esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Fault Detection Performance
The high loss value of 78%, on the other hand, indicates a significant difference between the actual and predicted labels, indicating that the model may not be completely accurate in capturing the intricate patterns present in the data. In contrast, the ResNet-50 model demonstrated superior performance with an accuracy of 85% and a significantly lower loss value of 42%. This improvement suggests that ResNet-50's deeper architecture and advanced feature extraction capabilities allow it to more effectively recognize intricate patterns and features in the solar panel images, resulting in more accurate predictions and a lower prediction error. The reduced loss in the ResNet-50 model indicates that it is better at minimizing errors in its predictions, making it more reliable for fault detection in solar panels. However, even with this enhanced performance, the presence of a 42% loss value reveals that there is still room for further refinement to enhance accuracy and reduce errors. This could involve fine-tuning the model parameters, improving the quality of the training data, or integrating additional data preprocessing steps. Overall, the analysis underscores the potential of ResNet-50 as a more effective model for solar panel defect detection compared to VGG-16, particularly in terms of accuracy and loss. Nonetheless, both models exhibit promising capabilities, and with further optimization, they could provide even more reliable tools for maintaining and monitoring the efficiency of solar energy systems. Figure 6: Loss and accuracy results: (**a**) CNN, (**b**) ResNet-50. Figure 7: Prediction results: (**a**) CNN, (**b**) ResNet-50.
# Novel Applications of Microbial Fuel Cells in Sensors and Biosensors
## 5 MFC-Based Biosensors for Water Toxicity Detection  
Detection of toxicity in water is a crucial parameter to determine necessary actions for providing safe water within the appropriate degree of quality for consumption by humans, animals, and crops. To this effect, MFCs appear as appropriate sensitive systems, since any toxicant present in the aqueous feedstock will affect the metabolic activity of microbes, and therefore the rate of substrate intake, which is, as already mentioned above, directly related to the current output of an MFC. Therefore, any change in the presence and content of toxicants in fluent water can be easily detected by monitoring the perturbations in the electric current generated by MFCs [128; 147], reducing the time and costs (personal, reactants, and analysis equipment) compared to conventional methods [148].  
Among the most significant advances in the last 10 years is the simple and planar silicon-based miniaturized MFC, developed as a prompt response toxicity biosensor by [PERSON] and co-workers in 2011 [149]. Regardless of the biosensing application, maximum power density of about 6.5 \(\mu\)W/cm\({}^{2}\) was reported to be provided by the silicon-based MFC.  
Eventually, performance and sensitivity to different toxicants in an MFC-based toxicity sensor strongly depends on the type of electroactive microbes used in the cell. Therefore, the selection of microorganisms with specially enhanced sensitivity to specific toxicants is critical to design appropriate devices according to the required monitoring needs.
## 2 Background and Related Work  
The meticulous documentation and subsequent analysis of work accidents are crucial for gaining insights into the factors contributing to occupational hazards and legal compliance. Reports of incidents in hazardous work environments can be analyzed to identify common hazards, which will help prevent future accidents. While some studies do this, most focus on causes involving physical risks. [PERSON] et al. (2021) [6] proposed an alternative approach and shed light on the causes of accidents in forestry from the perspective of employee failure and fatigue. It is interesting to note that 70% of incidents are attributed, at least in part, to worker negligence. Secondly, 78% of the causes attributable to worker error show signs of fatigue. This suggests that a significant number of forestry accidents are due to worker fatigue.  
A wide array of studies have been dedicated to examining different aspects of accident records. [PERSON] et al. (2014) [7], for example, analyzed timber harvesting accidents in Austria. This line of research extends to other countries, with notable studies in Poland [8], Slovakia [9; 10], Italy [11], and Brazil [12]. Additionally, cross-national comparative research has been conducted [13]. Notably, a considerable segment of this research is centered on understanding accidents involving chainsw use or motor-manual tree filling [14; 15; 16].  
The adoption of machine learning (ML) in accident prediction is chiefly attributed to two key factors: its explanatory capacity, which aids in identifying primary causes of accidents [17], and its predictive ability, enhancing accident prevention efforts [18]. This methodology is increasingly used in various high-risk professions to analyze occupational accidents. For instance, ML has been applied to study serious accidents in the chemical industry [19] and the steel industry [20]. In the field of electrical engineering, [PERSON] et al. (2021) [21] explored the use of deep learning and boosted tree algorithms to evaluate their effectiveness in safety risk management. Further examples of ML's application in accident prediction can be found in studies such as [22; 23; 24; 25].  
