esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Dataset  
Two public datasets were selected for this experiment: WordNet and Freebase. WordNet is an English lexical database constructed by Princeton University. It contains a vast collection of English words along with their hypernyms, hyponyms, synonyms, and more. Freebase, on the other hand, is an open and semi-structured graph database developed by Google. It encompasses 39 million entities from various domains in the real world, such as art, abstract concepts, objects, and more. Specifically, we used two subsets from WordNet: WN18 and WN11, as well as two subsets from Freebase: FB15K and FB13. The details of the entity number, relationship number, training set, verification set, and test set of these datasets are shown in Table 1.
## 4 Calibration Methodology  
Distinct methodologies (see Table 1) for calibration have been used, depending on whether the entire building or just the technical systems were calibrated with real data sets. After calibration and validation with standard metrics (see e.g., Section 4.1.2), the model responses to control actions were further tested independently by team members to ensure that physical expectations were met (setpoint responses, energy consumption patterns by system activation, etc.).
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 3 Evaluation Methodology
### Peak Signal-to-Noise Ratio (PSNR)  
Although this is the most well-known FR objective image quality metric, PSNR values [24] often deviate significantly from subjective results. The PSNR value is calculated as:  
\[\text{PSNR}=10\times\log_{10}\left(\frac{MAX_{I}^{2}}{MSE}\right), \tag{1}\]  
where \(MAX_{I}\) is the maximum possible pixel intensity (which, in 8-bit images, is 255), and \(MSE\) is the mean square error between the reference image pixel value and the compressed image pixel value. The reason why PSNR quality assessment often deviates from subjective results is that it is not adapted to Human Visual System (HVS) characteristics: it involves a simple pixel value comparison. Typical values for the PSNR in lossy 8-bit image and video compression are between 30 and 50 dB, where higher is better.  
Figure 1: Script execution flowchart.
## 2 Sector-Specific Applications
### Automotive Industry  
The automotive sector, characterized by complex supply chains and dynamic production environments, has embraced low-code platforms to enhance agility and operational efficiency [28]. LCDPs empower engineers to design applications for predictive maintenance, real-time data integration, and workflow automation without relying on traditional software development cycles [29; 30; 31; 32; 33; 34; 35; 36]. For instance, predictive maintenance systems built on platforms like Siemens MindSphere integrate IoT sensor data from assembly lines to detect equipment anomalies in real time, reducing unplanned downtime [17]. These systems leverage WebSocket protocols to dynamically update maintenance schedules based on sensor telemetry, ensuring alignment with production targets. Furthermore, by leveraging a low-code platform, Thinkmoney achieved the development of a novel banking application in merely 14 weeks and finalized its online banking system in 26 weeks, reducing the R&D timeline by a significant margin. In response to the pandemic, New York City  
Figure 1: Comparative workflow: low-code vs. traditional software development lifecycles. leveraged Unqork's low-code platform to deploy a COVID-19 information portal in just 72 h, underscoring the agility of low-code solutions in crisis scenarios [37]. A key advantage of low-code in the automotive sector lies in its ability to unify fragmented IT ecosystems. Legacy systems such as ERPs and CRMs are often siloed, but platforms like vf-OS [38; 39; 40] provide open APIs to seamlessly connect these systems with IoT devices and machine learning modules [28]. This integration supports customized workflows for quality control, where non-technical staff can visually design dashboards
# Pursuing High Quality Phase-Only Liquid Crystal on Silicon (LCoS) Devices
## 8 Conclusions  
LCoS technology has been developed for nearly four decades. However, due to its higher production cost and lower performance than DMD, the latter continues to dominating the present-day projection display market. Recently, high-resolution and fine pixel size LCoS backplanes have been produced by several vendors, and appear to offer more promising spatial light modulation than full high definition displays. Techniques for assessing various characteristics of high-resolution LCoS SLMs have been discussed in this review. The physics and modeling of LCs in parallel aligned LCoS were not discussed in detail due to the limited length and scope of this paper. The physics and electro-optical effects of tilted LCs, and addressed digital driving in the parallel aligned LCoS, have been thoroughly discussed by [PERSON] et al. and [PERSON], et al. [68; 69; 70]. During the course of developing high quality SLM panels, the phase precision, phase stability, and output phase accuracy need to be meticulously adjusted on a linearly calibrated phase panel. The novel high-quality and well-calibrated panels can generate phase-only holography and project faithful CGH design, which can be useful for 2D/3D holographic video projectors and optical telecommunications. The high phase precision, fast response time, good phase stability, and high phase accuracy of LCoS panels opens more possibilities for various applications, as discussed above. Though the benefits of LCoS panels are many, endeavors should be made to offer them at a competitive price to the public in the near future.  
