esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Influence Factors on Deformation Allowance of Loess Tunnel  
### Analysis of Deformation Allowance Under Grade IV Surrounding Rock  
The initial deformation of tunnel support is induced by the intricate interaction between loess and the uncertain factors involved. Hence, the field-measured data exhibit significant discreteness. If the deformation allowance, calculated based on the guaranteed rate, exceeds 80%, the tunnel can be considered to have suitable stability. However, tunnel deformation may vary due to regional differences in the quality of the loess soil. Therefore, the soil is divided into clayey loess, silty loess, and sandy loess for analysis. Considering that the crown settlement in the measured data exceeds surrounding convergence, the deformation allowance for the loess tunnel design should prioritize crown settlement. In the loess tunnel section with grade IV surrounding rock dominance, data from 68 monitoring sections for crown settlement and 46 monitoring sections for surrounding convergence were collected and analyzed. Analyzing Figure 5, when the vault's deformation allowance is set at 10 cm, 15 cm, 20 cm, and 25 cm, the corresponding guarantee rates are 55.89%, 82.53%, 94.11%, and 100%, respectively. In clayey loess and silty loess sections, the tunnel deformation allowance is set to 15 cm, and in sandy loess sections, it is set to 20 cm. Based on the findings in Figure 6, setting the deformation allowance for both sides of the tunnel side wall at 40 mm, 80 mm, 120 mm, and 170 mm yields guarantee rates of 58.69%, 84.78%, 97.82%, and 100%, respectively. Furthermore, in sandy loess sections, the side wall deformation allowance for tunnel measurements can be set at 12 cm, taking into account soil quality. In clayey and silty loess sections, the tunnel deformation allowance can be set to 4 cm.  
Figure 5: Analysis of deformation allowance caused by crown settlement of grade IV surrounding rock.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 2 Vehicle Model
### Kinematic Error Model  
The vehicle dynamics model exhibits certain limitations when applied to controller design. Consequently, an error model is often incorporated to convert the vehicle dynamics model into a dynamic error model [15], as illustrated in Figure 2.  
In the diagram, \(e_{d}\) indicates the lateral error, and \(e_{\varphi}\) denotes the heading error. R corresponds to the radius of curvature.  
The control objective of the path tracking model is to make the lateral error \(e_{d}\) and the heading angle error \(e_{\varphi}\) converge to zero. Considering the small-angle assumption, combining the path tracking model with the dynamic model, one can further obtain the dynamic error model. This leads to the state-space equation of the dynamic error model.  
\[\dot{x}=Ax+Bu+C_{\xi} \tag{3}\]  
where \(x=\begin{bmatrix}e_{d}&\dot{e}_{d}&e_{\varphi}&\dot{e}_{\varphi}\end{bmatrix} ^{T}\); \(u=\dot{\delta}\); \(\xi\) is the road curvature.  
\[A=\begin{bmatrix}0&1&0&0\\ 0&\frac{C_{f}+C_{r}}{mV_{x}}&-\frac{C_{f}+C_{r}}{m}&\frac{l_{f}C_{f}-l_{r}C_{ r}}{mV_{x}}\\ 0&0&\frac{0}{1}\\ 0&\frac{l_{f}C_{f}-l_{r}C_{r}}{mV_{x}}&-\frac{l_{f}C_{f}-l_{r}C_{r}}{l_{x}}& \frac{l_{f}^{2}C_{f}+l_{r}C_{r}}{l_{x}v_{x}}\end{bmatrix};B=\begin{bmatrix}0 \\ -\frac{C_{f}}{m}\\ 0\\ -\frac{l_{r}C_{f}}{l_{z}}\end{bmatrix};C=\begin{bmatrix}0\\ \frac{l_{f}C_{f}-l_{r}C_{r}}{m}-V_{x}^{2}\\ 0\\ \frac{l_{f}C_{f}+\dot{\theta}C_{r}}{l_{z}}\end{bmatrix} \tag{4}\]
# High Performance Receiver Design for RX Carrier Aggregation
## 3 LNTA Topologies and Simulation Results
### Three-coil Ballun LNTA  
The three-coil LNTA in Figure 6 enables CA operation with magnetically-coupled inductors. LNTA tank impedance is lowered by a factor of two due to two inductive coupled loads. With N CA support, the number of magnetically-coupled coils increases linearly. The gain of LNTA is reduced and NF is increased with this approach. Moreover, isolation between different CA paths is limited by the coupling coefficient (\(k_{CA1}\leftrightarrow k_{CA2}\)). Two intra-band CA simulation with this approach gives 51.6 dB gain and 3.2 dB NF. Compared to the single-carrier receiver case, 5 dB gain reduction is observed close to the expected 6 dB loss. NF increase is due to the insufficient gain of the LNTA, and more noise contribution from the subsequent stages accordingly. Even with twice the current consumed in LNTA, moderate improvements are achieved with 53 dB and 2.67 dB of gain and NF, respectively.
