esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Price Forecasting
#### 4.3.1 Data Training and Prediction at the Hourly Level  
For the hourly data, the original data were utilized to generate the data set, with a sliding window size of 24 and a stride of 1, via data augmentation. During pre-processing, standardization and normalization were carried out. The training data were enhanced by generating additional data, with the training set being composed of generated data and a portion of the original data set. Additionally, a similarity comparison was conducted using t-SNE.  
The experimental results of forecasting were evaluated according to the MAE, RMSE, MAPE, and \(R^{2}\) values. Multi-scale prediction for the three commodities was performed based on the hourly or daily data. The parameter settings were as follows: kernel size of 3, 30 and 100 iterations, learning rate of 0.001, time window set to 5, and Adam optimizer.  
From Table 2, we can see that the TCN-attention and TCN models took less time than GRU or LSTM, and TCN-attention generally performed better overall. Meanwhile, the outcomes of training data for garlic, ginger, and chili with 30 and 100 iterations are presented. Traditional methods such as SVM, RF, and ARIMAX, along with univariate analysis, were added at an hourly level for chili, as shown in Table 3. It can be seen that the accuracy for different species is lower than that of the proposed method. In Table 4, the forecasting results for the next 5 and 10 h on the test sets are presented. Compared with GRU and LSTM, TCN-Attention had better accuracy, slightly outperforming TCN. The comparison of various forecasting methods, including TCN, GRU, LSTM, and TCN-Attention, is illustrated in Figure 8. The figures depict a performance comparison between 30 epochs and 100 epochs for forecasting within the next 10 days. Additionally, the proposed model tended to converge after 30 training iterations
# Generative Image Steganography via Encoding Pose Keypoints
## 3 The Proposed Method  
This paper proposes a generative image steganography via encoding pose keypoints, as shown in Figure 1. First, the sender generates sequences of pose keypoints driven by secret information using an LSTM-based sequence generation network. The keypoints sequences are then drawn into keypoint connectivity graphs. Next, the graphs are input into the pose-guided image generation model (DPTN-TA) with an arbitrary person image to obtain the images with the target poses. The sender uploads the images to the public channel to transmit the secret information. Finally, the receiver receives the stego-images from the public channel, extracts pose keypoints using an improved YOLOv8 pose estimation model and then extracts the secret information using the sequence generation model.
# Rheological, Textural, and Sensorial Characterization of Walnut Butter
## 3 Results
### Rheological Parameters  
The rheological properties of the two types of walnut butter were studied. Both types are concentrated suspensions and differ depending on the type of sweetener used and the amount of emulsifier used. The first type of walnut butter is a stabilized suspension consisting of solid walnut particles in maple syrup (non-Newtonian fluid), and the second type of walnut butter is an unstabilized suspension consisting of walnut particles and Jerusalem artichoke powder. The emulsifier (xanthan gum) is used as a gelling agent and stabilizer and the modification of the rheological characteristics is essential for the modification of the sensory properties of the food [45]. Xanthan gum is mainly effective due to its ability to be absorbed by various interferences. Figure 2 shows the data of the flow curves obtained for shearing the walnut butter samples from 0 to 500 s\({}^{-1}\). It is possible to observe a decrease in shear stress from M1 to S3, suggesting a decrease in shear stress with a gradual increase in the proportion of xanthan gum cow in the formulation. However, all the analyzed samples showed a similar behavior of thinning by shearing (pseudoplastic), in which a decrease in viscosity with the shear rate is observed. Adding maple syrup to walnut butter reduces the viscosity of the butter and the stress of production. Its use as a sweetener in walnut butter made it more mobile and led to greater liquidity. In the case of walnut butter with Jerusalem artichoke powder, the highest viscosity was registered for S6, which is the sample with 2% xanthan gum as a stabilizer (Figure 2b). It is observed that as the emulsifier (xanthan gum) concentration increased, there was no significant difference between the samples. Lissajous curves were obtained, sinusoidal in shape, with increases and decreases in viscosity due to the fact that no oil was added to the walnut butter in addition to the oils present in the walnut kernel.  
According to [PERSON], et al. [20], the shear thinning behavior is usually associated with the presence of any structure that was totally or partially broken down with an increasing shear rate [20]. The thinning behavior of shear was also observed by [PERSON] et al. [18] who studied the effect of different levels (0, 0.5, and 1.5%) of emulsifying agents (mono-diglycerides and lecithin) on the rheological properties of walnut butter. It was previously reported that the presence of high-molecular-weight substances, nut proteins, and polysaccharides is also responsible for their high shear-thinning behavior [18].
