esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results And Discussion
### Number of Handovers  
The graph shown in Figure 5 indicates the number of handovers under different velocities of the mobile node. Increasing the handover increases the overhead and its effects on delay; on the other hand, it increases the drop rate, reduces the throughput, and increases the delay of the system. This analysis uses four kinds of wireless channels with different groups of nodes. The speed of the portable hub (mobile node) increases the effect on the performance of the node and the network-based scenario. In the case of different velocities, the condition of handoff changes, though it is extremely hard to decide which sort of sensor group handovers reduce the delay, and the effect on the number of handover failures is reduced. These decisions result in the use of static, dynamic, and optimized base algorithms. The use of bandwidth is improved by selecting an effective sensor channel or vertical handovers and an effective bandwidth that employs hybrid and single optimization approaches. In the proposed approach, the use of hybrid optimization helps in improving the static base algorithms, such as RSS. The use of RSS helps in the selection of a static threshold, and this static threshold is optimized via an optimization algorithm. In most of the cases, the optimization approach does not converge and provides a random answer, which further reduces the performance of time delay. In the proposed approach, cuckoo search and GA are also used. This methodology iteratively improves the performance of cuckoo search.  
For the velocity of 60 km/h, the handovers are lowest, with fewer than two; however, beyond this velocity, there is a slight increase in number of handovers compared to RSS-cuckoo search. The graph indicates a significant improvement in handovers using the proposed hybrid approach compared to the existing approach and other single optimization methodologies.  
Figure 4: HFP of the hybrid model.
## 5 Conclusions  
The intention of the analyses presented here was to indicate possible modifications to the standard GPS/EGNOS algorithm, with a view to using it in its most optimal form in smartphone-type mobile devices. The results obtained indicate the use of steady-state values other than those recommended for aeronautical purposes related to the smoothing of observations. The smoothing of code observations alone for mobile devices has a positive impact on the quality of positioning. For autonomous positioning, the signal-to-noise-related weighting model gives better accuracy results than the satellite elevation-related observation weighting model. However, in the case of positioning using SBAS data, the EGNOS weighting model performs best. Interfering with the components of the corrections to the pseudorange indicated that in order to process fewer data, it is possible to dispense with range rate corrections without degrading positioning accuracy. In the case of vertical positioning, the use of ionospheric corrections is of great importance for accuracy.  
Conceptualization, G.G. and A.C.; Data curation, D.T.; Formal analysis, K.K.; Funding acquisition, G.G.; Investigation, G.G.; Methodology, G.G.; Project administration, G.G.; Resources, G.G.; Software, G.G.; Supervision, K.K.; Validation, A.C.; Visualization, D.T.; Writing--original draft, G.G.; Writing--review and editing, A.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Warmia and Mazury in Olsztyn.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 7: HPE and VPE values for GPS/EGNOS positioning smoothed with carrier-phase observations using a smoothing window of 20 s with different variants of pseudorange correction components (upper figures—Xiaomi Mi8, lower figures—Septentrio AsteRx2).  
Conflicts of Interest: Author [PERSON] was employed by the company Four Point. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Related Work  
### Internet of Things  
The Internet of Things is a huge network of information sensors connected with the Internet [12; 13; 14]. Its purpose is to connect all things with the network to achieve automatic real-time object identification, location, tracking, and monitoring, and trigger the corresponding events. With the spread of mobile devices and advances in computing and ICT(Information and Communication Technology), the Internet of Everything is no longer a fantasy. In order to ensure the consistency of the development of the IoT, many researchers and institutions adopted a unified hierarchical standard. The Internet of Things can be divided into four layers: the device, network, platform, and service layers [15].  
At the device layer, every-day objects have added sensors to sense the environment. The device sends the collected information to the gateway for subsequent processing and receives the response data. It is estimated that around 20 million connected devices will be online and generate data by 2020, providing unprecedented opportunities for service providers to enhance customer experience, offer game-changing innovations, and boost profits. Data no longer live in the walled garden of a traditional data center; they exist at the edges, in transit, in the cloud, and often at the middle of those paths. It may not even be clear who owns an IoT device, the rights associated with the data, and who is responsible for ensuring the integrity and security of the data in question. When these fundamental issues are not addressed, it is difficult for service providers to provide clarity and transparency to customers about their data practices.  
