esa-sceva/satcom-chunk-collection huggingface dataset:
## Modeling and Analyzing the Energy Efficiency Using the Proposed Approach
### Modeling the Energy Efficiency in Each Cluster  
According to the approach presented, each cluster which includes records with the most similarity must be analyzed separately to identify factors which influence the energy efficiency group. The corresponding rules of the decision trees' (C5.0) branches in the separate analysis of each cluster are given in Table 4. The percentage presented in the tables is explained in Section 3. The findings of Table 4 show that:  
In the Low-consumption cluster:  
* Economic tariff 7 Electricity in this cluster has also shown its impact. More than 34% of the homes that have this tariff have the D label, while more than 46% of the homes which do not use this tariff are labeled C. By comparing rules 2 and 3, it can also be concluded that the use of this tariff in small houses has a greater impact on poor energy efficiency. * Survey households in this cluster also state that a greater percentage of homes that do not use this electricity tariff 7 are better in energy efficiency than those using tariff 7. This different percentage in the small houses of this cluster (area less than 51 m\({}^{2}\)) is shown in Table 5.
* Rules 4 and 5 stated that newly built houses are in good condition in terms of energy efficiency and old ones have a poor energy efficiency. The survey reveals that 36.9% of households living in newly built houses have an A or B label, and 3.28% of the old ones have a G label. Figure 3: Size of clusters. In the medium-consumption cluster:  
* Rules 6 and 8 stated that, firstly, large old houses have not a good energy efficiency. Also, they state that among these homes, the ones which use electricity tariff 7 are better off. * Rules 9 and 10 refer to the role of house structures in energy efficiency. So, in newly-constructed houses with a flat structure the energy efficiency groups are A and B, and other house structures have a C label.
# Weakly-Supervised Multilingual Medical NER for Symptom Extraction for Low-Resource Languages
## 3 Results
### Models' Comparison with Existing Models  
To evaluate the effectiveness of our multilingual NER models, we compared their performance with three existing baseline models trained for English, Italian, and Spanish NER tasks. These existing models were tested on the same dataset used for our models to ensure a fair and consistent evaluation. The baseline models used for comparison were domain-specific NER systems tailored for each language. The English baseline was the Stanza i2b2 model [18], which combines word embeddings with forward and backward LSTM-based character-level language models and a CRF decoder, trained on clinical notes to extract entities such as problems, tests, and treatments. The Italian baseline, MedPsyNIT [20], was built on BioBIT and fine-tuned on clinical data from four Italian hospitals, targeting psychiatric and medical entities through low-resource fine-tuning. The Spanish baseline, lcampillos/roberta-es-clinical-trials-ner [21], adapted a biomedical RoBERTa model to identify anatomical, chemical, pathological, and procedural entities from clinical trial texts using the CT-EBM-SP corpus. Table 7 displays the self-reported F1 score of the models.  
In our test set, our models outperformed the existing ones across all three languages, demonstrating superior precision, recall, and F1 scores, as shown in Table 8. For English, our model achieved an F1 score of 80.07%, surpassing the baseline model's performance of 67.69% by a significant margin. Similarly, in Italian, our model attained an F1 score of 75.60%, compared to the baseline model, which achieved an F1 score of 57.06%. In Spanish, our model's F1 score of 77.61% showed a clear improvement in handling complex linguistic nuances over the existing model, which achieved an F1 score of 62.60%.  
The Italian and Spanish models use different labels from our labels. To perform the comparison we carried outlabel mapping to match the labels in the Spanish model, as displayed in Table 9.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Model** & **Language** & **F1 Score** \\ \hline i2b2 [18] & English & 88.13\% \\ MedPsyNIT [20] & Italian & 89.53\% \\ roberta-es-clinical-trials-ner [21] & Spanish & 86.47\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Self-reported F1 score of the models used for comparison with our fine-tuned models.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Language** & **Existing Model** & **Existing Models’ Performance** & **Fine-Tuned Models’ Performance** \\ \hline English & i2b2 [18] & 67.69\% & **80.07\%** \\ \hline Italian & MedPsyNIT [20] & 57.06\% & **75.60\%** \\ \hline Spanish & roberta-es-clinical-trials-ner [21] & 62.60\% & **77.61\%** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of our fine-tuned models with the existing models. The existing models are the state-of-the-art models found in the literature for specific models.
