esa-sceva/satcom-chunk-collection huggingface dataset:
# Motives for Instagram Use and Topics of Interest among Young Adults
## 4 Results
### Influence of Professional Training Background on Motive for Instagram Use
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Motives** & **Training** & **N** & **M** & **SD** & \(p\) \\ \hline
1. To follow my friends & Design & 172 & 3.59 & 1.228 & 0.003 \({}^{**}\) \\  & Engineering & 135 & 3.97 & 1.000 & 0.003 \({}^{**}\) \\ \hline
2. To see updates of my friends & Design & 172 & 3.74 & 1.188 & \\  & Engineering & 135 & 4.13 & 0.904 & 0.001 \({}^{**}\) \\ \hline
3. To Interact with others (comment \& like) & Design & 172 & 3.01 & 1.228 & \\  & Engineering & 135 & 3.44 & 1.048 & 0.001 \({}^{**}\) \\ \hline
4. To see the updates of those I follow & Design & 172 & 3.87 & 1.180 & \\  & Engineering & 135 & 3.59 & 1.082 & \\ \hline
7. To appreciate different lifestyles and photography works & Design & 172 & 4.27 & 0.897 & \\  & Engineering & 135 & 3.89 & 1.056 & \\ \hline
8. To seek creative inspiration & Design & 172 & 4.02 & 1.017 & \\  & Engineering & 135 & 3.25 & 1.183 & \\ \hline
9. To look at current fashion brand trends & Design & 172 & 3.63 & 1.144 & \\  & Engineering & 135 & 3.17 & 1.249 & \\ \hline
10. To look at current fashion wear and outfits & Design & 172 & 3.59 & 1.154 & \\  & Engineering & 135 & 3.26 & 1.287 & \\ \hline
25. To Show off my pets & Design & 172 & 1.99 & 1.264 & \\  & Engineering & 135 & 2.36 & 1.308 & \\ \hline \hline \end{tabular} \({}^{*}p<0.05\), \({}^{**}p<0.01\), \({}^{***}p<0.001\).  
\end{table} 
Table 1: Influence of professional training background on motive for Instagram use. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Motives** & **Gender** & **N** & **M** & **SD** & \(p\) \\ \hline
8. To seek creative inspiration & Male & 133 & 3.52 & 1.172 & 0.032 \({}^{*}\) \\  & Female & 174 & 3.80 & 1.131 & \\ \hline
15. To escape awkward situations (pretending to be busy or in traffic) & Male & 133 & 2.95 & 1.328 & \\  & Female & 174 & 3.33 & 1.255 & \\ \hline
22. To become popular (gain followers) & Male & 133 & 2.47 & 1.259 & \\  & Female & 174 & 2.14 & 1.050 & \\ \hline
24. To self-promote myself & Male & 133 & 2.88 & 1.332 & \\  & Female & 174 & 2.39 & 1.116 & \\ \hline
26. To create art & Male & 133 & 3.11 & 1.298 & \\  & Female & 174 & 2.68 & 1.148 & \\ \hline
27. To show off my photography skills & Male & 133 & 3.15 & 1.379 & \\  & Female & 174 & 2.59 & 1.207 & \\ \hline \hline \end{tabular} \({}^{*}p<0.05\), \({}^{***}p<0.001\).  
\end{table} 
Table 2: Influence of gender on motives for Instagram use (male vs. female).
## Materials and Methods  
### Site Description and Experimental Design  
The experiment took place at the remote sensing field test facility of Forschungszentrum Julich in Selhausen (Niederzier, Germany). Two 2 by 2 metre wooden boxes were built with a soil depth of 40 cm (Figures 1 and 2).  
The boxes were filled with the soil from the site, containing 13% sand, 70% silt and 17% clay, according to the USDA textural classification [25]. The soil was placed on top of a gravel layer, with geotextile separating the silty loam from the gravel layer. This setup allows standing water to drain away, limiting its water storage capacity. Gravel and stones were removed from the soil.  
