esa-sceva/satcom-chunk-collection huggingface dataset:
# An In-Depth Assessment of the New BDS-3 B1C and B2a Signals
## 6 Quality of Carrier Phase Observations Based on Zero Baseline  
Carrier phase observations are currently the most accurate observations for GNSS measurement, the measurement accuracy of which is on the order of millimeters. In this section, the authors analyze the accuracy of carrier phase observations. The zero-baseline [18] observation method was used to evaluate the accuracy of carrier phase observation. In Section 6.1, double-difference observations are used to evaluate the accuracy of the carrier, while in Section 6.2, the zero-baseline solution of different navigation systems is used.
## 3 Experimental and Results  
### Experimental Setup  
The first experiment was conducted in a large-scale flow loop channel at the University of Wollongong (UOW), Australia (Figure 5). The channel was a rectangular open channel with a length, width and height of 11 m, 0.3 m and 0.45 m, respectively. Water was supplied to the channel from a constant head tank. The test section was located 6 m downstream of the inlet. The velocity was measured using a Dantec two-component LDA system. The equipment used in the experiment included a 60 mm fiber optic probe, a 400 mm focal length front lens, a 300 mW continuous wave argon ion laser, and the transmit optics of the beam splitter Bragg cell and signal processor. In order to improve accuracy, particles were uniformly mixed into the fluid. In the experiment, the water depth was controlled by opening a baffle in the downstream tail water tank. When the required water depth was reached, the water depths upstream and downstream of the LDA installation position were measured, and the channel inlets had four positions at: X = 1.0 m, 2.6 m, 4.0 m and 6 m. When the water depths measured at these locations were equal, it was considered that the fluid in the rectangular open channel was uniformly flowing.  
Figure 4: Location map of the characteristic points and the line of depth average velocity (LDAV) in different flow partitions.  
The second experiment was carried out in a 6.3 m long, 0.8 m wide, 0.6 m deep rectangular inclined water tank at the Fluid Mechanics Laboratory of China Agricultural University (CAU), China, as shown in Figure 6. The main components of the channel were the head tank, the tail tank, the glass channel and the circular duct circulation system. The head tank was aligned with the center of the channel and was symmetrical with the center line of the glass water channel. Meanwhile, in order to make the flow velocity more uniform, a honeycomb steel plate was added at the entrance of the channel. An adjustable tail gate was installed at the end of the channel to change the water depth. Furthermore, Q was measured using an electromagnetic flowmeter installed on the inlet pipe. The flow velocity was measured using an acoustic doppler velocimetry (ADV). ADV is based on the principle of the acoustic doppler effect--the acoustic signal emits an ultrasonic signal from the probe, which is scattered by the particles in the water stream and then the scattered signal is received by the receiving probe. The ADV measures fluid velocity by comparing the Doppler phase shift of coherent acoustic pulses along three axes and transforming these to horizontal and vertical components.  
The experimental conditions were varied by adjusting the flow rate and flow depth.
## 5 Performance Evaluation and Simulation
### Experiment Settings and Measurements  
Experiments are carried out using both Cooja and TensorFlow. Cooja is built for IoT simulations, especially ad-hoc networks. TensorFlow provides APIs that encourage ML and have a shorter compilation time than other ML frameworks. It is assumed that the battery stage of the IoT objects is within normal distribution. To maximize the IoT object's performance, the object chooses the best offloading policy, with computation tasks offloading to the target edge node. This model has 12 controllers and cells and three computational nodes for the consensus process, incorporating four edge nodes and one private cloud node. In addition, 8 servers are installed in the blockchain mechanism. Besides, Table 3 formalizes the key parameter settings. The compute resource status of the cloud server and edge nodes for the consensus process is categorized into four levels: low, medium, high, and extremely high. Therefore, the compute overhead condition of the cloud server and edge node consensus process is classified as cheap, ordinary, and costly. Moreover, the workload arrival space has been set at = {10 units/s, 20 units/s,..., 100 units/s}. Additionally, H = {20 ms/unit, 30 ms/unit,..., 60 ms/unit} is the network congestion space. We build the environment states and extract their associated energy-generating distributions using real-world green energy harvesting traces, includingwind and solar. The time is set at 5 s for two adjacent time slot decisions. The method will infer by experimenting with the optimal offloading policy of the IoT object in the MDP. As expected, the radio channel between IoT objects and edge nodes is stable. An IoT object recharged by an RF energy harvester was simulated to obtain real emergency care advice. The computational assignments are produced at 100 kb/s, with every bit requiring 1000 CPU cycles to complete. The IoT object uses the energy harvesting concept to determine the amount of renewable energy provided by the EH component in this time slot. The EH efficiency is 0.51, and the RF transmitter produces 3 W of corresponding isotropically radiated energy power. Finally, each edge node consumes 150 watts of electricity. Nevertheless, each edge node has a maximum service rate of 20 units/s.