Faced with these problems and challenges caused by the huge amount of data generated by IoT devices, many industry insiders have started to turn their eyes to this place for research. Companies such as Microsoft, Bosch, Samsung, and Fujitsu are already collaborating on a blockchainlike market for Internet of Things data, which they hope will one day allow any connected sensor or device to independently and cheaply obtain data. If successful, the market could provide powerful applications for supply chains, smart cities, artificial intelligence, and other use cases. If done well, such markets could illustrate how companies can better balance the twin requirements of IoT data security and open data sharing.  
In general, in the context of the Internet of Things, there are a large number of Internet of Things terminal devices generating a large amount of data. In addition, Internet of Things devices themselves have limited capabilities and poor attack resistance, resulting in extremely low security of a large amount of data generated in the process of transmission, exchange, and sharing. How to effectively solve this problem is the focus of this paper.
## 2 Preliminaries
### The 2-Tuple Linguistic Method  
In instances concerning qualitative aspects that prove challenging to evaluate with exact values, this paper introduces the Binary Semantic Model to handle fuzzy linguistic information. This method is a continuous information representation model that can prevent information loss and inadequacy during the aggregation process. To articulate it, the following will introduce binary semantic pairs representing linguistic information and the binary semantic operators used for transforming and aggregating linguistic information.  
The expert evaluation information after aggregation expresses a semantic interval number, and this paper adopts a binary semantic word calculation method to convert the semantic interval number into a numerical interval number. The binary semantic calculation process is relatively simple, and the calculation result is more accurate. The primary focus lies in the formulation of a linguistic representation model that encapsulates the decision maker's linguistic information using 2-tuples \((s_{i},\alpha_{i})\), \(s_{i}\in S\) and \(\alpha_{i}\in[-0.5,0.5)\), where \(s_{i}\) denotes the linguistic description of the information, and \(\alpha_{i}\) is a numerical value signifying the extent of transformation from the initial outcome \(\beta\) to the nearest index label \(i\) within the linguistic term set \((s_{i}\in S)\), a process referred to as symbolic translation.  
**Definition 3**[34]. _Given a semantic evaluation term set \(S\), \(S=\{s_{i}:i=0,1,2,\cdots,g\}\), \(s_{i}\in S\) is any semantic evaluation term, and its corresponding 2-tuple can be obtained through the conversion function \(\varphi\) as follows:_  
\[\left\{\begin{array}{l}\varphi:S\to S\times[-0.5,0.5)\\ \varphi(s_{i})=(s_{i},0)\end{array}\right., \tag{6}\]  
**Definition 4**[34]. _Let \(S=\{s_{i}:i=0,1,2,\cdots,g\}\) be a linguistic term set and \(\beta\in[0,g]\) a value that underpins the outcome of the symbolic aggregation operation; the 2-tuple, conveying information equivalent to \(\beta\), is derived using the following function:_  
\[\Delta[0,g]\to S\times[-0.5,0.5], \tag{7}\]  
\[\Delta\beta=(s_{i},\alpha_{i})\begin{cases}s_{i}\;,&i=round(\beta)\\ \alpha_{i}=\beta-i\;,&\alpha_{i}\in[-0.5,0.5)\end{cases}, \tag{8}\]  
_where round denotes the standard rounding operation,\(s_{i}\) signifies the index label closest to the value \"\(\beta\)\", and \"\(\alpha\)\" represents the symbolic translation value._  
Conversely, let \((s_{i},\alpha_{i})\) be a 2-tuple; there always exists a function \(\Delta^{-1}\) that, when applied to a 2-tuple, yields its corresponding numerical value \(\beta\in[0,g]\) and the following function is considered:  
\[\Delta^{-1}:S\times[0.5,-0.5)\rightarrow[0,g], \tag{9}\]  
\[\Delta^{-1}(s_{i},\alpha_{i})=i+\alpha_{i}=\beta. \tag{10}\]
## 5 Experimental Evaluation
### Public Sentiment on Healthy Lifestyle after Vaccination
#### 5.2.3 Sentiment Timeline for Vaccination Tweets  
Observing Figures 10 and 11, we can see some dissimilarities. As described in Section 5.1.3, VADER shows some misclassification for Twitter Dataset 2 as well. We manually inspected some tweets, and some misclassified example tweets with the sentiment are given in Table 6.  
