esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment
### Algorithm
#### 3.1.3 Performance Metrics  
In the academic realm, time complexity and space complexity are widely recognized metrics for assessing the complexity of a network model. Typically, time complexity is quantified using the number of floating-point operations (FLOPs), whereas space complexity is often measured in terms of the model's parameter count. In our experiment, we have adopted these two indices--FLOPs and the number of parameters--to evaluate the complexity of our model.  
The mean average precision (mAP) is calculated as the mean of the average precision (AP) for each category. This metric effectively gauges the precision performance of a  
Figure 8: Chart of categories and number of instances.  
Figure 7: Example visualization of selected images from the HSRD dataset.  
network model in multi-class tasks. In our experiment, we utilize the mAP value at a common confidence threshold of 0.5 as the principal metric for evaluating the model's accuracy. The calculation formula for the _mAP_ value is as follows:  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP(i) \tag{6}\]  
In Formula (5), \(N\) is the total number of categories, and _AP_(i) is the average accuracy of the i category.  
The frames per second (FPS) value, indicative of the number of frames processed per second, serves as a crucial metric for assessing the efficiency and performance of an algorithm. A higher FPS value implies superior real-time performance of the model, signifying its capability to process data more swiftly. The formula for calculating FPS is as follows:  
\[FPS=\frac{1}{T_{\text{pre}}+T_{\text{in}}+T_{\text{nms}}} \tag{7}\]  
In Formula (6), \(T_{pre}\) denotes the average preprocessing time per frame, \(T_{in}\) represents the average inference time per frame, and \(T_{nms}\) indicates the average post-processing time per frame. The units for \(T_{pre}\), \(T_{in}\), and \(T_{nms}\) are all in seconds.  
When evaluating object detection models, box_loss reflects the accuracy of bounding box predictions, obj_loss measures the precision of predicting the presence of objects, and cls_loss indicates the correctness of classification. The levels of these three loss metrics are indicative of the model's performance in terms of localization, object detection, and classification. As these aspects are critical for model optimization, we have incorporated these loss indicators into our evaluation metrics.
# Modelling and Analysis of Hybrid Transformation for Lossless Big Medical Image Compression
## 2 Literature Survey and Critiques
A wavelet-based approach was developed to compress and encrypt fused images by selecting salient and less salient information. Fusion is performed by error measurement with compression, and encryption methods use pseudo-random number sequences and Huffman coding. The results showed that the proposed method was better than all others [19]. DWT was used for medical big data image compression. The results showed that the proposed algorithm has better security and performance than previous works [20]. This research used DCT, DWT, and watermark methods to store patient data. The results showed that the proposed system performs better un-detectability [21]. The modulation function was applied, and many advantages were achieved regarding good resolution, overcoming LSB compensation, and creating large storage space to achieve a sizeable hidden volume [22]. The Embedded Zero tree Wavelet (EZW) algorithm provides an efficient technique for low bit rates and high compression and advanced EZW techniquesfor lossless and lossless grayscale and color image compression [23]. The simple and optimized LSB replacement method and genetic algorithm were developed for image embedding. The results showed that the embedded image was not significantly affected, and the hiding strategy was improved [24]. The new type of steganography using the LSB replacement and pixel value difference (PVD) methods was developed to improve image quality and maximize the hidden space [25; 26; 27; 28]. The entropy, run length, and dictionary-based compression techniques were developed to achieve lossless compression [29; 30; 31; 32; 33]. The DNN and principal component analysis strategies were applied to predict data compression using entropy values. The images were divided into blocks, and then applied compression [34].
