esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Future Directions
### Multi-Connectivity  
Multi-connectivity, which allows UEs to connect with multiple BSs simultaneously, can be leveraged to enhance HO and mobility performance in 5G and B5G HetNets. By maintaining multiple connections simultaneously, multi-connectivity can enable seamless HO between different network types, such as 5G, WiFi, and cellular networks. This approach improves network capacity, reduces congestion and network failures, and provides better connectivity and user experience even if one of the connections is interrupted. Multi-connectivity can also improve coverage and signal quality by leveraging multiple networks, provide load balancing to distribute traffic over multiple connections, reduce delays caused by traffic overload, and offer redundancy by maintaining backup connections in case of network failures. Additionally, multi-connectivity enables QoS optimization to select the best network for specific applications based on QoS requirements. By doing so, this approach enhances network efficiency, reduces delays, and ultimately improves HO and mobility performance in 5G and B5G HetNets.
# Nationwide Flood Monitoring for Disaster Risk Reduction Using Multiple Satellite Data
## 4 Discussion
The new algorithm proposed in this study is expected to be a useful tool in disaster risk reduction, not only to monitor floods but also to standardize nationwide flood monitoring using combined multiple satellite images with the SfWi, and thus to promote global flood mapping and flood forecasting systems.
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 3 Verification with Cantilever Beam Vibration
### Results of Edges by Different Detection Algorithms  
The proposed method uses the Canny operator to locate the rough pixel-level edge, and applies the ZOM-based algorithm to relocate the precise subpixel-level edge as shown in Figure 7. It can be seen that the subpixel-level edges are much closer to the true edge than pixel-level edges. That is, the ZOM-based subpixel algorithm significantly improves the accuracy of edge detection.  
The performance of the proposed method was compared with the other two methods, i.e., the PAE-based algorithm and the method proposed by [PERSON] [31] combining the Sobel operator with the ZOM-based algorithm, as shown in Figure 8. As can be seen in Figure 8a, many edge points are missing on the top part of the beam, and the method from the literature detected a lot of noise edge points at the bottom, while the result of the PAE-base method shown in Figure 8b is slighter better but still with a few missing edge points and many noise points. In contrast, the proposed method has detected a very complete edge and has almost no noise point as shown in Figure 8c. This indicates that the proposed method has very good performance in edge detection.  
Figure 8: \(Cont\).  
Figure 7: Edge points extraction of the cantilever beam.
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 3 System Model
### Energy-Consumption Model  
In our duplication-based task offloading algorithm, we utilize the processor idle time to duplicate predecessor tasks, This can avoid the transfer of data between edge network and user devices and finally improve the overall completion time of applications. However, extra energy consumption will be caused by the duplicate subtask. Thus, in our work, we maintain the focus on the energy consumption of user devices. The computation consumption (\(E_{i}^{c}\)) and communication consumption(\(E_{i}^{t}\)) constituted the main parts of the energy cost of user devices \(i\). The overall energy consumption (\(E_{i}\)) is effect by the offloading scheme (\(D_{i}\)) and the duplication decision \(I_{i,j}\), which is defined as follows:  
\[I_{i,j}=\left\{\begin{array}{ll}1&\quad\text{if }T_{i,j}\text{ has a duplicate subtask }T_{i,i^{\prime}}\\ 0&\quad\text{otherwise}\end{array}\right. \tag{9}\]  
Furthermore, the \(I_{i}=\{I_{i,1},I_{i,2},\ldots,I_{i,|v_{i}|}\}\) represents the duplication scheme of user device \(i\), and thus \(\mathbf{I}=I_{1}\times I_{2}\times\ldots\times I_{N}\) denotes a possible duplication strategy of user devices in the edge network.  
The computation consumption depends on the subtasks executed locally:  
\[E_{i}^{c}=\sum_{j=1:L_{ij}=-i}^{|n_{i}|}m_{ij}\delta_{i}+\sum_{j=1:d_{ij}\
eq-i}^{ |n_{i}|}I_{i,j}m_{i,j}\delta_{i} \tag{10}\]  
where the \(m_{i,j}\) denote the required CPU cycle to accomplish the \(T_{i,j}\), and the \(\delta_{i}\) denotes the energy consumption per CPU cycle in user devices \(i\).  