## 1 Introduction
### Fundamental Theoretical Calculations  
[PERSON] et al. demonstrated the basic theoretical calculations in 1969 that were helpful in the design and selection process of SEM [63]. Their work helped in identifying the correct probe size for SEM. The aberrations of the diffraction and electron gun, as well as the first-order image of the field emission tip, aid in determining the size of the examined SEM probe for the specimen. The diameter (\(d_{s}\)) of an effective source leads to an image with the following correlation:  
\[d_{s}=2 mR\bigg{(}\frac{\overline{v_{T}}}{v_{1}}\bigg{)}^{1}\bigg{/}_{2} \tag{1}\]  
where \(m\) is the magnification of the gun, \(R\) represents the actual radius of the tip, \(\overline{v_{T}}\) indicates the average transverse energy of electrons exiting the tip (\(\sim\)0.2 V), and \(v_{1}\) is the emission voltage needed to produce a 1 \(\upmu\)amp emission current. The term \(\bigg{(}\frac{\overline{v_{T}}}{\overline{v_{1}}}\bigg{)}^{\frac{1}{2}}\) is the characteristic of a field emission source and indicates the reduction factor of the effective source size.  
The theoretical spot size is affected by the aberrations of the gun with the following two terms:  
\[d_{a}=\frac{mC_{s}\alpha_{1}^{3}}{2} \tag{2}\]  
\[d_{c}=mC_{c}\alpha_{1}\Delta V \tag{3}\]  
where \(d_{a}\) is the spherical aberration, \(d_{c}\) is the chromatic aberration, \(C_{s}\) is the spherical aberration constant, \(C_{c}\) indicates the chromatic aberration constant, \(\alpha_{1}\)  is the entrance half angle of the electron beam, and \(\Delta V\) represents the total energy spread of electrons. \(\Delta V\) is maintained such that the total energy spread of electrons leaving the tip remains at 0.2 V, while variations in \(V_{0}\) and \(V_{1}\) can be considered negligible [63].  
At the defining aperture, the diffraction effect should be included. The diffraction contribution to the final spot size (\(d_{d}\)) can be calculated as follows:  
\[d_{d}=\frac{0.6m\lambda_{1}}{\alpha_{1}} \tag{4}\]  
where \(\lambda_{1}\) represents the electron wavelength at the first anode. The focused spot diameter can be estimated by combining the four terms as follows [63]:  
\[d_{rms}=\sqrt{d_{s}^{2}+d_{a}^{2}+d_{c}^{2}+d_{d}^{2}} \tag{5}\]
## 4 Overview of GNSSs
### Glonass
#### 4.3.2 GLONASS Space Segment  
The GLONASS space segment, evenly distributed across three orbital planes with an inclination of 64.8\({}^{\circ}\), consists of 21 operational satellites and three backups. The longitudes of the ascending nodes of each plane differ by 120\({}^{\circ}\) from plane to plane. In each orbital plane there are 8 satellites 45\({}^{\circ}\) apart in latitude. At an altitude of 19,100 km, the orbital period is 11 h 15 min and 44 s. In September 2016, the number of operational satellites in orbit was increased to 27, of which 24 are GLONASS-M and GLONASS-K1 satellites with full operational capability [81].
