esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Statistical Significance Analysis and Model Comparison
#### 4.5.2 Post-Hoc Tukey's HSD Test  
Following the ANOVA test, which identified significant differences among models, we conducted a Tukey's Honestly Significant Difference (HSD) test as a post-hoc analysis. [PERSON]'s HSD test is designed to determine which specific model pairs exhibit statistically significant differences. This method compares all possible pairs of models and adjusts for multiple comparisons to control the family-wise error rate. The test provides \(p\)-values that indicate the likelihood that performance differences between two models occurred by chance. \(p\)-values less than 0.05 suggest a statistically significant difference between model performances. This analysis offers a detailed understanding of how each model compares against others, as summarized in Table 17.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **F-Statistic** & \(p\)**-Value** \\ \hline \(R^{2}\) & 87.42 & \textless{}0.001 \\ MSE & 132.67 & \textless{}0.001 \\ MAE & 109.83 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 16: ANOVA results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(R^{2}\) **(Mean \(\pm\) SD)** & **MSE (Mean \(\pm\) SD)** & **MAE (Mean \(\pm\) SD)** \\ \hline MLR & 0.843 \(\pm\) 0.019 & 0.0084 \(\pm\) 0.0012 & 0.059 \(\pm\) 0.004 \\ GP & 0.827 \(\pm\) 0.025 & 0.0033 \(\pm\) 0.0008 & 0.040 \(\pm\) 0.003 \\ XGBoost & 0.916 \(\pm\) 0.015 & 0.0012 \(\pm\) 0.0005 & 0.028 \(\pm\) 0.0002 \\ GP-XGBoost & 0.927 \(\pm\) 0.011 & 0.0010 \(\pm\) 0.0004 & 0.027 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Statistical summary of model performance (30 runs).
## 2 The IDIVS Concept and Application Domains
### Application Areas
#### 2.4.1 Smart Buildings  
An application in smart buildings is that an IDIVS is used to detect human activities, e.g., meeting type, presentation, discussions, and/or work type, silent work, phone, or video calls. Other IDIVSs can detect related states, such as the number of people per room or per square metre. The idea is that the user can provide feedback on the activities (either existing or new). In other applications, an IDIVS can be used to detect complex  
Figure 2: The information properties and input/output of IDIVS.  
physical properties, such as air quality (including temperature and odour). The sensors may be both stationary sensors, e.g., motion sensors, sound level detectors, image/camera sensors, as well as mobile sensors in smartphones or on movable furniture. Potential applications (services) exist, for instance, connected to building and equipment automation, e.g., controlling blinds and presentation equipment, HVAC control adapted to the number of people present; guidance of people, e.g., finding a suitable room for the work at hand; and gathering usage data of a building, e.g., for re-allocating the types of rooms in a building or maintenance. Other examples of potential applications in smart buildings can be found in the review by [PERSON] et al. [16].
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 3 Methodology
### Capacity KPA
Additionally, if it is further considered that the ASMT includes advanced CD&R functionalities (Scenario 2), some of the time associated with CD&R tasks could decrease, resulting in a change in the ATCO WL, and thus in the capacity. Appendix A shows the tasks and their associated times, defined in RAMS for the calculation of the ATCO WL, which were extracted from [42]. These tasks are classified into five categories: Conflict Search, Flight Data Management, R/T Task, Co-ordination, Radar Task. The Radar Task category includes those tasks associated with CD&R actions (Table A1, Appendix A). This category is divided into seven different tasks (Radar 1-7) depending on the _LOS_ conditions (stabilized flight, same direction, etc.). Each of these tasks encompasses a set of actions, as follows:  
* Detection: generic ATCO action for conflict detection between aircraft pairs and the associated tasks. * Decision making and communication: determination of the conflict resolution maneuver and communication to the pilot (readback/hearback procedure). * Resolution: ATCO monitoring of the implementation of the aircraft resolution maneuver. Different meetings were arranged with experts from the European Union Aviation Safety Agency (EASA) and Spanish Air Navigation Service Provider (ANSP), to quantify the different actions (detection, decision making and communication, and resolution) in these tasks (Radar 1-7 tasks). Figure 3 shows an example of how Radar 4 Tasks were divided into the different actions, along with the associated times. Based on expert judgement gathered from meetings with Spanish ANSP and EASA experts, and in accordance with the ASMT prototype tool [33], a 50% improvement in the detection action and a 20% improvement in the decision making action is estimated if the ASMT with CD&R functionalities is available for the ATCO in a scenario. The 50% improvement in detection is primarily attributed to a reduction in the time required to determine whether any of the aircraft in the vicinity may lead to a conflict with other aircraft. Meanwhile, the 20% enhancement in decision making action is due to  
