esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Raspberry Pi Experimentation  
The Raspberry Pi is a popular platform for implementing traffic sign recognition systems due to its low cost, compact size, and ability to interface with various sensors and cameras. The use of Raspberry Pi in traffic sign recognition systems involves implementing computer vision, machine learning, and deep learning algorithms on the platform. It can be easily integrated into vehicles or installed on roadside cameras to provide real-time information to drivers and traffic control systems. However, the performance of Raspberry Pi-based systems can be limited by the computational power of the system.  
Some works on TSR systems have been the subject of experimentation on the Raspberry Pi board. In fact, the authors in [50] study the effect of shading on the road sign recognition system running on the Raspberry Pi, but the performance of the system is not clearly stated. Concerning [51], the authors develop a robotic traffic sign detection and recognition system using an edge detection algorithm running on a Raspberry Pi to detect traffic signs. However, the performance of the developed system is not evaluated in a real-world environment that could have a significant impact, such as light reflection on the captured traffic sign image. In addition, the authors of [52] develop a traffic sign recognition system using a Raspberry Pi, focusing only on speed signs and taking into account the stability of colour detection as a function of daylight. The results show that the system has an accuracy of 80% and a processing time of up to 2 s.  
According to [53], a real-time traffic sign detection and recognition system using the Raspberry Pi 3 is implemented using machine learning algorithms to identify the type of traffic sign and send alerts to the driver, considering five different classes of traffic signs. The results show that the average accuracy of detection and identification of traffic sign images of the five traffic sign classes is above 90%, and the maximum average time to determine the type of traffic sign in the system is 3.44 s when the car is driving at 50 km/h.  
In this work, the proposed TSR system outperforms the cited approaches in terms of accuracy, with a score of 99%, which means that it correctly identifies a large majority of traffic signs. In terms of execution time, the proposed TSR system is the fastest one, with an average of 0.15 s. This makes it a practical solution for real-time traffic sign recognition applications.
## 4 Simulation Results  
Employing appropriate symmetry conditions (see Figure 6), a quarter of the MTS structure was simulated using ANSYS HFSS software package. Two TM and TE modes are excited by a magnetic dipole oriented towards the \(y\)-axis. The total near-field components at 13.5 GHz obtained by the combination of both modes guided on the structure are depicted in Figure 7.  
Figure 6: Details of the rotationally symmetric metasurface composed by pixel B subwavelength elements. Total radius \(R=50u=157\) mm. The structure is excited by a magnetic dipole oriented towards the \(y\)-axis. The metal patches are shown in dark blue, whereas the light blue color represents the dielectric substrate.  
Figure 7: (**a**) Total field vector at 13.5 GHz over the MTS obtained as the combination of TM and TE modes; (**b**) \(Re(E_{x})\) and (**c**) \(Re(E_{y})\) components’ magnitude over the surface.  
The TM and TE modes were appropriately excited on the structure, with propagation that was synchronized and in-phase. Due to this fact, the TM and TE modes' field combination generates a total field oriented towards the \(x\)-axis (Figure 7a). Additionally, the decoupled propagation of both modes ensures negligible cross polar field excitation. In consequence, \(Re(E_{y})\) is negligible in Figure 7c.  
Figure 8 depicts the total \(Re(E_{x})\) field in the aperture at \(\varphi=0^{\circ}\), \(45^{\circ}\) and \(90^{\circ}\) planes, obtained as a combination of the synchronized TM and TE SW fields.  
As expected, the SW wavelengths of both modes are in agreement, \(\lambda_{TM}=\lambda_{TE}=10.52\) mm (Figure 8b). This feature is in agreement with the predicted phase velocity values related to the unit cell dispersion analysis carried out in Section 3.2, where \(\beta_{TM}=\beta_{TE}=597\) rad/m. It has to be noted that the synchronism of both modes is perfectly maintained in the center of the antenna. Nevertheless, for the outer positions (\(r_{i}>5.5\lambda_{0}\)), there is some phase difference between the modes. The reason for this effect may be related to the cartesian lattice for the surface implementation, and its effect on the TE dispersion properties when the patch is rotated azimuthally.