## 3 Results and Discussion
### Effects of Waxy Barley Bran Blend Ratio
#### 3.3.3 \(\beta\)-Glucan, TPC, and Antioxidant Activity
OBLP had higher values for TPC (165.23 mg CTN eq/100 g), H-ORAC (16.68 \(\upmu\)mol TE/g), and DPPH (1.26 \(\upmu\)mol TE/g) than IBLP (TPC: 221.88 mg CTN eq/100 g, H-ORAC: 39.63 \(\upmu\)mol TE/g, DPPH: 4.68 \(\upmu\)mol TE/g) [9]. Therefore, the TPC values and antioxidant activities of the 30% OBLP replacement group tended to be higher than the IBLP replacement groups. Based on the previous values, the TPC and antioxidant activities of the breads with IBLP and OBLP substitutions per piece (70 g) were calculated as follows: 10% IBLP replacement group (TPC: 7.58 mg CTN eq/100 g, H-ORAC: 76.51 \(\upmu\)mol TE/g, DPPH: 5.78 \(\upmu\)mol TE/g), 30% IBLP replacement group (TPC: 21.88 mg CTN eq/100 g, H-ORAC: 220.86 \(\upmu\)mol TE/g, DPPH: 16.68 \(\upmu\)mol TE/g), 50% IBLP replacement group (TPC: 35.05 mg CTN eq/100 g, H-ORAC: 355.82 \(\upmu\)mol TE/g, DPPH: 26.73 \(\upmu\)mol TE/g), and 30% OBLP replacement group (TPC: 27.29 mg CTN eq/100 g, H-ORAC: 487.48 \(\upmu\)mol TE/g, DPPH: 57.57 \(\upmu\)mol TE/g). While TPC and antioxidant properties of the wheat flour used for sample preparation were not directly measured, previous studies have demonstrated the retention of these properties in bread [52; 53; 54]. Naked barley is also rich in 156 phenolic compounds [25]. Barley bran contains the following polyphenols, which contribute to its antioxidant properties: catechins, procyanidins, prodelphinidins, quercetin and ferulic acid [55; 56]. Together, these findings demonstrate that the TPC and antioxidant activity is relatively maintained in bread with IBLP and OBLP substitutions, which may contribute to the maintenance and improvement of consumer health.
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 5 Radio Frequency (RF) and Infrared Wake-Up Mechanisms and Implementation  
### Proposed RF Design and Implementation  
The RF wake-up approach is the latest innovation towards achieving energy saving for wireless networks. There are two types of implantation methods: (1) the method that uses active wake-up receivers and utilizes energy from a battery, and (2) the method that uses passive wake-up receivers and harvests energy from the wake-up radio. In our approach, we find that active wake-up receivers have a much better performance in range and a higher rate of success in changing between listening and wake-up modes. In addition, the energy consumption for the RF receiver can be as low as serval mW while it is in its listening state. Figure 5 summarizes the RF-based wake-up design proposed in this paper.  
Figure 4: An actual WSN deployed in the field, using the same sensor modules tested in this paper.  
To implement the design in Figure 5 we choose to use a commercial product, AS3933, as the wake-up receiver, which is further attached to the Waspmote sensor node. AS3933 is a 3-channel low power amplitude-shift-keyed (ASK) receiver, which is able to generate a wake-up signal upon detection of a data signal that uses an LF carrier with a frequency range of 15-150 kHz. The receiver's output is connected to the MCU's interrupt pin at the sensor board. When the AS3933 receives an RF signal, it decodes and checks whether the signal matches the pre-stored pattern. Once confirmed, the receiver will send out a pulse to the interrupt pin to wake up the MCU. Figure 6a shows the hardware setup of the three components in our RF wake-up prototype.  
By adding the RF antenna to the UAV and programming the micro-controller on the UAV to generate a Manchester wake-up pattern, we were able to use the UAV as a control hub to wake up sensor nodes in range. Since the MCU was in sleep mode using this out-band wake-up method, one interesting question was how long it would take for the MCU to wake up after the wake-up signal was sent out. If this procedure took a significant amount of time, then we needed to consider this delay as a drawback for this RF wake-up mechanism. This potential pitfall is carefully studied in this paper.  
Figure 5: Design for the radio frequency (RF)-based active wake-up mechanism.  
Figure 6: Hardware components of the two wake-up systems: (**a**) RF wake-up and (**b**) infrared wake-up.
