esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Work and Main Contribution
However, the availability of data from the different WSs is not synchronous, thus making it difficult to make an intervention decision because it is often based on observing several physical quantities simultaneously. Unlike most previous works, which only deal with the energy autonomy of a single WS, our study proposes a solution for optimizing the performance of a network of several WSs. The main goal was to ensure the energy autonomy of the WSN by giving all the WSs the ability to communicate regardless of their location in the industrial process. For this, a hierarchy of the different WSs of the WSN was established. This classification considers the residual energy and the expected energy harvest rate for each WS. After this classification, WSs with an energy deficit were powered by the WSs with an energy supplement. This allowed using the harvested energy to increase the data transfer rate of the whole WSN. Hence, this paper's second contribution consists of implementing a _Hierarchical Energy-Balancing Protocol_ (_HEBP_). A conceptual view of the proposed protocol is presented in Section 4. The combined solutions (PHPV and HEPB) aimed to improves the network's QoS. The rest of the paper is organized as follows: Section 2 describes the design method and the tools used. In Section 3, we discuss the implementation of the proposed predictor (PHPV). Finally, Section 4 deals with the efficient management of harvestable energy with HEPB.
## 5 Results
### Baseline Versus Automatic Hierarchy Method: Results
#### 5.1.3 Efficiency  
As Figure 5c shows, questions 5 through 6 assessed efficiency. A higher score suggests that the method contributes to increased efficiency. For Q5, the question asked whether the method helps lower interaction time for exploring the records. The participants showed a statistically significant difference between the two methods (\(Z=-3.01\), \(p<0.01\)), with automatic hierarchy (\(M=6.42\), \(SD=0.51\)) being rated higher than the baseline (\(M=2.00\), \(SD=1.71\)). Q6 asked whether the method helps shorten exploration times. The participants showed a statistically significant difference between the two methods (\(Z=-3.11\), \(p<0.01\)), with automatic hierarchy (\(M=6.75\), \(SD=0.45\)) being rated higher than the baseline (\(M=1.67\), \(SD=0.89\)).
## 4 Experimental Settings and Datasets
### Hardware Platform  
In the experimental setup, the hardware platform comprised three GY-521 sensors. These sensors were interfaced with the Arduino device using jumper wires for electrical communication, and Bluetooth modules (HC-05) were also connected via GY-521 sensors. All the modules including GY-521, HC-05, and Arduino Uno along with a 9-Volt battery were fixed in a specially designed protective case that was then mounted on a belt and tied on the human body at the arm, leg, and neck position, as shown in Figure 12. Therefore, no sensor component would misbehave during the activities. The Bluetooth HC-05 transceiver modules were responsible for wireless communication and 9-Volt batteries were used with the setup to ensure uninterrupted data collection. The open-source Arduino software (IDE) was used to simulate operation in a real-time environment. During the trials, no loss of data occurred.  
The GY-521 sensor provides six-degree-of-freedom (DOF) motion tracking. It consists of a three-axis accelerometer and a three-axis gyroscope embedded in a small chip, and it is based on a micro electro mechanical system (MEMS). The purpose of using the GY-521 sensor is its built-in digital motion processor (DMP) for motion processing. We received data angles of yaw, roll, and pitch with the GY-521. Thus, the burden of a host computer in the manipulation of motion data was minimized.  
The limitation with the current setup is the 9-Volt battery. The power of the battery can operate the system for up to two days. There is a need to recharge or to replace the battery often to operate the prototype system for longer periods.