## Materials and Methods
### Antioxidant In Vitro Activity
#### 2.4.2 ABTS (Diammonium Salt) Method  
The ABTS scavenging activity was measured according to previous work with some modifications [17]. The ABTS radical cations were prepared by mixing 25 mL of 7 mM ABTS solution with 88 \(\upmu\)L potassium persulfate (140 mM) solution. The solution obtained was incubated in the dark for 16 h. The ABTS solution was then diluted with ethanol 96% conveniently until obtaining an absorbance at 734 nm of 0.70 \(\pm\) 0.02. Then, 100 \(\upmu\)L of extracts (0-10 mg mL\({}^{-1}\)) were combined with 3 mL of the ABTS solution and homogenized with a vortex. The reaction mixture was incubated at room temperature (\(25\pm 2\)\({}^{\circ}\)C) for 10 min and then the absorbance was measured at 734 nm against a blank (ABTS solution with 100 \(\upmu\)L of methanol: water (80:20)) using a spectrophotometer (Biomate-3, Thermospectronic, Mobile, AL, USA).
# Vehicular Edge-Computing Framework for Making Use of Parking and Charging Electric Vehicles
## 3 System Model and Definitions
### Utility Function for Edge Server  
As the service provider, the edge servers receive the payment from the requesting vehicle at the cost of computing energy consumption. Consequently, the utility function of an edge server can be defined as the received reward from providing computing services minus its computing energy consumption, which is expressed as:  
\[U_{VEC}=\sum_{i=1}^{n}(p_{iv}q_{iv}-E_{iv}^{c})=\sum_{i=1}^{n}\left(p_{iv}q_{iv}- \phi\frac{\alpha q_{iv}}{C_{v}}\right). \tag{11}\]
# Changes in Soil Moisture Improved by High-Performance Ester Materials under Dry-Wet Cycles
## 2 Materials and Methods
### Methods and Content
#### 2.2.1 Dry-Wet Cycle Test  
The equipment used for this dry-wet cycle test is shown in Figure 2, which mainly included a tray, a modified cutting ring device (as shown in Figure 2a), and a soil moisture EC detection instrument (as shown in Figure 2b). The 200 cm\({}^{3}\) modified cutting ring covered the bottom to prevent a large amount of the soil sample from being lost during the dry-wet cycle, and holes were made in the bottom cover to allow the soil to absorb and dehumidify. The required parameters for soil moisture and the EC detector were obtained by inserting a sensor into the soil sample.  
Figure 1: Principle of soil improvement with high-performance ester materials under dry–wet cycling conditions.  
Figure 2: _Cont._  
200 g of soil samples were taken for each test group, and high-performance ester materials were mixed into the soil according to the designed proportions outlined in Table 1. After being evenly mixed, the soil samples were filled into the cutting ring. There were total of 16 test groups, including 1 control group (CK) and 15 groups with different material ratios. The soil samples were placed in an oven for drying at 60 \({}^{\circ}\)C. Once dry, the test groups were placed in a tray to absorb moisture for 8 h before fully absorbing it. A detector (as shown in Figure 2) was used to test the water holding capacity and EC of each group at three different positions. An average value was taken as the results and the samples were put back into the oven for drying. The next dry-wet cycle was then prepared for and disturbance of the test soil samples was minimized during the test process. A total of 22 dry-wet cycles were conducted in this experiment and corresponding data were tested.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Group** & \
\begin{tabular}{c} **Water-Retaining** \\ **Materials (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Adhesive Materials** \\ **(\%)** \\ \end{tabular} & **Notes** \\ \hline CK (0) & 0 & 0 & \\ \hline
1 & 0.06 & 0.03 & \\ \hline
2 & 0.12 & 0.03 & \\ \hline
3 & 0.18 & 0.03 & \\ \hline
4 & 0.06 & 0.06 &
\begin{tabular}{c} Added \\ water-retaining and \\ bonding materials \\ \end{tabular} \\ \hline
6 & 0.18 & 0.06 & \\ \hline
7 & 0.06 & 0.09 & \\ \hline
8 & 0.12 & 0.09 & \\ \hline
9 & 0.18 & 0.09 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ratios of high-performance ester materials for soil improvement experiments.  
Figure 2: Design of dry–wet cycle test: (**a**) Modified cutting ring; (**b**) Soil moisture and EC sensor; (**c**) Dry–wet cycle test device.  
In addition, 3 g of water-retaining material was taken without being mixed with soil and used as test group 16 for the dry-wet cycle test to study the response of the water-retaining material by itself.
## 3 Frame Optimization Design
### Size Optimization
#### 3.3.2 Optimization of Frame Size  
The purpose of size optimization is to reduce the wall thickness of less stressed pipe fittings, thereby achieving a lightweight design (some pipe fittings are determined according to rules) and improving the power performance of racing cars.  