Figure 3: Radar 4 task split (from Appendix A) based on expert judgement. ASMT functionality to present a range of conflict resolution maneuvers to the ATCO after having detected a conflict. Therefore, the ATCO time associated with the evaluation of the resolution maneuver is reduced. In Figure 3, for the Radar 4 task split, this means a time reduction of 6 s in detection and 5 s in decision making.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 3 Attention Mechanism in MDE Methods
### Transformer-Based Attention Methods
#### 3.3.2 Self-Supervised Training  
[PERSON] et al. presented a ViT-based MDE method that adopts self-supervised training in combination with supervised training [67]. Unlike DPT, it uses non-local fusion blocks instead of common fusion blocks to gather long-range contexts in the decoder stage. [PERSON] et al. proposed a self-supervised method that uses Transformers for both the depth network and the pose network [68]. The highlight of the method lies in that a simplified Transformer is used instead of the original architecture, i.e., (1) the learnable layer of the generating \(V\) matrix is replaced by column-wise average pooling, and (2) the feature maps are firstly reduced in space before feeding to the attention module. In addition, the features of the depth network are introduced into the pose network in a spatial cross-attention manner. To reduce the complexity and parameter size, the pooling pyramid vision Transformer was proposed for MDE [69], in which spatial reduction and pooling operations are applied to matrix \(K\), \(V\), and \(Q\), respectively. [PERSON] et al. proposed Transformer-based depth estimation via the self-supervised learning method (TransDSSL) [70]. The highlights of TransDSSL are: (1) the pixel-wise attention module is used as a skip connection to supply details from the encoder stages, and (2) using the estimated depth of the high-resolution layer to guide the training of the low-resolution feature maps. The pixel-wise attention module is shown in Figure 17.  
Figure 16: Overview of DPT architecture (image: courtesy to [PERSON] et al. [10]).  
[PERSON] et al. constructed a self-supervised MDE method [71] by readapting DPT for the depth network and the decoder part of Monodepth2 [3] for the pose network. In their method, a data efficient image Transformer (DeiT) [72] is used in the pose network to estimate the camera pose and camera intrinsics simultaneously.
## 3 Feature Extraction
### Time Domain Feature Extraction
#### 3.2.4 Waveform Length (WL)  
Waveform length refers the signal feature obtained by accumulating the length of the signal energy (amplitude) within the number of data points and normalizing it. It reflects multiple physical quantities of signal such as action time, signal frequency, and amplitude.  
\[RMS=\sqrt{\frac{1}{N}\sum_{i=1}^{N}x_{i}^{2}} \tag{6}\]
# Analyzing and Controlling Inter-Head Diversity in Multi-Head Attention
## 6 Experiments on Controlling Inter-Head Similarity Methods
### Experimental Details  
* **Data and Setups:** We test our proposed methods on machine translation tasks with De-En WMT17 corpus [31], Ru-En UN corpus, and Zh-En UN corpus [15]. For WMT17 and UN corpus, we sample 2.5 M sentences randomly from each training set for training and use the whole development/test sets, similar to the setup of [PERSON] et al. [6]. Each corpus has development set consisting of 16,573 and 4000 sentences respectively and test set consisting of 3004 and 4000 sentences respectively. We also test our methods on a language modeling task with the Penn Treebank corpus [32]. We follow the rest of details as mentioned in Section 5
* **Model architectures:** We set a baseline model as an encoder-decoder Transformer with 6 layers, 512 hidden size, and 8 heads for every machine translation task. For language modeling, we use only the decoder part of Transformer only with 2 layers, 256 hidden sizes, and 4 heads. For each model named with ortho and hsic, we add each regularization term \(L_{ortho}\) and \(L_{h\_{lic}}\) to Transformer's default loss term. We choose the value of hyperparameters _Drophead rate_, \(\lambda_{ortho}\) and \(lambda_{lskic}\) by grid search on De\(\rightarrow\)En IWSLT17 task; _Drophead rate\(=0.1\)_, \(\lambda_{ortho}=1.0\)_, and \(\lambda_{h\_{lic}}=10^{-7}\). We apply the same values for other models.
