esa-sceva/satcom-chunk-collection huggingface dataset:
# FEM Analysis of Superplastic-Forming Process to Manufacture a Hemispherical Shell
## 4 Results and Discussion
### Numerical Results Connected with a Conical Section Blank  
Once the five simulations were performed, the values at the pole, \(s_{p}\), and at the edge, \(s_{e}\), of the thicknesses of the manufactured hemispherical shell were determined. These values were fitted through a straight line and represented in the \(s_{p}\)-\(s_{min}\) and \(s_{e}\)-\(s_{min}\) graphs (see Figure 8). From Figure 8, it can be seen that \(s_{p}=s_{e}\) for a single value of \(s_{min}\) (\(s_{min}=0.903\) mm). This value represents the optimal minimum thickness if a blank characterised by a conical section profile is used. Equation (2) allows for the determination of the optimal value of \(s_{max}\) (\(s_{max}=1.193\) mm).  
Using the blank characterised by the optimal values of \(s_{min}\) and \(s_{max}\), a numerical simulation that highlights the thickness distribution of the hemispherical shell in the  
Figure 8: Thickness values obtained, though finite element analysis, at the pole and edge of the hemispherical shell using a conical blank section characterised by different \(s_{min}\) values.  
Figure 7: Thickness distribution in the hemispherical shell obtained via both 3D and 2D FEM analyses.  
optimal conditions is performed. Figure 9 shows the comparison between the thickness distribution obtained using a blank with a conical section profile and that obtained with a constant thickness blank. From Figure 9, it is evident that the thicknesses of the shell obtained with a blank with a conical section profile are distributed more uniformly than in the case in which a blank with a constant thickness is used.  
This means that the thickness distribution is more uniform when moving from the constant-thickness blank to the conical blank. The uniformity of the thicknesses in the hemispherical shell can be measured using the following expression:  
\[A=\left(1-\frac{s_{p}}{s_{e}}\right)100, \tag{5}\]  
In Equation (5), \(s_{e}\) is the maximum thickness value, and \(s_{p}\) is the minimum thickness value at the edge and at the pole of the manufactured hemispherical shell, respectively. If the two values are the same, \(A\)% tends to zero, making the distribution of the shell thicknesses uniform. The value of \(A\)% decreased from \(\approx\)55% when using a constant-thickness blank to \(\approx\)16% with a conical blank; so, the final distribution of the thicknesses of the hemispherical shell for a conical blank is more uniform than that of a constant-thickness blank.
## 1 Introduction
[12] have previously addressed the issue of inexact measurements metrically, taking up the simple model of interval-bounded independent per-sample error which is unrelated across samples in the sense of chosen afresh upon every sample. We expand their analysis by decomposing the error into an unknown yet fixed offset and an independent per-sample error and show that in this setting, monitoring of temporal properties no longer coincides with collecting Boolean combinations of predicates evaluated pointwise over best-possible per-sample state estimates, but can be genuinely more informative in that it infers determinate truth values for monitoring conditions that interval-based evaluation remains inconclusive about. For the model-free as well as for the (certain or uncertain) linear model-based case, we provide optimal evaluation algorithms based on affine arithmetic [13] and SAT modulo theory solving over linear arithmetic [14; 15]. Beyond uncertain sensing, we also address the issues of partial observation (w.r.t. both state variables and time instants) in uncertain linear systems. In all these cases, the reductions to proof obligations in affine arithmetic provide conclusive monitoring verdicts in many cases where interval-valued state estimations and subsequent interval-based evaluation of temporal monitoring properties inherently remains inconclusive, which we demonstrate by means of examples. We furthermore prove that our affine-arithmetic reductions are optimal in that they are as precise as a monitor operating under metric uncertainty can possibly be: they do not only provide sound verdicts throughout, but are also optimally informed in that they always yield a conclusive verdict whenever this is justified by the formula semantics. Any reduction to even richer extensions of interval arithmetic, like [16], would consequently fail to provide additional gains in precision. To achieve these results, we first in Section 2 review the definition of Signal Temporal Logic [7], which we use as the formalism of choice for illustration. We then provide the metric error model for measurements (Section 3) and based on it define the monitoring problem under metric uncertainty (Section 4) including rigorous criteria for soundness, completeness, and precision of monitoring algorithms. The subsequent two sections develop optimal monitoring algorithms based on reductions to affine arithmetic, where Section 5 covers the model-free case and Section 6 treats optimal monitoring when a (potentially uncertain) affine model of system dynamics is given. Both sections provide illustrative examples of the constructions. Section 7, finally, investigates the worst-case complexity of the monitoring problem under uncertainty.
