esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Improved Fisher Kernel Function
The gradient \(U_{X}\) of the log-likelihood function, called as the Fisher score, plays a fundamental role in the process of derivation and application. The local metric \(M_{\Theta}\) defines a distance between \(P(X|\theta)\) and \(P(X|\theta+\delta)\), as represented by the following form:  
\[D(\theta,\theta+\delta)=\frac{1}{2}\delta^{T}\mathrm{I}\delta \tag{11}\]  
The Fisher score \(U_{X}\) maps the instance \(X\) into a feature vector and specifies its steepness in \(P(X|\theta+\delta)\). Later, called as the natural gradient, the gradient is derived from the ordinary gradient by:  
\[\phi(X)=\mathrm{I}^{-1}U_{Xi} \tag{12}\]  
The mapping \(X\rightarrow\phi(X)\) that transforms the instance \(X\) into a feature vector is called a natural mapping, and the value of the parameter \(\theta\) is set. The core of this mapping is the inner product of these feature vectors related to the Riemann system:  
\[K(X_{i},X_{j})\propto\phi(X_{i})^{T}\mathrm{I}\phi(X_{i}) \tag{13}\]  
The Fisher score is core of the [PERSON] algorithm. In fact, in the logistic regression model, as shown above, the matrix appears in the Gaussian covariance associated with the feature vector, helping explain that this information matrix does not actually exist, with this simple core function as:  
\[K_{U}(X_{i},X_{j})\propto U_{X_{i}}^{T}U_{X_{j}} \tag{14}\]  
A suitable alternative is provided for the Fisher kernel function. From a metric point of view, for \(c,c_{0}\geq 0\), the following kernel function serves as an equivalent kernel,  
\[\hat{K}\Big{(}X_{i},X_{j}\Big{)}=cK\Big{(}X_{i},X_{j}\Big{)}+c_{0}K\Big{(}X_{ i},X_{j}\Big{)} \tag{15}\]  
In the logistic regression model, the incremental constants exert an effect on the prior change of the bias term in the kernel, with the incremental factor \(c\) related to the remaining parameter change of the whole. Finally, the Fisher kernel function is directly substituted into the \(K(X_{i},X)=X_{i}^{T}\Sigma X\) in the support vector machine as a kernel function, which is substituted into the support vector machine decision function. \[f(x)=\text{sgn}(g(x))=\text{sgn}(\sum_{i=1}^{l}\alpha_{i}y_{i}(\phi(x)\cdot \phi(x_{i}))+b) \tag{16}\]  
The inner product \((\phi(x)\cdot\phi(x_{i}))\) corresponds to the sample function \(k(x_{i}\cdot x_{j})\), thus improving support vector machine method based on Fisher kernel function, namely, FK-SVM method.
# Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application
## 5 Conclusions  
In this paper, two dual-band antennas according to the demand of WLAN for multi frequency communication are designed. One of them is a dual-band printed monopole antenna, which operates in two frequency bands of 2.4 GHz-2.52 GHz and 4.5 GHz-7.5 GHz, and the impedance bandwidth reaches 120 MHz and 3 GHz, respectively, covering 2.4 GHz, 5.2 GHz and 5.8 GHz--three working frequency bands of WLAN. In addition, the gain of the antenna is greater than 4 dB, and the volume of this antenna is only 16 \(\times\) 29 \(\times\) 1.6 mm\({}^{3}\), implementing the miniaturization of the antenna. The second antenna is a dual-band microstrip antenna, which works in two frequency bands of 2.4 GHz-2.46 GHz and 5.16 GHz-5.4 GHz, and realizes the miniaturization and dual-band operation by adopting the slotting technology. The two designed antennas have a higher gain and a favourable transmission characteristic in the operating band, which is in accordance with the requirements of WLAN communication under more complicated conditions.  
This research is partially supported by the Natural Science Foundation of China (No. 61471260 and 61271324) and the Natural Science Foundation of Tianjin: 16 JCYBJC16000.  