## 6 Monitoring and Ensuring System Reliability
### Case Study 2: [PERSON] and [PERSON], [159]  
[PERSON] and [PERSON]'s system architecture focused on reliability through redundancy, incorporating duplicate soil moisture sensors and fail-safe irrigation scheduling. Table 8 presents the key aspects of their implementation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Aspect** & **Details \({}^{*}\)** \\ \hline Step 1: Real-time data collection and transmission & Hourly transmission of sensor data from WSUs to WIU via ZigBee; remote access to system status and performance data through GPRS uplink \\ \hline Step 2: Data storage and processing & Edge computing using microcontrollers; sensor data uploaded to web server for remote monitoring \\ \hline Step 3: Predictive modeling and irrigation scheduling & Soil moisture and temperature threshold-based control algorithm; fixed irrigation durations triggered when thresholds exceeded \\ \hline Step 4: Application of insights & Solenoid valves directly controlled by digital outputs of WIU microcontroller; irrigation amounts based on real-time sensor feedback \\ \hline System security, robustness, and monitoring & Alert notifications sent via GPRS for system failures; solar power and rechargeable batteries for continuous operation; web application enables real-time remote monitoring \\ \hline Cost and ease of use & Estimated total cost of USD 1380 for electronic components; designed for low cost and use in geographically isolated areas; modular design allows easy system expansion \\ \hline Benefits and limitations & Water savings of up to 90\% compared to traditional irrigation practices; solar-powered, enabling use in remote areas without grid power; remote alerts help quickly identify and resolve issues \\ \hline \hline \end{tabular} \({}^{*}\) WSU: wireless sensor unit; WIU: wireless information unit; GPRS: general packet radio service.  
\end{table}
Table 7: Key aspects of the fully autonomous irrigation.
## 2 Materials and Methods
### Data Sets  
Four data sets were used to represent environmental conditions within the experimental orchard (1 m\({}^{2}\)/pixel): multispectral aerial imagery, microclimatic data logs, presence-absence data of citrus pests, and georeferenced soil samples.  
Multispectral imagery was captured using an unmanned aerial system (UAS) integrated by a low-cost quadcopter (3 DR Solo, discontinued in 2019) and a cheap sports camera (Ekken 4K, 60 FPS) modified with a planar lens designed for vegetation analysis (i.e., NDVI-7; red-edge 750 nm, green 500-565 nm, blue 450-485 nm). Recent studies have used similar platforms to approach vegetation biophysical features [8; 18; 19]. The UAS was deployed once over the experimental orchard on October 17 th of 2018 approximately at zenith (between 14:00 and 14:30 h. local time) to guarantee maximum radiation conditions and minimum shadow effects. This UAS followed a photogrammetric route designed to take images with 60% side overlap and 80% vertical overlap at a flight altitude of 50 meters above the takeoff site. A set of 12 fixed ground control points (e.g., georeferenced vinyl squares on the ground) was used to facilitate aerial image spatial referencing.  
Microclimatic data was sampled using nine Arduino-based data loggers assembled and programmed in the Biogeography laboratory of the Instituto de Ecologia, A.C. (INECOL). Arduino is an open-source electronics prototyping platform based on simple, customizable hardware and software [20]. Data loggers were installed evenly across the orchard below fully grown trees, while ambient temperature and humidity sensors were placed 15 cm above the ground as in [PERSON] et al. [8]. Loggers were set to record information with a frequency of 60 minutes for 20 days, from October 28 th to November 17 th of the year 2018.  
Georeferenced soil samples and presence-absence data of _Pltyophthora_ sp. \"foot rot\" and \"brown rot\" were collected from 73 randomly selected Tahiti line trees. Two stages of foot rot were considered. Resinous wounds and callus tissue near the grafting area were associated with \"active\" and \"inactive\" foot rot infections, respectively [21]. After careful inspection of each tree, a soil sample of approximately 400 gr was collected from the uppermost 30 cm of the topsoil, where roots of citrus trees mainly develop [22]. Once in the laboratory, 100 gr of each available sample were used to prepare 1:5 dilutions in demineralized water as in [PERSON] et al. [8]. Such dilutions were used to measure soil pH and electrical conductivity (EC) values using a multipurpose sensor for monitoring water quality (YERYI TDS/EC/PH/TEMP meter).
## 2 Review of PV Inverter Efficiency
### Formulation of Weighted Efficiency  
The general approach to derive the weighted efficiency can be found in several documents, particularly the IEC 61683 and the EN 50530 Standard [6; 7; 8]. These standards recommended that the weighted efficiency equation to be developed in a controlled laboratory environment using the PV array simulator. The output of the simulator emulates the output of a real PV panels, which is connected to the inverter being tested. The simulator is fed by a one-year \(G\) profile that is averaged as a one-day dataset. To compute the efficiency, the input and output power data (i.e., the \(P_{ac}\) and \(P_{dc}\)) of the inverter need to be recorded. Another important feature is that the weighted efficiency is computed with the assumption that the back panel temperature (\(T\)) is kept constant at 25 \({}^{\circ}\)C. Only \(G\) is made variable so that the performance of the inverter can be made comparable when tested in a different climate.  
