esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Outcomes and Estimation
#### 3.2.2. Test for Differences between Treatment Effects
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**A/B (n = 15)**} & \multicolumn{4}{c}{**B/A (n = 14)**} \\ \cline{2-10}  & **Average Rank** & **Mdn** & **IQR** & **Average Rank** & **Mdn** & **IQR** & **Z** & **\(p\)** & **r** \\ \hline Hz wrist [cyc/s] & 9.23 & 0.00 & 0.30 & 21.18 & 0.35 & 0.50 & \(-\)3.81 & \(<\)0.001 & 0.71 \\ MaxROM wrist [mm] & 9.60 & \(-\)0.20 & 1.80 & 20.79 & 3.35 & 5.55 & \(-\)3.54 & \(<\)0.001 & 0.66 \\ HzF5 & 9.47 & 0.00 & 0.80 & 20.93 & 0.30 & 0.40 & \(-\)3.65 & \(<\)0.001 & 0.68 \\ MaxROM F5 & 13.83 & 0.30 & 2.00 & 16.25 & 0.40 & 4.10 & \(-\)0.77 & 0.444 & 0.14 \\ HzF4 & 10.03 & 0.00 & 0.80 & 20.32 & 0.30 & 0.40 & \(-\)3.28 & 0.001 & 0.61 \\ MaxROM F4 & 13.37 & 0.20 & 2.00 & 16.75 & 1.15 & 2.65 & \(-\)1.07 & 0.285 & 0.20 \\ HzF5 & 9.50 & 0.00 & 0.70 & 20.89 & 0.35 & 0.40 & \(-\)3.62 & \(<\)0.001 & 0.67 \\ MaxROM F3 & 11.00 & \(-\)0.10 & 1.70 & 19.29 & 1.10 & 2.75 & \(-\)2.63 & 0.009 & 0.49 \\ HzF2 & 9.53 & 0.00 & 0.80 & 20.86 & 0.30 & 0.40 & \(-\)3.60 & \(<\)0.001 & 0.93 \\ MaxROM F2 & 11.90 & \(-\)0.30 & 1.30 & 18.32 & 0.30 & 2.43 & \(-\)2.03 & 0.042 & 0.52 \\ HzF1 & 12.67 & 0.00 & 0.50 & 17.50 & 0.30 & 0.58 & \(-\)1.53 & 0.125 & 0.40 \\ MaxROM F1 & 9.77 & \(-\)0.30 & 2.10 & 20.61 & 0.60 & 2.73 & \(-\)3.43 & 0.001 & 0.89 \\ Grip strength [kg] & 12.00 & \(-\)0.10 & 0.80 & 18.21 & 0.45 & 2.95 & \(-\)1.97 & 0.049 & 0.51 \\ \hline \hline \end{tabular} A/B—A-tipstim® group (first measurement), B-Placebo (second measurement); B/A; B-placebo group (first measurement), A–tipstim® (second measurement); ROM—range of motion; one cycle = the movement from flexion to extension. Bolded \(p\)-values are statistically significant alt level \(p<0.05\).  
\end{table} 
Table 5: Comparison of groups in terms of parameter differences from the first and second measurement without stabilization. 2.3. Relationships between Tipstim(r) Parameters (Median/Ulnar Sensation) and Motor Coordination Parameters  
Using the Spearman correlation coefficient, the relationship between the tipstim(r) parameters (median and ulnar sensation) and the motor coordination parameters in the non-stabilized and stabilized position was checked. The correlation coefficients for the performed analyses are presented in Table 8.
## 3 Results and discussion
### Climate Zone of the Anti-Icing Asphalt Mixture  
Because the climatic conditions in different regions of the world are very different, the climate zone of the anti-icing asphalt pavement is presented for ensuring that the anti-ice asphalt road can adapt to the climate. Firstly, the extremely minimum temperature, snowfall intensity and the coldest month's average temperature of a region in the last 30 years should be considered to ensure the continuous self-snow effect of anti-icing filler. Secondly, the high temperature indicators and the rainfall are considered to make anti-ice asphalt pavement performance to meet the requirements. Taking China as an example to introduce the method of climate zone, the climate zone index is shown in Table 13, the climate zone boundaries are shown in Figure 17.  
Region 1: The area with the extremely minimum temperature in the last 30 years between \(-9\) and \(-21.5\)\({}^{\circ}\)C is chilling region. In this region, the average temperature in the coldest month is above \(-10\)\({}^{\circ}\)C; in terms of snowfall intensity, moderate and light snows frequently fall. Because snow can be melted in a short time, the anti-icing asphalt mixture is most applicable.  
