esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Methods
### Feature Selection  
In Thailand, the EGAT system makes forecasts for the next day at 2 pm, which is 10 h to 34 h ahead. On Fridays, EGAT needs to forecast until Monday because the EGAT office is closed on weekends. For short-term forecasting, Thailand typically uses data up to 106 h ahead, especially during long holidays. This study, however, is limited to using data up to 2 pm to forecast only for the next day.  
To simplify the model, the lagged terms and prediction horizon specific to Thailand's practice are illustrated in Figure 12. Many authors, such as [12, 52], have proposed and implemented a feature selection strategy in their model. To forecast the demand on a particular day \(d\), forecasting starts at 2 pm, which is 10 h ahead. Now, the historical demand data from \(HH=0\) to \(HH=28\) of \(d-1\) (the previous day) and \(HH=29\) to \(HH=47\) of \(d-2\) is taken and represented by the variable _load1d_cut2 pm_. Similarly, the lagged demand for two days ahead is included in our model and represented by the variable _load2d_cut2 pm_, and so on.  
Figure 11: (**a**) Data input structure of FNN model and (**b**) data input structure of DNN model.  
Figure 12: Demand prediction horizon: practice of EGAT in Thailand.  
Our forecasting models incorporated external variables in the form of a weather variable (specifically, temperature) and its interactions with days and months. Previous studies have suggested the inclusion of dummy variables and their interactions for improved forecasting accuracy. For instance, [PERSON] et al. (2017) proposed the use of dummy variables and their interactions, while [PERSON] (2018) applied different day-type dummy variables for each day of the week, considering Sunday as a public holiday. The variables were grouped into four categories: deterministic, temperature, lagged, and interactions, as presented in Table 3.
## 4 Results
### Thermogravimetric Analysis  
Figure 4 presents the TGA and DTG of PP/PE, PP/PE/SCB, and PP/PE/SW blend composites, and their respective degradation temperatures are summarized in Table 4. The PP/PE blend showed two-stage decomposition which is related to the degradation of neat PP and PE, respectively, in the blend [15]. Furthermore, the PP/PE/SCB and PP/PE/SW blend composites displayed two degradation steps. In this case, the first step shifted to lower temperatures compared to the pristine blend. It is known that low thermal stability of fiber/cellulose has the potential to lower the degradation temperatures of polymer composites [2,13]. However, in this study, the fiber seems to have an intermediary degradation temperature with the PP. The thermal decomposition results in Table 4 indicate that all the blend composites are thermally less stable compared to the neat PP/PE blend. None, except poor interfacial interactions, can better explain the observation as seen from SEM images. In fact, cellulose containing blend composites appeared to be the least thermally stable compared to untreated ones. While cellulose derivatives and thermal degradation mechanisms are possible reasons [20], vacant space and poor distribution of fiber, as witnessed in SEM, and resulting crystallinity could also account for the observations. On the contrary, [PERSON] et al. [15], in their study, observed an increase in thermal stability with the introduction of WP. They related the improved thermal stability to WP char decomposition, which acted as a barrier that delayed the heat transfer to the polymer matrices. In this study, this is not necessarily the case because the introduction of untreated filler and cellulose appeared to have accelerated the degradation of the PP/PE.  
Figure 3: XRD spectra of PP/PE/SCB and PP/PE/SW blend composites.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Deconvolution CI (\%)** & **Peak Height CI (\%)** & **CS (nm)** \\ \hline PP/PE & 61.2 & 27.30 & 14.64 \\ PP/PE/SCB untreated 5\% & 32.6 & 24.79 & 22.27 \\ PP/PE/SCB cellulose 5\% & 24.9 & 21.94 & 23.27 \\ PP/PE/SW untreated 5\% & 27.9 & 23.80 & 30.60 \\ PP/PE/SW cellulose 5\% & 21.8 & 17.07 & 34.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Crystallinity index of materials.
