esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analysis
### Friction Coefficient  
When the initial shift speed difference was 600 r/min, the moment of inertia of the synchronized part was 0.04 Kg-m\({}^{2}\). After compensation, the test and simulated friction coefficient results were compared, as shown in Figure 12.  
Figure 11: Comparison of sliding-friction work before and after compensation.  
Figure 12: Friction coefficient after compensation.  
In the experiment, the temperature increased first and then decreased. At the initial stage of friction, the energy of heat flux density input was greater than the energy of heat conduction and convective heat transfer taken away, and the temperature increased speedily. With the decrease in speed difference at both ends of the synchronizer, the heat flux and convective heat transfer coefficient of the friction cone decreased gradually. When the energy of heat flux density input was equal to the energy taken away, the temperature of the synchronizer ring friction cone reached the maximum. As the speed difference at both ends of the synchronizer decreased continuously, the temperature of the synchronizer ring friction surface reduced gradually until the end of synchronization process. It can be seen from Figure 12 that after the compensation control, the friction coefficient simulation and test values during the synchronization of the synchronizer had been fluctuating around the expected value, which satisfied the expected control target. However, the fluctuation coefficient of the friction coefficient test value during the synchronization process was larger than expected--the expected friction coefficient was 0.08, and the friction coefficient obtained by the fitting was slightly lower than 0.08. Therefore, it was necessary to carry out a large number of tests in the later stage, and a better friction coefficient compensation control of various MAP, and consider various errors in the compensation control process to obtain a better control performance.  
By comparing the synchronization time, shock degree, sliding-friction work, and friction coefficient before and after the synchronization of the synchronizer, it can be concluded that the compensation control strategy can maintain the friction coefficient of the synchronization near the expected friction coefficient. The synchronization time was shortened, and the sliding-friction work consumed in the synchronization phase was reduced. It is only the shock degree in the synchronization phase that was slightly larger, but the maximum value of the shock degree in the synchronous phase was smaller than the shock degree in the uncompensated phase. It can be seen from the performance evaluation indicators of these synchronization stages that the friction coefficient compensation control strategy can have a good effect. From the perspective of shortening the synchronization time, the reduction in the synchronization time can reduce the shift interruption time, which is crucial for shift quality.
## 3 Methodology
### Loss Function  
The workhorse of our approach is the way the neural network loss function is defined. The total loss \(\mathcal{L}_{total}\) is composed of the data term \(\mathcal{L}_{data}\), \(\mathcal{L}_{IC}\) and the physics loss term \(\mathcal{L}_{physics}\):  
\[\mathcal{L}_{total}=\mathcal{L}_{data}+\mathcal{L}_{IC}+\lambda\mathcal{L}_{ physics} \tag{5}\]  
such that  
\[\mathcal{L}_{data}=\frac{1}{N}\sum_{i=1}^{N}(x_{i}^{*}-\hat{x}_{i})^{2}+\frac {1}{N}\sum_{i=1}^{N}(\hat{x}_{i}^{*}-\hat{x}_{i})^{2} \tag{6}\]  
and  
\[\mathcal{L}_{IC}=(x_{0}-\hat{x}(0))^{2}+(\hat{x}_{0}-\hat{x}(0))^{2}. \tag{7}\]  
Here, \(x_{i}^{*}\) and \(\hat{x}_{i}^{*}\) are the displacement and velocity from the data, \(\hat{x}_{i}\) and \(\hat{\hat{x}}_{i}\) are displacement and velocity predictions from the neural network, \(\lambda\) represents the regularization term, \(x_{0}\) and \(\hat{x}_{0}\) are the initial conditions.The task of \(\mathcal{L}_{data}\) and \(\mathcal{L}_{IC}\) is to constrain the neural network predictions using the data.  
The physics loss \(\mathcal{L}_{physics}\) term is where the physics information is infused into the neural networks and is given by,  
\[\mathcal{L}_{physics}=\frac{1}{N}\sum_{i=1}^{N}\biggl{(}\frac{D\hat{\hat{x}}_{ i}}{Dt_{i}}+\delta\hat{x}_{i}+\alpha\hat{x}_{i}+\beta\hat{x}_{i}^{3}-\hat{f}_{i} \biggr{)}^{2}. \tag{8}\]  
This equation is obtained by rearranging Equation (1) and replacing velocities and displacements with their equivalent neural network predictions. For calculating the acceleration from velocity prediction, we make use of automatic differentiation, which is represented by \(\frac{D}{Dt_{i}}\) in the above equation. The job of \(\mathcal{L}_{physics}\) is to force the \(\hat{f}_{i}\) to take values that obey the governing equation.
