esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Antiviral and Cytotoxic Activities of Compounds 1-8  
The compounds were dissolved in DMSO to 20 mg/mL for storage. The Vero cells with 80% confluence in 96-well plates were added with different concentrations of compounds (0.31, 0.625, 1.25, 2.5, 5, 10, and 20 mg/mL) in a DMEM medium (2% FBS) with or without PRV virus at 100 TCID50 (50% cell culture infectious dose). After 48 h post infection, the supernatant was discarded and the cells were washed twice. A MTT assay was used for cell viability calculation. The effective antiviral concentration (with PRV virus) and cytotoxicity concentration (without PRV virus) were expressed as EC50 and CC50, respectively. EC50 and CC50 were calculated by using SPSS 16.0 software, and each dilution had four replicates. The selectivity index (SI) was used as the result of EC50/CC50. Ribavirin and acyclovir were used as the positive controls.
# A New Hybrid Hierarchical Roadside Unit Deployment Scheme Combined with Parking Cars
## 2 Related Work
Furthermore, recently, they further introduced a new approach for parked car self-organization, which advances existing techniques in several ways; the proposed mechanisms optimize networks of parked cars beyond their initial grouping, which leads to a more efficient selection of cars to act as RSUs [18]. Inspired by all the conventional and new concepts mentioned above, we propose a new hybrid hierarchical deployment scheme for roadside units incorporating parked cars as temporary roadside units for VANETs. In our approach, we first solve the problem of how to choose the appropriate cars serving as temporary RSUs. We then optimize the deployment of RSUs by considering the dynamic nature of temporary RSUs, which requires sufficient connectivity robustness.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 7 Conclusions
### Contributions and Implications for the Field of DVC  
This review contributes to the DVC field by offering a comprehensive overview of existing techniques, datasets, and challenges. By synthesizing information from a multitude of sources, this review presents a clear picture of the current DVC research landscape and identifies key trends and avenues for future exploration.  
The implications of this review for the DVC field are multifaceted. Firstly, it emphasizes the importance of ongoing innovation in model architectures and training strategies to tackle the challenges posed by video complexity and natural language ambiguity. Researchers should delve into new methods for more effectively integrating multimodal information and developing robust and scalable models capable of handling large-scale video data.  
Secondly, the review underscores the need for diverse and high-quality datasets to support the development and evaluation of DVC models. As the field advances, there will be a growing demand for datasets that encompass a wider array of video genres, languages, and cultural contexts. Additionally, there is a need for datasets that incorporate more challenging and realistic scenarios, such as videos captured in unconstrained environments or with low-quality cameras.  
Finally, this review draws attention to the broader implications of DVC technology for applications like video retrieval, video summarization, and assistive technologies for the visually impaired. As DVC models become more accurate and efficient, they have the potential to revolutionize these and other domains by facilitating more intuitive and natural interactions with video content.
## 2 Background and Motivations
Thus, it is intuitive to understand that the higher the agreement, whatever it is, the \"sounder\" the majority and the more reliable the dataset. The easiest way to represent agreement is in terms of the percent of agreement (\(P_{o}\)), that is the ratio between the number of times the raters agree and the total number of joint ratings (see Figure 4). However, as is well known [7; 16; 17], this rate overestimates agreement (and hence reliability) in that (intuitively) raters can agree by chance, and not because they really agree on how to label a specific case. We call this latter case genuine agreement. To take chance into consideration, and discount it from agreement estimation, several metrics have been proposed [18] to assess the reliability of the data produced when at least one rater involved disagrees with the others in regard to some rating: here, we can mention the Pearson r, the Spearman Rho,  
Figure 2: The figure depicts the average, minimum, and maximum accuracy of the datasets obtainable for a given number of raters, each with an average accuracy of \(85\%\pm 1\%\), by simple majority voting among the raters for each case. The three estimates of accuracy were obtained by an analytical simulation, computed through random sampling from a simulated population of 100 individuals with the above-mentioned characteristics and by computing, respectively, the average, minimum, and maximum observed value among the extracted samples. the intra-class correlation coefficient, the concordance correlation coefficient, and the most commonly adopted ones, that is [PERSON]'s kappa (for two raters), the Fleiss kappa (for three or more raters) [19], and [PERSON]'s _a_[20]. Although these metrics adopt a model of chance to account for the effect on agreement, they present some limitations, like restrictions on the data type they can be applied to, their ability to work with missing ratings, or their being undefined for single cases. Moreover, they are not free from known paradoxes, which have been discussed in some specialist works [7; 21; 22; 23; 24; 25], among which we will discuss the main ones in Section 3.2 to show how our proposal is less prone to these shortcomings. Traditionally, inter-rater agreement scores are considered good proxy indicators of the reliability of data (e.g., [26]). However, as we argue in the following section, this may clearly be seen as a simplistic view that does not account for the multi-faceted nature of reliability.