## 2 Existing ASs and the Related Market Design
### Categories of ASs
#### 2.1.1 Frequency Control  
TSOs oversee the regulation of frequency and are tasked with keeping an equilibrium between electricity production and consumption, which is crucial for the operation of the grid. This entails managing the active power produced and/or consumed to maintain the supply-demand equilibrium within the electrical power system's 200 mHz bands. A designated portion of the active power reserve is allocated for precise frequency regulation. In instances where the system frequency falls below the specified threshold, frequency control backup mechanisms come into play. Frequency control is further categorized into three distinct classifications:  
* Primary frequency control,
* Secondary frequency control,
* Tertiary frequency control.  
In response to fluctuations in power system frequency, the initial action involves the activation of primary frequency controls. These controls, situated within the \(\pm\)200 mHz band, actively address and sustain the frequency oscillations. Subsequently, secondary frequency control is engaged to restore the system frequency to its nominal value. Tertiary frequency control, in turn, is instrumental in maintaining a balance between production and consumption. Complementary to secondary control, tertiary frequency control reinforces secondary reserves and indirectly enhances the suitability of primary control reserves [32; 35].  
Figure 1: Classification of ASs [34].  
The system frequency resource response times, critical for the restoration efforts in European countries, are outlined as follows:  
* The coordination of primary frequency control sources is required within a 30-s time-frame, ensuring stability for a minimum of 15 min.
* The activation of secondary frequency control reserves must occur within 30 s; the aim is to restore the frequency to its nominal level within 15 min.
* Tertiary frequency reserves are likewise anticipated to reach their maximum power output within the 15 min timeframe.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 9 Case Study 2: Cisco System Breach by Yanluowang Ransomware Gang
### Future Directions
#### 9.7.6 Automated Response: Swift Containment and Mitigation of Malware Intrusions  
Intrusion detection systems can be augmented with automated response mechanisms to enable rapid containment and mitigation of malware intrusions.  
* **Range of Automated Responses:** Developing automated response actions ranging from isolating affected systems to deploying countermeasures against specific malware strains.
* **Safety and Reliability:** Ensuring the safety and reliability of automated responses through careful consideration of potential risks and the use of machine learning for intelligent response strategies.
## 3 SQM Metrics  
Two types of correlators are considered in this section, namely the tracking and monitoring correlators, where the tracking correlators are used for tracking the signal and the monitoring correlators are used for signal quality monitoring. SQM metrics are defined as the combination of different tracking and monitoring correlators. The conventional Ratio and Delta SQM metrics are considered here as they make the base of most SQM metrics defined in the literature (e.g. [10; 26; 27; 12]). The single sided ratio test (also known as asymmetric ratio or simply ratio test) is usually defined as the late correlator output divided by the prompt value. The reason for monitoring this ratio test is that the late side of the correlation curve is usually more distorted by typically delayed reflected signals [29].  
The Delta metric (also known as symmetric ratio test) is a symmetric indicator which is designed to detect asymmetric correlation peaks based on the difference of either in-phase or absolute value of early minus late correlator outputs normalized by the prompt correlator [29]. The Double-Delta metric (as a specific case of SAM metric) is also defined here as the difference between two pairs of early-late correlators normalized by the prompt correlator.  
Under nominal conditions, in low multipath open sky environments, calm ionospheric condition and the absence of other GNSS signal degradation errors, the output of each SQM metric is a random process whose statistics (e.g., mean and variance) is determined based on the location of the constituent correlators and receiver noise. References [10; 24] investigated the nominal statistics of different SQM metrics for BPSK(1) and BOC(1,1) signaling schemes. The methodology is described in Appendix A. Table 1 summarizes the nominal mean and variance of the defined SQM metrics in which the tracking and monitoring early-late correlator spacings are 0.2 and 1 chips, respectively.  