## 3 Results
### Relationship Between the Seven VIs and AGB at Four Different Stages  
We used linear and nonlinear (logarithmic, parabolic, power, and exponential) regression analysis to investigate VIs with AGB at four different growth stages. Table 5 showed the best AGB estimation models, respectively. The results indicated that the accuracy of AGB estimation models that were based on the seven VIs were different at different growth stages. Figure 3: Correlation coefficient results of AGB and canopy spectral reflectance at four different stages: (a) Seeding stage; (b) Bolting stage; (c) Flowering stage; and, (d) Pod stage. At the seeding stage, the R\({}^{2}_{\rm cal}\) and RMSEC of calibration models varied, respectively, from 0.38-0.75 and 516.02-820.85 kg/hm\({}^{2}\), while the R\({}^{2}_{\rm val}\), RMSEP, and RPD of validation models were between 0.35-0.62, 634.92-808.95 kg/hm\({}^{2}\), and 1.23-1.57. Through comparing the accuracy of models, models that were based on CIred edge and Green normalized difference vegetation index (GNDVI) proved to have good fitting characteristics, relatively high precision models. At the bolting stage, R\({}^{2}_{\rm cal}\) and RMSEC varied, respectively, from 0.60-0.90 and 548.12-1116.29 kg/hm\({}^{2}\), while R\({}^{2}_{\rm val}\), RMSEV, and RPD were between 0.64-0.95, 563.53-1200.59 kg/hm\({}^{2}\), and 1.58-3.36; model 8 based on CIred edge proved to have good fitting characteristics, relatively high precision model. At the flowering stage, the accuracy of AGB estimation models that were based on VIs were relatively low, and R\({}^{2}_{\rm cal}\) and RMSEC varied, respectively, from 0.06-0.51 and 1725.98-2689.12 kg/hm\({}^{2}\), while R\({}^{2}_{\rm val}\), RMSEV, and RPD of validation models were between 0.20-0.73, 1383.12-2359.91 kg/hm\({}^{2}\), and 1.14-1.94. Model 15 based on CIred edge proved to have good fitting characteristics, relatively high precision models. At the pod stage, R\({}^{2}_{\rm cal}\) and RMSEC of calibration models varied, respectively, from 0.22-0.68 and 2570.50-4003.84 kg/hm\({}^{2}\), while the R\({}^{2}_{\rm val}\), RMSEV, and RPD of the validation models were between 0.20-0.61, 2768.50-3959.06 kg/hm\({}^{2}\), and 1.10-1.57; models 24, 25, and 26 based on NDVI, GNDVI, and RVI proved to have good fitting characteristics, relatively high precision models, respectively. Figure 4 showed the relationship between CIred edge and AGB. CIred edge performed better in estimating AGB among the seven VIs at four different growth stages. However, the results indicated a low correlation between CIred edge and AGB at the flowering and pod stage. During reproductive growth (flowering and pod stage), photosynthetic products were mostly stored in reproductive organs (e.g., flower and pod), while photosynthetic products were mostly stored in stems and leaves during vegetative growth [40]. The canopy morphology structure of WOR also changed significantly. Thus, it was difficult to monitor AGB by VIs that are based on visible and near infrared bands.
## 4 Results
### Obtaining the Peak Load Profile  
This section describes and illustrates, through examples, the following processes:  
* Determining a normalized load profile, characterized as a distribution that minimizes entropy while adhering to specified load requirements (Section 4.1.1);
* Extracting a segment of the load profile, specifically the load during peak periods, and subsequently smoothing its trajectory (Section 4.1.2);
* Scaling a normalized load during peak periods in accordance with predefined load requirements (Section 4.1.3).
# Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
## 3 On-Board Processing Unit Design
### Software Design  
This subsection is organized in three parts, starting with a description of the booting procedure and operating system in Section 3.3.1, and what choices were made in their design with regard to integrity, redundancy, and locality. The second part discusses the main application of the OPU in Sections 3.3.2 and 3.3.3, and the third part discusses the on-board processing pipeline application and architecture in Section 3.4.