In the following sections, we introduce two datasets, followed by a series of experiments to uncover the explanatory variables influencing forest accidents. We conclude by outlining future research directions.
## 1 Introduction
### Environmental Impact of Stadiums and Potential for Sustainability  
Among all building typologies, stadiums have the highest environmental impact; in particular, football stadiums consume a considerable amount of energy on the day of an event due to the need for lighting, heating, cooling, and other electrical systems. Here is why UEFA, as a guardian of the world's most popular sport, developed a Guide to Sustainable Stadiums, which gives the best practices in a wide range of areas including energy savings [20]. According to this, the installation of PV panels on stadium roofs plays a key role in achieving the green transition. A stadium, used a few times a week, can become an electrical power station working every day, without environmental or architectural impact, thanks to the installation of photovoltaic panels on the roofs [21; 22]. Thus, it could provide clean electricity to the surrounding community. There are many examples around world of using PV systems in sports stadiums to save and/or export energy to a local utility grid [23].  
The first stadium in the world where PV panels, with a capacity of 259 kWp, were installed is Schwarzwald-Stadion in Freiburg in Germany. This pioneering installation on the stadium roof took place in 1995.  
The JC Arena is already one of the world's most sustainable and multi-functional stadiums, and it continues to implement innovative smart energy solutions for the venue, its visitors, and the surrounding neighborhood. With a 1 MWp solar installation on its roof, the stadium currently generates approximately 8% of its electricity needs [24; 25].  
The biggest rooftop PV power plant, with an installed capacity of 1.42 MWp and an area of 11,530 m\({}^{2}\), is mounted at Mineirao Football Stadium in Brazil [26].  
In 2018, [PERSON] et al. [27] proposed a cluster of actions to reduce the energy use of Dacia Arena: two hypothetical system PV system layouts were proposed and evaluated for providing electricity. The PV system can annually generate up to 815,000 kWh. If a heat pump is installed, approximately 500,000 kWh of that total becomes surplus energy, which is not directly utilized by the arena. Instead, it is fed into the energy grid and supplied to nearby neighborhoods to offset the stadium's carbon emissions.  
In 2020, [PERSON] et al. [21] examined the applications of integrated and applied photovoltaic technologies on ten landmark buildings characterized by unusual geometries like stadiums, highlighting the aesthetics of their architecture and quality of PV integration based on a proposed set of seven criteria.
# A Fuzzy Based Handover Decision Scheme for Mobile Devices Using Predictive Model
## 7 Conclusions  
A Fuzzy based Vertical Handover Decision Algorithm Using Predictive Model for Next Generation network is proposed by us in this paper. A MFNN (Multi-layer Feed Forward Network) technique has been used to determine the next cell of the user along with best hand off time. In the case that the predicted cell does not satisfy the requirement, then the multiple-attribute Access Network Selection Function (ANSF) is used to obtain a suitable access network. A fuzzy logic decision is used to obtain the best network and assure the user about the ongoing session.  
In the case of handover from WiMAX network to UMTS network, FVHDA execute better from VHDA by 14.2% in handover_delay, 18% in delivery ratio, and 17% bandwidth utilization. In the case of handover from UMTS network to WiMAX network, FVHDA outperforms VHDA by 6.14% in handover_delay, 3.24% in delivery ratio, and 3.32% in bandwidth utilization. In both the cases of handover, FVHDA outperforms VHDA.  
[PERSON] and [PERSON] contributed to the main idea and the methodology of the research. [PERSON], [PERSON] and [PERSON] designed the experiment, performed the simulations, and wrote the original manuscript. [PERSON], [PERSON] and [PERSON] contributed significantly to improving the technical and grammatical contents of the manuscript. [PERSON], [PERSON] and [PERSON], reviewed the manuscript and provided valuable suggestions to further refine the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Dongseo University, \"Dongseo Cluster Project\" Research Fund of 2021 (DSU-20210004).  
The authors declare no conflict of interest.
# Expert Recommendation for Answering Questions on Social Media
## 4 Performance Evaluation
Figure 10 shows the precision according to the change in the number of expert recommendations. The proposed method showed about 8.1% higher precision than the existing methods, on average. The proposed method demonstrated a higher precision compared to the existing methods when five persons were recommended. Furthermore, better precision was continuously shown when more experts were recommended. The existing methods did not consider the human relations and answer quality of users. Furthermore, since the hierarchical relationship for the user question was not included, the expertise score was evaluated as low. Figure 10: Precision according to the number of expert recommendations. Figure 9: Recall according to the number of expert recommendations. Figure 11 shows the F-measure according to the change in the number of expert recommendations. The proposed method showed about 6.5% higher F-measure than the existing methods, on average. The existing methods did not consider the human relations and answer quality of users. Furthermore, since the hierarchical relationship for the user question was not included, the real experts were not evaluated with high scores, and the expertise score was evaluated as low.
