esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results
Although most of the studies (freq. = 36, pct. = 49.32%) did not specify the particular operating system on which their application was running, from the ones that did, android (freq. = 24, pct. = 32.88%) was the preferred operating system, followed by iOS (freq. = 6, pct. = 8.22%), both android and iOS (freq. = 5, pct. = 6.85%) and Windows Holographic OS (freq. = 2, pct. = 2.74%), as it can also be seen in Figure 6 (RQ11). This fact can be justified when taking into consideration the operating systems' worldwide market share [233] and the fact that the most popular augmented reality Software Development Kits (SDKs) natively support the development of applications for the Android operating system. The Unity platform (freq. = 26, pct. = 35.62%) was the most widely used development tool along with Vutoria engine and SDK (freq. = 15, pct. = 20.55%) (RQ11). It is worth noting that the majority of the studies (freq. = 34, pct. = 46.85%) did not specify which development tools were used for the creation of their application. Regarding the devices used during the experiments (Figure 7), mobile devices had the overwhelming majority as they were used in a total of 61 studies (83.56%) with only a few studies utilizing specialized equipment, such as Microsoft HoloLens (freq. = 2, pct. = 2.74%), HTC Vive Pro HMD (freq. = 1, pct. = 1.37%), Leap Motion Controller (freq. = 1, pct. = 1.37%), and Touchizer [228] (freq. = 1, pct. = 1.37%), while 7 (9.59%) studies did not specify the particular devices that were used (RQ12). As far as the gamification elements used are concerned, the applications mostly used points, scores, leaderboards, game-like features, mini games and puzzles, virtual rewards (e.g., badges, achievements, tokens, etc. ), objectives, quests and tasks, quiz questions, challenges and difficulty levels, instant feedback, timer, and digital storytelling (RQ13). Moreover, studies capitalized on students' competitive spirit and collaborative learning activities. Role-play and digital storytelling were also the main aspects of certain applications while other studies used additional external material in the form of cards, board games, slides, learning sheets, etc. Figure 5: Empirical studies: research methods.
# Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
## 4 Experimental Evaluation
### Experimental Case 1: Address Matching without Any Similarity Metrics  
The input data go through the address parsing process first. The parsing process divides the input address data into city, district, road, and building number. Since we assume that there are no errors or mistakes in the city and district names and building numbers, this methodology simply matches them without a similarity metric for further processes.  
The address matching without any similarity metrics shows 71.78% of accuracy when Input data 3 with 70% of correct matches are used (Table 2). As the percentage of correct matches in the input data increases, the accuracy also rises as expected. Mismatched addresses are addresses matched with incorrect addresses, whereas no addresses have matched with the unmatched addresses. Since matching for aliases with official names in the simple matching is impossible without any similarity metrics, there is no mismatched input address. Instead, there is 28.22% of unmatched input addresses in Input data 3. After that, the address locating process geocodes all the 1524 input addresses, as illustrated in Figure 9. A total of 32.74% of input addresses are geocoded on corresponding parcels using Input data 3. The same process geocoded the rest of the addresses outside the corresponding parcels.  
In order to understand positional accuracy, the distances between all input addresses and corresponding parcels were also calculated and explored, excluding mismatched addresses, which may have considerable distances. On average, the geocoded points are 4.43 m further away from their corresponding parcels using Input data 3. As the percentage of correct matches increases from Input data 1 to Input data 3, the mean distance decreases because the number of addresses geocoded on corresponding parcels, which has 0 m distance, rises.  
Figure 8: Road network data and its schema.
### Research Scope and Methodology  
The scope of this study is to develop prototype smart sensing safety shoes with FSR sensors to effectively distinguish whether a construction site worker is carrying a heavy object that can burden the worker's musculoskeletal system. In addition, a mobile app was developed to primarily store pressure strength data measured from a total of seven channels of fiber pressure sensors attached to the left and right insoles of the smart sensing safety shoes that then transmits them to a web-based DB (Database). Depending on how the worker lifts the object, pressure may not be applied evenly to both feet. Therefore, the sensing data values measured from the seven FSR sensors may not be completely uniform. However, the sum of the pressure transmitted to both feet can be seen as a direct reflection of the weight of the object. Therefore, in this study, rather than using each sensor value, we mainly use the sum of the sensor values to determine the weight of the heavy object. Finally, a machine-learning-based sensing data analysis model created by Python was developed to track how much weight a construction worker lifted while wearing the smart sensing safety shoes.  
