esa-sceva/satcom-chunk-collection huggingface dataset:
# A Palladium-Deposited Molybdenum Disulfide-Based Hydrogen Sensor at Room Temperature
## 4 Results
### Gas Measurement Method  
This section provides a detailed description of the gas measurement technique, encompassing the test conditions, setup protocols, and gas introduction methods.  
The gas sensing measurements were conducted within a controlled environment, specifically a gas probe station chamber. During these measurements, the sensor was subjected to a DC voltage of 3.5 V. The H\({}_{2}\) concentration was precisely controlled using a mass flow controller (MFC), ensuring accurate and repeatable test conditions. To minimize the influence of other gases and maintain current stability, the sensor was first exposed to N\({}_{2}\) gas at a flow rate of 100 SCCM for 1 min. Subsequently, H\({}_{2}\) gas was introduced at a flow rate of 20 SCCM, allowing for the measurement of changes in current as a result of H\({}_{2}\) presence.
# Study on the Influence of a Rigid Wall on Cavitation in Underwater Explosions Near the Free Surface
## 3 Results and Discussions
### Effect of Rigid Wall on Medium-Charge Underwater Explosion near Free Surface
It can be observed that the presence of a rigid wall leads to two occurrences of cavitation in the flow field. Compared to the condition without a rigid wall, the complete collapse of the first cavitation event is accelerated from 2.86 ms to 2.56 ms. Although the volume of the second cavitation event is smaller, its duration is longer. Figure 13 displays the pressure-time curves at measurement points located at distances of 0.5 m and 1.0 m from the symmetry axis, at a water depth of 0.2 m. From Figure 13, it is evident that the presence of a rigid wall has a significant impact on the collapse pressure of cavitation induced by a moderate charge size underwater. Not only does the rigid wall cause cavitation to collapse earlier, but it also significantly increases the collapse pressure. These findings highlight the influence of a rigid wall on the dynamics of cavitation during underwater explosions. The presence of the wall alters the cavitation evolution, resulting in an earlier collapse and increased collapse pressure. These insights contribute to a better understanding of the intricate interactions between the charge size, water depth, and rigid boundaries in underwater explosion-induced cavitation phenomena. Figure 9: Schematic of underwater explosion near a free surface with a rigid wall (**a**) and mesh strategy of the fluid domain (**b**). Every 1 in 10 lines is plotted in the right figure. Figure 10: Numerical results of density (**top**)-pressure (**middle**), and cavitation domain (**bottom**) under the condition of an underwater explosion near the free surface without a rigid wall at \(t=0.8\), \(12.1\), \(16.2\), \(20.26\), \(30.4\), and \(6.0\) ms (from left to right). The red and area represents the cavitation domain in the bottom, contour plots. Figure 11: Numerical results of density (**top**), pressure (**middle**), and cavitation domain (**bottom**) under the condition of an underwater explosion near the free surface with a rigid wall at \(\pm\) 0.8, 12, 16, 20, 26, 3.0, 4.0, and 6.0 ms (from left to right). The red area represents the cavitation domain in the bottom contour plots. The findings of this section contribute to our understanding of the complex interplay between the charge size, water depth, and the presence of boundaries in underwater explosion-induced cavitation near the free surface. Such knowledge is invaluable for various applications, ranging from underwater blast mitigation strategies to the design and safety considerations of marine structures and vehicles operating in explosive environments. Continued research in this field is crucial for advancing our understanding and developing effective measures to mitigate the potentially destructive effects of underwater explosions.
## 2 Literature Review  
Web search activity is a topic of great interest in several academic disciplines. It is an indicator of public interest in the epidemiology field. For instance, the volume of the Google web search queries is an efficient, real-time public interest indicator compared with traditional surveillance measurements [7]. It is also an economic indicator, as economists use web search volume to forecast unemployment rates, household expenditures, and housing prices [8; 9; 10]. Furthermore, the web search volume also predicts the stock price in the financial market [11].  
Not surprisingly, web search activity is an important topic among marketing researchers. On one hand, some researchers focus on the role of web searches as a leading indicator of sales or the demand for new products. Web search trends predict new car sales in the US automobile industry [5]. Web search volume predicts the demand for tour and retail products [12]. A cinema admission forecasting model is more accurately forecasted when cinema-related Google Trends search data are included in the model [3]. Similarly, the predictive power of forecasting models for the box office revenue of a new movie improves when web search data are employed [2]. A recent analysis of Google Trends search data in Finland showed that the peaks in prime time for streaming media services have replaced television primetime [13]. On the other hand, other researchers investigated the distinctive characteristics of web searchers. Younger and more educated consumers prefer the internet over traditional research vehicles because the internet reduces reaching costs and provides information more efficiently than interpersonal sources or print information [14]. Web searchers prefer to use short queries and only see one page of search results [4].  
