esa-sceva/satcom-chunk-collection huggingface dataset:
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 2 Reservoir-Scheduling Problem
### Constraints  
To complete the scheduling model, the constraints of the scheduling problem are defined as:  
1. Hydraulic Connection \[\left\{\begin{array}{l}I_{i,t}=O_{i-1,t}+R_{i,t}\\ O_{i-1,t}=Q_{i-1,t}+S_{i-1,t}\end{array}\right.\] (2)  
\(I_{i,t}\) is the inflow of reservoir \(i\) in time \(t\); \(O_{i-1,t}\) is the outflow of reservoir \(i-1\) in time \(t\); \(R_{i,t}\) is the interval inflow between reservoir \(i-1\) and \(i\); \(S_{i-1,t}\) is spillage of the upstream reservoir \(i-1\); \(Q_{i-1,t}\) is the generation flow of the upstream reservoir \(i-1\).
2. Water-Balance Constraint \[V_{i,t}=V_{i,t-1}+(I_{i,t}-O_{i,t})\cdot\Delta t\] (3) \(V_{i,t}\) is the reservoir storage of reservoir \(i\) in time \(t\).
3. Water-Level Constraints \[Z_{i,t}^{\min}\leq Z_{i,t}\leq Z_{i,t}^{\max}\] (4) \[|Z_{i,t}-Z_{i,t-1}|\leq Z_{i,t}^{step}\] (5)  
\(Z_{i,t}^{min}\) and \(Z_{i,t}^{max}\) are, respectively, the lower and upper bounds of the water level; \(Z_{i}^{step}\) is the limitation of water-level variation.
4. Outflow Constraint \[O_{i,t}^{\min}\leq O_{i,t}\leq O_{i,t}^{\max}\] (6) \(O_{i,t}^{min}\) and \(O_{i,t}^{max}\) are, respectively, the minimum and maximum outflows of reservoir \(i\).
5. Output Constraint \[N_{i,t}^{\min}\leq N_{i,t}\leq N_{i,t}^{\max}\] (7) \(N_{i,t}^{min}\) and \(N_{i,t}^{max}\) are, respectively, the minimum and maximum power output of reservoir \(i\).
6. Boundary Condition \[Z_{i,0}=Z_{i,start},Z_{i,T}=Z_{i,end}\] (8) \(Z_{i,start}\) and \(Z_{i,end}\) are, respectively, the initial and terminal water levels of reservoir \(i\).
###### Abstract  
The university course scheduling problem (UCSP) is a challenging combinatorial optimization problem that requires optimization of the quality of the schedule and resource utilization while meeting multiple constraints involving courses, teachers, students, and classrooms. Although various algorithms have been applied to solve the UCSP, most of the existing methods are limited to scheduling independent courses, neglecting the impact of joint courses on the overall scheduling results. To address this limitation, this paper proposed an innovative mixed-integer linear programming model capable of handling the complex constraints of both joint and independent courses simultaneously. To improve the computational efficiency and solution quality, a hybrid method combining a genetic algorithm and dynamic programming, named POGA-DP, was designed. Compared to the traditional algorithms, POGA-DP introduced exchange operations based on a judgment mechanism and mutation operations with a forced repair mechanism to effectively avoid local optima. Additionally, by incorporating a greedy algorithm for classroom allocation, the utilization of classroom resources was further enhanced. To verify the performance of the new method, this study not only tested it on real UCSP instances at Beijing Forestry University but also conducted comparative experiments with several classic algorithms, including a traditional GA, Ant Colony Optimization (ACO), the Producer-Srcounger Method (PSM), and particle swarm optimization (PSO). The results showed that POGA-DP improved the scheduling quality by 46.99% compared to that of the traditional GA and reduced classroom usage by up to 29.27%. Furthermore, POGA-DP increased the classroom utilization by 0.989% compared to that with the traditional GA and demonstrated an outstanding performance in solving joint course scheduling problems. This study also analyzed the stability of the scheduling results, revealing that POGA-DP maintained a high level of consistency in scheduling across adjacent weeks, proving its feasibility and stability in practical applications. In conclusion, POGA-DP outperformed the existing algorithms in the UCSP, making it particularly suitable for efficient scheduling under complex constraints.  
