esa-sceva/satcom-chunk-collection huggingface dataset:
# BWLM: A Balanced Weight Learning Mechanism for Long-Tailed Image Recognition
## 4 Experiments
### Long-Tailed Image Recognition
We find that as IF increases, the accuracy of existing methods decreases faster than our BWLM. When the IF = [10, 100], the accuracy decreases by 17-25%, while our method controls the accuracy decrease to around 10%. In order to visually observe the descent speed, we have selected several comparison methods in Figure 5b to compare with our method. We can intuitively see that the accuracy of CE+CR [64], Focal+CR [64], and CB+CR [64] decreases significantly with the increase of IF. When IF < 100, they still have an advantage. When IF \(\geq\) 100, our model surpasses other comparison methods, and our BWLM performs best among all IFs compared to other methods that add weight decay. To sum up, our method performs better on long-tailed datasets with large IF, which also proves that our model can solve more extreme long-tailed image recognition problems. Training curves on ImageNet-LT and CIFAR100-LT are shown in Figure 6. It can be seen that BWLM has a significant advantage in big data and has been proven through 150 generations of training. Figure 7 shows the per-class accuracy for CIFAR100-LT (IF = 200). The impact of the long-tailed problem on the naive model is evident, as there is a significant decline in performance as the class IDs go from small to large. In contrast, the BWLM mitigates the steepness of this decline. It is visually apparent that BWLM substantially improves the classification results for Medium and Few classes without compromising the classification performance of Many classes. This improvement is particularly pronounced for Few classes, providing compelling evidence of the significant balancing capability of BWLM. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Imbalance Factor (IF)** & **10** & **50** & **100** & **200** \\ \hline CE [31] & 55.71 & 43.85 & 38.32 & 34.84 \\ CE+CB [31] & 57.99 & 45.32 & 39.60 & 36.23 \\ KD [65] & **59.22** & 45.49 & 40.32 & - \\ Focal [47] & 55.78 & 44.32 & 38.41 & - \\ LDAM [1] & 56.91 & - & 39.60 & - \\ Mixup [66] & 58.02 & 44.99 & 39.54 & - \\ Focal+CB [31] & 57.99 & 45.17 & 39.60 & 35.62 \\ CE+DRW [1] & 58.10 & 46.50 & 41.00 & 36.90 \\ CE+CR [64] & 57.40 & 45.10 & 40.50 & 36.90 \\ Focal+CR [64] & 58.30 & 45.20 & 40.20 & 37.50 \\ CB+CR [64] & 59.20 & **46.80** & 40.70 & 38.50 \\ \hline \hline \multicolumn{5}{c}{Our methods} \\ \hline Weights Transfer & & & \\ +CE & 38.8 & 40.5 & 37.8 & 34.8 \\ +CEW & 50.0 & 43.8 & 41.1 & 38.3 \\ +CBW & 51.3 & 44.7 & 41.5 & 39.5 \\ +BSW & 51.5 & 44.9 & 40.9 & 39.1 \\ +BWLM & 51.6 & 45.1 & **41.5** & **39.6** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The accuracy (%) on CIFAR100-LT with different IFs. We can combine Figure 5b to analyze the rate of accuracy decline.The best results are shown in **underline bold**.
## 4 Validation and Verification Process
### Impacts on Aircraft Fleet Maintenance Cost and Product Availability  
Before and after optimization, the functional relationship between availability and the AFMC presents a good convergence and convergence accuracy and is also within the controllable range. In addition, the most important goal of product selection through a mathematical optimization model was to enhance product availability and reduce maintenance costs. After 300 iterations, according to the non-dominated sorting and crowding degree, the algorithm outputs the optimal decision plan among the 300 population individuals. For 300 kinds of decision-making schemes, the advantages and disadvantages are compared again according to the fitness function; the optimized decision scheme is shown in Figure 8. The results in Table 2 show that the AFMC was reduced by 20.71%, and availability increased by 2.576%. The validity of the model and method has been verified.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**State** & **Availability** & **AFMC/USD** \\ \hline Before optimization & 0.856017805 & 510.5 \\ After optimization & 0.87807647 & 404.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of optimization objectives.  