## 3 Discussion  
Compared to traditional monitoring methods, differential radar interferometry shows significant advantages in multi-scale deformation monitoring for cultural heritage. Generally, every heritage site has its own context related to the evolution of human civilization during a certain historical period. Establishing deformation monitoring systems based on the commonness and specificity of cultural heritage is a challenging task. With advances in radar imaging systems oriented toward high-resolution, multi-frequency and multi-platform, it is possible to select optimum differential radar interferometry for establishing systems that satisfy end-user's requirements of monitoring in cultural heritage sites. Space-borne radar interferometry systems can be used as a regular approach in large-scale deformation monitoring for the whole heritage site; while the ground-based system shows its significant advantage in extracting detailed displacement information of heritage structures by virtue of its flexible placement and high data acquisition rate. Besides, compared to the ground-based system, the space-borne system will be the better choice when access to the site is difficult or when the availability of historical space-borne radar data archives can add value to understanding deformation processes in heritage sites over time.
# University Student Dropout Prediction Using Pretrained Language Models
## 5 Analysis
### Effect of Different Types for Hypothesis  
We conducted additional experiments to understand the effect of NLI task recasting on the dropout prediction task and the impact of the hypothesis on performance. We compared the model before the NLI recasting (_Classification_) with that after the NLI recasting (_NLI_). Because a previous study [43] showed that discrete tokens may be suboptimal, we ran experiments with discrete and continuous hypotheses. The results of these experiments are shown in Table 6. First, the model with the discrete hypothesis (_NLI\({}_{Discrete}\)_) outperformed _Classification_ by up to 5.38% in terms of the F1-score. We also observed that the performance was lower or higher depending on the discrete hypothesis. This suggested that the optimal discrete hypothesis we considered may not have been the best choice. Therefore, we conducted additional experiments using a continuous hypothesis instead of a discrete hypothesis. We found that _NLI\({}_{Continuous}\)_ outperformed _NLI\({}_{Discrete}\)_ by 3.32% in terms of the F1-score. Based on this result, our proposed model prepended the continuous hypothesis to the key and value of each layer to better reflect the prediction value. Our proposed model (_Ours_) outperformed _NLI\({}_{Continuous}\)_ by 13.58%. These results indicated that the continuous hypothesis was more suitable than the discrete hypothesis when recasting the task to the NLI task, and attaching them to each layer was more helpful for model prediction than attaching them to the input embeddings alone.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Modality**} & \multicolumn{4}{c}{**Dropout Prediction**} \\ \cline{2-9}  & **Dem.** & **Aca.** & **Cou.** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{4}{*}{**STUD\({}_{BERT}\)**} & ✗ & ✓ & ✓ & 0.884 & 0.333 & 0.143 & 0.200 \\  & ✓ & ✗ & ✓ & 0.892 & 0.424 & 0.182 & 0.255 \\  & ✓ & ✓ & ✗ & 0.885 & 0.429 & 0.390 & 0.408 \\  & ✓ & ✓ & ✓ & **0.894** & **0.478** & **0.416** & **0.444** \\ \hline \multirow{4}{*}{**STUD\({}_{SimCSE-BERT}\)**} & ✗ & ✓ & ✓ & 0.759 & 0.182 & 0.390 & 0.248 \\  & ✓ & ✗ & ✓ & 0.874 & 0.350 & 0.273 & 0.307 \\ \cline{1-1}  & ✓ & ✓ & ✗ & 0.896 & 0.483 & 0.364 & 0.415 \\ \cline{1-1}  & ✓ & ✓ & ✓ & **0.901** & **0.516** & **0.416** & **0.460** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation analysis of the STUD model.
