi} \end{bmatrix}\cdot Combined_{h}\big{)} \tag{8}\]where _Concat_ denotes the vector concatenation operation, and \(\gamma_{hi}\) and \(\beta_{hi}\) are the layer normalization parameters for each attention head.  
Also, let \(X\) be the input matrix \(L\cdot\cdot d\), where \(L\) represents the length of the text (number of tokens) and \(d\) is the dimension of the embedding vector. Weight matrices are defined for queries (\(W_{Q}\)), keys (\(W_{K}\)), and values (\(W_{V}\)) with the dimensions \(d\cdot\cdot\cdot d_{k}\), where \(d_{k}\) is the dimension of the key:  
\[Q=X\cdot W_{Q},\ K=X\cdot W_{K},\ V=X\cdot W_{V} \tag{9}\]  
in matrix form is expressed as:  
\[\begin{bmatrix}Q\\ K\\ V\end{bmatrix}=\begin{bmatrix}X\cdot W_{Q}\\ X\cdot W_{K}\\ X\cdot W_{V}\end{bmatrix} \tag{10}\]  
This formulation illustrates the use of the attention mechanism and ensures that the model is able to consider relevant information while considering the current context and avoiding the inclusion of future data during processing.  
And the attention function for each token \(i\) is defined as:  
\[Attention_{i}=Softmax\left(\frac{Q_{i}(K_{i})^{T}}{\sqrt{dk}}\right)\cdot V_{i} \tag{11}\]  
where \(Q_{i}\), \(K_{i}\), and \(V_{i}\) are the rows of the matrices \(Q\), \(K\), and \(V\), respectively.  
The _Softmax_ function generates a probability distribution, and scaling with \(\sqrt{dk}\) helps to stabilize the training.
## 4 Case Study: Application to Missouri Crash Dataset
### Classification Results  
To evaluate the effectiveness of the proposed framework, we applied the fine-tuned GPT-3.5-turbo model to a dataset of 6400 police-reported crash records. The model was tasked with classifying whether each crash occurred in a construction zone by leveraging both structured crash attributes and unstructured narrative descriptions.  
Among the 91 crashes originally labeled as construction zone-related, only 13 were correctly identified by the model, highlighting a conservative prediction strategy designed to minimize false positives. Despite this, the model achieved high overall classification performance across the entire dataset, particularly excelling in accurately identifying non-construction zone crashes.  
Table 3 summarizes the model's performance using standard classification metrics, reported separately for each class and averaged across the dataset.  
Figure 4 visualizes the confusion matrix for construction zone involvement. The model accurately classified 6307 non-construction zone crashes and misclassified only 2 of them, indicating a low false-positive rate. However, 78 construction zone crashes were incorrectly predicted as non-construction zone, indicating a high false-negative rate. This pattern suggests a conservative bias, favoring precision over recall--an approach suitable for data quality auditing, where minimizing false positives can reduce the expert review workload.  
The model's precision-first behavior is further reflected in its excellent performance in non-construction zone cases (F1 = 99.37%) and limited success with construction zone cases (recall = 14.29%). While this limits full recall coverage, the trade-off supports the goal of flagging only high-confidence anomalies in large-scale crash datasets.  
Figure 4: Confusion matrix of GPT-3.5’s predictions for construction zone involvement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Non-Czone (0)** & **Czone (1)** & **Overall/Avg** \\ \hline Precision & 98.78\% & 86.67\% & – \\ Recall & 99.97\% & 14.29\% & – \\ F1 score & 99.37\% & 24.53\% & – \\ Support (No. of cases) & 6309 & 91 & 6400 \\ Accuracy & – & – & 98.75\% \\ Macro-average F1 score & – & – & 61.95\% \\ Weighted-average F1 score & – & – & 98.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification metrics for construction zone detection (dataset, \(n\) = 6400).