[PERSON] and [PERSON] designed the overall evaluation criteria. In addition, they wrote and revised the paper. [PERSON] and [PERSON] contributed to the experiment and statistical analysis of data.  
The authors declare no conflict of interest.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### Gfdm-OQAM Transceiver
#### 4.2.1 System Description of GFDM-OQAM Transceiver
## 3 Materials and Methods
### Redundancy of Hardware in the Architecture of RED/BLACK Communication  
Redundancy is pivotal in the RED/BLACK architecture to assure the continuity and integrity of sensitive classified environments alongside the availability of routine aviation communications. Redundancy in aviation communication systems is not just a measure of reliability; it is a necessity for safety and security. The critical nature of these communications requires that there is no single point of failure. Both the RED (secure) and BLACK  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Applications of** & \\
**RED/BLACK Concept** & \\ \hline \multirow{4}{*}{Security Protocols} & - & The RED/BLACK concept is supported by strict protocols and procedures. Personnel are trained to understand the importance of the separation and to handle each type of \\  & - & Regular security audits and checks are conducted to ensure that the separation is maintained \\  & and that no cross-contamination occurs between RED and BLACK systems. \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._(non-secure) domains must maintain continuous operation, as any disruption could have serious implications.  
Continuous monitoring systems of RED/BLACK architecture are implemented to detect failures or degradations in system performance. Upon detection of a failure, automated systems switch operations to the redundant systems without manual intervention, ensuring minimal disruption. RED requires failover mechanisms to handle fast security key rotations alongside equipment switchovers. BLACK leverages standards-based redundancy protocols. Blending standards where feasible improves cost efficiencies without diluting security capabilities.
## 3 Results
### Nutrient Use Efficiency of Zn and Se Treatments  
The nutrient use efficiency (NUE) of Zn and Se biofortification treatments in soybean crops was calculated (Table 4). The biofortification treatments did not influence grain yield nor vegetative mass yield (all above-ground organs except the grain). This study's average soybean vegetative mass and grain yield were similar (3.9 t/ha).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Applied Foliar Fertilizer** & \begin{tabular}{c} **Yield** \\ **(t/ha)** \\ \end{tabular} & \begin{tabular}{c} **Zn Removal** \\ **(g/ha)** \\ \end{tabular} & \begin{tabular}{c} **Se Removal** \\ **(mg/ha)** \\ \end{tabular} & \
\begin{tabular}{c} **Zn Use Efficiency** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Se Use Efficiency** \\ **(\%)** \\ \end{tabular} \\ \hline
**Grain** & & & & & \\ \hline Control & 3.9 & 180.1 b & 212.0 e & - & - \\ Se\_1 (10 g/ha Se) & 4.0 & 253.0 b & 4527.1 d & - & 43.2 a \\ Se\_2 (20 g/ha Se) & 4.0 & 184.7 b & 8136.3 c & - & 39.6 a \\ Se\_3 (30 g/ha Se) & 3.9 & 176.7 a & 11391.1 a & - & 37.3 ab \\ Zn\_1 (3 kg/ha Zn) & 4.0 & 257.7 a & 200.8 e & 2.6 a & - \\ Zn\_2 (6 kg/ha Zn) & 3.9 & 259.3 a & 165.0 e & 1.3 b & - \\ Se\_3 Zn\_2 (30 g/ha Se + 6 kg ha Zn) & 3.8 & 184.0 a & 9700.3 b & 1.2 b & 31.6 c \\ \hline Average & 3.9 & 213.6 & 4904.7 & 2.6 & 37.9 \\ \hline
**Vegetative mass** & & & & & \\ \hline Control & 3.7 & 23.4 c & 77.0 e & - & - \\ Se\_1 (10 g/ha Se) & 4.0 & 24.4 c & 745.1 d & - & 6.7 a \\ Se\_2 (20 g/ha Se) & 4.0 & 24.4 c & 1240.0 c & - & 5.8 ab \\ Se\_3 (30 g/ha Se) & 3.8 & 25.6 c & 1788.2 a & - & 5.7 ab \\ Zn\_1 (3 kg/ha Zn) & 4.0 & 156.8 b & 146.5 d & 4.4 a & - \\ Zn\_2 (6 kg/ha Zn) & 3.9 & 187.14 a & 81.0 d & 2.7 b & - \\ Se\_3 Zn\_2 (30 g/ha Se + 6 kg ha Zn) & 3.8 & 196.8 a & 1472.3 b & 2.9 b & 4.7 b \\ \hline Average & 3.9 & 91.2 & 792.9 & 3.6 & 5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Zn and Se biofortification NUE in soybean.  
