esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effectiveness Outcomes
#### 3.2.3 External Apical Root Resorption  
The emergence of PSLB systems, which utilize wires that passively engage in a tube without active springs and offer more flexibility and less friction, has reignited concerns about their impact on apical resorption. [PERSON] et al. (2016) [54] conducted a retrospective study on periapical radiographs. They concluded that there was no significant difference in external apical root resorption between patients treated using the self-ligating Damon system or with conventional ligating appliances. According to [PERSON] et al. (2018) [55], the comparison of external root resorption (ERR) volumetrically in maxillary incisors induced by orthodontic treatment using PSLBs (Damon Q, DQ) or conventional brackets (Titanium Orthos, TO) with the help of cone-beam computed tomography (CBCT) showed that it is not possible to claim the superiority of one bracket system over the other based only on root resorption pattern or amount. However, the study found a higher incidence of slanted ERR in patients treated using the conventional system, which requires further research to identify the possible specific causes (Table 7).
## 1 Introduction  
Due to the scarcity of measurement devices in Distribution Networks (DNs), Distribution System State Estimation (DSSE) calculation is challenging [1]. In DNNs, the lack of measurements causes issues with observability and the ability to perform DSSE computations. Generating pseudo-measurements, which are acquired from load forecasting based on Advanced Metering Infrastructure (AMI) measurements, is a common way to address these issues [2; 3]. However, the quality of the pseudo-measurements is low in both speed and accuracy [4]. Conventional measurements from Control and Data Acquisition (SCADA) measurements, along with pseudo-measurements, are used to perform State Estimation (SE) calculations using low sample rates [5; 6], and SCADA measurements are asynchronous and available with a long interval time [7]. In addition, the conventional DSSE results are not precise enough, since SCADA measurements have low accuracy and are much higher than the pseudo-measurement [5; 8]. Moreover, new features, including DGs, two-way communications, electric vehicles, variable loads, etc., will increase the need for accurate DSSE calculations [9; 10]. Therefore, installing smart measurement devices, such as Phasor Measurement Units (PMUs) and micro PMUs (\(\mu\)PMUs), currently involve grid modernization projects to improve DSSE results [11; 12]. PMUs are extensively used in Transmission Networks (TNs) [13]. However, because DNs have different characteristics from TNs, PMUs are not as commonly used in DNs [14]. Some DN characteristics are:  
1. DNs typically have radial or weakly meshed topology.
2. The placement of measurement tools is not feasible at all buses due to economic constraints and DN configuration with multiple buses.
3. Radial DNs have a high resistance-to-reactance ratio (\(r/x\)).  
Since PMU measurements are available in a synchronous form and can achieve high sampling rates, the incorporation of PMU measurements along with traditional measurements into SE calculations improves DSSE results, as well as increases the observability, resiliency, and reliability levels of DNs [15; 16; 17]. However, replacing conventional measurement devices with advanced tools is not possible over a short period of time; it may take several years to modernize a DN. Because of the presence of both traditional and advanced measurement data during the modernization process, managing, controlling, and performing SE calculations can be challenging [18].
## 1 Introduction
### Materials and Methods
#### 2.1.2 Computing Yield Losses  
We used the AGRESTE database ([[https://agreste.agriculture.gouv.fr](https://agreste.agriculture.gouv.fr)]([https://agreste.agriculture.gouv.fr](https://agreste.agriculture.gouv.fr)), accessed date: 5 March 2019), which refers to yield by crop and department in the historical period (1989-2018 for soft winter wheat and winter barley and 2000-2018 for grassland) with one value by year and by crop produced and declared on a given surface. Yield losses for the \(n\)-th year were computed by comparing the annual yield with a yield reference defined by the Olympic average over 5 years. This methodology is used in agricultural public policies like crop insurance [43]. The crop yield loss computation using the Olympic average is presented here:  
\[\textit{Yield loss}_{c,n}=\frac{\textit{Yield}_{c,n}-(\sum_{n-5}^{n-1}\textit{ yield}_{c}-Max(\sum_{n-5}^{n-1}\textit{yield}_{c})-Min(\sum_{n-5}^{n-1}\textit{ yield}_{c}))}{(\sum_{n-5}^{n-1}\textit{yield}_{c}-Max(\sum_{n-5}^{n-1} \textit{yield}_{c})-Min(\sum_{n-5}^{n-1}\textit{yield}_{c}))}\]  
In which \(c\) is the culture and \(n\) is the year.  