The transmission consumption is mainly affected by the data exchange between user devices and the edge server. It can be formulated as follows:  
\[E_{i}^{t}=\sum_{\begin{subarray}{c}j=2:d_{ij}-1\rangle=-i\\ d_{ij}+d_{ij}\
eq\
eq\end{subarray}}^{|n_{i}|}(1-I_{i,j})C_{(j-1),j}^{k}\times q _{i} \tag{11}\]  
The \(q_{i}\) is the transmission power of user devices \(i\), and the \(C_{(j-1),j}^{k}\) is the data exchanging time between the \(T_{i,(j-1)}\) and \(T_{i,j}\). From Equation (11), the essential condition of communication consumption: (1) The predecessor subtask \(T_{i,j-1}\) is executed in the local processor. (2) The \(T_{i,j-1}\) has no duplicate subtask. (3) Either the subtask \(T_{i,j}\) or its duplication is executed in the edge server.
# Practical Application of Mesh Opportunistic Networks
## 2 Theoretical Background
### Privacy and Security in Mesh OppNets  
The privacy and security of personal messages are among the most important things that interest users, companies, and public administrations today. We know that, through automated processes that use more or less biased algorithms, our connected society accumulates digital dossiers of people online [14]. The problem with this is not simply the loss of control over personal information, the existence of \"proto big brother\" controllers, or omniscient corporations or portals but rather, the problem is the bureaucratic processing of uncontrolled data that affects our lives [15], constituting an authentic digital tatto [16; 17; 18]. In this sense, mesh OppNets involve interactions among multiple decentralized nodes [19], not in terms of a social network but rather, through mesh networks that establish opportunistic connections that can occupy the urban space. These communication networks do  
Figure 2: Implementation of goTenna Mesh devices (April 2023) in: (**A**) U.S.; (**B**) in part of Europe. This is not exhaustive as only publicly communicated nodes appear; and (**C**) regional groups of this system where users discuss ideas and issues. Note that we cannot know the use given to these devices. Source: [[https://imeshyou.gotennamesh.com/](https://imeshyou.gotennamesh.com/)]([https://imeshyou.gotennamesh.com/](https://imeshyou.gotennamesh.com/)) (accessed on 6 April 2023), courtesy of goTenna Mesh Support.  
not require formal leadership, or a command post or control center, nor do they require a vertical organization responsible for distributing information or instructions. Rather, these systems advocate networking as a way of life [19]. In this context, this type of network offers new options in terms of the privacy and security regulations of personal communications.  
Although there does not currently seem to be an appropriate approach to these situations, it is understood that regulating mesh OppNets should take place within the framework of the unstoppable phenomenon of convergence between the information and communications technologies [20]. Such approaches must align with the new discipline of computer law, which assumes the right to computer freedom as a personal modality recognized by citizens, legally protecting the \"computer identity\" of each individual [21]. Alternatively, mesh network proposals are emerging, such as payment gateways for Bitcoins using Blockchain or other cryptocurrency transactions, employing protocols with decentralized networks [22]. Without a need for the Internet, these mesh OppNets protocols mask the physical situation of the sender or receiver, and they avoid telephone identification as they do not use a SIM card or IP address [23; 24].
# Double Deep Q-Network with a Dual-Agent for Traffic Signal Control
## 5 Results and Discussion
### Simulation Setup and Parameters  
The open source simulator SUMO (version 1.3.1 in this paper) based on the future vehicular network provides necessary vehicular information, such as positions and waiting time, which helps the agent calculate rewards and extract states. In this paper, traffic environment settings follow the research [6]. The intersection is composed of four roads, as shown in Figure 1. Each incoming road has four lanes, where the right-most lane (\(L0\)) is for straight and right-turn traffic flow, two middle lanes (\(L1\), \(L2\)) allow vehicles to go straight only, and the left inner lane (\(L3\)) allows vehicles to turn left.  
