esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method for Evaluating Stability of Manholes during Liquefaction
### Quantity of Manhole Uplifting (\(\Delta f\))  
The quantity (\(\Delta f\)) of manhole uplifting can be estimated using Equation (9) [15], assuming that the backfill soil is completely liquefied, and the excavation range is sufficiently larger than the diameter of manhole, as shown in Figure 4.  
\[\Delta f=\left(1-\frac{\gamma_{m}}{\gamma_{sat}}\right)h-\left(1-\frac{\beta \gamma_{t}}{\gamma_{sat}}\right)d-\frac{F_{S}}{\pi\gamma_{sat}}\left(\frac{2 }{c}\right)^{2} \tag{9}\]  
where \(\Delta f\) is the quantity of displacement for manhole uplifting, \(\gamma_{m}\) is the manhole unit volume weight, \(\gamma_{sat}\) is the soil saturation unit volume weight, \(h\) is the vertical length of the manhole, \(\beta\) is the excess pore water pressure ratio, \(\gamma_{t}\) is the unit volume weight of the non-liquefied soil layer shallower than the groundwater level, \(d\) is the depth of the groundwater level from the ground surface, \(F_{S}\) is the frictional force acting on the peripheral surface of the manhole, and \(c\) is the diameter of the manhole.  
Figure 4: Relative relationship between excavation range and manhole assumed in analysis.
# Power Prediction of Bifacial Si PV Module with Different Reflection Conditions on Rooftop
## 2 Modeling and Method
### Prediction of Output of the Bifacial Module  
The output power of the bifacial module (\(P_{bifacial}\)) is assumed to be the sum of the front output power (\(P_{front}\)) and the rear output power (\(P_{rear}\)).  
After the bifacial module is manufactured, the outputs of the front side and the rear side of the module are measured under the Standard Test Conditions (\(STC\)). As the measured output power is reflected without absorbing all the irradiance, it is corrected by the coefficient \(\kappa\) which is the ratio of the total area of the module to the area without the cell and ribbon. It is assumed that the difference between the front output and the rear output is the additional output power, as shown in Equation (14).  
\[P_{STC,additional}=\kappa P_{STC,front}-P_{STC,rear} \tag{14}\]  
\(I_{rear}\) in Equation (15) is obtained by using the position of the measurement point and the date time by installing it at the measuring point of the bifacial module and measuring the solar irradiance of the front (\(I_{real,front}\)).  
Using Equations (15) and (16), the output from the rear side is predicted.  
\[1000\;\mathrm{W/m^{2}}\;:\;P_{STC,rear}=I_{rear}\;:P_{real,rear} \tag{15}\]  
\[P_{real,rear}=\frac{P_{STC,rear}\;\times\;I_{rear}}{1000\;\mathrm{W/m^{2}}} \tag{16}\]  
Similarly, the output from the front side is predicted using Equations (17) and (18).  
\[1000\;\mathrm{W/m^{2}}\;:\;P_{STC,front}=I_{front}\;:P_{real,front} \tag{17}\]  
\[P_{real,front}=\frac{P_{STC,front}\;\times\;I_{front}}{1000\;\mathrm{W/m^{2}}} \tag{18}\]  
The output from the front side and rear side can be predicted using the above, and the total output of the bifacial module can be represented as the sum of these values, as shown in Equation (19).  
\[P_{real,total}=P_{real,front}+P_{real,rear} \tag{19}\]
## 2 Materials and Methods
### Finite Element Simulation  
The FE simulations applied with the proposed method were performed in an environment, including humeral motion, pretension of the supraspinatus tendon, and miscellaneous conditions, as shown in Figure 5. The humeral motion is generated by screw motion with relaxation control of the boundary condition. The dummy model for the relaxation control of the displacement boundary condition in the FE modeling is defined as the guidance for humeral motion. The humeral model, the actual object of analysis, is defined as the follower. Additionally, the proximal end of the supraspinatus tendon is pulled constantly by a force of 10 N to maintain pretension [31]. The miscellaneous conditions include scapular fixation, a tie condition, and a contact condition. The scapular, which is the basis of humeral motion, is fixed at the initial position. The humerus and supraspinatus tendon are tied based on the footprint. In the contact condition between soft tissues, the applied coefficient of friction is 0 to represent slip caused by actual moisture. The bone models, defined as a rigid body, are modeled by linear quadrilateral elements, which are shell elements (S4), in order to reduce the computations. The soft tissue models, which are likely to come into contact with one another, are modeled by linear tetrahedron elements (C3D4).  