Figure 2 represents yield losses for soft winter wheat, winter barley, and grassland on the French farm scale for the historical period 2000-2018. Over this period, yields were affected by several events:  
* The most significant soft winter wheat and winter barley yield losses were registered for the 2016 excess water event (27% and 17%, respectively) and the 2003 drought (14% and 16%, respectively).  
Figure 1: Crossing the SAFRAN-grid with Graphic Plot Register for DOWKI computation at the departmental scale.  
* The most important grassland yield losses were registered for the 2003 drought (32%) and the 2011 drought (21%).  
Cereals seem to be sensitive to two natural extreme hazards--excess water and droughts--and grassland specifically to droughts. Over the historical period, there were two extreme drought events: in 2003 and in 2011. These two years saw largescale severe droughts, the worst droughts in 30 years. On average, these two events caused 25% crop losses for grassland and 10% crop losses for soft winter wheat. In order to characterize the extreme events in the future, we used these two extreme droughts as a reference.
# Far Off-Resonance Laser Frequency Stabilization Technology
## 4 Far Off-Resonance Laser Frequency Stabilization Using Transfer Cavities
PBS: polarizing beam splitter; PD: photo detector; PZT: piezoelectric transducer. (Figure reproduced from [67]). Furthermore, the cavity length and FSR were observed to be inversely and linearly proportional to the range of laser frequency detuning [21]. With the development of optical processing technology, the finesse of the F-P cavity is significantly increasing, and it can meet the needs of special experiments. However, the transfer cavity frequency stabilization method needs two or more lasers, which increases the volume and the complexity of the optical path. Although the requirements of the F-P cavity are much simpler than those in the PDH, it still requires more design and calculation than some methods. Besides, a laser is stabilized as the reference in an additional approach which should be able to reach a high stability. Those factors limit the scope of the applications. Another method that uses frequency transmission to stabilize the frequency is an optical frequency comb. Femtosecond laser and optical comb technology have been used in absolute frequency measurement and research on optical clocks [68; 69; 70; 71; 72; 73]. At present, laser frequency stabilization technology based on optical frequency combs has been able to achieve very good results. In 2001, [PERSON] et al. locked the laser on the transition line of the iodine atom, and the stability was transferred to each comb component in the entire optical octave bandwidth with an accuracy of \(3.5\times 10^{-15}\). Moreover, the stability of the optical clock was measured as \(4.6\times 10^{-13}\) over a year [74]. In 2014, [PERSON] et al. at the Centre for Metrology and Accreditation used the saturation absorption spectrum of Rb atoms as the frequency standard to lock the laser on the optical comb, and finally, the laser linewidth was in the order of MHz [75]. Moreover, the optical frequency comb is also important for detuning and frequency stabilization. In 2017, [PERSON] used an optical comb to achieve \(1.5~{}\upmu\)m laser frequency stabilization with a stability of the order of \(10^{-12}\) and showed that the method can achieve detuning above 10 GHz [76].
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 2 Likelihood-Based Modulation Recognition Methods
### Generalized Likelihood Ratio Test (GLRT)  
GLRT treats unknown parameters as deterministic quantities and reduces computational complexity by performing maximum likelihood estimation on a symbol-by-symbol basis:  
\[\Gamma_{\text{GLRT}}=\prod_{n=0}^{N-1}\underset{\mathbf{u}_{i}}{\text{max}} \Gamma(r_{n}|H_{i},\mathbf{u}_{i}), \tag{8}\]  
where \(\underset{\mathbf{u}_{i}}{\text{max}}\) represents the maximum likelihood estimation (MLE) of the parameter \(\mathbf{u}_{i}\). This method is more efficient in dealing with unknown parameters but may introduce bias for some nested modulation types.
## 4 Narrowband Interference Suppression Algorithm Based on SVD with Guidance Signal
### Acquisition of the Amount of Narrowband Interference  
To accurately obtain the amount of narrowband interference in noisy PD signals, Figure 5 shows FSWT's frequency-amplitude mean curve, with peaks indicating the center frequencies of interference and PD signals. The yellow line in Figure 5 is the threshold obtained by the smaller threshold method.  