###### Abstract  
In this paper, we propose a novel data augmentation technique employing multivariate Gaussian distribution (DA-MGD) for neural network (NN)-based blood pressure (BP) estimation, which incorporates the relationship between the features in a multi-dimensional feature vector to describe the correlated real-valued random variables successfully. To verify the proposed algorithm against the conventional algorithm, we compare the results in terms of mean error (ME) with standard deviation and Pearson correlation using 110 subjects contributed to the database (DB) which includes the systolic BP (SBP), diastolic BP (DBP), photoplethysmography (PPG) signal, and electrocardiography (ECG) signal. For each subject, 3 times (or 6 times) measurements are accomplished in which the PPG and ECG signals are recorded for 20 s. And, to compare with the performance of the BP estimation (BPE) using the data augmentation algorithms, we train the BPE model using the two-stage system, called the stacked NN. Since the proposed algorithm can express properly the correlation between the features than the conventional algorithm, the errors turn out lower compared to the conventional algorithm, which shows the superiority of our approach.  
data augmentation; multivariate Gaussian distribution; deep learning; blood pressure Article
# Cellulose Nanofibril (CNF) Films and Xylan from Hot Water Extracted Birch Kraft Pulps
## 4 Conclusions  
Hot water extractions of kraft pulp can be applied to remove and recover the hemicellulose prior to fibrillation of the pulp for the manufacture of CNF films. The extraction of hemicelluloses seemingly decreases the efficiency of fibrillation, as indicated by the higher number of fibre fragments in the CNF dispersions obtained by TEMPO oxidation and subsequent fluidization of the pulps. In any case, the CNF from both kraft and water-extracted pulps show an interconnected network of fibrils in the nanometre range and form smooth and transparent films after addition of sorbitol as plasticizer. The CNF films from water-extracted bleached pulps have similar stiffness but lower tensile strength than the films made from bleached kraft pulp, presumably due to the lower hemicellulose (i.e., xylan) content. Despite the lower strength, the mechanical properties of the films should be sufficient for many food packaging applications. Moreover, the extraction of hemicelluloses from bleached pulp apparently results in CNF films with lower hydrophilicity and enhanced barrier properties to oxygen and water vapour, although films having the same thickness should be prepared in order to confirm this effect. On the other hand, the presence of lignin in the pulp (and in the CNF) leads to films with poorer mechanical and barrier properties.  
Based on the results of this study, hot water extraction of bleached birch kraft pulp would be a suitable process to produce CNF films with improved barrier properties for food packaging, while obtaining a low molar mass xylan side-stream for high-value applications (e.g., prebiotics). The use of unbleached pulps would be preferable to obtain a higher molar mass xylan but would not be desirable for the manufacture of CNF films, as all properties except ductility appear to deteriorate.  
[PERSON] and [PERSON] were responsible for the conceptualization, investigation and data validation of this study. [PERSON] wrote the original draft and [PERSON] participated in the review and editing process.  
This research was funded by the Academy of Finland through the PURCELL project (decision number 312470).  
[PERSON], [PERSON], [PERSON], and [PERSON] from VTT Technical Research Centre of Finland Ltd. are thanked for their skiful support with SEC analyses of water-extracted pulps and aqueous hydrolyses, preparation of CNF and films, AFM imaging of CNF dispersions, and determination of mechanical and barrier properties of the films.  
The authors declare no conflict 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.
# Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
## 5 Results and Analysis
### Performance of Short-Baseline RTK Positioning
Figure 16 displays the distribution of carrier phase observation residuals of ambiguity-fixed solutions derived from GPS/IRNSS integrated short-baseline RTK for GPS and IRNSS satellites. The residual analysis here is based on the 7-day datasets at the stations YARR and YAR3. The GPS phase observation residuals are mainly concentrated within 1.5 cm, while those of IRNSS satellites are mainly within 2.0 cm. The average value and the RMS value of the GPS phase observation residuals are \(-\)3 and 5 mm, respectively, while the two statistical values of the IRNSS phase observation residuals are 5 and 12 mm, respectively. GPS satellites exhibit better performance than IRNSS satellites in terms of the two residual statistics. The phase observation residuals of both GPS and IRNSS satellites are approximately normally distributed, which demonstrates that the employed short-baseline RTK mathematical model is also rigorous, and the various biases and errors have also been properly handled. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**System Combination** & **Service Rate** & **ASR** & **Average Error** & **STD Statistic** \\  & & & **(East/North/Up)** & **(East/North/Up)** \\ \hline IRNSS-only & 71.5\% & 64.0\% & 5.4/–21.1/–0.2 mm & 7.8/19.2/29.0 mm \\ GPS-only & 100.0\% & 99.5\% & –0.3/–0.1/–3.4 mm & 3.0/2.8/9.2 mm \\ GPS/IRNSS & 100.0\% & 99.9\% & 0.1/–2.2/3.0 mm & 2.7/3.7/10.2 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Service rate, ASR, mean positioning bias, and STD value of the positioning error for the short-baseline RTK with different system combinations based on the datasets on 7–13 July 2019.