In order to verify the effectiveness of the smart sensing safety shoes (shoe size: 275-280 mm) developed in this study, an experiment was conducted on five healthy adult males between the ages of 25 to 36 under the assumption that construction site workers carry heavy objects. The experiment was carried out in such a way that collected pressure change data measured by the FSR sensors when a construction worker was standing without weight (body weight +0 kg) and when the worker was standing with additional weight (increased up to 20 kg in 5 kg increments (+)).  
The collected data was analyzed to determine the correlation between the sensing data and the weight of the object held by the construction worker through the primary analysis of a scatter diagram (scatter plot) and linear regression. However, due to the nature of sensitive sensing data, it was found that the \(R^{2}\) value for explaining the degree of estimating the weight of a heavy object appears relatively low due to the distribution of the wide data value. In order to solve this problem, the second analysis of the experiment was carried out through the development of an analysis model applying ML (machine learning) classification algorithms. The results show that it is possible to obtain higher accuracy in estimating the weight of the package carried by the construction worker.
## 2 Analysis of the Operating Mechanism of a Hybrid Plant Station Primary Frequency Regulation
### Research on EEMD Algorithms Suitable for Power Frequency Division
Equation (23) illustrates that the top \(a\) IMF component with a higher frequency is assumed by the SCESS, while the P2H device assumes the rest. In the EEMD algorithm, the parameters that have an impact on the power frequency division result include the random white noise sequence \(n_{i}(t)\), the pre-set standard deviation \(\sigma\) (in simulation, the parameter is set as Nstd, which is the ratio of additional noise standard deviation to input data standard deviation), the number of white noise groups \(m\), and the number of high-frequency IMF components undertaken by the SCESS during power component recombination \(a\). Where white noise sequence \(n_{i}(t)\) is a random parameter, the influence of other parameters Nstd, \(m\), and \(a\) of EEMD is shown in Figure 4.  
The EEMD algorithm assigns the frequency regulation power instruction (a) to the P2H device \(P_{\text{P2H}}\) and the SCESS \(P_{\text{SCESS}}\), as depicted in Figure 5. Various parameter combinations lead to drastically diverse frequency division processing outcomes from EEMD, which in turn provide distinct frequency regulation effects. Of them, parameter m mostly influences signal amplitude in EEMD signal processing, while parameter a primarily influences signal frequency. To achieve better results, distinct EEMD parameters should be chosen for various frequency regulation requirements.
## 2 Materials and Methods
### Amplification of 16S rRNA Genes  
Bacterial communities composed at taxonomic resolution were analyzed with 16S rRNA gene amplification and sequencing protocol [25]. The V3-V4 regions of bacterial 16S rRNA were amplified by using the 341F/805R primer set (341F: 5\({}^{\prime}\)-CCTACGGGNGGCWG CAG-3\({}^{\prime}\), 805R: 5\({}^{\prime}\)-GACTACHVGGGTATCTAATCC-3\({}^{\prime}\)). The PCR condition was: 94 \({}^{\circ}\)C for 3 min (1 cycle), 94 \({}^{\circ}\)C for 20 s/55 \({}^{\circ}\)C for 30 s/72 \({}^{\circ}\)C for 30 s (6 cycles), 94 \({}^{\circ}\)C for 15 s/68 \({}^{\circ}\)C for 15 s/72 \({}^{\circ}\)C for 20 s (30 cycles), and a last step of 72 \({}^{\circ}\)C for 5 min [26]. The PCR products were sequenced on an Illumina HiSeq2500 platform (Health Genomics Bioinformatics Technology Co., Ltd., Beijing, China).
# A Survey on Parameters Affecting MANET Performance
## 4 Evaluation Metrics and Performance Analysis in MANETs  
Different evaluation metrics are used to define the characteristics of the MANET performance under certain conditions. After researchers set up the simulation environment and define the parameters needed to control the MANET environment, the results of the simulation tool need to be evaluated. To analyze the network performance, some metrics are used as follows:  
* THPT: Throughput is the rate of successfully delivered packets that reached the receiver node per time slot [43]. Throughput is affected by topology changes, noise on communication links, the power of transmission from the source node, and the existence of malicious nodes affecting the throughput ratio.
* AETED: Average end-to-end delay is the average time taken to send a packet to the destination node [44]. This delay is due to many reasons such as route discovery queuing and process latency, delays caused by wireless links, and processing delays at both the sender and the receiver sides.  
Figure 6: The different types of MANETs’ parameters.  