At the same time, combined with the classical threshold method, a smaller threshold method is introduced to filter WGN. The expression of the smaller threshold method is shown in (21).  
\[T=\gamma\cdot\sigma\sqrt{2\ln N} \tag{21}\]  
In (21), \(N\) is the sampling data point and \(\sigma\) is the standard deviation of the signal. In order to ensure that the corresponding frequency peaks of the PD signal and the narrowband interference can be obtained at the same time, \(\gamma\) is usually taken as 0.1-0.5. Based on the large difference in the amplitude of the narrowband interference selected in this paper, \(\gamma\) is taken as 0.4. All narrowband interference frequency peaks and PD signal center frequency peaks can be obtained by a smaller threshold method.  
Figure 5: Frequency–amplitude mean curve.  
In order to further distinguish the frequency of the two signals, the skewness of each peak frequency in Figure 5 is acquired, as shown in Table 3. From Table 3, it can be seen that the skewness value of peak points 1, 2, 3, 5 and 6 is less than 0, and it is determined that peak points 1, 2, 3, 5 and 6 correspond to the center frequency of narrowband interference, and the number of the narrowband interference is 5.
## 2 Related Research
### Method Using Statistical Models
#### 2.2.3 Particle Filter  
The particle filter combines Bayesian estimation and the method of Monte Carlo approximation [31], and considers N particle samples to represent the prediction and state distributions. The state distribution is simulated by the particle samples, and the particle weights and position distribution at \(t\) are adjusted using samples rather than integration operations, and the state distribution is updated according to the adjusted particles. Particle filtering consists of four parts: initialization, sequential inertial importance sampling, attaching new weights, and sampling. The two most important parts are sequential importance sampling [29] and resampling [32; 33].
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 2 Materials and Methods
### Spatial Variables  
Several factors control urban growth; these include spatial variables such as slope, elevation, water bodies, the existing urban land area, roads and population density, which influence the shaping pattern of future urban growth [50]. In this study, we used slopes, roads, existing built-up areas and water bodies as spatial variables to determine urban growth. Steep slopes are known to be less suitable for urban growth, and the lower the slopes, the more suitable they are for urban expansion [16]. Following [PERSON] and [PERSON] [15] and [PERSON] et al. [14], we considered a slope below 25% as suitable for built-up developments and gave it a value of 1, while a value of 0 was given to a slope above 25% because they were unsuitable. We excluded areas very close to roads as no further developments were expected. For this reason, we created a 50 m buffer along and around the roads, and areas within the buffer were given a value of 0 (unsuitable), and areas located more than 50 m were given a value of 1, as they were suitable. We gave a value of 0 to existing built-up areas, as they were unsuitable for further growth and already developed, and a value of 1 was given to undeveloped areas. We gave a value of 0 to existing waterbodies since they already exist, and a value of 1 was given to areas without water bodies.
# Laser-Ultrasound-Based Grain Size Gauge for the Hot Strip Mill
## 1 Introduction  
The properties of high-strength steel, and many other steels, depend to a large extent on the austenite grain size prior to cooling. It is, therefore, very desirable to be able to characterize the austenite microstructure during the hot rolling process. However, the temperatures of the material of interest in a hot strip mill ranges between 800-1200 \({}^{\circ}\)C, and the material moves at 1-10 m/s. Due to these severe conditions, the only method capable of measuring the microstructure in bulk is laser ultrasonics (LUS), which is a non-contact measurement technique for generating and detecting ultrasonic waves in a material using lasers. There are a few variations of how this technique can be employed. However, the most common setup is to use a short-pulsed Q-switched laser directed to the surface of the material to generate the ultrasound and use another laser to detect the response. The short high-energy pulses of the Q-switched laser create plasma on the surface, which, due to thermal expansion, generates an ultrasonic shockwave that propagates through the material. The ultrasonic response is then measured by analyzing the Doppler shift of the surface reflection on the material usually from a single-longitudinal-mode laser. A more thorough description of the LUS technique can be found elsewhere in the literature, e.g., [PERSON] and Drain [1].  
The laser ultrasonic technique that was developed in the 1980s and the 1990s has been commercially employed in the metal industry to monitor the wall thickness during the production of seamless tubes [2] since the beginning of the 2000s. There are currently tens of these LUS-based thickness gauges installed in tube mills around the world.  