Figure 10: Public sentiment for Twitter Dataset 2 on healthy lifestyle with timeline using TextBlob.  
Figure 9: Sentiment percentage distribution using raw and pre-proceseed tweets for Twitter Dataset 2 on healthy lifestyle.  
2.4. Top Frequency Words (Positive, Negative, Neutral) for Post-Vacination Tweets on Healthy Lifestyle  
In Figure 12, we show the high frequency word distribution for each topic, categorized into negative, neutral, and positive. Using word frequencies, no useful information can be observed. as shown in Figure A6. Later, using the log-likelihood values, we could better capture information for the positive words such as \"stay, safe, well, happy, party, thank,\" as given in Figure 12. In Figure 12c, we see the idea of travel ban in India and travel risk in Florida. However, the negative words are still scattered for other topics. Generating word clouds combining all topics provides a better overview of the negative words such as \"ban, sick, stop, emergency, death, mask, risk\" given in Figure 13. In Figure 13, at a glance, we can see that people are more reluctant to \"mask\" and lean towards \"party\" after getting vaccinated.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Original Tweet** & **TextBlob** & **VADER** \\ \hline Today marks my two weeks post second dose, so I am fully vaccinated. Still going to do the thing and wear my & & \\ mask and social distance, though. I never know what difficulties people around me may have, so I would be & positive & negative \\ mortified if I caught this again and spread it to someone else. & & \\ \hline \hline Finally got the first Dose of \#covid19 Vaccine. & & \\ Get Vaccident & & \\ We will still wear mask and practice social distancing.. Lets fight together against Covid. & & \\ \#stayseaanddekeesafe & & \\ \#govcaccience & & \\ \#vacciesafety & & \\ \#Vacination2021 & & \\ \hline \hline No. Frankly I’m not changing social distancing practice until two or three weeks after we’ve both had our second & & \\ dose of vaccine. https://to/OhgAwLmuLq (accessed on 5 June 2021) & & \\ \hline \hline Received my First Dose of COVID Vaccination. Urge all eligible (18-45 years)to get your shot soon at your nearest & & \\ Vaccination Centres. Vaccination Kamp; Social Distancing are the only few weapons to defeat the virus. & & \\ findiafightsCorona \#WebVaccine \#OccWIN \#VacinationForAll \#VearMasks [[https://t.co/fsCKguM9](https://t.co/fsCKguM9) jr]([https://t.co/fsCKguM9](https://t.co/fsCKguM9) jr) (accessed on 5 June 2021) & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Random tweets from Twitter Dataset 2 on social distancing.  
Figure 11: Public sentiment for Twitter Dataset 2 on healthy lifestyle with timeline using VADER.
## 3 Case Study
### Testing  
Proceeding to analyze the geometry of the opening of the web frame for the passage of the secondary stiffener, for this purpose, two different cut-out shapes have been chosen within a 530 \(\times\) 430 \(\times\) 20 mm steel plate (Young's modulus equal to 210 GPa, Poisson's ratio equal to 0.3, and yield stress equal to 235 MPa) in relation to the standard secondary stiffener (W.200 \(\times\) 8/FB.90 \(\times\) 14). Figure 6 illustrates the test specimens and their respective dimensions.  
The specimens consist of two welds: one linking the opening to the side shell and another connecting the opening to the standard stiffener (only for opening \(b\)). The weld between the opening and the standard stiffener is replaced with a standard bending moment of 7.5 kN\(\cdot\)m and a shear force of 15 kN, with a load ratio (\(R\)) of \(-1\). The load scenario entails a uniformly distributed load of 200 kN on the web frame, resulting in a SENT-type condition (Single Edge Notch Tension). Figure 7 illustrates the load condition as well as the adjustments made to the specimens for testing on the machine.  
