esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 In-Network Computing via ICN
### Reactive Executor Selection Strategies
#### 4.2.2 Centralized Approaches  
In [61], the executor selection is managed by a delegated node that oversees an edge domain. After receiving the service request from a consumer, the supervisor pols the edge nodes under its supervision to collect availability information, including the expected computation time and the cost of computation. Information is retrieved during a decision time interval and then the node with the best offer, e.g., lowest cost or shortest expected computation time, is selected for the computation.  
A softwarized edge network is instead considered in [31], where an SDN controller with global knowledge of the domain orchestrates routing and in-network computing services. When a service request is received, the controller first checks if the same service has been already executed and the result can be directly reused. If this is not the case, the controller evaluates the computational and storage resources of candidate nodes, ensuring they have sufficient capacity to execute the requested service. By considering the network topology and link latencies, the overall service computing time is calculated as the sum of three time contributions: (i) the time to retrieve input data, (ii) the time to execute the service, (iii) the time to deliver the output back to the consumer. The node that minimizes the overall service provisioning time is selected as the executor. If no suitable node is available, the request may fail or be redirected to another domain.
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 5 Enhancing Safety Through Large Language Models
### Human Factors and Error Reduction  
Human factors are a leading cause of maritime accidents, often resulting from cognitive overload, fatigue, miscommunication, or procedural errors. LLMs offer solutions to mitigate these issues by reducing the cognitive load on crew members and minimizing human errors through automated support.  
By automating routine tasks such as data entry, report generation, and information retrieval, LLMs allow crew members to focus on critical decision-making and operational duties. The models can provide real-time assistance by answering queries, offering reminders of safety procedures, and verifying that actions align with established protocols. This immediate support reduces the likelihood of mistakes caused by oversight or lack of information.  
Furthermore, LLMs can implement automated checks and balances within operational workflows. For example, when crew members input data or make decisions, the models can cross-reference this information against safety guidelines and alert users to potential discrepancies. By serving as an intelligent safeguard, LLMs help prevent errors from progressing into incidents, thereby enhancing the overall safety of maritime operations.
# Mixed Reality Enhanced User Interactive Path Planning for Omnidirectional Mobile Robot
## 5 The Description of VFH* Algorithm
### Modification of VFH* Algorithm
#### 5.2.2 The Candidate Direction Selection  
After determining the passable sectors, we added the process of sieving the sectors. When the passable sector is the whole circle, which means the robot can move in any direction, the direction towards the target point is undoubtedly a better choice. To avoid unnecessary cost calculation and ensure the accuracy of direction selection, we reduced the passable sector in this case to the area near the target sector, as shown in the Figure 5a.  
Besides, when the passable sector is large (greater than 80\({}^{\circ}\)), the direction close to the sector boundary is not a good choice. We preferred choosing some directions inside the sector, which were theoretically be safer. Therefore, for the passable sectors, in this case, we also made an appropriate reduction, as shown in the Figure 5b.  
As the obstacles in the constructed map will be inflated, the passage area will be safer. However, when some obstacles are close to each other, the operation of obstacle inflation will make the previously passable area impassable due to the narrow detected passable area. To reduce the occurrence of the above situation, when the detected passable sector angle is between 10\({}^{\circ}\) to 20\({}^{\circ}\), we will adjust the passable angle to make it passable in the above situation, as shown in the Figure 5c.  
If the range of candidate directions does not fall into the above three cases, the generated candidate directions are not adjusted. The above candidate direction selection strategy is expressed as follows:  
\[C_{d}=\begin{cases}[c_{rt}-\frac{20^{\circ}}{\alpha},c_{rt}+\frac{20^{\circ}}{ \alpha}]&(\beta=360^{\circ})\\ &[c_{l}-\frac{15^{\circ}}{\alpha},c_{r}+\frac{15^{\circ}}{\alpha}]&(80^{\circ} <\beta<360^{\circ})\\ &\frac{c_{l}+c_{r}}{2}&(10^{\circ}<\beta<20^{\circ})\\ &[c_{l},c_{r}]&others\end{cases} \tag{15}\]  
where \(C_{d}\) is the range of candidate direction sieved, \(c_{rt}\) is the sector from the robot's current position to the target point, \(\beta\) is the angle of the passable sectors, \(\alpha\) is the angular resolution of sectors, \(c_{l}\) and \(c_{r}\) are the left and right boundaries of the passable sector. We selected \(\alpha=2^{\circ}\) in this experiment.  
