esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The \(2^{k}\) Factorial Analysis
The fourth column, labeled \(AB\), is the product of the entries in columns \(A\) and \(B\). The four observations are listed in a column vector next to this matrix. The column vector is labeled \(y\) and consists of the results corresponding to the factor levels listed under columns \(A\) and \(B\). The next step is to multiply the entries in column \(I\) by those in column \(y\) and put their sum under column \(I\). The entries in column \(A\) are now multiplied by those in column \(y\) and the sum is entered under column \(A\). This operation of column multiplication is repeated for the remaining two columns of the matrix. The sums under each column are divided by 4 to give the corresponding coefficients of the regression model. The importance of a factor depends on the proportion of the metric _total variation_ explained by the factor. The total variation of \(y\) is also known as Sum of Squares Total (SST), which can be calculated as follows:  
\[Total\ variation\ of\ y=SST=\sum_{i=1}^{2^{2}}(y_{i}-\overline{y})^{2} \tag{9}\]  
where \(\overline{y}\) denotes the mean of the responses from all four experiments. For a \(2^{2}\) design, the variation can be divided into three parts:  
\[SST = 2^{2}q_{A}^{2}+2^{2}q_{B}^{2}+2^{2}q_{AB}^{2} \tag{10}\]  
These parts can be expressed as a fraction; for example:  
\[Fraction\ of\ variation\ explained\ by\ A=\frac{SSA}{SST}=\frac{2^{2}q_{A}^{2}}{SST} \tag{11}\]  
Hence, we can indicate the percentage of variation of each studied metric explained by each factor. The more percentage of variation, the more impact this factor has in the measured metric. In our example, we found that the density of vehicles accounts for 89.01% (_i.e._, \(\frac{2^{2}\times(-0.225)^{2}}{0.2275}\)) of the total variation of the warning notification time, the speed of the vehicles accounts for 9.89% (_i.e._, \(\frac{2^{2}\times(-0.075)^{2}}{0.2275}\)), and their combination accounts for the remaining 1.10% (_i.e._, \(\frac{2^{2}\times 0.025^{2}}{0.2275}\)). Therefore, in our selected example the density of vehicles is the most important factor that affects the warning notification time. \begin{table}
\begin{tabular}{c c c c c} \hline
**I** & **A** & **B** & **AB** & **y** \\ \hline
1 & \(1\) & \(-1\) & \(-1\) & \(0.5\) s \\
1 & \(-1\) & \(1\) & \(-1\) & \(0.8\) s \\
1 & \(1\) & \(1\) & \(1\) & \(0.4\) s \\ \hline
2.7 & \(-0.9\) & \(-0.3\) & \(0.1\) & Total \\
0.675 & \(-0.225\) & \(-0.075\) & \(0.025\) & Total/4 \\ \hline \end{tabular}
\end{table} 
Table 3: Sign table method of calculating the effects of the factors in a \(2^{2}\) design. The outcome of the \(2^{k}\) factorial analysis allows us in sorting out factors in the order of impact. At the beginning of any performance study, the number of factors and their levels could usually be large. A full factorial design with such a large number of factors and levels may not be the best use of available effort. The first step should be to reduce the number of factors and to choose those factors that have a significant impact on performance.
## Experimental Results
### Ablation Study  
To further investigate the effectiveness of SaSLM and answer **RQ3**, we conducted an ablation study. We investigated the effects of personalized positional weights and the self-attentive layer. The positional weights account for the order of the predicted item ratings, and the self-attention ensures the awareness of other items in the set during subset selection. We first define subset learning model (SLM), which is the basic model of SaSLM without positional-weights and self-attention. We added up positional-weights and self-attention to SLM, denoted by SLM\(+\)_pos_ and SLM\(+\)_att_, respectively.  
Results are reported in Table 6, where performances in both set rating predication and item-rating prediction are shown. SaSLM generally performed the best, except on the NetEase dataset.  
As shown in Table 6, results on both RealSet-explicit and RealSet-implicit datasets are similar. Without positional weights and self-attention, SLM performs poorly. Only adding self-attention to SLM did not improve the performance. In comparison with positional weights, the performance improved greatly. By combining positional weights and self-attention, the best results were achieved by SaSLM.  