Region 2: The area with the extremely minimum temperature in the last 30 years between \(-21\) and \(-37\)\({}^{\circ}\)C is cold region. In this region, the average temperature in the coldest month is above \(-15\)\({}^{\circ}\)C. Moderate snowfalls frequently occur while heavy snowfalls occasionally occur. Because of the variance of temperature, the anti-icing asphalt mixture is also applicable.  
Region 3: The area with the extremely minimum temperature in the last 30 years above \(-9\)\({}^{\circ}\)C is warm region. In this region, the average temperature in the coldest month is above \(0\)\({}^{\circ}\)C. Light snowfalls most frequently occur. The frozen rain or melted snow in some regions could be iced again, and hence the anti-icing asphalt mixture can be paved in some special sections such as bridges and tunnel entrances.  
Region 4: The area with the extremely minimum temperature in the last 30 years below \(-37\)\({}^{\circ}\)C is severely cold region. Moderate and heavy snowfalls dominate the snowfalls in this region, and blizzard occasionally occur. The anti-icing filler is ineffective for a long time in this region. Hence, the anti-icing filler is not suitable in this region. However, the afore-mentioned experiments in Section 3.2 proved that the application of the anti-icing filler can reduce the adhesion between pavement and ice-snow. Hence, anti-icing asphalt mixtures are applicable to some special sections such as bridges and tunnel entrances for accelerating the removal of ice and snow.  
Figure 16: Dropping experiment with different loading times.
## 3 Results  
### Diurnal Variation Trends of Environmental Observations and Canopy-Air Temperature Differences  
We observed the diurnal patterns in meteorological data to assess the weather conditions during the observation period and to analyze the impacts of environmental stresses on the diurnal variations in the vegetation structure and physiology. Figure 4a-c shows the diurnal variations in Total Rad, VPD, and Ta, respectively, during the observation period. The instability of Total Rad suggested overcast or cloudy weather conditions on DOYs 223, 224, 229, 230, 232, and 233. Under clear-sky conditions, all the observations exhibited similar trends, increasing from the morning, peaking at noon, and then declining as the day progressed, with consistent diurnal patterns and slight variations. On cloudy days, the midday peak Ta was significantly reduced, Total Rad showed considerable variability, and the VPD was notably lower.  
The diurnal variation in vegetation physiology is influenced by both environmental factors and water stress, and \(\rm\Delta T\), which is defined as the canopy-air temperature difference, can reflect the response of vegetation to different environmental and water conditions. Under clear-sky conditions, there were significant differences in the diurnal variations in \(\rm\Delta T\) under varying water stress levels, whereas the diurnal variations in Total Rad, VPD, and Ta slightly differed. Notably, \(\rm\Delta T\) typically began to increase in the morning, peaked at noon, and then gradually declined. From DOYs 222 to 233, as water stress increased in the irr2 plot, the noon peak in \(\rm\Delta T\) correspondingly rose. Following irrigation, a significant reduction in \(\rm\Delta T\) was noted in both plots. Under cloudy conditions, the diurnal trend of \(\rm\Delta T\) remained consistently low throughout the day, in contrast to the trend observed under clear-sky conditions (Figure 4d). In this experiment, \(\rm\Delta T\) accurately reflected the degree of water stress under clear-sky conditions.  
Figure 4: The diurnal variations of total daily radiation (Total Rad) (**a**), vapor pressure deficit (VPD) (**b**), air temperature (Ta) (**c**), and canopy–air temperature difference (\(\rm\Delta T\)) (**d**) under the two irrigation treatments (irr1 and irr2) during the observation period from DOYs 222 to 234. The red and blue dashed vertical lines indicate the rewatering times in the irr1 and irr2 plots, respectively.
## 2 Literature Review
### Different Sensors for Ship Perception  
Currently, ship perception methods are mainly divided into two categories: active perception and passive perception. Active perception methods include mariner radar, sonar, SAR, etc., while passive perception methods include optical remote sensing, cameras, AISs, etc.  
