esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The efficiency analysis of a star gear train under multiple working conditions was carried out, including complete uniform load distribution under ideal conditions, actual load distribution without any load-sharing measures, and load distribution with errors. The results show that the unbalanced power split caused by load distribution significantly affects the efficiency of the star gear train and the vibration of the gearbox. The results are as follows:  
1. The total efficiency of the star gear train can be regarded as a parallel unit composed of multiple shunt branches, which can be improved by improving the efficiency of the shunt branch and adjusting the load coefficient of the branch.
2. The adjustment of the load coefficient affecting gear train efficiency can be realized by adjusting the center distance and center distance azimuth between the sun gear and the inner gear ring. Regardless of the change in the load coefficient between the shunt branches, the sum of the meshing forces of the five meshing pairs in the inner or outer meshing group is fixed. Ideally, the five meshing forces form a regular pentagon.
3. The load-sharing coefficient of the gear train decreases with the increase in power. Without load-sharing measures, the load coefficient of the star gear train with five-way power splitting is about 1.16, which is slightly lower than the efficiency of theoretical load sharing.
4. The uneven distribution of five shunt loads not only reduces the efficiency of the gear train, but also increases the vibration of the gearbox. The greater the load-sharing coefficient, the lower the efficiency and the larger the vibration.  
Conceptualization, S.W.; Formal analysis, D.L. ([PERSON]); Investigation, Y.Y.; Writing--original draft, D.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by the National Key R&D Program of China (No. 2018 YFB2001502).  
Figure 10: Vector displacement curve diagram of inner gear.
## 3 Results
### Treatment Comparisons and Efficacy Scores  
On XXpire low treatments, none of the insecticide-method combination treatments significantly reduced _B. tabaci_ densities, compared with the nontreated check treatment (Table 2; Figure 4). For XXpire high treatments, the numbers of _B. tabaci_ were significantly lower in the spench treatment at 14 DAA (Figure 4B), and in the foliar treatment at 21 DAA (Figure 4C), than in the nontreated check treatment (Table 2). For Zylam treatments, the numbers of _B. tabaci_ were significantly lower in spench treatments than in nontreated check treatments at 7 (Figure 4A), 14 (Figure 4B), 21 (Figure 4C), and 28 (Figure 4D) DAA. Foliar applications (foliar and spench) significantly reduced _B. tabaci_ densities, compared with the nontreated check treatment in terms of Zylam treatment at 28 DAA (Table 2; Figure 4D). The drench treatment did not affect _B. tabaci_ densities for any insecticide-method combination treatments, compared with the nontreated check treatment, at any days after application.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Treatment**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**7 DAA**a **} & \multicolumn{2}{c}{**14 DAA**} & \multicolumn{2}{c}{**21 DAA**} & \multicolumn{2}{c}{**28 DAA**} \\ \cline{3-10}  & & \(t\) & \(p\) & \(F\) & \(p\) & \(F\) & \(p\) & \(F\) & \(p\) \\ \hline \multirow{3}{*}{XXpire low} & Foliar & \(-1.1\) & 0.279 & \(-0.5\) & 0.867 & 0.0 & 0.991 & 1.2 & 0.263 \\  & Spench & \(1.1\) & 0.274 & \(1.7\) & 0.122 & 2.0 & 0.064 & 0.6 & 0.547 \\  & Drench & \(-0.5\) & 0.595 & \(-1.2\) & 0.259 & \(-0.9\) & 0.382 & \(-1.5\)* & 0.166 \\  & Foliar & \(0.1\) & 0.942 & \(-0.3\) & 0.802 & 2.2 & **0.042** & 1.1 & 0.301 \\  & Spench & \(1.3\) & 0.223 & 2.4 & **0.032** & 1.7 & 0.117 & 1.6 & 0.130 \\  & Drench & \(-0.8\) & 0.453 & 0.8 & 0.444 & 0.2 & 0.848 & 1.1 & 0.308 \\  & Foliar & \(0.7\) & 0.503 & 1.7 & 0.112 & 1.9 & 0.077 & 2.4 & **0.029** \\  & Spench & \(2.3\) & **0.038** & 3.2 & **0.007** & 3.1 & **0.009** & 2.7 & **0.018** \\  & Drench & \(-0.2\) & 0.879 & 1.2 & 0.239 & 1.9 & **0.073** & 0.7 & 0.521 \\ \hline \hline \end{tabular}
\end{table}
Table 2: All densities of _B. tabaci_ observed on poinsetia leaves treated, with the specific insecticide–method combination compared with nontreated check treatment in the greenhouse, were analyzed using Student\({}^{\prime}\)s \(t\)-test.