On the YouShu dataset, however, the positional weights deteriorated the results. Although SaSLM performed the best, its advancement mostly came from the self-attentive layer. Similarly, on the NetEase dataset, the positional weights did not provide further performance gain, whereas applying self-attention alone greatly improved the performance and achieved the best results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Method** & **Set-Level RMSE** & **Item-Level RMSE** \\ \hline \multirow{8}{*}{**YouShu**} & MFSet & 0.4690 & 0.5680 \\  & BCF & 0.4216 & 0.7045 \\  & BGCN & 0.4467 & 0.7082 \\ \cline{2-4}  & ARM-MF & 0.4979 & 0.5013 \\  & VOARM-MF & 0.5000 & 0.5018 \\  & SaSLM-MF & 0.4979 & **0.5000** \\ \cline{2-4}  & ARM-NCF & 0.4343 & 0.6083 \\  & VOARM-NCF & **0.4184** & 0.7794 \\  & SaSLM-NCF & 0.5586 & 0.5133 \\ \hline \multirow{8}{*}{**NetEase**} & MFSet & 0.4881 & 0.5128 \\  & BCF & **0.4074** & 0.7035 \\  & BGCN & 0.6305 & 0.7073 \\ \cline{1-1} \cline{2-4}  & ARM-MF & 0.4985 & 0.5002 \\  & VOARM-MF & 0.4953 & 0.5005 \\  & SaSLM-MF & 0.4985 & **0.5001** \\ \cline{1-1} \cline{2-4}  & ARM-NCF & 0.4517 & 0.6535 \\  & VOARM-NCF & 0.4456 & 0.7200 \\  & SaSLM-NCF & 0.4461 & 0.6058 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of set-rating prediction with sets of various sizes.  
In short, both the positional weights and the self-attentive layer play important roles in SPL. The positional weights were effective on RealSet but not on YouShu and NetEase, indicating that the positional weights should be considered only when the sets contain a few items. Unlike positional weights, the self-attentive layer shows effectiveness when the set contains more items.
## 1 Introduction
### Digitisation and FM  
New technologies have transformed many people's daily lives and have revolutionised several traditional industry practices aiming to achieve efficiency, accuracy, and precision. This evolution has gained momentum due to advancements in technologies such as the Internet of Things (IoT), big data, cloud computing and cyber-physical systems [19].  
The strengths of these innovations 4.0 lie in monitoring, controlling, interoperability, real-time information processing and process self-optimisation [19]. The physical world's connection with the virtual world enables products and components to create a self-adapting and self-managing communication network [20].  
In the construction sector, the first attempt at digitisation aiming to increase the sector's efficiency has already been seen with the spread of BIM [21].
## 5 Experimental Results
### Inference Result of DNN  
The hardware specifications used in the experiment for inference were a ASUS GL504G laptop with an Intel(r) Core(tm) i7-8750H CPU and an NVIDIA GeForce RTX2070 GPU. Table 4 shows the results of comparing the ground cost of the test dataset to the prediction cost of the DNN. The left column of Table 4a is the sum of the average travel times of all the agents for the assignment of the PDDL solver in the test dataset. The right column of Table 4a shows the agent's bottleneck of the longest average travel time when all agents started simultaneously and completed all jobs. Table 4b shows the sum and bottleneck of the average travel time of all agents for the assignment result based on the predictions of the DNN. Table 4c presents the reduction ratio of the average travel time for the assignment based on the DNN prediction or PDDL solver calculation.  
As a result of the assignment between multiple agents and multiple jobs by inference of the DNN, the average travel time increased by a maximum of \(13\%\) in the case of a bottleneck compared with the ground truth. The result of the DNN inference is sometimes better than the cost value of the ground truth in the case of the sum of the average travel times, indicating that the PDDL solver's job assignment was not optimal globally.  