Various maritime perception methods have their own advantages and limitations. Mariner radar is virtually unaffected by visibility, lighting, and noise but it is expensive, has blind spots, and may experience reduced detection accuracy in the presence of sea clutter. In contrast, sonar performs well in detecting, positioning, and tracking underwater targets [16]. However, sonar images often suffer from poor quality, low contrast, and low resolution, requiring additional image enhancement processing for target identification and localization. Simultaneously, SAR systems have unique advantages in ship detection [17]. However, when dealing with both stationary and moving targets, SAR systems may exhibit image distortion and blurring [18], which makes the extraction of target behavior information more complicated. Optical remote sensing technology has garnered significant attention in recent years, especially in target identification and situational awareness [19]. However, remote sensing images are influenced by factors such as noise, seasonal climate, and light intensity, making them unable to work reliably in all weather conditions. In comparison to other sensors, cameras, as passive sensors similar to the human eye, operate by capturing information about the color, contours, and textures of objects. Cameras are widely used in areas such as driverless and target recognition [20]. Additionally, cameras can be used to measure the distance to the target and determine the direction and position of the target. Due to their advantages of high-precision measurement and not revealing the position of the detection system, cameras have demonstrated high application value in many fields [21]. AIS data contains a wealth of information to ensure the safety of maritime navigation. It is widely used in ship behavior analysis, route planning, and collision avoidance decision-making research [22; 23].  
Table 1 presents the characteristics of the primary ship perception methods. From this, we can conclude that relying solely on a single sensor has significant limitations. It fails to provide sufficient accuracy and completeness in capturing ship navigation dynamic data. In contrast, a multi-source data fusion framework leverages the strengths of multiple sensors, enabling a more comprehensive and precise perception of the navigation environment. This approach is particularly well suited for diverse and complex application scenarios.
## 1 Introduction  
The spindle is the core functional component of a machine tool and its performance directly determines the technical level of the machine tool. With the promotion of Industry 4.0 technology, intelligent spindles integrated with actuators and sensors will be the direction of future development of machine tool spindles [1; 2]. These actuators are considered as legs and arms of intelligent spindles, including chatter suppression actuators [3; 4; 5; 6], thermal error compensation actuators [7; 8], bearing preload actuators [9; 10], active balancing actuators [11] and so forth.  
Active balancing actuators are used to automatically reduce the unbalance vibration of machine tool spindles during the working process. Although the spindles are accurately pre-balanced by the manufacturer before delivery, due to asymmetrical installation of tools/workpieces, positioning errors between tool and spindle or even non-uniform wear of tools in cutting, the rotor system will still produce new mass imbalances, which can result in increased vibration of the machine tool and decreased machining accuracy and efficiency. Therefore, it is necessary to frequently do dynamic balancing of spindles in workshops. In general, the dynamic balancing of spindles is a manual and iterative process, requiring experienced technicians and a lot of time [12; 13; 14]. For intelligent spindles, the balancing process must be automatic, without human intervention, so the requirement of engineering promotes the development of active balancing actuators. In recent years, several types of active balancing actuators have been invented, including magnetic-bearing type [15; 16], liquid-type [17; 18; 19], electromechanical type [20] and electromagnetic-ring type [21; 22]. Because electromagnetic-ring actuators have the advantages of compact structure, fast response speed and high accuracy, they have great potential to integrate into the machine tool spindles and have been developed as a commercial product by several manufacturers [23; 24]. However, existing electromagnetic-ring actuators can be only installed outside of the rotor, generally at the root of the extension section of the spindle and waste part of the spindle processing space. If the actuator can be installed inside the rotor, it is better for intelligent spindle integration and its scope of engineering application can be extended.  
To solve the problem, this work proposes a novel electromagnetic-ring actuator for the hollow rotor of the machine tool spindle. The paper is organized as follows: Section 2 describes the working principle of the actuator, including the balancing system, the magnetic circuit, the step rotation and differences from existing actuators. Section 3 analyzes the basic performance of the actuator, such as the balancing ability, balancing accuracy and self-lock capacity. Section 4 introduces the control strategy of the actuator and develops the phase detection and control program. To verify the effectiveness of the novel actuator, function and active balancing experiments are carried out in Section 5 and conclusions are presented in Section 6.
## 2 UAV Network Communication Model and Architecture
### Decentralized Communication Architecture
#### 2.2.3 Multi-Layered Ad Hoc Swarm Network  
The \"multi-layer swarm ad hoc network\" architecture is a type of architecture developed from the \"multi-group swarm ad hoc network\". It is suitable for many different UAV models (Figure 7). In this architecture, a group of adjacent UAVs of the same type forms an ad hoc network. It serves as the first layer of the communication architecture. Different UAV groups rely on the gateway UAV to perform GTG communication. It is the second layer. The nearest gateway UAV communicating with the infrastructure is the third layer of the architecture. Communication between any two UAVs in the \"multi-layer swarm ad hoc network\" architecture does not require infrastructure forwarding. Mutual communication of UAVs in the same group takes place at the first level. Communication between UAVs in different groups is routed through the gateway UAV. Data packets pass through the first and second layers, respectively. Therefore, there is no SPOF in the \"multi-layer swarm ad hoc network\".  
