esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Far-Field Distance Calculation  
In [45], the problem of evaluating the Fraunhofer zone (FZ) for FDA with equal inter-element spacing is studied. The relative frequency band \(\delta f=(f_{max}-f_{min})/(f_{max}+f_{min})\) and overlap factor \(\xi_{ov}=\Delta f/\Delta S_{part}\) are defined and help characterize FZ error for FDA with \(f_{n}=f_{0}+n\Delta f\). \(\delta f\) characterizes the bandwidth of the array with respect to the array center frequency, and \(\xi_{ov}\) characterizes the spectral overlap between array elements with respect to the partial signal spectrum \(\Delta S_{part}\) and inter-element frequency offset \(\Delta f\).  
In this work, the bandwidth of each collection is small relative to the carrier frequency of the array, yielding \(\delta f\ll 1\). Additionally, since each array element radiates a constant tone, \(\Delta S_{part}\to 0\), yielding \(\xi_{ov}\gg 1\). Thus, we make the monochromatic assumption when calculating the range to the farfield \(R_{ff}\). Following [45,46], this range is  
\[R_{ff}\geq\frac{2D^{2}}{\lambda_{0}}=\frac{2((N-1)\frac{\lambda_{0}}{2})^{2}}{ \lambda_{0}}=\frac{1}{2}\lambda_{0}(N-1)^{2}. \tag{2}\]  
The far-field distance for each collection is calculated using Equation (2) and summarized in Table 3.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Identity and Access Management
#### 4.4.4 Role-Based Access Control  
Role-Based Access Control (RBAC) regulates access to network resources according to the roles designated to users within an organization. RBAC authorization employs the rbac.authorization.k8s.io API group to facilitate authorization determinations, allowing dynamic policy adjustments via the Kubernetes API. Reference source not located. The implementation of RBAC in Kubernetes consists of a two-step procedure as detailed below. The initial step is to establish a Role or ClusterRole. The latter is a global object, whereas the former is a namespace-scoped object. A Role or ClusterRole comprises verbs, resources, and subjects that delineate the permitted actions on a resource. The subsequent step involves establishing a ClusterRoleBinding, wherein the privileges outlined in the initial step are conferred upon the user or group. Figure 10 illustrates the creation of an RBAC role that confers read-only access to Pods within the test namespace, with the verbs specifying permitted actions on resources (get, list, and watch):  
Role-Based Access Control (RBAC) is a crucial element of Kubernetes' authorization strategy, offering a systematic and secure approach to managing permissions within a cluster. RBAC delineates Roles and Role Bindings to guarantee that users, groups, and service accounts possess only the permissions requisite for their designated functions. Effectively implementing RBAC enhances security and facilitates adherence to governance policies and industry standards. The effective execution of RBAC necessitates meticulous planning and continuous oversight. Administrators must assess user and service account needs to establish suitable roles with the least required permissions.
## 2 Literature Review  
### Mental Health Management Through Remote Healthcare Monitoring Systems  
Effective management of mental health is essential for enhancing the quality of life for individuals with mental health conditions. Remote healthcare monitoring systems have become an asset in this regard, providing numerous benefits compared to conventional methods. AI-powered medical devices that collect precise clinical data remotely assist health professionals in evaluating conditions with high accuracy, reducing the need for in-person assessments and ensuring consistent monitoring. However, to fully appreciate the advantages of remote healthcare monitoring systems, it is important to examine traditional approaches to mental health management and their inherent limitations. It is important to have an understanding of these shortcomings in order to address the need for integrating AI-powered solutions for the improvement of patient outcomes.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 3 Simulation Test
### Case 1: Nanhua District
#### 3.1.1 Image Shooting Distance  
The planning result of the grid-type shows that the average shooting distance is 194 m, while the average shooting distance of the contour-type is 199 m. From this result, the shooting distance of the contour-type is closer to the setting value of 200 m. The  
Figure 4: Case 1: Route planning results. (**a**) Grid-type mission planning, (**b**) contour-type mission planning.  