Table 5 shows the inference computation time for the DNN, which has a time complexity of \(O(1)\) because it requires an average of approximately 20 ms of constant time, regardless of the number of agents and jobs.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multicolumn{1}{c}{\multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Agents (\(j\)) and** \\ **Jobs (\(k\))** \\ \end{tabular} }} & \multicolumn{6}{c}{**Cost (Average Travel Time [s])**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**Ground Cost**} & \multicolumn{2}{c}{**Prediction Cost**} & \multicolumn{2}{c}{**Average Travel Time**} \\  & & \multicolumn{2}{c}{**in the Test Dataset**} & \multicolumn{2}{c}{**of the DeConNet**} & \multicolumn{2}{c}{**Reduction Ratio**} \\  & & \multicolumn{2}{c}{**(a)**} & \multicolumn{2}{c}{**(b)**} & \multicolumn{2}{c}{**(c)**} \\ \hline \(j\) & \(k\) & \(G_{S}\) & \(G_{B}\) & \(P_{S}\) & \(P_{B}\) & \(P_{S}\)/\(G_{S}\) & \(P_{B}\)/\
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 6 Conclusions
The Simulation Model  
Without loss of generality (the simulator--independently of its complexity--is treated by the optimisation algorithm as a _black-box_), we define only the kinematic model of a ship, which describes the motion of the ship without consideration of forces (an example of a more complex model can be found, for instance, in [50]). The optimisation problem under consideration (see Section 3) has two objectives: the _passage time_ and the corresponding _fuel consumption_ are to be _minimised_. They are defined in the following way:  
_Passage time (duration)_, calculated as a sum of passage times for each linear path segment, \(r\):  
\[t_{AB}=\sum_{r=1}^{n_{r}-1}\Delta I_{r}\sum_{i=1}^{s_{\text{c}}}\frac{1}{w_{s} (x_{r_{i}},y_{r_{i}},v_{s_{r}},t_{r_{i}})},\] (A1)  
_Fuel consumption (total)_, as above--accumulated over all segments:  
\[f_{\text{c}}=\sum_{r=1}^{n_{r}-1}\int_{0}^{t_{fr}}\Bigl{[}0.0625+0.001v_{s_{r} }^{3}(t)\Bigr{]}dt,\] (A2)  
where (\(n_{r}-1\)) is the number of segments forming a single ab-path, \(\Delta I_{r}\) is the length of the \(r\)-th segment, \(s_{\text{c}}\) is the number of subsegments used to approximate the integral \(t_{fr}=\int_{0}^{\Delta I_{r}}\frac{dl}{w_{s}}\), \(w_{s}\) is the absolute speed (SOG--_Speed Over the Ground_) of the ship (along the \(r\)-th linear segment), and \(v_{s_{r}}\) is the speed of the ship relative to the water (STW--_Speed Through Water_). The ship absolute speed (\(SOG\)) depends on the _tidal stream_ (typically given in the form of a vector field), which in the simulations is assumed to be as follows (see Figures 1 and 4):  
\[\mathbf{v}_{t}=c_{1}[\cos(\alpha)\ \hat{\mathbf{x}}+\sin(\alpha)\ \hat{\mathbf{y}}]=v_{t_{x}}\ \hat{\mathbf{x}}+v_{t_{y}}\ \hat{\mathbf{y}},\] (A3)where: (a) For the _test problem 1_, shown in Figure 1 and denoted in the text by _[Prob1]_:  
\[ \begin{split} c_{1}&=2.25\bigg{(}1+\frac{y+108,000}{21 6,000}\bigg{)}\sin(0.000145\ t),\\ \alpha&=\arctan\bigg{(}\frac{3x}{216,000}-2\bigg{)} \end{split}  \tag{10}\]  
(b) For the _test problem 2_, shown in Figure 4 and denoted in the text by _[Prob2]_  
\[ \begin{split} c_{1}&=-1.125+\frac{5.625}{216,000}(y- 108,000)\ \sin(0.000145\ t),\\ \alpha&=\arctan(0.25\ \pi\ \cos(\pi/216,000\ x) \end{split}  \tag{11}\]  
Finally, _the ship absolute speed_ (along the given path segment \(p_{s_{i}}\)) is defined as follows:  
\[w_{s}=v_{s}[\cos(\gamma-\beta)]+v_{t_{x}}\cos(\beta)+v_{t_{y}}\sin(\beta), \tag{12}\]  
where \(\beta\) is the angle (slope) of \(p_{s_{i}}\) and  
\[\gamma=\beta+\arcsin\bigg{(}\frac{v_{t_{x}}\sin\beta-v_{t_{y}}cos\beta}{v_{s} }\bigg{)}.\]  
(a) Each ab-path is a continuous piecewise-linear function (formed by the sequence of the corresponding graph edges). (b) Each control input sequence is a piecewise-constant function (constant along each linear path segment).