Figure 4: The threshold setting policy. (**a**) The low threshold setting. (**b**) The high threshold setting.
# Web Scraping Scientific Repositories for Augmented Relevant Literature Search Using CRISP-DM
## 2 Research Motivation & Questions  
Visual exploration of bibliographic databases postures a key role towards maintaining the transparency, quality and consistency of the systematic reviews through specific software (CiteSpace, VOSviewer, etc.) [6]. This had contributed significantly towards reinforcing advancements which could be mostly visible in biomedical journals by applying systematic review methodologies [7]. Despite the fact, these principles could also be very relevant to almost any other knowledge domain embarking on achieving the earlier explained educational research objectives.  
On the other hand, advanced analytical algorithms also have high aptitude towards complementing these methods of visual exploration. These signs could be observed through the propositions of applying the combinations of text mining techniques to reduce the time reviewing relevant literature [8]. This implicitly addressed a methodological approach that works on identifying relevant literature searches through extractive summarization.  
However, the spoken-of advanced analytical methods, as well as the visual exploration software, had somewhat suffered from the user interface lack of flexibility, as well as the restriction to data access. For instance, these advanced analytical methods had been set to retrieve data from open access repositories only; with an example of downloading a research paper corpus from a corpus database like Semantic Scholar ([[http://labs.semanticscholar.org/corpus/](http://labs.semanticscholar.org/corpus/)]([http://labs.semanticscholar.org/corpus/](http://labs.semanticscholar.org/corpus/))). This corpus database exposes both data samples, or the full data set through API call or Command Line Interface that is restricted to open access repositories (please see Appendix A.1). Likely, visual exploration software sees that the primary source of input data are only repositories like the Web of Science and PubMed [9].  
These methods are without a doubt very valuable to researchers. Still, this lack of flexible user interface and restriction to data access poses key obstructions to executing systematic reviews in a timely manner. The sought-after stage of augmenting systematic reviews deems to be a challenge as a result.  
Correspondingly, these earlier spoken-of pointers spark the discussion for the following interrogation: How can researchers extend the reach of applying extractive summarization for a relevant literature search beyond open access repositories, if not all scientific repositories especially,that many repositories do not follow open access standards? In addition, whether the technology landscape possess rather user-friendly interfaces enabling researchers to execute wider advanced analytical methods serving these principles of systematic reviews?
# Two-Factor Authentication Scheme for Mobile Money: A Review of Threat Models and Countermeasures
## 2 Related Work
### Various Attacks on Mobile Money System  
An outsider or unauthorised person can attack the MMS at various levels. There are 11 attack points (AP) that are utilised by adversaries, as shown in Figure 4.  
Figure 3: Mobile money authentication phase.  
The attack on mobile money users, i.e., authentication attack at AP 1: The attacker gets access to the user's PIN through shoulder surfing since the PIN used is only four or five digits long and is unmasked [39]. Once the attacker accesses the PIN, they can perform a fraudulent transaction. The attacker can also perform a brute force attack because of the simplicity of the PIN [11; 23; 44].
* The attacks on the mobile money communication channels (AP 2, AP 6, AP 9): Attackers can hack or control the traffic into the MMS and manipulate accounts to perform transactions or gain benefits [11; 23; 44].
* The attack on the mobile money app server at AP 3: The adversary attacks the server and makes it unavailable to both mobile money users and agents. According to [PERSON] et al. [11], attackers target mobile money servers and overwhelm them with fake traffic to block requests from mobile money users and agents. Attackers also install malware on the mobile money app server to deduct a small amount of money from the mobile money user's and agent's wallet and deposits it into the attacker's account without them realising [18].
* The attack on IT administrator (AP 4): The intruder, insider, or unauthorised person can hack into the administrator's computer and change the credentials so that the administrator cannot access the system.
* The attack on the mobile money agent (AP 7): The adversary uses a shoulder surfing technique to steal the agent's commission PIN or gives the wrong PIN repeatedly when making a transaction to get access to the agents' PIN. According to [PERSON] and [PERSON] [12] and [PERSON] [13], the adversary gives the wrong mobile phone number repeatedly to get an agent's PIN and uses the PIN to gain unauthorised access to the agent's float account.  