## 3 Results and Discussion
### MSCR Test Result Analysis  
#### 3.3.1 Jnr and R before Aging  
Non-recoverable creep compliance (Jnr) and recovery (R) were used to evaluate the high temperature of HCRMB with different amounts of sulfur [31]. Jnr at 0.1 kPa and 3.2 kPa are denoted as Jnr0.1 and Jnr3.2, respectively. R is defined in the same way as Jnr at different stress conditions. According to Figures 7 and 8, the Jnr0.1 value is higher for 70# compared to HCRMB with different sulfur content, and the ranking of Jnr0.1 is 20 TB_0.45 ul \(<\) 40 TB_0.45 ul \(<\) 20 TB_0.25 ul \(<\) 40 TB_0.25 ul \(<\) 70#. In addition, the same ranking of R0.1 and R3.2 is 70#\(<\) 20 TB_0.25 ul \(<\) 40 TB_0.25 ul \(<\) 40 TB_0.45 ul \(<\) 20 TB_0.45 ul, indicating that the increase of sulfur doping can improve the high temperature performance of HCRMB. This is due to the increase in sulfur doping, which promotes the reaction between bitumen and sulfur, resulting in an increase in asphaltene and a change in bitumen structure to a gel-type structure, which manifests itself in the increased high temperature performance of HCRMB.
# Dynamic Tuning and Multi-Task Learning-Based Model for Multimodal Sentiment Analysis
## 3 Methodology
### Task Definition  
MSA aims to decipher sample sentiment states by harnessing multiple signals, encompassing text (\(I_{t}\)), visual (\(I_{v}\)), and audio (\(I_{a}\)) modalities. Task types within this domain are typically categorized into two broad classes: classification and regression. Focusing on the latter, the proposed DMMSA model takes \(I_{t}\), \(I_{v}\), and \(I_{a}\) as inputs, yielding an output sentiment intensity value \(y^{*}\), constrained within the actual interval \([-R,R]\), where \(R\) defines the upper and lower bounds of the sentiment score.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 1 Introduction
Thereby, as already pointed out by several reviews published in recent years, despite the fact that these sensing technologies allow for detecting food spoilage with high sensitivity, they are not yet suitable for industrial applications [27; 28; 29]. In this term, optical sensors have been increasingly investigated and show great potential for the food packaging sectors. Indeed, these systems are low-cost, do not require electronic connections or read-out devices, and are suitable for use by untrained operators, represented by the distribution chain operators and the consumers. Such sensors can be based on several working principles including colorimetric assay, fluorogenic receptors, photonic structures, and plasmonic particles. In all cases, one of the main advantages of these sensors is the response that is easy-to-read using the naked-eye due to the simple color variations. With this in mind, this review aims to provide a concise but comprehensive summary of the recent developments concerning the real-time monitoring of food products via innovative packaging technologies mostly based on optical detection systems. Hence, we first briefly introduce the main indicators of food quality and how they can be exploited to perform real-time monitoring. Subsequently, we report a detailed description of the efforts made in developing \"optical\" intelligent packages that can be directly employed for monitoring food quality. Finally, a short summary of the current challenges and future perspectives is reported.
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 3 Materials and Methods
### ILTIS-N Model Applications  
ILTIS-N applications serve as the human-computer interface shown in Figure 6. These applications display real-time track infrastructure status and mediate dispatcher commands to the SIMIS W interlocking system. They can also be used to quickly diagnose signalling systems on the model railway.  
The graphical design closely resembles the real ILTIS-N system (see Figure 7). For this model railway application, only the most essential system functions were implemented,such as station dispatching, track consent changes, and other functions related to dispatcher-station communication.  
Each ILTIS-N application relies on dictionaries containing all objects with which it interacts. Each dictionary holds information about objects of the same class. The dictionary \"dictNav\" contains information about all model railway signals. This dictionary controls access to object attributes of signals like the current status of the signal, its name, type of signal, ID number, and other relevant data [8]. New data from the JSON format is assigned to these objects, which are then represented by standardized graphical symbols (see Figure 8) per railway standard ZSR D 101/T 101 Annex 8c. Symbols for all potential track scenarios were created, saved in.bmp format, and organized into directories. As new data are assigned to objects, control functions (for example \"vyberCestu\", \"ackiaPriecestie\", \"akciaTS\" [8]) are called to update their appearances in the graphical interface.  