1. Design variables: Use the inner and outer diameters of each pipe fitting on the frame as design variables. Because the frame is a symmetrical structure about the longitudinal plane, setting two symmetrical pipe fittings as a variable effectively improves computational efficiency. According to specific needs, the range of values for each variable should be clarified. The initial value of the inner circle radius for this racing frame is 10.3 mm, with a variation range of 1-14 mm. The initial value of the outer circle radius is 12.7 mm, with a variation range of 1-3 mm (as shown in Figure 16); 2. Establish responses: In the responses panel, establish three responses: mass, stress, and displacement;  
Figure 16: Definition of design variables for size optimization.  
3. Constraints: 1 Because the maximum stress of the frame under three typical working conditions is 114.6 MPa, which is lower than the allowable stress and has a large optimization space, the maximum stress is set to not exceed 200 MPa. 2 Respectively constrain the displacement to not exceed the original value under various working conditions; for example, the displacement does not exceed 0.3792 mm under bending conditions;
4. Set objective function: With the overall goal of minimizing the net weight of the vehicle frame, use the accuracy and output card provided in Hypermesh to define the accompanying variables and output sensitivity analysis results.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### The Textbook DTW Alignment Algorithm
#### 3.1.1 Definitions  
Let \(\mathcal{A}\) and \(\mathcal{B}\) denote two feature spaces, and let \(\mathcal{A}^{*}\) and \(\mathcal{B}^{*}\) denote the sets of all sequences over \(\mathcal{A}\) and \(\mathcal{B}\), respectively. Let \(\mathbf{x}\) be a sequence, and let \(|\mathbf{x}|\) denote its length, and \(\mathbf{x}[1:n]\) be the subsequence containing its \(n\) first elements.  
A notion of the correspondence between the ordered elements of two sequences \(\mathbf{a}\in\mathcal{A}^{*}\) and \(\mathbf{b}\in\mathcal{B}^{*}\) can be depicted by a sequence \(\boldsymbol{\pi}\) of the ordered tuples of \(\mathbf{a}\) and \(\mathbf{b}\) indices:  
\[\boldsymbol{\pi}=\{\pi_{k}\}\text{ where }\pi_{k}=(i_{k},j_{k})\in\{1,\ldots,| \mathbf{a}|\}\times\{1,\ldots,|\mathbf{b}|\}\quad\forall k\in\{1,\ldots,| \boldsymbol{\pi}|\}\]  
\(\boldsymbol{\pi}\) is called a _pathway_[2], a _warping_ function_[5], a _warping_ or _alignment path_, or simply a _path_ if it also satisfies some additional constraints:  
* Boundaries: \(\boldsymbol{\pi}\) starts and ends at the beginning and end of each sequence, i.e,.: \[\pi_{1}=(1,1)\text{ and }\pi_{|\boldsymbol{\pi}|}=(|\mathbf{a}|,|\mathbf{b}|)\] (1)
* Monotonicity: \(\boldsymbol{\pi}\) does not go backward, i.e,.: \[i_{k-1}\leq i_{k}\text{ and }j_{k-1}\leq j_{k}\qquad\forall k\in\{1,\ldots,| \boldsymbol{\pi}|\}\] (2)  
The Figure 2 illustrates these constraints:  
The transitions between successive points on path \(\boldsymbol{\pi}\) are typically limited to a set of permitted gaps. For instance, the textbook alignment algorithm imposes a maximum shift of one element on both axis, i.e.,:  
\[\pi_{k}-\pi_{k-1}\in\{(1,1),(1,0),(0,1)\} \tag{3}\]  
The Figure 3 illustrates the textbook-permitted transitions.  
Figure 2: A monotonically evolving alignment path between two sequences \(\mathbf{a}\) and \(\mathbf{b}\).  
