esa-sceva/satcom-chunk-collection huggingface dataset:
# Selecting Bioassay Test Species at the Screening Level of Soil Ecological Risk Assessments
## 3 Results and Discussion
### Ecotoxicological Analysis  
The bioassay results are shown in Figure 2. In the plant assay, the inhibition of shoot growth for _V. radiata_ compared with that in the reference soil was observed in the order of A1, A3, and A2; that for _O. sativa_ was observed in the order of A1 and A3 (\(p<0.05\)). The growth of both plants was higher in the A4 soil, and the growth of rice was significantly higher in the A2 soil (\(p<0.05\)) than in the reference soil. In the earthworm soil acute assay, the mortality of _E. andrei_ after 14 days of exposure was not significantly higher in the contaminated soil (A1-A4) than in the reference soil (\(p<0.05\)). For the collembola assay, none of the contaminated soil samples showed a significant decrease in the survival  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil** & **pH** & **EC (S/m)** & **TOC (\%)** & **Texture** &
\begin{tabular}{c} **Sum of Cations** \\ **(cmol/kg soil)** \\ \end{tabular} \\ \hline R & 7.5 & 0.011 & 1.9 & Sandy loam & 112.6 \\ A1 & 7.2 & 0.019 & 0.9 & Sandy loam & 184.1 \\ A2 & 7.2 & 0.024 & 4.9 & Sandy loam & 254.8 \\ A3 & 6.5 & 0.019 & 1.2 & Sandy loam & 183.8 \\ A4 & 7.1 & 0.012 & 3.2 & Loamy sand & 253.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical characteristics of reference and test soils in North Chungcheong Province, Korea, used in soil ecological risk assessments.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil** & \multicolumn{5}{c}{**Concentration of Heavy Metal (mg/kg or mg/L)**} \\ \cline{2-7}
**Sample** & **Cr** & **Ni** & **Cu** & **Zn** & **As** & **Cd** & **Pb** \\ \hline R & 5.8 & 4.3 & 7.3 & 20 & 3.9 & 0.8 & 6.8 \\ A1 & 10.7 & 72.6 & 2597 & 10,545 & 36,982 & 117 & 10756 \\ A2 & 2.2 & 40.9 & 312 & 1505 & 2264 & 18 & 1912 \\ A3 & 12.4 & 63.3 & 2440 & 6367 & 9459 & 75 & 6985 \\ A4 & 3.9 & 23.2 & 108 & 210 & 235 & 4.2 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total concentrations of heavy metals in test soil samples in North Chungcheong Province, Korea, used in soil ecological risk assessments, as determined with inductively coupled plasma optic emission spectrometry (ICP-OES).  
of adults. However, the reproduction rate significantly decreased in A3 (\(p<0.05\)). The reproduction rate of nematodes was significantly decreased in the order of A3, A1, and A4 (\(p<0.05\)). The biomass of _C. infusionum_ was significantly lower in A1 and A3 than in the reference soil (\(p<0.05\)); however, there was no significant difference between A2 and A4 (\(p>0.05\)). For soil bioassays, species sensitivity was high for _C. infusionum_, _V. radiata_, C. elegans_, _F. candidia_, _O. sativa_, and _E. anfrei_ (Figure 3). Except for the _E. anfrei_ and acute _F. candidia_ assays, almost all species used in this study were found to be sensitive to A1 and A3. This appears to reflect the high total concentrations of heavy metals in the A1 and A3 soils.
## 3 Results and Discussion
### Implementing the Two Networks on A Low-Cost Microcontroller  
The aim of this work is to find an accurate forecasting neural network that is suitable for using as sub-module in PV monitoring, PV fault detection, or any PV system that requires PV output power prediction. To further investigate the two developed networks, both trained ANNs are loaded on the same hardware, namely the ATmega2560 8-bit low cost microcontroller. Both networks were implemented using the topologies shown in Figures 2 and 4. Equations (1)-(3) were used for implementing the neurons with weights (\(w_{\bar{y}}\), \(w_{jk}\), and \(w_{kl}\)) obtained from the training phase of the neural networks. The same data set is used as input on both implementations and each neural network output is recorded along with the execution time, see Table 6. As shown in Table 6, there is a difference between values obtained from MATLAB simulation and hardware implementation. This is because the hardware implementation on low cost microcontroller requires some rounding in values and requires some tradeoff between memory and value precision. However, we notice that the NARX network showed better performance in term of execution time, also better accuracy over the DFF network compared to the results obtained in simulation. Using NARX network an output power value is obtained with a higher accuracy of \(\pm\)0.18 W and with half the execution time compared to the DFF network. Using the DFF network the same output power value calculation requires double the execution time of the NARX network and with lower accuracy of \(\pm\)0.59 W.