Conceptualization: H.-M.P.C. and J.-P.Y.; writing--original draft preparation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-M.P.C. and S.-T.W.; experimental work, [PERSON]-P.Y., [PERSON]-T.Y., [PERSON]-N.H. and Y.H.; supervision, [PERSON]-M.P.C. and [PERSON]-T.W.  
The financial support is provided by the Ministry of Science and Technology of the Republic of China under Grant Nos. 105-2622-E-009-012-CC2, 105-2221-E-009-090, and MOST 106-2221-E-009-111; and is partially supported by the Research Team of Photonic Technologies and Intelligent Systems at NCTU within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.  
The authors are grateful for the LCoS photos listed in Figure 1 provided by Jasper Display.
## 2 Formulation and Methodology
\tag{11}\]  
Taking joint finite sine and cosine transforms on both sides of Equation (3), it gives  
\[\int_{0}^{a}\int_{0}^{b}\
abla_{o}^{4}W(x,y)\sin\alpha_{m}x\cos\beta_{n}ydxdy- \omega^{2}\rho h\hat{W}(m,n)=0 \tag{12}\]  
where  
\[\
abla_{o}^{4}=D_{x}\frac{\partial^{4}}{\partial x^{4}}+2H\frac{\partial^{4} }{\partial x^{2}\partial y^{2}}+D_{y}\frac{\partial^{4}}{\partial y^{4}} \tag{13}\]Using integration by parts and considering the boundary conditions of Equations (7), the joint finite sine and cosine transforms of the fourth derivatives in Equation (12) are given by  
\[ \begin{split}\int_{0}^{a}\int_{0}^{b}W_{xxxxx}\sin\alpha_{m}x\cos \beta_{n}ydxdy=&\alpha_{m}^{4}\hat{\Psi}(m,n)\\ &-\alpha_{m}\Big{[}(-1)^{m}\hat{W}_{,xx}\left(a,n\right)-\hat{W}_ {,xx}\left(0,n\right)\Big{]}\\ \int_{0}^{a}\int_{0}^{b}W_{,xxyy}\sin\alpha_{m}x\cos\beta_{n}ydxdy=& \alpha_{m}^{2}\beta_{n}^{2}\hat{W}(m,n)\\ &-\alpha_{m}^{2}\Big{[}(-1)^{n}\tilde{W}_{,yy}\left(m,b\right)- \tilde{W}_{,yy}\left(m,0\right)\Big{]}\\ \int_{0}^{a}\int_{0}^{b}W_{,yyyy}\sin\alpha_{m}x\cos\beta_{n}ydxdy=& \beta_{n}^{4}\hat{W}(m,n)\\ &+\Big{[}(-1)^{n}\tilde{W}_{,yyyy}\left(m,b\right)-\tilde{W}_{,yyyy }\left(m,0\right)\Big{]}\\ &-\beta_{n}^{2}\Big{[}(-1)^{n}\tilde{W}_{,yy}\left(m,b\right)- \tilde{W}_{,yy}\left(m,0\right)\Big{]}\end{split}  \tag{14c}\]  
in which \(\hat{W}_{,xx}\left(0,n\right)\) and \(\hat{W}_{,xx}\left(a,n\right)\) are determined from FCT with respect to \(y\) at edges (\(x=0\) and \(x=a\)). Similarly, \(\tilde{W}_{,yy}\left(m,0\right)\) and \(\tilde{W}_{,yy}\left(m,b\right)\) are obtained from two free edges by FST. They are expressed as  
\[\tilde{W}_{,xx}\left(0,n\right)=\int_{0}^{b}W_{,xx}\left(0,y\right)\cos\beta_ {n}ydy \tag{15a}\] \[\tilde{W}_{,xx}\left(a,n\right)=\int_{0}^{b}W_{,xx}\left(a,y\right)\cos\beta_ {n}ydy\] (15b) \[\tilde{W}_{,yy}\left(m,0\right)=\int_{0}^{a}W_{,xx}\left(x,0\right)\sin\beta_ {n}xdx\] (15c) \[\tilde{W}_{,yyy}\left(m,b\right)=\int_{0}^{a}W_{,xx}\left(x,b\right)\sin\beta_ {n}xdx \tag{15d}\]  
It can be obtained from Equation (7d) that  
\[ \begin{split}\Big{[}(-1)^{n}\tilde{W}_{,yyyy}\left(m,b\right)- \tilde{W}_{,yyyy}\left(m,0\right)\Big{]}=\frac{H+2D_{xy}}{D_{y}}\alpha_{m}^{2} \Big{[}(-1)^{n}\tilde{W}_{,yy}\left(m,b\right)-\tilde{W}_{,yy}\left(m,0\right) \Big{]}\end{split}  \tag{16}\]  
Substituting Equation (16) into Equation (14c) yields  
