esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Description
### Actuator Disk Model  
In line with the experiments, the rotor of a horizontal-axis wind turbine is modelled in the computations as an actuator disk [9], where the effect of the blades on the wind flow is reproduced by forces distributed over a disk. As the actual geometry of the blades is not reproduced, the load of the turbine is distributed over the area swept by the rotor. The simplest conception of the model is employed here, where it is assumed that the forces over the AD point only in the axial direction, opposite to the flow and are distributed uniformly over the disk. If \(U_{\infty}\) is the inflow velocity, the force is calculated as  
\[F_{x}=-\frac{1}{2}\,\rho U_{\infty}^{2}C_{T}A, \tag{6}\]  
Figure 2: Introduction of synthetic turbulence field into the LES.  
where \(A\) is the area of the disk. The diameter of the disk is \(D=0.1\) m and \(C_{\rm T}\) is equal to the values shown in Table 1. Since the introduction of the forces represents an abrupt discontinuity in the flow field, large velocity gradients occur in the vicinity of the AD and spatial oscillations (wiggles) on the velocity field may appear due to pressure-velocity decoupling inherent to collocated grids. To avoid this effect, the forces that comprise the AD are distributed in the axisymmetric direction. This is done by taking the convolution of the forces with a Gaussian distribution  
\[g(x)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left(-\frac{x^{2}}{2\sigma^{2}}\right). \tag{7}\]  
In this manner, the value of the standard deviation \(\sigma\) (i.e., the distribution width) will define the thickness of the disk. The force distribution is defined between the limits \([-3\sigma,3\sigma]\) so that it contains 99.7% of magnitude of the forces computed for the original--one cell thick--disk. A value of \(\sigma=2\Delta x\) is used, yielding a disk thickness equal to \(12\Delta x\). Therefore, the absolute magnitude of the thickness changes according to the cell length.
## 5 Evaluation
### Loss Recovery Delay
ProbCache and Prob randomly cache the passing chunks. Some chunks that do not experience loss will occupy the cache space, so that only a small number of lost packets are recovered through multicast tree nodes. Therefore, the NLRD achieved by ProbCache and Prob in combination with various cache allocation mechanisms is relatively large. When the cache size is 900, based on the cache allocation results of DCAM, the NLRD of CAPC is reduced by 10.78%, 11.21%, and 12.06% compared with LCE, Prob, and ProbCache, respectively. In conclusion, among all combinations, DCAM combined with CAPC can achieve the smallest NLRD, and the DCAM-CAPC can reduce NLRD by up to 31.44%, 27.07%, 30.71%, 3.2%, 5.2%, 11.65%, 11.21%, 11.67%, 12.11%, 12.06%, and 12.14% compared with AARM-LCE, DCAM-LCE, Equal-LCE, AARM-CAPC, Equal-CAPC, AARM-Prob, DCAM-Prob, Equal-Prob, AARM-ProbCache, DCAM-ProbCache, and Equal-ProbCache, respectively.
# Some Results on Shop Scheduling with S-Precedence Constraints among Job Tasks
## 4 Further Research  
In this paper we established some preliminary complexity results for perhaps the most basic cases of shop problems with s-precedence constraints. Here, we briefly elaborate on possible research directions.  
* _Job shop problem with three jobs._ The job shop problem with more than two jobs is NP-hard. This is a direct consequence of the fact that \(J|s-prec|C_{max}\) can be viewed as a generalization of \(J||C_{max}\), which is NP-hard with three jobs [11].  
**Theorem 5**: \(J|n=3,s-prec|C_{max}\) _is NP-hard._  
Proof.: Consider an instance \(I\) of \(J|n=3|C_{max}\), in which \(O_{i}^{k}\) denotes the \(i\)-th task of job \(J^{k}\) in \(I\), having processing time \(p_{i}^{k}\) on machine \(M(O_{i}^{k})\).  
