esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Aerosol Infection, Treatment and CFU Assay  
For animal studies, 8-10 weeks old C57 BL/6 mice (n = 42; 21 male and 21 female mice) were purchased from the Jackson Laboratory (Bar Harbor, ME, USA). Bacterial stocks were generated as described previously [22]. Mice were exposed for 40 min to nebulized bacteria at a density optimized to deliver a standard low dose of around 50-100 CFU using Glas-Col Full Body Inhalation Exposure as described previously [23; 24; 25; 26; 27; 28]. To determine the actual infection dose, six mice (3 male and 3 female) were sacrificed at three hours post-infection (3 h p.i.), lung homogenate was plated on Middlebrook 7H11 media (Difco BD, Franklin Lakes, NJ, USA) and CFU were counted after 4-6 weeks of incubation. After infection, mice were segregated into two groups: (1) untreated, and (2) DEM (diethylmaleate, Santa Cruz Biotechnology, TX, USA)-treated. Each group contained 18 mice (9 female and 9 male). DEM was administered daily (50 mM in 100 \(\upmu\)L) by oral gavage for eight weeks. At specific time points (2-, 4- and 8-weeks post-infection), six mice (3 male and 3 female) from each group were euthanized and approximately 0.7 mL of blood was collected by cardiac puncture. A typical necroy was performed, and lungs, spleens, and livers were harvested. The left lung and a portion of the spleen (25 mg) and liver (0.5 g) were homogenized in PBS with 0.05% Tween 80. Homogenates were serially diluted and plated on Middlebrook 7H11 media. The plates were incubated at 37 \({}^{\circ}\)C and CFUs were counted after 4-6 weeks. The lower lobe of the right lung as well as a portion of spleen and liver were fixed in 10% neutral buffered formalin and used for histology studies. Plasma and lung lysates were filtered through a 0.2-micron filter and used for downstream works. All animal procedures were performed in bio-safety level 3 facilities as per the approved procedures of the Rutgers University Institutional Animal Care and Use Committee.
## 5 Attacks and Countermeasures
### Countermeasures in MP2P
Otherwise, a routing approach would be required that is based on flooding the network when the requested node does not exist. On the other hand, generating and maintaining a second routing table in parallel to the main routing table as proposed by [PERSON] _et al._ could consume large amounts of the available bandwidth. Still, this approach could be reasonable in MP2P implementations that maintain routing tables by harnessing overheard messages. To sum it up, the resource restriction of MP2P networks affects most security measures that were proposed for P2P systems operating on top of a less resource-constraint underlay such as the Internet. Most security mechanisms proposed for MANETs are still applicable in the context of MP2P but can be improved by harnessing cross-layer information. To the best of our knowledge, only sparse related work on security in MP2P networks exists. [PERSON] _et al_. proposed a face-to-face authentication system for MP2P networks [47]. As nodes are mobile, nodes can be carried during bootstrapping to the bootstrapping node for a direct authentication. The authentication process is based on social interaction as the owner of the bootstrapping node has to decide whether a node may join the network. [PERSON] _et al._ propose a cryptographic approach to secure the integrated Scalable Source Routing (SSR) protocol [48]. They authenticate routing messages and certify links to prevent malicious nodes from forging messages based on a PKI. In the PEERS project [49], a basic framework for MP2P security mechanisms was developed. By now, only the framework itself is introduced and no further MP2P security mechanisms were proposed.
## 5 Results and Discussion  
### Operational and Static Storm Analysis  
This analysis aims to verify the structural integrity of the offshore platform, ensuring compliance with all applicable regulations regarding loads and stress distributions in both the substructure and superstructure, as well as in the pile foundation system. The results are presented in terms of unity checks (UC), which represent the inverse of the factor of safety, defined as the ratio of the maximum design load to the allowable load. This is also referred to as the utilization ratio, where a UC value below 1.0 indicates that the component meets the required design criteria, as specified in Section 6.2 of API RP 2A WSD 22 nd Edition [35].  