A tripod with a hyperspectral instrument (Fluorescence box--FloX) was placed in between the boxes. The tripod was installed in such a way that the spectrometer could pivot between the boxes. During the vegetation development phase, identical irrigation schemes were applied to both boxes. After the leaf development phase, the boxes were subject to different irrigation regimes, causing one box to become significantly dryer compared to the other. By default, the FloX instrument was installed to measure the dry box. During specific days, the cross-arm on the tripod was turned for the FloX to measure over the reference box.  
Figure 1: Schematic view of the box design.  
The test facility was located next to the Integrated Carbon Observatory System (ICOS) and TERENO (Terrestrial Environmental Observatories) test site at Selhausen, from which data on air humidity and air temperature were used. Soil moisture, soil temperature and soil water potential (matric potential) were monitored in the plant boxes themselves. Table 1 gives an overview of the sensors used. The soil water availability was estimated by means of the soil water potential, expressed in pressure heads (h). These were converted to a pF value (Equation (1)). Soil moisture and soil water potential provide complementary information. Soil moisture is more commonly used in modelling of the hydrological cycle as it is easier to measure at large scale [26], whereas soil water potential corresponds more closely to the water the plant can extract from the soil [27] ([PERSON], 2018).  
\[\mathrm{pF}=-\log(\mathrm{h}) \tag{1}\]  
Two measurement campaigns were conducted. The mustard was sown on May 29 th (DOY 149), 2021 and harvested on August 2 nd (DOY 214), 2021. The lettuce was planted (not sown) on August 7 th (DOY 219), 2021 and harvested on September 22 nd (DOY 265), 2021.
###### Abstract  
Background: Temporary anchorage devices (TADs) are broadly used in orthodontic treatment. TADs must be placed accurately to avoid collision against tooth roots. To place miniscrews with a higher accuracy, a digitally designed surgical guide was proposed in previous studies. However, to our knowledge, there have been no articles that have assessed the minimal required length to achieve good drilling accuracy. The objective of the present study was to evaluate the accuracy of pre-drilling using computer-aided designed surgical guides with different lengths. Methods: A typodont model was scanned, and surgical guides of 3.0, 4.5, and 6.0 mm were designed. Duplicated typodonts and the surgical guides were printed with a 3D printer. Using these models and surgical guides, pre-drillings were performed. Freehand drilling was also conducted for the control. The drilled models were scanned with micro-computed tomography to evaluate the accuracy. Results: The mean errors at the tip of the drill were 0.44 mm, 0.61 mm, 0.41 mm, and 0.24 mm for the freehand drilling, and 3.0, 4.5, and 6.0 mm for the surgical guide, respectively. Conclusion: The results suggested that a longer surgical guide was recommended to achieve good insertion accuracy for a narrow interradicular space, and that 3.0 mm was enough when an error of 1.0 mm was acceptable for the insertion site.  
t  
temporary anchorage device; TADs; miniscrews; CBCT; pre-drilling; orthodontic anchor screws; surgical guide
## 4 Discussion
In fact, only two conditions were selected for each variable (\(d=l_{\rm thigh}\) or 0.5\(\cdot l_{\rm thigh}\); C\({}_{\rm GR}\) shifted at rearfoot or forefoot, C\({}_{\rm wall}\) shifted at the pelvic or scapular zone). However, for each variable, the two selected conditions constitute appropriate boundaries that enclose the natural range of the variable variation. For example, a \(d\) distance slightly lower than 0.5\(\cdot l_{\rm thigh}\) with the knee flexed at 45\({}^{\circ}\) was not accessible to all participants due to the lack of necessary ankle dorsiflexion mobility. Likewise, a distance \(d\) slightly greater than \(l_{\rm thigh}\) caused limited stability between the foot and the ground (with a marked tendency of the foot to slide forward) in some participants. The boundary values of the variables are also the most appropriate in order to identify the conditions that maximise muscle activity and to determine these maximum levels. Another limitation is related to the postural position maintained during the exercise. All participants reported a clear self-perception of their ability to shift C\({}_{\rm wall}\) between the scapular and the glutelae zone and C\({}_{\rm GR}\) between the rearfoot and forefoot while maintaining a static wall-squat position with neutral spine alignment [32], vertical trunk orientation, and the whole foot plant on the ground. The posture that participants assumed during the exercise was carefully controlled by the investigators, even though the forward tilt angle of the pelvis was not precisely determined. Critically, this angle can influence the degree of lumbar lordosis and muscle activity in the lower back and lower limbs. Further research is, therefore, needed to investigate the effect of this variable. A further limitation stems from the fact that the exact position of C\({}_{\rm wall}\) and C\({}_{\rm GR}\) during the trials was not measured experimentally. This would have required the use of force platforms placed under each foot and on the wall supporting the back. Shifts in C\({}_{\rm wall}\) and C\({}_{\rm GR}\) were voluntarily performed and freely controlled by the participants without the use of any feedback signal. Aside from the lack of this information in the present study, our experimental setup may be useful to therapists working in rehabilitation environments that are not equipped with technologically advanced devices.