Note that the Double-Delta metric has been defined here based on the difference between monitoring and tracking pairs of early-late correlators. Under the alternate hypothesis, when multipath occurs, this definition of Double-Delta will result in similar performance as the Delta metric (defined based on the difference of monitoring correlators). This is because DLL keeps the tracking early-minus-late value around zero. However, under the null hypothesis (i.e., multipath free conditions), the difference of two pairs of early-late correlators alters the SQM metric variance based on the correlation between the tracking and monitoring correlators as shown in Table 1. This will affect detection performance and sensitivity whilst the number of monitoring correlators is the same as the Delta metric.
## 2 Materials and Methods
### Application Rate Control Model  
The real-time fertilizing and seeding application rate of seed-fertilize drill machine can be expressed with the following equation:  
\[M(t)=\frac{c\cdot F(t)}{B\cdot V(t)}, \tag{1}\]  
where \(M(t)\) is the fertilizing or seeding application rate (kg/ha), \(F(t)\) is the material mass flow rate (kg/min), \(B\) is the operation width (m), \(V(t)\) is the vehicle speed (km/h), and \(c\) refers to the unit conversion coefficient between different variables. The relationship model between the rotation speed \(R(t)\) of the fluted-roller driving shaft and the mass flow rate \(F(t)\) of the seed-fertilize drill machine can be established as a linear equation by calibration, and can be expressed as:  
\[F(t)=k\cdot R(t)+b, \tag{2}\]  
where \(k\) is the ratio coefficient of the relationship model between the mass flow rate and rotation speed and represents the amount of material flowing from each single rotation of the fluted rollers (kg/r) and \(b\) is the constant term of the fitting equation (kg/min).  
Using Equations (1) and (2), the rotation speed of the fluted-roller driving shaft can be expressed as Equation (3).  
\[R(t)=\frac{1}{k}(\frac{M(t)\cdot B\cdot V(t)}{c}-b), \tag{3}\]  
Equation (3) is the basic control model for controlling the application rate of the seed-fertilize drill machine with fluted rollers. In practice, the main controller calculates the driving shaft rotation speed in real-time, according to the relationship model shown in Equation (3), combining the target application rate with the vehicle speed to ensure that the actual application rate matches the target application rate.
# Assessing Voltage Stability in Distribution Networks:
## 5 Case Analysis  
This paper conducts a case study of a distribution network with distributed PVs, using the IEEE 33-node distribution system as an example to validate the effectiveness of the proposed voltage stability assessment method [41]. Figure 4 illustrates the topology of the distribution network.  
The IEEE 33-node system has a total of 32 branches with a base power of 10 MVA and a base voltage of 12.66 kV, where node 1 is set as the slack node. The loads follow normal distribution with the expected values by referring to the IEEE 33-node standard test system, and the standard deviation is set at 10% of the expected values. In Figure 4, PV represents distributed PV, and dashed lines indicate the options for connection, thereby facilitating discussions of future scenarios. PV output obeys Beta distribution, which is calculated as the PQ node. The shape parameters are \(\alpha=3.613\), \(\beta=5.938\). The PV conversion efficiency is \(\eta=13.44\%\).  
The proposed model was established using MATLAB R2021b and the Matpower 7.1 toolkit. In the MATLAB cooler, the power models for the load and PV were constructed through coding. The Matpower software package was then used to import the model data of the IEEE 33-node system, and the continuous power flow calculation and stochastic analysis were carried out to evaluate the voltage stability of the distribution network. The testing system was implemented on a computer (Manufactured by Lenovo Group and purchased in Xuzhou, China) with an Intel i7-700 HQ CPU (2.8 GHz) and 16 GB of RAM.  
Without considering the stochastic factors, the CPF calculation was performed by gradually increasing the load at each node in proportion, while maintaining a constant power factor. The \(\lambda\)-\(V\) curve of each node in the system can be obtained by calculation, and the voltage-stability critical point of the node can be obtained. Taking the curve of Node 16 as an example, as shown in Figure 5.  
In Figure 5, \(\lambda\) is called the load parameter, which is the ratio of the active power absorbed by the load at a certain moment to the base state active power. \(V_{\text{cr}}\) is the critical voltage of the node.  
