esa-sceva/satcom-chunk-collection huggingface dataset:
## 9 Routing in CRSNs
### CRSN Routing Modules
#### 9.1.2.3.1 Complete Avoidance of PU Zones
##### 9.1.2.3.2 FCC Interference Temperature Limit  
The idea behind this scheme is that SUs are allowed to transmit in the same spectrum in the presence of PUs as long as they do not exceed a set interference temperature level above the noise floor. Although the interference temperature limit concept was terminated in 2007 by the FCC [74] based on a lack of specific implementation rules, research to define these rules is ongoing because of its suitability in the realms of CRSNs. This scheme requires the SU to know the location of the PU, which necessitates polynomial calculations for precise interference measurement. An example of a study based on this model is [75], which models the interference temperature dynamics of a primary channel with a Baum-Welch trained HMM that they proved to be statistically stable. SUs use the trained HMM to predict the channel's interference temperature in future time slots and calculate the channel availability metric value for the channel in question. This value is, in turn, used by the SU underlay for primary channel selection and transmission. Ref [76] presents a mathematical model that serves as an interference avoiding mechanism for aggregated interference to the primary networks. In a bid to explore the possibilities of CRSNs amidst various interferences, [9] proposes a model that determines the optimum transmit power required to achieve a desired throughput by measuring the interference temperature. However, various studies have consistently shown the limitations of this method. Additionally, a partially observable Markov decision process (POMDP) framework for a decentralized cognitive MAC for OSA has also been proposed [77]. The scheme properly resolves the hidden and exposed terminal issues and exploits opportunities at the slot level. The results showed optimized SU performance while limiting the interference perceived by the PUs.
## 3 Results and Discussions
### Lifetime Estimate of the Prototype Reformer
From Figure 5, it is seen that the lifetime based on the average tube temperatures and pressures of the prototype reformer is approximately the same order of magnitude as the design lifetime. On the other hand, the lifetime based on the average tube temperatures and pressures of the periodic model (not shown in Figure 5) is approximately \(10^{12}\) years, which is obviously unreasonable as compared to the design lifetime. It should be noted that the catalyst tubes can be ruptured in practical operation because the temperatures and pressures may exceed their average values. The lifetimes estimated from the maximum tube temperatures and pressures shown in Figure 5 reveal this fact. The catalyst tubes at the upstream areas can be ruptured at four years of service. It can also be observed from Figures 4 and 5 that each catalyst tube has individual lifetime due to the different temperature and pressure in each catalyst tube. The catalyst tubes in the downstream areas have longer lifetimes because they are closer to the reformer exit where the divergent shape of the reformer top portion  
Figure 3: Correlation diagram of the Larson–Miller parameter [34]. (roof) provides larger heat dissipation space. On the other hand, the catalyst tubes in the upstream areas have shorter lifetimes because they are farther from the reformer exit and hence the heat dissipation is worse. From the above comparisons, it is found that obvious difference exists between the simulation results of the prototype and the periodic model. Therefore, the prototype is adopted for subsequent discussion.
## 2 System Model and Channel Model
### System Model
#### 2.1.1 Local Model Computation  
At this step, UDs use local sample subsets to update the global model, which is received from BS. Assuming that local sample subsets are enough in each UD, from which \(D_{k}\) are taken out for local training. Let \(c_{k}\) denotes the number of CPU cycles required to train one sample data, and \(f_{k}\) denotes CPU frequency. The time and energy consumed on local training of the \(k\)-th UD can be expressed as:  
\[\tau_{k}^{l}=L\frac{c_{k}|D_{k}|}{f_{k}}, \tag{1}\]  
\[e_{k}^{l}=L\frac{\alpha_{k}}{2}{f_{k}}^{2}c_{k}D_{k}, \tag{2}\]  
where \(L\) is the number of local iteration, and \(\alpha_{k}/2\) denotes the effective capacitance coefficient of the k-th UD's computing chipset [21].
# Low-Cost Automation for Gravity Compensation of Robotic Arm
## 5 Proposed Implementation
### Packages Construction of ROS Application Programming Interface (API) into Raspberry Pi Card (RPi)  
Most of the packages required by the Kuka youBot manipulator robot controller have been installed simultaneously with ROS Indigo.  
Figure 7: Cascaded PID regulation of the joint’s motor TMCM-1610-KR controller board.  
To establish communication with the robotic arm, programming is developed through the manipulator API, it requires several controllers that must be run simultaneously and continuously so that there is no disconnection when sending orders from the RPI card.  