The \"multi-layered ad hoc swarm network\" architecture rapidly deploys network reconstruction when the number of UAVs changes in the swarm. Therefore, this architecture  
Figure 6: Multi-group swarm ad-hoc network diagram.  
Figure 7: Multi-layer ad hoc swarm network diagram.  
is suitable for complex mission scenarios. These missions are characterized by a large number of UAVs, frequent network structure changes, and a high frequency of communication between UAV nodes. When constructing this network structure, determining the number of layers is very important for the system to operate effectively [34].
## 5 Experimental Results
### Memory Analysis
#### 5.2.2 Wu-Manber Algorithm  
In the pattern-matching process with the WM algorithm, the shift distance of each character block belonging to the patterns is stored in a SHIFT table. Using this distance, the character block of any pattern is searched over the target text. PREFIX tables are used to distinguish patterns containing suffixes with the same hash values. If the suffix and prefix hash values on the target text are correct for one or more patterns, the target text and candidate patterns are compared character by character to complete the pattern detection. Patterns must be held in the GPU memory to perform this comparison. All patterns have been combined within a character array on the GPU memory. The starting and ending points of each pattern in the character array must be known in order to obtain a pattern from this array. Therefore, an integer array is created, besides the character array. The number of entries in the WM tables increases with the number of patterns and the number of characters in the alphabet that form the pattern set. Table 8 shows the memory space required for the WM tables specially created for datasets containing different numbers of patterns.
# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 2 Materials and Methods
### Classification Model  
The support vector machine model is formulated to estimate the relationship between the acquired predictor and the encounter situation resulting from the survey response. The model's input is a variable obtained through the process described in Section 2.2.2. The output is a situation awareness result predicted using a trained model. The input and output data pairs were divided into training-validation data and test data. At this time, the division ratio of the data was 7:3. After that, the training-validation data were again divided into training data and validation data at the same rate. It is because the optimal hyperparameter is obtained using the grid search (yellow box of Figure 1). Finally, we constructed the final model using the optimal hyperparameters obtained through the grid search. In this section, a support vector model applied to the model is described, and the hyperparameters are described. Finally, the validation method for the model is described.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
###### Abstract  
Photovoltaic panels can be affected by partial shading, which causes some shaded cells to consume the energy generated by other cells of the panel. That is, shaded cells stop operating in the first quadrant and start operating in the second quadrant, with negative voltage at their terminals, causing power losses and other negative effects in the cells. The Bishop model is an accurate representation of the cells behavior at the second quadrant, but estimating its parameters is not a trivial task. Therefore, this paper presents a procedure to estimate the parameters of the Bishop model by using the Chu-Beasley optimization technique. The effectiveness of this procedure was evaluated using different accuracy measures such as RMSE and MAPE, obtaining values lower than 0.5%. In addition, the results of this study demonstrate that it is essential to estimate all the parameters of the Bishop model, illustrate the variation in the parameters according to the cell technology and show the strong influence of the shunt resistance into the behavior at the second quadrant.  
Bishop model; single-diode model; solar cell; optimization methods; I-V curve Article
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 2 Display to Camera Communication System
The frequency coefficients of the reference image are simply subtracted from those of the data-embedded image. In this manner, the data can be extracted in the form of symbols \(\hat{\mathbf{S}}\), which are de-mapped and channel-decoded to obtain the output message in the form of a binary data vector \(\hat{\mathbf{b}}\).
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 3 Results
### Landslide Monitoring and Investigation
[PERSON] et al. [59] compared slide dimensions observed from satellite (IRS LISS-IV) with geophysical investigation and determined the depth and slip surface geometry using 2D ERT and GPR) for Kaliganga river valley in Uttarakhand. [PERSON] et al. [55] used ERT to understand the subsurface movement to determine the probable failure plane of the Pakhi landslide, Uttarakhand. [PERSON] et al. [60] conducted a geophysical study at Lanta Khola Landslide, Sikkim using very low-frequency (VLF) electromagnetic survey to understand the subsurface structure. The results reveal the presence of a water-saturated zone at the subsurface level of the slide. The literature review reveals that only a handful of studies have been carried out for landslide monitoring and investigation; considering the increased quantity of the available remote sensing data sources, the focus needs to shift towards the use of high spatial and temporal resolution data, which would lead to near real-time monitoring results. The framework using sub surface investigation is ideal at a local scale and would fulfill the demands of a community but would be difficult to manage in long term due to the high installation and operational costs. However, in sections where the landslide problem is immense, which is slide specific, such as Paglajhora landslide (Darjeeling), Singtam landslide (Sikkim), or Kotropi landslide (Himachal Pradesh), monitoring using instruments would still be required as the region is highly vulnerable and satellite images at currently available resolution may not be able to accurately identify failure planar sections.