## 1 Introduction
The encrypted data are increased to introduce a large number of keywords and the trapdoor generation algorithm is used to solve the out of order problem without loss of data [18]. The personal data are kept secret in the cloud to protect sensitive data and remove the constraints. The data owner encrypts the data that will be outsourced to the cloud by the fine grained-access control. Between users and the cloud, the information will be leaked during the collusion time and it can be avoided by the safety data sheet (SDS) frame [19; 20]. The similarity index is proposed to protect the data from insecurity and the m-index is encrypted to support the neighbor's queries. The key policy attribute based signature (KP-ABS) scheme composes the signer's private key into two components. The other users cannot forge the signature [21; 22]. In this proposed work, blowfish hybridized weighted attribute-based encryption (BH-WABE) algorithm is developed for securing the data stored in the cloud. An attribute encryption scheme with more authority is more suitable for data access control cloud storage systems because the user can be held by multiple institutions to manage property, and access to policy data owners to use the property that may be defined in different institutions. Traditional single authority to manage all user attributes dense steel, easy to degrade system performance. In addition, a single authority solution requires a completely honest authorized body; it is difficult to meet the security requirements of cloud computing environments. Weighted attribute-based encryption is hybridized with the blowfish algorithm for encryption purposes. Encryption, key generation, and decryption are ensured with the blowfish algorithm. A key contribution in this paper is summarized as follows:  
* We propose a novel data collaboration scheme for secure read and write operations in cloud computing that allows a symmetric encryption algorithm for effective key management to reduce computational overhead. A full delegation approach-based hybridized encryption (BH-WABE) that is employed for the outsourced data should be secure. * We provide a verification method for the outsourced encryption and decryption. If the cloud returns incorrect results, users can notice it immediately by running the corresponding verification algorithm. Therefore, the user can access the data anywhere and anytime using any device. The computational cost is low, which is introduced by ABE in the user side. * We provide a security and performance analysis of our scheme, which shows that our scheme is both secure and highly efficient.
## 5 Results and Discussion
### Stage II  
For further testing, two configurations from each of the three groups described above were selected. These were 6, 7, 10, 11, 13, and 16. Their detailed results are presented in Table 5. Based on Table 4, we selected two LiDAR configurations. The less demanding one used the Dual Return mode, a 180-degree field of view, 1024 resolution, and 10 frames per second. It required an average throughput of approximately 85 Mbips. The second, more demanding configuration had the same parameters but operated in the Dual Return mode with a 360-degree field of view and an average throughput of about 165 Mbips. It met the S2 requirement for laser mapping. As shown in Table 5, all base station configurations were able to transmit traffic generated by the first LiDAR configuration without loss, while the second configuration's traffic exceeded the capabilities of several of them.  
Figure 5: Results from separate latency and throughput measurements for each configuration in Stage I.
## 5 Result and Discussion
### Protocol Comparison: Load Metric  
Load parameter in routing protocols is an important metric used to calculate the best path for network traffic. It is used to avoid congested links and balance the traffic load across the network. It refers to the current level of traffic or utilization on a particular link or network path. It is typically expressed as a percentage of the link's capacity such as 25 percent load. In comparison to the default OLSR [16] and AODV [26], the proposed OLSR has demonstrated a decrease in load. The value of delay obtained is highlighted in Table 3 and the comparison graph for all three techniques observed is highlighted in Figure 6.