Let \(\mathcal{S}:\mathcal{A}\times\mathcal{B}\rightarrow\mathbb{R}\) denote a similarity measure between elements of \(\mathcal{A}\) and \(\mathcal{B}\) (the textbook DTW uses a _cost_ measure, but we use here a _similarity_ measure to draw the parallel with the CTC alignment below). Aligning the sequences \(\mathbf{a}\in\mathcal{A}^{*}\) and \(\mathbf{b}\in\mathcal{B}^{*}\) consists in finding the path \(\boldsymbol{\pi}^{*}\) that maximises the sum of the similarity measures of its elements:  
\[\boldsymbol{\pi}^{*} \triangleq\arg\max_{\boldsymbol{\pi}}\sum_{(i,j)\in\boldsymbol{ \pi}}\mathcal{S}(a_{i},b_{j}) \tag{4}\] \[\mathrm{A}^{*} \triangleq\sum_{(i,j)\in\boldsymbol{\pi}^{*}}\mathcal{S}(a_{i},b_ {j}) \tag{5}\]  
\(\boldsymbol{\pi}^{*}\) is called the _optimal path_, and \(\mathrm{A}^{*}\) is called the _optimal alignment measure_.
# Speech Act Theory as an Evaluation Tool for Human-Agent Communication
## 4 Evaluation of Human-Agent Communication Using SAT
### Speech Act Theory as an Evaluation Tool  
Speech Act Theory has been used to interpret communication. In [21], a method based on Speech Act Theory was introduced to discover the relationship between speech acts in human conversations and how to foster the analysis of business process management performance. In another study [38], speech act theory was used to examine utilising speech acts in computer-mediated communication. The study applied qualitative analysis to investigate various types of speech acts that are manifested in the status messages of the _Whatsapp_ mobile application. That study identified were four speech acts in _Whatsapp_ status messages, with different levels of occurrence. Although that study used SAT to evaluate the distribution of _Whatsapp_ status messages, the study used only 86 status messages posted by 23 participants. Also, the study did a descriptive analysis and did not go beyond describing the frequency of messages appearing in each type of act. In another study [39], SAT was used as a tool to support the supervised classification of twitter messages.  
Another study [8] presented a framework that used SAT as a tool to evaluate deception in computer-mediated communication. The proposed framework analysed emails in business-to-business environments to detect deception in business messages in order to help managers and decision makers determine whether their business partners were being deceptive. That framework assessed three levels: the ability of word use, message developments, and intertextual exchange cues.
# Optimal Cluster Expansion-Based Intrusion Tolerant System to Prevent Denial of Service Attacks
###### Abstract  
In this study, we propose an optimal cluster expansion-based intrusion-tolerant system (ITS) that can maintain quality of service (QoS) under a massive denial of service (DoS) attack. Our proposed scheme conserves resources while maintaining good QoS by optimally increasing and decreasing cluster size. To evaluate the performance of the proposed scheme, we use a CloudSim simulator and compare our proposed scheme with an existing conventional adaptive cluster transformation (ACT) scheme. Our simulation results show that the proposed scheme outperforms the conventional ACT scheme in terms of better QoS and lower resource consumption.  
Keywords: intrusion-tolerant system; optimal cluster expansion; denial of service (DoS); virtual machine (VM); queuing theory +
Footnote †: journal: Applied Sciences
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### Energy Consumption and Algorithm Complexity Analysis
The final results show that MSA incurs approximately 33% more computational cost compared to the conventional algorithm. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Notation** & **Meaning** & **Value** \\ \hline \(P_{TX}\) & Transmission power & 545 mW \\ \(P_{RX}\) & Reception power & 90 mW \\ \(P_{DRX}\) & Power consumption during light sleep & 3 mW \\ \(P_{PSM}\) & Power consumption during deep sleep & 0.015 mW \\ \(T_{msg1}\) & Msg1 duration & 0.01 s \\ \(T_{msg2}\) & Msg2 duration & 0.01 s \\ \(T_{msg3}\) & Msg3 duration & 0.01 s \\ \(T_{msg4}\) & Msg4 duration & 0.01 s \\ \(T_{RTT}\) & RTT between UE and satellite & 0.03 s \\ \(T_{date}\) & UL date duration & 0.5 s \\ \(T_{DRX}\) & DRX cycle & 1 s \\ \(T_{PSM}\) & PSM cycle & 100 s \\ \(E_{atti}\) & Power consumption of elevation sensor & 1 mW \\ \(N_{GNSS}\) & The number of GNSS satellites & 4 \\ \(T_{GNSS-acq}\) & GNSS acquisition duration & 5–20 s \\ \(T_{GNSS-track}\) & GNSS tracking duration & 1 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Meaning and value of important notations. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Complexity** \\ \hline MSA & \(O\big{(}1.39e^{6}\cdot n\big{)}\) \\ Maximum Likelihood & \(O\big{(}1.04e^{6}\cdot n\big{)}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of algorithm complexity.