## 7 Limitations of Nanomaterials  
Nanotechnology provides a variety of processes for removing pharmaceutical compounds from wastewater or surface waters. It does, however, show promising results in lab experiments, but its readiness in pilot-scale studies varies greatly. Their commercialization and transition to pilot studies face a number of challenges, including cost-effectiveness, technical obstacles, potential human and environmental risk, and so on. The pilot-scale application of nanomaterials in removing pharmaceutical compounds from surface and wastewater sources faces two major challenges. The first is to evaluate the material's effectiveness and performance in both systems, as well as the removal efficiency and applicability of various techniques to validate the nanoparticles by enabling sensing techniques. Second, the long-term efficacy of these nanomaterials is still unknown because most reported studies were conducted in labs for a short period of time. Despite nanoparticles' high removal efficiencies, the adaptation of these technologies to pilot studies is solely dependent on potential risk and cost-effectiveness. Except for nanoscale ion-oxide, nano TiO\({}_{2}\), and polymeric nanofibers, the current cost of nanomaterials is prohibitively high. However, by reusing and retaining the nanomaterials, this can be improved. By increasing the reactivity and selectivity of the nanomaterials, surface modification of nanoparticles results in the removal of pharmaceutical compounds. Derivatization improves photo-catalytic activity by combining the nanomaterial with a catalytic metal, increasing the efficiency and affinity of the nanoparticles towards the contaminant. The use of modified materials to remove NSAIDs and ARV drugs from wastewater is minimal, and different approaches should be considered to overcome the role of other contaminants such as complex organic compounds, antibiotics, heavy metal ions, dyes, viruses, and so on. Although these functionalized nanoparticles are made using low-cost and simple methods, producing them on a large scale remains difficult. It is also critical to consider this from an engineering standpoint. Compatibility between current water treatment processes and the aforementioned nanotechnologies must also be addressed. Focus should be placed on implementing nanotechnology with minimal changes to the existing distribution system and treatment plant.
## 2 WSN-Based ITS Applications
### Traffic Law Enforcement Applications  
Traffic law enforcement applications can be considered as a special case of traffic safety applications, since one of the final goals of traffic laws is to increase safety. Currently, traffic law violations are usually detected and put into effect when a police officer or a traffic enforcement vehicle is nearby. WSNs though, offer permanent monitoring of the locations where they are deployed, enabling to automate the process of reporting infractions. Some laws which can be supervised are speeding, illegal parking, going through red traffic lights, unauthorized use of bus lanes or access to restricted or congestion charge areas; yet the first two are those typically included in applications so far. Applications such as [15,16] detect speed limit violations with high precision through the collaboration between adjacent nodes. They rely on cameras triggered upon the detection of the infraction, whose photographs are sent to a Traffic Management Center (TMC) where they are processed and stored. In addition, it is also possible to warn drivers by means of Variable Message Signs (VMS) before proceeding to fining. Illegal parking is also detected by [16] using sensor nodes placed next to curbs which, in turn, after warning through a loudspeaker, activate a camera that takes a picture of the license plate number of the vehicle.  
Another application related to traffic law enforcement is post-accident investigation, performed in order to determine responsibilities after an accident. WSNs deployed along the road for a particular purpose get data that is used within a short period of time to fulfill this purpose, e.g., traffic safety. However, it is also possible to permanently store this information and use it later to investigate the causes of the accident (car accident forensics). Although WSN devices are, by nature, constrained devices, they are less and less constrained in storage capacity and can thus hold an important amount of information. Subsequently, this information can be wirelessly gathered by some super-users of the system with special privileges through secure access methods [17], allowing to judge the drivers' driving style and taking into account the road conditions at the moment of the accident.