In principle, the idea of weighted efficiency is based on the irradiance-duration (\(G\) vs. time) curve. An example of such curve is shown in Figure 2. In this case, the inverter operates at four levels of operation. The level of operation is related to \(P_{dc}\)/\(P_{dc\_rated}\), where \(P_{dc}\) is the dc input power to the inverter (which is proportional to \(G\)), while \(P_{dc\_rated}\) is the rated dc power of the inverter (given by the data sheet). The choice for the number of level of operation is arbitrary. Any number can be used as long as the \(P_{ac}\), \(P_{dc}\), and \(G\) data are available. However, to simplify the efficiency formula, \(P_{dc}\)/\(P_{dc\_rated}\) are grouped into several ranges.  
The formula for the weighted efficiency, (\(\eta_{\text{WT}}\)) is calculated as the sum of the product efficiency (at each level of operation) and its corresponding weightage, as given by (2). The weightage refers to the amount of time (\(T_{n}\)) that the inverter operates (at a specific level of operation) divided by the sum of irradiance level for the whole duration of the inverter operating time (\(T_{\text{WT}}\)), i.e., \(T_{n}\)/\(T_{\text{WT}}\). The efficiency is simply the ratio of the output (\(P_{ac}\)) to input (\(P_{dc}\)) power at a specific level of operation.  
\[\eta_{WT}=\sum\
olimits_{T=1}^{T=n}\!\!\left(\frac{T_{n}}{T_{\text{WT}}}\eta T /Tn\right) \tag{2}\]  
For the irradiance-duration curve shown in Figure 2,  
\[\eta_{\text{WT}}=\frac{1T_{1}}{T_{\text{WT}}}.\eta_{1/4}+\frac{2T_{2}}{T_{ \text{WT}}}.\eta_{2/4}+\frac{3T_{3}}{T_{\text{WT}}}.\eta_{3/4}+\frac{4T_{4}}{ T_{\text{WT}}}.\eta_{4/4} \tag{3}\]  
where  
\(\eta_{\text{WT}}=\text{weighted efficiency}\)  
\(T_{\text{WT}}=1T_{1}+2T_{2}+3T_{3}+4T_{4}\)  
Figure 2: Example of irradiance-duration curve with four levels of operation [6; 7; 8].  
\(\eta_{1/4}\) = is the efficiency value when \(P_{dc}\) is 1/4 of \(P_{dc\_rated}\)
## 4 Experimental Results
### MLP-Based IoT Attack Classification Mechanism  
This experiment uses the MLP attack classification model. The total amount of processed data in the Bot-IoT dataset is 2,927,407 records, and its classes include DoS, DDoS, reconnaissance, theft, and normal. The entire dataset will be divided into three parts: training dataset, validation dataset, and test dataset. The data amounts of the training dataset, validation dataset, and test dataset are 70%, 10%, and 20% of the total data amount, respectively.  
The training dataset is the primary part of the dataset used to teach a machine learning model. It includes labeled examples pairing input data with the corresponding target labels. The model learns by fine-tuning its internal parameters as it analyzes the training dataset repeatedly, striving to make highly accurate predictions. This iterative process continues until the model's performance on the training data reaches an acceptable level through adjustments based on prediction errors. The validation dataset is a separate part of the data used to optimize the model's hyperparameters and track its performance during training. As training progresses, the model's hyperparameters are fine-tuned based on performance metrics measured on the validation data. This process ensures that the model generalizes effectively to new data and avoids overfitting the training dataset. The test dataset is a distinct subset of data that the model has not encountered during training or validation. Its purpose is to assess the final performance of the trained model and its ability to generalize to new, unseen data.  
The experimental results of MLP-based IoT attack classification are shown in Figure 4. When the 129 th training epoch is completed, the early stopping mechanism is triggered to prevent subsequent overtraining from affecting classification performance. Throughout the training process, as shown in Figure 4a, the accuracy of training and validation is maintained at 99%; as shown in Figure 4b, the loss values of training and validation are below 1%. It can be clearly seen that the curves of overall accuracy and loss value show a gradual convergence trend. The training time and testing time are 900 s and 40 s, respectively.  
The performance analysis of the MLP's IoT attack classification is shown in Table 6. It can be found that the accuracy, precision, and F1-score of the \"Theft\" class are all lower than 76%. Although the \"Normal\" class has good accuracy, the recall rate is only 88.22% and lower than the other three main classes. The reason for this result is that the amount of training data for the two classes is too small, which affects the classification effect of the model.