The API provides the main libraries and the different configuration files for the robot control system, this is showed like a combination of decoupled functional subsystems, that is, the arm and the platform are defined as the set of different joints, within the API there are three main classes used to control the Manipulator, Base and Both simultaneously of the Kuka youBot.  
Additionally, the API provides a hierarchy where the diagnosis of the slaves connected to the Ethercat port is included, offering the possibility of obtaining electrical parameters on the motors of the rotary joints, so that it can always work under optimal characteristics, ensuring the correct operation of the robotic arm.  
The Figure 8 shows a summary of the architecture of the operation of the YouBot API, the arrows represent the data flow which occurs through EtherCAT communication, the data transfer between the user and the robot is done through a non-buffer blocking, that is, the driver can read, write and get a data message immediately.  
* **Youbot driver:** is the main class of the youBot API, allows the control of the Kuka youBot using the RPI's Ethernet port as an EtherCat communication port. The configuration file permits access to the subsystems of the platform, the arm, and the gripper through the definition of the parameters of rotary joints.
* **ROS interface driver:** Defines the classes and packages of the youBot driver for the control through ROS, it also contains the executable file that allows the initialization of the driver for the handshake between the RPI and the robotic arm through TCP protocol. This driver has the following packets: (i) brics_actuator this package contains all the types of messages that can be sent to the robot's joints, the main ones being those of position, torque, and speed. (ii) std_msgs contains the type of data that is going to be sent through the user applications developed in C++, these can be of type string, float, bool, integer, among others. (iii) pr2_msgs this package defines messages and specific types of pr2 services such as detailed information about its power board and fingertip pressure sensors.
* **Youbot Description**: The ROS integration on the Kuka youBot includes a robot model in Unified Robot Description Format (URDF), this package describes the geometry of various elements and kinematic chains.
## 4 Highly Dynamic Simulations and Results
### Simulation Conditions and Track Settings  
According to the data book of ADI Co., Ltd. product, three typical gyroscopes are selected; they are the high-precision gyroscope ADIS16490, the middle-precision gyroscope ADIS16448, and the low-precision gyroscope ADIS16300 (Analog Devices, Norwood, MA. USA). The main error parameters of the gyroscopes are shown in Table 1, and the bias errors of accelerometers are set as 16 mg/20 mg/60 mg.  
The parameters of the GPS system are as follows: the carrier-to-noise ratio (CNR) is 50 dB-Hz, the bandwidth of the carrier tracking loop is 20 Hz, the coherent integration time is 1 ms, the Allan standard deviation of the frequency is \(10^{-6}\) Hz, and the carrier wavelength of GPS L1 is 0.19 m. For the deeply coupled system, the calculation cycle of INS and the cycle of INS-aided loop control are both 10 ms, and the filter cycle time is 0.1 s. A typical dynamic trajectory of an X-43 hypersonic aircraft is set as the reference track from running take-off until level flight [15]. The initial position is in Nanjing where the coordinate is (18.80\({}^{\circ}\), 32.03\({}^{\circ}\), 10.00 m), the initial velocity is 0 m/s, and the initial heading angle is 90\({}^{\circ}\). The 3D trajectory is as shown in Figure 2, and each stage of the dynamic trajectory is as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gyros ID** & \begin{tabular}{c} **Bias Repeatability** \\ **(\({}^{\circ}\)/s)** \\ \end{tabular} & \begin{tabular}{c} **White Noise** \\ **(\({}^{\circ}\)/s)** \\ \end{tabular} & \
\begin{tabular}{c} **g-Sensitivity** \\ **(\({}^{\circ}\)/s)** \\ \end{tabular} &
\begin{tabular}{c} **50 g-Sensitivity** \\ **(\({}^{\circ}\)/s)** \\ \end{tabular} \\ \hline ADIS16490 & 0.05 & 0.05 & 0.005 & 0.25 \\ ADIS16448 & 0.50 & 0.27 & 0.015 & 0.75 \\ ADIS16300 & 2.00 & 1.10 & 0.050 & 2.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error parameters of three typical gyroscopes from ADI Co., Ltd.
# Assessment of Quad-Frequency Long-Baseline Positioning with BeiDou-3 and Galileo Observations
## 2 Materials and Methods  
In this section, we describe the quad-frequency DD observation equations and stochastic model. We analyzed the theoretical a priori precision of quad-frequency and three different triple-frequency EWL/WL combinations in BDS-3 and Galileo. Four publicly available signals of BDS-3 and Galileo are shown in Table 1. The order number in the second column is a simplification of the two system signals, \(f_{1}\), \(f_{2}\), \(f_{3}\), \(f_{4}\) correspond to B1C, B1I, B3I, and B2a for BDS-3 and to E1, E5a, E5b, and E6 for Galileo, respectively.  