## 6 Simulation Results
#### Scalability Solutions  
The simulation model was utilized to assess the system's response when the frequency of transaction confirmation was heightened as the blockchain network became congested. Figure 20 shows a comparison between the simulation results in the case of constant \(f_{BC}\) (green color) and the simulation results when \(f_{BC}\) doubles as the blockchain network becomes congested (blue color). It is clear that in the case of doubled transaction validation frequency, the utilization of the APUs increases from 40% of the available capacity to 60% at the end of the game. The available maximum production capacity of the manufacturing system in this time frame does not differ much from the capacity in the case without increasing the frequency of transaction confirmation, as this frequency is limited by the production function. However, in the future, even in the case of the maximum possible frequency of service provision, a difference would appear as a result of more service executions. The APUs in the manufacturing system would be upgraded more, and their frequency of service provision would be higher. When \(f_{BC}\) is doubled, the frequency of the provided services increases by approximately 40%, and does not completely follow  
Figure 19: Time point to congestion in the game for different frequencies of transaction confirmation.  
the maximum production capacity, as agents perform staking and unstakingtransactions during this time as well.  
Simply increasing the frequency of transaction validation in the blockchain network while not affecting the other properties of the system is not possible due to technological limitations (i.e., the Scalability Trilemma). Solutions are emerging to increase the scalability of blockchain technology, which may allow systems to increase their scalability with arbitrary expense in terms of security and decentralization (e.g., sharding, sidechains). Depending on the amount transferred by the transaction and their importance, users can decide how secure or expensive a network they prefer to use for their transactions. In the case of BBSM, transactions for service provision could be arranged according to such a hierarchy. Therefore, the decision about whether a certain user selects an additional channel (increased \(f_{BC}\)) is not included in the presented Figure 20. This raises interesting questions for future work; we intend to examine the effects of scalability enhancing technologies on the performance of the BBSM concept.
## 2 Materials and Methods
### Field Experiments
#### 2.4.2 RSSI Test Platform  
The RSSI localization algorithm has two phases: (1) channel modeling based on the sampled data; and (2) the evaluation of the localization accuracy performed using the channel data. Theoretically, the channel modeling process only needs to be performed once, but because the radio channel is unpredictable, it is not unusual to sample the data repeatedly to improve the channel model. The localization algorithm uses the distance to the known nodes determined during the channel modeling phase.
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
## 2 Results and Discussion
### Core Flooding Test
#### 2.4.3 Gel Dehydration  
Gel dehydration refers to the loss of water from the gel particles due to the pressure difference between the fracture and the matrix, leading to the gel being concentrated. Gel dehydration can significantly affect the gel's propagation into the fracture. As the gel becomes more concentrated, it also becomes stronger, requiring a higher pressure gradient for proper placement. Furthermore, severe gel dehydration can deteriorate the gel pack coherency and result in low residual resistance factors at low water injection flow rates.  
A quick assessment of CMC/PAMBA PPG dehydration was conducted by monitoring the effluent particle size during gel injection for both large (600-800 microns) and small (250-600 microns) particles using the weight difference method. In this method, the collected (dehydrated) gel particles are weighed and then placed in an oven at a temperature of 80 \({}^{\circ}\)C for 3 days until completely dry, with no change in weight noticed. The difference between the water weight absorbed during the initial swelling and the water weight evaporated during the drying process represents the amount of water lost during the gel injection. Dividing this by the initially absorbed water weight and multiplying by 100 gives the dehydration percentage. The small particles dehydrated by approximately 9%, while the large particles dehydrated by about 17%. Figure 19 displays the gel after post-water flood for both sizes, and the minor dehydration observed for the smaller particles resulted in better packing.
## 3 Background
### Three Lines Model  
The Three Lines Model, originally known as the Three Lines of Defense Model, provides a comprehensive framework for managing risk and establishing effective governance in organizations [27]. Its most recent revision, conducted by the Institute of Internal Auditors (IIA) in 2020, revamped the model to improve its adaptability, scalability, and ease of implementation across various organizational structures [11].  