Figure 6: _Cont_.  
The NUE of Se was the highest with the application of 10 g/ha Se (Se_1) and 20 g/ha Se (Se_2) where it was on average 43.2% for the grain and 6.7% for vegetative mass (stem, pods, and leaves). The Zn NUE was the highest with 3 kg/ha foliar treatment for both grain and vegetative mass (2.6% and 4.4%, respectively), which was significantly different (\(p<0.05\)) from the other Zn treatments.
## 2 Materials and Methods
### Proposed Framework Architecture
#### 2.3.5 Data Security and Privacy Module  
The final core component of the framework is the data security and privacy module, which is crucial for protecting the integrity of the system and the trust of its users.  
Measures are taken at multiple levels: device authentication at the IoT layer, encryption of data in transit across the network, secure data storage and access controls in the data layer, and AI-driven security analytics to detect and respond to threats in real time. Given the sensitive nature of educational data (student personal information, grades, research data) and the extensive connectivity of a smart campus, the system implements a defense-in-depth strategy.  
Authentication and Access Control: All IoT devices and users interacting with the system are authenticated (using methods like certificates or secure credentials) to prevent unauthorised access. Role-based access control ensures that, for instance, only authorised staff can view certain dashboards or control critical systems.  
Encryption and Secure Communication: Data from sensors are encrypted end-to-end from the device to the server, often using lightweight IoT cryptography for sensor constraints. Campus networks are secured via protocols (WPA3 for Wi-Fi, VPN tunnels for remote access, etc.), reducing the risk of eavesdropping or tampering as data flows.  
Figure 5: Adaptive scheduling system operation.  
AI-driven Threat Detection: The module employs AI to monitor network traffic and user behaviour for anomalies which could indicate cyberattacks or breaches. For example, an unusually high data transfer from a device at odd hours might signal a compromised node. AI techniques for anomaly-based intrusion detection are applied, allowing the system to flag potential threats faster than manual monitoring [61].  
Privacy Preservation: Since the system collects extensive data (including possibly personal data like IDs when tracking assets or logs from Learning Management System (LMS)), compliance is ensured with privacy regulations (such as GDPR or FERPA in the educational context). Techniques like data anonymisation, aggregation, and strict data retention policies are built in. Users (students and staff) are informed and given appropriate control over personal data usage, providing trust.  
Figure 6 illustrates this layered design, depicting device-level authentication, encryption locks on data streams, secure repositories with controlled access, AI monitoring of system logs, and an incident response pathway which isolates problematic devices or alerts responsible personnel.  
Figure 6: Architecture of data security and privacy module.
