esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Execution Time Comparison  
In this subsection, we compare the execution time of the proposed schemes with that of the existing schemes. Table 3 shows that the proposed schemes (EDADS-1 and EDADS-2) consume less time for feature selection and anomaly detection as compared to the existing schemes. The feature selection time of the two proposed schemes is similar because both schemes utilize GA. However, the detection time of EDADS-2 is slightly less than that of EDADS-1. The reason is that EDADS-2 utilizes only normal features for training from the test data. On the other hand, EDADS-1 employs all the features from the test data for training because the data are unlabeled for this case. Table 3 shows that for NCA, the execution time is higher than the proposed schemes. Table 3 clearly shows that the FE technique [7] employing PCA requires more time. The PCA-based approach achieves the dimensionality reduction by transforming original samples with binary values into new samples with numeric values while making the execution time much longer than other schemes.  
Figure 11: \(F_{1}\) score of the proposed EDADS-1 and EDADS-2 in standard IEEE 14-, 39-, 57- and 118-bus systems with varying numbers of training samples. (**a**) \(F_{1}\) score of EDADS-1; (**b**) \(F_{1}\) score of EDADS-2.  
Figure 12: \(F_{1}\) score of the proposed EDADS-1 and EDADS-2 in standard IEEE 14-, 39-, 57- and 118-bus systems with varying numbers of compromised load profiles. (**a**) EDADS-1; (**b**) [EDADS-2.
## 2 Methods of Encapsulation
### Freeze-Drying  
Freeze-drying encapsulation requires creating a solution comprising the desired compound and the encapsulation materials, which is subsequently transformed into microcapsules using the freeze-drying method [31]. Freeze-drying is used for the microencapsulation of natural flavors, extracts, lipids, probiotics, and various bioactive substances. While freeze-drying is a straightforward technique well suited for encapsulating aromatic compounds, it does require a lengthy dehydration period [32; 33]. The preservation of volatile compounds during freeze-drying is contingent on the chemical characteristics of the system and the process parameters [34].  
Figure 2: Spray-drying encapsulation (created with BioRender.com (accessed on 26 November 2023)).  
Prior to the commencement of microencapsulation, a solution is prepared, and frequently, one of its constituents is starch. Native starch is inherently hydrophilic, but through the esterification of starch's hydroxyl groups with a hydrophobic substituent, starch transforms into an amphiphilic substance. The resulting derivative of starch in this process is referred to as octenyl succinin anhydride (n-OSA) [35].  
The microencapsulation process through freeze-drying takes place under low-temperature and low-pressure conditions. These specific conditions ensure a high degree of preservation for thermolabile compounds that would degrade or undergo alterations in alternative microencapsulation methods. Substances such as unsaturated fatty acids can be successfully microencapsulated using this method [36]. In addition, some types of molecules, such as coacetvates or complexes, benefit from the use of FD for isolation [6]. FD is a method suitable for volatile compounds because it does not require the use of high temperatures, which could cause the thermal decomposition of compounds. It allows for the preservation of the original properties in terms of the color, aroma, taste, texture, or biological activity. It does, however, involve the possibility of losses during production and an increase in the release time due to the formation of a porous wall between the compounds and the surroundings. Despite its numerous advantages, freeze-drying is consistently regarded as the most costly method for producing dehydrated products. This is due to the high energy consumption associated with the necessary freezing of the product, followed by a long primary drying process when sublimation occurs, and a secondary drying process when evaporation occurs. It is also bound up with the long duration of each of these processes [37]. A diagram of the FD encapsulation method is shown in Figure 3.
