esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
LFC Load frequency control PV Photovoltaic ESSs Energy storage systems PID Proportional integral derivative SMC Sliding mode control GFTSMC Global fast terminal sliding mode control AGFTSMC Adaptive global fast terminal sliding mode control
## 4 Investigating Parameters' Impact on Beacon Rate Adaptation  
Several parameters impact beacon rate adaptation. In this section, our discussion focuses on five influential parameters and their impact on beacon rate adaptation.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 3 Materials and Methods
### YOLO Family
#### 3.5.3 YOLOv10  
The YOLOv10 [17] architecture represents a significant evolution in real-time object detection. One of the main innovations of YOLOv10 is the introduction of consistent dual assignments, combining \"one-to-many\" and \"one-to-one\" strategies in training. This allows the model to learn efficiently without relying on NMS (non-maximum suppression), reducing latency and ensuring more accurate predictions during inference. The approach promotes rich supervision by aligning the training and inference processes in a holistic way. Its main improvements include:  
* Backbone YOLOv10 introduces CSPNet++, a new backbone architecture that combines CSP (Cross-Stage Partial) with EfficientNet-B3. This hybrid architecture takes advantage of the compound scaling efficiency of EfficientNet and the gradient flow improvements of CSP, leading to a highly efficient feature-extraction process.
* With no neck, YOLOv10 adopts PAN (Path Aggregation Network), which improves the fusion of features at multiple scales, enabling better detection of objects in different sizes and conditions. This approach improves spatial representation and facilitates the efficient propagation of information within the network.
* The detection head maintains the traditional multi-scale approach of YOLO models, but optimizes the allocation of computational resources to make inference faster and more accurate. With these structural improvements, YOLOv10 is able to achieve superior performance without compromising speed, making it an ideal solution for real-time applications.  
The YOLOv10 is available in different variants, including YOLOv10n, YOLOv10s, YOLOv10m, YOLOv10b, YOLOv10l, and YOLOv10x, providing options for balancing speed and accuracy according to the needs of the application. Its improved design allows it to adapt efficiently to different scenarios, from detecting small objects in complex environments to high-performance applications with a lower computational load.
## 6 Conclusions  
A spatiotemporal joint cleaning technology based on CC-VC-UKF is proposed to solve the problem of inconsistent measurement data caused by the non-zero mean and non-Gaussian distribution measurement deviation caused by the spatial distance of the measurement equipment. The measurement data of each piece of measurement equipment is filtered based on CC-VC-UKF according to the temporal correlation of the measurement data. Then, taking the filtered data as the reference sequence, the data sequence to be cleaned is filtered based on the CC-VC-UKF algorithm according to the spatial correlation of the measured data. Through simulation verification, the following conclusions are drawn:  
1. Compared with other advanced algorithms, such as LSTM and deep neural network, the spatiotemporal joint cleaning technology based on the proposed CC-VC-UKF algorithm improves the accuracy and quality of data cleaning to a certain extent.
2. For the non-zero mean non-Gaussian distribution measurement deviation caused by the spatial distance of the measurement equipment and the measurement deviation caused by negative data input, the spatiotemporal joint cleaning technology based on CC-VC-UKF can better reduce the fluctuation of the measurement deviation and can convert the negative part of the measurement deviation from the normal range into a more reasonable normal value.
3. In this study, the CC-VC algorithm is also applied to the traditional EKF, which ignores the influence of high-order terms in the linearization process. Thus, the experimental results show that applying the CC-VC algorithm to UKF can obtain higher accuracy data than applying it to EKF.  
Conceptualization: Z.S.; methodology: Z.S. and C.L.; validation: Z.S., C.L. and B.L.; data curation: Z.S., C.L. and B.L.; writing--review and editing: Z.S., C.L. and B.L.; project administration: Z.S., J.Z., C.C. and Q.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by State Grid Corporation of China headquarters science and technology project under grant number 5500-202055466A-0-0-00.  
Not applicable.  