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Simulation Results
#### 6.3.1 Network Lifetime Evaluation
In addition to the reasons mentioned before, which made the proposed approach outperform the other algorithms in the first scenario, the proposed approach effectively bypasses the danger zones and discovers a safer path for the mobile sink through its modified environmental impact metric. Simultaneously, it tries to reduce the influence of environmental factors on the routing performance of urgent data packets by skipping the risk zone and finding the safest pathways to the mobile sink. In prior research [18; 19; 22; 24], the mobile sink would pass hazardous zones since they do not address environmental influence on network performance. This makes the mobile sink easy to be cut off due to environmental reason, causing the network failure. Therefore, all the previous works in [18; 19; 22; 24] achieve roughly the same lifetime as network failure once the sink node visits a cluster head in a danger zone. In contrast, environmental events occur at 400 seconds from the start of the network operation.
## 4 Experiment  
### Dataset Preparation  
#### 4.1.1 Original Dataset  
In this study, data were acquired from two principal sources: on-site collection and publicly available datasets. Detecting non-PPE poses a unique challenge because real-world imagery often depicts workers fully equipped with PPE, yet identifying instances where PPE is missing is vital. For the on-site component, data were gathered from five construction sites in South Korea by mounting a GoPro HERO9 Black camera on the helmets of 35 workers during peak hours. Video footage was recorded continuously, and frames were extracted at regular intervals to capture authentic site conditions (Figure 9a).  
Figure 9: Example images of constructed datasets.  
Although the initial data-collection effort yielded 54,329 images, a balanced sampling strategy was applied to ensure adequate representation of both fully compliant (all PPE worn) and non-compliant (missing PPE) scenarios, reducing the on-site dataset to 1207 images. In addition, two publicly available construction datasets were incorporated: 530 images from the Safety Helmet Wearing Dataset (SHWD) [29] and 565 images from the Safety Helmet Computer Vision Project (SHCVP) [30]. These resources broadened the range of visual contexts for non-PPE detection. Overall, 2302 images were compiled from both the on-site collection and public sources, with illustrative examples shown in Figure 9b.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.3 Hyper-Parameter Tuning  
Hyper-parameter tuning is crucial as these parameters, which are set manually rather than learned from the data, dictate the behavior and efficacy of the learning algorithm. However, choosing the right hyper-parameters is complex, as their optimal values depend on both the data characteristics and the algorithm's learning capacity. Moreover, appropriate hyper-parameters significantly influence the model's ability to generalize and yield accurate predictions. Therefore, the process involves meticulously testing various hyper-parameter combinations to identify the most effective settings that enhance model performance, typically assessed on a validation set or through cross-validation methods. Among tuning techniques, grid search is favored in many classical ML scenarios for its simplicity and comprehensiveness. It methodically explores every potential combination within a defined grid, ensuring that no possible option is overlooked, thereby increasing the likelihood of discovering the optimal configuration.
* Grid search optimization: In our research, we utilize a grid search to fine-tune hyper-parameters. This method thoroughly explores all parameter combinations within a grid, employing cross-validation to pinpoint optimal hyperparameters. It ensures a comprehensive exploration of the configuration space, ultimately improving model performance. Moreover, the simplicity and effectiveness of grid searches are undersocored by our experiment, where we employ a grid search tailored for each baseline technique alongside 5-fold cross-validation to optimize hyperparameters. Further details on the best parameters can be found in Table 2.
## 5 Home Automation System Based on Zig-Bee
### The Architecture Related to Zig-Bee Protocol  
The Zig-Bee protocol generally consists of four layers:  
1. The first layer of the Zig-Bee protocol is called the Application Layer (APL). This layer converts input to digital data, change input to digital data, and digital data to output [74].
2. The next layer specifies the source and destination of the information. The logical transformation of addresses to be understandable for layering is done in this layer. Also, traffic network control is done in this layer. One of the most essential devices in computer networks, called the router, operates in this layer because, in this layer, information packets (Packets) deal with the IP protocol for the route, and the router works with IP [75].
3. (MAC): This layer employs the carrier sensed multiple access with collision avoidance (CSMA-CA) mechanism for channel access, personal area network (PAN) association, disassociation, network synchronization beacons, and device security [75].
4. (PHY): This layer includes radio communication (modulation and demodulation), which is responsible for transmission and reception. With a frequency of 2.4 GHz in this layer, information can be sent and received up to 250 kbps [74].