## 1 Introduction
This involves a set of techniques that enables networks to remain fully operational during outages, typically caused by link failures due to fiber cuts or equipment failures at the network nodes. To ensure survivability, networks rely on two main approaches: protection and restoration techniques [12]. The former uses deployed backup resources (e.g., optical channels, wavelengths, etc.) that are automatically activated when a failure occurs, while the latter dynamically discovers and allocates resources after a failure is detected. A commonly used protection technique operating in the optical domain is known as 1 + 1 Optical Channel (OCh) protection [13]. In this scheme, the source node transmits two identical optical signals over two physically disjoint optical channels. As a consequence, both signals must use the same wavelength, which increases overall wavelength consumption and adds complexity to the wavelength assignment strategies. An important question to address is how survivability affects wavelength requirements and how it depends on topological parameters. As far as we know, this problem has received limited attention in literature. For instance, in [8; 14], the authors analyzed the impact of a link restoration technique on wavelength requirements using lower bounds and heuristics, respectively. Similarly, ref. [15] investigated the issue using ILP (integer linear programming) and heuristic formulations to assess dedicated and shared path protection, as well as shared link protection. However, none of these studies have considered the influence of topological parameters in their analysis. In this paper, we revisit the problem of wavelength assignment in optical backbone networks to understand the dependency of wavelength requirements on topological pa rameters, while also considering survivability features. Although great emphasis has been placed on algebraic connectivity, additional topological parameters not previously considered by other authors, such as spectral radius and robustness, are also examined. Furthermore, by relying on regression techniques, simple formulas are derived to predict wavelength requirements and the wavelength increment factor, assuming a \(1+1\) OCh protection scheme, with this factor being defined as the ratio between the number of wavelengths required with protection to the number without protection. The paper is organized in five sections. Section 2 describes the model used to characterize the optical network, defines the topological parameters considered in the study, and defines the protection strategy adopted in this work. In Section 3, we describe the algorithms employed, with emphasis on the problem of computing disjoint node paths. Section 4 presents the methodologies for generating synthetic data sets and provides a discussion of the results, also focusing on the derivation of formulas to estimate wavelength requirements. Finally, Section 5 summarizes and concludes the paper.
# Pharmaceuticals in Coastal Waters: An UHPLC-TOF-MS Multi-Residue Approach
## 3 Results and Discussion
### Method Development
#### 3.1.2 UHPLC-TOF-MS Performance  
Optimizing the chromatographic separation of a high number of analytes with different chemical properties in one single run can be challenging. The resulting final method will reflect a compromise between several key steps including ionization efficiency, peaks shape, resolution and time of analyses [21]. The process started with the selection of the most suitable analytical column and, therefore, four columns with varying length and pore diameter were tested, namely Acquity BEH C18 (2.1 \(\times\) 100 mm and 1.7 \(\upmu\)m), Acquity HSS C18 (2.1 \(\times\) 100 mm and 1.8 \(\upmu\)m), Kinetex Biphenyl (2.1 \(\times\) 50 mm and 1.7 \(\upmu\)m) and Zorbax Eclipse Plus C18 (2.1 \(\times\) 50 mm and 1.8 \(\upmu\)m). The full comparisons of results obtained for the four columns tested are presented in Figure 3. Based on the number of compounds detected, the reverse-phase Zorbax Eclipse Plus C18 column (2.1 \(\times\) 50 mm and 1.8 \(\upmu\)m) was selected. One known advantage of using smaller particles packed columns, in this case 1.8 \(\upmu\)m, is the  
Figure 2: Minimum, mean and maximum recoveries achieved with SPE with and without preconditioning the OASIS HLB.  
elution of analytes in a narrower, more concentrated band that increases resolution, peak capacity (efficiency) and linear velocity [22]. Additionally, to enhance the separation of target analytes, a width of 2.1 mm was preferred with a flow rate of 500 \(\upmu\)L/min. As for the length, and considering the gradient time, the 50 mm column produced the highest peak capacity. Given the high number of analytes, it is necessary to ensure maximum resolution to decrease matrix suppression caused by their simultaneous elution [21].  
Electrospray ionization is greatly influenced by analytes chargeability and is particularly suited for polar compounds. When using the positive ion mode (ESI+), a pH modifier is usually added to the solvent to enhance the signal intensities of the target analytes, in particular of weak bases which are more extensively protonated in acidic medium [23]. In this work, formic acid was selected with the aqueous phase (A) consisting of water acidified with formic acid 0.1% to avoid signal suppression effects. The ideal concentration of formic acid was selected in previous laboratory tests by performing a set of analyses with variation of formic acid in water ranging from 0.05% to 0.3%. Results are presented in Figure 4 in which it is clear that the best recoveries were achieved with 0.1% of formic acid. As for the organic phase, acetonitrile was selected since it reduced the background noise. Overall, the resolution was improved with the mobile phases chosen, with a total run time of 12 min.  
Figure 3: Minimum, mean and maximum recoveries obtained for the variation of mobile phase composition, in percentage of formic acid.
## 1 Introduction
### Theories of Organizational Safety
#### 1.3.1 Theory of High Reliability Organizations  
The theory of \"high reliability organizations\" (HRO) was developed with the aim to understand which factors determine that complex, high-risk organizations, such as those in the aeronautics sector and the nuclear industry, are able to maintain high safety levels [26]. HRO are seen as organizations capable of maintaining an error-free performance for long periods of time [27]. In this way, even if they are organizations operating in complex, high-risk environments, they are mostly accident-free [28].  