# Trust Model of Privacy-Concerned, Emotionally Aware Agents in a Cooperative Logistics Problem
## 4 Proposed Trust Model
The decision to answer a cooperation request from another agent depends upon the following criteria:  
* The mood (current feeling) of the trusting agent: Positive emotions (joy and surprise) of the agent encourage trusting decisions, whereas negative (sadness and fear) and antisocial (anger) emotions discourage trusting decisions, with a bonus/malus of 0.1 of trust required;
* Privacy issues involved in the trusting decision for the agent (in our logistic problem, the level of privacy associated with the box to be moved): If privacy issues are involved for with subject agent, then 0.1 less trust is assigned to the other agent;
* How much the other agent is trusted, where trust is computed based the previous performance of the other agent in interactions with the subject agent (in our logistic problem, the level of success of the other agent in previously moving boxes of the subject agent): success (delivery of the box without delay) is associated with an increase of 0.1 in trust, whereas each cycle of delay translates to a \(-\)0.05 decrease in trust;
* How much the other agent may help (in our logistic problem, the number of boxes already carried and assigned boxes with their time requests and relative paths to their destination): for each box already being carried, 0.05 more trust to is required to accept the cooperation offer. The perceived emotion of the other agent does not directly influence both trusting decisions, i.e., the influence is indirect: perceived alien emotion influences the emotion of the subject agent, and such own emotion influences the corresponding trusting decision. In the same way, privacy issues associated with the to-be-trusted agent do not directly influence both trusting decisions; they influence the alien emotion, which influences the emotion of the subject agent, and such emotions influence the corresponding trusting decision.
## 6 Evaluation
### Accuracy Evaluation of Energy Model
#### 6.3.3 Impact of Task Generation Rate  
This experiment executes four scenarios, with task generation rates of 0.05, 0.1, 0.2, and 0.3 s, using Application 2. All scenarios were configured with 500 tasks, 100 IoT devices and 1 MEC server. Figure 6 shows that small task generation rates flood the network with tasks, rapidly consuming all local resources. The x-axis shows the execution time in seconds, and the y-axis indicates the number of tasks. Remembering, 50% are critical tasks, and 50% are regular tasks.  
The critical tasks are immediately launched to IoT devices with a 0.05 s task generation rate. However, as IoT processing is slowest than CC and MEC server, the tasks have a slow time completion. In contrast, regular tasks are launched to MEC and CC. As a result, the local network reaches an overhead quickly. With a 0.1 s task generation rate, critical tasks are completed in a more balanced fashion in IoT devices. The MEC servers can execute more tasks in comparison with CC.  
More critical tasks can be executed in MEC servers with 0.2 s of task generation rate. Thus, CC resources are little-used in comparison to before generation task rates. Task generation rates should be designed with a time interval that favors local resource usage. It may help reduce total costs depending on the application and the costs of each allocation policy, as with the 0.3 s generation task rate shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & \(\mathbf{u_{E}}\) & \(\mathbf{u_{T}}\) & \begin{tabular}{c} **Cost** \\ **10-3** \\ \end{tabular} & \begin{tabular}{c} **\(\mathbf{E_{Tual}}\)** \\ **mj** \\ \end{tabular} & \begin{tabular}{c} **Tual** \\ **ms** \\ \end{tabular} & \
\begin{tabular}{c} **Freq.** \\ **MHz** \\ \end{tabular} &
\begin{tabular}{c} **Voltage** \\ **V** \\ \end{tabular} & **Policy** \\ \hline C1 & 1/5 & 4/5 & 18.59 & 145.50 & 33.36 & 1,500 & 1.20 & MEC \\ C2 & 2/5 & 3/5 & 25.97 & 142.76 & 34.69 & 750 & 0.83 & MEC \\ C3 & 3/5 & 2/5 & 33.18 & 142.76 & 34.69 & 750 & 0.83 & MEC \\ C4 & 4/5 & 1/5 & 35.44 & 70.40 & 250.00 & 8 & 4.00 & IoT \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cost coefficients for energy and time variation for Application 2.  
By configuring deadlines with very restrictive time limits, the experiments showed that critical tasks were canceled because the scheduler could not find an allocation policy to achieve task completion. Deadlines must be appropriately configured to have sufficient time allocation to process and complete the task correctly and avoid this behavior.