Table 2 is pseudo-code of FE simulation applied with the proposed method. The spring constant (K) of truss is assumed to be \(K_{x}=K_{y}=K_{z}\), which is an isotropic feature, and be analyzed step by step. If spring constants are increased, the synchronization between the guidance model and follower model increases, and this can come close to the case where the displacement boundary condition is given directly to the analyzed model.  
\begin{table}
\begin{tabular}{l} \hline
**Data** Finite element model by the acquired shoulder geometry \\
**Result** Shoulder motion without excessive distortion and its contact stress measurement result \\
**For**\(K_{x}=1\) to n **do** \\ Boundary condition generation for humeral motion based on screw axis \\ Loading condition generation for initial pretension of supraspinatus tendon \\
**if** not at excessive distortion of finite element **then** \\ Displacement boundary condition relaxation by penalty function occurrence \\ Contact stress measurement between supraspinatus tendon and labrum \\
**else** \\
**close;** \\
**end** \\ \hline \end{tabular}
\end{table}
Table 2: Pseudo-code for FE simulations applied with the proposed method.  
Figure 5: Finite element simulation environment.
## 2 Results and Discussion
### Circular Dichroism Analysis
In this respect, the selective and potent activity of **1** and selected lactam bridged analogues (**4**, **6**, and **8**, MIC = 4 uM) towards _E. coli_, in the pres \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Microorganism** & \multicolumn{10}{c}{**Minimum Inhibitory Concentration (μM)**} \\ \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Cation Adjusted Media**}} & \multicolumn{10}{c}{**Compound**} \\ \cline{3-14}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **P \({}^{*}\)** & **A \({}^{*}\)** \\ \hline Gram-negative & & & & & & & & & & & & & \\ _E. coli_ (ATCC 25922) & Y & 2 & 4 & 16 & 4 & 8 & 4 & 8 & 4 & 8 & 1 & - \\  & N & 1 & 1 & 8 & 2 & 2 & 2 & 2 & 1 & 2 & 1 & - \\ _P. aeruginosa_ (SVB-B9) \({}^{\rm T}\) & Y & 16 & 32 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & 32 & 64 & & 2 \\  & N & 2 & 4 & 32 & 16 & 16 & 8 & 16 & 8 & 16 & 2 & - \\ \hline Gram-positive & & & & & & & & & & & & & \\ _S. aureus_ (ATCC 29213) & Y & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & - & \textgreater{}64 \\  & N & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & - & \textgreater{}64 \\ \hline Fungi & & & & & & & & & & & & & \\ C. _ albicans_ (SC314) \({}^{\rm T}\) & N/A & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & \textgreater{}64 & 1 \\ \hline \end{tabular}  
* P = polymynxin, A = amphotericin B; \({}^{\rm T}\) = Type strain.  
\end{table} 
Table 1: Minimal inhibitory concentrations (MICs) for synthetics (**1**) and analogues (**2–9**). ence of physiologically relevant levels of divalent cations is both intriguing and exciting. The two-fold increased selectivity of **4** and **6** amongst the tested Gram-negative species is particularly promising.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 3 Model Mathematical Formulation
### Variables and Parameters  
Let us introduce the following variables and parameters:  
* \(x_{it}\): Binary decision variable indicating whether staff member \(i\) is assigned to time slot \(t\).
* \(y_{pt}\): Binary decision variable indicating whether patient \(p\) is assigned to time slot \(t\).
* \(z_{nt}\): Binary decision variable indicating whether resource \(r\) is allocated at time slot \(t\).