# Sustaining Rice Production through Biofertilization with N\({}_{2}\)-Fixing Cyanobacteria
###### Abstract  
Current agricultural productivity depends on an exogenous nutrient supply to crops. This is of special relevance in cereal production, a fundamental part of the trophic chain that plays a vital role in the human diet. However, our agricultural practices entail highly detrimental side-effects from an environmental point of view. Long-term nitrogen fertilization in croplands results in degradation of soil, water, and air quality, producing eutrophication and subsequently contributing to global warming. In accordance with this, there is a biotechnological interest in using nitrogen-fixing microorganisms to enhance crop growth without adding chemically synthesized nitrogen fertilizers. This is particularly beneficial in paddy fields, where about 60% of the synthetic fertilizer that has been applied is dissolved in the water and washed away. In these agricultural systems, N\({}_{2}\)-fixing cyanobacteria show a promising biotechnological potential as biofertilizers, improving soil fertility while reducing the environmental impact of the agricultural practice. In the current study, Andalusian paddy fields have been explored to isolate N\({}_{2}\)-fixing cyanobacteria. These endogenous microorganisms have been subsequently re-introduced in a field trial in order to enhance rice production. Our results provide valuable insights regarding the use of an alternative natural source of nitrogen for rice production.  
Keywords:cyanobacteria; rice; biofertilizer; N\({}_{2}\)-fixation; PGPB +
Footnote †: journal: Applied Sciences
# Energy-Efficient IoT-Based Light Control System in Smart Indoor Agriculture
## 1 Introduction
These lights are controlled by a set of microcontrollers, each one of them is used to monitor the growth conditions in a specific growth chamber. Pulse width modulation (PWM) method is implemented in each microcontroller to dim the LEDs and change the obtained light intensities to the required levels. An additional microcontroller is used as a server to supervise all the growth chambers together from one place. A distributed control architecture is implemented in the server to remotely control the growth lights in multiple growth chambers by communicating with the microcontrollers in each one of them. The control architecture should have a low computational cost to avoid increasing the latency of the network and facilitate the data handling. The set of microcontrollers used in this case constructs a 6 LoWPAN (IPv6 over low-power wireless personal area networks) network, which means that it exchanges data packets using the IPv6 protocol. The communication between the microcontrollers in this network can be subject to multiple constraints on data transmission, processing time, and energy consumption. RPL (routing protocol for low-power and lossy networks) protocol is used to transfer data in this kind of constrained network thanks to its low power consumption and its specific design for multi-hop and multi-point communications. In brief, the main contributions of the paper are the proposed architecture that improves the energy-efficiency of the plant factory by using economic LED lights, an energy-efficient PWM method instead of amplitude modulation for light dimming, a low computational cost-control method, and a 6 LoWPAN network with the RPL routing protocol for constrained networks to perform tasks remotely. The remainder of this article is organized as follows: Section 2 presents related works and similar IoT and control systems developed previously in the agricultural field. Section 3 explains the plant light requirements and shows why the automation of light control is needed. Section 4 shows the proposed control architecture and provides details about its different components and the used techniques. Section 5 discusses the implementation of the system and evaluates the obtained results. Finally, Section 6 concludes the article and provides hints about future research.
# 5G-MEC Testbeds for V2X Applications
## 3 Existing Testbeds and Implementations
### 5G V2X Testbeds  
The 5G-V2X testbeds are essential for testing V2X services without using the ETSI MEC architecture. For instance, these testbeds can be used to gauge the effectiveness of various edge and fog computing architectures that are alternatives to the ETSI MEC architecture. Additionally, these testbeds enable the execution of experiments to assess the effectiveness of well-known communication systems in a real-world setting (for instance, the network effectiveness of vehicle communications at real car speeds). The testbeds enable the testing of novel network protocols, for example, reducing the power consumption on the radio access network or increasing the exchanged data reliability and/or security. Finally, the testbeds can also be utilized to test new technologies on radio interfaces such as mmWave and evaluate the performance achieved by the cooperation among different Radio Access Technologies (RAT) in a multi-RAT scenario. The main challenges of using 5G-V2X testbeds are as follows:  
* The testbed/experiment complexity for troubleshooting and programming.
* Interoperability between MNOs and vendors for validating the end-to-end solutions.
* Real-world scenarios, such as network conditions and environmental factors, can impact testbed conditions.
* Security and safety issues of the connected vehicle and controlled environment to test the experimental analysis.