We can define an instance \(I^{\prime}\) of \(J|n=3,s-prec|C_{max}\) with the same number of machines. The three jobs of \(I^{\prime}\) are obtained replacing each task \(O_{i}^{k}\) of \(I\) with a sequence of two tasks \(O_{i^{\prime}}^{k}\) and \(O_{i^{\prime}}^{k}\), in which \(O_{i^{\prime}}^{k}\) precedes \(O_{i^{\prime}}^{k}\), \(O_{i^{\prime}}^{k}\) has length \(p_{i}^{k}\) and requires machine \(M(O_{i}^{k})\), while \(O_{i^{\prime}}^{k}\) has sufficiently small length \(\varepsilon>0\) and also requires machine \(M(O_{i}^{k})\). As a consequence, in \(J|s-prec|C_{max}\), the task \(O_{i+1^{\prime}}^{k}\) cannot start before \(O_{i^{\prime}}^{k}\) is started, but since \(M(O_{i^{\prime}}^{k})=M(O_{i^{\prime\prime}}^{k})=M(O_{i}^{k})\), this can only occur after \(O_{i^{\prime}}^{k}\) is finished. So, for sufficiently small \(\varepsilon\), a feasible schedule for \(I^{\prime}\) having makespan \(\leq K+me\) exists if and only if a feasible schedule for \(I\) exists having makespan \(\leq K\).  
Notice that the above reduction cannot be applied to \(F|n=3,s-prec|C_{\max}\), since in the flow shop each job visits all machines exactly once. In fact, the complexity of \(Fm|s-prec|C_{\max}\) is open, even for fixed \(m\geq 3\) or fixed \(n\geq 3\).  
* _Open problems with two jobs._ The approach in Section 2 for \(J2|n=2,s-prec|C_{\max}\) cannot be trivially extended to more than two machines. The complexity of this case is open. Additionally, an open issue is whether a more efficient algorithm can be devised for \(J2|n=2,s-prec|C_{\max}\), and a strongly polynomial algorithm for \(Fm|n=2,s-prec|C_{\max}\).  
Conceptualization, A.A. F.R. and S.S.; methodology, A.A. F.R. and S.S.; validation, A.A. F.R. and S.S.; formal analysis, A.A. F.R. and S.S.; investigation, A.A. F.R. and S.S.; resources, A.A. F.R. and S.S.; writing-original draft preparation, A.A. F.R. and S.S.; writing-review and editing, A.A. F.R. and S.S.; visualization, A.A. F.R. and S.S.; supervision, A.A. F.R. and S.S.  
This research received no external funding.  
The authors declare no conflict of interest.
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
## 4 Collaborative Transmission Mechanism Based on Q-Learning for Vehicular Networks  
### Selection of Reinforcement Learning Algorithm  
In reinforcement learning, there is always a quandary of exploitation dilemma [13]. Only by forming a compromise among explorations can we achieve a positive learning effect in practical applications. Among them, there are two commonly used algorithms for exploration. One of them is the greedy algorithm [14], in which the agent directly selects the action with the highest value in the corresponding state as the decision and the environment uses the reward matrix to feedback the \(Q\) matrix. This algorithm needs to set a certain exploration probability and the agent randomly selects the action to avoid falling into the local extreme value in the exploration state. The details can be found in Algorithm 3 as follows.  
```
1:Initialize Q(s,a) arbitrary
2:Repeat (for all episode):
3:if random \(<P_{\text{investgate}}\)
4: Random take action \(a\)
5:else:
6: Take action a which is the greatest in \(Q(s^{\prime},a)\)
7:\(Q(s_{t},a_{t})=(1-a)Q(s_{t},a_{t})+a(r_{t+1}+\gamma max_{a}Q(s_{t+1},a))\)
8:Update s'
```  
**Algorithm 3** Q-learning algorithm 1 (\(\epsilon\)-greedy)  
The other is the softmax exploration algorithm [15], in which the agent must normalize the probability of each action according to the softmax function. The details can be found in Algorithm 4 as follows.  