## 4 Results in Practical Cases
### Results by Simulation Practical Cases
#### 4.1.1 HC Control Method Simulation Results  
The first practical case is based on the HC method according to operation in (6), shown in Figure 2. The use of three ANNs, working in parallel, allows us to obtain the fundamental components of the phase currents. The subtraction of these components from the load currents gives the reference signals for the power converter. This test was carried out with a 50 Hz frequency and a load consisting of a three-phase thyristor regulator with firing angle of 90\({}^{\circ}\), followed by RL series branches with values shown in Table 2.  
Figure 6 shows the load and source currents resulting in this case. Whereas the load _ITHD_ is 43%, the value at the source is reduced to 2.4%.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters of the System Setup** & **Value** \\ \hline V source & 400 V \\ Nominal frequency & 50 Hz \\ Three-phase load impedance & 30 \(\Omega\), 40 mH, series \\ AC regulator thyristors & firing angle 90\({}^{\circ}\) \\ Filter series inductance & 18 mH \\ Inverter control & hysteresis band \(\pm\) 0.03 \\ Sampling frequency & 2500 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main electric system parameters of the test models.  
Another test was carried out to analyze the dynamic response in case of load change. The connection of an additional load in parallel with the former nonlinear one shown in Figure 3a was established at time \(t\) = 0.08 s. It is a linear load consisting of RL series branches of values 60 \(\Omega\) and 80 mH. Figure 7 shows the resulting waveforms, where it can be seen that two electric cycles after the change, the source currents reach the new steady state. The load \(ITHD\) after the change is 24%, and 1.7% for the source currents. The system behavior for some other tested changes showed similar performance.
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 1 Introduction
However, integrating too many switches into the metasurface element makes it difficult to enhance element efficiency. To verify that the single switch topology can enable a RRA element to regulate THz waves at low loss levels, we propose a design concept for THz wave RRA elements and experimentally demonstrate their amplitude control capability. The element consists of a split-ring resonator, a control line, and a single switch. The equivalent impedance of the tunable switch is extracted through probe testing and Thru-Reflect-Line (TRL) calibration testing. The design approach of the RRA element enables the resonance frequency of the splitting to be regulated by the impedance changes in the switch. Therefore, a 30 \(\times\) 24-element prototype is designed, fabricated, and measured to verify the design concept. The main novelty of this work is the application of lower-frequency RRA element design methodologies in the terahertz band, achieving low-loss amplitude control of terahertz waves using CMOS technology. The remainder of this paper is organized as follows. Section 2 introduces the design concept and the RRA element structure is proposed in Section 3. Section 4 shows the fabrication and measurement of the 30 \(\times\) 24 prototype. Finally, the conclusion is given in Section 5, and defining abbreviations is given in the Abbreviations section.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Trickle timer-Based Solutions  
A Trickle timer is used to exchange connectivity information with local nodes by broadcasting control messages. Messages transmission interval is constrained and it increases with the network stability. When the stability is compromised by a mobile node, the interval is reset to a minimum value. Proposed solutions use a lower Trickle timer to solve the issue of Trickle timer and lack of identification of mobile nodes.