The authors declare no conflict of interest.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 1 Introduction
* The rate ranges and characteristics of some real videos are analysed and these videos are applied in the simulations. From the real-time flow rate and total utility, we find that the algorithm converges rapidly and each UAV can intelligently select and maintain a stable video uplink rate, so that a reasonable allocation of wireless resources could be achieved. The influence of total channel bandwidth and cost factor is also analyzed. The rest of this paper is organized as follows. In Section 2, the related works are summarized. In Section 3, we describe the system model in detail and discuss our preliminary goal. In addition, the utility function is introduced here. In Section 4, we model the problem as a potential game and prove the existence of correlated equilibrium. We adopt a distributed self-learning algorithm to solve the model in Section 5. The experimental results on real video data and some discussions are settled in Section 6. In addition, we draw the conclusions in Section 7.
# LTDA-MAC v2.0: Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks +
## 4 Conclusions  
The LTDA-MAC protocol facilitates efficient packet scheduling in LUASNs without the need for synchronized clocks at the network nodes. The key part of the LTDA-MAC protocol is the real-time application of a heuristic optimization algorithm to produce a packet schedule with spatial spectrum reuse tailored to the given deployment scenario. In this paper, we proposed a new greedy optimization algorithm for deriving LTDA-MAC packet schedules that produces reliably better solutions and is 2-3 orders of magnitude more computationally efficient than our previous GA + PSO optimization method. Simulations of LUASNs deployed on 2 km and 20 km underwater pipelines showed that LTDA-MAC can exploit long propagation delays and the interference pattern in a given network deployment to considerably improve the network throughput, compared with the conventional STDMA approach, despite the lack of clock synchronization in LTDA-MAC. Continuing from our previous work on single-hop and dual-hop TDA-MAC, LTDA-MAC equipped with the new greedy schedule optimization algorithm is a key step in our further work on TDA-MAC for unsynchronized scheduling in UASNs with general multi-hop topologies.  
Conceptualization, N.M.; methodology, N.M., P.D.M. and Y.Z.; software, N.M.; validation, N.M., P.D.M. and Y.Z.; formal analysis, N.M.; investigation, N.M., P.D.M. and Y.Z.; resources, P.D.M. and Y.Z.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, P.D.M., Y.Z.; visualization, N.M.; project administration, P.D.M.; funding acquisition, P.D.M., Y.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) through the USMART (EP/P017975/1) and Full-Duplex (EP/R003297/1) projects.  
The authors declare no conflict of interest.
## 2 Results
### Functional Characterization of Three Diterpene Synthases  
Among seven identified diterpene synthase genes (four in TPS-c and three in TPS-e/f subfamily), _Sb_TPS8, _Sb_TPS9, and _Sb_TPS12 were present as full-length sequences. The open reading frames of these diTPS were directly amplified from root cDNA. The function of _Sb_TPS8, _Sb_TPS9, and _Sb_TPS12 were identified using in vitro assays with GGPP as a substrate. The product was analyzed by gas chromatography-mass spectrometry (GC-MS). The known enzymes _Sm_CPS1 (normal-CPP synthase) and _Sm_KSL1 (militadiene synthase) from _Sa. militorrhiza_ produced militoradiene [20; 28], and _Sm_CPS5 (_ent_-CPP synthase) together with _Af_KS (_ent_-kaurene synthase) from _A. thaliana_ produced _ent_-kaurene with GGPP as substrate [32] were used as positive controls (Figure 6).  
The purified recombinant _Sb_TPS8 and _Sb_TPS9 (Figure S5) individually reacted with the known enzyme of _Sm_KSL1 and _At_KS. _Sb_TPS8 in combination with _Sm_KSL1 formed militadiene (Figure 6),  
Figure 6: GC-MS analysis of in vitro assays with _S. barbata_ diTPS. (**A**) Extracted ion chromatograms (EIC) of \(m/z\) 257 from in vitro assays with _Sb_CPS coupled with different _Sb_KSL and microbial co-expression of _Sb_TPS12. The characterized _Sm_CPS5 (_ent_-CPP synthase from _Sa. militorrhiza_), _Sm_CPS1 (normal-CPP synthase from _Sa. militorrhiza_), _Sm_KSL1 (militadiene synthase from _Sa. militorrhiza_), and _At_KS (_ent_-kaurene synthase from _A. thaliana_) were used as positive controls. a. militoradiene; b. _ent_-kaurene; c. ent or (+)-copalol. (**B**) Corresponding mass spectra of recombinant enzyme assay products of militriadiene and _ent_-kaurene.  