```
1:Initialize Q(s,a) arbitrary
2:Repeat (for all episode):
3:\(A=max_{a_{1}}\frac{Q^{(s^{\prime},a)}}{\sum_{t^{\prime}=1}^{s^{\prime}}Q^{(s^ {\prime},a^{\prime})}}\)
4:Take action a correspond with A
5:\(Q(s_{t},a_{t})=(1-a)Q(s_{t},a_{t})+a(r_{t+1}+\gamma max_{a}Q(s_{t+1},a))\)
6:Update s'
```  
**Algorithm 4** Q-learning algorithm 2 (softmax)  
From the above, the greedy method is adopted to realize the collaboration between collaborative multi-point transmission and joint interference coordination mechanism in vehicular networks, and so we select it in this paper.
## 4 Fire detection algorithms
### Identification of potential fire pixels  
The identification of the potential fire pixels is based on the fixed threshold algorithm. A pixel is confirmed as a potential fire pixel if \(T_{3}\)\(>308\) K, \(T_{34}\)\(>8\) K, \(\rho_{2}<0.3\), where \(T_{34}=T_{3}-\ T_{4}\), otherwise it is classified as non-fire. It is assumed that the brightness temperature of one pixel is larger than 308 K if it includes fire. The difference of brightness temperature between MIR and TIR channels mainly aims at eliminating the land surfaces with high temperatures other than fires, such as bare soil, rock, and road. Meanwhile, it is assumed that the reflectance of NIR channel is very low when fire has occurred [23]. These relatively low thresholds are expected to improve the likelihood of detecting smaller and/or cooler fires which are frequently missed when more conservative thresholds are used. The threshold of MIR channel is very important for fire detection because the active fires show maximum spectral emission in the MIR spectral range.  
The difference of brightness temperature between MIR and TIR channel has been simulated as the function of fraction of fire area under US Standard Atmosphere condition and 23 km visibility. The results are shown in Figure 2. The simulated fraction of fire area ranges from 0.01% to 100% and the temperature ranges from 600 K to 1,200 K. It is clear that the difference of brightness temperatures tend to be zero when the fire fraction is close to 100%. As a result, the large fires may be omitted. The absolute threshold based test described above is necessary.
## 5 Conclusions  
This study shows that grayscale images and background removal significantly improve model performance for DR detection. By eliminating irrelevant background elements, the models were able to focus on critical retinal features and lesions, leading to improved performance. Model 1 achieved a validation accuracy of 90.39%, while Model 2, using normal RGB images, slightly outperformed it with a validation accuracy of 90.60%. Despite these results, Model 3, which was trained with fewer epochs, showed the importance of adequate training duration for optimal performance, with a validation accuracy of 87.64%.  
A key finding of this study is that simple yet effective techniques, such as grayscale conversion and background removal, can greatly enhance model accuracy, especially in compact models with just over three million parameters. This is particularly relevant for resource-constrained environments where computational power and training time may be limited. Furthermore, the results indicate that while grayscale images reduce computational complexity and maintain high accuracy, additional color information can provide slight improvements in performance when ample computational resources are available.  
Comparing our models with previous works, such as [PERSON] et al. (2023) [22] and [PERSON] et al. (2023) [24], shows that our models achieved competitive accuracy, particularly in DR classification into five levels. These results suggest that our models could play a crucial role in early DR diagnosis and treatment, providing a simpler alternative to more complex architectures.  
However, for real-world application, further validation is needed on diverse datasets beyond EyePACS to ensure that the models are robust across different regions and patient populations. This future research will be critical in ensuring the models' reliability in varied clinical settings. Despite these limitations, this work demonstrates the value of using efficient deep learning models with advanced preprocessing techniques in medical imaging tasks, potentially paving the way for more accessible and scalable diagnostic tools in healthcare.  
