esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Well Diffusion Method  
The 24-h bacterial cultures were grown on TSA (Tryptic Soy Agar, Merck, Darmstadt, Germany) slants at 36 \(\pm\) 0.5 \({}^{\circ}\)C. After incubation, 1 mL of sterile saline solution (0.9% NaCl) was added to each test tube. The bacterial culture was then collected from the surface of the medium using a sterile loop. The final bacterial inoculum density was determined spectrophotometrically (DEN-1 McFarland Densitometer, Biosan, Riga, Latvia) and adjusted to 1.0-2.0 \(\times\) 108 CFU/mL, which corresponds to the standard cell density used in antimicrobial susceptibility testing. The resulting suspension was transferred into cooled, still-liquid Mueller-Hinton agar (Merck) and gently mixed. The inoculated medium was then poured into sterile Petri dishes and allowed to solidify. After the agar solidified, six wells (8 mm in diameter) were made on each plate using sterile cork borers. In the final step, 100 \(\upmu\)L of the test extracts were added to each well, and the plates were incubated for 24 h at 36 \(\pm\) 0.5 \({}^{\circ}\)C. After incubation, the inhibition zones were measured, excluding the well diameter.  
Based on the analysis, extracts with antibacterial properties were identified and subsequently subjected to tests assessing their combined effect. These tests involved combining selected extracts in pairs according to a defined matrix and applying 100 \(\upmu\)L of the resulting mixture to the designated wells in the well-diffusion method [72]. The result was presented as an average of two repetitions.
# Packet Injection Exploiting Attack and Mitigation in Software-Defined Networks
## 3 Packet Injection Exploiting Attack
### Threat Experiment
**Attack impact on OpenFlow switch:** We counted the occupancy of the switch flow table under different conditions, and the results are shown in Figure 6. As mentioned above, when an attacker implements the forging destination MAC address attack, the controller will delete unmatched packets. Since no more flow entries are installed, the flow table occupancy under the forging destination MAC attack is similar to the normal network state, as shown in Figure 6a,b. Figure 6c shows the occupancy of the flow table under the packet injection exploiting attack. As we can see, the number of flow table entries increases rapidly, reaching a maximum of 800. This is because the controller calculates the forwarding path for unmatched packets and installs plenty of flow rules.
## 3 Results and Discussion
### Comparison of Different Neural Network Architectures in UWB IR Signal Processing  
One of the most important problems, as defined previously, is the choice of proper NN architecture for UWB IR signal processing. This topic was studied and is described in detail in [30]. The essential outcomes are as follows:  
* The difference in proper waste detection accuracy for simple NN architectures (multi-layer perceptron and convolution NN) and dense NN architectures (Transformer and ResNet) in the case of the monostatic radar is up to 1.9% in favor of dense NN architectures; at the same time, this means a difference in counts of mistakenly recognized objects of up to four times;
* In the case of simple NN architectures switching from monostatic to multistatic radar increases the detection accuracy up to 1.8%;
* In the case of most dense NN architectures, switching from monostatic to multistatic radar increases the detection accuracy only by a fraction of a percent.
## 4 Experimental Design  
The purpose of this section is to provide technical information on the inner workings of the simulation scenario, and it is intended for fellow researchers who would want to replicate our experiment. If the focus of the reader is only on the model, this section can be skipped safely.  
The experiments were conducted using a series of outdoor simulated scenarios, each populated with three types of nodes: stationary nodes, pedestrians, and vehicles. Some of these nodes have positioning capabilities (e.g., a computing device with GPS), and they interact with each other through a mobile _ad hoc_ network (MANET) to sense and share contextual elements.  
All scenarios were designed using the Network Simulator 3 (ns-3), a networking simulator built as a core system with a set of interchangeable libraries that can be linked or imported to user programs (simulations). It provides substantial support for simulation of networking scenarios and is highly scalable, extensible, and modular [32]. The versatility of the ns-3 allows users to write simulation scenarios (based on already validated models) using only the features they want to represent, as well as adding their own libraries to the core of the simulator.  