genetic algorithms; university course scheduling problem; dynamic programming; judgment mechanism; forced mutation operation +
Footnote †: journal: _Algorithms_
## 2 Problem Description
### Medium-Level Planning
#### 2.2.2 ICU Is the Bottleneck Resource (R*)  
The planning is performed based on the capacity of the ICU when it is identified as the bottleneck resource. Figure 4 represents when the intensive care unit becomes the system's bottleneck. In this case, the ICU's capacity is fixed, and this capacity information is sent through a rope to the operating room. Operating room planning and scheduling now take the capacity information of the ICU, and the patient allocation to the operating rooms is performed based on the capacity of the bottleneck of the ICU.  
\[obj=\text{maximize}\sum_{p=1}^{p}\gamma_{pi}^{d\tau}\ \forall 0<d\leq D\ \forall\tau \tag{24}\]  
\[\sum_{i=1}^{I}\sum_{d=1}^{D}\gamma_{pi}^{d\tau}=1\ \forall p\in P\ \forall t \tag{25}\]  
\[\sum_{p=1}^{p}\gamma_{pi}^{d\tau}\leq N_{id\tau}\ \forall 0<d\leq D\ \forall t \tag{26}\]  
\[N_{id\tau}^{occ}=N_{i(d-1)\tau}^{occ}-N_{i(d-1)\tau}^{disc}+N_{id\tau}^{o}+N_ {id\tau}^{\text{exp}}\ \forall i\in I\ \forall 0<d\leq D\ \forall\tau \tag{27}\]  
\[N_{id\tau}^{occ}\leq N_{id\tau}\ \forall i\in I_{d\tau}\ \forall 0<d\leq D\ \forall\tau \tag{28}\]  
\[t_{pi}^{disc}=t_{pi}^{d\tau}+LOS_{p}^{i}\ \forall i\in I\ \forall p\in P \tag{29}\]  
The following are the objective function and constraints of medium-level planning when the ICU becomes a bottleneck. The objective function given by Equation (24) aims to maximize the number of patients requiring a bed in the ICU.  
Equation (25) ensures that on a given day \(d\) of the planning horizon \(\tau\), a patient can be assigned to one bed only in ICU. Equation (26) ensures that the number of patients requiring the ICU is less than the total number of ICU beds. Equation (27) provides the method of calculating the number of occupied beds in the ICU, where \(N_{id\tau}^{disc}\) is the number of beds released after the discharge of patients from the ICU. \(N_{id\tau}^{o}\) and \(N_{id\tau}^{\text{exp}}\) are the number of beds required in the ICU for the patients from the operating room and other expected patients. Similarly, based on the information on the available capacity of the ICU, Equation (28) puts an upper bound on the number of beds occupied in the ICU that should be less than or equal to the total number of beds in the ICU. Where \(N_{id\tau}\) is the total number of beds in the ICU on the day \(d\) of the planning horizon \(\tau\). \(N_{id\tau}^{occ}\) is the number of occupied beds in the ICU on \(d\) of planning horizon \(\tau\). Equation (29) provides the patient's discharge time from the ICU. Based on the available capacity of the ICU, the ICU patients' schedule is released to the operating room, and operating room planning is performed based on the released schedule of the ICU. The information on the bed occupancy in the ICU is then sent to the operating rooms to plan surgeries as per the available capacity of the ICU.
# Review of the Main Cable Shape Control of the Suspension Bridge
## 1 Introduction  
Suspension bridges play a vital role in modern bridge structures, with their good structural performance, huge spanning capacity, and beautiful shape. The progress of modern calculation theory [1], high-strength materials [2], computer technology [3], and construction methods [4; 5] has made the suspension bridge develop rapidly, and the maximum span record has been repeatedly updated. In 2022, the completion of the Canakkale 1915 Bridge [6] in Turkey marked that the suspension bridge's span has entered the era of two kilometers. The Zhang Jinggao Yangtze River Bridge, now under construction in China, will continue to refresh the suspension bridge's record for the longest span, with a main span of 2300 m. The bridge construction is gradually expanding to important sea-crossing channels and deep mountain canyons, where the construction conditions are more complex, and the span and scale of the bridge are larger. Undoubtedly, new challenges are put forward to the structural design method, construction method, and control technology, mainly manifested in (1) a large span and a more prominent structural nonlinearity [7; 8], (2) a longer construction period and more complex construction method [9; 10], and (3) greater sensitivity to environmental factors and construction errors [11]. The main cable's shape has a direct bearing on a long-span suspension bridge's safety and appearance. To guarantee that the finished bridge's geometric alignment complies with design specifications and that the structure's internal forces reach the optimal state, strict construction control must be applied in the construction process [12]. The suspension bridge's cable shape control has always been the focus of attention due to the difficulty of constructional control. As a suspension bridge's primary load-bearing component, it is difficult to alter the alignment once it has been erected. The erection error of the free cable shape will have a substantial impact on subsequent construction stages. Therefore, when erecting the main cable, it is essential to guarantee the shape's precision to the greatest extent possible [13], and there should be a control and evaluation standard for its error. To match the ideal state to the maximum extent, the free cable shape needs to rely on reliable calculation parameters and accuratecalculation methods, coupled with strict manufacturing and construction quality control. This paper reviews the current status of research on the control of the suspension bridge's main cable shape from three aspects: main cable shape-finding, construction control technology, and control analysis. The current research's shortcomings are summarized, and the future development directions of this field are discussed.