which was identified by comparison of the retention time and mass spectrum to the product of combination of _Sm_CPS1 and _Sm_KSL1 from _Sa. miltiorrhiza_. No product was observed when combining _Sb_TPS8 and _At_KS (specific to _ent_-CPP), showing that _Sb_TPS8 was a normal-copalyl diphosphate synthase. The similar combination of _Sb_TPS9 with _At_KS and _Sm_KSL1 showed that _Sb_TPS9 only produced _ent_-kaurene when combined with _At_KS (Figure 6); thus _Sb_TPS9 was found to be an _ent_-copalyl diphosphate synthase.  
Furthermore, _Sb_TPS12 was found to produce miltiradiene when combined with _Sb_TPS8 (Figure 6), thus _Sb_TPS12 was identified to be a miltiradiene synthase in _S. burbata_. However, KSL from different origins were found to have extreme promiscivity [34; 39; 40]. Thus, to probe the substrate promiscuity of _Sb_TPS12, a previously reported modular metabolic engineering system was utilized [40; 41]. This system enables facile co-expression of CPSs and KSLs in _E. coli_ with high production. Twelve different class II diterpene cyclases with varied product outcomes [40] were assembled into the metabolic engineering system for co-expression with _Sb_TPS12. As expected, _Sb_TPS12 co-expressed with copalyl diphosphate synthase produced the same product of miltiradiene with an in vitro assay. No obvious stable activity was found with the other 11 class II diTPS synthases.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Key Generation
#### 4.1.4 Error Correction Code and Privacy Amplification  
To resolve the errors in the subsifted key, both the units exhibit Reed-Solomon (R-S) code. It is an error reconciliation algorithm that corrects multi-bit error with a low computational overhead [35; 36]. The R-S code also evaluates the confidentiality and integrity of the subsifted key. The RTU encodes the key, which involves adding extra parity bits and sends the codeword to the receiver. The MTU receives the codeword, decodes it, and resolves the errors. Meanwhile, the eavesdropper fails to read the subsiftedkey [36; 37].  
The Reed Solomon(RS) error correction code has two modules: RS encoder and RS decoder. The RS encoder module involves encoding the information with a Generator polynomial to obtain the codeword. Therefore, it provides confidentiality to the information, however not to the codeword. The codeword is sent to the decoder, which applies a decoding algorithm to correct the errors in the received codeword. When both units perform error correction, they obtain the same subsifted key. However, to reduce any information leakage during the R-S code, the MTU and the RTU hash the subsifted key to obtain the final key. This mechanism is called privacy amplification, as it increases the key secrecy.