Real-time responsiveness is crucial for these applications. Initially, single-threaded processing was considered, but it caused application responsiveness issues. Data retrieval from the REST API server often interfered with other graphical window actions, resulting in a loss of responsiveness. Consequently, multithreading was implemented to separate data processing and updating from control algorithms, allowing each application to filter and process only the data relevant to its functions. To carry this out, QThread library was used.  
Figure 6: Simple block schematic of real-time system [12].  
Figure 7: Main window of our ILTIS-N model application for dispatching centre (Closed arrows represent main signals, open arrows represent marshalling signals, combined arrows represent combined signals) [8].
## 4 Results
### Flow and Velocity Measurements  
Figure 5 shows the Bland-Altman analyses for the flow volume measured by the deep learning methods and ground truth. For both the HC and MM cohorts, good agreement in segmentation and flow volume measurements can be seen for all three deep learning models. The differences in flow between the ground truth and deep learning models ranged between \(\pm\)1.8 mL/s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Healthy** \\ **Control** \\ **Baseline** \\ **(_n = 53_)** \\ \end{tabular} & \begin{tabular}{c} **Moyamoya** \\ **Baseline** \\ **(_n = 16_)** \\ \end{tabular} & \begin{tabular}{c} **Healthy** \\ **Control** \\ **Diamox** \\ **(_n = 54_)** \\ \end{tabular} & \
\begin{tabular}{c} **Moyamoya** \\ **Diamox** \\ **(_n = 30_)** \\ \end{tabular} &
\begin{tabular}{c} **All Subjects** \\ **in All** \\ **Conditions** \\ **(_n = 153_)** \\ \end{tabular} \\ \hline
**U-Net** & 0.92 \(\pm\) 0.03 & 0.58 \(\pm\) 0.31 & 0.92 \(\pm\) 0.05 & 0.73 \(\pm\) 0.18 & 0.81 \(\pm\) 0.21 \\ \hline
**Attention U-Net** & 0.87 \(\pm\) 0.06 & 0.69 \(\pm\) 0.21 & 0.90 \(\pm\) 0.05 & 0.76 \(\pm\) 0.15 & 0.85 \(\pm\) 0.13 \\ \hline
**Nested U-Net** & 0.85 \(\pm\) 0.11 & 0.79 \(\pm\) 0.21 & 0.91 \(\pm\) 0.06 & 0.80 \(\pm\) 0.13 & 0.85 \(\pm\) 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dice scores achieved by each model using the manual segmentations as the ground truth.  
Figure 4: Receiver Operating Characteristic (ROC) curve for the Standard, Attention, and Nested U-Net Architectures. The areas under the curves (AUCs) are 0.96, 0.99, and 0.98 for U-Net, Attention U-Net, and Neste U-Net, respectively.
## 4 Experimental Results
The derived fPC scores were utilized to predict the forthcoming 20 cycles. Case 2 demonstrates the first 120 cycles for the training purpose, whereas Case 3 depicts the first 140 cycles for the initial training. Each time, the model tested and predicted the subsequent 20 cycles using the training fPC scores. The results indicate that fPCA prediction model based on current, voltage, and temperature performs better at the beginning stage rather than at the later-period prediction of the battery life. The higher MAPE (%) for the last case is due to the inconsistent data trend of the monitored data, which is a common behavior of Li-ion batteries under load at the end of the lifespan. The dataset only comprises 161 cycles of monitored data since it reaches to the end of its lifetime. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **Case 1** & **Case 2** & **Case 3** \\ \hline  & Training cycle & Training cycle & Training cycle \\  & 1–100 & 1–120 & 1–140 \\  & Testing cycle & Testing cycle & Testing cycle \\  & 101–120 & 121–140 & 141–160 \\ \hline RMSE & 0.009 & 0.02 & 0.04 \\ MAPE (\%) & 0.44 & 1.74 & 3.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction performance of battery No 7 for three separate cases. Figure 6: Capacity prediction results of Case \(1\).
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 4 Research Challenges
### Interoperability Challenge  
Installation of 6G networks and smart city infrastructures is carried out by different service providers, regulatory bodies, and technology companies. Lack of worldwide standardization of HO protocols can lead to interoperability issues, leading to connectivityloss while crossing over to another network. Facilitating transparent HO between different technologies, including terrestrial, satellite, and aerial networks, requires universal HO standards to be created. In addition, regulatory limits between areas may shape network rollouts and HO schemes. Harmonized collaboration between academia, industry, and standardization forums such as 3 GPP and ITU is needed to introduce standardized HO frameworks. Future studies need to resolve compatibility issues and establish global mobility management norms so that seamless, cross-domain handover in future networks is facilitated [58].