## 4 Application Characteristics
### Onboard Image Processing and the YOLO Model for Ship Detection
#### 4.2.2 Ship Detection with YOLOv5 Model  
Except for the dual-line array push-sweep imaging mode of HN-1 01, the (You Only Look Once) YOLOv5 model [45] is employed for ship detection in the downloaded images of HSC satellites. It consists of three steps. First, the ship's geometric features, including length, width, area, aspect ratio, and shape information from the remote sensing images, are recorded to construct the data set. Second, by integrating the basic geographic data and a pattern recognition method, the automatic segmentation of land, islands, and ocean is performed to reduce the negative impact of islands on ship detection. Third, the ship images are loaded into the system and the ship detection is performed. The output provides quantitative information on ship type, coordinates, geolocation (longitude and latitude), confidence, and size.  
The data set used to optimize the YOLOv5 model contains the remote sensing images (HN-1 01/02 and WC-1 01/02) of various ships. The corresponding labels and positions of ships were marked manually to ensure the quality of samples. The proportions of training, validation, and test sets are 80%, 10%, and 10%, respectively. During the training procedure, the pre-train weights were first employed to initial the YOLOv5 model, which is helpful for promoting the convergence of the model. The mean square error and cross-entropy loss were both adopted to measure the difference between true labels and prediction. The stochastic gradient descent (SGD) [46] optimizer was adopted to update all parameters during back propagation.  
To evaluate the performance of the YOLOv5 model, the precision, recall, and f1-score were utilized. The confidences to determine the ship class and different ships were set to the optimized values of 0.65 and 0.20, respectively. After training, the best values of precision, recall, and f1-score reached 92.3%, 93.6%, and 92.9%, respectively. The visualization of several detection results is shown in Figure 6.  
In the real scenarios, the minimum size of the moving ships that could be detected correctly was 70 m (14 pixels) using 5 m spatial resolution images and 25 m (16.6 pixels) using 1.5 m spatial resolution images. Both examples are shown in Figure 7. However, the sizes of ships were roughly calculated according to the detection frame. Therefore, there may be some deviations in the results, which needs further evaluation.