The present analysis was conducted using the Structural Analysis Computer System (SACS), evaluating mechanical behavior in structural elements, stress ratios (UCs), pile foundation performance--including nonlinear soil-pile interaction, hydrostatic collapse, and joint strength assessments (both penetration-based and resistance-based). The evaluation adheres to API RP 2A WSD 22 nd Edition [35], AISC, and IMCA standards. The results of the operational and static storm analyses are presented below, with a summarized overview in Figure 6.  
* Stress Interaction and Unity Check (UC) Analysis. The maximum UC in the sub-structure (jacket) was found to be 0.93, while the maximum UC in the superstructure (topside) was 0.81, both of which are below the threshold value of 1.0, ensuring compliance with regulatory standards.  
* Tubular Joint Penetration Analysis. Tubular joints were evaluated for penetration load and structural resistance (STRN), with results of 0.968 and 0.783, respectively. Since both values are below unity, they comply with the current standards.
* Pile Stress Interaction and Load Safety Factors. The maximum UC for piles above the seabed (mudline) was 0.774, whereas for piles below the seabed, it was 0.754, both within the acceptable range. The axial load safety factors in the foundation exceeded the minimum regulatory requirements, with an operational value of 2.18 (>2.00 required) and storm condition value of 1.58 (>1.50 required), confirming compliance with industry standards.
* For pre-installed but uninstalled structures: Reinforcement using 1.00-inch (25.4 mm) thick rings with a 12-inch (305 mm) spacing, as guided by SACS redesign recommendations. However, this approach may be impractical for installed structures due to the high cost of underwater welding.
- For structures in the design phase: Modifications include increasing pipe thickness or incorporating anchor bolts to enhance structural resistance.
- For installed structures: Cementing the affected elements can enhance structural properties and mitigate hydrostatic collapse risks.  
It is important to note that this document presents a structural assessment and potential solutions. The final decision on the optimal technical and economic approach will be determined by the structural engineering team. In this case study, the implementation of hydrostatic collapse rings was selected as the preferred solution.
## 1 Introduction
The authors in [30] proposed the Alamouti STBC transmitter diversity technique along with the IRS-assist MISO wireless communication system to mitigate the effect of phase shift noise and quantization noise. The authors in [31] proposed an IRS-based Alamouti scheme that allows the IRS to transmit coded information while reflecting the incident Space Shift Keying (SSK) signals. In [32], the authors proposed the IRS-assisted received spatial modulation (RSM) scheme with Alamouti STBC. In [33], they proposed an Alamouti STBC, generalized spatial modulation, and IRS incorporated into a SWIPT system to increase the spectral efficiency. These papers did not consider IRS-aided OSTBC for four antennas with code rate %. Different from the previous-mentioned works, this paper investigates the deployment of higher-order modulation schemes for an Alamouti and OSTBC IRS-aided system compared to the conventional non-IRS system for four transmit antennas. The main contribution of the presented work can be summarized in the following points:  
* The paper employs a combination of OSTBC and IRS technology, to reduce the required bit energy to noise power spectral density (\(\text{E}_{\text{b}}/\text{N}_{\text{o}}\)) in case of the MISO system. * In contrast to prior work, OSTBC is utilized instead of QOSTBC to prevent ISI. * Unlike the previous work, the paper utilizes OSTBC with a % code rate combined with IRS. * A detailed mathematical model is introduced for OSTBC combined with IRS. * The proposed systems of OSTBC are simulated in case of different modulation techniques (QPSK, 16 QAM, 64 QAM, and 256 QAM). * The system performance is evaluated and compared in both cases with IRS and without IRS to assess the benefits of the IRS integration. The obtained results show that by employing OSTBC as well as increasing the number of transmitting antennas, the system's performance improves. In addition, the BERdecreases by increasing the number of reflecting elements of the IRS due to the constructive reflections from the IRS elements. The rest of the paper is organized as follows: In Section 2, the system model of OSTBC-assisted IRS is presented. The mathematical model for Alamouti and OSTBC-assisted IRS is presented in Section 3. Section 4 presents the simulation results and related discussions. The paper then concludes in Section 5 with some potential future research directions.