## 2 Materials and Methods
### Analytical Strain Equations in Cylindrical Coordinate System
Carrying out the transformations and assuming that the \(xz\)-plane is the sample's plane of deformational symmetry (\(x\mathrel{\widehat{=}}\theta=0\)), the following equations for the normal strains \(\varepsilon_{r}\), \(\varepsilon_{\theta}\) and \(\varepsilon_{z}\) in relation to the isotropy plane inclination \(\beta\) in cylindrical coordinates are obtained [16; 25]:  
\[\varepsilon_{r}=\bigg{[}\left(-\frac{\
u\sin^{2}\beta}{E}-\frac{\
u^{\prime} \cos^{2}\beta}{E^{\prime}}\right)\sin^{2}\theta+\frac{1}{4}\bigg{[}\bigg{(} \frac{1}{E}-\frac{1}{G^{\prime}}+\frac{1}{E^{\prime}}\bigg{)}\sin^{2}2\beta- \frac{\
u^{\prime}(3+\cos 4\beta)}{E^{\prime}}\bigg{]}\cos^{2}\theta\bigg{]} \sigma_{z} \tag{2}\]  
\[\varepsilon_{\theta}=\bigg{[}\left(-\frac{\
u\sin^{2}\beta}{E}-\frac{\
u^{ \prime}\cos^{2}\beta}{E^{\prime}}\right)\cos^{2}\theta+\frac{1}{4}\bigg{[} \bigg{(}\frac{1}{E}-\frac{1}{G^{\prime}}+\frac{1}{E^{\prime}}\bigg{)}\sin^{2}2 \beta-\frac{\
u^{\prime}(3+\cos 4\beta)}{E^{\prime}}\bigg{]}\sin^{2}\theta \bigg{]}\sigma_{z} \tag{3}\]  
\[\varepsilon_{z}=\bigg{[}\frac{\sin^{4}\beta}{E}+\frac{\cos^{4}\beta}{E^{\prime }}+\frac{1}{4}\bigg{(}\frac{1}{G^{\prime}}-\frac{2\
u^{\prime}}{E^{\prime}} \bigg{)}\sin^{2}2\beta\bigg{]}\sigma_{z} \tag{4}\]  
Figure 1: Transformation of the local Cartesian coordinate system \(x^{\prime}y^{\prime}z^{\prime}\) of a transversely isotropic cylindrical sample into the global cylindrical coordinate system \(r\theta z\) in two steps using transformation matrices \(\Omega_{\beta}\) and \(\Omega_{\theta}\). The local \(x^{\prime}\)-axis follows the inclination \(\beta\) of the isotropy planes within the sample’s symmetry plane, which defines the direction \(\theta=0\) (based on [PERSON] et al. [25]). Equations (2)-(4) are exclusively valid under the assumption of a uniform axial stress state. This case is only satisfied for ideal test boundary conditions without interface friction between the sample and loading platens. Figure 2 illustrates the orientation of the acting normal strain components within the cylindrical coordinate system \(r\theta z\).
## 4 Vehicle Dynamics Model
### Full-Car Dynamics Model
#### 4.2.2 Dynamics of Vehicle System  
Based on [PERSON]'s law, dynamics equations of the 9-DOF full-car model are as follows.  
1. Equation (51) for vehicle body vertical motion \[\sum_{i=1}^{4}F_{si}=-m_{s}\ddot{z}_{s}\] (51) where \(F_{si}\) is suspension force. Corresponding equations can be obtained as follows. \[\begin{array}{l}F_{s1}=-k_{s1}(z_{b1}-z_{w1})-c_{p1}(\dot{z}_{b1}-\dot{z}_{ w1});\\ F_{s2}=-k_{s2}(z_{b2}-z_{w2})-c_{p2}(\dot{z}_{b2}-\dot{z}_{w2});\\ F_{s3}=-k_{s3}(z_{b3}-z_{w3})-c_{p3}(\dot{z}_{b3}-\dot{z}_{w3});\\ F_{s4}=-k_{s4}(z_{b4}-z_{w4})-c_{p4}(\dot{z}_{b4}-\dot{z}_{w4});\end{array}\] (52) And, \[\begin{array}{l}z_{b1}=z_{b}+\frac{B}{2}\sin\phi-a\sin\theta;\\ z_{b2}=z_{b}-\frac{B}{2}\sin\phi-a\sin\theta;\\ z_{b3}=z_{b}+\frac{B}{2}\sin\phi+b\sin\theta;\\ z_{b4}=z_{b}-\frac{B}{2}\sin\phi+b\sin\theta;\end{array}\] (53)Equation (54) for unsprung mass vertical motion  
\[\begin{array}{l}m_{w1}\tilde{z}_{w1}=-k_{s1}(z_{w1}-z_{b1})-c_{p1}(\hat{z}_{w1} -\hat{z}_{b1})-k_{t1}(z_{w1}-z_{r1});\\ m_{w2}\tilde{z}_{w2}=-k_{s2}(z_{w2}-z_{b2})-c_{p2}(\hat{z}_{w2}-\hat{z}_{b2})-k_ {t2}(z_{w2}-z_{r2});\\ m_{w3}\tilde{z}_{w3}=-k_{s3}(z_{w3}-z_{b3})-c_{p3}(z_{w3}-\hat{z}_{b3})-k_{t3}(z _{w3}-z_{r3});\\ m_{w4}\tilde{z}_{w4}=-k_{s4}(z_{w4}-z_{b4})-c_{p4}(\hat{z}_{w4}-\hat{z}_{b4})-k _{t4}(z_{w4}-z_{r4});\end{array} \tag{54}\]  
3. Equation (55) for vehicle body pitch motion \[\sum_{i=1}^{4}M_{yi}=I_{yi}\ddot{\theta}=(F_{s1}+F_{s2})a-(F_{s3}+F_{s4})b+m_{ s}g\hat{h}_{roll}\sin\theta\] (55) where \(M_{yi}\), \(i=1\), \(2\), \(3\), \(4\) is vehicle roll moment.