# An Efficient Framework for Securing the Smart City Communication Networks
## 3 Description of Our Proposed Cyber Security Framework for Smart Cities
### Secure Two-Way Communication by Using an Authentication and Key Exchange Scheme  
The authentication and session key discovery portion of secure two-way communication work is presented in Figure 3. Every message, \(m\), will be authenticated and encrypted with key \(K\). Now, every smart meter transmits \(m\) to the control center. This \(m\) is a trusted communication, which requests a session key for its communication with the control center. A control center generates a \(K_{SMP}\), which includes a smart meter's public key \(K_{SMCC}\) and a ticket generated by the control center to secure the communication between them \(T_{CC}\). Upon receiving the \(K_{SMP}\), a smart meter immediately decrypts it. Another message, \(K_{SMCC}\), is sent by the smart meter to the control center, which is a key session between them. This message contains \(T_{CC}\) and random secret number \(S_{SM}\). When the message is received, the control center decrypts both \(T_{CC}\) and \(S_{SM}\); then, it sends the message back to the smart meter as \(S_{SM}+1\), which is a value of that message encrypted between them in order to protect the two-way communication.  
For the authentication and key exchange scheme, which is used between a smart meter and the control center to securely provide a two-way communication as outlined above, two tasks configurations, authentication and encryption tasks, are illustrated below in detail.
# Parameter Optimization for Ship Antiroll Gyros
## 4 Mathematical Model for Parameter Optimization of Antiroll Gyro
### Constraint Conditions  
The dimensional constraint of the gyro rotor was obtained based on the maximum allowable mounting size of the gyro, given the assumption that the gyro's maximum allowable mounting size is \(h_{l}\times h_{w}\times h_{h}\), where \(h_{l}\), \(h_{w}\), and \(h_{h}\) are the length, width, and height of the mounting size. As shown in Figure 4, the rotor's inner diameter \(D_{1}\), the rotor's outer diameter \(D_{2}\), the rotor's shaft diameter \(D_{3}\), the rotor thickness \(H_{1}\), and the ribbed plate thickness \(H_{3}\) met the following constraints:  
\[\left\{\begin{array}{l}D_{2}<D_{1}<\min(h_{1},h_{w},h_{h}),a_{4}<H_{1}<\min(h _{1},h_{w},h_{h})\\ a_{1}<D_{2},a_{2}<D_{3}<a_{3},a_{5}<H_{3}<a_{6}\end{array}\right. \tag{22}\]  
where \(a_{i},\ i\in\{1,2,\cdots,6\}\) are all positive, and their specific values were selected according to the actual size of the gyro's rotor.  
The dimensional constraint of motor speed \(\omega_{m}\) was obtained based on the available power of the ship's electrical system. As \(\omega_{m}=\frac{9550P}{T}\), where \(P\) is the rated power and \(T\) is the rated torque, given the assumption that the power cap is \(P_{s}\), the \(\omega\) met the following constraints:  
\[a_{7}<\omega_{m}\leq\frac{9550P_{s}}{T} \tag{23}\]  
where \(a_{7}\) is positive, and its specific value was selected empirically between \(1050\) and \(1150\).  
The constraint of the gyro's damping coefficient was expressed as \(a_{8}\leq C\leq a_{9}\), in which \(a_{8}\) and \(a_{9}\) were obtained from Figure 6. The constraint of the mass of the gyro rotor was \(M\)s \(\leq a_{10}\), where \(a_{10}\) was selected according to gyro's size. The constraint of the roll reduction rate was \(TT\geq a_{11}\), where the specific value of \(a_{11}\) was given by the ship's designer.  