Figure 5: Sectional area curve for the \(V_{0}\) version.  
Figure 6: Dimensions of the geometry and test specimens (in mm). (**a**) Opening \(a\), (**b**) test specimen of opening \(a\), (**c**) opening \(b\), and (**d**) test specimen of opening \(b\).  
Each specimen is positioned within the fatigue machine using two integrated auxiliary structures, one above and one below, to simulate the intended design load. The upper transition structure includes a U-block bolted and welded to another component supporting a link connected to the machine, ensuring alignment and load consistency. Similarly, the lower transition structure features an identical U-block connected to a parallelepiped element, also linked to the machine. This setup is depicted in Figure 8.  
The experimental loading condition follows a similar geometric scaling process as previously, but this time, it is scaled down by a factor of 2.5. The stress range is constrained within \(\pm\)25% of the mean stress component (\(R\) = 0.6). The load is gradually applied to the specimens in an upward continuous manner at a rate of 0.5 kN/s until reaching the desired design load, initiating the testing process. In order to record strain and displacement data, an axial extensometer is attached to the model. The standard load from the upper load cell operates at a designed frequency of 5 Hz.
# A Multi-Branch DQN-Based Transponder Resource Allocation Approach for Satellite Communications
## 4 Experiments
### Datasets and Implementation  
#### 4.1.1 Generation of Datasets  
Due to the absence of standard satellite resource allocation datasets, the reinforcement learning model lacks a sufficient amount of data for training. In view of this, this paper adopts two generation methods to randomly generate the satellite task list dataset. The first method is the zero-waste satellite task list dataset (ZW-Dataset) generation method, which is more conducive to comparing the performance between different allocation methods. The second is the non-zero waste dataset (NZW-Dataset) generation method, which is more consistent with the actual condition of a satellite task list.  
The ZW-Dataset generation method refers to the binned data generation algorithm proposed by [22]. Its core idea is to continuously divide a large task that fills the satellite resource pool, and randomly select subtasks with an area of not less than \(2m/\gamma\) for cutting each time, where \(m\) is the maximum area of the task in the task sequence and \(\gamma\) is the aspect ratio parameter. Finally, it is divided into a list of several tasks that satisfy a certain area ratio \(\gamma\) and an aspect ratio \(\rho\) (both set to 3 in this paper). The optimal allocation result of the tasks can be known on the ZW-Dataset due to the zero-waste setting. The generation method of ZW-Dataset is demonstrated in Table 4.  
By setting different parameters in the NZW-Dataset generation method, we can control the number of satellite tasks in the dataset, as well as the resource occupation in the time and frequency ranges. In this paper, the frequency and time in the resource pool are divided into ten segments and each task occupies at least one of them. The time range for the generated dataset is set to \(t_{i}\in[\Delta t/N,\ \Delta t/2]\) and the time range to \(f_{i}\in[\Delta f/N,\ \Delta f/2]\).
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 4 Experimental Results and Analysis
### Convergence Period Analysis
#### 4.3.2 Mobile EDs Scenario  
Figure 9 shows the convergence period and \(PSR\) of mobile EDs, where both typical ADR and ADR+ require 15 h and 14 h, respectively. Further, in Figure 9a, we can observe two types of convergence period (in the case of typical ADR and ADR+), which are the initial convergence period and convergence period caused by the mobility. The initial convergence period occurs owing to a high SF (because all EDs initially start transmitting packets with SF = 12). However, after a short period of stability, \(PSR\) decreases, and both typical ADR and ADR+ suffer from a convergence period. This convergence period is caused when a mobile ED receives a \(LinkADRreq\) MAC command containing SF and/or TP from the NS; the propagation environment might have been drastically altered, resulting in massive packet loss [16; 32].  