# Effect of High-Speed Train-Induced Wind on Trackside UAV Thrust Near Railway Bridge
## 4 Conclusions  
The objective of this research was to experimentally investigate the characteristics of track-side wind induced by high-speed trains and its effects on the thrust of a typical quad-copter propeller near the railway bridge. In this study, 3-axis ultrasonic anemometers and a UAV thrust measurement system were installed on top of a PSC box girder bridge. Moreover, the changes in train-induced wind and UAV thrust were monitored along the track during the passage of two high-speed trains. The wind velocity sharply increased when the leading power cars of trains passed, gradually increased while the passenger cars passed, and more than doubled when the trailing power cars passed. The maximum wind velocity of the KTX was approximately 9.3% higher than that of the SRT, owing to its larger total length. Even after the passage of the trains, the train-induced wind slowly decreased owing to the influence of the wake. The measured train-induced wind characteristics can be used to establish a UAV operation plan when high-speed trains pass. Additionally, since the wind velocity in the train travel direction was confirmed to be dominant, it is appropriate to avoid operating the UAV in the train's width direction when a train-induced wind occurs. The maximum wind velocity measured at 2.8 m from the train surface was approximately 62% lower than that measured at 0.8 m. The maximum wind velocity according to the distance from the train surface, can be used to set the accessible distance of the UAV.  
It was found that the UAV thrust increased by 20% owing to the train-induced wind when the leading power car of a high-speed train passed and by up to 60% when the trailing power car passed. The thrust was maintained in the \(\pm\)8.8% range in a relatively stable manner during the passage of the passenger cars after that of the leading power car. From the research results, it was confirmed that the thrust of the UAV, which was kept constant, suddenly increased when the leading and trailing power cars of a high-speed train passed over the bridge. A sudden thrust increase during the passage of a high-speed train may cause errors in UAV control systems. The experimental results presented here can be used to conduct further research on a robust control and a variable pitch-propeller that can control the thrust of UAV propellers.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments. [PERSON], [PERSON], and [PERSON] designed the UAV thrust measurement system, and [PERSON] and [PERSON] assisted with the experiments. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (20 DPIW-C153340-02) from Unmanned Vehicle and SW platform research program related to Public Procurement for Innovation funded by the Ministry of Land, Infrastructure, and Transport (MOLIT) of Korea government and Korea Agency for Infrastructure Technology Advancement (KAIA).  
Figure 8: UAV thrust measured at the trackside on top of the Wolsan bridge during the passage of a KTX train.
## 3 5G HetNet Services, Architecture, and Related Technologies
### 5G Technologies
#### 3.3.1 Heterogeneous Network  
Heterogeneous networks (HetNets) are a promising technology for 5G and Beyond 5G (B5G) that offers high capacity, high data rates, low latency, and wider coverage, especially to UEs at the cell edges. HetNet utilizes a mixture of MBSs underlaid with small base stations (SBSs) or small cells (SCs) and different RATs to provide cellular service and fulfill the demand of NG-WCS. The MBSs and SBSs operate dynamically over different spatial and time scales. To ensure full coverage and achieve high data rates in densely populated environments with tall buildings and obstacles, the deployment of SCs plays a critical role. The basic idea behind SC deployment is to bring the network closer to users. The SCs are generally categorized as femtocells, picocells, and microcells, where femtocells are the  
Figure 3: The 5G SA and NSA architecture.  
smallest and can handle a few users (5-10 users), picocells are in the middle, covering 250 m and can handle 100 users, while microcells are the largest, covering 1-2 km and can be used in large events [52].  
Due to their low power consumption, low cost, easy installation, and reliability features, SCs have been incorporated into the 5G network. The SCs can be installed both inside and outside of buildings, providing excellent service to subscribers. Low cost refers to the cost of deploying a single 5G cell rather than the deployment cost of the entire 5G system. The deployment of SCs also reduces the burden on macro cells and improves the UEs' QoS by utilizing a cell load-balancing strategy [53]. The installation of UD-HetNet over macro-cells is beneficial for both operators and users and effectively maximizes the capacity of the 5G system [54]. The main effect of UD-SC deployment is the ICI between the macro cell (MC) and the SC, and this is further boosted when a UE within the coverage of the SC receives signals from the MC. As a result, the SINR is reduced in UD-SC, which affects the network performance and user experience [55; 56]. In addition, the performance of the network is based on the number of successful HOs during user mobility. Users may be high-speed (such as those in fast vehicles) or very low-speed (such as pedestrians). Therefore, when planning the HO process, both of these scenarios should be taken into consideration to ensure the successful implementation of HOs in HetNet.