4. Equation (56) for vehicle body roll motion \[\sum_{i=1}^{4}M_{xi}=I_{xi}\ddot{\phi}=(F_{s2}+F_{s4})\frac{B}{2}-(F_{s1}+F_{ s3})\frac{B}{2}+m_{s}h_{roll}(g\sin\theta+a_{sy})\] (56) where \(M_{xi}\), \(i=1\), \(2\), \(3\), \(4\) is vehicle roll moment.
5. Equation (57) for vehicle lateral motion \[\sum_{i=1}^{4}F_{yi}=m_{s}a_{sy}+\sum_{i=1}^{4}m_{xi}a_{wyi}=(F_{y1}+F_{y2}) \cos\delta_{f}+(F_{y3}+F_{y4})\cos\delta_{r};\] (57) where \(F_{yi}\), \(i=1\), \(2\), \(3\), \(4\), is tire lateral force.
6. Equation (58) for the vehicle yaw motion \[\begin{array}{ll}\sum M=I_{z}\dot{\omega}&=b(F_{y3}+F_{y4})\cos\delta_{r}-a (F_{y1}+F_{y2})\cos\delta_{f}\\ &-\frac{B}{2}(F_{y2}-F_{y1})\sin\delta_{r}-\frac{B}{2}(F_{y4}+F_{y3})\sin \delta_{f}\\ &+M_{z1}+M_{z2}+M_{z3}+M_{z4}\end{array}\] (58) where \(M_{zi}\), \(i=1\), \(2\), \(3\), \(4\) is tire self-aligning moment. Thus, Equation (59) can be expressed as follows. \[\begin{array}{ll}\mathrm{RI}_{full}=\frac{m_{u3}\tilde{z}_{u3}+F_{s3}+m_{u3} \tilde{z}_{u3}+F_{s4}+m_{u4}\tilde{z}_{u3}+F_{s4}+m_{u4}\tilde{z}_{u3}-m_{u1} \tilde{z}_{u3}-F_{s1}-m_{u1}\tilde{z}_{u3}-m_{u2}\tilde{z}_{u3}-F_{s2}-m_{u2} \tilde{z}}{m_{u3}\tilde{z}_{u3}+F_{s3}+m_{u4}\tilde{z}_{u3}+F_{s4}+m_{u4}\tilde {z}_{u3}-m_{u1}\tilde{z}_{u3}-m_{u1}\tilde{z}_{u3}-m_{u2}\tilde{z}_{u3}-F_{s2} -m_{u2}\tilde{z}}\end{array}\] (59)
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 4 Machine-Learning Models
### Logistic Regression  
Binary Logistic Regression models the relationship between a set of independent variables and a binary dependent variable. The goal is to find the best model that describes the relationship between the dependent variable and multiple independent variables.  
The Logistic Regression's dependent variable could be binary or categorical and the independent ones could be a mixture of continuous, categorical, and binary variables.  