* \(o_{i}\): Binary decision variable indicating whether staff member \(i\) works overtime.
* \(z_{ip}\): Binary decision variable indicating whether staff member \(i\) serves patient \(p\).
* \(W_{it}\): The weight associated with assigning staff member \(i\) to time slot \(t\). This could represent the cost or importance of this assignment.
* \(I_{ijt}\): The interaction weight between staff members \(i\) and \(j\) at time slot \(t\) when both are assigned to work. This could reflect the benefit or cost of having these two staff members work together.
* \(C_{pt}\): The cost associated with assigning patient \(p\) to time slot \(t\). This could represent the cost of providing service to this patient at this time.
* \(D_{rt}\): The cost associated with allocating resource \(r\) at time slot \(t\). This could represent the cost of using this resource at this time.
* \(P_{pt}\): The penalty associated with patient \(p\) not receiving service during time slot \(t\). This could reflect the negative impact of not serving this patient at this time.
* \(O_{i}\): The overtime weight for staff member \(i\) when they work overtime. This could represent the extra cost or penalty for having this staff member work beyond their regular hours.
* \(U_{p}\): The weight associated with patient \(p\) not receiving service when staff member \(i\) serves them. This could reflect the negative impact of not serving this patient when this staff member is working.
* \(S_{i}\): The skill level of staff member \(i\) required for serving patients during time slot \(t\). This could represent the ability of this staff member to provide service.
* \(D_{t}\): The demand for staff at time slot \(t\). This could represent the number of staff members needed at this time.
* \(H_{i}\): The maximum working hours for staff member \(i\). This could represent the maximum number of time slots this staff member can be assigned to.
* \(M_{ipk}\): The minimum skill level of staff member \(i\) required for serving patient \(p\) in task \(k\) during time slot \(t\). This could represent the minimum ability needed to provide this service.
* \(B\): The budget constraint. This could represent the maximum total cost of staff assignments and resource allocations.
* \(F_{ij}\): The maximum allowed difference in working hours between staff members \(i\) and \(j\). This could represent the maximum disparity in workload between these two staff members.
## 2 Results and Discussion
### Microstructure of CRMP  
A changed moisture or fat content would affect the cheese microstructure [21]. To evaluate the effect of the EPS on the microstructure of CRMP, scanning electron microscopy was performed. This technique allows the preservation of the original structure of the sample as far as possible because of the gradual replacement of the water by ethanol.  
The microscopic studies of three different amplifications of CRMP were demonstrated in Figure 3. DAC (Figure 3-c) displayed its protein matrix (black thick arrow) formed by casein micelles. Fat globules (white thick arrow) were largely spherical in shape with the diameter of \(\sim\)1.5 \(\upmu\)m, and were present in the matrix of proteins. Fat globules, as well as water (pockets in microscope), can function as casein networks breakers [44; 47]. However, the water in large pockets would lose its stability under pressure or long storage causing the dehydration of cheese.  