# A High Capacity Watermarking Technique for the Printed Document
## 3 Proposed Model
### Pixel Flipping Strategy  
This paper flips the character boundary pixels by adjusting the high frequency component of the Fourier descriptor. The high-frequency component only affects the fineness of the boundary, and has little effect on the overall shape of the character, so we use it for pixel flipping. The flipping strategy is as follows:  
1. According to the result of Section 3.1, inputting character \(a_{s}\) and its corresponding pixel flipping rate \(f_{s}\).
2. If \(\left|f_{s}\right|\leq 0.002\), it is considered that the character does not need to be flipped, and it can be left as it is; otherwise, it will enter step 3.
3. Extracting the boundary of the character \(a_{s}\) and calculating the Fourier descriptor \(F_{s}(u)\) of the character \(a_{s}\) according to Equation (4), where \(u=0,1,2,\cdots,K_{s}-1\) and \(K_{s}\) is the number of boundary points of the character \(a_{s}\). Let \(P_{s}=K_{s}-2\), \(\beta_{s-best}=10\), \(a_{s-best}=a_{s}\).
4. Reconstructing the boundary with Equation (5), then filling the boundary to get the character \(\hat{a}_{s}\).
5. If \(f_{s}<0\), then let \(a_{s-new}=a_{s}\ \cap\ \hat{a}_{s}\); otherwise \(a_{s-new}=a_{s}\ \cup\ \hat{a}_{s}\). Then calculating the print scan invariant \(x_{s-new}\) of the character \(a_{s-new}\) and getting the difference between \(x_{s-new}\) and the target \(\hat{x}_{s}\). Let \(\beta_{s}=|x_{s-new}-\hat{x}_{s}|\).
6. If \(\beta_{s}<\beta_{s-best}\), then let \(\beta_{s-best}=\beta_{s}\), \(a_{s-best}=a_{s-new}\).
7. If \(\beta_{s-best}\leq 0.002\) or \(P_{s}<T_{s}\), Then the flipping process ends, replacing the original character \(a_{s}\) with \(a_{s-best}\); Otherwise, let \(P_{s}=P_{s}-1\), jump to step 4 and continue the flipping process.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 5 Summary of Observations
The correlation coefficient is not too significant: 91% of pairs of incident links are less than 0.6 in the correlation coefficient. Additionally, not all of the observed correlations are _good_ (positive), and sometimes, they are _bad_ (negative), unlike what is expected. The weak and irregular link correlation also indicates the obvious spatial difference among links. With such an observation, the upper layer protocols cannot reliably estimate the qualities of all the out-going links only according to the quality of a particular single link, unless they can assure significant correlation between two given incident links. In summary, low-power wireless links deployed in forest environments are easily affected by the complex forest environment and then are both unreliable and extremely hard to precisely predict. Our experimental study discloses the importance of the following issues that need to be considered or addressed in practical wireless sensor networks deployed in forests: (1) how to model and evaluate the link validity with as effective and resource-efficient metrics as possible; (2) how to guarantee the reliable data delivery over the data-ack link of high asymmetry; (3) how to deploy wireless sensor nodes in forests such that the network topology can be kept efficiently connected with a high probability; (4) how to employ and schedule mobile sinks, such as mobile phones [9; 32], to collect the sensory data, if the sensor network is unavoidably segmented due to poor links; and (5) how to achieve optimized cooperative designs across the link, the MAC and the routing layers [33] to further save the limited network energy.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 5 Optimal Dispatching of Micro-Energy-Grid
### Constraint Equation  
#### 5.2.1 Thermoelectric Power Balance Constraints  
\[P_{PV}(t,s)+P_{buy}(t,s)+P_{sell}(t,s)+P_{CHP}(t)+P_{d}(t)\] \[=P_{ntsl}(t)+P_{tsl}(t)+P_{AHP}(t)+P_{c}(t) \tag{18}\] \[H_{CHP}(t)+H_{AHP}(t)+H_{d}(t)=H_{load}(t)+H_{c}(t) \tag{19}\]  
\(P_{c}(t)\) and \(P_{d}(t)\) are the charging and discharging power at moment \(t\), where a positive value means the remaining energy stored in the battery from the grid or PV power generation, while a negative value represents the energy released from the battery to the electric load. \(P_{ntsl}(t)\) and \(P_{tsl}(t)\) are the non-time-shifting load and time-shifting load at moment \(t\), \(P_{AHP}(t)\) is the electric power required by ASHP at moment \(t\), and \(H_{CHP}(t)\) is the heat-generating power of CHP at moment \(t\). \(H_{AHP}(t)\) is the heating power of ASHP at moment \(t\). \(H_{c}(t)\) and \(H_{d}(t)\) are the charging and discharging power at moment \(t\), where a positive value represents the phase change heat storage device absorbs heat, while a negative value represents it releases heat. \(H_{load}(t)\) is the heat load at moment \(t\).