* Regulatory compliance with operating a 5G V2X testbed.  
The selected 5G-V2X testbeds are discussed below.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 3 Design Considerations
### Design of Resonant Capacitance \(C_{s}\)  
According to stage 2 in Section 2 with \(C_{s1}\) = \(C_{s2}\) = \(C_{s}\), from Equation (7), we can obtain the following:  
\[V_{c}=\frac{I_{in}\times T_{2}}{2C_{s}} \tag{41}\]  
From Equation (32), we can get the relationship between \(V_{in}\) and \(V_{c}\), with \(\alpha\) being positive and smaller than 1, as shown in Equation (42):  
\[V_{c}=\frac{V_{in}}{1-D-\alpha}>V_{o} \tag{42}\]  
During stage 2, \(v_{ds1}\) rises from zero to \(V_{c}\) and \(v_{ds2}\) falls from \(V_{c}\) to zero. Based on the IRF540 datasheet and its characteristic curves of rising time \(t_{r}\) and falling time \(t_{f}\) versus drain current \(I_{ds}\), we can know that the value of \(T_{2}\) is about 0.02 \(\upmu\)s. Hence, from Equations (41) and (42), we can attain the inequation of the resonance capacitance \(C_{s}\) to be  
\[C_{s}<\frac{4.21\times 0.02\ \upmu\text{s}}{42\times 2}=1\text{nF} \tag{43}\]  
As the resonant capacitance \(C_{s}\) is equal to the parasitic capacitance of the power switch, two IRF540 MOSFETs with typical output capacitance of 125 pF are chosen for \(S_{1}\) and \(S_{2}\) to meet the requirements of Equation (43).
## 5 Iron, ROS and Iron Chelators That Can Cause DNA Damage in Cancer
### Iron Chelators and ROS
#### 5.1.6 Triapine and Deferiprone  
Triapine is a tridentate iron chelator initially designed to inhibit RnR, discussed above, but it is also involved in Fenton chemistry. It has demonstrated it can produce hydroxyl radicals as its Fe complexes can participate in redox cycling, and thus it was shown to result in breaks in plasmid DNA [205]. It is mainly used in combination therapy as treatment by itself has not been as successful in clinical trials [206]. Deferiprone is one of the most commonly used orally active tridentate iron chelators; it has been used against prostate and cervical cancer cell lines. It is largely redox-inactive due to its low redox potential resulting in the inhibition of the Fenton reaction and lipid peroxidation [207]. Despite this deferiprone can also under certain conditions be pro-oxidant reacting where iron transfers its electron to deferiprone starting a cascade of redox reactions [177; 207]. In cancer, deferiprone originally was shown to inhibit growth and induce apoptosis [1]. Subsequently, deferiprone demonstrated it could cause one-electron reduction to form superoxide, which can inactivate aconitase and play a role in Fenton chemistry [177]. Deferiprone has also been shown to target cancer stem cells in MCF7 and T47D breast cancer cells, targeting mitochondrial oxygen consumption and glycolysis and increasing ROS production [208]. These iron chelators like the ones above have this ability to redox cycle and produce ROS under certain conditions. These chelators have found great use in cancer, as they can tip the scale to a toxic amount of ROS while limiting iron available.