# Electropolishing--A Practical Method for Accessing Voids in Metal Films for Analyses
## 5 Conclusions  
It has been shown that electropolishing is a convenient method for preparing metal thin films for void analyses. During the electropolishing process, material is removed from the sample while, at the same time, micro corrugation is levelled out. The accessed surface is very well-suited for interior degradation void analyses. As the voids are accessed in lateral direction of the film, statistical analyses can be performed over a large area. With a conventional cross-sectional preparation (embedded-mechanical cross-section or FIB), this is only possible with a substantially higher effort, as the accessed area is constrained by the low thickness. Electropolishing is not limited to copper; moreover, other metals such as aluminum, silver, gold, titanium, etc., can be prepared after adjustment of the electrolyte and the polishing parameters. Possible applications in terms of preparation for void analyses comprise reliability testing in microelectronics, microelectromechanical systems (MEMS), and flexible electronics but also process control in metal depositions.  
**Author Contributions:** Conceptualization, S.M.; methodology, S.M.; software, M.K.; validation, S.M. and M.K.; formal analysis, S.M. and M.K.; investigation, S.M. and S.K.; data curation, S.M. and M.K.; writing--original draft preparation, S.M.; writing--review and editing, S.K., J.Z. and M.J.C.; visualization, S.M.; supervision, S.K. and J.Z.; project administration, J.Z. and M.J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Austrian Research Promotion Agency (FFG, Project No. 881110).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study may be available upon reasonable request by contacting the corresponding author. Restrictions apply to the availability of these data.  
**Acknowledgments:** The authors would like to express their gratitude to [PERSON] for the supervision during the electrochemical experiments and to [PERSON] for providing the finite element simulations presented in this publication.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion  
### The Evaluation of the Performance of CM-QOS-AODV, CM-GPSR, QOS-AODV, and GPSR Protocols  
The evaluation of the performance of CM-QOS-AODV, CM-GPSR, QOS-AODV, and GPSR protocols in terms of throughput, delay, and packet delivery ratio (PDR) is presented in this section. These performance metrics were assessed under varying conditions, including different traffic generation rates, packet sizes, mobility speeds, and pause times. The comparative analysis was conducted within a hybrid Vehicular Ad Hoc Network (VANET) scenario. This series of experiments aims to compare the performance of CM-QOS-AODV, CM-GPSR, QOS-AODV, and GPSR protocols under varying traffic generation conditions. The evaluation focuses on throughput, packet delivery ratio (PDR), and delay to assess the protocols' efficiency. The primary goal was to examine how different traffic generation rates impact the performance of CM-QOS-AODV and CM-GPSR and to compare their efficiencies with those of QOS-AODV and GPSR. Traffic generation follows a burst application model, where traffic is directed toward a destination, which then responds to the source node. The generated traffic is controlled by adjusting the time interval between consecutive packets. Table 6 details the amount of traffic generated by a node for the time intervals used in these experiments.
## 3 Spectral Linewidth  
The theoretical formula for the spectral linewidth of ECDL has been investigated in many papers [18]. Consider an external cavity semiconductor laser comprising a laser diode with cavity length \(l\) and an external mirror at a distance \(L\). The linewidth is written in the following formula [19].  