A central element shared by the authors of this approach is the principle that accidents are unavoidable. The theory of HRO stands, in practice, as an academic counter-proposal to the \"normal accident theory\" (NAT) formulated by [PERSON] [29]. The NAT assumes a certain fatalism on the unavoidability of accidents in complex organizations. An unavoidability caused by high-coupling between technology and organization and the unpredictability of the system, in which a failed part could affect all others and the system as a whole.  
On the contrary, the theory of HRO proclaims that technologically complex organizations have a set of processes that effectively prevent and detect catastrophic failure [27; 30]. The consistency and stability required for failure-free operation would be achieved through a set of technical and organizational features, such as (a) management commitment to ensure safety is an overriding priority at all levels, (b) existence of redundancy systems, (c) decentralization of the decision-making process, (d) ongoing technical training of personnel, (e) organizational learning based on operational events, (f) promotion of communication, and (g) reward for individuals who report problems [31; 32].  
It is worth noting that the approach of the HRO theory ended by converging with some aspects of resilience engineering, integrating an approach both preventive and adaptive within safety management. Prevention requires the identification of events that should not occur and precursor events which might lead to them, as well as the creation of procedures to prevent undesirable events [33]. In addition to prevention, organizational reliability requires resilience [34] to handle organizational variations while maintaining stable systems [33; 35].  
In this sense, [PERSON] and [PERSON] [36] point to five key aspects to maintaining high safety standards: (1) a preoccupation with failure, (2) a reluctance to simplify, (3) a sensitivity to operations, (4) a commitment to resilience, and (5) deference to expertise. The first three address the capacity of an organization to anticipate unexpected problems, whereas the last two refer to the capacity of the organization to contain unexpected problems once they have occurred. In other words, high reliability organizations are able to detect and manage unexpected events while sustaining reliable performance.
## 4 Results and Discussion
### Radial Velocity Distributions  
Figure 9 shows the contours of the radial velocities in the classification chamber corresponding to the different guide cones. The negative sign denotes a direction oriented toward the center of the rotor cage. According to the radial direction, from Figure 9, it can be seen that, with the guide cone of \(d\) = 808 mm, the radial velocity distribution was quite uniform in the separation chamber, while the radial velocities in zone (I) were higher than those in zone (II) in the remaining cases. High radial velocities were concentrated around the impact plate. In this region, they exhibited positive values, which meant that they were directed toward the walls of the classification chamber. This meant that the material particles were carried by the airflow that passed the impact plate toward the wall. As a result of these collisions, coarse material particles lost their kinetic energy and fell, becoming a coarse product.  
Figure 10 shows the radial velocity under different distances from the axis of the rotor cage. With similar trends under the four different guide cones, the radial velocities are approximately zero in the near-wall region. After this, they increased significantly and displayed negative values in the annular zone. This meant that the airflow was oriented toward the rotor axis, transporting material particles into the rotor cage (as clearly shown in Figure 3a). In the rotor outlet, the radial velocities were the highest, but they declined sharply inside the rotor cage. This trend was in opposition to the t  
Figure 9: Contours of radial velocities in the classification chamber. (**a**–**d**) correspond to different guide cones with \(d\) of 708, 808, 758, and 658 mm.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 4 Security Hardening of Kubernetes Components
### Control Plane Security
#### 4.1.1 Securing Kubernetes API  
The Kubernetes API server listens on port 6443 on the first non-localhost network interface, secured by TLS. In production, the API server uses port 443. The --secure-port and --bind-address flags can change this port's IP address. The port is changed from the default to 8443, and the API server is bound to the control plane node's private IP address, as shown in the example below. As shown in Figure 2, /etc/kubernetes/manifests/kubernayer.yaml is modified:  
Firewall rules should also be configured to restrict API server access.  
API servers provide certificates for secure communication. Private certificate authorities (CAs) or public key infrastructures tied to trusted CAs can issue this certificate. The certificate and private key are in /etc/kubernetes/pki/. Figure 2 shows the \"-tls-cert-file\" and \"-tls-private-key-file\" options. That encrypts all Kubernetes API server traffic with HTTPS using the specified certificates. To establish secure connections, the client must present a copy of the CA certificate configured in ~/.kube/config and present its own TLS certificate. TLS security depends on cipher suite strength. It is crucial to enable only strong cipher suites like AES-GCM and disable weaker ones.  
HTTP requests undergo authentication after TLS is established. An HTTP request is used for authentication, which checks headers and client certificates. Client certificates, passwords, plain tokens, bootstrap tokens, and JSON Web Tokens are authentication modules. Multiple authentication modules can be configured to try sequentially until one succeeds.  
If the request cannot be authenticated, HTTP status 401 is returned. Some authenticators provide group memberships, but not all. Kubernetes uses usernames for access control and logging but does not store them or other user data.