Figure 4: The red dashed line with an arrow represents an attack point (AP) on different components of the MMS.  
The attack on the bank server (AP 10): The attacker makes the bank server unavailable to the mobile money user who wishes to transfer money from the bank account to the mobile money wallet through a denial-of-service attack.  
The attacks on notification message channel and modifications of messages (AP 5, AP 8, AP 11): Attackers can hack into the communication channel of the notification message, modify the message, and later resend it to the intended recipient [45; 46].
## 4 Results
### Relations and Similarity Analysis for AT, RH, SR, BP, PR, and SD
Regarding PR, there were two station pairs (i.e., L1 vs. L2 in group G5, and C2 vs. C4 in G6) available; where moderate-to-strong relations, such as r-values (\(\geq\)0.69) and AAE (\(\leq\)0.92 mm), and unacceptable similarities, such as PS value (\(\leq\)9.25%) were observed. Regarding SD, data for only one station pair (i.e., C2 vs. C4 in group G6) \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & **G5** & & & & **G6** & & **G7** \\ \cline{3-13}  & & & **L1** & **L1** & **L1** & **L2** & **L2** & **R2** & **C2** & **C2** & **C4** & **JE308** \\  & & & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** & **vs.** \\  & & & **JE306** & **L2** & **R2** & **JE306** & **R2** & **JE306** & **C4** & **JE323** & **JE323** & **JE312** \\ \hline  & n & 1039 & 3227 & 1726 & 1055 & 1726 & 23,847 & 2060 & 1112 & 1112 & 40,860 \\ AT & AAE (\({}^{\circ}\)C) & 1.07 & 0.88 & 1.24 & 1.17 & 0.98 & 1.81 & 0.98 & 2.20 & 1.82 & 2.04 \\  & r & 1.00 & 1.00 & 1.00 & 1.00 & 1.00 & 0.99 & 1.00 & 0.99 & 0.99 & 0.98 \\  & PS (\%) & 53.90 & 68.79 & 50.75 & 53.55 & 57.13 & 40.50 & 67.28 & 30.13 & 32.55 & 35.68 \\ \hline  & n & 1039 & 3116 & 1244 & 1055 & 1243 & 22,499 & 2060 & 1112 & 1112 & 28,762 \\ RH & AAE (\%) & 5.4 & 4.23 & 4.99 & 5.62 & 3.79 & 6.49 & 4.55 & 4.97 & 5.08 & 7.53 \\  & r & 0.91 & 0.92 & 0.88 & 0.95 & 0.92 & 0.89 & 0.92 & 0.90 & 0.88 & 0.86 \\  & PS (\%) & **84.22** & **92.49** & **90.51** & **90.90** & **96.62** & **80.60** & **91.41** & **88.58** & **87.32** & 74.61 \\ \hline  & n & - & - & - & - & - & - & 19,333 & 2060 & 1112 & 1112 & 24,680 \\ SR & AAE (W/m\({}^{2}\)) & - & - & - & - & - & - & 73.52 & 23.10 & 43.27 & 37.18 & 69.65 \\  & r & - & - & - & - & - & 0.86 & 0.96 & 0.84 & 0.85 & 0.87 \\  & PS (\%) & - & - & - & - & - & 31.57 & 29.84 & 17.72 & 21.12 & 36.93 \\ \hline  & n & - & - & - & - & - & 23,860 & 2060 & 1112 & 1112 & 25,053 \\ BP & AAE (kPa) & - & - & - & - & - & 7.86 & 1.57 & 0.45 & 1.82 & 5.28 \\  & r & - & - & - & - & - & 0.14 & 0.82 & 0.77 & 0.98 & 0.37 \\  & PS (\%) & - & - & - & - & - & **92.85** & 0.44 & **96.67** & 0.00 & **99.94** \\ \hline  & n & - & 3083 & - & - & - & - & 2002 & - & - & - \\ PR & AAE (mm) & - & 0.68 & - & - & - & 0.92 & - & - & - \\  & r & - & 0.77 & - & - & - & - & 0.69 & - & - & - \\  & PS (\%) & - & 9.25 & - & - & - & - & 8.85 & - & - & - \\ \hline  & n & - & - & - & - & - & - & 1786 & - & - & - \\ SD & AAE (cm) & - & - & - & - & - & - & 2.16 & - & - & - \\  & r & - & - & - & - & - & - & 0.97 & - & - & - \\  & PS (\%) & - & - & - & - & - & - & **85.95** & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The analysis of similarity and relations among the meteorological parameters for G5 and G6 groups, measured at 2 m height. The sign ‘-’ meant the measurements were unavailable for comparison, and highlighted bold values meant strong PS with \(\geq\)75%. was available; where strong relations, such as r-value (=0.97) and AAE (=2.16 cm), and acceptable similarities, such as PS value (=85.95%) were seen.