Figure 5: Case 1: Analysis of route planning results. (**a**) Image shooting distance, (**b**) overlap, (**c**) elevation difference.  
maximum value of the grid-type is 213 m, and the minimum value is 173 m. Only 64% of the images in the 200 \(\pm\) 10 m range are available. In contrast, the maximum value of 209 m and the minimum value of 178 m for the contour-type, and 95% of the images are in the 200 \(\pm\) 10 m range. The results showed that the contour-type method could maintain a better shooting distance.
# Systematic Encoding and Shortening of PAC Codes
## 1 Introduction  
PAC codes are a class of linear block codes designed to improve the performance of polar codes by combining channel polarization with convolutional coding [1]. It has been shown that PAC codes can perform better than polar codes [1], in some instances performing close to the theoretical limits for finite-length codes.  
Given the potential of PAC codes for applications requiring extreme reliability at short block-lengths, it is of interest to investigate various aspects of PAC codes that may be important in practice. In this paper, we study systematic encoding and shortening of PAC codes. Systematic encoding is of interest mainly because it provides a better bit error rate (BER) performance compared to non-systematic encoding. Code shortening is important as a means of providing flexibility is choosing the code length. The BER advantage of systematic coding is illustrated in Figure 1 for a PAC code of length \(N=128\) and rate \(R=1/2\) on an additive Gaussian noise channel with binary modulation. A better BER performance is important in concatenation schemes where an outer code corrects the bit errors left over by an inner PAC code.  
In Section 2, we give a definition of PAC codes and their non-systematic encoding. In Section 3, we develop a method for systematic encoding of PAC codes. In Section 4, we indicate how the systematic encoding method of Section 3 can be used for shortening PAC codes.  
Throughout, we restrict attention to PAC codes over the binary field \(\mathbb{F}_{2}=\{0,1\}\). All algebraic operations are over vector spaces over \(\mathbb{F}_{2}\). \(\mathbb{F}_{2}^{N}\) will denote row vectors of length \(N\) over \(\mathbb{F}_{2}\) and \(\mathbb{F}_{2}^{N\times M}\) will denote matrices with \(N\) rows and \(M\) columns. For any \(\mathbf{v}=(v_{1},\ldots,v_{N})\in\mathbb{F}_{2}^{N}\) and \(\mathcal{A}\subset\{1,2,\ldots,N\}\), let \(\mathcal{V}_{\mathcal{A}}\) denote the subvector \((v_{i}:i\in\mathcal{A})\). For any \(\mathbf{G}\in\mathbb{F}_{2}^{N\times M}\), \(\mathcal{A}\subset\{1,2,\ldots,N\}\), and \(\mathcal{B}\subset\{1,2,\ldots,M\}\), let \(\mathbf{G}_{\mathcal{A}\mathcal{B}}\) denote the matrix obtained after deleting the rows of \(\mathbf{G}\) not in \(\mathcal{A}\) and columns of \(\mathbf{G}\) not in \(\mathcal{B}\). The notation \(\mathbf{0}\) denotes a vector or matrix all of whose elements are \(0\) and \(\mathbf{I}\) denotes an identity matrix.
## 4 Numerical Simulation and Discussions
### Modal Simulation  
In order to study the fluid structure interaction between the pipeline and the fluid, and to verify the correctness of the friction model, the modal analysis is performed first. It is assumed that the pipeline is full of fluid, and the pipeline ends are sealed by screw plugs in the process of modal analysis. The two pipeline ends are free, and screw plugs are loaded at both ends as additional mass.  
The mechanical shock excitation \(F_{r}[1(t)-1(t-T)]\) is applied to the initial end of the pipeline. The average impact force is \(F_{r}\) = 200 N, and the impact duration is \(T\) = 2 ms. After the Laplace transformation, the corresponding frequency domain excitation is \((F_{r}/s)(1-e^{-sT})\), the excitation form of the pipeline can be expressed as  
\[\left\{\begin{array}{l}\mathbf{Q}_{0}(s)=\left[\begin{array}{cccccccc}0&- (F/s)(1-e^{-sT})&0&0&0&0&0&0\\ \mathbf{Q}_{L}(s)=\left[\begin{array}{cccccccc}0&0&0&0&0&0&0&0\\ \end{array}\right]^{\text{T}}\end{array}\right. \tag{31}\]  
Based on the above mechanical shock excitation and frequency-domain transfer matrix method, the axial vibration velocity response of the two measuring points on the pipeline are shown in Figure 3.  