## 1 Introduction

Table 1: Comparisons of state-of-the-art works. related to the phases during the determination of the optimum values for the parameters of a system from all probable values, including minimum profitability. This stability was compared with the traditional SCA. The simple application and cost-efficient computational overhead of the MSCA and the HMSCACSA algorithms may provide a solution to compound optimization tasks. The main contributions of this study are as follows:  
1. We include the modified LHS (originating from the SCA) operation within the MSCA. 2. We develop an emerging sine-cosine method based on the hybrid CSA, known as HMSCACSA. This gives the fastest convergence curve, improves the capabilities in a global search domain with sensitive parametric analysis, and minimizes CPU time. 3. We provide a performance comparison of the HMSCACSA with recent state-of-the-art hybrid metaheuristic algorithms including PSOGWO [39], PSOABC [40], and PSOGSA [41]. The literature review indicated that the modification and hybridization of the SCA comprises information on the exploration and exploitation of global optimal solutions. This proposed method was tested using six benchmark test functions from the IEEE CEC 2017 standards [27] and IEEE CEC 2014 [28] to validate the performance of feature optimization. The mean and standard deviations were tabulated to evaluate the performance of the suggested method for the benchmark of the optimization of the mathematical function. Few feature optimizations were present with an improved CPU time and faster convergence speed, while the minimum fitness optimization was tested to illustrate the improvement that the proposed method represented. The diverse sizes of parameter sensitivity for the population, dimension, and diversity plot were fine-tuned to show the improved capabilities of the global domain search of the SCA. Three hybrid algorithms--hybrid PSOABC, PSOGWO and PSOGSA--were benchmarked against the proposed MSCA and HMSCACSA. The rest of this paper is organized as follows: The basic methodology used to design the proposed algorithm is outlined in Section 2. In Section 3, the proposed MSCA using LHS is described in detail, including how it is implemented and working principles. The second method, which is the HMSCACSA, is explained in Section 4. Section 5 discusses the performance of the proposed algorithms using six benchmark mathematical functions tested within the IEEE CEC 2014 [28] and a recent benchmark test function within the IEEE CEC 2017 [27]. The analysis, simulation, and statistical results are presented in this section to report the efficiency of both methods. Section 6 presents the conclusions and the planned future work.
## 4 Discussion
First, some papers do not show the threshold tuning (that might be different than the one used in this paper, where we focused on diminishing the number of false negatives). Second, we do not use official splits for the datasets, even though some validation or test splits have manual annotations versus automatic annotations for the training split. Third, we must prefer using PR-AUC to interpret the results, as shown in [70], given the severe imbalance of the dataset and the expected low prevalence of pneumothorax in a real-world setting. Last, even though we use the 'no finding' label to help the model learn features from other pathologies, we do not train the model to identify other pathologies different than pneumothorax, which is something that could further help the model (even more with pathologies with free air such as pneumoperitoneum, subcutaneous emphysema, etc.) Our work also has some limitations. First, other training strategies could have improved the results, such as the use of more negative samples from already available datasets (using batch dynamic sampling instead of choosing a fixed number of negative samples) or freezing more layers from the pretrained base model. Second, we also could have chosen a model architecture (an ensemble of models, custom models, etc., such as [84]) or image size (bigger image size) that yielded better results, but we preferred to use simpler models that could have ImageNet-pretrained weights and that could be retrained in a local server with an accessible GPU. Third, we do not carry out a subgroup analysis on the test datasets to dissect the model performance by pneumothorax size (small or large), location, or radiographic projection (posteroanterior or anteroposterior). That would show the specific needs to obtain better metrics on difficult samples. Although the usage of saliency maps has been shown to be useful for clinical interpretability [85], it is not something that can be trusted to always show the localization of a disease [86]. We believe object detection or segmentation models are much more useful in clinical use, both due to their capacity to detect and localize the pathology correctly, and also due to their interpretability capabilities [53; 87]. Further work should analyze the utility of segmentation in real-time pneumothorax triaging, as well as a prospective real workflow with a clinician-in-the-loop or radiologist-in-the-loop trial to assess the real model impact, false-alarm fatigue, and potential utility in terms of the detected pathology and reduced time-to-diagnosis. Given the current metrics, many models (including ours) are best positioned as a worklist-prioritization tool, flagging high-risk cases for expedited review, rather than a full autonomous diagnosis, which would require almost 100% recall with less false alarms. Appropriate site-specific threshold calibration and continuous performance monitoring is needed in order to use these models in a clinical scenario.
## 4 Inspection Results
### Piece-Part Inspection Software  
The software for piece-part inspection correctly detected 42.2% of defects, while incorrectly identified 27.8% of non-defective blades as defective. This equals an inspection accuracy of 48.8%. The processing time for each image ranged from 186 to 219 ms. An average inspection time of 203 ms was achieved. When processing the same set of images twice, the results remained unchanged. Thus, the repeatability of the inspection software was 100%.  