\[ \begin{split}&\int_{0}^{a}\int_{0}^{b}W_{,yyyy}\sin\alpha_{m}x \cos\beta_{n}ydxdy\\ =&\beta_{n}^{4}\hat{W}(m,n)+\left[\frac{H+2D_{xy}}{D_ {y}}\alpha_{m}^{2}-\beta_{n}^{2}\right]\Big{[}(-1)^{n}\tilde{W}_{,yy}\left(m,b \right)-\tilde{W}_{,yy}\left(m,0\right)\Big{]}\end{split}  \tag{17}\]  
Then, substituting Equations (14a), (14b) and (17) into Equation (12) yields  
\[ \begin{split}\hat{W}(m,n)=&\frac{1}{\Omega_{mn}- \omega^{2}\rho h}\Bigg{\{}\alpha_{m}D_{x}\Big{[}(-1)^{m}\tilde{W}_{,xx}\left(a, n\right)-\tilde{W}_{,xx}\left(0,n\right)\Big{]}\\ &+D_{y}(v_{x}\alpha_{m}^{2}+\beta_{n}^{2})\Big{[}(-1)^{n}\tilde{W} _{,yy}\left(m,b\right)-\tilde{W}_{,yy}\left(m,0\right)\Big{]}\Bigg{\}}\end{split}
## 3 Results and Discussion
### PVA/GA Conditioning and Characterization  
In order to obtain a coating as uniform as possible, the experimental conditions and formulations were optimized. For the hydrogel preparation, a PVA/GA mixture (% by weight) with a 4:1 ratio was found as suitable according to the literature [46]. A semiquantitative analysis of the FTIR spectra of PVA and PVA cross-linked with GA was carried to monitor the chemically cross-linking between PVA and GA and characterize the products. The FTIR spectra shown in Figure 4 (FTIR spectra of PVA/GA (Figure 4b)) reveal two important bands at 2850 and 2750 cm\({}^{-1}\) of C-H stretching related to aldehydes, an absorption duplet with peaks attributed to the alkyl chain [19]. Additionally, a strong band from carbonyl group was verified (C = O at 1720-1740 cm\({}^{-1}\)). These bands overlapped with PVA bands in these regions. However, by crosslinking PVA with GA, the O-H stretching vibration peak at 3330-3350 cm\({}^{-1}\) was relatively decreased when compared to pure PVA. This could indicate a possible formation of acetal bridges. Moreover, the C-O stretching at approximately 1100 cm\({}^{-1}\) in pure PVA was replaced by a broader absorption band (from 1000 to 1140 cm\({}^{-1}\)), which can be attributed to the ether (C-O) and the acetal ring (C-O-C). According to the literature, these acetal bridges could be considered as confirmation of the PVA/GA crosslinking reaction [50].  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Gel-Coat (Surface)**} & **Cutoff Length** & **Roughness (Ra)** & **Total Roughness (Ra)** & **Standard Deviation** \\  & **(cm)** & **Average (\(\upmu\)m)** & **Average (\(\upmu\)m)** & **Standard Deviation** \\ \hline \multirow{4}{*}{Substrate 1} & 1.65 & 0.42 & & \\  & 3.30 & 0.41 & 0.42 & 0.015 \\  & 4.95 & 0.44 & & \\  & 1.65 & 0.46 & & \\ Substrate 2 & 3.30 & 0.48 & 0.46 & 0.025 \\  & 4.95 & 0.43 & & \\  & 1.65 & 0.57 & & \\ Substrate 3 & 3.30 & 0.40 & 0.49 & 0.085 \\  & 4.95 & 0
## 3 Results and Discussions
### Physicochemical Characterization  
The results in Table 3 indicate the data for the physicochemical characterization and zeta potential of the Raw-RGL and AC-RGL. BET results indicated that the surface area of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **Mass (\%)** & **Atom (\%)** \\ \hline C & 77.15 & 86.05 \\ \hline O & 11.46 & 9.60 \\ \hline Mg & 1.30 & 0.72 \\ \hline Si & 1.33 & 0.64 \\ \hline Cl & 1.42 & 0.54 \\ \hline K & 1.63 & 0.56 \\ \hline Ca & 5.70 & 1.96 \\ \hline Total & 100.00 & 100.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The surface element analysis of AC-RGL adsorbent.  