Figure 8: Product life information after optimization. MTBF—mean time between failures.  
Figure 7: Distribution and convergence accuracy between availability and AFMC. (AFMC: Aircraft fleet maintenance cost). (**a**) Distribution law; (**b**) Convergence accuracy.
## 4 Results and Discussion
### Operating Energy Costs  
Data on operating costs includes energy requirements for maintenance of the system, e.g., washing solar systems, replacing worn parts, including the energy required to build spare parts, energy requirements for operating the systems, such as control systems, or, if necessary, the energy associated with the fuel cycle (including the energy content of any fuel consumed). Such inputs were mainly associated with CSP, where natural gas must sometimes be burned to maintain steam operating temperatures or to restart the steam turbine after an overnight shut-down. Data taken from [39; 41; 42; 43; 45; 46].  
There was insufficient data to distinguish fixed and variable operating costs, as is done in economic analyses. As such, all operating costs have been aggregated and are displayed in both units of kWh\({}_{e}\)/W\({}_{p}\) and kWh\({}_{e}\)/kWh\({}_{e}\).  
Figure 2 shows the distribution of estimates for operating costs. Data could be found only for CSP technologies. As can be seen, tower CSP has higher operating costs than trough. This may be due to natural gas consumption necessary to maintain higher temperatures in the event of cloud cover [69].  
Figure 2: Operating cost [kWh\({}_{e}\)/kWh\({}_{e}\)] of CSP technologies.
# Merging-Squeeze-Excitation Feature Fusion for Human Activity Recognition Using Wearable Sensors
## 6 Experimental Results and Discussion
### Proposed Merging-Squeeze-Excitation Feature Fusion
#### 6.3.1 MSE Feature Fusion  
Table 9 presents the accuracy scores of the MSE feature fusion proposed in Section 4 according to the merging methods, DL models, and datasets. We have the following results:  
* The highest accuracy score in each dataset is indicated by the asterisk (*). The MSE feature fusion using the minimum merging and AlexNet achieves the highest accuracy score of 99.17% for the PAMAP2 dataset. The MSE feature fusion using the average merging and AlexNet achieves the highest accuracy scores of 98.32% and 98.04% for the DaLiAc and DSAD datasets, respectively.
* We compare the accuracy scores of the MSE feature fusion to those of the baseline architectures in Section 6.2. According to the highest accuracy scores obtained from these architectures, the results show that the MSE feature fusion outperforms the baseline models.
* Among the considered merging methods, the MSE feature fusion using the addition merging offers the worst accuracy scores. The MSE feature fusion architectures using the other merging methods provide the same level of performance. Their accuracy scores are rather close to each other. We do not have a conclusive result on which merging method is the best.
* Among the considered DL models used as feature extractors, the MSE feature fusion using ALexNet outperforms the MSE feature fusion using the other DL models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**PAMAP2**} & \multicolumn{3}{c}{**DaLiAc**} & \multicolumn{3}{c}{**DSAD**} \\ \cline{2-10}  & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** & **LeNet5** & **AlexNet** & **VGG16** \\ \hline Single-Branch Model & 96.79 & **98.77 *** & 98.75 & 95.37 & **97.60 *** & 95.53 & 91.39 & **97.18 *** & 96.04 \\ Multi-branch Model & 98.73 & 98.51 & 97.85 & 96.09 & 97.10 & 94.49 & 94.54 & 96.59 & 93.31 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy scores (%) of the baseline architectures on classifying the PAMAP2, DaLiAc, and DSAD datasets where LeNet5, AlexNet, and VGG16 are applied as feature extractors. The asterisk (*) indicates the highest accuracy score of each dataset.