The 2020 revisions, while maintaining the essence of the model, attempted to increase its versatility and eliminate some of its inherent limitations. One significant change is removing the term \"defense\", suggesting a shift from rigid, siloed functions to a more fluid, collaborative approach to risk management [11]. The updated model also emphasizes the oversight and strategic roles of the board of directors and that all parts of an organization contribute to its governance and risk management [27].  
The Three Lines Model provides a structured approach to risk management that aligns organizational objectives with risk identification, assessment, and mitigation strategies [28].  
The first line includes operational management, which identifies, assesses, and manages risks in their respective areas. The second line consists of specialized risk management and compliance functions, which oversee risk monitoring, policy development, and implementation of internal controls. The third line comprises the internal audit function, which provides independent assurance and evaluation of risk management practices. The governing body is responsible for establishing strategic direction and ensuring the effectiveness of the organization's governance system. The external assurance providers give additional assurance to satisfy legislative and regulatory expectations that protect the interests of stakeholders and requests by management and the governing body to complement internal sources of assurance [27].  
The model emphasizes the distinct but complementary roles of the first line (operations), the second line (risk and compliance functions), and the third line (internal audit) while emphasizing that responsibility for risk management and internal controls ultimately rests with management [11].  
The model further redefines organizational management, risk management, governance, external assurance providers and internal audit roles, viewing them as interconnected but distinct components of an over-arching governance framework [27]. This shift represents a move from the original model's defense perspective to a 'lines of sight' viewpoint, focusing on organizational accountability and risk management rather than solely on risk control [29].
# A Hybrid Control Approach for the Swing Free Transportation of a Double Pendulum with a Quadrotor
## 2 Dynamic Modeling of the System
### Double Pendulum System  
The highly nonlinear dynamics of double pendulums are still of interest to scientists [41]. Transportation of a payload by a hook attached to the quadrotor corresponds to this double pendulum paradigm, where the hook is the first mass and the payload the second [40]. Figure 1 shows the experimental setup, where the bars of the pendulum are assumed massless. The equations describing the coordinates of the double driven pendulum masses are given in Equation (1), where \(x_{0}\) and \(z_{0}\) represent the quadrotor instantaneous position, and the rest of coordinates refer to the absolute position of every punctual mass. In order to simplify computation, in this paper we will work in 2D, i.e., over the \(X-Z\) plane:  
\[\begin{cases}x_{1}=x_{0}+L_{1}\sin(\theta_{1})\\ z_{1}=z_{0}-L_{1}\cos(\theta_{1})\\ x_{2}=x_{1}+L_{2}\sin(\theta_{2})\\ z_{2}=z_{1}-L_{2}\cos(\theta_{2})\\ \end{cases} \tag{1}\]  
A computer algebra program was used to infer the equations for \(\ddot{\theta}_{1}\) and \(\ddot{\theta}_{2}\) in terms of \(\left(\ddot{\theta}_{1},\ddot{\theta}_{2}\right)=f\left(\theta_{1},\theta_{2},\dot{\theta}_{1},\dot{\theta}_{2}\right)\). The resulting equations are omitted due to its big size. The dynamics of the double pendulum are summarized in Figure 2, where \(T_{1}\) and \(T_{2}\) are the tension of the first and second link, respectively. Thus, the tension affecting the quadrotor is \(T_{1}\). Applying fundamental results of Applied Mechanics, we can deduce the dynamic equations expressed in the horizontal and vertical coordinates given by Equation (2):  
\[\begin{array}{rcl}m_{1}\ddot{x}_{1}&=&-T_{1}\sin\theta_{1}+T_{2}\sin\theta_{2 }\\ m_{1}\ddot{z}_{1}&=&T_{1}\cos\theta_{1}-T_{2}\cos\theta_{2}-m_{1}g\\ m_{2}\ddot{x}_{2}&=&-T_{2}\sin\theta_{2}\\ m_{2}\ddot{z}_{2}&=&T_{2}\cos\theta_{2}-m_{2}g\\ \end{array} \tag{2}\]  
Again, with the help of an algebraic manipulation program, we derive the equations for \(T_{1}\) and \(T_{2}\), which we omit here for reasons of space. We can consider the quadrotor+slung load system as an quadrotor with bounded disturbances. Specifically, we take into account the \(Z\) and \(X\) projections of \(T_{1}\), namely \(T_{1X}\) and \(T_{1Z}\), for use in the following subsection.  