# Techniques for Robust Imputation in Incomplete Two-Way Tables
## 2 Material and Methods
### rSVD84 (Procedure Based on [PERSON] et al., 2021 and [PERSON] 1984)  
Consider a dimension \(\boldsymbol{Y}\) matrix \((n\times p)\) with possible missing entries. (i) Using the observed information, calculate the vectors of trimmed means (i.e., the means when acertain percentage of the most extreme observations are ignored; we use 10% or 20% following [25]) by columns and by rows, \(\mathbf{b}(1\times p)\) and \(\mathbf{a}(n\times 1)\) respectively. (ii) Determine the presence of outliers in vector \(\mathbf{a}\) by any technique that is preferred for univariate outliers (for example, the quartile method) and if there is any discrepant data replace it with a trimmed mean of the elements of \(\mathbf{a}\). (iii) Update the elements of \(\mathbf{b}\) and \(\mathbf{a}\) as follows: \(b_{c}=med\{|y_{r,c}/a_{r}|;r=1,\dots,n\ \}\) and \(a_{r}=med\{|y_{r,c}/b_{c}|;c=1,\dots,p\ \}\). Repeat steps (ii) and (iii) with the updated vectors \(\mathbf{b}\) and \(\mathbf{a}\) until some convergence criteria are attained. Once the stability of vectors \(\mathbf{b}\) and \(\mathbf{a}\) has been achieved, a robust low-rank approximation of \(\mathbf{Y}\) is obtained by means of the \(\mathbf{ab^{\mathrm{T}}}\) product whose element signals must be the same as those of \(\mathbf{Y}\). To obtain the first singular value and the first right and left singular vector of the robust SVD, the standard SVD over \(\mathbf{ab^{\mathrm{T}}}\) is applied and both the first singular value and the first right and left singular vector are recorded. To obtain the second singular value and the second right and left vector, the same procedure described in this section is done, but replacing the matrix \(\mathbf{Y}\) with \(\mathbf{Y}\)-\(\mathbf{ab^{\mathrm{T}}}\). This cyclic strategy deflating the matrix continues until the desired number of robust SVD components-rSVD84 is obtained. In the Appendix A of this manuscript a function is provided in R [13] that calculates the rSVD84 for any complete or incomplete matrix.  
Pseudocode  
1. \(\mathbf{Y}\) <- incomplete matrix
2. \(\mathbf{Y^{*}}\) <- initial completed matrix (columns mean)
3. Calculate a robust singular value decomposition on \(\mathbf{Y^{*}}\) Section 2.3 (rSVD).
4. The rSVD contains the require imputations.
We firstly extended original research of the four-coil WPT system to elucidate a number of crucial system concepts, including the maximum power efficiency and the optimal load. Then, a single-layer square thin unit cell of loaded spiral resonators was designed. Accordingly, these units were assembled in a 5 \(\times\) 5 slab periodically to achieve a negative magnetic permeability. Furthermore, the tuning scheme was proposed to contribute to the transfer property. In addition, the Class-E power amplifier was employed as the inverter circuit topology to adapt to the highly operating frequency of 2.80 MHz. By applying this technology, the system energy efficiency could be improved by 4.26% and 9.13% at distances of 100 cm and 200 cm, respectively. The proposed system is significant as a power supply for electric applications in relatively far distances, such as a mobile phone and tablet PC. In particular, this technology can make the power grid much smarter by supplying high voltage power pole tower on-line monitoring terminal devices.