## 3 Results  
### Distribution of Study Types  
Of the 60 studies analyzed, the most prominent category is original and empirical research, comprising 37 studies. These include experimental trials, observational assessments, and mixed-methods investigations conducted in real healthcare environments, such as ICUs, NICUs, dental clinics, and psychotherapy settings. They provide insights into how acoustic interventions affect patient stress, recovery, cognitive focus, and staff well-being. Beyond these, systematic and scoping reviews appear in nine studies, providing structured syntheses of current evidence. These include [PERSON] & [PERSON] (2018) on the effects of nature sounds on stress [63], [PERSON] et al. (2023) on how environmental conditions affect mental health [36], [PERSON] et al. (2024) on inpatient exposure to nature [15], [PERSON] et al. (2024) summarizing hospital noise impacts [31], [PERSON] et al. (2021) focusing on ICU soundscapes [38], [PERSON] et al. (2024) investigating existing research gaps in the design of inpatient healthcare environments, highlighting the need for more evidence-based, interdisciplinary approaches to architectural features, such as acoustics, lighting, and spatial layout, that directly impact patient outcomes and staff performance [27] and [PERSON] et al. (2023) reviewing principles of acoustic comfort in smart healthcare environments [35]. Narrative reviews and conceptual papers, represented in 10 studies, offer theoretical perspectives and reflective syntheses. Notable examples include [PERSON] (2024), who maps the emotional role of acoustics in biophilic design [20], [PERSON] et al. (2022) calling for sustainable acoustic protocols in dental offices [2], and [PERSON] (2020) discussing natural sound as positive distraction [55]. Additionally, there are five studies classified as book chapters or full-length books. These include [PERSON] et al. (2024), who explore how architectural features impact emotional and pain responses [28], [PERSON] (2017) on the stress-reducing power of auditory nature [65], [PERSON] (2020) examining soundscapes and views in healing [57]. [PERSON] [64], and [PERSON] & [PERSON] (2021) discuss restorative design in urban health planning [67]. Additionally, six studies are literature or applied reviews. These include [PERSON] & [PERSON] (2016) on noise and care satisfaction [68], [PERSON] et al. (2024) on environmental comfort elements like biophilic acoustics [33], [PERSON] &[PERSON] (2016) proposing a health-focused design framework [66], and [PERSON] et al. (2024) on thematic sound analysis in dental clinical soundscapes [4]. Lastly, a small but impactful set of design frameworks and evaluation studies (two to three studies) explores design methods more directly. For example, [PERSON] and [PERSON] (2016) provide a framework that connects the acoustic environment with occupant health outcomes [66].  
In Figure 2, we present a pie chart illustrating the distribution of study types among the 60 reviewed articles.
## 3 Method  
This paper proposes a reflection removal method for black box videos based on the combination of image completion and illumination compensation. We use the gradient reconstruction and completion of transmitted images as a solution to the problem of reflection removal. Some of the reflected images in the video may shake due to vehicle bumps, and traditional feature matching region extraction methods cannot fully extract the target region. Based on the MSER feature region extraction method, we decompose the video into video frames and combine all of its frames into an image sequence. This article utilizes the phenomenon that the reflective imaging position and overall shape of the windshield do not change due to the driving of the vehicle and are only related to the direction of sunlight exposure. Different reflected images presented under different lighting conditions is compensated for through the addition of a loss function. Finally, the image is reconstructed using a motion gradient method through image completion to achieve the effect of reflection removal. Our approach comprises two key parts: (1) The extraction of the reflection region, which entails illumination adjustment and MSER region matching of the front and back picture frames; (2) The removal of reflections from images. Image differentiation is used together with the IRLS method and a penalty function for motion map decomposition to produce a background layer sequence. Figure 2 depicts our approach in broad strokes.  
Figure 2: An overview of our method. The blue module is used for video framing operations, selecting images with reflective areas, calculating the number of images used in the image sequence (represented by the green module), and then adding lighting compensation for image completion (represented by the orange module). Reflection removal is achieved through the difference between the image and the reflection layer (represented by the pink module). Finally, image collection and video restoration (represented by the purple module) are carried out.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 4 Materials and Methods  
In this section, a new preamble structure is designed first. On this basis, we propose a new timing synchronization algorithm and its improved algorithm. Next, the correlation peaks extracted by the synchronization algorithm are used to estimate the frequency offset.