Figure 2: Forces in the double pendulum system.
## 3 MJTAR Protocol
### E-IFTIS Overview
#### 3.3.2 E-IFITS Concept  
The following Figure 4 illustrates the operational concept of the proposed E-IFITS.  
Figure 4: Concept of the enhanced IFTIS distributed mechanism.  
Figure 3: Infrastructure-free traffic information system mechanism.  
In our enhancement, we introduce an additional field to the existing CDP to accommodate pheromone information, transforming it into a cell pheromone packet (CPP). We posit that the quantity of pheromone contained within the CPP correlates with the number of vehicles on the road. Consequently, group leaders traversing the road collect pheromone data from neighboring vehicles within the same cell and incorporate them into the CPP for storage and transmission. The following Figure 5 describes the CPP packet format.  
The CPP is divided into two structures: CPP master and CPP subset. The CPP master structure serves as a packet to store the pheromone collected from each cell and transmit it to the J_begin junction. It encompasses fields for identifying the road ID, transmission time, cell's center position, and a list detailing the pheromone amount within the cell. On the other hand, the CPP subset structure functions as an agent packet responsible for consolidating CPP master packets received from multiple roads at the one-hop junction into a unified group. Subsequently, these consolidated data are forwarded to the J_begin junction. Upon reaching the J_begin junction, the vehicle collects both the CPP master and CPP subset packets. Subsequently, it utilizes the gathered information to populate the CPP management table, storing details regarding junction locations and pheromone information. Nevertheless, it cannot gather CPPs during early morning hours when vehicle density is low or in road conditions devoid of moving vehicles. In such scenarios, a hybrid approach may be suggested, wherein E-IFTIS is employed during peak traffic periods in the morning or evening when vehicle density is high, while RSU traffic surveillance systems [12; 38; 39; 40] are utilized during off-peak hours. The subsequent sections will elaborate on the CPP creation procedure and the method for maintaining CPP in detail.
## 4 Simulation Results  
This section presents the simulation results for end-to-end delay, packet loss rate, and throughput for the urban rail transit communication network under the proposed protocol. In order to validate the performance of our algorithm, we compare it with the LEACH clustering protocol and AODV routing protocol. LEACH utilizes random numbers to elect cluster heads periodically and AODV is an on-demand routing protocol based on node sequence number, and both are classical algorithms used in ad hoc networks.
## 6 Energy Management
### Smart Energy Management
#### 6.2.2 Energy Management by Considering Household Occupancy  
Occupant behaviour has an extensive impact on energy consumption and building controls. Unaware energy consumption can add one-third to a building's designed energy performance [101]. Former research has demonstrated that utilization of real-time occupancy information for controlling the lighting can save a remarkable amount of the electrical energy used for lighting [102]. Recent investigation is concentrated on the development of energy intelligent buildings that incorporates residents' activities and behaviour as a significant element for energy management systems. A home automation system should be able to recognize whether or not a house is occupied [103]. In addition, many home automation systems also require forecasting when a house is going to be occupied. When a home is not occupied, the automation system will turn off or dim all the artificial lighting, reducing ventilation rate, and setback the heating setpoint. Upon predicted occupancy, the system can trigger heating in advance to heat the house back to the normal setpoint prior to occupancy [103].  
A smart heating system should tackle two primary requirements: Reducing energy consumption while maintaining the comfort of occupants. The smartness of the system typically depends on its ability of adaption to prevalent environmental circumstances, the household specifications, and the behaviour of the residents. Utilizing information and communication technology to automatically and intelligently control heating systems has been investigated for years. Smart space conditioning devices, such as the Neurothermostat [104], the GPS Thermostat [105], the Smart Thermostat [106], NEST learning thermostat, and Eco Bee's Smart-Si [107], and several others [108, 109, 110, 53], have been appeared.
## 2 Materials and Methods
### Experimental Design  
#### 2.5.1 Evaluation Indicators  
In evaluating model performance for object detection, several critical indicators are utilized: precision (P), recall (R), average precision (AP), mean average precision (mAP), and parameter quantity (Params). These metrics gauge the effectiveness and efficiency of the model comprehensively.  