## 2 Materials and Methods
### Tracking  
After the workers are detected in a video frame, a track is assigned to each of them. For this, we apply Kalman filtering, which relies on a motion model only. An appearance model as it is provided by other tracking approaches would be redundant since our YOLO detector already takes care of this. For the purpose of tracking pedestrian workers, a simple motion model that only includes a worker's two-dimensional location \((x,y)\) and velocity \((v_{x},v_{y})\) is sufficient. According to this, our motion model describes the transition from one state \(t\) to the next state \(t+1\), as shown in Equation (2).  
\[\begin{pmatrix}x_{t+1}\\ y_{t+1}\\ v_{x,t+1}\\ v_{y,t+1}\end{pmatrix}=\begin{pmatrix}1&0&\Delta t&0\\ 0&1&0&\Delta t\\ 0&0&1&0\\ 0&0&0&1\end{pmatrix}\begin{pmatrix}x_{t}\\ y_{t}\\ v_{x,t}\\ v_{y,t}\end{pmatrix} \tag{2}\]  
Beginning with an initial detection of a worker, the motion model is used to predict this worker's location in the ensuing video frame. Afterwards, the subsequent frame is evaluated. The workers'locations predicted by the Kalman filter in the preceding frame and the detections made by YOLO in the current frame are, then, matched using the Hungarian method. We use the locations of the matching detections to update the estimation of each track towards the actual worker's position. This prevents the tracks from drifting off. Detections that do not match any pre-existing track will create a new track. This way, our detector serves for both, initializing tracks and providing evidence for existing tracks. Those tracks for which no evidence is found will persist for a maximum of 12 frames without further evidence. From this, we can derive the workers' walking path trajectories as well as their prospective walking directions.  
Although the Kalman filter was originally developed for linear tracking, it copes with non-linearity to some extent [31]. However, the pedestrian workers' movements can be highly non-linear. In the short time period between only a few consecutive video frames, though, the workers' movements can be assumed to be almost linear. For this reason, the tracks should be updated at least on every second frame. This drastically decreases the probability of occurrence of non-linearities. Considering this, the Kalman filter has to deal with non-linear state transitions only if workers are not detected for several video frames. An adequate detection beforehand is, thus, a necessary requirement for reliable tracking.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 7 FIT-IoT Performance Evaluation
### 1 to Many Experiments  
The first set of 1-to-many experiments, MQTT-3 and CoAP-3, involve 1 sender and 94 receivers, where the sender sends 5 messages (subscribed by each receiver/client), separated by an IAT of 1000 ms. Then, MQTT-4 and CoAP-4 involve a higher number (5000) of very frequent messages (1 ms IAT). Results are summarised in Figure 9.  
In comparison to the prior experiment with 1 sender and 1 receiver (rf. to Figure 8), MQTT slightly increases the average TTC from 35 ms to 136.97 ms, while CoAP shows a significant difference, from 185.17 ms to 4220 ms.  
Increasing the frequency of messages has more impact in MQTT than in CoAP. In fact, for the MQTT case there is a packet loss of 65%. While CoAP incurred a packet loss of 0.04%. This seems to imply that MQTT brokers cannot cope well with a lot of information being requested in parallel by different subscribers. We believe that this is due to the lack of queueing in MQTT, and also to the way that the messages are served.  
CoAP, on the other hand, attained a slightly higher average TTC (approximately 300 ms) when compared to the experiment run with not so frequent messages. We believe this occurs due to the way the CoAP server stores the content by senders: the initial setup process of communication between the sender and CoAP is possibly only set once; the reliability process with exponential backoff adjusts to the frequency of messages, thus resulting in a more stable TTC across all experiments.