## 3 Result
### Validation of LSM and Comparison with Field Data  
AHP prepares an LSM of the study area. The AUC curve is drawn using a validating dataset. The dataset is divided into training (70%) and testing (30%) datasets. The testing dataset is used for the validation procedure. The AUC value obtained for this study region is 96.8%. An acceptable model has an AUC value from 0.70 to 0.8, a good hazard model has an AUC from 0.8 to 0.9, and an outstanding model has an AUC greater than 0.9. Our findings show that our model had an AUC of 96.8% (Figure 6), suggesting an extend  
Figure 5: Final landslide susceptibility map of the part of the Darjeeling district.  
To evaluate the accuracy of a predictive model, it is important to compare the model's predictions to real-world data. In the case of a landslide susceptibility map developed using the analytic hierarchy process (AHP) model, one method of validation is to use a success rate curve. The success rate curve is constructed by plotting the cumulative percentage of observed landslide occurrence against the cumulative percentage of the study area in decreasing the landslide susceptibility index (LSI) values [21; 73]. The area under the success rate curve can be used to assess the accuracy of the prediction [53]. To validate the AHP model's landslide susceptibility map, 30% of the data were randomly selected and used for model validation. The results of the success rate curve analysis for the AHP model showed an area under the curve of 0.813, corresponding to a prediction accuracy of 81.30% (Figure 7). The results show good accuracy of the model.  
Figure 6: Overall model’s accuracy is represented in AUC-ROC curve.  
Figure 7: Accuracy of the model represented in a success rate curve.
## 4 Results
### Usability Evaluation  
Usability was evaluated using the SUS questionnaire. The results of the SUS score are presented in Table 3. ChatGPT obtained the highest SUS score (SUS = 79.03). Taguette and Gemini achieved similar results with SUS values of 74.95 and 75.08, respectively. Based on the scale's benchmark, all usability scores are acceptable and can be categorized as good. Based on some interpretations of the lower limits, ChatGPT could also be considered a promotor. Detailed SUS interpretation for all tools is visualized in Figure 5. The reliability of the results obtained with the SUS questionnaire was measured with Cronbach's alpha and reached \(\alpha=0.814\), indicating very good levels of reliability. Before analysis, all negatively stated items were reversed to avoid negative alpha values. Detailed analysis of reliability for SUS showed very good reliability for ChatGPT and Gemini and an acceptable level of reliability for Taguette. Detailed results are presented in Table 3. Overall, the usability of all three evaluated tools can be considered good, although some positive variance was observed in the ChatGPT score.  
The difference in SUS scores between the tools was tested with the Kruskal-Wallis H Test, which showed no significant difference in the observed tools with \(\chi^{2}(2)=4.918\), \(p=0.086\), with a rather small effect size (\(\eta^{2}=0.0119\)). An additional pairwise comparison between the SUS scores of the three tools with the Dunn's Test showed a significant difference between the SUS scores of Taguette and ChatGPT (\(p=0.037\)). No other differences were statistically significant. Results are presented in Table 4. This further indicates that  
Figure 5: SUS score interpretation of the tools, interpretation based on [82].  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Tool** & **SUS Score** & **Cron. Alpha** \\ \hline Taguette & 74.95 & 0.766 \\ ChatGPT & 79.03 & 0.848 \\ Gemini & 75.08 & 0.852 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detailed SUS scores of observed tools.  
there was no statistical difference between the usability of the AI tools used for the observed two UX evaluation tasks.