## 4 Results and Discussion
Moreover, these results show that the correspondence between the simulation and the experimental results is quite acceptable, while considering various types of influencing factors, including the non-uniform physical properties of the sample, the assumptions made in the 2D simulation while the experiment is in 3D, the probe calibration and the measurement errors caused by the analogue electronic devices involved in the experiment, the variation of the lift-off distance leading in weaker coupling between the probe's electromagnetic field and the specimen, and the machining of cracks. A finite element modeling simulation was used to measure the accuracy detection of the horizontal probe. The comparison of the experimental and simulation data proved the accuracy of the proposed probe.
## 4 Applications of Intelligent Acoustic Monitoring
### Public Safety and Security
#### 4.1.2 Explosion Detection Using Smartphones  
[PERSON] et al. in [100] noted that monitoring explosions was challenging due to the uneven distribution of infrasound and seismoacoustic sensor networks. They proposed using smartphone sensors for explosion detection. The authors combined two machine learning models (D-YAMNet [106] and LFM [107]) into an ensemble model for smartphone explosion detection. They observed that explosion detection on smartphone sensors using machine learning was a relatively new endeavour, and no specialised models were available for evaluating the performance of ensemble models.
## 3 Probabilistic Analysis
### Reliability Analysis
#### 3.2.1 Surrogate Model  
Comparisons between different surrogate models have been conducted, in which a combination between kriging metamodels and subset simulation (AK-SS) has been proven to be efficient in describing non-linear limit state functions, allowing a great representation of the behavior of the function using an experimental design of 19 models [29]. Therefore, an experimental design of 20 runs generated using the Latin Hypercube Sampling Method was used. Then, a Kriging surrogate model using UQlab was created and validated based on the random variables previously defined. The surrogate model uses a universal trend type, an anisotropic ellipsoidal Matern 5/2 correlation function, used to define the Gaussian process and cross-validation estimation method. For the validation of the surrogate model, two different sources of error estimation were implemented: the leave one out method and a validation experimental design set of 10 runs using LHS.
# On the Evaluation of Complex Networks Designs for an Energy-Efficient IP/WDM Core Network
## 3 Complex Network Designs
### Random Networks  
The purpose of random networks as first proposed by [PERSON] ([PERSON]) in 1959 was to model networks with n nodes and generate a homogeneous graph G (n,p) by randomly  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nodal Degree (\(k\))** & Nodal degree or degree of a node (\(k\)) presents the sum of links (neighboring nodes) directly connected to the node (\(i\)). \\ \hline
**Degree Distribution\(p(k)\)** & It is the probability distribution of a node, and it can be obtained by summing all nodes with different \(k\) links and dividing it by the total number of nodes in the whole topology. \\ \hline
**Diameter** & The diameter of a network is the length of the shortest path between the farthest-distanced nodes. \\ \hline
**Path Length (\(L\)) and** & Path length (\(L\)) between any source (\(s\)) and destination (\(d\)) nodes in the network is the sum of all the traversed links between s and d, while the mean shortest path length \(\langle\textbf{L}\rangle\) is the average of all shortest paths between all pairs of s and d nodes. \\
**Mean Shortest Path Length (\(L\))** & \(\langle L\rangle=\frac{1}{N(N-1)}\sum\limits_{s\
eq d}L_{sd}\) \\  & where \(\textbf{{L}}_{sd}\) is the length of the shortest path between nodes s and d and N is the total number of nodes in the network. \\ \hline
**Clustering Coefficient C,** & \(\langle\textbf{{C}}\rangle\) **and \(\textbf{{C}}(\textbf{{k}})\)** \\ \hline
**Average Clustering Coefficient \(\langle\textbf{{C}}\rangle\) and \(\textbf{{C}}(\textbf{{k}})\)** & \(\textbf{{C}}_{i}\) \\ \hline
**Coefficient \(\langle\textbf{{C}}\rangle\) and \(\textbf{{C}}(\textbf{{k}})\)** & \(\textbf{{C}}_{i}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network property definitions.  
selecting a pair of nodes with equal probability (p) and assign edges to connect them with the prespecified probability [9]. The generated random graph has p[n(n\(-\)1)/2] randomly placed edges connecting a pair of nodes with independent probability (p). Random graphs have a low mean path length which is directly proportional to the network size [7].