The nodes mobility was modeled using the BonnMotion simulator [33], since this tool includes well-known models that represent people mobility in the study scenarios, particularly for disaster areas [34]. The mobility models considered in this work have a widely analyzed and validated mathematical basis [22; 35; 36].  
The following subsection describes the parameters of the simulated environment and the conditions under which the simulations are performed, as well as the implementation of the experimental test bed.
## 1 Introduction
Furthermore, the incorporation of an adaptive term enables accurate and rapid estimation of phase currents. (3) Voltage signals are used in most fault diagnosis methods for T\({}^{2}\)3L inverters. Nevertheless, this method necessitates additional voltage sensors, which adds more potential fault spots and increases hardware expenses. Moreover, such methods become ineffective when voltage signals are unavailable. To address this challenge, the proposed method can achieve accurate detection and location of faulty switches only through the current signal, despite only minor differences in fault characteristics. The paper is organized as follows: Section 2 describes the hybrid logical dynamic model of the T\({}^{2}\)3L inverter and investigates the effects of various switch faults on the inverter's output. Section 3 introduces a diagnostic scheme integrating the IASMO with the adaptive threshold. Section 4 validates the proposed method through simulations and experimental tests on the power electronics training system (PTS). Section 5 presents the diagnosis performance analysis and comparative performance evaluation. Finally, Section 6 concludes the paper and outlines directions for future research.
# Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights
## 7 Conclusions  
This paper presented a comprehensive review of the frequency-domain FxLMS algorithms in terms of convergence performance, stability, and computational burden. Practical applications include active noise control of open windows [53], the ventilation system [54], and automotive cabins [55], where several thousands of coefficients are required to model the control filters. The frequency-domain algorithms used in ANC and system identification are quite different. First, broadband ANC is sensitive to algorithm latency, and a large delay may cause the system to fail. Second, direct use of an FDAF for ANC results in a delay in the weight update and the observation of the error signal, which reduces the maximum step size of the algorithm.  
This paper reviewed the delayless filtering methods and the modified filtered-x structures to address the two delay problems. Specifically, the update-first approach was proposed to improve the stability of the FPBFxLMS algorithms. Several frequency-domain FxLMS algorithms have a very high peak complexity and thus are not practical for real-time systems, although these algorithms are correct in theory. A computationally efficient delayless frequency-domain algorithm was proposed that combines the hybrid fast filtering approaches and the modified filtered-x structure, which completely removes the aforementioned two types of delays. Extensive simulations were carried out to evaluate the convergence performance of the state-of-the-art frequency-domain FxLMS algorithms.  
However, more work needs to be done regarding the frequency-domain FxLMS algorithms. For instance, the step size bound of the traditional frequency-domain FxLMS algorithms in Tables 1-4 should be determined. Analyzing the statistical convergence behavior of the frequency-domain FxLMS algorithms is an interesting topic [56; 57; 58; 59].  
[PERSON] performed the experiments and wrote the paper; [PERSON], [PERSON] and [PERSON] gave valuable comments and edited the paper which improves the quality of the article; [PERSON] conceived the experiments and proofread the paper.  
This work of [PERSON] was supported by National Natural Science Fund of China under Grants 61501449 and 11674348, Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant 2018027, and IACAS Young Elite Researcher Project QNYC201722. The work of [PERSON] was supported by a grant of the Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI, project number PN-III-P3-3.1-PM-RO-CN-2018-0099/28 BM/2018, within PNCDI III.  
We thank [PERSON] for preparing the impulse responses used in the paper.  
The authors declare no conflict of interest.  
Figure 11: Algorithm stability of the traditional and modified structures. (**a**) \(\mu=0.01\). (**b**) \(\mu=0.08\).