PDR: Packet delivery ratio is the ratio of packets that are received by the destination across the overall transmitted packets from the source node [45]. The packet delivery ratio represents the maximum throughput that can be achieved by the MANET network.
* PLR: Packet loss ratio is the opposite of PDR; PLR measures the total lost packets that did not reach the destination node across the overall transmitted packets [46].
* ROR: Routing overhead ratio is the size of control and header packets needed by the protocol for route discovery and maintenance over the total data packets received by the destination node [47].
* NRL: Normalized routing load is the ratio between the total number of control packets sent by a source node over the total number of data packets received by a destination node [48]. An increase in normalized routing load metric indicates the efficiency of the used routing protocol.
* NL: The network load is the average amount of data packets that are being carried by the entire network over time [49]. Increasing the network load ratio increases the possibility of data collision in the wireless medium.  
Figure 7 is a conclusion of the evaluation metric terms used in MANETs.
# Assessing the Security and Privacy of Baby Monitor Apps
## 3 Materials and Methods
### Reviewing the Confidentiality of Monitoring Video Streams
#### 3.2.2 Methodology  
Even in the case of properly set-up transport encryption of video streams, MitM attacks may compromise the monitoring streams' confidentiality by allowing eavesdropping or recording of the transmitted video footage. Therefore, next to evaluating the implemented encryption mechanisms intended to protect transmitted video streams, we checked each application for security issues in the applied network protocols and software implementations:  
1. Using the results of a traffic capture performed during a baby monitoring session (see Section 3.1), we evaluated the collected data regarding utilized network security measures and potential clear text transmissions.
2. By statically analyzing the app using the Mobile Security Framework (MobSF) [27], we evaluated the overall security state of each app. We obtained an overview of potential security issues and verified these issues by performing a code review using JADX [28].
3. We performed an additional security analysis for each application to identify security issues that were not detected by automatic static analysis. The analysis consisted of a manual review of the source code using JADX [28], where we focused on identifying potential issues that compromise the confidentiality of transmitted video streams.
## 5 Discussion  
In the CTM, the T-complete functions have to be derived by finding the general solutions of the governing equation. The solutions are expressed as a series using the linear combination of the T-complete functions. Since the T-complete functions satisfy the governing equation, we may place the boundary collocation points only on the boundary of the domain. In addition, the source point of the CTM is only one. The CTM requires the evaluation of a coefficient for each term in the series. On the other hand, the MFS adopts the fundamental solutions as its basis function. Since the fundamental solution has only one term, the MFS has to collocate many source points outside the domain to construct its basis function as a series. The MFS also requires the evaluation of a coefficient for each term in the series. For the CTM, the ill-posedness is often found when higher order terms of the basis functions are used. For the MFS, there is only one basis function but it requires many source points. Although the MFS does not require higher order terms of basis functions, the position of the source points in the MFS, however, is very sensitive to the results due to the singular characteristic of the fundamental solution of the differential operator.  
In this article, we propose a novel method using non-singular basis functions instead of the singular solution. The collocation scheme of the proposed method is similar to the MFS. The non-singular basis  
Figure 22: Comparison of results with the analytical solution. (**a**) Absolute error in the first layer, (**b**) absolute error in the second layer, (**c**) relative error in the first layer and (**d**) relative error in the second layer.  
functions are adopted from the cylindrical harmonics. Due to the adoption of the non-singular basis function, the locations of the source points are not sensitive to the results. This proposed method resolves a major difficulty in the MFS for finding appropriate locations for source points. From the results of the numerical examples, the proposed method was found to be superior in accuracy.  
For modeling heterogeneous multi-layer composite materials, the DDM is adopted. The idea of the DDM for modeling composite materials is that the subdomains intersecting at the interface must satisfy flux conservation and the continuity of the temperature for the problem. We successfully integrated the DDM into the hybrid boundary-type meshless method. Two examples of heat conduction problems in heterogeneous multi-layer composite materials were investigated. The results obtained from the examples reveal that the proposed method can be applied to heterogeneous multi-layer composite materials. In addition, the collocation scheme of the proposed hybrid boundary-type meshless method is relatively simple because we only need to place the collocation points on the boundary of the domain. This would be advantageous for engineering problems involving the design of irregular shapes in applied sciences.