Conceptualization, C.S. and K.L.; methodology, C.S. and K.L.; software, C.S. and K.L.; validation, C.S. and S.P.; formal analysis, C.S. and S.P.; investigation, C.S., S.P. and T.A.; data curation, C.S. and K.L.; writing--original draft preparation, C.S., K.L., S.P. and T.A.; writing--review and editing, C.S. and K.L.; visualization, C.S. All authors have read and agreed to the published version of the manuscript.  
This research work was partially supported by Chiang Mai University.  
Not applicable.  
Data Availability StatementData in this research paper will be shared upon request with the corresponding author.  
This work was supported by the Excellence Center in Logistics and Supply Chain Management, Department of Industrial Engineering, Faculty of Engineering, Chiang Mai University.  
The authors declare no conflicts of interest.
# Spatial and Temporal Sampling Properties of a Large GNSS-R Satellite Constellation
## 5 Conclusions  
Large constellations of smallsats in low Earth orbit can provide spatial and temporal coverages with greater sampling density relative to constellations with fewer large satellites. This increase in coverage is primarily characterized by three factors and quantified by zonal and global measurements. The selection of satellite inclinations allows designers to choose which latitude zones are receiving more coverage while also optimizing global visibility. Meanwhile, dividing a constellation into multiple orbit planes allows designers to further improve global and zonal temporal coverage figures. Additionally, the quality and quantity of the permissible sensed data can be controlled by selecting an appropriate RCG threshold in accordance with constellation's onboard GNSS-R instruments. Ultimately, these parameters allow for designers to iteratively evaluate constellation prototypes and maximize spatial and temporal coverage both globally and in regions of interest.  
When designing a constellation, each mission will have its own set of objectives where it wants to maximize its coverage. However, the most generic definition of a successful constellation design would be maximizing global coverage metrics while keeping zonal coverage metrics evenly distributed. Through an iterative process of adjusting the satellite inclinations of 3-plane constellations, it has been found that a constellation with planes of 8 satellites orbiting at inclinations of 50\({}^{\circ}\), 75\({}^{\circ}\) and 80\({}^{\circ}\) meet these design goals. Although it may be possible to adjust the definition of success to meet specific mission requirements, this constellation has been found to provide quite good overall coverage both globally and zonally.  
Conceptualization, C.R. and S.G.; methodology, J.W., C.R. and S.G.; software, J.W.; validation, J.W., C.R. and S.G.; formal analysis, J.W., C.R. and S.G.; investigation, J.W., C.R. and S.G.; resources, C.R.; data curation, C.R.; writing--original draft preparation, J.W., C.R. and S.G.; writing--review and editing, J.W., C.R. and S.G.; visualization, [PERSON]; supervision, [PERSON] and  
Figure 10: Simulated measurements by two GNSS-R constellations of Hurricane Ida during landfall on 29 August 2021. The left column corresponds to an 8-satellite constellation and the right to one with 24 satellites, all at 30\({}^{\circ}\) inclination. Rows correspond to 6 h time intervals centered on 0600, 1200, 1800 and 2400 UTC. GNSS-R tracks are overlaid on GOES-16 Band 8 (6.17 μm) images at the center times. Landfall occurred at 1655 UTC.  
S.G.; project administration, [PERSON]; funding acquisition, C.R. All authors have read and agreed to the published version of the manuscript.  
This research and the APC were funded by NASA contract number 80 LARC21 DA003.  