It can be seen that the center frequency of the B1C and B2a signals of BDS-3 are the same as the center frequency of the Galileo E1 and E5a signals. Therefore, some combinations with the same wavelength can be formed from the two systems.
# A Linear Fitting Algorithm Based on Modified Random Sample Consensus
## 2 Common Linear Fitting Algorithm  
### Weighted Least Squares Method  
The WLS assigns different weights to data points during the fitting process. Compared to traditional least squares, WLS applies weights to each point in the dataset to reflect the importance or confidence of different points, thereby achieving a more reliable fitting result.  
Suppose a data point set \((x_{i},y_{i})\) satisfying the following relationship:  
\[y=ax+b \tag{1}\]Here, \(a\) represents the slope and \(b\) is the intercept. The objective of the WLS is to minimize the weighted sum of squared errors, which can be expressed as follows:  
\[\min\sum_{i=1}^{n}w_{i}(y_{i}-(ax_{i}+b)^{2}) \tag{2}\]  
where \(w_{i}\) is the weight associated with the \(i\)-th point. To achieve the minimum value of the sum of squared errors, we need to take the partial derivatives of \(S\) with respect to \(a\) and \(b\), respectively:  
\[\frac{\partial}{\partial a}\sum_{i=1}^{n}w_{i}(y_{i}-(ax_{i}+b)^{2})=-2\sum_{ i=1}^{n}w_{i}x_{i}(y_{i}-(ax_{i}+b))=0 \tag{3}\]  
\[\frac{\partial}{\partial b}\sum_{i=1}^{n}w_{i}(y_{i}-(ax_{i}+b)^{2})=-2\sum_{ i=1}^{n}w_{i}(y_{i}-(ax_{i}+b))=0 \tag{4}\]  
The above equations can be expanded in matrix form as  
\[\begin{pmatrix}\sum_{i=1}^{n}w_{i}x_{i}&\sum_{i=1}^{n}w_{i}\\ \sum_{i=1}^{n}w_{i}x_{i}^{2}&\sum_{i=1}^{n}w_{i}x_{i}\end{pmatrix}\begin{pmatrix} a\\ b\end{pmatrix}=\begin{pmatrix}\sum_{i=1}^{n}w_{i}y_{i}\\ \sum_{i=1}^{n}w_{i}x_{i}y_{i}\end{pmatrix} \tag{5}\]  
By solving the system of equations represented by this matrix form, we can effectively obtain the parameters \(a\) and \(b\) of the fitted model.
## 1 Introduction
Additional work in this area was provided by [PERSON] and [PERSON] [43], who examined the stability of a lumped axial-torsional model. It was found in that work that under certain parameter values, vibrations can coexist with periodic solutions (period-1 and period-2). This behavior was demonstrated through numerical simulations. In the work carried out by [PERSON] and [PERSON] [44], drill string stability was defined, with the main focus being on the importance of proportional damping coefficients as well as rock intrinsic energy in defining stable drilling conditions. From their findings, it can be concluded that rock intrinsic energy is an important parameter concerning bit-rock interaction introduced by [PERSON] et al. [45]. System damping was found to have little influence on the minimum rotational speed for stable drilling. However, intrinsic rock energy raised a downside deviation of the stability boundary and accommodated lowering the maximum permissible WOB. Another key finding concerns the common assumption of constant axial velocity at the top of the drill string. Research suggests that this simplification could significantly influence stability predictions and should be carefully reconsidered in dynamic modeling [46]. In spite of the valuable insights from these studies, the challenge of translating results from downscale experiments and their applicability to full-scale drilling operations remains unresolved. Scaling effects introduce discrepancies in material properties, geometric constraints, and applied forces, potentially limiting the applicability of downscale findings to real-world scenarios [47, 48]. While some studies have examined torsional vibrations across different scales, no comprehensive comparative work directly assesses how scaling influences the dynamics of vibration reported. This study will address this gap in a systematic comparison between torsional vibrations of downscale and upscale drill strings under various torque conditions. In this study, we propose to look at torsional vibration intensities, stick-slip phenomena, and dynamic responses for different depths of the string, which would further help in understanding how scaling affects drilling vibrations. By relating downscale results to upscale results, this work sheds light on how experimental design can be better optimized and predictive modeling enhanced for drilling operations.