## 3 System Model
### ADP Weighted MV-MOS Model
Owing to the service time of RSU for vehicle users is limited, it may cause the vehicle user to only complete a small part of the download task within the time of the RSU service range, which means bad QoE. When the vehicle user leaves the RSU service coverage area, MBS will continue to distribute the unfinished part of the replicas of video. This is very likely to be difficult to meet the QoE requirements of the corresponding bitrate replicas of video due to the low MBS transmission rate, resulting in serious video service fail. To deal with the above problems, it introduces the evaluation index of the ADP of vehicle users into the QoE evaluation model, then proposes an ADP weighted MV-MOS model that comprehensively considers the completion of the video download task. The calculation formula of ADP for mobile video is expressed as \[P_{ADP}\Big{(}f_{i},\ell_{j},\mathcal{R}_{a}\Big{(}t_{y}^{x}\Big{)}\Big{)}= \begin{cases} \sum\limits_{z=1}^{t_{SN}}\mathcal{R}_{a}\big{(}t_{y,z}^{x}\big{)}\triangle t _{y,z}^{x},\sum\limits_{z=1}^{t_{SN}}\mathcal{R}_{a}\Big{(}t_{y,z}^{x}\Big{)} \triangle t_{y,z}^{x}<g_{j}\\ 1\,\ \sum\limits_{z=1}^{t_{SN}}\mathcal{R}_{a}\Big{(}t_{y,z}^{x}\Big{)} \triangle t_{y,z}^{x}\geq g_{j}\end{cases} , \tag{34}\]  
where \(\mathcal{R}_{a}\Big{(}t_{y}^{x}\Big{)}\) is transmission rate, \(g_{j}\) is file size of the video bitrate level \(\ell_{j}\). When vehicle users obtain video from the MECS, the \(P_{MEC}\Big{(}t_{y}^{x}\Big{)}\) can be expressed as  
\[P_{MEC}\Big{(}t_{y}^{x}\Big{)}=\sum\limits_{i\in F}\sum\limits_{j\in L}x_{f_{ i},\ell_{j}}\Big{(}t_{y}^{x}\Big{)}P_{\mathcal{F}}^{x}(f_{i})P_{ADP}\Big{(}f_{i}, \ell_{j},\mathcal{R}_{MEC}\Big{(}t_{y}^{x}\Big{)}\Big{)}. \tag{35}\]  
When a vehicle user obtains a video from MBS through the backhaul at the time slot \(t_{y}^{x}\), the \(P_{BH}\Big{(}t_{y}^{x}\Big{)}\) is calculated as  
\[P_{BH}\Big{(}t_{y}^{x}\Big{)}=\sum\limits_{i\in F}\sum\limits_{j\in L}x_{f_{ i},\ell_{j}}\Big{(}t_{y}^{x}\Big{)}P_{\mathcal{F}}^{x}(f_{i})P_{ADP}\Big{(}f_{i}, \ell_{j},\mathcal{R}_{Bh}\Big{(}t_{y}^{x}\Big{)}\Big{)}. \tag{36}\]  
Hence, the average ADP at time slot \(t_{y}^{x}\) is calculated as  
\[\overline{P}_{ADP}\Big{(}t_{y}^{x}\Big{)}=\sum\limits_{K=0}^{\mathbb{E}\big{[} U(t_{y}^{x})\big{]}}P_{Ar}(\mathbb{E}\Big{[}U\Big{(}t_{y}^{x}\Big{)}\Big{]},K) \left(\frac{KP_{BH}\Big{(}t_{y}^{x}\Big{)}}{\mathbb{E}\Big{[}U\Big{(}t_{y}^{x }\Big{)}\Big{]}}+\left(\frac{\left(\mathbb{E}\Big{[}U\Big{(}t_{y}^{x}\Big{)} \Big{]}-K\right)P_{MEC}\Big{(}t_{y}^{x}\Big{)}}{\mathbb{E}\Big{[}U\Big{(}t_{y} ^{x}\Big{)}\Big{]}}\right), \tag{37}\]  
In summary, the ADP weighted MV-MOS at the time slot \(t_{y}^{x}\) is expressed as  
\[J\Big{(}t_{y}^{x}\Big{)}=\overline{P}_{ADP}\Big{(}t_{y}^{x}\Big{)}Q_{MOS} \Big{(}t_{y}^{x}\Big{)}. \tag{38}\]