# A Novel Approach for Semantic Extractive Text Summarization
###### Abstract
It cannot judge how important information is given in the body of sentences. LSA works on distributional semantics, calculates the cosine similarity between keywords and the remaining text, and, based on the highest similarity score, gives the highest similar sentences; however, it cannot filter irrelevant text and one of the major drawbacks of this model is it cannot filter repetitive sentences. Thus, it produces redundancy. The important events in the form of dates remain left in the document. LexRank's algorithm is based on Eigenvector centrality, and sentences are placed at the vertex of a graph while the weights are assigned based on a semantic similarity that calculates the cosine similarity among sentences. During sentence overlapping, the redundant sentences are also retrieved, which have to be removed. TextRank's algorithm is based on Eigenvector centrality, and sentences are placed at the vertex of a graph, and weights are assigned based on lexical similarity. One of the major drawbacks of this model is sentence overlapping, which shows redundancy. In KL divergence, redundancy would only lower the efficiency of the algorithm, as adding redundant sentences would affect the unigram distribution of words and increase the divergence from the source document set. Table 1 shows comparisons based on the shortcomings of the algorithms. Here, the symbol means \"allowed\", and the symbol means \"not allowed\". Column one shows the features of the different text summarizers, including the one proposed in this paper. Columns two to seventh show the comparison among them on the basis of selected features shown in column one. As shown in the table LexRank's algorithms allow redundancy, while the proposed algorithm does not allow it. The same criteria are followed in the remaining features, such as different algorithms allowing some features while some do not allow others. It is clearly shown from the table that one of the major advantages of the proposed model is that it allows all the features and excludes redundancy as compared to the other algorithms. The proposed algorithm's detailed results are shown in the results section. This paper contains five sections. Section 1 describes the introduction of the research work carried out in this paper. Section 2 describes related work that has been done in relation to text summarization techniques. Section 3 describes the implementation of the methodology and the dataset used for the experiment. Section 4 presents the results of the experiments that were conducted. Section 5 presents the conclusion and future work. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Features** & **LexRank** & **Edmonson** & **LSA** & **TextRank** & **Kl-Divergence** & **Proposed** \\ \hline Redundancy & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ Semantics & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ Events/Dates & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ Chronological Order & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ CR/RR Balancing & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of algorithms based on their shortcomings.
# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
## 2 Related Work
### Deep Learning Models for ICD Coding
The use of Bidirectional Encoder Representations from Transformers (BERT) models, which uses a transformer architecture with multi-head attention, and especially BioBERT has improved the overall recall values at the expense of precision compared to CNN and LSTM models when applied in the ICD-10 coding task at CLEF eHealth in 2019 [25], a finding which we have confirmed in our experiments. Therefore, we do not report on experiments with this architecture in this survey. Finally, [PERSON] et al. [26] survey the literature on the benefits, limitations, implementation, and impact of computer-assisted clinical coding on clinical coding professionals. They conclude that human coders could be greatly helped by current technologies and are likely to become clinical coding editors in an effort to raise the quality of the overall clinical coding process. [PERSON] et al. [11] review deep learning models for EHR systems by examining architectures, technical aspects, and clinical applications. Their paper discusses shortcomings of the current techniques and future research directions among which the authors cite ICD coding of free clinical text as one of the future challenges.
# Improving Energy Efficiency in LoRaWAN Networks with Multiple Gateways
## 5 EE-LoRa Algorithm
### Power Control  
The current consumption at transmission \(I_{tx}\) depends on the TP level \(l\). Table 8 gives \(I_{tx}\) when \(l\) varies between 2 and 14 dBm for a SX1272 LoRa transceiver obtained from the Semtech LoRa Calculator [29]. For instance, decreasing the TP level from 14 dBm to 2 dBm will mitigate \(I_{tx}\) from 44 to 24 mA. As a result, in order to minimize the energy consumption, we will reduce the TP for all nodes to the bare minimum required to ensure communication reliability.  