The first four simulation modes of the two measuring points are described in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter Name** & **Numerical Value** & **Parameter Name** & **Numerical Value** \\ \hline \(L_{1}\) & 49.613 mm & pipeline density/\(\rho_{p}\) & 7800 kg/m\({}^{3}\) \\ \(L_{3}\) & 85.216 mm & Young’s modulus/E & 213 GPa \\ \(L_{5}\) & 162.253 mm & shear modulus/G & 81.9 GPa \\ \(R_{2}\) & 28.575 mm & Poisson ratio/\(\
u\) & 0.33 \\ \(R_{4}\) & 28.575 mm & fluid density/\(\rho_{f}\) & 872 kg/m\({}^{3}\) \\ thickness of pipeline/\(e\) & 0.889 mm & Bulk modulus/K & 1.95 GPa \\ bending angle \(\theta_{2}\) & 1.595 rad & Kinematic viscosity/\(\
u\) & 19.7 mm\({}^{2}\)/s \\ bending angle \(\theta_{4}\) & 1.480 rad & Material & 6061-T6 \\ inner radius of pipeline/\(r\) & 3.874 mm & hydraulic oil & 10 \# \\ \hline \hline \end{tabular}
\end{table}
Table 1: The precise parameters of the pipeline and fluid.  
Figure 3: Axial velocity response curve of pipeline.  
It can be observed from Figure 3 and Table 2 that there will be four formants from 0 Hz to 2000 Hz, and the corresponding axial vibration velocity value of the second formant is lower than the other formants obviously. Thus, it is reasonable to infer that the major vibration mode of the pipeline is not the axial vibration under the second-order natural frequency.
# Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid +
## 3 Power Grid ICS Network Architectures  
ICS Networks should be logically separated into the following zones [26]:  
* Level 5, Enterprise Network--Used for managing business-related activities.
* Level 4, Site Planning and Logistics Network--Used for managing production work flows.
* Level 3, Site Manufacturing Operations and Control--Used to manage control plant operations that produce the desired end product.
* Level 2, Area Control--Used for supervising, monitoring, and controlling the physical processes.
* Level 1, Basic Control--Sensing and manipulating the physical processes.
* Level 0, Physical Process--The physical process happens here.  
As depicted in Figure 1, the OT network resides in Levels 0-2 while the IT network resides in Levels 3-5.  
Since IT-OT convergence is a relatively new phenomenon, many Operational Technologies are still insecure by design [4]. IT technologies have evolved alongside the various threats to networking and computing technologies while OT networks were isolated until relatively recently. In [26], CISA recommends various traditional methods to implement a defense-in-depth strategy at the enterprise zone. This differs compared to traditional IT security services, shown in CISA's recommended firewall rule set layer, depicted in Figure 2. In particular, they recommend the use of a DMZ to provide logical separation of the enterprise zone from the Internet; virtual private networks to secure remote access connections; firewalls to restrict connections to trusted entities and content between zones; and various host security controls such as antivirus software, patch management, intrusion detection systems, etc.  
While the above defense-in-depth strategy is a great start to securing ICS networks, the OT portion of the network may still require additional security controls to improve an organization's overall risk posture. Organizations responsible for power transmission and distribution are responsible for assets distributed over vast geog  
Figure 2: CISA Recommended Firewall Rule Set Layers.  
these systems typically use SCADA technologies to monitor and control these distributed systems. A typical SCADA system is shown in Figure 3.  
Beyond the transmission and distribution domains in the smart grid, there are also the generation, customer, markets, operations, and service provider domains as defined in the NIST Smart Grid Framework 3.0 [27]. The domains that are of primary concern in this paper are the transmission, distribution, operations and customer domains of the smart grid. The next two sections of this paper will focus on the potential cyber threats to the Smart Grid and potential countermeasures.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 4 Network of TA Systems
### Lon Dependent Degradation of Antitoxin  
A well-studied role of stress-inducible proteases in TA systems is their ability to preferentially degrade antitoxins, which frees toxins to exert toxic functions only under stress conditions [158; 159; 2; 15]. The degradation of antitoxins, which is a negative transcriptional repressor, also leads to the induction of the expression of its own module and other genes [136; 57; 78].  