The method by which vehicles are generated and released into the network has a major impact on the quality of any traffic simulation. The most popular vehicle production method is to randomly sample from a probability distribution numbers that respect to the time interval between vehicles. This work does not break from this method totally, however we struggle for implementing a nuanced version which better models realistic scenarios. Empirical research has demonstrated that different vehicle flow rates are suitably approximated by different probability distributions [23]. In this paper, the vehicle production rate follows the Bernoulli process, and the probability \(p\) (referred as production rate) setting can be seen in Table 2. The roads of east-west (direct routes), for example, have 1/6 vehicle production probability for the road 0 and road 1 respectively. The sum of production rates of all roadsis 1, which represents that one vehicle enters into the intersection every second. It is obvious that the traffic flow density is 3600 veh/h in the intersection.  
To simulate the intersection traffic flow and traffic signal control, it is significant to set appropriate traffic environment parameters. In this work, the simulated intersection is a 500 m \(\times\) 500 m area, and each observable road (from the stop line) length \(l\) is set to be 160 m. In the SUMO simulation environment, the vehicle length is 5 m and the minimum safety distance is 2.5 m. Therefore the cell length \(c\) is set to be 8 m. Road speed limit is set to be 70 km/h. The time intervals \(\tau_{p}\), \(\tau_{y}\), \(\tau_{a}\) are all set to be 6 s. This interval setting gives enough time for driver to react and alleviates pressures of the real-time control in traffic system.  
To acquire an effective traffic signal control policy, the DDQN with dual-agent algorithm is trained for 500 episodes, each of which contains 1800 s. Detailed parameter settings of dual-agent networks are illustrated in Table 3. In the network training process, we employ the ADAptive Moment estimation (Adam) [24] that updates the learning rate considering both first-order and second-order moment with the stochastic gradient descent procedure. The learning rate is set to be 0.0002.
## 4 Discussion
These models and approaches are tailored to the specific characteristics of the data and the requirements of each task, reflecting the rich methodological diversity in the field of affective computing and emotion recognition and sentiment analysis. Specific programming languages and frameworks are predominantly used in the implementation of these AI models to facilitate the use of large datasets and complex architectures. Python is the most widely used language due to its versatility and because it has a wide range of specialized libraries. Frameworks such as TensorFlow, PyTorch, and Keras are essential for the development of deep neural networks and other advanced models. In the realm of RQ3, which asks what are the most frequently employed sources of information and data-bases in research related to \"affective computing\", \"sentiment analysis\", and \"emotion recognition\", and what limitations do studies encounter when using these sources, it has been observed that sources of information and databases, particularly in the text analytics domain, have witnessed substantial growth due to the availability of large volumes of data from sources like social media, product reviews, and blog comments. Deep learning models, transformer-based models like BERT, and the combination of deep learning with different techniques have become prominent trends in text analytics. Nevertheless, the availability of databases for physiological signals or the fusion of physical and physiological signals remains limited, and standard protocols for generating these databases have yet to be established. Consequently, comparing results across various studies remains challenging. Thus, it is evident that there is still a considerable journey ahead in the analysis of emotions using non-EEG physiological signals, which are presently somewhat confined to controlled laboratory applications, paving a trend for the use of non-intrusive sensors and wireless technology to facilitate data collection in natural environments, providing valuable insights into real-world emotional states. As technology advances and interdisciplinary collaboration continues, it is expected that these limitations will be addressed, paving the way for more robust and comprehensive emotion analysis in a wide range of practical applications. The analysis of these trends in \"affective computing\", \"sentiment analysis\", and \"emotion recognition\" research provides a comprehensive understanding of the evolving landscape in data sources, analysis models, and applications. Researchers must consider these trends and the advantages and limitations of databases when conducting studies in these domains.
## 2 Materials and Methods
### Mapping and Differential Gene Expression Analysis  
The raw reads were filtered with FASTX-Toolkit and then aligned to the maize reference genome (B73_RefGen_v3 annotation build (5b+)) using default parameters in Tophat2 [54]. The aligned sequences were assembled using Cufflinks and compared with the annotated reference genome using Cuffmerge [55]. Unmapped reads were imported to CLC Genomics Workbench (v7.5.1 CLC Bio). For the de novo assembly, we employed the following parameters: the mismatch count and maximum gap were set to 2, the insertion and deletion costs were set to 3, the minimum contig length was set to 200 bp, and the length fraction and similarity parameters were set to 0.5 and 0.9, respectively. The sequence reads were submitted to NCBI Short Read Archive (SRA) under accession numbers SRP082534 (B73) and SRP135093 (Ki11).  