# A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery
## 2 Review of Early Fault Diagnosis Approaches  
The EFD of rotating machinery has attracted substantial attention from both academia and industry. According to the difference of the implementation way for EFD of rotating machinery, we can divide it into two main categories: FFD methods and AI methods. We will briefly introduce the principles of the most commonly used FFD methods, including WT, EMD, EEMD, LMD, EWT, VMD, and sparse decomposition. Then, a framework of AI methods is introduced and the most commonly used classifiers are described in detail.
# Open Loop Position Control of Soft Hydraulic Actuators for Minimally Invasive Surgery
###### Abstract  
Minimally invasive surgery (MIS) presents many constraints on the design of robotic devices that can assist medical staff with a procedure. The limitations of conventional, rigid robotic devices have sparked interest in soft robotic devices for medical applications. However, problems still remain with the force exertion and positioning capabilities of soft robotic actuators, in conjunction with size restrictions necessary for MIS. In this article we present hydraulically actuated soft actuators that demonstrate highly repeatable open loop positioning and the ability to exert significant forces in the context of MIS. Open loop position control is achieved by changing the actuator volume, which causes contraction. In one degree of freedom (DOF) configurations, root mean square error (RMSE) values of 0.471 mm, 1.506 mm, and 0.350 mm were recorded for a single actuator against gravity, a single actuator with a pulley, and a horizontal antagonistic configuration, respectively. Hysteresis values of 0.711 mm, 0.958 mm, and 0.515 mm were reported in these experiments. In addition, different numbers of soft actuators were used in configurations two and three DOFs to demonstrate position control. When deactivated, the soft actuators are low-profile and flexible as they are constructed from thin films. As such, a robot with a deployable structure and three soft actuators was constructed. The robot is therefore able to reversibly transition from low to high volume and stiffness, which has potential applications in MIS. A user successfully controlled the deployable robot in a circle tracing task.  
soft robotics; minimally invasive surgery; parallel mechanism +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Flow Rate  
In 5G VANETs, where ultra-reliable and low-latency communication is critical, understanding flow rate dynamics is useful for optimising network performance and maintaining consistent data delivery. In our implementation, flow rate was closely monitored at both the SDN gateway and the gNB to assess the system's adaptability to varying vehicular loads whilst maintaining dynamic QoS management.  
At the SDN gateway, we implemented rate-limiting to structurally manage the transmission of data from Camera, LiDAR, and RADAR sensors. This was particularly important given the constrained computation and network resources at the edge. As illustrated in Figure 8, Camera sensors achieved the highest flow rate, increasing from an average of 2.3 packets per second (PPS) in Test 1 to 6.6 PPS in Test 3, an approximate increase of 187%. LiDAR and RADAR sensors also recorded gains, rising from 1.4 PPS to 4 PPS and 0.6 PPS to 1.9 PPS, respectively. These results demonstrate the system's scalability and its ability to maintain reliable throughputs under increasing load, especially for high-bandwidth sensors such as cameras.  
Figure 8: Average flow rate at SDN gateway.  
Figure 7: Average flow duration.  