## 1 Introduction
Reference [16] proposed a new tightly integrated navigation method consisting of a SINS and a pressure sensor (PS), in which beam measurements are used without converting them to 3D velocity in harsh GNSS environments. Referencing [17] for single satellite navigation and positioning in challenging environments, a navigation and positioning algorithm based on clock bias elimination was given, but no corresponding solutions were given for other positioning situations. At present, there are advanced algorithms that provide feasible reference solutions for the low-cost and high-precision positioning accuracy of integrated navigation in challenging environments. Reference [18] proposed an application data fusion algorithm for indirect centralized (IC) integrated SINS/GNSS. The main purpose was to improve the positioning accuracy, performance and reliability of low-cost SINS/GNSS integrated navigation systems. The authors of [19; 20; 21] proposed a low-cost, high-precision multisensor fusion navigation and positioning design for challenging multipath and non-line-of-sight (NLOS) environments. Reference [22] proposed a new positioning method to improve accuracy in challenging environments by adapting to the random noise of microelectromechanical systems (MEMS-INS) and accurately estimating INS errors. However, this type of algorithm usually needs to observe four satellites. The newly proposed algorithm does not rely on altimeters and continuous observations, as it is a dynamic positioning algorithm. Switching between LEO satellites can bring the following three advantages:  
1. In military application scenarios, a certain degree of anti-interference ability can be guaranteed by switching. The shorter the switching time is, the less likely it is to be interfered with by the enemy. 2. Since the bandwidth resource is also an important frequency band resource, switching between LEO satellites can avoid occupying the bandwidth for a long time, thereby making full use of the bandwidth resource and avoiding bandwidth waste. 3. Through switching, the LEO satellite navigation service can be used as long as possible while ensuring the LEO communication function. This is mainly based on the ICN perspective. With the gradual deployment of LEO satellites, such as SpaceX, OneWeb and Hongyan (China), our algorithm has practical significance and practical value. It is a low-cost, low-complexity and anti-jamming algorithm that can be used in harsh environments, such as lush forests, canyons, cities with high-rise buildings and high-latitude areas with few visible satellites, without relying on traditional GNSSs. It provides a navigation plan for outdoor travel, expedition, scientific research and field exploration search and rescue personnel.
## 7 Conclusions  
Successfully receiving query messages is essential for sensor nodes to collect and report sensing data in response to the queries. Localized discovery and localized recovery of query losses are required for sensor nodes due to the large network size. This paper introduces a localized approach for the discovery and recovery of query losses in sensor networks. We employ the mechanisms of distributed detector clustering, local query name resolution, and collective operation of detectors to achieve both scalability and high success rate of loss discovery and recovery. We proved the scalability and the high successful delivery rate of the proposed approach. We extended the analysis of the approach to the MAC and PHY layers to find appropriate collective operation of loss detectors. Our simulation results imply that the proposed approach achieved short route length of recovery, high successful discovery, and high recovery rates.  
The proposed approach can be applied to the on-demand information retrieval in sensor networks. Since it employs a fully distributed and local operation while achieving a global improvement of query dissemination, it has an especially good performance in large size sensor networks and can be applied without depending on specific broadcast mechanisms.
# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 3 Materials and Methods
### Search Execution  
Selecting precise search terms to retrieve broad and relevant articles was challenging. We combined different search terms and their synonyms into logical search strings (S1-S4). After preliminary searches yielded few articles, we focused on articles published in the last three decades, between 1 January 1994 and 1 January 2024. Articles not retrieved by the S1 and S2 search strings were retrieved by quoting the search phrases in the S3 and S4 logical search strings. These logical search strings were used to search the Google Scholar [97,98] and PubMed databases [99]:  
* S1: ((EEGOR Electroencephalogram) AND (Seizure detection OR Machine learning seizure detection OR Deep learning seizure detection OR Interical spike detection OR Ictal spike detection) AND (Rodent model of epilepsy OR Mouse model of epilepsy).
* S2: ((EEG OR Electroencephalogram) AND (Seizure prediction OR Machine learning seizure prediction OR Deep learning seizure prediction OR Spike detection OR Preictal spike detection) AND (Rodent model of epilepsy OR Mouse model of epilepsy) OR Rate model of epilepsy).
* S3: ((\"EEG\" OR \"Electroencephalogram\" OR \"iEEG\") AND (\"Seizure detection\" OR \"Machine learning seizure detection\" OR \"Deep learning seizure detection\" OR \"Interical spike detection\" OR \"Ical spike detection\") AND (\"Rodent model of epilepsy\" OR \"Mouse model of epilepsy\" OR \"Rat model of epilepsy\")).
* S4: ((\"EEG\" OR \"Electroencephalogram\" OR \"iEEG\") AND (\"Seizure prediction\" OR \"Machine learning seizure prediction\" OR \"Deep learning seizure prediction\" OR \"Spike detection\" OR \"Preictal spike detection\") AND (\"Rodent model of epilepsy\" OR \"Mouse model of epilepsy\" OR \"Rat model of epilepsy\")).  