# Study on Stress-Strain Relationship of Coir Fiber-Reinforced Red Clay Based on Duncan-Chang Model
## 4 Duncan-Chang Model and Parameter Analysis of Coir Fiber-Reinforced Red Clay
### Parameter Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fiber Content (\%)** & **Confining Pressure (\& **\(\mathbf{kPa}\)**)** & **Parameter \(a\)** & **Parameter \(b\)** & \(\mathbf{E_{i}}\)** & \(\left(\mathbf{\sigma_{1}\text{-}\sigma_{3}}\right)_{\text{ult}}\) (\(\mathbf{kPa}\))** & \(\mathbf{R_{f}}\) \\ \hline \multirow{4}{*}{0} & 100 & 0.00471 & 0.00930 & 212.31 & 107.53 & 0.9951 \\  & 200 & 0.00386 & 0.00577 & 259.07 & 173.31 & 0.9809 \\  & 300 & 0.00315 & 0.00365 & 317.46 & 273.97 & 0.9892 \\  & 400 & 0.00223 & 0.00252 & 448.43 & 396.83 & 0.9777 \\ \hline \multirow{4}{*}{0.2} & 100 & 0.00418 & 0.00850 & 239.23 & 117.61 & 0.8673 \\  & 200 & 0.00207 & 0.00513 & 483.09 & 194.93 & 0.9952 \\  & 300 & 0.00160 & 0.00333 & 625.00 & 300.30 & 0.9923 \\  & 400 & 0.00127 & 0.00238 & 787.40 & 420.17 & 0.9758 \\ \hline \multirow{4}{*}{0.25} & 100 & 0.00385 & 0.00906 & 259.74 & 110.38 & 0.9966 \\  & 200 & 0.00177 & 0.00527 & 564.97 & 189.75 & 0.9960 \\  & 300 & 0.00149 & 0.00318 & 671.14 & 314.47 & 0.9762 \\  & 400 & 0.00113 & 0.00240 & 884.96 & 416.67 & 0.9648 \\ \hline \multirow{4}{*}{0.3} & 100 & 0.00330 & 0.00851 & 303.03 & 117.51 & 0.9786 \\  & 200 & 0.00202 & 0.00422 & 495.05 & 236.97 & 0.9875 \\ \cline{1-1}  & 300 & 0.00139 & 0.00278 & 719.42 & 359.71 & 0.9591 \\ \cline{1-1}  & 400 & 0.00087 & 0.00205 & 1149.43 & 487.80 & 0.9635 \\ \hline \multirow{4}{*}{0.35} & 100 & 0.00453 & 0.00862 & 220.75 & 115.98 & 0.9743 \\  & 200 & 0.00279 & 0.00505 & 358.43 & 198.02 & 0.9948 \\ \cline{1-1}  & 300 & 0.00187 & 0.00296 & 534.76 & 337.84 & 0.9502 \\ \cline{1-1}  & 400 & 0.00138 & 0.00223 & 724.64 & 448.43 & 0.9634 \\ \hline \multirow{4}{*}{0.4} & 100 & 0.00460 & 0.00826 & 217.39 & 121.05 & 0.9665 \\ \cline{1-1}  & 200 & 0.00355 & 0.00482 & 281.69 & 207.47 & 0.9736 \\ \cline{1-1}  & 300 & 0.00298 & 0.00297 & 335.57 & 336.70 & 0.9474 \\ \cline{1-1}  & 400 & 0.00201 & 0.00227 & 497.51 & 440.53 & 0.9670 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tangent deformation modulus and related parameters.
## 1 Introduction
The main contributions of our study are as follows:  
* Correlational properties between channel state parameters in mmWave/THz systems are in the order of at least 30-60 cm, with AoD being characterized by much stronger dependence as compared to AoA, which is 300-600 times higher than the wavelength at 300 GHz;
* Representation of the channel state parameters in a form convenient for modern state-of-the-art video frame interpolation and prediction techniques;
* ML prediction results, showing that reliable prediction accuracy of AoA and AoD angles is feasible up to 60 cm distance, with AoA being characterized by a higher mean squared error (MSE). The rest of the paper is organized as follows. First, in Section 2, we overview the related work. Further, in Section 3, by utilizing a ray-tracing tool, we quantify the correlation properties of the main channel parameters. ML-based mechanisms for channel state parameter prediction are introduced in Section 4, while their evaluation is provided in Section 4.4. Conclusions are drawn in the last section.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 5 Heuristic Approach