While the prior marketing literature focused on web search effects or web searchers' characteristics, how a web search is generated has been minimally discussed. This question is important for marketers, because when marketers launch new products to the market, they often spend a large amount of money on marketing activities to compel consumers to search for information about the products. To implement these marketing activities successfully, they should understand the relative effectiveness of marketing activities, particularly in the contemporary environment, where online services and mobile devices are major sources of information.  
Therefore, this paper investigates how marketing activities generate consumers' web search behavior. Specifically, we test our hypotheses in the film industry, where the quality of each product is difficult to convey to consumers through other marketing activities [15]. Note that movie marketers perform various marketing activities to attract consumers' attention and deliver detailed information about the movies before they are officially released. In this paper, we focus on three popular marketing activities: news media, social network marketing, and film star promotions.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 5 Mror: Mobile Reliable Opportunistic Routing for CRSN
### Receiver Contention Prioritization  
In the design of the receiver contention prioritization scheme, more priority is granted nodes that make better advancement towards the sink, to transmit the packet. With respect to the location information, the contention region is divided into Q priority regions, _i.e._, \(A_{i}\), where \(i=1,2,...,Q\). Nodes that return a positive value for the \(I_{DIFD}\) operation determine their priority regions based on the location information stored in the header of the data packet. Afterwards, according to their respective priority regions, each node delays sending its ACKpacket.  
A delay window size of short inter-frame space (SIFS), \(SIFS_{i}\), is tagged to each priority region, \(A_{i}\). Accordingly, all potential next hop nodes will delay for \(\sum_{j=1}^{i-1}SIFS_{j}+sifS_{i}\); where \(sif_{i}\) is randomly chosen, such that \(sifS_{i}\in[0,EIFS]\), where the extended inter-frame space \((EIFS)=SIFS_{i}-SIFS_{i-1},\forall i\). This configuration implies that nodes that will compete to relay the packet will be only those that reside in the same priority region. Once a contesting node successfully sends its ACK packet, all nodes undergoing delay will immediately drop the data packet when they overhear the ACK packet. The ACK is used to notify all of the delaying nodes about the successful selection of a next-hop node. Figure 8 is used to present a case for three priority regions.  
When node \(i\) intends to forward a data packet, it simply broadcasts the packet through its data channel; nodes that returned a positive value for \(I_{DIFD}\) initialize their SIFS according to their priority region (Figure 6). Among all of the regions, nodes within the region \(A_{1}\) will first contend in broadcasting the ACK packet after \(SIFS_{1}\) seconds. When a node is successful, others refrain from broadcasting ACK and subsequently drop the packet. In a case where no ACK is received by the delaying nodes after \(SIFS_{2}\) seconds, nodes in \(A_{2}\) commence to compete in sending their ACK, and so on. Note that organizing the nodes in VCGs helps to prevent the event of two nodes sending an ACK packet without hearing each other. However, the collision of ACK packets from the same region may occur. This case results in ACK being sent by a lower-priority node. MROR does not resolve this problem because the cost of resolving such outweighs the gains. Moreover, such an occurrence is very low, because the scheme segregates nodes into contention regions.  
On the other hand, it is also possible that node \(i\) did not receive an ACK. This can occur as a result of the following: (i) due to mobility, the eligible node falls into a local minimum area; or (ii) due to the failure of the node;s neighbors. In such a case, node \(i\) makes \(k\) attempts, after which it concludes the occurrence of a local minimum situation. It afterwards switches to the route maintenance module, which helps in resolving the issue.