Although _S. thermophilus_ SP1.1 (Figure 3g,h,i) did not obviously affect the microstructure, the use of _S. thermophilus_ TM11 (Figure 3d,e,f) in CRMP greatly changed the protein matrix with more opened cavities. The cavities were numerous and small. These results agreed with the reported studies in which a more open structure was observed in Mozzarella [9], Feta [48] and Panela [21] cheese made with the EPS-forming culture of _S. thermophilus_. Accordingly, these cavities could contain more water, which was in agreement with the increasing moisture and yield of CRMP.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### Lightweight Cryptography
Although attackers can partly compromise security, the authors still announced that this method shows good robustness against ciphertext-only attacks (COAs) and known plaintext attacks (KPAs). With theoretical considerations and numerical evidence, the results showed that the effort needed to reveal the original signal is well beyond what an adversary could bear, especially for KPAs. TRAKS [19] is a unified key manager and distribution scheme for the European Rail Traffic Management System (ERTMS). It uses pseudorandom functions (PRFs) and a shared secret to generate a dynamic key, which reduces the deployment overhead. Moreover, it is backward compatible with the existing scheme used by ERTMS and accounts for the standardization of postquantum cryptography, as the ERTMS is designed with a long lifespan. The security of the proposed key generation protocol is discussed with a game-based approach and implementation in EuroRadio. TRAKS is proven to be as secure as the existing scheme while supporting additional key management benefits. [PERSON] et al. [106] proposed a communication and obfuscation management architecture (COMA) to securely activate obfuscated circuits made in untrusted foundries. COMA does not need to store the obfuscation unlock key on the untrusted chip and changes the key after each unlock attempt. It protects communications to/from the COMA-protected device with two novel mechanisms for encrypted connection: ultrasecure and ultrafast. They demonstrated that COMA reduces the area overhead by 14% compared to the latest key management architectures while allowing them to identify each IC chip and remove the need to implement a secure memory in the untrusted foundry. As the computational resources in IoT devices are constrained, [PERSON] et al. [62] outsourced the cryptographic operations of attribute-based encryption (ABE) to clouds for the security of message distribution. Moreover, to keep the user's attributes from being leaked by cloud services, they used multiple clouds to complete the outsourced computations. The parallel-cloud scheme provides stronger security protection than the chain-cloud scheme but is less flexible and expressive. Evaluated with two commercial cloud services and IoT simulators, the proposed scheme was proven to be reliable and competent.
# Robust Estimators in Geodetic Networks Based on a New Metaheuristic: Independent Vortices Search
## 3 Independent Vortices Search  
All of the REs (except for IRLS) require a numerical optimization method. The MH has to explore values for the parameters which generate the residuals from the Gauss-Markov model. The objective function of the RE is then evaluated and used for the computation of the solution fitness. Then, the MH generates new candidate solutions, continuing the process until it reaches a pre-defined limit [20].  
For the numerical optimization, we developed the Independent Vortices Search (IVS), based on the Vortex Search algorithm (VS) [32] and its modification, the Modified Vortex Search algorithm (MVS) [33]. While VS works with one vortex, MVS can explore many vortices at once, exchanging information. We will present more details below.
Our contribution to the ongoing research in industrial control systems anomaly detection using Modbus over TCP is as follows:  
* This paper presents extended results over our previous work by presenting a detailed cluster-based entropy analysis on selected network traffic features. The entropy analysis is used more so to study particular features than for classifier analysis. * It extends the analysis using a classification-based approach. The results show that, overall, the classification results of the DOS attacks were much higher than the MITM attacks. In terms of classifiers, J48 and random forest had the best classification results. * This is the first work that has used packet cluster-based entropy analysis to detect anomalies using Modbus over TCP. * We wanted to study if two Modbus features, in addition to the three TCP/IP features, can be used to classify DOS and MITM attacks with a high accuracy. The rest of the paper is organized as follows. Section 1.1 presents the background necessary to understand this research. Section 2 discusses the related literature in this area. Section 3 discusses the data used in this research and our previous work. Sections 4 and 5 discuss methodology and results using entropy analysis and classification analysis, respectively. Section 6 concludes the paper, and Section 7 presents the future works.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Improved Spatial Pyramid Matching (SPM) Model
#### Multi-Feature Extraction  
In comparison with normal rails without corrugation, typical corrugation rails exhibit distinctive textural features. A widely-used method for texture feature extraction is the local binary pattern (LBP). To extract the textural characteristics of rail image more effectually, the rotation invariant Uniform LBP [20] that the number of patterns decreased from 256 to 59 is used in this paper. It significantly compresses the dimension of the eigenvector and reduces the effect of high-frequency noise, allowing for better representation of image information under a variety of complex conditions.  
Another local feature descriptor with excellent performance is SIFT, which has been widely applied since was first reported [36]. In this paper, the dense_SIFT proposed by [PERSON] et al. [21] is applied. It takes a fixed-size mask and extracts the dense_SIFT patches from left to right and top to bottom on the image with a certain step size and then extracts 128-dimensional SIFT descriptors from each patch as the local feature descriptors. Compared to the traditional SIFT, dense_SIFT reduces the computation complexity and extracts dense_SIFT features that can be better used for linear classification.  