Fragments per kilobase of transcript per million mapped reads (FPKM) values were calculated for each gene. Differentially expressed genes (DEGs) under drought stress were identified by Cuffdiff. DEGs were defined by a false discovery rate (FDR) [56] less than 0.05 and an absolute log2 fold change value greater than 1. In addition, differential usage of exons and alternative splicing events were detected using DEXSeq [57] with an FDR under 0.05 and threshold absolute fold change over 2.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.7 Integrated Access Backhaul (IAB)  
The inclusion of IAB technology in 3 GPP Release 16 was a critical building step in the deployment of mmWave networks. IAB technology is a key Release16 feature in 5G NR that enables quick and affordable mmWave deployments. The IAB terminals share the same interface and frequency for access and backhaul. IAB nodes are required to fill isolated coverage gaps, increase system capacity, and provide fiber coverage for sparse fiber deployments. In addition, the IAB technology deploys networks using a multi-hop strategy and permits the deployment of mmWave base stations with or without fiber backhaul transmission. It operates by employing a fiber or cable connection to have a portion of the deployed BSs act as donor nodes. IAB allows operators to install alternative backhaul where necessary while utilizing their current mmWave spectrum licenses without the need for additional interference studies that would be subject to regulatory scrutiny.
# Initial Experimentation of a Real-Time 5G mmWave Downlink Positioning Testbed +
## 5 Field Campaign
### Impact of the Receiver Orientation  
The receiver azimuth orientation also has a significant impact on the beam alignment between the transmitter and the receiver. In order to assess this impact on the FR2 AoA estimation performance, several tests were repeated for different receiver orientations compared to the perfect alignment studied in the previous section, i.e., with UE orientations at \(0^{\circ},30^{\circ}\) and \(-30^{\circ}\). The static periods of these tests were used to obtain the beam pattern for each orientation. As it is shown in Figure 5c, the beam pattern of the Sivers EVK is not symmetric, and there are abrupt changes in the RSRP between \(-13.5^{\circ}\) to \(-18^{\circ}\) and between \(13.5^{\circ}\) and \(18^{\circ}\), which correspond to angle offsets of \(45^{\circ}\) with respect to the antenna boresight. This reduces the sensitivity of the AoA to small changes in the receiver orientation, especially when perfectly aligned. Future work can explore enhanced AoA weighting functions optimized for the beam response of the antenna array or based on iterative fitting, as in [24].  
Figure 5: Normalized power measurements per beam angle: (**a**) RSRP per beam angle over the full UE trajectory and azimuth orientation of \(0^{\circ}\); (**b**) average RSRP per beam angle at static UE locations within the moving trajectory and azimuth orientation of \(0^{\circ}\); (**c**) average RSRP per beam angle for azimuth orientation of \(0^{\circ}\), \(-30^{\circ}\) and \(30^{\circ}\) during static UE periods.