## 4 Semantic Interoperability Provision in Heterogeneous Healthcare Service Environments
### Semantic Interoperability in Healthcare Data Models
#### 4.2.4 Deployment of Learned Semantic Alignments in the Target Ontology Catalog  
Semantic interoperability provision can enhance data sharing in an environment where healthcare applications could utilize the information from diverse models that are, although designed for different applications, refer to the same data. In such scenarios, it is very difficult to integrate such data automatically. Existing approaches of semantic alignment require the mechanisms to align these data models with the help of human or tool efforts. We propose a setup of automatic semantic alignments in the face of diverse semantic models (as shown in Figure 11). We design a set of microservices which enable the semantic processing into features such as semantic annotation, semantic query processing, and semantic alignments processing. Moreover, a base ontology catalog has been set up to provide the repositories and data processing and management mechanism based on the WoO infrastructure. The learning mechanism has been designed as the microservice process to enable learning of the ontology representations, which are input to the semantic alignment process to compute the semantic similarities between data models. Depending on the semantic matches, entries are stored in the base ontology instances. These instances collectively form an interoperable shared data model to provide semantic coherence of multiple data models. A base ontology generated from the semantic alignment process could be used by other systems to deal with the heterogeneity of the chosen system; this enables the development of applications to form service flows on the heterogeneous data which are semantically synchronized from multiple systems.  
Figure 11: Semantic interoperability processing with the base ontology catalog.
## 2 Current Issues, System Rationale and Objectives
### Implementation Rationale  
The combinatory approach of a HUD and a gesture interface intent to facilitate the multiple and diverse infotainment systems that currently distract the driver. Moreover, the mobile and wireless communication systems and protocols have come under intense research focus in concert with the continuing development of mobile services, applications, and devices offering a broader spectrum of communication conduits to the users [18; 30].  
Figure 1: Screenshot of the fully functional Augmented Reality (AR) Head-Up Display (HUD) and conventional Head-Down Display (HDD) interfaces in the Virtual Reality (VR) driving simulator.  
As this technology progresses at a faster rate than its integration to the vehicles by the automotive industry, a large segment of the concurrent car-market, namely used and older vehicle-models, will require a retrospective integration. In particular, the used vehicles' market is significantly larger, representing sales numbers of used versus new models in the ratio of 3:1 in the UK, 2.6:1 in the US, 2.4:1 in France, and 2:1 in Germany, amongst other countries with similar market trends [31]. Notably, any further financial turbulence in worldwide or national level increases further this ratio difference in favour of the used cars market.  
As such, the provision of an off-the-shelf consumer electronic system presents a viable option for retrofitting to older vehicle fleets. This resolves a major issue in which most of the older vehicles are becoming disassociated from the current driver's digital ecosystem due to incompatibility between the vehicular electronics/infotainment systems and the latest mobile technologies. As the proposed system aims to increase the safety and regulate the use of the latter devices in the car, this retrospective compatibility offers democratisation of technology and enhances the socioeconomic equality. To this end, the proposed HUD system offers a combinatory approach of existing hardware components and proprietary software. The point of using readily available hardware was to avoid the reliance on each automotive manufacturer's proprietary systems.  
The proposed HUD system could be devised in an adaptable and flexible positioning of the individual hardware components (i.e., glass combiner, mini-HUD projector, micro-computer unit, and gesture recognition sensor). The in-house developed software will also aid the user in the system calibration and alignment between the components, accommodating the different design aspects of various dashboards and instrumentation panel hubs. A prototype version was utilised for the simulation and evaluation of the system.  
Consequently, the complete and streamlined version of the system will be transferable to new vehicles as well, either as a retrospective system or as a bespoke system directly embedded to new vehicle models during the manufacturing process.
## 5 Simulations  
The proposed robust stochastic optimization (RSO) algorithm is first evaluated with simulations before moving to field experiments. In this section, we begin with presenting the types of simulation cases, evaluation methods and metrics. Then, we continue to present the simulation setup and finally present the results with analysis. The following simulation cases are considered:  
**Case 1: Considering an LOS scenario without spatio-temporal noise**  
In this case, we assume that the radio signal is not blocked by large obstacles, and hence, the RSS has a line-of-sight from the source to the destination device. Furthermore, we assume that the RSSspatial variations due to shadowing effects are negligible, as the environment is free of obstacles, and the multipath fading effects on RSS are filtered out using the temporal and spatial filters.
**Case 2: Considering an LOS scenario with spatio-temporal noise**: In Case 2, we use LOS assumptions as in Case 1. However, the RSS spatial variations due to shadowing and multipath effects are included (considered) to some extent, even though we assume that we filter out most of the variations in the RSS (including the measurement noise of the device).