# Real-Time Detection Method for Center and Attitude Precise Positioning of Cross Laser-Pattern
## 2 System Composition
### Image Acquisition System  
The image acquisition system consists of three parts: laser receiving screen, industrial camera fixed behind screen and dust shield. The laser receiving screen is made of transparent acrylic board with the size of 5 mm \(\times\) 200 mm \(\times\) 200 mm. A band-pass filter film matching with the laser frequency is coated on the exterior surface of the receiving screen; this allows the cross laser-pattern to go through the screen, and together with the dust shield, it can effectively reduce the interference of stray light to the system. A thin layer of diffuse reflection coating is present on the interior surface of the screen; on this layer, the cross-laser beam forms a cross laser-pattern with Lambertian reflection. To acquire the cross laser-pattern image, the industrial camera in this paper uses AVT Mako-U503B with a frame rate of 14 FPS, a resolution of 2592 \(\times\) 1944 pixels and pixel size of 2.2 \(\upmu\)m \(\times\) 2.2 \(\upmu\)m. The camera lens used is a Computar M0814-MP; this is a megapixel fixed focus lens with a focal length of 8 mm, working distance of 0.1 m\(\sim\)\(\infty\) and aperture range of F1.4\(-\)F16C. The image acquisition system is fixed at the measured point, which consists position and attitude measurement system together with the laser emission system. The cross laser is used as the measurement datum, and the cross laser-pattern image on the receiving screen is acquired by the industrial camera in real time.  
In order to verify the robustness of the algorithm to the uneven distribution of energy under the influence of smoke and water mist, this paper simulated the environmental impact of smoke and water mist as shown in Figure 3. A smoke generator and an ultrasonic atomizer were used to generate smoke and water mist, an acrylic tube was used to collect the smoke and water mist in the light path of the cross laser-pattern.  
The acquired digital image of cross laser-pattern under simulated smoke and water mist environment is shown in Figure 4:  
Figure 4: (**a**) Digital image of cross laser-pattern under simulated smoke and water mist environment. (**b**) Energy distribution of corss laser-pattern under the influence of smoke and water mist.  
Figure 3: The simulated smoke and water mist environment.
# Exploring the Evolution of Autonomous Vehicle Acceptance through Hands-On Demonstrations
## 4 Results and Discussion
### Future Users Survey Results
#### 4.2.3 Autonomous Vehicles and Insurance Expectations  
Lastly, potential future users were asked about their expectations regarding autonomous vehicles in the insurance sector (Figure 10).  
The results reveal the following observations:  
* Of all respondents, 96% believe that autonomous vehicles will continue to require insurance.
* More than 90% of respondents believe that insurance should be equal in price, cheaper or non-existent.  
Figure 8: Future users’ willingness to use autonomous delivery services.  
Figure 9: Age vs. confidence of future users driving among autonomous vehicles.  
It is interesting to find that 96% of respondents believe that autonomous vehicles will continue to require insurance. Moreover, most (57%) think that this insurance should be cheaper. Based on the premise that autonomous vehicles' accident rate would be lower, it seems that the higher cost of the technology associated with autonomous driving is not perceived by the general public; therefore, they expect lower insurance rates. This demonstrates that users expect the future self-driving car to be more efficient and safer than current manned vehicles; have fewer accidents; and, hence, have fewer claims and cheaper insurance.
# Efficient Regression Testing of Software Product Lines by Reducing Redundant Test Executions
## 5 Evaluation
### Results and Discussion
#### 5.3.1 RQ1
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject Product Family**} & \multicolumn{4}{c}{**Full Selection**} & \multicolumn{4}{c}{**Ekstazi\_SPL Selection**} \\ \cline{2-9}  & \multirow{2}{*}{**\#Sel**} & **Ehaustive Execution** & \multirow{2}{*}{**CPLTE**} & **Savings** & \multirow{2}{*}{**\#Sel**} & **Ehaustive Execution** & \multirow{2}{*}{**CPLTE**} & **Savings** \\ \hline MobileMedia (4) & 913 & 2711 & 1602 & 40.9\% & 303.4 & 813.9 & 617.2 & 24.2\% \\ TankWar (5) & 735 & 2968 & 1941 & 34.6\% & 178.8 & 669.3 & 608.7 & 9.0\% \\ Prevayler (3) & 1157 & 2940 & 1231 & 58.1\% & 139.8 & 311.8 & 176.1 & 43.5\% \\ Prevayler (5) & 2144 & 7499 & 2314 & 69.1\% & 324.8 & 799.1 & 399.4 & 50.0\% \\ MobileRSSReader (3) & 2764 & 8227 & 3036 & 63.1\% & 204.9 & 605.7 & 261.5 & 56.8\% \\ MobileRSSReader (5) & 4258 & 21,090 & 4707 & 77.7\% & 317.0 & 1559.6 & 411.5 & 73.6\% \\ Lampiro (4) & 5265 & 20,810 & 5330 & 74.3\% & 468.2 & 1729.5 & 503.7 & 70.9\% \\ Lampiro (6) & 7492 & 44,386 & 7725 & 82.6\% & 692.3 & 3818.3 & 833.0 & 78.2\% \\ BerkeleyDB (3) & 9027 & 24,504 & 16,390 & 33.1\% & 789.4 & 1925.1 & 1732.1 & 10.0\% \\ BerkeleyDB (5) & 15,161 & 66,525 & 34,659 & 47.9\% & 1764.5 & 7025.1 & 6038.3 & 14.0\% \\ BerkeleyDB (7) & 20,858 & 126,314 & 50,231 & 60.2\% & 2473.0 & 13,339.2 & 10,436.0 & 21.8\% \\ BerkeleyDB (10) & 24,352 & 200,534 & 59,960 & 70.1\% & 3346.5 & 24,634.5 & 17,712.2 & 28.1\% \\ Average & & & & 59.3\% & & & & 40.0\% \\ \hline \hline \end{tabular} \#Sel: average number of test cases selected.  