## 3 Real-Time Interference Cancellation
\begin{array}{ll}y_{sp}(t_{2})&=\cos A\Big{(}1-\frac{B^{2}}{2}\Big{)}-B\sin A +j\Big{(}\sin A\Big{(}1-\frac{B^{2}}{2}\Big{)}+B\cos A\Big{)}\\ &=(\cos A+j\sin A)-\frac{B^{2}}{2}\cos A-B\sin A+j\Big{(}-\frac{B^{2}}{2}\sin A +B\cos A\Big{)}\\ &=(\cos A+j\sin A)+jB(\cos A+j\sin A)-\frac{B^{2}}{2}(\cos A+j\sin A)\end{array}  \tag{17}\]  
The above Equation is a modification of the time sampled at \(t_{2}\) s, and in this modified Equation, the function of the time sampled at \(t_{1}\) can be found. Applying this function is equivalent to Equation (18). \[y_{sp}(t_{2})=y_{sp}(t_{1})+jBy_{sp}(t_{1})-\frac{B^{2}}{2}y_{sp}(t_{1}) \tag{18}\]  
The purpose of this method is to find the frequency difference \(\Delta f\) from IEMI, so to sum up the above Equations for \(B\), the expressions are arranged in perfect squares form. Equation (19) is the process of arranging a perfect square expression. \[ \begin{array}{ll}y_{sp}(t_{2})-\frac{1}{2}y_{sp}(t_{1})&=\frac{1}{2}y_{sp}( t_{1})+jBy_{sp}(t_{1})-\frac{B^{2}}{2}y_{sp}(t_{1})\\ &=\frac{1}{2}y_{sp}(t_{1})\Big{(}1+j2B-B^{2}\Big{)}\\ &=\frac{1}{2}y_{sp}(t_{1})(1-jB)^{2}\end{array}  \tag{19}\]  
After arranging this for \(B\), \(\Delta f\) is obtained by substituting \(2\pi\Delta f\Delta t\) into \(B\) in Equation (19). \[\Delta f=\frac{1}{j2\pi\Delta t}\Bigg{(}1-\sqrt{2\frac{y_{sp}(t_{2})}{y_{sp}( t_{1})}-1}\Bigg{)} \tag{20}\]  
The above Equation can be used to estimate the difference between two frequencies using the sampled signal at \(t_{1}\) and \(t_{2}\) time. Theoretically, the frequency difference can be predicted using only two samplings. However, in the presence of noise, the accuracy is very low. Signals are sampled for a specific period to overcome this problem, and then averaging is used to estimate frequency difference. This is similar to the averaging or average filter technique used in many measurement instruments and shows good agreement when white Gaussian noise is presented. By applying this method to the Equation for estimating the frequency difference, it can be expressed as Equation (21). \[\Delta f=\frac{1}{N}\sum_{k=1}^{N}\Delta f_{k},\ \Delta f_{k}=\frac{1}{j2\pi \Delta t}\Bigg{(}1-\sqrt{2\frac{y_{sp}(t_{k+1})}{y_{sp}(t_{k})}-1}\Bigg{)} \tag{21}\]  
Applying this averaging estimation method to the MPM calculation, part of the active cancellation algorithm, can reduce the overall simulation time. The newly proposed algorithm is shown below in Figure 6.
## 4 Results and Discussions
### Discussions
#### 4.2.3 Asynchronous Behavior Management  
The system's ability to handle asynchronous behavior resulted in a seamless user experience, with real-time updates occurring without noticeable interruptions. This represents a significant improvement over traditional systems that may experience delays or communication collisions. [PERSON] et al. [23] addressed the modeling and analysis of critical properties in interactive mobile systems using Petri nets. Our approach aligns with these principles, showcasing the efficacy of Petri nets in managing complex interactions within mobile applications.
# Pattern-Based and Visual Analytics for Visitor Analysis on Websites
## 5 Traffic Segmentation and Characterization
### Case Study Scenario: Segmentation by Country  
Next, we applied the proposed approach to contrast visitors from Mexico versus those from Asia. Again, we aim to extract patterns which marketing teams can observe to get interesting trends from both types of visitors.  
For this scenario, the pattern mining algorithm will work with two classes: Mexico and Asia. The first step is to label the data. We obtain class label by manually labelling the data depending to the country associated with each visit. If a visit does not belong to either class, it is left out of the analysis. Next, we will present the results in the form of a summary of distinctive traits.  
Again, we analyzed the patterns which have zero support for the opposite class, given that they show characteristics exclusive to the class of interest. In the Mexican class, we observe two interesting patterns related to the visit duration. These patterns tell us that half of visits from Mexico have a duration longer than 100 s and 13 percent have a duration of less than five seconds; from this we can infer that the rest of the visitors (37 percent) have a visit duration between 5 and 100 s. A possible business goal could be to try to engage such 13% of visitors that stay in the site less than five seconds.  
Also, since the patterns have zero support, we can further and infer that Asian visitors browse the site an stay at most 100 s. Again, this can be actionable information, if the business is interested expanding to a new market, then it can aim to optimize the duration of Asian visits.