Then, the constraint conditions can be summarized as follows:  
\[\left\{\begin{array}{l}D_{2}<D_{1}<\min(h_{1},h_{w},h_{h}),a_{1}<D_{2}\\ a_{4}<H_{1}<\min(h_{1},h_{w},h_{h}),a_{2}<D_{3}<a_{3}\\ a_{5}<H_{3}<a_{6},a_{7}<\omega_{m}\leq\frac{9550P_{s}}{T}\\ a_{8}\leq C\leq a_{9},M\text{s}\leq a_{10},a_{11}\leq TT\end{array}\right. \tag{24}\]
# Improving Threat Detection in Wazuh Using Machine Learning Techniques
## 3 Materials and Methods
### Training Supervised Machine Learning Models
#### 3.6.2 Random Forest Classifier (RF)  
The Random Forest Classifier (RF) [20] is a widely used ensemble learning method that aggregates multiple decision trees to enhance predictive performance. It reduces overfitting and improves generalization by averaging or taking a majority vote from several decision trees trained on different subsets of the training data.  
**Implementation Steps:**  
* Trained an ExtraTrees classifier to perform feature selection using SelectFromModel;
* Trained the RFC model with n_estimators = 100 using the selected features;
* Used the trained RFC model to make predictions on the test set.  
After training the RFC model, we extracted the important features using the feature importance method. This analysis revealed the relative importance of each feature in the model's decision-making, as shown in Figure 3. The results obtained indicate that the following features have a significant influence on the predictions of the RFC model:
# Experimental Demonstration of Terahertz-Wave Signal Generation for 6G Communication Systems
## 1 Introduction
### Uni-Travelling Carrier Photodiode (UTC-PD)  
The rapid advancement of photonics technology is significantly enhancing signal processing speeds within related systems. Devices such as photodiodes offer the advantage  
Figure 1: Terahertz generation techniques classification.  
of both high saturation output and high speed, which contribute to the development and improvement of large-capacity communication systems. By combining an optical amplifier with a high saturation power photodiode, it is possible to extend bandwidth, eliminate the need for post-amplification electronics, and simplify the receiver configuration [22]. UTC-PDs are particularly promising for these applications due to their unique operational mode [23].  
UTC-PDs are a type of pin-junction photodiode that utilizes selected electrons as active carriers. Their structure features a thin \(p\)-type absorber where electrons are generated as minority carriers, which then spread or accelerate towards the collector. The high velocity of electrons traveling through the depleted collector allows UTC-PDs to outperform conventional pin photodiodes in terms of photo response [24].  
These photodiodes are reported to have a bandwidth of 150 GHz [25] and exhibit a high saturation output current due to the reduced space charge effects in the depletion layer caused by the rapid electron velocities [26]. Since their introduction in 1997 [27], UTC-PDs have been employed as photo-mixer chips, operating at frequencies ranging from 75 to 170 GHz. In 2003, the integration of UTC-PDs with planar antennas was demonstrated, achieving operation frequencies exceeding 1 THz [28]. Furthermore, integrated antennas in UTC photo-mixers have been shown to operate at frequencies above 2 THz [29]. UTC-PDs also utilize traveling-wave designs, which provide a slower frequency response roll-off and facilitate better integration. In [30], UTC-PDs were reported to produce output powers of 24 uW at 914 GHz and 148 uW at 457 GHz. Additionally, a THz wireless link achieving 160 Gbps was realized using a single UTC-PD in the 300-500 GHz range, as noted in [31].
# An Image Authentication Scheme Using Merkle Tree Mechanisms
## 2 Background
### Blockchain Technology  
Blockchain [9; 10] technology is composed of multiple technologies, namely cryptography, mathematics, consensus algorithms, and economic models. It is a secure, shared and distributed ledger (database) that records all transactional data as what are called blocks. The blockchain use P2P networks and consensus mechanisms to solve the problem of distributed data synchronization, and so it is not necessary to have a centralized trusted authority. Bitcoin is one of most famous applications using the blockchain technique [5; 11; 12].  