## 4 Case Studies
### Corrective Line Flow Control  
For transmission line flow control, we focus on controlling key transmission corridors with a high risk of overloading issues in East China. Based on historical observation, one provincial power grid in East China, which contains five key transmission corridors, was selected for verifying the effectiveness of the proposed RL-based control framework because of overloading concerns during the summer peak period over the past few years. To test the performance of the RL-based control in simultaneously regulating multiple line flows, a total of 51,891 valid EMS snapshots were collected that represented actual system operating conditions from January 2020 to July 2020 to form a dataset. The control objective is set to ensure the active power flows of the five selected transmission corridors are below 90% of their corresponding operating limits, also known as the pink line used in the control center. Moreover, an additional term representing the total amount of control actions is  
Figure 4: Performance of RL agent for regulating voltage profiles and transmission losses.  
Figure 5: Loss values of Pi, V, Q1, and Q2 of the SAC agent during the training phase.  
used to penalize more actions in the reward function to minimize the amount of controls for mitigating line overloading issues.  
In this case study, the state space consists of line flows and active power outputs of selected generators that can be dispatched in real time for emergency control. The action space consists of the active power outputs of the selected 150 generators. Among all the episodes collected in the above dataset, 67% of the cases are used for training the RL agent, while the remaining 33% are used for testing purposes. The reward accumulation vs. episode is plotted in Figure 6 (upper). It can be observed that the RL agent learns to control line flows within 90% of their limits using a maximum number of three iterations. During the control process, the maximum amount of
## 2 Methods and Data
### Two Assessment Methods Based on the Newmark Model
In previous studies, various researchers have obtained numerous empirical formulas in different regions, but these formulas often exhibit regional characteristics and lack universality; the same formula may yield significantly different fitting results in various regions [42; 43]. Therefore, it is advisable to employ fitting formulas that are most suitable for the specific study area. To ensure regional applicability, we utilized an empirical formula derived from previous research--specifically based on cumulative displacement records from the Wenchuan strong earthquake--to calculate the cumulative displacement of the rock-soil masses in the current earthquake event (refer to Formula (3)) [36]. \[\mathrm{lg}D_{\mathrm{N}}=0.194+\mathrm{lg}[\left(1-\frac{A_{\mathrm{c}}}{PGA }\right)^{2.262}*\left(\frac{A_{\mathrm{c}}}{PGA}\right)^{-1.754}] \tag{3}\]  
The formula utilizes \(D_{\mathrm{N}}\) to represent the permanent cumulative displacement (in cm), while \(A_{\mathrm{c}}\) and \(PGA\) denote the critical acceleration (in g) and peak ground acceleration (in g), respectively. Notably, the empirical formula boasts an impressive coefficient of determination \(R^{2}\) of 91.4%. Figure 2 illustrates the processes of these two assessment methods, showing the similarities and differences between them. Both methods were based on calculations utilizing DEM data and geological maps to determine the critical acceleration (\(A_{\mathrm{c}}\)) of the rock-soil masses within the study area. Notably, the difference method employed the \(PGA\)-\(A_{\mathrm{c}}\) difference to directly assess the earthquake-triggered landslide risk, whereas the cumulative displacement method used an empirical formula to calculate the Newmark displacement based on \(PGA\)-\(A_{\mathrm{c}}\), and it subsequently evaluated the earthquake-triggered landslide risk based on the rock-soil mass's Newmark displacement. Finally, the assessment results of the two methods were verified using actual landslide data.