# A Filter Feature Selection Algorithm Based on Mutual Information for Intrusion Detection
## 1 Introduction
There are three penalty factors that affect feature subset selection: MI between features to reduce redundancy between features, MI between selected features and classes and MI between candidate features and classes. However, A weakness of the already reported feature selection algorithms is that they only consider the part of penalty factors to affect the feature subset selection and intrusion detection efficiency. Aiming at the shortcomings of the above algorithms, a new filter-based feature selection algorithm is proposed in the paper. The new algorithm considers three penalty factors to maximize relevancy and minimize redundancy between features. By using the proposed algorithm, the selected feature subset is superior to those selected by the above algorithms and the intrusion detection performance can be effectively improved in Section 4. The paper is structured as follows. In Section 2, we introduce some concepts about MI. Section 3 reports the proposed new filter-based feature selection algorithm. Experimental results with this feature selection algorithm are shown in Section 4. Finally, conclusions with a discussion on future work are presented in Section 5.
# A PEKS-Based NDN Strategy for Name Privacy
## 4 Details of Implementation
### Strategy  
The forwarding plane in NDN is called the strategy module [32] and the key to the resilience and efficiency of the NDN [2]. Strategies in NDN provide to make decision forwarding interest packets. Even though there are multiple strategies in NFD, only a single strategy handles to forward an interest packet. There are triggers to decide where the forwarding strategy needs to send the packet. Those triggers are:  
* After Receive Interest Trigger: When an interest packet arrives at a router, NFD checks whether the packet violates \"/localhost\" scope, whether it is looped, etc,. After all of those necessary checks are done, the incoming interest packet is needed to be forwarded. Then this trigger is invoked by the Incoming Interest pipeline, which is a module of NFD. If the interest packet is using PEKS_Strategy, testing with Trapdoor data structure done at this moment and forwards to PEKS_Strategy module to match InterestName with Trapdoor as described above subsection.
* After Receive Data Trigger: This trigger is invoked by the Incoming Data pipeline. In PEKS_Strategy, storing data packet to CS is done after this trigger is invoked.  
In abstract, when an interest packet is forwarded to newtop, PEKS_Strategy adapts the best route and when NFD receives trapdoor dissemination, it uses multicast. If there is no single match, it also multicast the interest packet to the network. If it is not a PEKS packet, NFD will use one of the other strategies to forward interest packet. PEKS_Strategy should be applied to all NFD in the network in order to use PEKS-based NDN.
## 5 Incipient Fault Model
### Incipient Fault Model for Sub-Cycle Disturbance  
As shown in Figure 18, the typical sub-cycle arc voltage and current waveforms, arc conductance variation, and their corresponding relationships are presented. The arc interval is defined as the period between the instantaneous rise and fall of the voltage/current. The conductance value at each sampling moment within this interval is sequentially calculated. During arc initiation, an abrupt change occurs over a very short period of approximately 0.04-0.08 ms, as observed in the experimental waveforms. Considering that the entire arc duration is approximately 4 ms, this rapid transition is ignored in the model to focus on the subsequent conductance variation, which dominates the arc's behavior. It can be observed that the conductance variation follows a trend similar to that of the arc current, exhibiting an initial increase followed by a decrease. The conductance reaches its maximum value at the current peak.  
Analyzing the fitted conductance variation curve reveals that the arc conductance follows a pattern approximately resembling a segment of a trigonometric function. To simplify the model representation, this study approximates the arc conductance variation using a partial sine wave. The conductance variation curve is considered as a segment of a sine function within the interval [0, \(\pi\)], where the peak of the sine function corresponds to the maximum conductance value. Thus, the fault model for sub-cycle arc can be expressed as:  
\[g_{f}=g_{\text{max}}\cdot\sin(2\pi\cdot f\cdot t+\theta)\quad t\in(t_{\text{st} },t_{\text{st}}+t_{0}) \tag{1}\]  
In the proposed model, the arc ignition time \(t_{\text{st}}\) and arc duration \(t_{0}\) can be directly obtained from experimental waveforms. The arc ignition time \(t_{\text{st}}\) is referenced to the voltage zero-crossing point and fluctuates randomly around the voltage peak, typically occurring within 3.5 to 6.3 ms. The maximum arc conductance \(g_{\text{max}}\) is determined from discrete experimental voltage and current sampling points. Since the conductance decay duration is significantly longer than the rising duration and follows approximately one-quarter of a sine wave period, the sine function frequency \(f\) can be approximated as the reciprocal of four times the conductance decay duration. The initial phase angle \(\theta\) of the sine function is calculated based on the ratio of the initial conductance to the maximum conductance, following the corresponding angular relationship. A smaller \(\theta\) indicates a longer conductance rise time. For the arcs occurring in humid air exposure conditions (3.2), the duration is extremely short and setting \(t_{0}\) within 0.3 to 0.5 ms in this model can effectively reproduce the arc waveform. The role and range of each model control parameter are summarized in Table 1.  