## 2 Motion Model Selection
### Exact Model  
In general, multirotor aerial vehicle (MAV) dynamics is described by six degrees of freedom. However, in planning followed by high-level controlling approaches, it is not required to define an actual motion model for planning, since a high-level controller consists of a fully-fledged quadrotor motion model. In most circumstances, the planner is composed of approximated quadrotor dynamics; this is due to computational complexity, which is not adequate for real-time onboard processing. Hence, the motion model selection process depends on the approach that formulates needs. In [58], the researchers proposed a six-degrees-of-freedom motion model, whose state vector is defined by \(\mathbf{x}=[\mathbf{p}^{\top},\mathbf{v}^{\top},\psi^{\top},\omega^{\top}]\), where \(\psi\), \(\mathbf{p}\), \(\mathbf{v}\) and \(\omega\) stand for orientation (rad), position (m), velocity (m/s), and angular velocity (rad/s) in \(\mathbb{R}^{3}\), respectively, with respect to a defined local coordinateframe. The system input or total trust that is applied for each of the motors is given by \(\mathbf{f}=[f_{1},f_{2},f_{3},f_{4}]^{T}\) (N). System dynamics is determined as \(\dot{\mathbf{x}}=[\dot{\mathbf{p}}^{\top},\mathbf{v}^{\top},\dot{\psi}^{\top}, \dot{\omega}^{\top}]\), where \(\dot{\mathbf{p}}=\mathbf{v}\), \(\dot{\mathbf{v}}=-g\cdot\mathbf{e}_{z}+\frac{(\mathbf{f}\cdot\exp\{\dot{ \psi}\},\mathbf{e}_{z}-k_{z}\cdot\mathbf{v})}{m}\), \(\dot{\psi}=\omega+\frac{1}{2}[\dot{\psi}]\cdot\omega+(1-\frac{1}{2}\frac{\| \psi\|}{tan(\frac{1}{2}\|\psi\|)}]\cdot\omega/\|\psi\|^{2}\), \(\dot{\omega}=J^{-1}(\rho(f_{2}-f_{4})\mathbf{e}_{x})+\rho(f_{3}-f_{1})\mathbf{ e}_{y}+k_{m}(f_{1}-f_{2}+f_{3}-f_{4})\mathbf{e}_{z}-[\omega]\cdot J \cdot w)\), \(g=9.8\) ms\({}^{-2}\) and \(\mathbf{e}_{i},~{}i=x,y,z\) stand for standard basis vectors in \(\mathbb{R}^{3}\), and \(k_{z},m,J,\rho\), and \(k_{m}\) are robot-specific constants.
## 5 Numerical Examples
### Simulation Verification
To check if the parameter estimator bias is too large, the hypothesis test based on Mahalanobis distance is implemented. It is used to assess whether the linearized model is a sufficient approximation. The results of six sets of tests and the threshold are compared in Figure 5. We can see that the test statistic of the first four sets of tests are all very small, the latter two sets of tests are very large, and some epoch data exceeds the threshold. It indicates that the bias of some epoch in Tests 5 and 6 are too large and the linearized model is not sufficiently approximate for UWB positioning. The bias is only intolerable for the relative large ranging error and ill-conditioned system geometry. The positioning system performances be ranked as Configuration 1 > Configuration 3 > Configuration 2 > Configuration 4 > Configuration 4 (large noise) and Configuration 5, from good to poor. Furthermore, the statistics of bias correction are listed in Table 1. It can be known that the bias correction effect in the coordinate component x and y can be ignored, and it is obvious in the coordinate component z. We can believe that it is more effective to use second-order bias correction when the measurement precision is relatively low or the configuration is bad.