Figure 4: FTIR spectra of Raw-RGL and AC-RGL adsorbents.  
AC-RGL was 17.69 m\({}^{2}\)/g, with a micropore of 0.50 cm\({}^{3}\)/g and a mesopore of 0.10 cm\({}^{3}\)/g. The total pore of AC-RGL was determined and found to be 0.60 cm\({}^{3}\)/g. When comparing the magnitude of the micropore volume with the total pore volume, this contributes approximately 80% of the total pore volume which demonstrate the high microporosity of the AC-RGL. The obtained results proposed that AC-RGL might be the efficient adsorbent in the removal of ibuprofen. The results of pH(PZC) for Raw-RGL and AC-RGL were found to be 7.32 and 6.61, respectively. The results for Raw-RGL were close to neutrality, while those of AC-RGL were slightly acidic. The maximum adsorption of ibuprofen on Raw-RGL was 74.12 mg/g with a zeta potential of 22.56 mV, while AC-RGL capacity was 113.76 mg/g with a zeta potential of 41.13 mV. The AC-RGL with a zeta potential of 41.13 mV adsorbed much more ibuprofen.
## 5 Simulations
### Packet Delivery Ratio  
In the simulation, SCR, PRoPHETv2, DRAFT and BUBBLE run in the four datasets respectively, and the simulation time is the duration of dataset, which is shown in Table 1. Simulation results are shown in Figure 6. Similar to [19], quartiles are used to analyze the experimental results. The experimental data is arranged in ascending order and then divided into four equal parts. Then, we can find In Figure 6 there are five signs (min, first quartile, median, third quartile and max) for the result of each algorithm. In some special situations, two or more signs may have the same value. For example, in the first part of Figure 6, to the result of BUBBLE, the first quartile and median have the same value. The quartiles shown in Figure 6 can reflect the distribution center, concentration and spread range of packet delivery ratio. As can be seen from Figure 6, SCR has a higher distribution center of packet delivery ratio than the other three algorithms, a small spread range and a focus on better range.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Infocom5** & **Infocom6** & **Cambridge** & **Intel** \\ \hline Number of Nodes & 41 & 98 & 52 & 9 \\ Buffer Size & 5 M & 5 M & 5 M \\ TTL & 60 min & 60 min & 2 days & 0.5 days \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of four experimental datasets in ONE.  
Figure 6: _Cont._  
With the simulation time varying, the results are shown in Figure 7. When simulation time is less than one day, SCR has similar results to the other algorithms. Due to the formation of clusters in the network requiring a process, in a short simulation time, there are not enough nodes in the local cluster for efficient data transmission. With the increasing of simulation time, SCR has a higher average packet delivery ratio than the other three algorithms. Compared with PRoPHETv2, DRAFT and BUBBLE, SCR has the higher packet delivery ratio, as shown in Table 3.  