## 3 Experimental Results and Analysis
### Simulation Experiment
It can be seen from Figure 11a that compared with the traditional SOBI method, the I-SOBI method has improved the noise reduction effect. This conclusion can be derived by magnifying the comparison analysis between the green and red lines. The de-noised outputs of I-SOBI (green line) are closer to the pure data (black line) than are the ones from the traditional SOBI method (red line). However, there are still high-frequency noises effects in the reconstructed signal. Therefore, the second-stage de-noising is carried out using the FFT with the A-CFS method. The cut-off frequency is selected through our A-CFS method, as proposed in early 2020. The results are shown in Figure 11b. It can be seen that the fluctuation curve of the de-noised signal (red line) is much smoother, and the random noise is significantly suppressed, making it much closer to the true value (black line). In order to more intuitively express the effectiveness of the proposed gradual de-noising method, Table 1 lists two objective evaluation indices, that is, the Signal-Noise-Ratio (SNR) and Root Mean Square Error (RMSE), as obtained from different methods. The reference value for index calculation is based on the pure data \(g_{1}(t)\) without noises. It can be seen that the traditional SOBI method failed to effectively reduce the signal noises, and even added extra noises due to the influences of separated components II and III. The I-SOBI method can improve the noise reduction accuracy to a certain extent. Compared with the traditional SOBI, the SNR is improved from 22.27 to 22.42, and the RMSE is reduced from 0.0545 to 0.0535. However, there are still residual noises in the outputs. After the second-stage de-noising is completed, the noise reduction effect is significantly improved. The proposed de-noising method outperforms any other method. The SNR value reaches 29.73, which is a 30% increase compared to original data. And the RMSE value is reduced to 0.0231, which is a 54% decrease compared to original data. The above simulation experiment results verify the effectiveness of the de-noising method proposed in this paper, further confirming its outstanding noises reduction capability. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Original Data** & **SOBI** & **I-SOBI** & **Proposed Method** \\ \hline SNR & 22.9318 & 22.2683 & 22.4245 & 29.7309 \\ RMSE & 0.0505 & 0.0545 & 0.0535 & 0.0231 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of three evaluation metrics in the simulation experiment.
# Evaluation of Linearization Methods for Control of the Pendubot
## 3 Control Problem
#### 3.1.2 Avoiding of Singular Points  
One of the limitations of analyzed method from Section 3.1 is a fact that the singular points occur, i.e., when \(\kappa_{1}\) in (19) goes to zero, the \(u_{\text{nonlin}}\) in (20) becomes unbounded. Hence, implementing the feedback (20) the inversion of term \(L_{g}L_{f}^{2}h\) has to be computed. Since the inversion can be made trivially only for \(x\in\mathcal{X}\) one can use the following robust inversion schemes  
\[\frac{1}{L_{g}L_{f}^{2}h}=\left\{\begin{array}{ccc}0&\text{for}&\left|L_{g}L_ {f}^{2}h\right|\leq\epsilon,\\ \frac{1}{L_{g}L_{f}^{2}h}&\text{for}&\left|L_{g}L_{f}^{2}h\right|>\epsilon, \end{array}\right. \tag{23}\]  
and  
\[\frac{1}{L_{g}L_{f}^{2}h}\approx\frac{L_{g}L_{f}^{2}h}{\left(L_{g}L_{f}^{2}h \right)^{2}+e^{2}}, \tag{24}\]  
while \(\epsilon>0\) is a design parameter.  
For illustration of this issue Figure 4 depicts a plot of (19) during one exemplary simulation trial.  
Figure 4: Decoupling matrix—\(L_{g}L_{f}^{2}h\)—during an exemplary simulation trial. The green part of the plot indicates when the swing-up controller is enabled.  
Figure 3: Solutions of zero dynamics (22) obtained for: (**a**) positive velocity, (**b**) negative velocity. The used values of parameters correspond to the particular system investigated in Section 4. The dotted lines present the value \(\theta_{r1}=\pi/2\) where the system is assumed to be stabilized.  
It can be easily noticed that (19) changes its sign many times in the given time horizon. Based on simulation results it was observed that application of this robust inversion makes it possible to enlarge the region of attraction of the analyzed algorithm and improves its performance. However, it violates locally the linearization procedure proposed by [15].
## 4 Case Study
### TOOCC Perspective
In this case study, the forecasts are exchanged between FM and NM through TOOCC, which acts as an intermediary between the two systems. Once the real-time scheduling results are obtained, they must be sent to the FM, i.e., Load 15 of the network, so that it can perform the forward local scheduling of energy. Once again, TOOCC has the role of receiving real-time results and forwards them to FM. The FM sends a request for the SCADA House Intelligent Management (SHIM) [72] platform, the execution of which is secured by the IDeS system. This platform is able to perform the local residential energy scheduling, considering the demands and restrictions of the NM. At this point, in real-time, Load 15 is able to use the results obtained from its own energy management, by acting directly on its physical devices. In order to do this, the FM sends the results to TOOCC, so that it can order the PLCMAS system to implement the necessary changes, once it is connected to the devices responsible for simulating the appliances of the house.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 4 Hamaliere Landslide Monitoring
### Application of the Model to a Real Geometry  
Before presenting the localization results, we will first apply the previously developed model to the real system geometry. The workflow is presented in Figure 7, showing how the real parameters come together with the geometry and model to compute the localization error mapping.  