Several observations were made, when processing the images and analysing the results. Since the software was developed for defects on the leading and trailing edges (e.g., nicks, dents, bends and tears), defects on the airfoil surface such as airfoil dents as well as defects on the tip (e.g., tip rub) were often missed. An example of a missed airfoil dent is shown in Figure 2d.  
Furthermore, it was found that defects with a pronounced or'sharp/rough' deformation of the edge led to higher detection rates. This was evident from the high inspection accuracies of tip curls, tears and nicks (example in Figure 2a), while smoother deformations like bends and dents showed lower performances (example in Figure 2c). Dirty blades with built-up deposits on the edges were the main reasons for false positives (refer to Figure 2b).  
Moreover, a white background led to the best detection rates, followed by a yellow background colour. It should be noted that the images are transformed into greyscale as part of the image processing. The difference in performance could be explained by lighter colours, such as white and yellow, leading to lighter grey tones, thus showing a higher contrast between the blade and background. This was also evident when processing images of shiny, silvery blades that showed a poor contrast to the background, when transformed into greyscale.
## 1 Introduction  
A current challenge is ensuring access to water, both for drinking and for industrial purposes. According to the latest report by the WHO and UNICEF, around 2.1 billion people around the world do not have access to drinking water where they live [1]. Lack of access to safe drinking water is causing, among other things, the worsening of the health of local communities. The decrease in available resources as well as in their quality is mainly caused by constantly developing urbanization and industrialization. Pollutants entering the environment can generally be divided into inorganic and organic pollutants. Conventional inorganic water pollutants include F\({}^{-}\) or NO\({}_{3}\)\({}^{-}\) alongside trace amounts of heavy metals (Pb, Cd, Cr, Ni, Zn, As, Hg, etc.). The effects of these contaminants on human health and the environment are relatively well described, and effective methods and technologies for treating water containing these types of contaminants have been developed. Organic pollutants pose a much greater challenge because they are usually very difficult to biodegrade, among which, endocrine disrupting compounds (EDCs) merit special attention.  
In most cases, these contaminants are not present in the water alone but in mixtures. Such situations are dangerous because there is still very little knowledge about undesirable synergistic effects. Many of these chemicals are not required to be monitored as they have no regulatory status. Increasingly, these compounds are detected in natural and drinking waters and wastewaters at relatively low concentration levels (ng dm\({}^{-3}\) and ug dm\({}^{-3}\), respectively) [2, 3], however, long-term exposure of living organisms to these contaminants is still under investigation. No effective technologies have yet been developed to remove all these contaminants simultaneously, even though some contaminants have been shown to decompose by a certain degree in selected processes. The subject of this work is a review of the application possibilities of both gamma radiation and electron beams for the decomposition of endocrine micropollutants in waters and wastewaters.
# Performance Analysis of Hybrid PDM-SAC-OCDMA-Enabled FSO Transmission Using ZCC Codes
## 7 Conclusions  
A novel FSO transmission that uses SAC-OCDMA with PDM for capacity enhancement is reported. Three users, each with a different ZCC code, are transmitted on \(X_{Polar}\) state, carrying 60 Gbps of information data. In addition, three additional users carrying 60 Gbps of information data assigned with the same three distinct ZCC codes are transmitted on \(Y_{Polar}\) state simultaneously. The proposed system is simulated considering different climate conditions (CW, LF, MF, HF, WSF, and DSF), and its performance is investigated in terms of log(BER), Q-factor, and eye diagrams. The results show that our proposed model can reach 10 km under CW, while for different fog conditions this range is decreased to 1.6 km (LF), 1 km (MF), and 0.76 km (HF). Additionally, FSO ranges of 1.2 km under WSF and 0.26 km under DSF are achieved. These ranges are considered at log(BER) \(\leq 10^{-9}\) and Q-factor \(\geq\) 6. Consequently, we suggest our proposed PDM-SAC-OCDMA-based FSO system for use in next generation high-speed applications and in remote areas. Furthermore, the proposed FSO transmission can be used for high-capacity wireless networks required for beyond 5G and 6G access networks. In this work, we have not considered cross-polarization effects due to FSO channel and practical, imperfect PDM combiner/splitter while performing simulations. In future works, the proposed FSO system will be experimentally validated to take into consideration real-time FSO channel imperfections and losses.  