# HEAD Access Control Metamodel: Distinct Design, Advanced Features, and New Opportunities
## 5 HEAD Metamodel: Development Approach to Access Control in Dynamic and Heterogeneous Environments
### Deriving Models
#### 5.2.2. Formalization of the Specified AC Models  
In this step, we encode the obtained AC models from the previous step, after specifying the needed AC components, using the DSL of the HEAD metamodel as an AC modeling language. The power of the DSL language of the HEAD metamodel is that it is simple and flexible to appropriately express any AC policy requirements, overcomes the complication of existing language expressions, and it is also independent of specific AC models. The flowchart in Figure 19 explains how this DSL works to instantiate various policy models. It can be interpreted as follows:  
Figure 18. Sample of DSL of HEAD metamodel [3].  
* Before defining any component(s), policy model should be specified with the needed attributes, for example, policy type (e.g., RBAC policy, ABAC policy, etc.), organization, etc.
* At least one (or more) \(E_{x}\) component must be created (with the needed attributes) since any AC model must include \(E_{x}\) entities (e.g., subject, object).
* \(I_{m}\) components (AUs and /or PUs) should be identified and created. Note that an AC model might not include an AU component(s) if, for example, the needed AC model is DAC. If the model to be instantiated is MAC or RBAC, then there exists an AU component (security level or role). Moreover, an AC model must include at least one PU component, for example, action, and it should be created.
* An AC model might not include \(S_{t}\) component(s), for example, in the DAC model context, and constraint components do not exist.
* If there exists additional components/attributes, they should be created, for example, to upgrade a policy. The HEAD metamodel allows for adding them.
* Finally, if all the needed components/attributes are created, then associations of components are handled while expressing the needed rule(s).  
Figure 20 shows an example of MAC instance as a result of this step. MAC components/attributes are defined and then the needed rule is expressed in terms of MAC elements (subject, object, security level, and operation).  
Figure 19: Instantiation of AC models using the DSL of HEAD metamodel.
## 5 Machine Learning and Feature Selection Enabled IDS
### Popular Datasets Adoption for ML/FS-Enabled IDS
#### 5.3.8 BoT-IoT Dataset  
The Bot-IoT is a diversified multi-class labeled dataset that is generated in a real-world testbed environment specifically designed for evaluating IoT botnet detection ([PERSON] et al., 2019). The testbed comprises three major components, including a Network Platform (NP), Simulated IoT Services (SIS), and Feature Extraction and Forensic Analytics (FEFA). The NP consists of network equipment, including firewalls and Terminal Access Points, along with virtual machines to generate malicious and benign traffic. The SIS utilizes a Node-red application to simulate IoT services, including a weather station. The FEFA component extracts features via the Argus application, where the feature vectors are then evaluated using variousstatistical models and ML classifiers for the classification of cyber-attacks. The dataset is generated through the use of five distinct IoT devices, each serving a specific function, such as a weather sensing node, an IoT-enabled fridge, motion lights, a remotely operated door, and an AC controlled by a smart thermostat. Additionally, a smart phone is employed as a weather station. These devices generate pertinent data, including temperature, humidity, and atmospheric pressure. The dataset encapsulates traces of various cyber-attacks, including DoS and DDoS with variations in protocols (HTTP, TCP, and UDP), port scans, keylogging, and data exfiltration attacks.