## 3 Results and Discussion
### Analysis of Elemental Compositions in Urban PMs and Incineration Asl
#### 3.3.1 Analysis of Elements of Incineration Ash by the \(k_{0}\)-INAA Method  
The KRISS ash CRM candidate with a similar matrix to airborne PM was analyzed using the \(k_{0}\)-INAA method. As this was a study to verify the applicability of the \(k_{0}\)-INAA method in the analysis of environmental samples, such as urban PMs, ash CRM candidate was also analyzed as part of the CRM development that is comparison target of urban PM CRM candidate. The analytical method was basically the same as that for the NIST SRM 1648a sample analysis. The ash samples were measured with slightly different cooling and counting times because the concentration levels of elements were slightly different. The concentration levels of the elements were compared with those of NIST SRM 1648a, and the results are shown in Table 2. The gamma-ray spectra of the KRISS ash CRM candidate and blank cellulose paper are shown in the Supplementary Materials (Figures S1 and S2). According to a source distribution study of PMs in Turkey, biomass and wood-burning contributions are dominated by K and Rb, as well as high concentrations of Ca, Mg, and Na [4]. The concentrations of these elements are high because the raw material was collected from an incineration facility. However, the concentrations of As (\(-84\%\), percentage difference relative to NIST SRM 1648a), Br (\(-100\%\)), Cd (\(-91\%\)), Cr (\(-40\%\)), and Hg (less than \(0.5\) mg/kg), generally known as toxic elements, are significantly lower than those in NIST SRM 1648a.
# Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Emotion**} \\ \hline
**Classifier/Measure** & **Angry** & **Calm** & **Disgust** & **Fear** & **Happy** & **Neutral** & **Sad** & **Surprise** \\ \hline \multicolumn{8}{c}{**RDF**} \\ \hline Precision & 100.0 & 100.0 & 100.0 & 91.0 & 95.0 & 64.0 & 90.0 & 100.0 \\ \hline Recall & 100.0 & 100.0 & 100.0 & 89.0 & 78.0 & 100.0 & 90.0 & 95.0 \\ \hline F1-score & 100.0 & 100.0 & 100.0 & 93.0 & 86.0 & 78.0 & 90.0 & 98.0 \\ \hline Accuracy & 96.0 & 93.0 & 92.0 & 90.0 & 94.0 & 91.0 & 96.0 & 97.6 \\ \hline \multicolumn{8}{c}{**GBM**} \\ \hline Precision & 100.0 & 100.0 & 100.0 & 87.0 & 79.0 & 55.0 & 80.0 & 86.0 \\ \hline Recall & 100.0 & 100.0 & 100.0 & 87.0 & 75.0 & 86.0 & 67.0 & 90.0 \\ \hline F1-score & 100.0 & 100.0 & 100.0 & 87.0 & 77.0 & 67.0 & 73.0 & 88.0 \\ \hline Accuracy & 88.0 & 84.0 & 83.0 & 82.0 & 85.0 & 85.0 & 88.0 & 98.6 \\ \hline \multicolumn{8}{c}{**ABC**} \\ \hline Precision & 94.0 & 93.0 & 91.0 & 78.0 & 71.0 & 79.0 & 81.0 & 89.0 \\ \hline Recall & 96.0 & 100.0 & 100.0 & 86.0 & 76.0 & 73.0 & 83.0 & 86.0 \\ \hline F1-score & 92.0 & 95.0 & 100.0 & 79.0 & 74.0 & 76.0 & 81.0 & 86.0 \\ \hline Accuracy & 86.0 & 82.0 & 83.0 & 81.0 & 87.0 & 82.0 & 88.0 & 92.6 \\ \hline \multicolumn{8}{c}{**BSVM**} \\ \hline Precision & 81.0 & 94.0 & 91.0 & 77.0 & 74.0 & 79.0 & 78.0 & 94.0 \\ \hline Recall & 92.0 & 89.0 & 90.0 & 76.0 & 74.0 & 77.0 & 74.0 & 90.0 \\ \hline F1-score & 94.0 & 91.0 & 91.0 & 77.0 & 74.0 & 78.0 & 76.0 & 92.0 \\ \hline Accuracy & 84.0 & 81.0 & 80.0 & 78.0 & 85.0 & 85.0 & 87.0 & 96.0 \\ \hline \multicolumn{8}{c}{**BMLP**} \\ \hline Precision & 76.0 & 89.0 & 86.0 & 72.0 & 70.0 & 76.0 & 74.0 & 83.0 \\ \hline Recall & 75.0 & 86.0 & 86.0 & 74.0 & 67.0 & 75.0 & 72.0 & 85.0 \\ \hline F1-score & 77.0 & 91.0 & 87.0 & 74.0 & 73.0 & 76.0 & 74.0 & 90.0 \\ \hline Accuracy & 85.0 & 81.0 & 78.0 & 73.0 & 78.0 & 77.0 & 78.0 & 84.