# Public-Key Cryptography Based on Tropical Circular Matrices
## 2 Tropical Matrix Semiring over Integer  
The definition of a semiring was first given by [PERSON] [30]. These are structures that satisfy all the properties of a ring, except for the existence of additive inverses. [PERSON], a Brazilian mathematician and computer scientist, discovered what is now known as the tropical semiring [31].  
**Definition 1**.: ([32]) _Let \(R\) be a non-empty set with binary operations \"\(+\)\" and \"\(\cdot\)\"; then, \(R\) is called a semiring if it satisfies the following conditions:_  
1. \((R,+)\) _is a commutative semigroup with an identity element_ \(0\)_;_
2. \((R,\cdot)\) _is a semigroup with an identity element_ \(1\
eq 0\)_;_
3. _Multiplication satisfies the left and right distribution law for addition;_
4. \((\forall a\in R)\)__\(a\cdot 0=0\cdot a=0\)_._  
_If \((R,\cdot)\) is commutative, then the semiring is called a commutative semiring._  
**Definition 2**.: ([24]) _The integer tropical commutative semiring is the set \(\mathcal{Z}=\mathbb{Z}\cup\{\infty\}\) with addition and multiplication as follows:_  
\[(\forall x,y\in\mathbb{Z})x\oplus y=\min(x,y),\ \ x\otimes y=x+y.\]  
\(\infty\) _satisfies the following equations:_  
\[(\forall x\in\mathbb{Z})\infty\oplus x=x,\ \ \infty\otimes x=\infty.\]  
_It is clear that \((\mathcal{Z},\oplus,\otimes)\) is a commutative semiring whose zero element and unitary element are \(\infty\) and \(0\), respectively._Let \(M_{k}(\mathcal{Z})\) be the set of all \(k\times k\) matrices over \(\mathcal{Z}\). We can also define the tropical matrix \(\oplus\) and \(\otimes\) operations.  
\[\left(\forall A=\left(a_{ij}\right)_{k\times k^{\prime}}B=\left(b_{ij}\right)_{ k\times k}\in M_{k}(\mathcal{Z})\right)A\oplus B=\left(a_{ij}\oplus b_{ij} \right)_{k\times k^{\prime}}A\otimes B=\left(\sum_{l=1}^{n}a_{il}\otimes b_{lj }\right)_{k\times k}.\]  
**Example 1**.: \[\begin{pmatrix}4&-5\\ 27&0\end{pmatrix}\oplus\begin{pmatrix}10&3\\ 1&9\end{pmatrix}=\begin{pmatrix}4&-5\\ 1&0\end{pmatrix}\] \[\begin{pmatrix}4&-5\\ 27&0\end{pmatrix}\otimes\begin{pmatrix}10&3\\ 1&9\end{pmatrix}=\begin{pmatrix}-4&4\\ 1&9\end{pmatrix}\] \[\begin{pmatrix}10&3\\ 1&9\end{pmatrix}\otimes\begin{pmatrix}4&-5\\ 27&0\end{pmatrix}=\begin{pmatrix}14&3\\ 5&-4\end{pmatrix}\]  
Let \(t\) be an integer. If a matrix \(A\) has the following form,  
\[A=\begin{pmatrix}a_{0}&a_{k-1}\otimes t&a_{k-2}\otimes t&\cdots&a_{1}\otimes t \\ a_{1}&a_{0}&a_{k-1}\otimes t&\cdots&a_{2}\otimes t\\ a_{2}&a_{1}&a_{0}&\cdots&a_{3}\otimes t\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ a_{k-1}&a_{k-2}&a_{k-3}&\cdots&a_{0}\end{pmatrix},\]  
then it is called an upper \(t\)-circular matrix. We denote \(A\) by \(\left[a_{0},a_{1},\cdots,a_{k-1}\right]_{k}^{t}\) or \(\left[a_{0},a_{1},\cdots,a_{k-1}\right]^{t}\). Let \(C_{k}^{t}=\left\{A\in M_{k}(\mathcal{Z})|A\text{ is upper $t$-circular matrix}\right\}\).  