Figure 18: A typical sub-cycle disturbance waveform: (**a**) voltage and current; (**b**) corresponding fault conductance values and the fitted curve.
## 3 Current Applications
### Retina
#### 3.2.3 Retinopathy of Prematurity  
Retinopathy of prematurity (ROP) is a vascular disorder that impacts infants born extremely premature or with low birth weight. The worst scenario of ROP is the occurrence of retinal detachment with subsequent blindness. The risk of these complications has been mitigated through vigilant screening and prompt interventions such as laser photocoagulation and anti-VEGF intravitreal injection. Despite these efforts, ROP continues to be the primary cause of visual impairment in children [56]. In 2020, [PERSON] and co-authors conducted a randomized controlled study examining the effectiveness of PBM using 670 nm red light in premature neonates with ROP. A 670 nm LED was placed on the isolette 20-25 cm above the baby 15 min each day and provided 9 J/cm\({}^{2}\) using a Warp 75 LED device (Quantum devices, Inc., Barneveld, WI, USA) until 34 weeks corrected gestational age. The pilot study aimed at assessing treatment feasibility and determining whether the interval and the dose, based on animal studies, yielded similar results in reducing ROP. The study suggested that 670 nm red light treatment did not significantly impact the growth parameters of preterm neonates but showed a trend towards a lower incidence of ROP, though not reaching statistical significance [57].
# Efficacy Improvement of Anomaly Detection by Using Intelligence Sharing Scheme
## 4 Proposed Architecture
### Reputation Model Usage Scenario  
In this section, a way to use a GRM to improve detection in participating networks is proposed.  
Firstly, enough data must be gathered to compute a meaningful GRM. If the time period is too long, the reputation model would suffer from modeling facts that may no longer be valid. If the time period is too short, the estimation of a reputation model could be noisy. The use of three days' worth of network data is proposed and evaluated. After this time period elapses, a GRM is computed and distributed among networks. The networks can then use the reputation model to improve the subsequent network traffic anomaly detection. Simultaneously, they gather data to build a novel GRM. After any set time period, the newly gathered data can be used to re-build the reputation model and the cycle continues. For simplicity, a once-built reputation model for another three days in experiments was evaluated. It would also be possible to refresh the system much more frequently to ensure maximum freshness of the reputation model.  
In effect, the GRM provides each network with a table of per-hostname anomaly scores. These anomaly scores can be used to improve anomaly detection in a network by performing a weighted average with the flow-specific anomaly scores. In proposed experiments, we strive to maximize the impact of the GRM on anomaly detection to achieve maximum contrast between the methods and to be able to better discern any differences between the two. Therefore, for every hostname recorded as malicious traffic data in the reputation model, we replace its anomaly score with the one from the reputation model. Here, no record of malicious traffic data in the reputation model exists, because the original anomaly score of the normalized flow was used accordingly.