## 3 DTSPN Intersecting Regions Algorithm
### Stage 3  
The third stage of the transformation converts the canonical GTSP to a \"clustered\" TSP. Problem \((P5)\) is a clustered TSP over the graph \(\mathcal{G}^{5}:=(\mathcal{N}^{5},\mathcal{A}^{5})\) with the corresponding cost vector \(c^{5}\) where \(\mathcal{N}^{5}=\mathcal{N}^{4}\). For every node set \(\mathcal{V}_{i}\) corresponding to nodes in \(\mathcal{N}^{4}\), define a cluster \(\mathcal{C}_{i}\) corresponding to the nodes in \(\mathcal{N}^{5}\). The nodes in each cluster are first enumerated. Let \(i^{1},i^{2},\ldots,i^{r}\) denote the ordered nodes of \(\mathcal{C}_{i}\) where \(r\) represents the cardinality of the cluster, \(r=|\mathcal{C}_{i}|\). Next, a zero cost cycle is created for each cluster by adding zero cost edges between consecutive nodes in each cluster and connecting the first node to the last. For each cluster \(i\) with \(r>1\), add the arcs \((i^{1},i^{2}),(i^{2},i^{3}),\ldots,(i^{r-1},i^{r}),(i^{r},i^{1})\) to \(\mathcal{A}^{5}\), and for each of these intra-cluster arcs assign a zero cost, _i.e._, \(c^{5}_{i^{1},i^{2}}=\cdots=c^{5}_{i^{r},i^{1}}=0\). The inter-set edges are then shifted so they emanate from the previous node in its cycle. For every inter-set arc \((i^{k},j^{l})\in\mathcal{A}^{4}\), with \(k>0\), create the arc \((i^{k-1},j^{l})\in\mathcal{A}^{5}\) with the corresponding cost, \(c^{5}_{i^{k-1},j^{l}}=c^{4}_{i^{k},j^{l}}\). For each interest arc \((i^{1},j^{l})\in\mathcal{A}^{4}\), create the arc \((i^{r},j^{l})\in\mathcal{A}^{5}\) with the corresponding cost, \(c^{5}_{i^{r},j^{l}}=c^{4}_{i^{1},j^{l}}\), where \(r=|\mathcal{C}_{i}|\). See Figure 4 for an example of Problem (P5).  
Finally, the clustered TSP is converted to an ATSP by adding a large finite cost to each inter-cluster arc cost.  
Problem \((P6)\) is a ATSP over the graph \(\mathcal{G}^{6}:=(\mathcal{N}^{6},\mathcal{A}^{6})\) with the corresponding cost vector \(c^{6}\) where \(\mathcal{N}^{6}=\mathcal{N}^{5}\) and \(\mathcal{A}^{6}=\mathcal{A}^{5}\). The arc costs are differ from \((P5)\) in the following way. For every arc \((i,j)\in\mathcal{A}^{6}\), if \(i\) and \(j\) belong to the same clusters in \((P5)\), then \(c^{6}_{i,j}=c^{5}_{i,j}\). If \(i\) and \(j\) belong to different clusters in \((P5)\), then  
\[c^{6}_{i,j}=c^{5}_{i,j}+\beta \tag{5}\]  
where  
\[\infty>\beta>\sum_{(i,j)\in\mathcal{A}^{5}}c^{5}_{i,j} \tag{6}\]  
An example can be seen in Figure 4, where \(\bar{c}_{i,j}\) depicts \(c^{6}_{i,j}\). The optimal tour is shown in red.  
The following theorem from [17] establishes the correctness of the transformation in Stage 3.  
**Theorem 3.3** ([PERSON] and [PERSON] [17]).: _Given a canonical GTSP in the form of \((P4)\) with \(n\) node sets, we can transform the problem into a standard TSP in the form of \((P6)\). Given an optimal solution \(y^{*}\) to \((P6)\) with \(c^{6}y^{*}<(n+1)\beta\), we can construct an optimal solution \(x^{*}\) to \((P4)\)._
## 2 DRFM Systems  
At the fundamental level, DRFM systems are a transceiver capable of digitizing, recording to memory, modifying, and re-transmitting the recorded waveform. Doing so requires a variety of sub-systems: receivers, Analogue-to-Digital Converters (ADC), Digital Signal Processing (DSP), Electronic Attack (EA) control system, Digital-to-Analogue Converters (DAC), and transmitters [11; 12; 13; 14]. This section discusses these various sub-systems, the associated distortions that occur when recording and re-transmitting a waveform, and the use of software defined EW systems to implement a DRFM. The intent is to provide the background necessary to understand how the DRFM systems were modeled and implemented digitally.  
DRFM systems have been in existence since the 1980s [15], with two themes driving their advance: digitizer performance, and a move to software defined systems. The first EW receivers were largely driven by single bit or mono-bit ADCs as the bandwidths required allowed for very restricted resolution with the existing technology [16] (this literally means the ADC had only a single bit for measuring amplitude, making the automatic gain control systems critical to the jammer performance).  
ADC and DAC performance is not the restricting factor anymore, as devices operating above at multiple giga-samples per second with 10 or more bits of resolution are common [17]. Instead, the bottleneck is the throughput of the resulting bit streams, especially in multi-channels commonly seen in direction finding.  
The other trend, software defined radios, is what allows for this research to occur, as previously, DRFMs could be modeled, or hardware implementations could be built at great expense. Now Field Programmable Gate Arrays (FPGA), and the associated model level tools, such as Xilinx System Generator or Lab View, allow for the creation of software defined EW systems in days, with a much narrower set of engineering skills.