**Proposition 1**.: _For any integer \(t\), \(C_{k}^{t}\) is a commutative sub-semiring of \(M_{k}(\mathcal{Z})\)._
###### Abstract  
The high electric field intensity achieved on the surface of sensors based on metasurfaces (metasensors) makes them an excellent alternative for sensing applications where the volume of the sample to be identified is tiny (for instance, thin-film sensing devices). Various shapes and geometries have been proposed recently for the design of these metasensors unit-cells (meta-atoms) such as split ring resonators or hole arrays, among others. In this paper, we propose, design, and evaluate two types of tripod metasurfaces with different complexity in their geometry. An in-depth comparison of their performance is presented when using them as thin-film sensor devices. The meta-atoms of the proposed metasensors consist of a simple tripod and a hollow tripod structure. From numerical calculations, it is shown that the best geometry to perform thin-film sensing is the compact hollow tripod (due to the highest electric field on its surface) with a mean sensitivity of \(3.72\times 10^{-5}\) nm\({}^{-1}\). Different modifications are made to this structure to improve this value, such as introducing arms in the design and rotating the metallic pattern 30 degrees. The best sensitivity achieved for extremely thin film analytes (5-25 nm thick) has an average value of \(1.42\times 10^{-4}\) nm, which translates into an extremely high improvement of 381% with respect to the initial hollow tripod structure. Finally, a comparison with other designs found in the literature shows that our design is at the top of the ranking, improving the overall performance by more than one order of magnitude. These results highlight the importance of using metastructures with more complex geometries so that a higher electric field intensity distribution and, therefore, designs with better performance can be obtained.  
terahertz; metasurfaces; tripod; thin-film; sensing; metasensors 2020
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 4 Solar Irradiance Forecasting Based on Deep Learning Techniques
### Solar Irradiance Forecasting Model Based on CNN  
Ref. [139] used a 3D-CNN model to forecast direct normal irradiance 10 min ahead of time. The proposed approach derived spatial and temporal features from cloud characteristics by integrating multiple consecutive ground-based cloud pictures. They developed the forecasting algorithm using GBC pictures and DNI data from a two-year period (2013 to 2014) obtained from NREL, and the model was able to achieve a forecasting accuracy of 17.06%. Ref. [33] presented a CNN-based forecasting method based on weather classification, as well as Generative Adversarial Networks (GAN). Initially, 33 different types of weather were integrated and classified into 10 new weather categories for data training. A data augmentation model using GAN was then applied to improve each weather category. Subsequently, the extended dataset that consisted of both original and generated solar irradiance data were used for CNN model training. The authors also used a weather  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Hourly Data**} & \multicolumn{3}{c}{**Half Hourly Data**} \\ \cline{2-7}
**Season** & **MAE** & **RMSE** & **MBE** & **MAE** & **RMSE** & **MBE** \\ \hline Spring & 0.75 & 1.51 & 0.02 & 0.69 & 1.43 & \(-\)0.02 \\ Summer & 0.98 & 2.04 & 0.31 & 0.87 & 1.84 & \(-\)0.05 \\ Autumn & 0.70 & 1.48 & 0.39 & 0.55 & 1.27 & 0.21 \\ Winter & 0.61 & 1.39 & \(-\)0.24 & 0.50 & 1.23 & \(-\)0.07 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of Single Step Ahead of PV Power Forecasting [138].  
classifier to evaluate solar brightness and investigated how GAN can increase forecast accuracy. Based on their observation, GAN could generate high-quality samples that precisely matched the unique features of the original data, as opposed to memorization of the training data. Next, ref. [140] developed a Solar Net using 20 layers of deep CNN to calculate the intra-hour global horizontal irradiance (GHI) (i.e., 10-min to one hour ahead with a 10-min pause). Solar Net used a single total sky image (TSI) with no feature engineering or numerical measurements as its input. According to numerical studies based on six years of public data, Solar Net produced multi-step forecasts, resulting in an nRMSE of 8.85% and an accuracy forecasting score of 25.14%, which were greater than those of other compared models.
## 3 Queuing Model and Dynamic Spectrum Algorithm
### Blocking Coefficient  
When the total number of channels occupied by SUs in a cognitive radio communication system is \(N\), the assignable channels of the CBS reaches a maximum. The newly arriving requests of \(SU_{q2}\) cannot receive a response from the CBS so that leading to enter the blocking state. The newly arriving request of \(SU_{q1}\) obeys the allocation of the CBS, and data transmission continues if \(SU_{q1}\) receives the channel of \(SU_{q2}\); if not, a blocking state is reached.  