The blockchain data structure is defined as an ordered back-linked record of blocks of transactions. It can be in a database or saved as a file. Each block can be recognized by a SHA256 cryptographic hash algorithm on the header of the block. Generally, the block is composed of two parts--the main data and the header. The main data contains a list of transactions, while the header includes a hash of the previous and current block, Merkle Root, timestamp, nonce, and other information. Figure 1 shows the structure of blockchain. A blockchain has the following six features:  
1. **Decentralized**: The basic structure of blockchain, the network is decentralized, meaning it has no need to rely on any server or node. The data can be recorded, stored, and updated by a group of nodes.  
2. **Transparency**: When data is transmitted on the blockchain, records on each node are open and transparent--this is the reason that blockchain can be trusted.
3. **Open Source**: The records of blockchain systems are publicly verifiable for any user, and the user can also use the blockchain system to develop any application.
4. **Autonomy**: Based on the consensus mechanism, each node in the blockchain can transmit or update data to each other in a secure situation. This idea is from a single entity to the entire system so that no one can interfere with it.
5. **Immutable**: Any records will always be kept and stored and will not be altered unless the remaining nodes have a record where greater than 51% of the record will be changed.
6. **Anonymity**: The blockchain technology solves the problem of trust on the node-to-node, so the data transmission or the transaction can be hidden, and only when the trader's blockchain address is known will it be exposed.
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 4 Approach
### Extending LQ for Improving QoE in Mobile Streaming
#### 4.2.3 Control Gain Table  
The optimal control gains \(K_{P}\) and \(K_{I}\) depend on the chunk length \(L\), the mean throughput \(C_{0}\), and the control weight \(\rho\). To enable LQ to continuously use optimal values for the parameters, LQE dynamically updates \(K_{P}\) and \(K_{I}\) according to chunk length and measured average bandwidth. However, solving the Riccati equation in a mobile device requires significant computational overhead. Therefore, we pre-compute \(K_{P}\) and \(K_{I}\) according to different chunk lengths and average bandwidths. The control gain table for a particular \(\rho\) represents this data as a two-dimensional array, indexed by the chunk length and the measured average throughput, where each entry includes corresponding optimal values for \(K_{p}\) and \(K_{l}\).  
Table 2 shows entries in the control gain table for \(\rho=10^{4}\) indexed by every 0.5 Mbps. Note that the final output of the LQE is \(\frac{10^{6}}{n_{k}}\) since we pre-compute this table according to bandwidth in Mbps. Figure 2 shows the flowchart of the LQE ABR logic to determine an appropriate bit rate for chunk downloading, where \(M_{it}\) and \(M_{d}\) are variables to count consecutive rate up/down selections, which is used for the counter-based switching logic described in Section 4.2.2.
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 3 Results and Discussion
### Effect of CBNPs on Dynamic Modulus of Asphalt  
The dynamic modulus indicates the stiffness of asphalt and can be used as a measure to study the resistance of asphalt against permanent deformation. The higher the dynamic modulus value, the higher the resistance against permanent deformation. Figure 6a,b show the relationship of dynamic modulus with loading frequency at 40 \({}^{\circ}\)C and 55 \({}^{\circ}\)C. As shown, the dynamic modulus of asphalt decreases with the decrease in loading frequency and increase in temperature. This is due the fact that asphalt is temperature-dependent and becomes soft with an increase in temperature which leads to a decrease in dynamic modulus [29]. Modified samples have greater values of dynamic modulus than the base asphalt samples. Moreover, 10% CBNPs-modified asphalt has shown the highest dynamic modulus values at 40 \({}^{\circ}\)C and 55 \({}^{\circ}\)C and at all loading frequencies. Nano-modified asphalt binder creates more contact forces with aggregate particles. Scattered nano-particles fill the gap between the aggregates which induces an improvement in dynamic modulus of asphalt and rutting resistance as well [28]. Hence, CBNPs have proven to be a promising modifier for enhancing the high-temperature performance of asphalt.  
Figure 6: (**a**) Graph of dynamic modulus and frequency at 40 \({}^{\circ}\)C; (**b**) graph of dynamic modulus and frequency at 55 \({}^{\circ}\)C.  
According to the Federal Highway Administration (FHWA), the structural and functional capacity of a pavement and its useful service life are key performance indicators and greatly impact the overall sustainability of a pavement [30]. This is because improved pavement performance leads to lower maintenance costs and longer service life, consequently conserving resources. It can be concluded from the results of the CBNPs-modified asphalt's performance testing that the addition of CBNPs in asphalt could help in building sustainable pavements.