## 4 Discussion
### Analysis of Staged Deployment Scheme  
The staged deployment of the LEO hybrid navigation constellation can be divided into three stages, and the three stages correspond to three scenarios in the simulation analysis. The first scenario analyzes the PDOP value of the 24 MEO satellite in the maximum case of the failure mode and compensates for the navigation performance degradation by increasing the LEO constellation. The second scenario analyzes the performance enhancement through the LEO navigation augmentation constellation under the normal operation of 24 MEO. The third scenario analyzes the differences between LEO independent navigation and GNSS.  
Figure 16 presents an analysis of the staged deployment of PDOP in various scenarios for LEO hybrid navigation constellations. In the absence of failures in the 24 MEO constellation, the average PDOP is 2.348. The most significant impact on satellite failure mode was observed with 14-22-35, resulting in an increase in the constellation average PDOP to 5.208. Following the addition of the first stage of LEO to augment the constellation, there was a recovery in the average PDOP to 2.206. Furthermore, with the inclusion of the second stage of LEO navigation augmentation constellation, there was a decrease in average PDOP to 1.456, thereby achieving higher precision navigation services. Additionally, for the  
Figure 15: The space phase of a single failure within each orbital plane: (**a**) failure modes of 14-22-35; (**b**) failure modes of 12-28-33; (**c**) failure modes of 11-24-32; (**d**) failure modes of 15-23-31.  
third stage of LEO independent navigation constellation, an average PDOP of 1.676 was achieved--outperforming that of the BDS-3 constellation, which had an average PDOP of 2.049.
Usually, an encoder consists of CNN, where CNN remains extracting features from the video frames, and a decoder, with the RNN model responsible for the sentence generation. However, with the rapid advancements of various neural network, researchers have now incorporated RNN features in the encoder stage [10; 11; 13; 14; 15; 16; 17]. They also found promising results from their RNN based LSTM frameworks. The LSTM is suggested to exhaust the evaporating angles issue by empowering the network to realize when to overlook past hidden positions and refresh hidden positions by incorporating memory cells. Therefore, we proposed a method called \"attention-based bidirectional LSTM and sequential LSTM\" (Att-BiL-SL) encoder-decoder model for describing the video in textual format. While most of the existing models work with single layered bi-LSTM, LSTM, or GRU approaches, the model of this study consists of two layers of attention-based bi-LSTM and one layer of sequential LSTM for video captioning. The model also extracts the universal and native temporal features from the video frames for smooth sentence generation that was absent in some previous models. With the mentioned two-layered attention-based bi-LSTM and one-layered sequential LSTM for extraction universal and native features, this study also includes word embedding with a soft attention mechanism in the encoder section to generate qualitative results. Through rigorous analysis, it is found that the model of this research work has outperformed various existing models. The residue of this paper is organized as follows. We review the previous video description generation methods and try to find the limitations of those models in Section 2. The proposed framework is illustrated in Section 3, accompanied by the overall process. In Section 4, we discuss the experimental procedure along with implementation details. In Section 5, the proposed model is compared with previous baseline models. Moreover, this section reveals the results of our model, which overtakes the various previous model. In Section 6, we discuss the quantitative analysis and limitations of our proposed method. Lastly, Section 7 accomplishes the paper by deliberating the model results and the possibility of future work.
# Purification of Produced Water by Solvents to Enhance Oil Recovery and Reuse Separated Droplets
## 4 Discussion  
### Hydrocarbons and Chlorinated Hydrocarbons  
#### 4.1.1 Linear Aliphatic Hydrocarbons  
Figure 2a illustrates the relationship between the volume of solvent per 100 mL of produced water and the percentage of residual oil removed, focusing on various aliphatic hydrocarbons. For hydrocarbons with low molecular weights (such as n-heptane, isootcane, and petroleum ether), oil recovery effectiveness remains moderate, peaking at 52%. Interestingly, a proportional increase in solvent volume correlates with enhanced oil recovery rates. However, substances like Vaseline (liquid) and liquid paraffin show no efficacy in  
this process. Notably, escalating Vaseline and paraffin volumes can inversely impact oil recovery, leading to a complete lack of recovery at 25 mL.  
Figure 2b reveals that hydrocarbons with 7 to 8 carbon atoms exhibit the best recovery, whereas the effectiveness notably declines for those with higher molecular weights. This is explained by the ability of smaller molecules to destabilize the oil-water emulsion interface, facilitating oil droplet coalescence. On the contrary, Vaseline and liquid paraffin, being macromolecules, contribute to the stabilization of these emulsions. The increased viscosity of these substances further amplifies this effect and is known to enhance the stability of emulsions.