\[\Delta\
u=\frac{\
u_{g}^{2}n\
u n_{sp}a_{m}g}{8\pi P_{out}}\ \ \left\{\frac{1}{D(r)+D_{1}(r)}+\frac{\alpha^{2} \beta^{2}}{D(r)}\right\} \tag{1}\]  
where \(\
u_{g}\) is the group velocity, \(h\) is the Plank constant. The spontaneous emission factor \(n_{sp}\) can be definited by the ratio of the spontaneous emission rate per laser mode to the stimulated emission rate per laser photon. \(\alpha\) is the linewidth enhancement factor. The factor \(\beta\) can be calculated from the phase condition for the longitudinal modes of the whole cavity [20]. The \(D(r)\) and \(D_{1}(r)\) are written as follows:  
\[D(r)=\frac{\left(1+r_{2}r_{1e}^{-1}\right)(1-r_{2}r_{1e})\text{ln}(1/r_{2}^{2} )}{2\left(1-r_{2}^{2}\right)\text{ln}(1/r_{2}r_{1e})} \tag{2}\]  
\[D_{1}(r)=\frac{n_{1}^{2}L_{c}}{2n^{2}l}\left(\frac{1}{1-r_{2}^{2}}\right)r_{1e} r_{2}\text{ln}\left(1/r_{2}^{2}\right) \tag{3}\]  
where \(r_{1e}\) is the effective reflectivity of the external cavity, \(r_{2}\) is the reflectivity of output surface (R\({}_{2}\) as shown in Figure 1), \(L_{c}\) is the coherent length, \(n\) is the refractive index of the diode, and \(n_{1}\) is the effective refractive index of the external cavity.  
We can achieve a narrow linewidth by controlling the structure and composition of the external cavity. According to the equations, the linewidth can be narrowed effectively by improving the \(r_{1e}\) and \(n_{1}\) of the external cavity. Therefore, the reasonable selection of external optical element parameters is very important.
## 3 Results and Discussion
### Dry Matter Yield Prediction  
The assessment of the leaf greenness index (SPAD) has been the subject of studies aimed at using this parameter to estimate the yield of crops. [PERSON] [48] made one of the first attempts to correlate nitrogen nutrition of meadow grasses with the yield of sward. [PERSON] et al. [31] conducted a study on maize and found that the yield of this species was significantly linearly correlated with the values of the SPAD index. This linear dependency indicates that the SPAD index can be used as a tool to estimate the yield of maize silage biomass as well as seed yields. Our study showed a strict positive correlation between DMY of maize and the values of the SPAD leaf greenness index (Table 5).  
The correlation coefficients calculated for both maize varieties were highly significant at all measurement dates. This fact shows that this index can be used to predict the yield of maize plants grown for silage (DMY of maize biomass) fertilized with different doses of nitrogen. The rectilinear regression model was used separately for each variety and for each SPAD index measurement date (three time points) to calculate the relationship between plant nitrogen nutrition with and DMY of maze biomass (Table 6). The highest R\({}^{2}\) value, indicating the best fit of the equation for the variety Delittop, was recorded at maize development phase BBCH 83 (beginning of wax maturity of caryopses) (3 rd date); for the variety Magittop, it was phase BBCH 51 (beginning of emergence of panicles) (1 st date).  
There are different opinions concerning the choice of the maize development phase when the SPAD index value enables the best estimation of biomass yield. According to [PERSON] et al. [31], yield prediction is the most precise when the SPAD index is measured at the beginning of cob formation (R1). According to [PERSON] et al. [49], readouts of chlorophyll content, interpreted based on the SPAD index, performed at the six-leaf development phase (V6) can also be used to predict and estimate the yield. [PERSON] et al. [50] observed the best correlation between the SPAD index values and yield at the 10-leaf development phase (V10). [PERSON] and [PERSON] [51] found that chlorophyll content expressed as the value of the SPAD index began to exhibit close relationship with seed size at the six-leaf development phase (V6). [PERSON] et al. [52] observed an even stronger correlation between the SPAD index values and maize yield at later stages of development. The results of [PERSON] [53] demonstrated that both SPAD values and changes in LAI values showed a positive correlation with the average DMY.
## 3 Wear Prediction Based on BP Neural Network
### Setting of BP Neuron Topology Parameter  
The topology of the neural network is shown in Figure 2, which is divided into output layer, hidden layer, and input layer. The non-linear mapping method between the three layers is used to approximate the result value with the set accuracy. For the set root mean square error value, the back propagation algorithm is adopted, therefore the samples are required for training, followed by input forward transmission, forming a complete closed-loop feedback system, and constantly improving the accuracy of the weight of each neuron to meet the accuracy requirements [23].  