Figure 6: Quartiles of packet delivery ratios.  
Figure 7: Packet delivery ratio comparisons.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 5 Issues and Challenges in 5G and Edge-Based V2X
### Immutability  
In vehicle networks, the distributed storing of 5G data on edge servers, and the processing of information between edge and vehicle users are susceptible to data modification and attacks by adversaries. The attackers can access personal information such as vehicle user information, or location information through unauthorized access and can modify the information. Such attacks can lead to data modifications and mutability of sensitive information. Thus, strong immutability features are required in 5G-based edge computing through technologies such as blockchain.
# Research on Design, Simulation, and Experiment of Separation Mechanism for Micro-Nano Satellites
## 2 Mission Analysis  
### Satellite Description  
As shown in Figure 1, MF is a micro-optical satellite designed based on the principles of \"modularity, standardization, lightweight, low cost, scalability, flexibility, and high resolution\". The designed orbit of the satellite is a 500 km sun-synchronous orbit. The resolution of remote sensing is better than 1.35 m, making it suitable for earth observation, new technology verification, scientific exploration, and other fields. The entire satellite structure system is supported by a frame made of the lightweight aluminum alloy 2A12, and the MF satellite weighs about 20 kg. The satellite is equipped with an expanded solar panel on the \(\pm\)Y direction. On the \(\pm\)Z side, there is a camera and various antennas. On the \(\pm\)X side, there is a propulsion system and antennas arrangement. As shown in Figure 2, the satellite's cabin contains an image processor, a gyroscope, a battery, and a reaction wheel, among other components. Outside the satellite's cabin are star trackers, various types of antennas, and other devices. As can be seen, the satellite's entire structure is compact.
# A Vision-Based Micro-Manipulation System
## 4 Tests and Results
### Image Recognition  
As a visual recognition tool, YOLOv7 was utilized to detect yeast cells and their coordinates for images received from a camera, installed in the micro-manipulation system. This study involved creating two training datasets containing yeast cell image patterns to assess the impact of image enhancement on YOLOv7 object detection. The first model utilized the training dataset with original raw yeast cell images, while the second model used the training dataset with images enhanced by FACE. This dual dataset, encompassing both original raw images and their FACE-enhanced counterparts, formed the basis for the subsequent development and validation of the YOLOv7 cell detection model. The 10-fold accuracy comparison between the FACE-enhanced training dataset and the dataset without enhancement for the two YOLOv7 cell detection models, as depicted in Table 2, demonstrates that the FACE model yields favorable results. It achieves higher accuracy in yeast cell detection, even with original raw input images featuring low-contrast features.  
To conduct a comprehensive evaluation of various object detection models and assess the congruence of their recognition outcomes with subsequent image processing, three distinct models were engaged--Mask R-CNN, YOLOv5, and YOLOv7--while maintaining a uniform label numbering system with a designated detection label number of 5. The findings from this comparative analysis unveiled that Mask R-CNN successfully identified three cells, YOLOv5 detected two cells and, impressively, YOLOv7 exhibited exemplary performance by accurately recognizing all five cells in the image. It is noteworthy that the superior accuracy demonstrated by YOLOv7, showcasing its ability to make highly precise predictions for each cell, outperformed the other models tested. A detailed breakdown of these comparative results is presented in Table 3.  
Table 4 shows the accuracy results for the Mask R-CNN, YOLOv5, and YOLOv7 models based on 10-fold accuracy analysis. It is clear from Table 4 that YOLOv7 performs better than both Mask R-CNN and YOLOv5 in terms of overall accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Fold** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Avg.** \\ \hline With FACE & 0.98 & 0.99 & 0.95 & 0.92 & 0.97 & 0.91 & 0.96 & 0.88 & 0.93 & 0.98 & 0.947 \\ \hline Without Enhancement & 0.98 & 0.95 & 0.92 & 0.90 & 0.95 & 0.90 & 0.85 & 0.92 & 0.98 & 0.99 & 0.934 \\ \hline \end{tabular}
\end{table}
Table 2: 10-fold accuracy comparison for with and without enhanced image.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 3 Platoon System  
### Platoon Structure Model  
According to the spatial position and functionalities, vehicles in a platoon can be classified into five roles: leader vehicle, tail vehicle, member vehicle, relay vehicle, and free vehicle. The structure of platoon is shown in Figure 1.  