The research community has furthermore shown the potential for measuring material properties in-line with LUS [3; 4; 5; 6; 7; 8]. More recently, it was also shown that LUS has the possibility of reliably measuring grain size during the hot rolling of steel strips in a hot strip mill [9; 10; 11]. The work presented herein summarizes the creation of a new LUS grain size gauge installed in the hot strip mill at Borlange in Sweden. This first permanent system to monitor the microstructure during hot rolling was installed in 2022, positioned after the last stand and before the run-out table, in the SSAB mill.  
In order to develop a gauge with a robust grain-size calculation, a new model and algorithm has been developed, which is corrected for temperature and does not require a reference sample [12]. Additionally, the grain size gauge has been equipped with optics for a long working distance of 600 mm and suitable protection for the harsh environment.
# Boosting Second Harmonic Radiation from AlGaAs Nanoantennas with Epsilon-Near-Zero Materials
## 1 Introduction
More specifically, we will focus on Second Harmonic (SH) generation and show that the presence of the ENZ can improve the scattered SH signal up to three orders of magnitude with respect to a dielectric substrate and up to two orders of magnitude with respect to a metallic one. This dramatic enhancement benefits a novel approach to improve nonlinear processes at the nanoscale.
## 2 Designing the \(4\times 1\) Microstrip Square Patch Array Antenna
### The Single Microstrip Square Patch Antenna
#### 2.2.2 Tapered Power Distribution
For the 4 \(\times\) 1 patch array antenna using uniform power distribution, HPBW for the y-z plane was 76.7 degrees at 24.125 GHz, whereas for the z-x plane, it was 22.0 degrees with a sidelobe level of \(-13.4\) dB. Figure 4: Design and electric field distribution of the 4 \(\times\) 1 microstrip square patch array antenna combined with a shunt-connected series feed network using a uniform power distribution: (**a**) the geometry and (**b**) the electric field distribution at 24.125 GHz. Figure 5: Design and electric field distribution of the 4 \(\times\) 1 microstrip square patch array antenna combined with a shunt-connected series feed networks using a tapered power distribution: (**a**) the geometry and (**b**) the electric field distribution at 24.125 GHz. Figure 6: Performance comparison of the 4 \(\times\) 1 microstrip square patch array antenna combined with shunt-connected series feed networks using uniform and tapered power distributions: (**a**) the input reflection coefficient, (**b**) the realized gain in the +z-axis direction, (**c**) the radiation patterns on the y–z plane at 24.125 GHz, and (**d**) the radiation patterns on the z–x plane at 24.125 GHz. For the 4 \(\times\) 1 patch array antenna using a tapered power distribution, the HPBW for the y-z plane was 75.5 degrees at 24.125 GHz, whereas for the z-x plane, it was 26.6 degrees with a side-lobe level of \(-\)21.3 dB. We observed that although the HPBW for the z-x plane increased by 4.6 degrees with a resulting maximum gain reduction, the sidelobe level for the z-x plane decreased by 7.9 dB when the tapered power distribution was used instead of the uniform power distribution. Design of 4 \(\times\) 1 Microstrip Square Patch Array Antenna Appended with Two Metallic Plates
## 3 New Compact Models
### The Complete Model  
We first define \(V^{R}\) as the set of vertices of \(V\) involved in at least one precedence relation as a head. Formally, \(V^{R}=\{t\in V\,|\,\exists s\in V:(s,t)\in R\}\). Let \(x_{ij}\) be a binary variable modeling if \(\operatorname{arc}{(i,j)}\in A\) is visited: \(x_{ij}=1\) if \((i,j)\in T\), and \(0\) otherwise. Let \(y_{i}\) be an integer variable used to identify the position of vertex \(i\in V\) along the only path of the arborescence connecting the root \(r\) to vertex \(i\) itself. Let \(u_{j}^{t}\) be a binary variable with indices representing vertex \(j\in V\) and vertex \(t\in V^{R}\). This variable will be used to model precedences. Let \(d_{j}\) be a continuous variable containing entering time of the flow at vertex \(j\in V\). Finally, let \(w_{j}\) be a continuous variable modeling the time waited at vertex \(j\) before letting the flow enter the node itself.  