Conceptualization, M.A.; Methodology, A.A.; Software, M.S.; Validation, S.A.A.E.-M.; Formal analysis, M.S. and S.A.A.E.-M.; Investigation, K.A. and S.A.A.E.-M. All authors have contributed equally to the work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Simulation Results
This is because _Random_ evenly distributes tasks across the MEC servers, preventing overloading on a single server, thereby achieving the lowest delay among the methods analyzed. Figure 5b illustrates the average delay as a function of task arrival rates. As the task arrival rate increases, the average delay shows an overall upward trend due to the higher task load. In particular, the _No partition_ method exhibits the worst delay performance because tasks are concentrated on a single MEC server without partitioning. As a result, it becomes highly inefficient in managing increasing task loads. Moreover, as mentioned in Figure 5a, the _Random_ approach achieves better delay performance compared to the proposed method. This improvement is attributed to the uniform distribution of tasks across the available MEC servers, which prevents server overload and reduces processing bottlenecks. Consequently, the _Random_ approach demonstrates a significant advantage in minimizing delay under high task arrival rates. In conclusion, task partitioning proves to be advantageous in scenarios involving high task loads. The proposed method demonstrates its best performance when the task arrival rate is at a moderate level. While the _Random_ approach achieves superior throughput performance, it exhibits negligible differences in delay compared to the proposed method. Future research will aim to enhance the proposed policy by incorporating certain advantages of the _Random_ approach, thereby addressing its current limitations and further improving performance. Also, the proposed paper shows that the performance varies depending on how the serving MECS \(K\) is determined. It shows that the smaller \(K\) is, the worse the performance is. The reason is that the more MECS are considered, the more the number of tasks to be divided increases, which reduces the delay and increases the throughput. However, if many serving MECSs are simply considered, the computational complexity  
Figure 4: Average throughput about number of server and task arrival rate: (**a**) Number of serving MECSs (K) (5, 8, 10); (**b**) Task arrival rate in a HST (20, 30, 40, 50). will increase significantly. Determining the appropriate \(K\) even in dynamic environment can be another research topic.
## 5 Experiments and Discussions
### Confusion Matrix and Visualization  
Table 3 lists the detailed classification of test set of DNST-GLCM-KSR scheme. The accuracy of positive sample (crack defect) is 95.50%; the accuracy of negative sample is 97.08%. There are four pseudo-defects misclassified as crack defects, and five crack defects are misclassified as pseudo-defects. This was due to the fact that inter-class defects have similar aspects in appearance. The false alarm rate of cracks is 4/110 = 3.64%, and the missing alarm rate is 5/111 = 4.5%. The above metrics meet the needs of engineering application of continuous casting slabs.  
Figure 6 shows the visualization of the DNST-GLCM-KSR test set. The result is a straight line, because the number of the feature dimensionality is 1. The pink circle represents the positive sample, the blue one represents the negative sample, and the upper left corner is a local enlargement image. The result of Figure 6 is consistent with that of Table 3, namely that four pseudo-defects are misclassified as crack and five crack defects are misclassified as pseudo-defects. From the graph, we can see that the DNST-GLCM-KSR features truly reflect the similarity between defect images. The intra-class scatter is small, and the inter-class scatter is large. The above analysis shows the method proposed can effectively recognize crack defects of continuous casting slabs in complex background images and interference factors.