## 6 Conclusions  
We addressed the problem of cost-aware planning of 5G packet-optical xHauI access networks, in which traffic (wavelengths) from a number of remote sites can be aggregated on the TPs. The main focus was on the optimized selection and placement of optical devices--OADMs and MUXs--on the TPs with the aim to minimize the overall network cost. To this end, an MILP optimization model for the network planning problem was formulated. Using the model, four different network deployment scenarios were investigated. The scenarios differed in the wavelength aggregation capabilities and in the flexibility of the use of different types of OADM/MUX devices.  
The obtained results indicate that the MIP optimization model proposed is scalable. Additionally, it is beneficial from the network cost perspective to consider larger sets of candidate routing paths. Regarding the cost savings from the flexible (optimized) application of OADMs, they can reach up to 35% when compared to the scenario with fixed-capacity OADMs, and up to above 45% with respect to the network using direct TPs (i.e., without OADMs). In general, the cost savings are higher in larger networks and in the networks with higher WDM capacity, whereas they decrease with the traffic load. Eventually, the cost of OADMs is relatively low with respect to the cost of optical fiber lease, which justifies the aggregation of wavelengths from intermediate remote sites on the TPs.  
Our future research will concern the optimized placement of OADMs in xHauI network protection scenarios, which will require a proper extension of the optimization model. Expecting a higher complexity of the optimization problem, we will focus as well on developing heuristics for the optimized planning of protected TPs in such networks.  
Conceptualization, M.K.; Funding acquisition, M.K.; Investigation, M.K. and M.J.; Methodology, M.K. and M.J.; Project administration, M.K.; Software, M.K.; Supervision, M.K.; Visualization, M.K.; Writing--original draft, M.K.; Writing--review and editing, M.K. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Science Centre, Poland, under grant number 2018/31/B/ST7/03456.  
The author declares no conflict of interest.
# Intra-Plot Variable N Fertilization in Winter Wheat through Machine Learning and Farmer Knowledge
## 3 Results and Discussion
### Validation of RF Ability to Predict Whole-Plot Yield Using Data from the Remaining Fields
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{3}{c}{**Coefficient of Determination (R\({}^{2}\))**} & \multicolumn{3}{c}{**Root mean Square Error (RMSE)**} \\ \hline
**Plot** & **MTRY = 2** & **MTRY = 7** & **MTRY = 14** & **MTRY = 2** & **MTRY = 7** & **MTRY = 14** \\ \hline Alto & 0.57 & 0.59 & 0.54 & 1694.20 & 1672.14 & 1646.13 \\ Apelarri & 0.27 & 0.42 & 0.46 & 1094.64 & 898.26 & 849.75 \\ Apelarri F & 0.86 & 0.88 & 0.88 & 1442.18 & 1332.08 & 1299.37 \\ Babea & 0.86 & 0.85 & 0.82 & 1142.54 & 1144.78 & 1104.33 \\ Barataua & 0.56 & 0.58 & 0.57 & 1070.95 & 1085.19 & 1101.12 \\ Iruleko & 0.57 & 0.60 & 0.62 & 1266.80 & 1285.71 & 1240.43 \\ Foronda & 0.70 & 0.68 & 0.65 & 919.62 & 1025.61 & 1129.08 \\ Kukura & 0.86 & 0.86 & 0.83 & 941.36 & 1085.36 & 1197.14 \\ Menor & 0.85 & 0.83 & 0.79 & 556.34 & 562.92 & 577.14 \\ Ollavarre & 0.21 & 0.13 & 0.01 & 1647.95 & 1473.98 & 1331.94 \\ Otalza & 0.60 & 0.59 & 0.56 & 872.88 & 850.42 & 962.20 \\ Parque & 0.38 & 0.39 & 0.39 & 968.63 & 960.87 & 999.55 \\ Prado & 0.32 & 0.47 & 0.49 & 1750.24 & 2229.17 & 2463.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Coefficient of determination (R\({}^{2}\)) and root mean square error (RMSE) of the predicted yield of each entire plot (validation) using the information from the rest of the plots. MTRY is indicative of the number of variables used to create each tree. [PERSON] et al. [20] had already reported an increase in the correlation between NDVI and final yield during the tillering phase.
# Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission
## 2 Materials and Methods
### Modified [PERSON]'s Algorithm  
Let the node for which we build the shortest path tree be called the root node. Any node that should send data can be the root.  
All channels between routers in the network can support bidirectional exchange. That is why each node can be the root of the classical and proposed algorithm. As a metric to obtain the shortest path trees, the number of hops in the path will be used.  