# Shear Wave Elastography Implementation on a Portable Research Ultrasound System: Initial Results
## 4 Discussion  
### System Validation  
The presented experimental results confirmed that the proposed system was capable of implementing the SWE technique. It was achieved by satisfying two key requirements: the generation of high-energy push pulses and the high-frame-rate data acquisition of shear wave propagation. The hardware capability of the push beam generation was proven for three different methods: FCUSE, SSI and FCUSE-SSI. Each of these techniques featured some kind of complexity: parallel focused beam generation (FCUSE), the generation of a sequence of beams with strict timing (SSI) or a combination of both of these features (FCUSE-SSI). The transmit flexibility of our system showed itself to be capable of implementing all of these techniques. The second requirement for SWE was achieved by the PWI data capture with a 5 kHz frame rate. The data quality allowed us to detect the small axial displacements in shear waves with various energy levels, which were generated using the various methods (Figure 8). The correct shear wave tracking enabled full SWS map reconstruction using the ToF correlation-based SWS estimation using the developed algorithm, preceded by the 3D shear wave motion data filtering in both the time and Fourier domains to improve the conditions for the correlation calculation.
# Legacy ICS Cybersecurity Assessment Using Hybrid Threat Modeling--An Oil and Gas Sector Case Study
## 2 Background and Related Work
### Related Work
They employed two scenarios: an aircraft maintenance scenario and a drone swarm scenario, in addition to the project's outcomes. No individual threat modeling method included all identified threats. The research team subsequently developed the Hybrid Threat Modeling Method (HTMM) [34]. Also, [PERSON] et al. [35] devised a new way to make an integrated TMM that combines asset-, attacker-, and software-centric methods. They then examined the integration of various techniques to create a unified output. The research answered the question of what the advantages and disadvantages are when using an integrated threat modeling approach. The technique consists of three questionnaires, one for each approach. They extract and combine each participant's response to a single questionnaire to identify the threats present in all the different approaches. The method presented is generic and depends on the participant's answers, which could vary greatly from participant to participant. Additionally, [PERSON] [36] developed a hybrid approach that combined multiple classic threat modeling techniques. [PERSON] studied the limitations of three threat models: STRIDE, Attack Tree, and Attack Library. Combining techniques allows us to overcome limitations such as a lack of countermeasures, varying levels of abstraction about scenarios, and ensuring completeness. The proposed procedure is linear. The process begins with a design analysis phase, then proceeds to the threat identification and categorization stages, utilizing STRIDE and attack trees, and concludes with a threat mitigation phase that utilizes attack libraries. Furthermore, [PERSON] et al. [37] present a quantitative, integrated threat modeling approach that merges software and attack-centric threat modeling techniques. They leverage the Common Vulnerability Scoring System (CVSS) to provide a standardized method of quantifying the low-level vulnerabilities in the attack trees. The paper concludes that providing a systemic, quantitative method of modeling system risk is one step toward providing a definitive, scientific approach to measuring the security of CPS systems. However, despite the benefits, there is still a need for further development of hybrid threat modeling frameworks specifically suited for legacy ICS cybersecurity assessments in oil and gas. Potential areas for improvement include industry-specific focus, enhanced usability that balances comprehensiveness with ease of use, and consideration of the specific challenges of legacy ICS environments. This paper presents a threat modeling approach for ICS focused on specific industries that involve classifying ICPS assets and then analyzing the cybersecurity vulnerabilities, threats, risks, impacts, and countermeasures.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 5 Experimental Validation
A proper phase delay compensation compensates for signal delays, reducing the error in the calculation of the real phase shift of the system (18). On the other hand, the exact filter inductance value is required to properly estimate the maximum phase shifts which determine power limits (17). The following procedure is used to estimate these values:  
* Delay compensation: To estimate \(n\), \(\delta^{\prime}\) is measured with \(P\) = 0 pu. Given a known grid frequency, \(n\) can be calculated using (18). In the experimental setup, a delay of three samples has been identified. * Filter inductance: To estimate \(L_{c}\), \(\delta^{\prime}\) values are measured under different \(P\) operating conditions. A linear regression of these \(P\)-\(\delta^{\prime}\) points can be used to obtain  