1. Leader vehicle: It refers to the first vehicle in a platoon. A leader vehicle is responsible for establishing and administering the platoon with the coordinate of Advanced Traffic Management System, e.g., controlling driving behavior of the vehicle in a platoon, collecting data from other vehicles on the road, and identifying and broadcasting the information of platoon. The movement of the leader vehicle is the reference movement for all remaining vehicles in the platoon to follow.
2. Tail vehicle: It locates at the end of a platoon. The tail vehicle is an essential hub for inter-platoon communication and is responsible for establishing a connection with the next platoon.
3. Member vehicle: It is a vehicle within the platoon that locates neither at the begin nor the end of a platoon. A member vehicle following a specified control algorithm receives messages from the leader vehicle and its preceding vehicle.  
Relay vehicle: Any member vehicles in a platoon can act as a relay vehicle, which is in charge of assisting leader vehicle in disseminating messages to all vehicles in a platoon.
5. Free vehicle: Vehicles that do not belong to any platoon are called free vehicles. A free vehicle sends a request to the leader vehicle of a platoon when it wants to join a platoon. After obtaining the permission of the leader vehicle, the free vehicle can perform join operation.  
In a platoon, the behavior of a vehicle (such as acceleration/deceleration and joining or leaving a platoon) not only depends on the driver's wishes but also obey management and constraint from platoon control center. The leader vehicle is generally regarded as the control center. A member vehicle transfers request messages to the leader vehicle within the same platoon when the driver wants to change driving behavior according to its own needs, e.g., destination and rest. Then, the leader vehicle makes a judgment based on the current traffic situation. If the leader vehicle responds to the member vehicle's request, all vehicles in the platoon have to adjust to maintain the stability of the platoon.
## 3 Materials and Methods
### Experimental Setup and Implementation
#### 3.3.5 Calculation of the Main Effects of Factors  
The main effect of a factor refers to how this factor influences the response when factor settings change from the low level to the high level. It is the difference between the mean responses at high level \(\text{M}_{\text{Factor}}{}^{+}\) and low level \(\text{M}_{\text{Factor}}{}^{-}\) (see Equation (5)):  
\[\text{E}_{\text{Factor}}=\text{ M}_{\text{Factor}}{}^{+}-\text{M}_{\text{ Factor}}{}^{-} \tag{5}\]  
where \(\text{M}_{\text{Factor}}{}^{+}\) and \(\text{M}_{\text{Factor}}{}^{-}\) are, respectively, the mean of the responses when Factor is at a high level (+1) and the mean of the responses when Factor is at a low level (\(-1\)).  
In our study and for comparison purposes, these averages were normalized by dividing each mean value by the standard deviation \(\sigma\) of the given values of the response (Equations (6) and (7)).  
\[\text{NormM}_{\text{Factor}}{}^{+}=\frac{\text{M}_{\text{Factor}}{}^{+}}{\sigma} \tag{6}\]  
\[\text{NormM}_{\text{Factor}}{}^{-}=\frac{\text{M}_{\text{Factor}}{}^{-}}{\sigma} \tag{7}\]  
where \(\text{NormM}_{\text{Factor}}{}^{+}\) and \(\text{NormM}_{\text{Factor}}{}^{-}\) are, respectively, the normalized main effects when Factor is set at a high level and low level. \(\sigma\) is the standard deviation of all values of the response.  
The plots of effects graphically represent these main effects with lines connecting the normalized values of mean responses at the low level with those calculated at the high level. The direction of the line indicates the sign of the effect (positive or negative), while the inclination of the line reveals the magnitude of the effect. When the line is horizontal, no effect is revealed.