The input data are a set of nodes (routers) N, a set of channels present in the network (graph arcs), a list of the distrustful nodes, and a root node.  
The flowchart diagram that depicts the proposed modified approach is represented in Figure 1.  
The proposed approach starts with an iterative process of assigning weight to each channel, depending on whether it is connected to a distrustful node. Channels connected to two distrustful nodes receive a weight of 2 \(\times\) M, while those connected to one distrustful node receive a weight of M. Channels without connected distrustful nodes receive a weight value of 1. This process is repeated for all channels.  
The value of M is obtained as the sum of all network channels. Therefore, the approach does not depend on the number of nodes or channels.  
After the weight assignment process, the Dijkstra algorithm is executed. Then, a weight of 1 is assigned to all channel weights, and the metrics of all paths are calculated.
## 3 Classification of Attacks on RPL-Based IoT  
Attacks targeting RPL-based IoT networks can be categorized into two primary classes: (a) the type of attacks stemming from vulnerabilities inherent in RPL itself and (b) those inherited from Wireless Sensor Networks (WSNs) [10]. Some of the common RPL-specific attacks encompass rank attacks, version attacks, DODAG Information Solicitation (DIS) attacks, neighbor attacks, and replay attacks. Attacks inherited from traditional WSNs include black hole and selective forwarding attacks, sinkhole attacks, wormhole attacks, Sybil attacks, and hello flood attacks. While these attacks have their roots in WSNs, their methodologies have evolved to adapt to the unique challenges posed by the IoT paradigm. These attacks are briefly discussed in the following subsections.
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing
### Statistical Event Generation and Segregation  
One of the unique aspects of ESDR is event segregation approach as it considers delay sensitivity associated with different types of events and assures efficient delivery to appropriate destination. We have devised a statistical model which considers different events occurring in sensing field. It is assumed that sensing node is able to determine the type of a particular sensed event and delay criticality associated with that event. In order to simplify the analysis occurring events are divided into three types: very critical, critical and normal/ordinary with the probability of occurrence \(p_{vc}\), \(p_{cr}\) and \(p_{no}\) respectively. Nodes with critical and very critical events are collectively termed as delay sensitive nodes and their data is termed as delay sensitive data. The nodes of each type of events are members of the sets defined as \(\mathcal{E}_{vc}\), \(\mathcal{E}_{cr}\) and \(\mathcal{E}_{no}\). Cardinality of these sets of node is given as:  
\[\left|\mathcal{E}_{vc}\right| =\left|p_{vc}\times\left|\mathcal{E}\right|\right| \tag{17a}\] \[\left|\mathcal{E}_{cr}\right| =\left|p_{cr}\times\left|\mathcal{E}\right|\right|\] (17b) \[\left|\mathcal{E}_{no}\right| =\left|p_{no}\times\left|\mathcal{E}\right|\right| \tag{17c}\]  
where sign \(\left\lfloor\cdot\right\rfloor\) represent the round off decimal value to nearest integer. The probability values are set as \(p_{vc}=0.111\), \(p_{cr}=0.222\) and \(p_{no}=0.666\) used in simulation section, which describe the number of nodes in each type of set. These values are assigned on the base of the assumption that more critical events are less likely to occur and vice versa. The values assigned to these probabilities ensure that; (i) the proportion of each type of nodes remain same throughout the network operation and (ii) the sum of all types of nodes is equal to the total number of data sensing nodes \(\left|\mathcal{E}\right|\) as given by the following equation.  
\[\left|\mathcal{E}_{vc}\right|+\left|\mathcal{E}_{cr}\right|+\left|\mathcal{E} _{no}\right|=\left|\mathcal{E}\right| \tag{17d}\]  
Considering the above mentioned event distribution and the probability values, it is ensured that a specific node of its lifetime \(r\) units (rounds or seconds), \(\left|66.6\times r\right|\) times it would sense ordinary event, \(\left|22.2\times r\right|\) times it would sense critical event and \(\left|11.1\times r\right|\) times it would be very critical event. This probability also ensures that at any time, \(\left|66.6\times\left|E\right|\right|\) out of \(\left|E\right|\) event sensing nodes would be ordinary nodes, \(\left|33.3\times\left|E\right|\right|\) nodes would be critical and \(\left|11.1\times\left|E\right|\right|\) nodes would sense very critical event. However in this model nodes are selected randomly as critical, very critical and normal but it is ensured that the length of each set of node is not changed.