Complementing this, flow rate analysis at the gNB further confirmed the system's ability to support high-throughput offloading. These flow rates represent not only the receipt of pre-processed data from the SDN gateway but also the rate at which Slice IDs were retrieved and subsequently used to dynamically route vehicular traffic to edge application servers. As illustrated in Figure 9, Camera sensor rate at the gNB increased from 8.4 PPS in Test 1 to 18 PPS in Test 3, an increase of approximately 115%. LiDAR and RADAR sensors followed a similar upward trend, with flow rates rising from 4.3 PPS to 11 PPS and 2.4 PPS to 5.9 PPS, respectively.  
The consistent increase in flow rates across the system reflects the robustness and adaptability of the SDN Controller algorithm in managing critical vehicular data. This scalability is particularly important for URLLC applications in 5G VANETs to maintain high throughput and stable transmission for autonomous driving, collision avoidance, and other mission-critical services.
## 4 Experiments
### Evaluation Indicators  
For the algorithmic models mentioned in this paper, the following four evaluation metrics will be used to evaluate them: \(PCK\), Confusion Matrix, model computation, and \(MACs\).  
The \(PCK\) indicator is used to measure the proportion of correctly estimated key points. Taking the total number of key points to be evaluated as \(k(k>0)\), the \(PCK\) indicator for the \(i(i\in(0,k))\)th key point is calculated as shown in Equation (9):  
\[PCK_{i}^{k}=\frac{\sum_{p}\delta\bigg{(}\frac{d_{pi}}{d_{p}^{def}}\leq T_{k} \bigg{)}}{\sum_{p}1} \tag{9}\]  
where the parameter \(p\) denotes the \(p\)th individual, the parameter \(T_{k}\) denotes the manually set threshold (\(T_{k}\in[0:0.01:0.1]\)), the parameter \(k\) denotes the \(k\)th threshold, the parameter \(d_{pi}\) denotes the Euclidean distance between the predicted value of the \(i\)th key point of the \(p\)th individual and the manually labeled value, and the parameter \(d_{p}^{def}\) denotes the scale factor of the \(p\)th individual. In the MPII dataset, this parameter denotes the Euclidean distance between the upper left point of the head and the right Euclidean distance of the lower left point of the head, and the parameter \(\delta\) takes a value of 1 if the condition holds and 0 otherwise.  
The confusion matrix indicator is used to more clearly and intuitively portray the error indicators in the specific classification results. In a binary classification problem, if the classifier judges a positive case as a positive case, a true positive case (TP) is generated; if the classifier judges a negative case as a negative one, it is considered a true negative case (TN). The other two cases are called False Negative (FN) and False Positive (FP). The corresponding confusion matrices are shown in Table 3.  
The amount of model computation indicates the number of computations of the model on the hardware unit, generally expressed as _FLOPs_ (floating point operations per second), which for a convolutional layer is shown in Equation (10):  
\[FLOPs_{conv}=k_{H}\times k_{W}\times C_{in}\times C_{out}\times H_{out}\times W_{ out} \tag{10}\]  
where \(k_{W}\) and \(k_{H}\) denote the width and height of the convolution kernel, respectively, \(C_{in}\) and \(C_{out}\) denote the number of channels of the input feature map and the output feature map, respectively, and \(W_{out}\) and \(H_{out}\) denote the width and height of the output feature map, respectively. For a fully connected layer, the _FLOPs_ calculation is shown in Equation (11).  
\[FLOPs_{FC}=C_{in}\times C_{out} \tag{11}\]  
MACs (multiply-accumulate computations) represent the cumulative number of multiplications and additions of the model. \(1 MACs\) contains one multiplication and one addition, which are calculated as shown in Equation (12).  
\[MACs=C_{out}\times H_{out}\times W_{out}\times C_{in}\times K_{H}\times K_{W} \tag{12}\]  
In this paper, the ptflops and _pytorch_model_summary_ modules provided by the torch deep learning framework will be used to calculate the computation and MACs of the model.