## 2 Materials and Methods
### Ground Truth  
For ground truth data, Google Earth was exploited via WMS (Web Map Service ) within the QGIS desktop free/open-source platform. Using high-resolution images (April 2022), supplemented by historical imagery from Google Earth Pro and extensive knowledge of the territory, 14,611 sampling points were selected within the AOI across various altitude bands and geographical arrangements to reduce spatial autocorrelation ([PERSON] et al., 2016). For the purpose of the study, five different land use categories were identified (Figure 2):  
1. Non-Photosynthetic Surfaces (NPSs), including urban areas, bare soils, rocks, and water bodies (1168 points).
2. Arable Lands (AL), extensive areas devoted to arable farming or annual crop cultivation, characterized by the absence of arboreal vegetation (3016 points).
3. Mixed Agricultural Areas (MAAs), encompassing tree vegetation associated with olive groves, vineyards, and various types of fruit orchards with diverse management practices (3060 points).
4. Vegetation (VEG), represent areas covered by varying degrees of woodland vegetation. Spontaneous vegetation, woodland, coppices, chestnut groves, riparian areas, and scattered vegetation were included. This class encompasses a wide range of habitats, from Mediterranean scrub to garigue, evergreen and deciduous oak forests, pine groves, and beech woods (4374 points).
5. Hazelnuts (HZs), comprising mature hazelnut plantations (>5 years) (2993 points).
## 4 Risk Analysis for Current Protocols
### Automated Identification System (AIS)
#### 4.4.1 Denial of Service  
Our analysis shows that with a bit rate of 9600 bits/s, AIS is extremely vulnerable against DoS attacks [39]. For example, timing attacks (a form of DoS) can be performed against AIS. The attacker can hinder availability of AIS stations by sending AIS messages that instruct the victim to delay transmission of messages for a certain time and repeat this process continuously.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 2 Experimental
### Bending Beam Rheometer Test  
A BBR test was employed to characterize the low-temperature performance of SBS modified asphalt in all combinations according to ASTM D6648. The test temperature was \(-12\), \(-18\)\({}^{\circ}\)C and the average results of three replicates were used as the testing results. Creep stiffness modulus (\(S\)) and creep rate (m-value) was employed to characterize the low temperature performance. The greater the creep stiffness modulus \(S\) value, the more elastic, less viscous and more brittle the asphalt, and the worse the low temperature resistance to deformation. The larger the creep slope \(m\) value, the stronger the anti-deformation ability of the asphalt and the better the low temperature performance [19; 20].
### Contributions  
This research tries to fill the research gaps identified above. The contributions of the paper mainly include the following three aspects, and are validated with comprehensive case studies.  
* General installation types of power electronic converters for ESS-based VSM, including a single ESS, and hybrid ESS realized by DC and AC coupled schemes, are introduced, which broaden the options of VSM realization compared with the existing control strategies based on specific energy storage installation.
* The steady-state energy variation of ESS regarding the control parameters of VSM, such as virtual governor, damping, and frequency integral control gain, is quantified, which provides a rough estimation of the margin needed when determining those parameters.
* A supplementary energy recovery control in addition to the VSM control on the grid-side DC-AC converter is proposed, thereby restoring the energy losses and ensuring  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Main Features** & **Main Limitations** & **Typical References** \\ \hline Ideal-source-based VSM & Energy resources on DC side not considered. & [13; 14; 15; 16; 17] \\ \hline Single-ESS-based VSM & The energy variation of ESS not analyzed. & [10; 11; 12] \\ \hline ESS-based VSM with SoC-limit triggered energy recovery & The energy variation of ESS not quantified. The energy reserve is small near the SoC limit. The event trigger control may incur abrupt change. & [19] \\ \hline Short-term ESS integrated with wind-turbine generator with SoC set-point control & The energy variation of ESS not quantified. & [21] \\ \hline DC-coupled hybrid ESS including a supercapacitor and a battery & The energy variation of ESS and thus energy recovery control not analyzed. & [23] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Some Typical research on ESS-based VSM.  
energy reserve for continuous VSM operation. The control parameter design ensures high stability and bandwidth separation between energy recovery and the frequency control loop.  