This paper takes the IEEE 33-node distribution system with PV as a case study, considering the uncertainties of both PV and load. The stability critical state of each node in the system was obtained through CPF calculation. The load margin index \(I_{\text{LM}}\) was obtained according to \(\lambda\), and the probability distribution model was set up to complete the probabilistic assessment of voltage stability.  
Figure 4: IEEE 33-node distribution system with distributed PVs.
## 3 Material and Methods
### Evaluation Measures  
To compare the performance of classifiers, several evaluation measures such as Area Under Curve (AUC), accuracy (ACC), precision, recall, and F1-score were used. The evaluation metrics formula is shown in the equations below.  
\[ACC=\frac{TP+TN}{TP+FN+TN+FP}\]  
\[Precision=\frac{TP}{TP+FP}\]  
\[Recall=\frac{TP}{TP+FN}\]  
\[F_{1}=\frac{2\times precision\times recall}{precision+recall}\]  
where true positive (_TP_) indicates the sample that is attack and also predicted as attack by the proposed model. True negative (_TN_) indicates the non-attack labeled sample predicted as non-attack. False positive (_FP_) indicates the sample that is originally non-attack but predicted as attack by the model. While false negative (_FN_) indicates the sample that is originally attack but predicted as non-attack by the model. _ACC_ represents the ratio of the number of correctly predicted samples to the total number of samples used during the testing phase. Precision indicates the usefulness of the proposed model, while recall indicates the completeness of the proposed model. While F1 indicates the ratio of precision and recall of the model. The AUC evaluation measure indicates the quality of the proposed model, a higher the value indicates better performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameter** & **Value** \\ \hline \multirow{2}{*}{DT} & max\_depth & 9 \\  & random\_state & 42 \\ \hline \multirow{4}{*}{Random Forest} & random State & 1 \\  & n-estimators & 100 \\  & max-depth & 15 \\  & min\_samples\_split & 5 \\ \hline \multirow{4}{*}{Gradient Boosting} & learning rate & 0.05 \\  & max\_depth & 3 \\  & max\_features & 0.5 \\  & random\_state & 42 \\ \hline \multirow{2}{*}{Ada Boost} & learning\_rate & 1.02 \\  & n\_estimators & 13 \\ \hline Extreme Gradient & eval\_metric & mlogloss \\ \hline \multirow{2}{*}{Light GBM} & learning\_rate & 0.1 \\  & max\_depth & \(-1\) \\ \hline \multirow{2}{*}{Cat Boosting} & iterations & 5 \\  & learning\_rate & 0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Models’ parameter values after optimization.
## 2 Conventional Direction Detection Technology
### Non-Marker-Based Approaches  
Non-marker-based approaches frequently utilize the strength of the signal transmitted from the ground station as a means of detecting the direction of the ground station. Though these have been highly reliable, they require heavier equipment for more accurate detection and a corresponding signal transmitter on the ground station side.  
The most straightforward method for detecting signals from a ground station is through signal detection [7]. The process of signal detection utilizes the Automatic Gain Control (AGC) signal of a digital receiving tuner, with the objective of measuring the strength of the signal received by the antenna.  
Radio frequency (RF) sensors quantify the angular deviation between the direction of the beacon wave transmitted from a ground station and the sensor's main axis. RF sensors are primarily employed for antenna control in satellite communications [7]. RF sensors comprising multiple power feed systems determine the direction of the beacon wave by calculating the difference in the received power for each feed system. Consequently, high precision RF sensors cause an increase in weight and therefore cannot be equipped on a small fixed-wing UAVs.  
The approach that uses signal strength to detect the ground station direction requires a device that transmits signals to the ground station. The installation of such a signal transmitter affects the cost of building a radio relay system and may cause operational feasibility problems. In addition, the installation of signal transmitters that require power may compromise the independence of the video transmission system using small fixed-wing UAVs. In particular, the signal transmitter may not function in certain environments, such as during disasters. In addition, this approach carries the risk of signal interference. Especially in congested wireless environments, such as urban areas, the accuracy of signal detection may be compromised by surrounding wireless systems. Given these concerns, the non-marker-based approach risks compromising the operational flexibility of small fixed-wing UAVs.