The mathematical formulations for these metrics are provided below:  
\[P=\frac{TP}{TP+FP} \tag{6}\]  
\[R=\frac{TP}{TP+FN} \tag{7}\]  
\[AP=\int_{0}^{1}P(R)d(R) \tag{8}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i} \tag{9}\]  
Here, true positives (TP) are correctly identified objects, false positives (FP) are incorrect identifications, and false negatives (FN) are objects that the model failed to detect. AP represents the area under the precision-recall curve, and mAP aggregates AP across all categories, offering a holistic measure of model accuracy. mAP50 assesses detection accuracy at a 50% intersection over union (IoU) threshold, while mAP50-95 averages accuracy across multiple IoU thresholds from 50% to 95%, offering a nuanced performance evaluation.  
These metrics are crucial for assessing both the accuracy in object detection and the computational efficiency vital for applications in resource-limited settings.
## Methods
### Mathematical Indices of Model Performance  
Three indices were used to evaluate the agreement between model simulations and observations: the correlation coefficient (\(R\)), the mean bias error (MBE), and the root mean square error (RMSE). The three indices are defined as follows:  
\[R=\frac{\sum\limits_{i=1}^{N}{(x_{i}-\overline{x})(\text{obs}_{i}-\overline{ \text{obs}})}}{\sqrt{\sum\limits_{i=1}^{N}{(x_{i}-\overline{x})^{2}\sqrt{\sum \limits_{i=1}^{N}{(\text{obs}_{i}-\overline{\text{obs}})^{2}}}}}} \tag{6}\]  
\[\text{MBE}=\frac{\sum\limits_{i=1}^{N}{(x_{i}-\text{obs}_{i})}}{N} \tag{7}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{N}{(x_{i}-\text{obs}_{i})}^{2}}{N}} \tag{8}\]  
where \(x\) is a model simulation either from CTL or NEW, obs is the corresponding observation, and \(\overline{x}\) and \(\overline{\text{obs}}\) are the mean of \(x\) and obs respectively. RMSE provides an estimate of the absolute bias in the model simulation; the smaller the value of RMSE, the better is the agreement between simulation and observations.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 4 RL Based Selection of TRxPs for RA
### Design
#### 4.4.2 Reward Function  
The reward obtained from the RA environment can be thought of as having an inverse relationship with the RA delay in successfully completing a procedure. A higher reward value means that the user completed the RA procedure with less delay and even within a shorter time before the delay budget expires. The reward value also encodes the delay budget given below. For instance, if a user cannot complete a random access within the delay limit, the reward value approaches zero. Otherwise, the reward is calculated as the inverse of the completion time. Mathematically, it is given by the following equation:  
\[r=1/D,\left\{\begin{array}{ll}D=D_{i}&\text{if }P(W\geq t)\leq\delta,\\ D\rightarrow\infty&\text{else},\end{array}\right. \tag{20}\]  
where \(D_{i}\) is the delay user experienced from TRxP \(i\). \(W\) is the waiting time of the user, and \(\delta\), a delay threshold.
## 3 The Proposed Method
### Framework Architecture  
As illustrated in Figure 2, the framework consists of three core modules: (1) detector: identifies welded nodes and provides details about their class and location based on the YOLOv4 technology [25], which is an advanced object detection algorithm that uses a deep convolutional neural network to detect and localize objects in real-time with high accuracy and speed; (2) tracker: utilizes information from the detector module to keep track of the welded nodes and their identity in the camera field of view (FoV) using an Intersection over Union (IoU)-based technique [26] which calculates the ratio of the intersection area between bounding boxes of objects in the current frame and the previous frame; and (3) classifier: receives the detection scores and the number of frames and identifies the final classification by ruling out the weak detections and giving an average score corresponding to the total number of remaining frames. Additionally, the framework utilizes four asynchronous  
Figure 1: Schematic diagram (Top view).  
input/output modules responsible for reading, writing, and streamlining the data. An SQL database [27], which is a structured collection of data that can be easily accessed, manipulated, and queried using the SQL programming language, is used to store the framework predictions, while Ignition(r) [28], a web-based industrial automation software, is employed to develop the User Interface (UI).