We have set the model geometry according to Table 1, which corresponds to the Harmaliere setup geometry. The number of antennas is now set to \(N_{a}=4\). The ground relative permittivity is set according to the literature for dry soils [56; 57], and the following results correspond to this dry soil scenario. In the case of a wet soil, we expect the relative permittivity to reach values around 25. In the model, this turned out generally to increase the phase error (and localization error) values by about 30%, which can represent millimeter to centimeter values depending on the context (see Section 4.2.2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Antenna No.** & **x (m)** & **y (m)** & **z (m)** \\ \hline
1 & 0 & 0 & 0 \\
2 & 0.018 & \(-\)0.034 & 1.55 \\
3 & 0.013 & \(-\)2.608 & 0.256 \\
4 & \(-\)0.338 & 2.148 & 0.287 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \hline \hline
**\(h_{antenna}\) (m)** & \multicolumn{2}{c}{3} \\
**\(h_{tag}\) (m)** & \multicolumn{2}{c}{1} \\ Ground relative permittivity & \multicolumn{2}{c}{2.4} \\ \hline \hline \end{tabular}
\end{table}
Table 1: (Up) Geometrical parameters for the positions of the four antennas in the Harmalière setup. (Down) Values of the main variables used in the two-ray model (see Figure 3); the height of the station is relative to the ground at the same position.  
Figure 6: Unwrapped phase variation for tag A, measured by four antennas at a frequency f = 865 MHz, from January 2021 to February 2022. The grey bar shows a period of missing data due to hardware failure. Data was directly available after replacement of the malfunctioning device.  
Figure 7: Schematic view of the workflow used to estimate the localization error and bias in the real-scenario Harmaliere geometry.
# A Review of Recent Advances and Challenges in Grocery Label Detection and Recognition
###### Abstract  
When compared with traditional local shops where the customer has a personalised service, in large retail departments, the client has to make his purchase decisions independently, mostly supported by the information available in the package. Additionally, people are becoming more aware of the importance of the food ingredients and demanding about the type of products they buy and the information provided in the package, despite it often being hard to interpret. Big shops such as supermarkets have also introduced important challenges for the retailer due to the large number of different products in the store, heterogeneous affluence and the daily needs of item repositioning. In this scenario, the automatic detection and recognition of products on the shelves or off the shelves has gained increased interest as the application of these technologies may improve the shopping experience through self-assisted shopping apps and autonomous shopping, or even benefit stock management with real-time inventory, automatic shelf monitoring and product tracking. These solutions can also have an important impact on customers with visual impairments. Despite recent developments in computer vision, automatic grocery product recognition is still very challenging, with most works focusing on the detection or recognition of a small number of products, often under controlled conditions. This paper discusses the challenges related to this problem and presents a review of proposed methods for retail product label processing, with a special focus on assisted analysis for customer support, including for the visually impaired. Moreover, it details the public datasets used in this topic and identifies their limitations, and discusses future research directions of related fields.  
retail; grocery products; computer vision; object detection; object recognition; text detection; text recognition; product label analysis +
Footnote †: journal: Medical Image Analysis  
12 Centre for Telecommunications and Multimedia at INESC TEC, Institute for Systems and Computer Engineering, Technology and Science, 4200-465 Porto, Portugal 14 Instituto Superior de Engenharia do Porto (ISEP), School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal 4