# Extracting Sentence Embeddings from Pretrained Transformer Models
## 4 Results
### Token Aggregation and Post-Processing Techniques
#### 4.1.1 Avg. versus B2S and B2S-100  
An interesting result, as seen in Table 4, is the comparison of simply averaged BERT tokens from many contexts (the Avg. model) and word vectors from a specially trained Word2 Vec-style model BERT2 Static (the B2S model) using the same BERT contexts, as described in Section 3.2.4. If no token aggregation and post-processing techniques are used, B2S is of similar performance in STS tasks (with average Spearman correlation of 56.7 versus 56.4), worse at clustering (with average clustering accuracy of 53.8 versus 55.2), and comparable in classification tasks (with average B2S accuracy of 76.9 versus 77.3) to the Avg. model. As we can see, the task performance differences are very small.  
Apart from training, the second main difference between the Avg. and B2S models is tokenization. The Avg. model uses sub-word tokens, the same as BERT (tokens of which it averages), while B2S models use a vocabulary of the full set of the most frequent words, 20 times the size of the BERT's. Due to this discrepancy, token aggregations for BERT and B2S models are applied differently, as idf statistics for both tokenizations are different; also, B2S does not have the equivalence of biased tokens such as BERT. However, it is very straightforward for B2S to filter out (do not use during averaging) a portion of the most frequent words. During our experiments, we varied this number and found that removing the 100 most frequent words works best, which improved the average Spearman correlation of STS tasks from 56.7 to 60.6 and the average accuracy of clustering tasks from 53.8 to 55.3 compared to full B2S, both with plain token averaging and no post-processing applied.  
Despite the additional option for B2S to remove the 100 most frequent tokens, using additional token aggregation and post-processing techniques allows our simple Avg. model to surpass both B2S and B2S-100 (see Table 4). Note that the removal of the most frequent words for B2S works in a similar way to weighted token aggregations or post-processing, the gains are not additive. Now the best average STS Spearman correlation score becomes 69.5 for Avg. versus 66.4 of B2S and the best average clustering accuracy of 62.2 for Avg. versus 57.2 of B2S-100. Additional techniques here helped a much simpler Avg. model outperform a much more complex learned B2S.  
The success of such a simple Avg. model inspired us to seek further improvement by combining it with the parent BERT model. For the combined BERT + Avg. model results, please see Section 4.3.
###### Abstract  
This study proposes a novel wireless power transfer (WPT) resonance compensation design method based on the SS topology with a self-limitation function that achieves fault tolerance without additional components when the secondary circuit is in a short and open state. Conventionally, WPT compensation topologies have constant current (CC) or constant voltage (CV) output characteristics. The CC and CV characteristics can lead to overvoltage and overcurrent, respectively. Several control systems have been proposed to counter this issue; however, they tend to increase the cost and weight of the system. The proposed topology has a self-limitation function that limits the voltage-ampere output. Here, the wireless charging system was designed after the parameters were obtained using the proposed analysis. The performance of the proposed design was verified by configuring a 400 W experimental prototype and comparing it with the conventional series-series (SS) topology. Experimental results indicated that the system could effectively limit the output voltage and current resulting from open-or short-circuit states.  
wireless power transfer (WPT); compensation topology; parameter design method; fault tolerance +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON], [PERSON], [PERSON], [PERSON].  
Novel Design Method in Wireless Charger for SS Topology with Current/Voltage Self-Limitation Function. _Appl. Sci._**2023**, 13, 1488.  
https://doi/10.3390/app1301488
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 5 Analysis of the Simulation and Test
### Field Test on an Embedded Linux Platform
#### 5.2.3 Adaptation of DRFC-TSP to the Different Topologies  
In order to evaluate the adaptation of DRFC-TSP to the different topologies, the six topologies shown in Figure 11 were simulated. The time offset of node 8 and its reference node is shown in Figure 12. In the topology shown in Figure 11(2) and topology shown in Figure 11(4), node 4 was selected as the reference node, although its offset was slightly larger compared with the cost of adding one hop. In the topology shown in Figure 11(5), node 8 selected node 6 as the reference node to synchronize, because the hop from root node 0 and time offset of node 6 were both the smallest. In the topology shown in Figure 11(3), nodes 2 and 6 exhibiting a small offset were selected as intermediate forwarding nodes, and this is the path with the least number of hops.  
Figure 11: (1–6) Synchronization in six different topologies.  
Figure 10: Time offset of node 8 and its reference of each route.