The rest of the paper is organized as follows. The general ESS types, the associated power electronic interface, and the general VSM control with energy recovery capability, are introduced in Section 2. Theoretical analysis of the energy variation of ESS in frequency response, the bandwidth rule to design the energy recovery control, and the whole procedure of designing ESS-based VSM, are given in Section 3. Various case studies are presented in Section 4 to validate the proposed general ESS-based VSM with energy recovery control. Section 5 points out the limitations of the current work. Section 6 concludes the article and gives recommendations on future research directions.
## 4 Analysis of Delays within a Router
### Theoretical Approach to Delays through a Router  
As discussed in section 3 a router is a specialized computer and therefore a basic equation can be defined by including delay parameters for the hardware (Dh) and the operating system (Dos). Applications such as NAT/PAT can be configured and is represented by (Da) and Services such as HTTP Server can also be used (Ds). Different protocols, e.g., IPv4, passed through the router will experience different delays and so a factor Dp has been included. When undertaking the practical work a great deal of time was spent reducing the number of packets that appeared on the network due to routing protocols so that the queuing delays were kept to a minimum however a factor Dq is added to account for this. Earlier work has shown that when configuring ACLs delays are introduced based on the type of ACL (Dta) used and the number of rules in an ACL Dnr. The model can be described as shown in the equation 1. Some of the variations in the delay are due to factors of queuing, resulting in.  
\[\text{Router Delay (Dr)}=\text{Dh}+\text{Dos}+\text{Da}+\text{Ds}+\text{ Dp}+\text{Dq}+\text{Dta}+\text{Dnr} \tag{1}\]  
Figure 5: Delay through router with Advanced OS.
# Positioning Energy-Neutral Devices: Technological Status and Hybrid RF-Acoustic Experiments
## 3 Hybrid Acoustic-RF Approaches for Positioning Energy-Neutral Devices
### Hardware Implementation, Power Consumption and Backscatter Demodulation
#### 3.4.2 Backscatter Demodulation  
With the addition of the local oscillator on the backscatter device, demodulation needs to be performed at the bistatic receiver. For this type of backscattering, two types of demodulation can be performed:  
1. OOK demodulation. The digitized acoustic signal drives a multiplexer that forwards either the local oscillator or no signal at all. Consequently, the load is switched at the local oscillator frequency \(f_{clo}\) or it is not switched. In the spectrum, this appears as a signal \(f_{clo}\) away from the RF carrier frequency that is turned on and off. As mentioned previously, OOK is very susceptible to noise, and the drift of the local oscillators can make the amplitude demodulation on the receiving radio impossible.
2. FSK demodulation. As the acoustic chirp \(\Delta f_{a}\) can be considered as a signal modulated in frequency, this chirp signal can be observed on both sides of the local oscillator frequency. The demodulation is done by performing a frequency translation and a decimating FIR filter on one of the sidebands. With this, only the portion of the wideband signal with the frequency decreasing chirp signal is saved to a buffer for later use.  
In this paper, FSK is used for demodulating the backscattered acoustic signal. In Figure 8, a snapshot of both the spectrum and the received signal in the time domain can be observed. The two sides of the spectrum are plotted, clearly showing the powerful RF carrier wave, the local oscillator frequency, and the two acoustic sidebands.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Microphone** & **Amplifier** & **Comparator** & **Oscillator** & **Multiplexer** & **Switches** & **LDO** \\ \cline{2-7} \(P_{agg,stable}\) (uW) & 213 & 185 & 2.5 & 112.5 & 197.5 & 2.5 & 42 \\ \(\Delta f_{on}\) (uS) & 140.5 & 932 & 452 & 223 & 986 & 842 & 810 \\ \(E_{avg,trans,all}\) (uJ) & & & & 1.66 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the measured power consumption per component and their wake-up time. Precision current measurements were performed at a reference voltage fixed at 2.5 V.