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage precision, recall and F1-score and accuracy on RAVDESS MFCC2. Table 8 shows the results of percentage precision, recall, F1-score and accuracy analysis of SAVEE MFCC1 features. In the results, the maximum percentage precision F1-score of the neutral emotion had achieved 94.0% using the RDF ensemble algorithm. This F1-score was quite high when compared to the F1-score obtained from RAVDESS database using the same features. The percentage increase in overall precision of neutral emotion was observed to be 133.33%, 77.36%, 18.57%, 12.66% and 10.39% using GBM, RDF, BMLP, ABC and BSVM respectively. Moreover, RDF and GBM algorithms achieved perfect prediction (100.0%) for recognizing the surprise emotion. The results show that MFCC features still achieved low recognition rates for the fear emotion as reported by other authors [25]. However, RDF (94.0%) and GBM (91.0%) show higher precision rates for the neutral emotion, which is higher than what was reported in [11].
###### Abstract  
The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime Autonomous Surface Ships requires the development of appropriate technologies, including intelligent navigation systems, which involves the identification of the current maritime traffic conditions and the prediction of future maritime traffic conditions. This study aims to develop an algorithm that predicts future maritime traffic conditions using historical data, with the goal of enhancing the performance of autonomous ships. Using several datasets, we trained and validated an artificial intelligence model using long short-term memory and evaluated the performance by considering several features such as the maritime traffic volume, maritime traffic congestion fluctuation range, fluctuation rate, etc. The algorithm was able to identify features for predicting maritime traffic conditions. The obtained results indicated that the highest performance of the model with a valid loss of 0.0835 was observed under the scenario with all trends and predictions. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The results of this study can be used to improve the performance of situation recognition systems in autonomous ships and can be applied to maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean.  
maritime autonomous surface ship; intelligent navigation system; prediction of maritime traffic condition; situation recognition system; long short-term memory
# Applications of Wireless Sensor Networks: An Up-to-Date Survey
## 2 Main WSN Applications
### Environmental Applications
#### 2.3.3 Emergency Alerting  
Proactive monitoring of the causes of natural disasters, can help to avoid these disasters or/and lower their cost. WSNs can be utilized for monitoring common disastrous causes in real time to provide proactive alerts in order to lower damage or even prevent disaster. Typical examples described in the rest of this subsection, are related to the monitoring of seismic activity, volcanic activity, forest fires, and tsunamis.  
Earthquakes can cause enormous damage to an occupied region where they take place. WSNs can be utilized to monitor seismic activity in real time in order to take precautionary measures and enable the authorities to act in advance. A real time seismic activity monitoring system is presented.  