In addition, Lon also seems to play a role in the cross-regulation of multiple TA systems. A study of _Salmonella_ and _Shigella_ showed that VapC overexpression induces _yetM-yoeB_ in a Lon protease-dependent manner [157]. HipA-mediated persister formation requires antitoxin degradation by Lon to activate multiple TA toxins [136]. Activation of Lon by overexpression of TA genes seems crucial for these phenomena. A variety of TA genes can activate Lon, for example ectopic expression of the antitoxin _prlF_ of _prlF-yhaV_ has been shown to activate Lon [78; 132]. A mutation on _prlF_ (_prlF1_ allele) also leads to Lon activation [132]. Although the consequence of such activation on regulation of other TA genes has not been directly examined, the degradation of antitoxins and release of cognate toxins are expected.
## 4 Results and Application
### Application of Coherent Integration Time Optimization Model  
The conformity between the model and the measured results indicates that the proposed model can better reflect the influence of coherent integration time on the altimetric precision. Consequently, the model can be used to determine the optimal coherent integration time to improve experimental data processing. In addition, the model can be applied to a variety of altimetry situations to provide a theoretical reference for data optimization processing.
## 3 Results and Discussion
### Strengths and Limitations  
This study reports the current status of IAQ in offices in an area with high interest due to outdoor air pollution conditions. The simultaneous measurements and questionnaire collection, in the specific time of the pandemic period, provided a unique opportunity to provide new insights to the literature. Furthermore, it follows our previous large-scale study in Europe through the OFFICAIR project [17], and it attempts to expand further our understanding of indoor pollutant concentrations and the possible relationships with commonly reported health complaints. Also, it is the first study in this region that tries to reveal the impact of the COVID-19 pandemic's countermeasures in the work environment.  
Some limitations, however, should be noted. Interpretation of the results should be done with caution because data on IEQ were self-reported. Results reveal associations between SBS symptoms and chemical pollutants, but it is difficult to verify these associations until further toxicological tests in labs are performed. It is noted that the statistical correlations found in this study should be interpreted with caution. The possibility that an association was found by chance should not be excluded. Despite the limited samples collected from office buildings and the questionnaire completed by participants, the analysis provides essential information. Further large-scale studies should be conducted to focus on the above-mentioned limitations. Furthermore, the fact that air pollutant concentrations were measured passively for five workdays provides an objective, representative picture of IAQ levels but limits the possibility of performing a more detailed source apportionment study.
# Overset Grid Assembler and Flow Solver with Adaptive Spatial Load Balancing
## 4 Hole Cutting  
The hole cutting task is for determining hole cells. One of the hole cutting methods involves usage of a hole map, which is a Cartesian mesh used to represent holes approximately. When hole maps are used in conjunction with the core donor search algorithm (explained in the next section), hole cells are determined exactly. There are exact methods which are not required to be used in conjunction with donor search. Direct cutter, for example, cuts a hole profile on overlapping mesh and determines interior hole cells with flood algorithm. However, direct cutting is found to have robustness issues with three-dimensional geometries.  
Hole mapping requires all wall and outer boundaries to be present in each processor. Therefore, local wall and outer boundaries are all-gathered. A Cartesian mesh which initially has one sub-block is made to fit around wall boundaries. If any sub-block of the Cartesian mesh intersects both wall and outer boundaries, the Cartesian mesh is refined by doubling each dimension. That is, the initial \(2\times 2\times 2\) Cartesian mesh becomes \(4\times 4\times 4\) and so on. Once no sub-block is left to intersect both wall and outer boundaries, the sub-blocks which intersect wall boundaries serve as an approximate hole profile. Next, flood algorithm identifies sub-blocks representing the volume of hole. The procedure is identical to that in [11], where implementation details can be found.  