A total of 3021 articles were retrieved through the four search strings. Precisely, Google Scholar returned 2443 articles, while PubMed returned 578. According to PRISMA protocol, the procedure for the literature search, document selection, and filtering is summarised in Figure 3.  
Figure 3: Review flowchart following the PRISMA statement, illustrating the process of identification, screening, determination of eligibility, and inclusion.
## 4 Results and Discussions
### Quantitative Comparisons
We compare our proposed model with several representative methods, including SRGAN [13], SRNTT [14], and RDN [15], as shown in Table 6. For the BSD100 and Urban100 datasets, our method yields excellent properties and outperforms the other methods on PSNR and SSIM. It reveals that the non-local architecture paid more attention to the different locations within a particular frame. Moreover, the above results also demonstrate the universality of our method in video inpainting and SR tasks, while no existing methods can deal with both situations.
## 2 Materials and Methods
### Statistical Analysis  
Studies were clustered into three groups based on the surgical approach used to treat peri-implantitis: (i) flap access, (ii) resective and (iii) reconstructive. Subgroups were plotted separately and contributed independently to the analysis if they reported using different disinfective agents. Probing depth (PD) reduction, recession (REC) increase, radiographic bone gain (RBG) and implant survival at last follow-up were considered the primary outcomes of the study. Data extraction relative to number of patients, number of implants, implant type, diagnostic criteria, pre-surgical protocol, surgical treatment modality, need of re-treatment, clinical outcomes, and follow-up length were extracted by two independent operators (RDG and BS) and compared. A third independent operator (HLW) was contacted in the case of disagreement. Study characteristics, quality and heterogeneity were assessed by the Cochrane Collaboration tool by two independent operators (RDG and BS). Statistical analysis was performed as weighted mean of studies using comparable treatment modality. Statistical analysis was conducted by a primary statistician (MVR) and validated by an independent statistician (ZC). The random-effect model was applied when performing the averages to account for methodological differences among studies. The data on means for each study were pooled using the mean value and standard error (SE) and were weighted by the inverse variance method. SEs were calculated with this measure SE = upper limit of 95% CI--mean/1.96. The number of implants and patients at the study inception, and those that became lost to follow-up, failed or experienced recurrence, were tallied to calculate survival and success. Forest plots were produced to graphically represent size effect and 95% CI of the outcomes. The heterogeneity of results across studies was evaluated using the I\({}^{2}\) statistic, which was interpreted accordingly: 0% to \(\geq\)25 (modest), 25% to 50% (moderate), 50% to 75% (substantial) and 75% to 100% (considerable). Differences were considered statistically significant for \(p<0.05\).
## 5 Applications in Food Products and Nutraceuticals
### Non-Food Items
#### 5.2.3 Biosorbent Production  
Although research is still limited, biosorbents derived from _Citrus_ waste have been studied as a sustainable solution for removing toxic metals and pesticides from wastewater. This material, when modified through various physic-chemical treatments, has shown innovative solutions to waste management problems [107]. High surface area and the presence of functional groups, such as hydroxyl and carboxyl in adsorbent substrates, make them effective in adsorbing metal ions [108]. Activated carbon from _Citrus_ waste is widely recognized as one of the most popular and effective biosorbents. Its cost-effectiveness and efficiency in removing contaminants make it a preferred choice for various adsorption applications [107]. [PERSON] et al. highlighted the effectiveness of activated carbon derived from sweet lime (_Citrus limettioides_ Tanaka) peel and seed in removing Nickel (II) ions from aqueous solutions [109]. The ideal conditions for adsorption were a contact time of 4 h and a pH range of 4.0 to 7.0. Also, functional groups, including hydroxyl, carboxylic, and sulfonic acids, were effectively involved. Additionally, the adsorbents could be reused effectively for up to five cycles, making them a practical and cost-effective option for Nickel (II) ion removal in wastewater treatment, offering an alternative to more expensive materials such as ion-exchange resins and carbon nanotubes [109]. On the other hand, activated carbon derived from mandarin seeds successfully removed up to 100% of some carbamate pesticides after up to three absorption cycles. Even after three cycles, this activated carbon showed retention indexes close to 90% for carbaryl, methiocarb, or pirimicarb, whilst it did drop its retention capacity to \(\approx\)30% with others such as metolcarb or isoprocarb [110]. Therefore, even though there is ongoing research regarding _Citrus_ seeds as biosorbent raw materials, peels have also been more extensively studied, and knowledge is still limited on their potential.