)\)--controller function
3:for each \(\Delta t_{I}\in T_{d}\), \(l=1,\ldots,|T|\)do:
4:for each \(\tau_{m}\in T_{\tau}\), \(m=1,\ldots,|T_{\tau}|\)do:
5: Calculate:
6:\(x_{i-1}^{*}=x_{i-1}+v_{i-1}\tau_{m}+a_{i-1}\frac{\tau_{m}^{*}}{2}\), \(v_{i-1}^{*}=v_{i-1}+a_{i-1}\tau_{m}\)
7:\(x_{i}^{*}=x_{i}+v_{i}\tau_{m}+a_{i}\frac{\tau_{m}^{*}}{2}\), \(v_{i}^{*}=v_{i}+a_{i}\tau_{m}\),
8:\(v_{0}^{*}=v_{0}+a_{0}\tau_{m}\),
9:\(a_{i}^{*}=f(v_{0}^{*},a_{0},x_{i-1}^{*},v_{i-1}^{*},a_{i-1},x_{i}^{*},v_{i}^{* },a_{i}^{*},d_{d})\),
10:\(d^{*}=x_{i-1}^{*}-x_{i}^{*}\), \(t=\tau\)
11:while\(d^{*}>d_{Th}\) & \(t<t_{Th}\) & \(v_{i}^{*}>0\)do:
12: Calculate:
13:\(x_{i-1}^{*}=x_{i-1}^{*}+v_{i-1}^{*}\Delta t_{I}+a_{i-1}\frac{\Delta t_{I}^{2}} {2}\), \(v_{i-1}^{*}=v_{i-1}^{*}+a_{i-1}\Delta t_{I}\)
14:\(x_{i}^{*}=x_{i}^{*}+v_{i}^{*}\Delta t_{I}+a_{i}^{*}\frac{\Delta t_{I}^{2}}{2}\), \(v_{i}^{*}=v_{i}^{*}+a_{i}^{*}\Delta t_{I}\),
15:\(v_{0}^{*}=v_{0}^{*}+a_{0}\Delta t_{I}\)
16:\(d^{*}=x_{i-1}^{*}-x_{i}^{*}\), \(t=t+\Delta t_{I}\),
17:\(a_{i}^{*}=f(v_{0}^{*},a_{0},x_{i-1}^{*},v_{i-1}^{*},a_{i-1},x_{i}^{*},v_{i}^{* },a_{i}^{*},d_{d})\)
18:if\(a_{i-1}^{*}-a_{i}^{*}>0\) & \(v_{i-1}^{*}-v_{i}^{*}>0\) & \(d^{*}>d_{Th}\)then:
19:\(t=\infty\)
20:break
21:endif
22:endwhile
23: Set: \(t_{m}^{*}=t\)
24:endfor
25: Select: \(m^{*}=\arg\max_{m}t_{m}^{*}\)
26: Set: \(\tau_{I}^{*}=\tau_{m^{*}}\)
27: Set: \(t_{I}^{**}=t_{m^{*}}^{*}\)
28:endfor
29: Select: \(l^{*}=\arg\max_{l}t_{l}^{**}\)
30: Set: \(\tau^{(sel)}=\tau_{l^{*}}^{*}\)
31: Set: \(\Delta t_{T_{\tau}}{}^{(sel)}=\Delta t_{l^{*}}\)return\((\tau^{(sel)},\Delta t_{T_{\tau}}{}^{(sel)})\)
32:endprocedure
```  
**Algorithm 1** Exhaustive search algorithm  
The approach proposed in Algorithm 1 provides a suboptimal solution, as it operates only with finite sets of \(T_{d}\) and \(T_{\tau}\). Furthermore, it assumes accurate knowledge of the current motion parameters of the preceding and the following vehicle, as well as an error-free exchange of information. Therefore, some emergency braking distance violations can be experienced even when it is in use, as the algorithm aims only at minimizing a chance of such an event. The number of such occurrences will depend on the acceleration disturbance changes and the sets of available values \(T_{d}\) and \(T_{\tau}\). The approach proposed with Algorithm 1 can be further extended by applying a hysteresis-based approach, where the final values of the messaging period are selected as the minimal value out of the past values, assuming a certain memory depth of \(r\) milliseconds. The aim of the use of hysteresis is to increase the robustness of the proposed solution to sudden and frequent acceleration changes that may lead to a loss of stability when less frequent messaging is used.