If the initial AABB of wall boundary does not intersect the outer boundary, there is no refinement required, and the AABB of wall boundaries is sufficient to represent the hole inside wall boundaries.
# Structural Optimization Method of a FinRay Finger for the Best Wrapping of Object
## 2 Materials and Methods  
### Finger Evaluation Methodology  
The evaluation of individual finger variants is based on their behaviour under load. The finger is constrained by a fixture on one side, and the gripped object is then pressed into the finger. To find out if the finger has wrapped around the gripped object, it is necessary to know the positions of at least three points. The first point is a contact point between the gripper and the gripped object. The second one is positioned before the first one in the direction towards finger's fixed base, and the third one in the opposite direction towards finger's tip. Positions of these three points on the finger can be easily measured. The position at the beginning of the finger does not change under load due to a fixed fixture. The maximum deflection appears at the point of contact. These three positions are on the finger's contact edge, and they are sufficient to describe the wrapping of the finger around the gripped object. Since the position at the beginning (fixed point) of the finger does not change, it is unnecessary to measure it. The positions of the remaining two points change under load and are defined by the distance by which the points move perpendicular to the contact surface in the unloaded state. Figure 1 shows these measured distances, where Ymax is the maximum deflection value and Yend is the value of the deflection at the fingertip.  
Yend and Ymax values were determined at each millimeter of compression. The millimeter step is chosen because of the different internal structures; the ratio of these values may differ depending on the magnitude of the deformation. From these values, the total finger deflection coefficient \(\Psi\) is calculated according to the following formula:  
\[\psi=\frac{\sum_{i=1}^{n}\frac{Y_{end_{i}}}{Y_{max_{i}}}}{n}, \tag{1}\]  
where \(\psi\) is the deflection coefficient, \(Y_{end_{i}}\) is the distance of the endpoint in the \(i\)-th millimeter of compression, \(Y_{max_{i}}\) is the maximum deflection of the finger in the \(i\)-th millimeter of compression, which is equal to the depth of compression of the steel roller into the finger in millimeters, n is the maximum depth of compression, which is 8 mm. The value of the maximum deflection of the finger 8 mm was determined based on the previous simulations of these sizes of fingers, in which there were failures of the simulations because of buckling in some tested structures at a greater value of deflection. There could be a different value of maximum deflection for another size, shape, or inner structure of the fingers.  
The smaller the deflection coefficient, the more finger's shape wraps around the manipulated object. Therefore, the most suitable type of finger is the one whose deflection coefficient \(\Psi\) is the smallest.
## 5 Discussion
### Pros and Cons of PODS  
The proposed PODS algorithm has the following characteristics, which provide good reproducibility, generality and transferability:  
1. It was completely implemented on the GEE platform, saving the time of data downloading and taking only several minutes to get the result. Otherwise, it would be computationally unfeasible to implement PODS on general-purpose personal computers because the data volume of Sentinel-1/2 covering the HJH plain, whose area is more than 10 thousand square kilometers, in the whole 2019, is hundreds of gigabytes.
2. The datasets used in PODS are those publicly accessible datasets, and neither prior knowledge of the local rice phenology information nor the cadastral field boundary data is required.  
The discrimination between rice and non-rice objects is determined by the occurrence of flood signals. However, commission error may occur in those land covers that have flooding phenomenon, such as fish pools and wetlands; omission error may occur in regions where flood signals are weak, e.g., in those regions where upland rice is adopted. Some other distinct characteristics of rice could be incorporated into the current rice classification module to reduce commission and omission error, such as the rapid growth in vegetative phase following the transplanting phase. The rapid growth signal can be captured by a surge in backscatter time series or vegetation index time series [24].  
In theory, the PODS algorithm can be applied to regions with different planting intensity and different terrains. However, when applied to different regions, the parameters need to be adjusted. Therefore, this study only selected Hangjiahu Plain as the study area. Double cropping is common in this region, where rice is in rotation with wheat, rapeseed, etc., but double cropping of rice is rare (about 10%). Thus, we didn't attempt to extract the double cropping rice from the identified rice planting area. The performance assessment of this algorithm in areas with different planting intensity and different terrain will be carried out in the follow-up research.