# Numerical Study on Transient Annular Pressure Caused by Hydration Heat during Well Cementing
## 2 Transient Model of Annular Pressure Caused by Hydration Heat during Well Cementing  
As illustrated in Figure 1, the cement slurry is circulated into the annulus between the casing and the formation. Then, the wellhead is shut in and the cement slurry is trapped in the annulus. At the same time, significant cement hydration starts. The heat generated by cement hydration results in transient temperatures and annular pressure.  
Figure 1: Heat transfer in wellbore during well cementing.
# Efficient Modulation Scheme for Intermediate Relay-Aided IoT Networks
## 1 Introduction
### Organization  
The remainder of the paper is organized as follows. Section 2 first describes the intermediate relay-aided network model. Section 3 presents the optimal modulation diversity design problem and the proposed solution. Section 4 provides and compares the numerical results to illustrate the benefit of the proposed approach. Finally, Section 5 concludes this work.
# Energy-Efficient BWP Configuration for Multi-Slice Users
## 2 Related Works
Hence, this work tackles the challenging problem of energy-efficient BWP configuration selection for users connected to multiple slices. The BWP configuration selection between a multi-numerology and single numerology is assessed for each user depending on multiple factors. The devised BWP configuration selection aims to strike a good balance between the size of the scanned BWP and the UE sojourn time in the network depending on the UE QoS and UE battery level. Two approaches are adopted:  
* A distributed approach based on a non-cooperative congestion game. * A centralized approach based on a global optimization problem. These approaches are compared against one another and against the legacy scheme where a single BWP for all services is selected for all users. Thus, the contributions of our work are summarized as follows:  
* Users connected to multiple slices are considered, which are rarely tackled in the SOTA. * The selection of a BWP configuration for multi-slice users is used to address the radio resource allocation problem unlike other works from the literature focusing on the PRB allocation. * This BWP configuration scheme is based on a novel concept where either the multi-numerology or single-numerology BWP configuration is selected for each multi-slice user while aiming to optimize the users' energy consumption and QoS. * The energy-efficiency aspect is considered in the BWP configuration selection process instead of using existing mechanisms from the standards such as DRX and the BWP Inactivity Timer to reduce users' energy consumption. * A centralized and a distributed approach are proposed and are compared against each other and against the legacy scheme. The proposed schemes are detailed in the next sections.
## 3 Results
### Remote Sensing Responses
Soybean \(\rm h_{c}\) was positively correlated with most of the VIs related with vegetation structure such as REIP, EVI, and OSAVI (R\({}^{2}\) between 0.4 and 0.6) and some chlorophyll indices like TCARI and TCARI/OSAVI (R\({}^{2}\)\(\sim\) 0.35, \(p\)-value \(<\) 0.01), while negatively correlated to PRI changes (R\({}^{2}\) = 0.54). Maize \(\rm h_{c}\) presented higher sensitivity than soybean \(\rm h_{c}\) to VIs with most R\({}^{2}\) between 0.73 and 0.96 (Figure 6 and Figure S4). Note that TCARI and TCARI/OSAVI decrease are linked to chl increase [39; 43]. In soybean, the strongest relations with VIs were found for chl with VIs used to detect pigment and photosynthetic efficiency, while no correlations were found with any of the structural VIs, except for rNDVI, which is also associated to water stress and REIP, which can estimate chlorophyll concentration. Even though maize VIs were also sensitivity to chl, the relation was opposite from soybean (e.g., TCARI), same as for \(\rm R_{700/670}\) or PRI. Maize VIs related to structural changes were sensitive to most of the variables (chl, \(\rm h_{c}\), \(\rm T_{r}\), \(\rm g_{s}\), and A), which was not the case for soybean (e.g., NDVI, EVI) (Figure 6 and Figure S4).
# Plant Electrical Signal Classification Based on Waveform Similarity
## 4 Results
### Classification Results of Our Proposed Method
#### 4.1.3 Template Matching Results  
We tested the 329 AP and non-AP waveforms described in Sections 4.1 and 4.2. To get a better threshold when adding new template or updating the template library, we compared different thresholds and select the threshold which showed the best performance. As shown in Table 3, we set different thresholds for every training and got different results. If the threshold for adding new templates is too small, the probability of adding non-AP as template will get bigger and the number of template library will increase fast. If it is too big, there will be few templates added into the template library, producing a low accuracy. Here, we set Append-Threshold = 0.91, and Update-threshold = 0.95 to get the best performance which classification rate is 96.0%.