An MILP model for the PCMCA-WT is as follows.  
\[\operatorname{minimize}{\sum_{(i,j)\in A}c_{ij}x_{ij}}+\sum_{i\in V}w_{i}\] (1) \[\text{subject to:}\sum_{(i,j)\in A}x_{ij}=1 \forall j\in V\backslash\{r\}\] (2) \[y_{i}-y_{j}+1\leq n(1-x_{ij}) \forall(i,j)\  
the instance as a Sequential Ordering Problem [34] using a nearest neighbor algorithm [41] is taken as the value of \(M\). This is a valid upper bound for the optimal cost of a PCMCA-WT instance, being a valid solution for the Sequential Ordering Problem a simple directed path that includes all the vertices of the graph, with the constraint that \(t\) never precede \(s\) for all \((s,t)\in R\). This implies that \(d_{t}\geq d_{s}\) for all \((s,t)\in R\).  
The model proposed in this section has a considerably smaller memory footprint compared to the polynomial-size model proposed for the PCMCA-WT in [38]. In detail, the number of variables is reduced from \(O(n^{3})\) to \(O(n^{2})\). The number of constraints remains instead in the order of \(O(n^{3})\), although now the hidden multiplicative factor depends on the number of vertices involved in at least one precedence as a head, instead of all the vertices. This makes the number of constraints much smaller. All together, these improvements reduce substantially the memory footprint of the model, with great advantages for practical tractability.
## 3 Materials and Methods  
### Materials  
#### 3.1.1 Development Tools  
The following tools were used for the development of the API and the mobile application:  
* API: Developed in PHP (version 8.0.2) using the Laravel framework (version 9.2). This framework was chosen for its ease in creating RESTful APIs and its robustness in managing routes and HTTP requests.
* Mobile Application: The application was developed in Kotlin (version 1.9.0) for Android mobile devices to leverage its native capabilities for geolocation and real-time communication.
* Geolocation: For distance calculations, the Haversine formula was implemented in the API, which receives the mobile device's geographic coordinates and calculates proximity to points of interest stored in a database.
* Database: MySQL (Ver 15.1 Distrib 10.3.39-MariaDB) was used to store the shopping centers' coordinates and promotional information.
* Petri Nets: Petri nets were used to model the asynchronous behavior of requests between users and the API.  
API Development Technology Environment
* Operating System: Ubuntu 20.04;
* Processor: Core i7.2.5 GHz;
* OS Type: 64-bit;
* RAM: 16 GB;
* Hard Drive: 1 TB.  
Mobile Application Technology Environment
* Device 1: Huawei smartphone with Android version 9, RAM 4 GB;
* Device 2 (emulated): Pixel 7 Pro with Android version 13, RAM 2 GB.  
IDE for Mobile Application Development
* IDE: Android Studio Iguana 1 2023.2.1 Canary 7.  
IDE for API Development
* IDE: Visual Studio Code version 1.94.2.  
The diagram in Figure 2 underscores the distributed nature of the system, highlighting the distinct separation between the mobile frontend and the backend infrastructure. It  
Figure 1: Representation of REST API architecture interacting between mobile client and database.  
also illustrates the integration of geolocation services and advanced modeling techniques, which are implemented to ensure efficient performance and a seamless user experience. This architectural design emphasizes the system's modular approach, facilitating scalability and maintainability while optimizing resource utilization across distributed components. In Figure 2, gray ellipses represent the main components of the system, including Mobile Application, API, MySQL, Laravel, PHP, Kotlin, Android, and Petri Nets. Green ellipses indicate development environment elements: Database, API development, modeling, and Mobile app development.
# Investigation into the Construction Response of Tunnels through Fault: Model Test
## 3 Design of the Model Test
### Model Size Determination  
As depicted in Figure 5, three monitoring sections, namely, the FL area, SL area, and TL area, are set up at distances of 10 cm, 30 cm, and 50 cm, respectively, from the tunnel portal. The SL area is the monitoring section where the tunnel intersects with the fault zone. The soil cover depth of the tunnel is 200 cm and the tunnel has a design width of 40 cm and a height of 34 cm. The soil layer thickness at the bottom of the tunnel is 70 cm. The tunnel model is constructed with a geometric similarity ratio of CL = 30. To investigate the effect of different fault dip angles, three different test conditions are designed; namely, 45\({}^{\circ}\) fault dip, 60\({}^{\circ}\) fault dip, and 75\({}^{\circ}\) fault dip.