In Figure 9a, G-ADR and EMA-ADR take 16 h and 3 h for the initial convergence period, respectively, but do not suffer a second convergence period. This is because both of the proposed  
Figure 9: Convergence period and PSR of mobile EDs with N = 500: (**a**) initial allocation of SF = 12, and (**b**) initial allocation of SF = 12 (for ADR and ADR+) and initial SF allocation with I-SFA (for G-ADR and EMA-ADR).  
schemes employ filters, which helps to reduce the convergence period and improve the PSR. As shown in Figure 9b, both G-ADR and EMA-ADR use I-SFA as an initial SF assignment during deployment. Unlike a static scenario, both the proposed G-ADR and EMA-ADR with the I-SFA scheme have a convergence period of 13 h and 3 h. In the case of G-ADR (with I-SFA), mobility has a considerable impact on the convergence period because the newly adopted parameters (i.e., both SF and TP) do not provide assurance and efficient communication. In such a case, the propagation environment might have changed radically by the time \(LinkADRreq\) command reaches the mobile ED. Hence, a new packet from this ED with recently adopted parameters can be lost. However, EMA-ADR (with and without I-SFA) performs exceptionally well under the mobility conditions, resulting in a reduced convergence period and high PSR. The detailed convergence periods of different mobile EDs in hours are shown in Table 5.
# Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services +
## 3 Preliminary Foglet-Middleware Design
### Selection of Criteria  
IoT technologies are becoming part of our everyday lives. For that reason, many works [55] have focused on improving connectivity and communication among numerous IoT devices as well as making their systems self-contained and autonomous by collecting, generating, and extracting accurate and timely information. To enhance further this vision, the benefits of the fog and cloud services are used effectively and efficiently for ensuring the sustainability of IoT services. However, the selection of the adequate services is a crucial task since it is based on a set of quantitative and qualitative parameters belonging to the characteristics of the fog-to-cloud platforms. This coordinated paradigm adopts two models, which are: the decentralized model that offers quick, even real-time, interaction and location-based services to closer smart objects, and the centralized model that is perfect at batch processing and provides the unlimited proper services necessary for the treatment of IoT data (Figure 2). Therefore, the choice of criteria for establishing preference relations between fog and cloud services depends on understanding the nature of the collaborative model.  
Figure 1: Foglet architecture.  
In this paper, we identify and analyze the criteria that have a direct influence on the selection phase and express clearly the most important characteristics of fog and cloud platforms. We consider ten criteria (Table 2), which are: usability, scalability, adaptability, performance, proximity, mobility, cost, network, availability, and security to be considered for this problem of fog/cloud service selection.
# Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels
## 1 Introduction
[21] proposed a ship wireless communication resource allocation method based on the ant colony algorithm to maximize the communication capacity as the objective function and used the ant colony algorithm to find its optimal solution. [PERSON] [22], based on the two indicators of throughput and fairness, established a slot resource allocation optimization model to achieve precise slot resource allocation in ship wireless communication networks. [PERSON] proposed a channel allocation method for maritime communication systems based on throughput and load balancing. [PERSON] [23] optimized a water communication network resource allocation strategy with the objectives of minimizing energy consumption and delay, solved it through convex optimization, and the simulation results showed that the scheme could achieve a good balance between energy consumption and delay. [PERSON] [24] introduced new communication technology into maritime ship communication systems and proposed a channel resource allocation scheme for system interference control to increase the number of devices in the network. A reasonable deployment of shore-based collaborative units can improve the network performance of ship networking and reduce construction costs. At present, more studies have focused on the deployment of mid-road side units instead of the deployment strategy of shore-based collaborative units in the Internet of Things. The deployment strategy of roadside units in highway scenarios has been studied to establish optimization goals for communication energy loss and network connectivity [25]. The deployment strategy of roadside units has also discussed to improve the positioning accuracy of vehicles [26]. A deployment strategy was proposed to minimize the number of roadside units and ensure the quality of traffic service [27]. The needs of ship-shore communication services and the Internet of Vehicles are quite different due to the special navigation methods of inland river ships. Therefore, the rational deployment of shore-based collaborative units is of great significance to the needs of the intelligent navigation business of inland ships.