(1) Determine the number of nodes in the input and output layer  
Since there are three input parameters, contact pressure, grinding speed, and material (characterized by elastic modulus), the number of nodes in the input layer \(n\) = 3, the wear rate of abrasive belt is the output parameter, so the number of nodes in the output layer is \(m\) = 1.  
(2) Determine the number of nodes in the hidden layer  
The number of hidden layers is related to the processing capacity of the network, which means more complex grid training and extended solution time. When there are too many hidden layer neurons, the network can learn well and achieve the expected network accuracy, but the generalization ability of the network will become worse due to the over fitting of the network to the weights. When the number of neurons is too small, the network performance cannot be brought into full play and it is difficult to complete sample learning [24]. The number of hidden layer neurons in the network is directly related to the complexity of the actual problem, the number of neurons in the input and output layers, and the setting of expected errors. At present, there is still a lack of a general calculation method, mainly referring to empirical formulas which are as follows [25]:  
\[L=\sqrt{n+m}+a \tag{5}\]  
where \(n\) is the number of input layer nodes, \(m\) is the number of output layer nodes, and \(a\) is a constant between one and ten.  
(3) Excitation function  
The commonly used excitation functions of BP neural network are ramp function and Sigmoid function. Due to the limited output range of Sigmoid excitation function, it is not easy to diverge during data transmission. Therefore, this article chooses the Sigmoid excitation function [26].  
\[f(x)=\frac{1}{1+e^{-ax}}\ 0\ <\ f(x)\ <\ 1 \tag{6}\]  
Figure 2: Topology of the neural network.  
\[f(x)=\frac{ae^{-ax}}{(1+e^{-ax})^{2}}=af(x)[1-f(x)] \tag{7}\]
## 2 Sensitivity-Based VPP Control Algorithm  
### Electrical Sensitivity Analysis  
The electrical sensitivity in power systems quantifies the relationship between changes in input variables and their impact on output variables. Specifically, it examines how variations in parameters, such as active power, influence critical system metrics, like the voltage magnitude, phase angle, or other operational parameters. For instance, in the context of the VPP, sensitivity analysis is conducted to determine how changes in the active power output of participating DERs impact the voltage at load buses.  
The calculation of electrical sensitivity begins with deriving the relationship between active power and voltage using the power flow equations. These equations form the foundation of the power system analysis, enabling the identification of how active and reactive power influence voltage magnitudes and phase angles throughout the grid. This relationship is represented mathematically using the Jacobian matrix, a key component of power flow analysis that expresses the linearized impact of power variations on voltage and angle changes.  
From the power flow equation represented in Equation (1), the expression for the change in active power can be separated as shown in Equation (2). However, in this paper, the phase angle was not considered, and therefore, Equation (2) was redefined as Equation (3).  
\[\begin{bmatrix}\Delta P\\ \Delta Q\end{bmatrix}=\begin{bmatrix}\frac{\partial P}{\partial R}&\frac{ \partial P}{\partial V}\\ \frac{\partial Q}{\partial R}&\frac{\partial Q}{\partial V}\end{bmatrix} \begin{bmatrix}\Delta\theta\\ \Delta V\end{bmatrix} \tag{1}\]  
\[[\Delta P]=\begin{bmatrix}\frac{\partial P}{\partial\theta}\end{bmatrix}\cdot [\Delta\theta]+\begin{bmatrix}\frac{\partial P}{\partial V}\end{bmatrix}\cdot [\Delta V] \tag{2}\]  
\[[\Delta P]\cong\begin{bmatrix}\frac{\partial P}{\partial V}\end{bmatrix}\cdot [\Delta V] \tag{3}\]  
The voltage change at the monitoring bus with respect to the active power variation at the generator bus is defined as the sensitivity matrix and is denoted as \(S_{ij}\), as shown in Equation (4). Here, \(i\) and \(j\) represent the generator bus and the monitoring bus, respectively.  
\[S_{ij}=\begin{bmatrix}\frac{\Delta V_{j}}{\Delta P_{i}}\end{bmatrix}=\begin{bmatrix} \frac{\partial V_{j}}{\partial P_{i}}\end{bmatrix} \tag{4}\]