## 3 Results and Discussion  
### Incident Light Intensity Characteristics on the Hood and Roof  
The average cosine losses for the hood and roof of the potential VIPV installation are summarized in Figures 9 and 10, respectively. These graphs show the average incident light intensity ratios at various solar azimuth angles and solar altitudes for each surface. By calculating these values, we identified the angle at which the highest solar intensity was maintained on each surface. In this context, the cosine loss averages represent the percentage of the incident light intensity. The analysis was based on 32,400 ray vectors, which capture solar altitudes ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\) and solar azimuths ranging from 0\({}^{\circ}\) to 360\({}^{\circ}\) in increments of 1\({}^{\circ}\). The vehicle's orientation was determined by the center of the vehicle body as the origin as follows: the positive x-axis indicates the direction of travel, the positive y-axis represents the left side of the vehicle body, and the positive z-axis corresponds to the direction of the roof. In this framework, the light ray vector considers the Sun's altitude as a positive angle measured clockwise from the z-axis, whereas the Sun's azimuth angle is a positive angle measured clockwise from the x-axis. For example, if the Sun has an azimuth angle of 90\({}^{\circ}\) and an altitude of 30\({}^{\circ}\), the rays of light are directed at an angle of 60\({}^{\circ}\) on the right side of the vehicle's body, as observed from the ground. The results showed a maximum value of 0.98 for both the roof and hood of the vehicle. This indicates that approximately 98% of the light was incident on the entire surface when the intensity of the rays at the specified angles was 100%. This measurement considers surface area. For the hood, the intensity of the incident light noticeably changed with variations in the solar azimuth angle, whereas the roof displayed only a minimal change. This discrepancy may be attributed to the unit normal vector of the hood being oriented differently from that of the roof, along with the effects of self-shadowing.
# Vineyard Gap Detection by Convolutional Neural Networks Fed by Multi-Spectral Images
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional Neural Networks \\ RCNN & Regional Convolutional Neural Networks \\ YOLO & You Only Look Once \\ SSD & Single Shot MultiBox Detection \\ SVM & Multiclass Support Vector Machine \\ MLR & Multinomial Logistic Regression \\ SGD & Stochastic Gradient Descent \\ ReLU & Rectified Linear Unit \\ mAP & Mean Average Precision \\ fps & Frames per Second \\ FPN & Feature Pyramid Network \\ RoI & Region of Interest \\ RPN & Region Proposal Network \\ IoU & Intersection over Union \\ FCN & Fully Convolutional Network \\ AR & Average Recall \\ SS & Selective Search \\ NDWI & Normalized Difference Water Index \\ CIR & Color-Infrared \\ NIR & Near-Infrared \\ RGB & Red-Green-Blue \\ GS & Gray-Scale \\ NDRE & Normalized Difference Red-Edge \\ NDVI & Normalized Difference Vegetation Index \\ UAV & Unmanned Aerial Vehicle \\ GDP & Gross Domestic Product \\ GS & Gray-Scale \\ PAN & Path Aggregation Network \\ SPP & Spatial Pyramid Pooling \\ NMS & Non Maximum Suppression \\ \end{tabular}  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Layer** & **Filters** & **Size** & **Input** & **Output** \\ \hline  
olo & & & & \\
17 route & 13 & & & \(19\times 19\times 256\) \\
18 conv & 128 & \(1\times 1\) & \(19\times 19\times 256\) & \(19\times 19\times 128\) \\
19 upsample & & \(2\times\) & \(19\times 19\times 128\) & \(38\times 38\times 128\) \\
20 route & 19 8 & \(3\times 3\) & \(38\times 38\times 384\) & \(38\times 38\times 384\) \\
21 conv & 256 & \(1\times 1\) & \(38\times 38\times 256\) & \(38\times 38\times 256\) \\
22 conv & 18 & & & \(38\times 38\times 18\) \\
23 yolo & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tiny YOLOv3 Architecture. Layer 16: [yolo] params: iou loss: mse, \(iou_{norm}\): \(0.75\), \(obj_{norm}\): \(1.00\), \(cls_{norm}\): \(1.00\), \(delta_{norm}\): \(1.00\), \(scale_{xy}\): \(1.00\).
## 3 Modelling and Simulation of an Aircraft Electric Braking System
### Fault Mode Integration and Simulation  
Having obtained the EBS normal performance model data, fault modes are selected according to their criticality and the variety of effects they could provide for data analysis purposes. Electrical and mechanical faults being critical, an electric motor open circuit (OC), intermittent open circuit (IOC) and motor jamming faults were selected for data processing. Undesirable situations as a result of these fault modes could lead to catastrophic accidents.  