# Preliminary Results on Tropospheric ZTD Estimation by Smartphone
## 2 Materials and Methods
### Case Study: Materials, Methods and Processing
#### 2.2.1 Case Study 1  
The acquisition test was performed on 11 December 2018; the smartphone acquisition lasted just over one hour and a half, starting from 11:49 a.m. until 1:30 p.m. UTC. This case study's chosen location was the rooftop of the Politecnico di Torino, where the TORI GNSS CORS, which belongs to the SPIN3 GNSS network, is installed. A few meters away from that location, a Xiaomi Mi 8 smartphone (referred to as TOSM for this case study) was placed. TORI, used as a reference, is a geodetic-level multi-frequency (L1, L2, and L5) and multi-constellations receiver. The GNSS receiver embedded in the Xiaomi Mi 8 (Broadcom BCM47755) can record multi-frequency (L1 and L5) and multi-constellations GNSS signals. The pseudo-ranges and carrier-phase measurements were collected via the GEO++ RINEX logger app (available on Google Play) and then processed using the previously described software.  
\begin{table}
\begin{tabular}{c c} \hline \hline RINEX format & RINEX 3 \\ Cut-off angle & 7.5\({}^{\circ}\) \\ Ephemeris & Final (MGEX) \\ Clocks & Final (MGEX) \\ Ionosphere & Ion-free \\ OTL & Based on Chalmers grid solution \\ Ambiguity resolution & PPP-AR \\ \hline \hline \end{tabular}
\end{table}
Table 1: Processing parameters for RTKLIB and CSRS-PPP software for both case studies.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 2 Smart Grid Network Architecture
### Network Planning Constraints
However, the information flows through different VLANs and with different priorities, explained as follows: (i) The energy consumption and voltage quality monitoring information from the meters is usually obtained in the AMI network through a pooling mechanism controlled by the control center, which uses an algorithm to make a scheduled pooling to distribute the reading throughout the day and avoid congestion. This algorithm, in general, can control the reading spatially (establishing different regions for the reading) and temporally (to perform the reading of different areas in different periods). An example of a scheduled smart meter reading algorithm is presented by the authors in [12]; (ii) Regarding the DAs (Backhaul network), they are considered high-priority devices; thus, their status is read more frequently (high-frequency reading) as the control center continuously monitors them and acts on them as quickly as required. Despite this high-frequency reading, it is essential to highlight that the number of DAs in a smart grid is considerably inferior to the number of smart meters. Thus, their traffic represents a high frequency of readings but for a small number of elements. * Finally, the AMI network function is not restricted to metering and billing. It comprises bidirectional communication that allows a remote to switch off/reconnect consumers' houses' energy--in addition to supporting \"last gasp\" alarms informing the lack of energy in consumers' houses and being able to map the defective sections and coordinate maintenance teams with greater assertiveness. The DAs communication network (wireless backhaul network) is provided by a backup energy system (batteries) to enable maneuvers even during shutdowns.
# Advances in Deep Space Probe Navigation
## 3 Exploring Further Advances in Deep Space Probe Navigation
### Advanced Filtering Algorithm  
Adaptive algorithms can identify the statistical characteristics of noise online, dynamically adjust filtering parameters, and adapt to changes in noise. Ref. [21] proposed a variational Bayesian implicit unscented Kalman filter (VBIUKF) method and applied it to celestial navigation using time delay measurement. Ref. [22] proposes an adaptive unscented Kalman filter based on sequential state difference (AUKF-SSD) to address the issue of divergence in spacecraft navigation systems and enhance navigation accuracy. Ref. [23] proposes a variable-dimensional adaptive IMM strong tracking filtering algorithm (VAIMM-STEKF) to estimate the spacecraft's position, velocity, and maneuvering acceleration state. Ref. [24] presents a novel parallel Q-learning extended Kalman filter (POEKF) to implement the measurement bias calibration. Ref. [25] developed an improved Q-learning-based extended Kalman filter (IQEKF) to obtain the accurate motion state estimate of both the spacecraft and the space targets based on the LOS direction measurements.