In view of the well-characterized ability and complementarity of the two local feature descriptors mentioned above, the image features are extracted by using Uniform LBP and dense_SIFT, respectively, which can reflect the different properties of the image data effectively. After that, these two features are used in combination to train the classifier. This can take full advantages of the two methods, therefore, a better classifier performance can be obtained.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 3 Rosenbrock-Algorithm-Based Beam Training  
### Problem Description  
Assume that the total number of beams at the transmitter and receiver are \(N_{t}\) and \(N_{r}\), respectively. For the most basic traversal search, the beam search will perform the \(N_{t}\times N_{r}\) training sequence to determine the optimal beam-pair, which consumes a lot of energy.  
From the system model analysis, the beam search problem can be transformed into the optimization problem of the 2-D discrete integer space, and the optimized objective function is the receiving SNR. However, the gradient information on the objective function is unknown in practice. The Rosenbrock search is very suitable for the optimization problem where explicit derivatives cannot be efficiently computed, but the objective function is not complicated to calculate. In this section, based on the RA, we develop an efficient beam training technology with evidently reduced overhead. To simplify the analysis, we employ the 1-D antenna array in the following detailed descriptions. With the beam index definition, the 1-D antenna array model can be easily extended to the 2-D antenna array case.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 3 Materials and Methods
### \(kNN\) Search
#### 3.4.1 Octree Searching  
To search \(kNN\) efficiently, we defined a searching ball \(B_{r}\) which takes query point \((x_{query},y_{query},z_{query})\) as the center and \(r\) as the radius, an \(octant\) with center \((x_{center},y_{center},\)\(z_{center})\) and edge length \((2l_{x},2l_{y},2l_{z})\). There are three cases of relationship between \(B_{r}\) and \(octant\) as follows:  
1. \(B_{r}\) lays inside \(octant\);
2. \(B_{r}\) contains \(octant\);
3. \(B_{r}\) intersects with \(octant\).  
Criteria of them as follows:  
1. Meeting \(|x_{query}-x_{center}|<l_{x}\), \(|y_{query}-y_{center}|<l_{y}\) and \(|z_{query}-z_{center}|<l_{z}\) at the same time, then \(B_{r}\) lays inside \(octant\).  
Figure 4: Two-level hybrid structure.  
2. Meeting \((|x_{query}-x_{center}|+l_{x})^{2}+(|y_{query}-y_{center}|+l_{y})^{2}+(|z_{query} -z_{center}|+l_{z})^{2}<r^{2}\), then \(B_{r}\) contains \(octant\).
3. It is a little complicated for this case. Three criteria are included: 1. There are three inequalities: \(|x_{query}-x_{center}|>r+l_{x}\), \(|y_{query}-y_{center}|>r+l_{y}\), \(|z_{query}-z_{center}|>r+l_{z}\). If one of them holds, then \(B_{r}\) do not intersect with \(octant\); 2. There are three inequalities: \(|x_{query}-x_{center}|<l_{x}\), \(|y_{query}-y_{center}|<l_{y}\), \(|z_{query}-z_{center}|<l_{z}\). If none or one of them holds, then \(B_{r}\) do not intersect with \(octant\); 3. If \(\sqrt{x_{d}^{2}+y_{d}^{2}+z_{d}^{2}}\geq r\), then \(B_{r}\) do not intersect with \(octant\). Where \(x_{d}=max(|x_{query}-x_{center}|-l_{x})\), \(y_{d}\) and \(z_{d}\) are likewise. If and only if none of the three criteria above holds, \(B_{r}\) intersects with \(octant\). In conclusion, given a searching ball \(B_{r}\) and a query point \((x_{query},y_{query},z_{query})\), octree searching finds all candidate 3D R*-trees corresponding to encoded leaf nodes which intersect with \(B_{r}\), contained by \(B_{r}\) or contains \(B_{r}\), as shown in Figure 4. The 3D R*-trees corresponding to the leaf nodes will be searched to find \(kNN\).