**Data Availability Statement:** No new data were created in performance of the work presented here.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Construction and Completion of the Knowledge Graph for Cow Estrus with the Association Rule Mining
## 5 Validation of Association Rules
### Boolean Functions in Normal Estrus Prediction  
The Boolean function validation model based on association rule learning proposed in this study incorporates the FP-Growth algorithm for extracting strong association rules and using them to construct a logical encoding, which in turn realized the accurate determination of the estrus status of heifers. Specifically, based on these strong association rules, we combined the feature variables to form a logical expression through logical operations such as and, or, and not. For example, if the result of frequent item set mining shows that 'increased body temperature and activity' or 'vulvar swelling' is a combination of strongly correlated features of cow estrus, the corresponding Boolean expression is shown in Equation (4) as an example:  
\[\text{Cow estrus}\ =\ (\text{T}\wedge\text{A})\vee\text{S} \tag{4}\]  
In this formula, T means increase in body temperature, A means increase in activity, and S means swelling of the vulva. If the combination of input features satisfies the logic expression, the Boolean function outputs 'True', indicating that the cow is in estrus; otherwise, it outputs 'False'. This Boolean function discriminant model based on association rule learning not only inherits the clarity and interpretability of Boolean logic, but also enhances the adaptability and accuracy of the cow estrus determination model through data-driven association rule mining. The specific flow chart is shown in Figure 10.
# Optical OFDM Error Floor Estimation by Means of OTDR Enhanced by Front-End Optical Preamplifier
## 2 Analysis
### OTDR Receiver Noise Floor Reduction
#### 2.4.1 Noise Figure of ODC Coupled OTDR  
As it can be seen in Figure 1, the OTDR backscatter firstly transverses the ODC and then the APD detector of the OTDR receiver, where the ODC's noise figure (as of a passive device) equals its intrinsic insertion loss [3].  
Furthermore, as, generally, the noise figure \(F_{1,2}\) of the cascaded two blocks is, to the large extent, determined by the noise figure \(F_{1}\) of the first block (with large gain \(G_{1}>>1\)) in the series:  
\[F_{1,2}=F_{1}+(F_{2}-1)/G_{1}\approx F_{1} \tag{13}\]  
the above classic relation shows that \(F_{2}\) (unless it is very large), does not contribute significantly to \(F_{1,2}\) characterizing the whole cascade [12].  
However, in case of a noise-rich (older-generation) OTDR receiver's APD [1], the passive front-end ODC with insertion loss \(A_{\text{DC}}\) (equal to its own NF), deteriorates the noise figure (13) of the cascade even further to [12]:  
\[F_{\text{OTDR}}=A_{\text{DC}}+(F_{\text{APD}}-1)\cdot A_{\text{DC}}=A_{\text{DC }}\cdot F_{\text{APD}} \tag{14}\]  
Equation (14), implies that, for wide class of OTDRs, we can justifiably consider the receiver noise figure, and thereby the noise floor, relatively high.  
On the contrary, state-of-the-art erbium-doped fiber amplifiers (EDFA) provide high and flat gain often in the range of 30 dB, as well as low noise figure below 4 dB [13; 14], which make them useful as OTDR external pre-amplifiers that could enable high-SNR BER testing [12; 14; 15; 16; 17; 18; 19; 20; 21].  
Moreover, a low-insertion-loss passive optical circulator (OC) is an appropriate device for signal separation needed for connecting an EDFA to the OTDR [3; 12].
# Improving Response Time for Crowd Management in Hajj
## 5 Results
Figure 10a,b show the PSNR and SSIM calculated after every one second at the base station for packets received from a UAV traveling at 30 km/h. Without PRoFFAN, the UAV's entire journey in the base station's range could transmit packets that contributed to PSNR, reaching a max value of 5 and SSIM of 0.19, respectively, in 86 s (represented by marker 4). However, the same quality of PSNR = 5 reached the base station in almost one-third of the time using PRoFFAN at 29 s (represented by marker 3). Assuming that the FANET application required a PSNR = 5 for processing the incoming image, without PRoFFAN, the image of PSNR = 5 was received in 86 s, whereas, with PRoFFAN enabled, the same quality of image reached the base station in 29 s. Hence, PRoFFAN saved 57 s.  