\end{table} 
Table 4: Number of test executions and savings of Checksum-based Product Line Test Execution (CPLTE).
# Logogram VR: Treadmill-Coupled VR with Word Reflective Content for Embodied Logogram Learning
## 1 Introduction  
Logograms are written language systems in which each character corresponds to a word. Although they generally have a longer history than phonograms do, logograms still play important roles in modern communication [1]. Currently, one fifth of the world's population uses logograms, mostly in east Asian countries. Speakers of Mandarin Chinese, which uses logograms in written communication, comprise the second most common language (L2) speakers, following English speakers [2]. Despite their prevalence, logograms are often perceived to be difficult to learn as a large number of characters are used [3, 4]. Learning logograms requires the memorization of thousands of distinctive characters, unlike segmental scripts where the alphabets represent sounds.  
The challenge of logogram acquisition is that the phonological structure and characters are unrelated. For instance, many written Chinese characters sound identical; this language system contains almost 8000 distinct words. The complexity of Chinese characters presents an additional obstacle to understanding them [5]. Indeed, reading and writing Chinese are viewed as more challenging than listening to and speaking Chinese [6].  
Although previous research on VR language education, including phonetic Chinese, has been successful in immersing and engaging students, these findings are not immediately transferable to logograms as they focused primarily on conversational Chinese or the Mandarin rendering of Chinese characters [7, 8]. There is a need for a different approach that incorporates the distinctive properties of logographic systems. Specifically, when one iscreating the education system, the complexity and relevance of the graphic configurations of the characters and the absence of a sound-script correlation should be considered.  
In this study, we offer Logogram VR, an immersive virtual reality platform for learning logographic languages. As their meanings are replicated in the virtual reality environment, hundreds of distinct characters can add variety to the learning scenario. In addition, the system incorporates walking and handwriting gestures, which are vital to the acquisition of the Chinese language [9; 10]. Furthermore, students can practice the listening and speaking components of logograms. To complete the scenario, they must articulate the meaning and pronunciation of each character after having finished the stroke. The participants indicated that the system's embodiment method is usable and provides a highly positive learning experience. The technique is extensible to a wide variety of Hanja and other countries' adaptations of Chinese characters.
## 5 Case Study: Problem Formulation
### Feasibility Assessment  
The needs, functionalities and constraints presented by both stakeholders and future users need to be scrutinized by the designer. After the comprehensive analysis, the general needs of a system supporting elderly people living alone with limited mobility or with the first signs of dementia are  
assessed as technically accomplishable and feasible. Furthermore, the performed research proved that the related functionalities and constraints concerning the working environments, activity recognition, vital signs' monitoring and behavior classification are also technically feasible at a moderate level of technical and algorithmic complexity. Nevertheless, the trade-off between the desired low price and the system's reliability and the further constraints has been acknowledged as challenging.