Figure 15: Test bench for experimental validation. (**a**) Laboratory setup. (**b**) Simplified schematic. from (5). \(L_{c}\) is estimated in 0.045 pu, a 10% smaller than the rated value provided by the manufacturer. Figure 18 compares two experimental tests for the AL strategy, labelled as \"Experimental\" and \"Experimental (adjusted)\". Both tests use the same delay compensation. However, the \"Experimental\" case is tuned according to the manufacturer' inductance value, whereas the \"Experimental (adjusted)\" case incorporates the estimated inductance. The 10% error in the \(L_{c}\) value produces a noticeable deviation in the \(\delta^{\prime}\) signal, which affects the accuracy of power limitation. In the first case, overload reaches 0.15 pu, twice the expected theoretical value. Additionally, a steady-state error of approximately 0.1 pu appears after the first frequency transient. These issues are effectively mitigated by using the proper inductance value, demonstrating the importance of fine-tuning AL strategies in real applications. Finally, the EFS strategy, shown in Figure 19, offers a consistent match between simulation and experimental results, with the expected overload limited to 0.05 pu. The \(P_{EFS}\) signal represents the frequency support power setpoint from the outer loop, which is limited to 1 pu to prevent overload. Although this power setpoint is saturated, the system retains some inertial response due to the dynamics of the inner PI controller. Overall, the experimental results closely match simulations and demonstrate the practical viability of each strategy. They also highlight the practical considerations for AL strategy. With both simulation and experimental results in hand, a comparative discussion is now presented to summarize the strengths and limitations of each strategy. Figure 16: Experimental validation of VP strategy. (**a**) Active power response. (**b**) Virtual power setpoint. Figure 17: Experimental validation of parallel PI strategy. (**a**) Active power response. (**b**) Angular frequency output of PI.
## 4 Discussion
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/app11135878/s1](https://www.mdpi.com/article/10.3390/app11135878/s1)]([https://www.mdpi.com/article/10.3390/app11135878/s1](https://www.mdpi.com/article/10.3390/app11135878/s1)), Figure S1: Histochemical analysis of the brains in wild-type (WT) mice after staining with fucoxanthin (Fx) and thioflavin S (ThS), Figure S2: Histochemical analysis of the brains in wild-type (WT) mice after staining with fucoxanthin (Fx) and anti-oligomeric tau antibody, Figure S3: Histochemical analysis of the brains in 5 XFAD mice after staining with fucoxanthin (Fx) and anti-oligomeric tau antibody. Figure S4: Histochemical analysis of the brains in APP-PS1 mice after staining with fucoxanthin (Fx) and anti-oligomeric tau antibody. **Author Contributions:** Conceptualization, A.-H.L., S.-H.Y. ; investigation and data curation, A.-H.L., S.-C.H., S.-H.Y. ; resources, I.P., S.Y., Y.K. ; Writing--original draft, A.-H.L., S.-H.Y. ; Writing--review and editing, A.-H.L., J.K., S.-H.Y. ; Funding acquisition, J.K., S.-H.Y. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **Thioflavin S** & \begin{tabular}{c} **A\(\beta\)-Specific Antibody** \\ **(6E10, A11, etc.)** \\ \end{tabular} & **Fucoxanthin** \\ \hline Duration & Less than 4 h & \(\sim\)16–24 h & Less than 4 h \\ \hline Additional chemicals & Ethanol & Fluorescent secondary antibodies, & \\  & & horse serum, PBS & Ethanol or PBS \\ \hline Cost & \(\sim\)\$5/1 g & \(\sim\)\$200–300/one A\(\beta\)-specific antibody & Need to estimate \\ \hline Specificity & Only fibrils containing \(\beta\)-sheet & \
\begin{tabular}{c} Depending on the A\(\beta\) form, different \\ specific antibodies are required \\ \end{tabular} & Need to further study \\ \hline Stability & Stable in alcohol base solution &
\begin{tabular}{c} Depending on the secondary \\ antibody conjugated with \\ fluorescent dye \\ \end{tabular} & Stable in water base solution \\ \hline Care after staining & Light sensitive & Light and temperature sensitive & Light sensitive \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of fluorescent dyes for detecting A\(\beta\) in the brain section. **Funding:** This work was funded by the Dongguk University Research Fund of 2019 (S-2019-G0001-00009), National Research Foundation of Korea (NRF-2020R1F1A1076063), and the Korea Institute of Industrial Technology (Project no. EO170047). **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Animal Care and Use Committee at Dongguk University. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix A Diesel Nozzle Flow Model  
This model provides a simple approach to correct the injected fuel droplet velocities and the initial fuel droplet diameters, owing to cavitation. The principle schematic of the diesel nozzle is shown in the figure below.  