Figure 3: Actuator and ABS Data.  
Figure 2: Aircraft Braking Data.  
Thus, the study of these faults is important to assure IVHM capabilities and increase the systems' reliability.  
The jamming condition is simulated by applying increased friction in the motor shaft. Some noticeable results are the lack of braking force as the actuator does not make contact with the brake, disturbance in the wheel slip, and no change in the aircraft velocity. Motor power is greatly increased as the actuator controller battles to overcome the jamming condition. This situation certainly has the potential to turn into a catastrophic accident.  
The OC and IOC are integrated into the model by opening the circuit with a switch. The OC opens 20 s after the braking procedure starts, halting the actuator activity completely, and displaying a complete disruption in the aircraft braking and the actuator power. Similarly, the IOC is set to occur every 5 s, which halts the EBS activity intermittently but still managing to stop the aircraft with a noticeable reduction in performance. Some data comparing the OC and IOC are shown in Figure 4.  
For the purpose of the development of an accurate reasoner to diagnose faults, the parameters that have unique characteristics and best represent a fault are of particular interest. It is desirable to obtain data and evaluate redundancies in order to reduce the computational load without compromising on accuracy.  
Figure 4: Comparison in Resultant Data from OC and IOC Faults: (**a**) OC Fault Resultant Data; (**b**) IOC Fault Resultant Data.
# An Analysis of the Short Utterance Problem for Speaker Characterization +
## 3 Formulation of the Embedding Extraction with Short Utterances
### Short Utterances  
Now we consider the short utterance scenario. According to the previous analysis, embeddings work well if the distribution of acoustic units \(\boldsymbol{\alpha}\) is similar to to the reference distribution and the particular contributions \(G_{i}(\mathbf{A})\) are estimated with low uncertainty. These two requirements are reassured as long as the utterance contains more and more data. Concerning short utterances, their low amount of data makes them likely to have their distribution of acoustic units \(\boldsymbol{\alpha}\) far from their reference. For the same reason short utterances may also suffer from large uncertainty in their phoneme estimations \(G_{i}(\mathbf{A})\). Hence degradation in short utterances can be explained by the following reasons:  
* **Errors in the contribution of phonemes**. Some contributions \(G_{i}(\mathbf{A})\) were estimated with very little information. Then the uncertainty of their estimation increases. Multiple values within this uncertainty range as \(G_{i}(\mathbf{A})^{\prime}\) can be estimated instead, committing the error \(E=G_{i}(\mathbf{A})^{\prime}-G_{i}(\mathbf{A})\).
* **Mismatch in the phoneme distribution**. The distribution of the weights \(\boldsymbol{\alpha}\) does not match the reference \(\mathbf{\overline{\boldsymbol{\alpha}}}\), defined by language characteristics. This degradation causes the error \(E=\sum_{i=1}^{M}(\boldsymbol{\alpha}_{i}-\mathbf{\overline{\boldsymbol{ \alpha}}}_{i})G_{i}(\mathbf{A})\). The extreme case happens when some acoustic units are not present in the utterance, that is, they are missing. In this situation their weight \(\boldsymbol{\alpha}_{i}\) are equal to zero, also forcing the missing estimations \(G_{i}(\mathbf{A})\) to be set to zero, as if they were occluded. The degradation due to the mismatch in the phoneme distribution is compatible with the errors in the contribution of phonemes.  
Traditionally errors have been attributed to the contributions per acoustic unit. This is specially true when traditional embeddings, for example, i-vectors, include an uncertainty term in its calculations. For this reason this sort of error was the first attempted to deal with, for example, see Reference [25]. However, to the best of our knowledge no previous work has covered the degradation due to the phoneme distribution, which can cause similar levels of degradation.