The steady state probability of the system state \((i,j)\) is donated as \(P_{i,j}\). The blocking probabilities of \(SU_{q1}\) and \(SU_{q2}\) are represented by \(S_{s1}\) and \(S_{s2}\), respectively. \(S_{s1}\) and \(S_{s2}\) are given by  
\[S_{s1}=P_{N,0}+\sum_{i=0}^{N-1}P_{i,N-i}p^{N-i} \tag{10}\]  
\[S_{s2}=\sum_{i=0}^{N}P_{i,N-i}. \tag{11}\]  
Because only \(SU_{q1}\) and \(SU_{q2}\) exist in the system, the system blocking coefficient \(S\) is defined as  
\[S=S_{s1}+S_{s2}. \tag{12}\]
# Review on the Role of GNSS Meteorology in Monitoring Water Vapor for Atmospheric Physics
## 4 Spatio-Temporal Analysis
### [PERSON] et al. [101] studied times-series of GNSS-derived IWV in South Africa, with the aim of detecting trends in the period. They used the autoregressive-moving-average (ARMA)(1,1) model, concluding that this model represents better the real trend, provided that more than five years of data are used. They also concluded that none of the two stations used had statistically significant trends.
## 5 Discussion
### Agricultural Land Change Dynamics
In brief, this study showed high accuracy of crop type map results for all five base years due to increasing high spectral and temporal information and several VIs with topographic variables. Specifically, the years 2001 and 2021 used both Landsat sensor datasets (L5 and L7) and (L8 and L9) for improving crop type classification. However, the five base years showed the same results as high misclassification between rice and cassava due to mixed spectral signatures. Finally, according to previous findings, future studies should consider other influential factors such as climate change, technological advancements and socio-economic dynamics, providing a more comprehensive analysis of crop type dynamics. Moreover, the integration of microwave and optical satellite image data from multiple sensors and involving different machine learning approaches (i.e., RF, DT, SVM and ANN) will be able to improve the crop type/LC classification accuracy.
## 5 Methods
### Simulation of Future Scenarios  
Once the model is calibrated to the study context, it is employed to simulate future scenarios. In particular, two future scenarios were simulated:  
* **Scenario A**: no intervention is carried out and the safety performance of the existing road under future traffic conditions is to be assessed;
* **Scenario B**: the redesign of the existing road, consisting of road rehabilitation and the implementation of bypasses is considered.  
To simulate future scenarios, it is assumed that the traffic on the road would be equal to the average traffic of the last two available years (2021 and 2022). In Scenario A, all traffic travels on the existing route. It is assumed that the traffic varies in constant steps between the known traffic sections. Thus, eight constant traffic zones were identified, as shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Facility Type**} & \multirow{2}{*}{**Case**} & \(N_{pred,\,n.c.}\) & \(N_{obs}\) & \multirow{2}{*}{\(C_{X}\)} & \multirow{2}{*}{\(CV\)} & \multirow{2}{*}{\(MAD\)} & \multirow{2}{*}{\(k\)} & \multirow{2}{*}{\% _CURE dev_} \\  & & [**crashes/9 years**] & & & & & & \\ \hline \multirow{4}{*}{**Segments**} & **Case 1.RS** & 196.30 & 158 & 0.80 & 0.18 & 0.70 & 0.081 & 25\% \\  & **Case 2.RS** & 165.15 & 134 & 0.81 & 0.17 & 0.67 & 0.069 & 4\% \\  & **Case 3.RS** & 159.61 & 129 & 0.81 & 0.16 & 0.74 & 0.056 & 2\% \\  & **Case 4.RS** & 153.14 & 121 & **0.79** & 0.15 & 0.73 & 0.048 & 1\% \\ \hline \multirow{2}{*}{**Intersections** & **Case 1.I** & 46.42 & 56 & 1.21 & 0.32 & 1.71 & 1.059 & 9\% \\ (**3 ST**) & **Case 2.I** & 45.68 & 51 & **1.12** & 0.32 & 1.59 & 0.942 & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Calibration results in terms of calibration factor and GOF parameters for different scenarios.  
Figure 2: Traffic zones for Scenario A.  
With reference to Scenario B, in the presence of the variant, part of the traffic will travel on the existing route and the remaining traffic will travel on the bypasses. The portion of traffic that will affect each of the five bypasses is assumed according to the specific bypassed area. In particular, the entity of traffic traveling on the existing route through residential areas is assumed in relation to the area extension, while the remaining traffic is assumed to be diverted to the bypasses, as shown in Table 9.  
The traffic distribution scheme assumed for Scenario B is illustrated in Figure 3.