The general form of Logistic Regression is as follows:  
\[z=a+b_{1}x_{1}+b_{2}x_{2}+b_{3}x_{3}+\ldots+b_{m}x_{m} \tag{6}\]  
\[y=\frac{1}{(1+e^{-z})} \tag{7}\]  
where \(x_{1},x_{2},\ldots,x_{m}\) is the feature vector and \(z\) is a linear combination function of the features. The parameters \(b_{1},b_{2},\ldots,b_{m}\) are the regression coefficients to be estimated. The output is between 0 and 1, and, usually, if the output is above the threshold of 0.5 the model predicts class 1 (positive) and otherwise class 0 (negative).
## 4 Demonstrated Application and Analysis  
### Demonstrated Application  
Five ground IoT terminal devices were installed in the most remote regions of the TP without a public communication network, as shown in Figure 5. On 23 October 2019, the first terminal device and AWS were installed at the end of the Muggangqiong glacier to collect meteorological data. This glacier is situated in the hinterland of the Qiangtang Plateau, which covers an area of approximately 708,000 km\({}^{2}\) with an average altitude ranging from 4700-5200 m and is referred to as the roof of the TP, which in turn is referred to as the Roof of the World. On 28 October 2019, the second terminal device and an AWS were installed on the slope opposite the Jialabailei glacier, which lies in the Yarlung Zangbo River Grand Canyon, the deepest canyon in the world. This area was the most challenging hiking area worldwide but is no longer accessible since a debris flow caused by ice avalanches originating from the Jialabailei glacier dammed the river on 16 October 2018.  
Three other terminal devices were installed in remote areas of the Qilian Mountains, which stretch more than 400 km along the northeastern edge of the Qinghai-Tibet Plateau. Water resources stemming from the Qilian Mountains feed the Hexi Corridor and supply five rivers, and Qinghai Lake is the largest inland lake in China. We installed the third terminal device combined with a snow depth monitor and AWS in the Dadongshuyakou snow-covered area within the Qilian Mountains on 20 December 2019. The fourth terminal device was installed with a tree stemflow meter and AWS in the Dayekou forest area of the Qilian Mountains on 27 December 2019, and the fifth device was deployed nearby on 10 August 2020, combined with ten dendrometers to monitor tree growth.  
Figure 4: National Tibetan Plateau/Third Pole Environment Data Center.
# Autonomous Loading System for Load-Haul-Dump (LHD) Machines Used in Underground Mining
## 4 Experiments and Analysis
### Offline Results Using Field Data: 2.5D Modeling of the Extraction Point  
Personnel cannot be inside a stope as it is forbidden for security reasons (i.e., rock can fall from the ceiling of the stope), so it was not possible to obtain accurate measurements of the ore pile characteristics. However, an accurate modeling of the ore pile is not really necessary, as it only needs to be detected, as well as having its width and inclination roughly estimated in order for the excavation algorithm to work.  
To obtain the datasets required to characterize our 2.5D modeling algorithm, the LHD was driven from a fixed distance of about 50 m from the loading point to the entrance of the stope, while all sensor and machine data was recorded. These datasets were captured at different times so the drawing point was not located in the same place, nor did the ore pile have the same shape. First, in each case the point cloud was computed and then the pile's width and inclination estimated. Figure 26 shows an example of the point cloud obtained while the LHD is approaching an extraction point, after registering the position of the points using the LIDAR-based odometry and the inclinometer data (see details in Section 3.2). The color of the points represents the distance to the LHD (in the x-axis) at the time of registration. In the case of the rock pile at the end of the tunnel, red indicates that the machine was further away when that reading was taken.  
Figure 26: Example of the scanning obtained while the LHD is approaching an extraction point. (**a**) Top view of the pile reconstruction. (**b**) Isometric view of the pile reconstruction.  
The results of several modeling attempts where the pile's width and inclination were estimated are summarized in Table 6. They consider ground truth width, predicted width, ground truth inclination, and predicted inclination. Both ground truth width and ground truth inclination were determined manually from the integrated point clouds. The average absolute error in the predicted width is 0.58 m, while the average absolute error in the inclination is 1.7 degrees. The precision achieved by the characterization procedure is sufficient for the task of autonomous loading.