**Case 3: Considering an NLOS scenario with spatio-temporal noise**: In this case, we assume that the radio signals have no direct line-of-sight between wireless devices and suffer blockage by several obstacles in the environment and, hence, qualify for NLOS conditions. Verifying the NLOS conditions is relevant to the algorithm, because the relay node aims to overcome the NLOS barriers in radio propagation and ideally convert the relayed channel from an NLOS link to two LOS links passing through the relay node. The variations in RSS due to shadowing and multipath effects are considered similar to Case 2.
## 6 NR Tracking Performance Analysis for Different NR and Receiver Configuration
### Impact of NR Signal Configuration on Tracking Accuracy
The proportion of reference signals in NR signal is dynamically configured according to channel quality [31], significantly affecting DLL tracking accuracy. According to NR protocol, the parameter _DMRS-Additional-Position_ adjusts PDSCH DMRS proportions. In the experiments, _DMRS-Additional-Position_ is configured to 0, 1, and 2, achieving reference signal proportions of 4.17%, 8.33%, and 12.5%, respectively. Its tracking accuracy is plotted in Figure 10, and we can find that the DLL tracking accuracy increases with the proportion of the reference signal. At 60 dBm transmitting power, elevating the reference signal proportion from 4.17% to 8.33% and 12.5% yields 31.52% and 44.95% accuracy enhancements, respectively. Increasing the reference signal power allocation enhances the CNR of the navigation signal. Notably, increasing the reference proportions reduces the communication capacity, and reference signals occupy the same resource blocks as data transmission. This one-to-one compromise suggests that a 12.5% reference signal allocation would consume 12.5% of the total channel capacity, leaving 87.5% capacity for data. The optimal ratio should be determined by specific application requirements for navigation accuracy versus data throughput. Overall, for fixed transmitting power, there are three NR configurations to enhance DLL tracking accuracy: first, maximizing signal bandwidth; second, the spectral of the NR signal is moved away from zero frequency as far as possible; and third, enhancing the proportion of the reference signal to increase navigation signal power.
# Enhancing Weather Forecasting Integrating LSTM and GA
###### Abstract  
Several sectors, such as agriculture and renewable energy systems, rely heavily on weather variables that are characterized by intermittent patterns. Many studies use regression and deep learning methods for weather forecasting to deal with this variability. This research employs regression models to estimate missing historical data and three different time horizons, incorporating long short-term memory (LSTM) to forecast short- to medium-term weather conditions at Quinta de Santa Barbara in the Douro region. Additionally, a genetic algorithm (GA) is used to optimize the LSTM hyperparameters. The results obtained show that the proposed optimized LSTM effectively reduced the evaluation metrics across different time horizons. The obtained results underscore the importance of accurate weather forecasting in making important decisions in various sectors.  
forecast horizon; weather variables; regression; long short-term memory; genetic algorithm +
Footnote †: journal: Applied sciences  
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# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 3 Adversarial Attacks on Sentiment Analysis  
Sentiment analysis systems are an important tool that provides summarized information on users' opinions to assist potential users and organizations in the decision-making process. Sentiment analysis systems attempt to determine the user experience with respect to a product or service based on the positive or negative connotations of the words used by users to express their opinions [3]. In recent years, the use of deep learning models for deploying sentiment analysis systems has become a widespread topic due to their good processing capacity and superior results achieved on large volumes of information. However, as described above, deep learning models are vulnerable to modified inputs, making sentiment analysis models vulnerable to adversarial attacks. This issue leads us to study how adversarial attacks operate in order to propose defenses that minimize the negative impact that adversarial examples can have on the results of sentiment analysis models, which could potentially affect decision-making.  
According to the characteristics of the reviewed works, and considering the discussed criteria in [20] for threat model design, we propose a new taxonomy specifically focused on adversarial attack methods for sentiment analysis models. Figure 7 presents the proposed taxonomy. In this taxonomy, six criteria are used to categorize the attack methods: (i) _model access_ refers to the knowledge of the attacked model when the attack is performed; (ii) _analysis level_ refers to the sentiment analysis level approached by the model under attack; (iii) _granularity_ refers to the level of granularity at which the modifications are made; (iv) _element selection_ refers to the way the methods select the element to be modified; (v) _strategy_ indicates the strategy by which the modification is performed; and (vi) _DNN model_ specifies the type of DNN being attacked. The main DNN architectures used for text applications are discussed in Section 2.6.  
Figure 7: Taxonomy of adversarial attacks on deep learning models used for sentiment analysis.