# Monitoring Forest Phenology in a Changing World
## 2 Monitoring Phenology
### Trade-Offs among Phenological Monitoring Techniques
#### 2.3.4 Data Processing and Interpretation
Future applications that provide easily accessible remotely sensed data, that require no pre-processing, or require no data manipulation experience represent an important development avenue for the study and monitoring of phenology.
# Delay Management in Regional Railway Transport
## 2 Materials and Methods
### Waiting Time Issue
#### 2.2.2 Bindings between Waiting Times, Infrastructure Capacity, and Timetable Stability
Equation (1) provides a calculation of a stability coefficient \(c_{stab}\) that reports how many timetable periods are needed for the total elimination of input delay \(t_{d,input}\), due to the existence of spaces and reserves in the timetable. (\(\sum t_{spaces,period}\) is the sum of spaces in one timetable period; \(\sum t_{resres,period}\) is the sum of reserves in one timetable period). \[c_{stab}=\frac{t_{d,input}}{\sum t_{spaces,period}+\sum t_{resresres, period}} \tag{1}\]  
Timetable stability cannot be marginalized while assigning transfer waiting times. Every delayed train disrupts the ride of all waiting trains, and the delay tends to spread through the net [18]. The greater the timetable interconnectedness, the greater the influence will be upon waiting times for delayed trains, because the number of affected trains increases disproportionally with the growing number of bindings in the timetable and the delay elimination time. For example, if there is a delay, it is transmitted from each train to \(v\) trains (where the intersection of the sets of trains to which the delay is transmitted is an empty set), there are \(n\) degrees until the delay is eliminated, then the total number of trains affected by the input delay can be described by a power function of order \(n\), as shown in Equation (2). The dependence of the number of affected trains on timetable  
Figure 1: Capacity balance according to UIC-Codex 406 [20; 21]. interconnectedness with 5 degrees of delay transmission is graphically displayed in Figure 2. Figure 3 describes the transmission of delay between trains and the gradual elimination of delay in the case of a stable timetable. The sum of the delays of all trains that were delayed (directly or indirectly) by the ride of delayed train A, with input delay \(t_{input,A}\), is described in the chart in Figure 4.  
\[N=\sum_{i=0}^{n}v^{i} \tag{2}\]  
Figure 3: Delay transmission between trains and gradual elimination of the delays. Source: the authors. Figure 2: Dependence of the number of trains affected by an input delay on the number of bindings of every train transmitting the delay when the delay is spread in 5 degrees. Source: the authors. For the reasons described above, transfer waiting times should have, at a maximum, such values that maintain the timetable's stability (e.g., reaching no more than a certain value of stability coefficient). This means that it should be possible to eliminate delay that is caused by waiting for a delayed connecting train, for an adequate amount of time using the reserves and spaces in the timetable.
# Energies of Mechanical Fractional-Order Elements: Causal Concept and Kernel Effects
## 2 Lorenzo-Hartley Concept: A Thorough Evaluation and New Causal Interpretation
### Two Examples from [PERSON] and [PERSON]
#### 2.4.2 Ramp Force  
With \(F(t)=t\) (a linear ramp),  
\[F(t)=\begin{cases}t,&0\leq t\leq 1\\ 0,&1<t\end{cases} \tag{16}\]  
From (3), displacement for \(0<t\leq 1\) is  
\[x(t)=\frac{1}{k_{\lambda}}\frac{1}{\Gamma(\lambda)}\int\limits_{0}^{t}\frac{ \tau}{(t-\tau)^{1-\lambda}}d\tau=\frac{1}{k_{\lambda}}\frac{t^{\lambda+1}}{ \Gamma(2+\lambda)},\quad 0<t\leq 1, \tag{17}\]  
meaning that we have  
\[\frac{dx(t)}{dt}=\frac{1}{k_{\lambda}}\frac{(1+\lambda)t^{\lambda}}{\Gamma(2+ \lambda)}=\frac{1}{k_{\lambda}}\frac{t^{\lambda}}{\Gamma(1+\lambda)},\quad 0<t \leq 1. \tag{18}\]  
The displacement for \(t\geq 1\) is  
\[x(t)=\frac{1}{k_{\lambda}}\frac{1}{\Gamma(\lambda)}\Biggl{[}\int\limits_{0}^{ 1}\frac{\tau}{\left(t-\tau\right)^{1-\lambda}}d\tau+\int\limits_{1}^{t}\frac{ 0}{\left(t-\tau\right)^{1-\lambda}}d\tau\Biggr{]},\quad t\geq 1, \tag{19}\]  
Figure 2: Work (energy) of a fractional Maxwell element for the causal Formulation (3): (**a**) short times, (**b**) medium times, and (**c**) long times. The labels along the axes are the same as in the work of [PERSON] and [PERSON] [6], allowing for easy comparison of the results. The dashed lines correspond to \(\lambda=0\).  