## 3 Results  
A wideband SIW-to-CPW transition is designed for measurement [21]. A prototype of the proposed antenna is fabricated using the single-layer printed circuit board (PCB) process, and the photograph is exhibited in Figure 9a. The optimized configuration is given in Table 1. The simulated and measured reflection coefficients are displayed in Figure 9b. It can be observed that the working bandwidth of proposed planar folded dipole is 55.7% (from 30.3 to 53.7 GHz), and the simulated and measured results are in good agreement. The measured gain and the cross-polarization are presented in Figure 10a and the simulated results are compared. The measured gain is between 5.1 and 6.4 dBi within the operating band. The maximum gain variation is less than 1.3 dBi. The measured cross-polarization levels are below \(-15\) dB over the working band. It is worth noting that it is possible to make a design with a better cross-polarization at the expense of the antenna gain. Here, we make a compromise that the proposed antenna has a moderately high gain and a relatively low cross-polarization over the operating band. The simulated and the measured efficiency are also presented in Figure 10b; the measured efficiency is larger than 80%.  
The radiation patterns in the yoz-plane and the xoz-plane for 35 and 45 GHz are shown in Figure 11a,b, respectively. Stable endfire radiation patterns are obtained and the measured radiation patterns well agree with the simulated ones. However, the cross  
Figure 8: The orthogonal gain components (Gain(\(\theta\)) and Gain(\(\varphi\))) of Antenna A and our proposed Antenna B; (a) Antenna A with different \(L_{\text{st}}\). (b) Antenna B with different \(L_{\text{st}}\).  
Figure 9: (a) The fabricated photograph. (b) The measured and simulated reflection coefficients of the proposed antenna.  
polarization patterns are asymmetric in the xoz-plane, which was probably caused by the SIW-to-CPW transition. The slots of the transition are distributed at one side of the substrate; it is expected to produce some leakage which make the cross-polarization patterns asymmetrical in the xoz-plane.  
A performance comparison of our work and some up-to-date MMW planar dipole antennas are summarized in Table 2. It can be observed that the proposed folded dipole has good balance for cross-polarization and the bandwidth. The proposed SIW-fed folded  
Figure 11: Radiation patterns for the proposed antenna in the yoz-plane (E-plane) and the xoz-plane (H-plane). (**a**) At 35 GHz. (**b**) At 45 GHz.  
Figure 10: (**a**) Measured and simulated gain, cross-polarization and (**b**) efficiency of the proposed antenna.  
dipole antenna has the widest impedance bandwidth while keeping a compact structure and a high radiation efficiency. Moreover, although a thick substrate with the thickness of 0.508 mm is used in this design, the proposed antenna still achieves a better cross-polarization level owing to the use of a folded structure.
## 5 Result and Discussion
### Experimental Setup  
For implementing the experiment, The Python programming language is used to implement the proposed experiment on a Google Colab Pro edition environment with a 25 GB GPU called Tesla P100. Cv2, NumPy, Pickle, TensorFlow, Keras, Matplotlib, are used as the python package. In addition, learning rates 0.001, 38 and 36 and 30 classes and adam optimizer used in the CNN architecture.  
For experiment, the dataset is divided into training and testing, where 70% of the dataset is considered as a training dataset and 30% is regarded as a test dataset. To increase the size of the training dataset, the augmentation technique is applied to the training dataset. Table 3 shows the number of training and testing images for each dataset before and after augmentation. The training dataset of the 38 BdSL, KU-BdSL, and Ishara-Lipi datasets was 8512, 1050, and 1260 images, respectively. After applying the augmentation technique, we have sequentially found 68,096, 15,750 and 18,900 images for the 38 BdSL, KU-BdSL and Ishara-Lipi datasets.