## 3 Results
### Photoconductive Characteristics  
The effectiveness of the photoconductivity of the ZnO NWs-PVDF NF layer structure is confirmed by I-V and I-t characteristics curves. As shown in Figure 6a, the I-V characteristic curve is analyzed by measuring the DC voltage sweep from -10 to 10 V with an increment of 0.01V. The I-V curves indicate the conductivity of the film under dark and light source radiation in atmospheric conditions. The resistance ratio (\(R_{\mathit{off}}/R_{\mathit{on}}\)) of the ZnO NWs-PVDF NF layer is 40.9. Light response performance has affected the density of the ZnO NWs on the layer [27]. The sensitivity (\(S\)) of the ZnO NWs-PVDF NF layer is expressed as Equation (3) [28]:  
\[S(\%)=\frac{I_{\mathit{on}}-I_{\mathit{off}}}{I_{\mathit{off}}}\text{-}100 \tag{3}\]  
where \(S\) is 39.37, from which I-V curve data are used to determine the sensitivity as a 1% truncated average. Compared with the present photodetectors, based on ZnO nanostructures [29], the ZnO NWs-PVDF NF layer demonstrates the applicability of a light-assisted polar gas molecule detector at room temperature. The time-dependent photocurrent response shown in Figure 6b is the I-t characteristic curve of ZnO NWs on a PVDF NF layer at 1 V DC bias for 1000 s under continuous light source radiation and the dark condition. Initially, photoconductivity response increases rapidly up to 0.028 \(\upmu\)A for 30.9 s. After a maximum current point, the current shows a decay power curve, y = a\(\cdot\)xb (a = 0.0624, b = \(-\)0.2184), through the curve fitting.
# Robust Controller Decorated by Nonlinear S Function and Its Application to Water Tank
## 3 Controller Design
\right]\) is added, the sensitivity function \(S=\mathbf{C}(s\mathbf{I}-\mathbf{A})^{-1}\mathbf{B}\) of the system can be obtained, followed by the complementary sensitivity function \(T\) of the system. \[T=1-S=\frac{T_{0}T_{1}s^{2}+(T_{0}+T_{1})s+1-T_{1}}{(T_{0}s+1)(T_{1}s+1)} \tag{36}\]  
According to the system robustness theorem, Formula (37) can be obtained as  
\[\left\|Tw\right\|_{\infty}\leq\rho \tag{37}\]  
Derivable  
\[\left\|T\right\|_{\infty}\leq 1 \tag{38}\]  
According to Formula (36), if you want to satisfy Formula (38), the damping coefficient of a second-order oscillation system requiring a complementary sensitivity function is greater than or equal to 1. \[\frac{T_{0}+T_{1}}{2\sqrt{T_{0}T_{1}}}\geq 1 \tag{39}\]  
Therefore,  
\[T_{0}^{2}+T_{1}^{2}-2T_{0}T_{1}\geq 0 \tag{40}\]  
According to [PERSON]'s inequality, it can be obtained that  
\[2T_{0}T_{1}\leq T_{1}^{2}+\frac{(2T_{0})^{2}}{4}=T_{1}^{2}+T_{0}^{2} \tag{41}\]  
Therefore, Formula (39) is automatically satisfied; hence under the condition that the state variables of water tank level control system satisfy \(\left\|\mathbf{X}\right\|_{\infty}>\rho/\sqrt{2(1-a^{2})}\), as long as \(T_{1}>0\) and \(T_{0}>0\), the robustness of the designed controller can be guaranteed, which is more relaxed than the original condition, and also consistent with the simulation results.
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 4 Experimental Section
### Ablation Experiments
#### 4.5.1 Model Design  
Some of the main modules are as follows:  
ENA(ENA-GNN): The introduction of node and edge cross-attention in the original GNN messaging process to enhance the relevance of the nodes and edges in relational reasoning.  
GW: Group re-weighting for different categories, using focal loss function while considering category equilibrium, aims to improve the prediction effect of low-frequency categories with few samples.  
UME: The unbiased meta-embedding vectors, which are obtained from category label learning, are grouped according to the sampling ratio of the categories, and their weights are reduced in the knowledge fusion process for the group where the common categories are located in order to avoid interfering with the training of the common categories.  