# A Real Time and Lossless Encoding Scheme for Patch Electrocardiogram Monitors
## 3 Results
### High-Bit Resolution  
With respect to the fixed-block mode, this research reserved 512 records as the block size and 512 units of output buffer space to identify three to eight suitable high-bit resolutions. The CRs are presented in Table 1 and Figure 8. The preferred CR was found at a resolution of seven bits on recording 100. Figure 9 shows the best case for high-bit and low-bit results for recording 100. Recording 107 provided the lowest performance of all 10 records because it contained an ECG signal with an abnormal \"paused\" heartbeat. The best CR of recording 107 was at a resolution of five bits for high bits, and it was the only recording that provided a high performance at five bits. Figure 10 presents the original ECG signal, which has a longer QRS complex than a standard ECG signal does. Since the QRS complex has a high-variation signal, high bits are used to represent the value and have short continual zeros. In contrast, zero separation exhibited a less favorable performance.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline  & **3 bits** & **4 bits** & **5 bits** & **6 bits** & **7 bits** & **8 bits** \\ \hline _100_ & 1.342 & 1.453  
Figure 8: Trend of high-bit resolution.  
Figure 9: Best case: MIT/BIH Arrhythmia Database recording 100 high bits = 7.  
the MIT database. However, while this feature works well for normal heartbeats, it performs poorly with MIT/BIH database recording 107, which features insignificant QRS complexes.
# An Inverter Topology for Wireless Power Transfer System with Multiple Transmitter Coils
## 4 Proposed Controller  
The control is implemented on both the primary side and the secondary side, as shown in Figure 1. The primary-side controller is adopted to find the proper transmitter coil pattern and regulate the output voltage. The secondary-side controller serves the detection operation and wireless data collection of the output voltage (\(V_{out}\)) to the primary side.
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 4 Position Solution
### Tightly Coupled GNSS and cmWave Cellular Positioning
\begin{matrix}\text{diag}\left(\mathbf{w}_{\text{sat}}\right) &\mathbf{0}_{M_{\text{sat}}\times M_{\text{BS}}}\\ \mathbf{0}_{M_{\text{BS}}\times M_{\text{sat}}}&\text{diag}\left(\mathbf{w}_{ \text{BS}}\right)\end{matrix} \right]. \tag{29}\]  
The cmWave observables are equally weighted because there is no prior knowledge of the LoS or NLoS conditions. In case of perfect knowledge of the propagation conditions, the weighting coefficients of the cmWave NLoS observables are set to zero, in order to only exploit the LoS observables with weights equal to one. The location problem can be evaluated in terms of position and timing with the geometric dilution of precision (GDOP), by using the true receiver position. This metric, which predicts the precision of the position solution, is here defined for the two-dimensional (2D) case as  
\[\text{GDOP}_{\text{2D}}=\sqrt{\text{tr}\left\{\left(\mathbf{G}_{\text{2D}}^{ \text{T}}\mathbf{G}_{\text{2D}}\right)^{-1}\right\}}, \tag{30}\]  
where \(\left[\mathbf{G}_{\text{2D}}\right]_{m,1:3}=\left[\frac{x_{m}-x}{\sqrt{(x_{m}- x)^{2}+(y_{m}-y)^{2}}}\quad\frac{y_{m}-y}{\sqrt{(x_{m}-x)^{2}+(y_{m}-y)^{2}}} \quad-1\right]\) is the reference 2D geometry matrix, and for the 3D case as  
\[\text{GDOP}_{\text{3D}}=\sqrt{\text{tr}\left\{\left(\mathbf{G}_{\text{3D}}^{ \text{T}}\mathbf{G}_{\text{3D}}\right)^{-1}\right\}}, \tag{31}\]  
where \(\left[\mathbf{G}_{\text{3D}}\right]_{m,1:4}=\left[ \begin{matrix}\frac{x_{m}-x}{ \left\|x_{m}-x\right\|}&\frac{y_{m}-y}{\left\|x_{m}-x\right\|}&\frac{z_{m}-z}{ \left\|x_{m}-x\right\|}&-1\end{matrix} \right]\) is the reference 3D geometry matrix. When the GDOP is above 6, there is a deficient geometry between transmitters and receiver that leads to position outages, while a GDOP value below 2 is excellent to achieve a precise solution. The positioning performance is then assessed with the 2D position error defined as \(\varepsilon_{\text{2D}}=\sqrt{(\hat{x}-x)^{2}+(\hat{y}-y)^{2}}\), and the 3D position error as \(\varepsilon_{\text{3D}}=\left\|\hat{\mathbf{x}}-\mathbf{x}\right\|\).