The discharge coefficient \(c_{d}\) is computed by phenomenological equations. Apart from flow field conditions, the geometrical features of the nozzle, such as the rounding of the inlet hole (inlet radius R) and the length-to-diameter ratio (L/D) of the nozzle hole also affect the discharge coefficient \(c_{d}\). These are introduced through the input parameters \(c_{1}\) and \(c_{2}\). In this way, the inlet pressure \(p_{1}\) for a turbulent flow is estimated:  
\[p_{1}=p_{2}+\frac{\rho}{2}\times\left(\frac{U_{geo}}{c_{d}}\right)^{2} \tag{1}\]  
where \(\rho\) is the density of the diesel fuel. At this time, the flow can be checked to know if the nozzle hole is cavitating under this condition. Assuming that the velocity profile is flat, by using [PERSON]'s expression [47; 48] for the contraction coefficient \(c_{c}\), the continuity will give the velocity at the smallest flow area at point \(c\):  
\[U_{c}=\frac{U_{geo}}{C_{c}} \tag{2}\]  
\(U_{geo}\) is the theoretical velocity of the laminar flow through the nozzle hole with the assumed flat velocity profile. The contraction coefficient is computed from the nozzle hole inlet rounding (see model parameter \(c_{1}\)). In the cavitation case, the potential flow theory allows the application of the Bernoulli equation from point 1 to \(c\) without any losses:  
\[p_{c}=p_{1}-\frac{\rho}{2}\times{U_{c}}^{2} \tag{3}\]  
If \(p_{c}\) is lower than \(p_{apap}\), it is assumed that the flow must be fully cavitating and a new inlet pressure and discharge coefficient are computed by:  
\[p_{1}=p_{vapor}+\frac{\rho}{2}\times{U_{c}}^{2} \tag{4}\]  
\[c_{d}=c_{c}\times\sqrt{K}=c_{c}\times\sqrt{\frac{p_{1}-p_{vapor}}{p_{1}-p_{2}}} \tag{5}\]  
The new effective conditions at the nozzle exit are now computed as:  
\[U_{eff}=U_{c}-\frac{p_{2}-p_{vapor}}{\rho\times U_{geo}} \tag{6}\]  
\[A_{eff}=A_{geo}\times\frac{U_{geo}}{U_{eff}} \tag{7}\]  
\[D_{eff}=\sqrt{\frac{4\times A_{eff}}{\pi}} \tag{8}\]  
More details of the model are given in [48].
# Morphological Study of _Bacillus thuringiensis_ Crystals and Spores
One of the most recent techniques used to monitor the growth kinetics and production of _B. thuringiensis_ spores and crystals is laser speckle imaging, a reliable and non-invasive technique based on light scattered by an illuminated medium [18]. In order to have accurate results and to understand the interaction between the light and the bacterial density, the size and shape of the scatterers in question should be known, along with whether these scatterers are in suspensions [19] or embedded in agar plates [20]. In the case of _B. thuringiensis_ spores and crystals, their size and morphology vary according to the culture conditions and the presence of additional chaperone proteins. For the latter, an example is that of the _B. thuringiensis_ var. _israelensis_ chaperone; others are accessory proteins p19 or p20, well-known for their association with anti-dipteran toxins. Both proteins were shown to be indispensable for correct folding of Cry and Cyt proteins [21; 22]. In fact, the effect of the 20 kDa protein on anti-coleopteran crystal toxins was highlighted in a 2011 study, in which this protein enhanced the volume, yield and solubility of Cry3A [23]. As for variations in culture conditions (culture media composition, pH, available oxygen and initial inoculum volume in the culture), several studies reported significant effects on the production and morphology of crystal inclusions, and the spore count at the start of the sporulation phase [24; 25; 26; 27]. In this study, we established a protocol for mining the morphological data and size distribution of _B. thuringiensis_ spores and crystals of different shapes. Our focus was on the morphological analysis of spores and crystals of three _B. thuringiensis_ var. _kurstaki_ strains with bipyramidal and cubic crystal; three var. _israelensis_ strains with spherical crystals; and one _B. thuringiensis_ strain belonging to an unknown serovar, but that is phylogenetically close to _B. thuringiensis_ var. _sichuansis_. The null hypothesis tested was that there were no significant differences in the various morphological aspects considered between the analyzed _B. thuringiensis_ strains.