In [22], the authors designed a warning system for earthquakes in order to increase the time before an earthquake so as to take precaution measures. The authors deployed a WSN in the island of Mauritius, which has high seismic activity. The system monitors seismic activity by utilizing primary waves (P-waves) and estimates local velocity and the hypocenter's location according to time delays in the arrival of the P-waves at the sensors.  
Volcanic Activity Monitoring  
Volcanoes can cause enormous damage to nearby towns or cities when they are activated. Before a volcano erupts, there are many signs that a WSN system can measure, proactively, in order to inform nearby citizens about the eruption. When such a system is applied, citizens can protect their families and belongings by transporting them outside of the area of the eruption, preventing further damage. Below is an example of such a system.  
In [23], a WSN based system to monitor volcanic activity components is proposed. The system is low cost, flexible, and easy to deploy and to maintain for remote locations. The users of the system can choose GPS data synchronization when the sensor nodes have signal reception, or a specific algorithm when they have not, to collect accurate timestamps of each sample. Pieces of the equipment used, are shown in Figure 11.  
Forest Fire Prevention  
Forest fires can destroy both animals and vegetation, thus resulting in huge ecological disasters. In order to prevent such disastrous events WSNs can be deployed within forests and monitor in real time related parameters in order to assist in forest fire prevention. One example of this type of WSN application is presented below.  
Due to the rapid climate change taking place, the necessity for proper forest fire prevention means is extremely pressing. Such an example is described in [24], which combines traditional methods (patrols, watchtowers, satellite imaging) to a WSN array monitoring system using ZigBee protocol [25]. Related parameters as temperature, humidity, etc. are monitored in real time and sent to a monitoring center in order to be analyzed. Then the system can make quick estimations of fire danger and inform the authorities.
###### Abstract  
The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkoder area, which occurred in the two last years (2017-2018). For instance, in March 2018 the Shkoder district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkoder lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkoder city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.  
Western Balkans; radar applications; inundation mapping; Sentinel-1; Sentinel-2; spatial planning +
Footnote †: journal: _Astronomical Journal_
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 2 Literature Review
### Key Concepts of Blockchain Technology  
The data structure of a blockchain consists of two main components: the block header and the block. The block header contains parameters, such as the index, previous block hash, timestamp, nonce, and Merkle root. These parameters play crucial roles in ensuring the integrity, security, and chronological order of the blockchain [83]. The index, also known as the block height, indicates the position of the block within the blockchain. The first block, called the genesis block, has an index of zero, and subsequent blocks increment the index [84]. The previous block hash establishes a link between blocks, thus ensuring the continuity and integrity of the blockchain, because altering a block would require recalculating the hash value for all subsequent blocks [85]. The timestamp indicates the time at which the block is created. It helps to order the blocks chronologically, which facilitates the sequence of events in the blockchain [86].  
In the process of blockchain mining, miners use a random value called a nonce to fulfill specific criteria in the consensus algorithm, thereby enhancing network security and resource requirements [87]. The Merkle root ensures transaction integrity through cryptographic hashing and creates a hierarchical structure called a Merkle tree [88]. A block's body contains validated transactions, which can vary based on the application; for construction workers' information management, these transactions might include registration, updates, access permissions, verification, authentication, and termination. These transactions are stored within the block and are extremely difficult to modify due to blockchain's immutability, thus ensuring data integrity and security [89].  
Figure 1 illustrates the data structure of a block, referred to as \"Block w,\" which includes four transactions: Tx 1, Tx 2, Tx 3, and Tx 4. In the blockchain, each block has a distinct hash, and it references the previous block, thus forming an unchangeable chain of data. A hash function is applied to the information within each transaction, resulting in unique cryptographic hash values, such as Hash1, Hash2, Hash3, and Hash4. These hash values serve as digital fingerprints for the transaction data, enabling efficient detection of any unauthorized modifications. Consequently, the blockchain ensures the prevention of unauthorized changes by facilitating efficient verification and validation processes.