MP(MP-DGCNN): A new point feature extraction and encoding network model with multi-scale-sampled point clouds, combined with a pyramid structure to jointly  
Figure 9: Visual qualitative results of scene graph classification (SGCls). Where the results with wrong or ignored predictions are shown in red, the results with correct predictions are shown in green, and the results with reasonable inferences with correct predictions but unlabeled true labels are shown in blue. Where (**a**) shows the instance segmentation mask of the scene; (**b**) shows the KSFPN [14] for the scene graph prediction and explicitly distinguishes the results with incorrect predictions for similar objects; (**c**) is the prediction results for our model.  
extract the multi-level geometric features of point clouds and improved initialization of the edge features.  
Table 4 shows the ablation tests of the different network parts of the model with the training strategy. From the experimental results, we can conclude that the model approach proposed in this paper has the best overall effect for the final experiment. Among them, MP-DGCNN has the most apparent overall improvement in the scene graph task (SGCls). It shows that the point feature extraction network is critical for the 3D scene graph prediction task and will directly determine the accuracy of the subsequent relational inference. In the predicate classification task, the ground-truth labels of the objects are known, and MP-DGCNN only encodes the feature of the edges, which has a weak impact on the final experimental results. While using the re-weighting group strategy will significantly improve the mR@K, which can alleviate the phenomenon of the long-tail distribution of data and balance the prediction effect of different grouping categories. Nevertheless, it accordingly decreases the R@K and ngR@K slightly, i.e., there is an inevitable negative impact on the overall prediction.
## 6 Simulation Verification and Analysis
### Robustness Analysis of the TDC-VSG Under the Inductance Variation in the Power Grid  
To verify the robustness of the TDC-VSG adaptive control strategy against variations in power grid inductance, the simulation is conducted under different inductance values while maintaining the same control parameters, as depicted in Figure 17. As illustrated in Figure 17, despite variations in power grid inductance values, the TDC-VSG adaptive control strategy significantly mitigates active power oscillations and frequency fluctuations within the system during given power and grid frequency variations. Consequently, the TDC-VSG control strategy demonstrates adaptability to varying power grid intensities, providing robust support for the stable operation of distributed power supplies connected to the grid.  
Figure 16: Results of output voltage and current under different control strategies.  
Figure 17: Simulation results of the TDC-VSG control strategy under different power inductance values.
# Detecting Jamming in Smart Grid Communications via Deep Learning
## 6 Performance Evaluation  
In this section, we analyze the performance of our proposed solution. We start by defining the metrics, and then we show that our analysis considers PLC links characterized by negligible BER, i.e., where the jammer does not affect the quality of the communication link, and, finally, we discuss performance for different scenario parameters, i.e., the distance between the jammer and the receiver, the size of the images generated from the \(I-Q\) samples, and, lastly, the relative jamming power.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 5 The Layered Architecture of the IoV and Emerging Technologies in Smart Cities
### Coordination Layer  
This layer is a very important layer that functions as the middle layer between the lower layer and the upper layer of AI. The coordination layer at its core is actually a virtual universal network coordination module, which is the centerpiece of the integration of different networks like Wi-Fi, 4G LTE (long-term evolution), or WAVE. Such integration is critical to furnish a complete data stream that makes communication data between multiple communicative sources of the IoV ecosystem flow. Supporting IoV application and effective communication over the entire network requires this layer to be able to harmonize data from different sources.
# Particle Swarm Optimisation for Emotion Recognition Systems: A Decade Review of the Literature
## 5 Discussion
### Data Mining Modalities  
Text mining is another modality for ERS, which involves analysing human emotions through text using data mining techniques. This method is commonly used in sentiment analysis [29]. One study found in the reviewed literature used PSO for feature selection in text mining to classify emotions [64]. The study extended previous research on feature selection classifiers to analyse text mining. The results showed that the impact of PSO on the accuracy of the classifiers was minimal when PSO feature selection was used. The study suggested that future research should focus on more datasets to better understand the impact of PSO and other variants of PSO on ERS through feature selection in data mining [64].