that is, from (18) we obtain  
\[\frac{dx(t)}{dt}=\frac{1}{k_{\lambda}}\frac{t^{\lambda}}{\Gamma(1+\lambda)}. \tag{20}\]  
Now, the work is  
\[W_{linear\ ramp}=\int\limits_{0}^{t}\underbrace{t}_{Force}\frac{1}{k_{\lambda}} \frac{t^{\lambda}}{\Gamma(1+\lambda)}\quad dt=\frac{1}{k_{\lambda}}\frac{t^{ \lambda+2}}{(2+\lambda)\Gamma(1+\lambda)},\quad t<1. \tag{21}\]  
The plots in Figure 3 show \(W_{linear\ ramp}(t)\) in two time intervals.  
Following this result, we have two distinct behaviors for \(0\leq\lambda\leq 1\) (the viscoelastic range) [6]:  
* For \(t\leq 1\), the work increases in time (Figure 3 in [6]).
* For \(t>1\) we have \(F(t)=0\); therefore, the added work is zero.
* For \(\lambda=1\), we have a pure viscous loss and there is no displacement when \(F(t)=0\).
* For \(\lambda=0\), the loss is in the elastic range and the displacement falls to zero immediately after the force is removed (\(F(t)=0\)) [6].  
**Remark 3**.: _The last comment concerning the case when \(\lambda=0\), stating that the immediate displacement falls to zero, is physically incorrect, as there are no processes changing with infinite rates. In this context, from (20) with \(\lambda=0\), we have \(\frac{dx(t)}{dt}=\frac{1}{k_{\lambda}}\), that is, the rate of the displacement is a constant. Moreover, there is a relaxation process from the maximal attained displacement down to zero after the force is removed; this model does not account for such effects._
## Abbreviations  
\(\mathbf{x}\) vector of the state  
\(\mathbf{x}_{k|k-1}\)\(\mathbf{x}\) of the prediction  
\(\mathbf{x}_{k|k}\)\(\mathbf{x}\) of the update at time index \(k\)  
\(\mathbf{z}\) vector of the measurement  
\(\mathbf{z}_{k|k-1}\)\(\mathbf{z}\) of the prediction  
\(\mathbf{z}_{k}\) the measurement at time index \(k\)  
\(\mathbf{v}_{k-1}\) vector of the process noise  
\(\mathbf{e}_{k}\) vector of the measurement noise  
\(\mathbf{Q}_{k-1}\) covariance of the process noise  
\(\mathbf{R}_{k}\) covariance of the measurement noise  
\(\mathbf{P}\) covariance of the state  
\(\mathbf{P}_{k|k-1}\)\(\mathbf{P}\) of the prediction  
\(\mathbf{x}_{k}\)\(\mathbf{P}\) of the update at time index \(k\)  
\(\mathbf{P}_{2,k|k-1}\) innovation covariance  
\(\mathbf{x}_{x,k|k-1}\) cross-covariance  
\(D_{k-1}\) history of input-measurement pairs up to \(k-1\)  
\(\mathbf{K}_{k}\) Kalman gain  
\(\mathbf{y}\) direction vector  
\(\mathbf{r}\) radius  
\(\mathbf{a}_{j}\)\(j\)-th sample point of the regular simplex  
\(\mathbf{\gamma}\) a set of the orthogonal transformation points  
\(\mathbf{\gamma}_{i}\)\(i\)-th orthogonal transformation point  
\(\mathbf{B}\) orthogonal matrix  
\(B_{i}\) orthogonal sample point  
\(w_{k}^{c}\) weight associated with \(k\)-th quadrature point  
\(w_{j}^{s}\) weight associated with \(\mathbf{a}_{j}\)  
\(n_{x}\) dimension of state space  
\(n_{z}\) dimension of measurement space  
\(n_{c}\) order of the Chebyshev-Laguerre polynomials