We consider in our work two different sets of TP levels that differ by TP step, listed in Table 9. The TP step of 3 dB used in the first set \(\mathcal{T}_{1}\) is the same applied in the ADR mechanism introduced by LoRaWAN [6]. However, we decrease the TP step in the second set \(\mathcal{T}_{2}\) to 1 dB. For example, having a TP level variation between 2 and 14 dBm for Sa X1272  
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt} p{28.5 pt}} \hline \hline
**Require:** Initial value \(\eta^{0}\), maximum tolerance \(\epsilon\geq 0\), iteration \(n\gets 0\) \\
1: & **do** \\
2: & \(\eta\gets\eta^{*}\) \\
3: & Solve (\(\hat{\mathcal{P}}\)) in (19) \\
4: & \\
5: & \(p^{n}\leftarrow\arg\underset{p(s)}{\max}\;(\mathcal{R}-\eta\mathcal{E})\) \\
6: & \(F^{(n)}\leftarrow\underset{p(s)}{\max}\;(\mathcal{R}-\eta\mathcal{E})\) \\
7: & \\
8: & \(\eta^{n+1}\leftarrow\frac{\mathcal{R}(p^{n})}{\mathcal{E}(p^{n})}\) \\
9: & \(n\gets n+1\) \\
10: & **while**\(F^{(n)}>\epsilon\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Current consumption at each TP.  
Figure 7: Example of adjusting the SNR thresholds.  
LoRa transceiver, \(\mathcal{T}_{1}\) is constituted by 5 levels while \(\mathcal{T}_{2}\) is composed of 13 TP levels. These two sets are used to evaluate the impact of increasing the number of TP levels later.  
After finding the \(computed\_SNR\_thr(s)\) at each SF and deducing the SF \(s\) for each node in the network as detailed in Section 5.1, and, having its \(SNR\) and initial TP denoted by \(old\_TP\), we can calculate its new TP denoted by \(new\_TP\) as follows:  
\[new\_TP=computed\_SNR\_thr(s)-SNR+old\_TP \tag{26}\]  
Since TP levels are discrete [23], the obtained \(TP\) value for each node is rounded to the nearest highest level in the used set of TPs.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 4 Experimental Results
### Evaluation of Performance for the Hyperspectral Image Application
#### 4.2.3 Steady Period Evaluation  
In order to perform a comparison between the different steady periods for the different light sources and illumination modes, a boxplot is presented in Figure 15 (data available in Supplementary Materials Table S1). The boxplot is generated using the ten repeated measurements made for every power supply in each illumination mode. In this boxplot, it can be observed that the shortest steady period is provided by CLS-1, and the longest steady period is provided by PLS. Additionally, it can be observed that the mean steady period trend is similar for the different illumination modes (reflectance and transmittance). However, the standard deviation of the steady period seems to be higher in the transmittance illumination mode. The measurement of the steady period supposes a useful tool for the comparison of the electrical performance of the different light sources. However, in the application of this light source for many HSI applications, the steady period is not relevant since the operator will wait a few minutes before the image acquisition, and the worst steady time for these lamps is lower than 2 s. In any case, for situations that require a fast light response, it is important to know the steady times of the different lamps.  
Figure 14: Examples of the light profiles provided for the different light sources and illumination modes. (**a**) PLS reflectance. (**b**) CLS1 reflectance. (**c**) CLS2 reflectance. (**d**) PLS transmittance. (**e**) CLS1 transmittance. (**f**) CLS2 transmittance.
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation
### CBR Performance  
#### 5.1.1 End-to-End Delay  
We first study the end-to-end delay at different moving speeds. Figure 7a shows the results of a low vehicular density environment with 100 vehicles, and Figure 7b shows a high density environment  
Figure 6: Road map of simulations.  
with 300 vehicles. As the moving speed increases, the network becomes more dynamic and thus the end-to-end delay is increased. It can be seen that the proposed CRB has a lower delay than other protocols at all relevant speeds. In a low density environment, road segments are likely to be disconnected. The CBR considers consecutive road segments instead of only the subsequent one. With the CRT Map, vehicles have wider-ranging traffic information to choose the more reliable delivery paths. In a high density environment, the network is more connected. GyTAR and RIVER have almost the same delay but the CBR can still outperform them. Similar results can be found in Figure 8, where we evaluate end-to-end delay with different vehicular densities at an average moving speed of 80 km/h. A high speed is chosen to create a high dynamic environment, which can highlight reliability of different routing schemes. Higher vehicular density can lower the delay since packets have less probabilities of being carried than forwarded. The proposed CBR can outperform in all cases.  
Figure 7: End-to-end delay vs. moving speed: (**a**) 100 vehicles; and (**b**) 300 vehicles.