The vertical line at 60 s mark in Figure 10a depicts the deadline of 60 s. Without PRoFFAN, the image of PSNR = 4 reached the base station (represented by marker 2). On the other hand, with PRoFFAN enabled, an image of PSNR = 13.4 had reached the base station in 60 s (represented by marker 1). In fact, at the 63 s (represented by mark 5), an image with a PSNR of 16.4 reached the base station. This improvement in quality of the image was achieved by enabling PRoFFAN. Hence, the application can use images of much better quality for processing. This is how PRoFFAN assists in improving response time and response efficiency in FANET applications with UAVs traveling at medium speeds. Figure 11a,b show the PSNR and SSIM calculated at after every one second at the base station for packets received from a UAV traveling at 60 km/h. Without PRoFFAN, the UAV entire journey in the base station's range could transmit packets that contributed to PSNR reaching a max value of 3.2 and SSIM of 0.16, respectively, in 25 s (represented by marker 4). However, the same quality image reached the base station in 13 s PRoFFAN (represented by marker 3). Assuming that the FANET application required a PSNR = 3.2 for processing the incoming image, without PRoFFAN, the image of PSNR = 3.2 was received in 25 s, whereas, with PRoFFAN enabled, the same quality of image reached the base station in 13 s. Hence, PRoFFAN saved 12 s.  
The vertical line at 30 s mark in Figure 11a depicts the deadline of 30 s. Without PRoFFAN, the image of PSNR = 3.2 reached the base station (represented by marker 2). On the other hand, with PRoFFAN enabled, an image of PSNR = 5.2 reached the base station in 30 s (represented by marker 1). In fact, at 49 s (represented by mark 5), an image with a PSNR of 9.7 had reached the base station. This improvement in the quality of the image was achieved by enabling PRoF  
Figure 9: Received images from UAV with speed 10 km/h. FAN. Hence, the application can use images of much better quality for processing. This is how PRoFFAN improves response time and response efficiency in FANET applications with UAVs traveling at medium speeds and higher speeds.
## 3 Empirical Results and Analysis
### Heterogeneity Test
There are differences in the degree of economic development, industrial structure, level of green technological innovation and environmental problems among different regions [38], so the impact of the policy on the _GTFP_ in different regions has regional differences [6]. Different from the other two regions, the industrial structure of southern China is mainly secondary industry, and the air quality and environmental level are superior to the other two regions. The APCP Action Plan is conducive to the transformation and optimization of industrial structure and green technological innovation in southern China, thus promoting the development of a green economy in the region [39]. It also confirms Hypothesis 2. As a consequence, when countries formulate corresponding environmental policies to solve pollution problems, they need to combine the emission of pollutants in different regions to achieve regional differentiated management and control. At the same time, regional joint prevention and control areas should be established to expand the scope of policy implementation [38], raise people's vigilance against environmental pollution, and establish a new, greener environment.
## Materials and Methods
### Model-Based Interleaved Biplane Fluoroscopy Image Tracking  
The new model-based tracking scheme for automated determination of 3D bone poses, termed model-based interleaved biplane fluoroscopy image tracking (MIBFT), was accomplished in three stages: (1) 2D/2D template registration, (2) frame interpolation, and (3) biplane 3D/2D image registration. The 6-dof of the bone at frame \(k\) is expressed by a transformation matrix \(\mathbf{T}(\overset{\rightarrow}{\mathrm{P}}^{k})\), parameterized by pose parameters \(\overset{\rightarrow}{\mathrm{P}}^{k}=\left\{T_{X^{\prime}}T_{Y^{\prime}}T_{ Z^{\prime}}\theta_{X^{\prime}}\theta_{Y^{\prime}}\theta_{Z}\right\}^{T}\). The three translation components \(T_{X^{\prime}}T_{Y^{\prime}}T_{Z}\) and rotation components \(\theta_{X^{\prime}}\theta_{Y^{\prime}}\theta_{Z}\) are represented with respect to \(G_{XYZ}\).