# A DDPG-Based USV Path-Planning Algorithm
## 3 Simulation
### Environment Modeling
#### 3.2.2 Design of Reward Function  
Proper reward configuration requires thoughtful consideration. If it is mishandled, an impractical reward scheme might lead to suboptimal results, which is even worse than a random strategy since it can incorrectly direct intelligent behavior. Typically, rewards are structured on a punitive basis until the objective is achieved, and these rules should not be overly complex. Therefore, a common reward scheme might consist of granting a significant bonus for meeting the goal and a minor penalty for failure to do so. To mitigate the drawbacks of sparse rewards, we have refined the incentives for the unmanned boat. The following outlines the guidelines for the interaction between the unmanned boat and its environment:  
1. **Goal Achievement**: A reward of +100 is given when the drone boat successfully reaches the final target. This substantial positive incentive is designed to guide the drone boat toward the target location;
2. **Collision Penalties**: If the drone boat encounters an obstacle or boundary, it incurs a penalty of \(-2\). Since these are undesired actions, the negative penalty encourages avoidance of these elements;
3. **Distance Rewards**: The rewards are set at +1 or \(-1\) depending on whether the drone boat's distance to the target is decreasing or increasing, respectively. This drives the drone boat to approach the target while avoiding diverging from it;
4. **Yaw Angle Rewards**: The smaller the yaw angle \(\alpha\) of the drone boat, the higher the reward. This serves to push the drone boat to minimize the difference between its actual and desired heading, smoothing its path, averting unnecessary trajectories, and shortening the planned route;
5. **Steering Angle Rewards**: Likewise, the more the steering angle \(\Delta\theta\) of the unmanned boat is reduced, the greater the reward is given. This incentivizes the unmanned boat to curtail the steering angle, which in turn decreases the turning torque and the boat's inclination on the water surface. As a result, this conserves energy, extends range, and diminishes the possibility of capsizing.  
To align with points 4 and 5, we utilize the cosine of the corresponding angle as the reward. Also, when applying the memory playback mechanism, we aggregate the rewards for a sequence of states, eight in length, and assign it to this macro-action. Such an arrangement enables the Critic network to appraise the macro action as either positive or negative, augmenting the network's discernment of different action sequence qualities. Consequently, the network's proficiency in evaluating the merits of various action sequences is bolstered.
# Three-Dimensional Division of Visible Light Communication Irradiation Area
## 2 Theoretical Model of VLC  
While studying VLC channels, LOS and NLOS channels are generally also taken into consideration. When considering the LOS channel alone, the dc gain \(H(0)\)[44] of the communication system is calculated accurately [2]. When considering the diffuse reflection channel, the light at the transmitter needs to pass through the diffuse reflection of the wall or other objects to reach the receiver. Figure 1 shows the theoretical model of the VLC system considered herein.  
In Figure 1, \(d\) is the distance between the transmitter and the receiver, and \(\phi\) is the angle. The field of view (FOV) at the receiver is \(\psi\). When the transmission power is \(P_{t}\), the irradiance (W/cm\({}^{2}\)) is \(I_{s}(d\),\(\phi)\)=\(P_{t}R_{o}(\phi)/d^{2}\). Therefore, the dc gain of the channel is written as  
\[H(0)_{LOS}=\begin{cases}\frac{A}{d^{2}}R_{o}(\phi)T_{s}(\psi)g( \psi)cos\psi,&0\leq\psi\leq\Psi_{c}\\ 0&\theta>\Psi_{c}\end{cases} \tag{1}\]  
where \(H(0)\) is the dc gain and \(A\) is the area of the detector. \(R_{o}(\phi)\) is the radiant intensity of the transmitter. \(T_{s}(\psi)\) is the signal transmission of the filter [45]. \(g(\psi)\) is the concentration gain, and \(\Psi_{c}\) is the maximum FOV of the receivable area (usually \(\Psi_{c}\leq\pi/2\)). In Equation (1), \(R_{o}(\phi)=[(m+1)/2\pi]cos^{m}\phi\), where \(m\) is the transmitter semi-angle at half power and is written as \(m=-ln2/ln(cos\Phi_{1/2})\). \(\Phi_{1/2}\) is the transmitter semi-angle. By substituting these values, Equation (1) is rewritten as  
\[H(0)_{LOS}=\begin{cases}\frac{(m+1)A}{2\pi d^{2}}cos^{m}\phi T_{s}( \psi)g(\psi)cos\psi,&0\leq\psi\leq\Psi_{c}\\ 0&\theta>\Psi_{c}\end{cases} \tag{2}\]  
where the dc gain is directly proportional to the area of the detector \(A\) and inversely proportional to the distance \(d^{2}\). Under certain conditions, the power efficiency of the LOS channel is maximized by increasing the radiation intensity of the transmitter \(R_{o}(\phi)\). When the receiver moves, the changes in other parameters (d, \(T_{s}(\psi)\) and \(g(\psi)\)) are compensated by changing the value of \(\phi\), thereby optimizing the communication efficiency of the communication system.