esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Synthetic Chemistry  
At the onset of our work, 5-chloroisatin **1a**, _L_-valine **2** and dipolaroophile **4d** were probed as model substrates to optimize the reaction conditions leading to pentacyclic compound **5d** (Scheme 2). The effects of solvents and temperature on the reaction time and yield were examined and the results are summarized in Table 1.  
Compound **5d** was isolated only with a low yield when the reaction was carried out in tetrahydrofuran (Table 1, entry 5). However, in refluxing methanol, the 1,3-dipolar cycloaddition reaction proceeds smoothly afforded the targeted spiro[oxidude-2,3\({}^{\prime}\)-pyrrolidine] **5d** with an excellent yield of 96% (Table 1, entry 4) as a racemic mixture.  
Having once established the optimal conditions (Table 1, entry 4), we next examined the scope of the reaction in regard to diverse \(\alpha\)-arylidene-succinimides **4**, as well as with isatin**1c** and various halogenated isatin derivatives **1a,b** (Schema 3, Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Entry** & **Solvent** & **T (\({}^{\circ}\)C)** & **Time (h)** & **Yield (\%) b** \\ \hline
1 & PhCH\({}_{3}\) & 110 & 24 & 69 \\
2 & CH\({}_{3}\)CN & 80 & 13 & 83 \\
3 & EtOH & 78 & 12 & 65 \\
4 & **MeOH** & **64** & **3** & **96** \\
5 & THF & 70 & 36 & 20 \\ \hline \end{tabular}  
* The reaction of **1a** (0.5 mmol), **2** (0.6 mmol) and **4d** (0.5 mmol) carried out in various solvents (5.0 mL). b Yield of the isolated product after purification.  
\end{table}
Table 1: Optimization of the reaction parameters for the synthesis of **5d a**.  
The electronic properties and position of the substituent on the aromatic cycle of dipo-laroophiles **4** had no substantial influence on the efficiency of this reaction. As summarized in Table 2, arylidene-succinimides **4** bearing an electron-neutral (H), electron-donating (e.g., 4-Me, 3-OMe, 4-OMe or 4-OH) groups or electron-withdrawing substituent (4-Br, 3-Br) reacted smoothly to afford the desired spiro[oxindole-2,3\({}^{\prime}\)-pyrrolidine] derivatives **5a**-**5q** in good to excellent yields. In addition, 5-fluorooisatin **1c** was a suitable substrate under standard conditions, although compounds **5i**-**5p** were obtained with somewhat lower yields (Table 2, entries 9-16). No spectroscopic evidence for a competing formation of isomeric compounds **6** bearing the
## 2 Materials and Methods
### Drift Potential Value (DPV)  
As for the deposition experiments, a spray mixture of Tartrazine at a concentration of 5 g 1-1 in water was used in the spray drift potential trials. A volume of about 50 mL of spray mix was systematically sampled at a nozzle outlet before each spray application in order to determine the spray mix concentration and to draw calibration curves (cf Section 2.3.2). After each trial, the Petri dishes were collected, washed with 10 mL of deionised water and placed in an orbital agitator to be shaken for 10 min. The Tartrazine concentration of every sample was determined by measuring the absorbance of the washing solution with a spectrometer (cf. Section 2.3.2). For each individual test, the deposition (expressed in mL cm\({}^{-2}\)) on each Petri dish collector (\(D_{i}\)) was calculated using equation (1). From the absolute depositions of the 20 covered collectors, the Drift Potential Value (DPV) was calculated using Equation (2) according to ISO22401 (2015):  
\[\text{DPV}=\frac{\sum_{1}^{20}D_{i}}{RSD}\ast 100 \tag{2}\]  
where DPV is the drift potential value (adim.), \(D_{i}\) is the spray deposition on each collector positioned in the covered slots (mL cm\({}^{-2}\)) calculated from the absorbance values and the calibration coefficient (cf. Section 2.3.2), and RSD (Reference Spray Deposit) is the theoretical amount of spray deposit in a treated area with a given application rate (mL cm\({}^{-2}\)). The \(RSD\) is equal to the application rate given in Table 1 but expressed as mL cm\({}^{-2}\). The absolute deposition on the uncovered Petri dishes was used to estimate the effective application rate.
## 5 SBS in a DOF  
The aim to achieve a large gain in a PSA relies on the ability to launch large pump powers into the fiber. Stimulated Brillouin scattering (SBS) poses a challenge to this pursuit since it limits the power that can be fed into the fiber. In fact, a high-power pump wave causes a density variation along the fiber's length that leads to formation of an acoustic wave. This propagating acoustic mode acts as a Bragg grating for the optical wave, resulting in forward and backward scattering. The backward scattered wave, known as the Stokes wave, experiences frequency shifting due to the Doppler effect. The Stokes wave, in turn, reinforces the acoustic wave, leading to further pump scattering and, consequently, a stronger Stokes wave. This nonlinear interaction between the pump, Stokes, and acoustic waves is known as electrostriction [41]. To overcome the problem of SBS, we investigate the SBS dynamics theoretically in this section and outline the implementability of a DOF for SBS mitigation.
# Enhancing Far-Field Speech Recognition with Mixer: A Novel Data Augmentation Approach
## 2 Related Work  
### Far-Field ASR  
Recently, large-scale pre-trained models have demonstrated notable success in FSR [11]. However, far-field data often suffer from signal degradation issues, prompting the development of specialized data augmentation strategies. Channel dropout [12] was introduced as a method for dropping frequency bands within a single-channel signal, similar to SpecAugment. To enhance multi-channel information, [PERSON] et al. [13] utilized audio channel dropping to augment data for training a speaker verification system that leverages beamforming as its frontend. Moreover, ChannelAugment [8] was proposed to address the limitation of both training and testing data being derived from a fixed array geometry. The distinguishing aspect of our research is the integration of different channels from multi-channel inputs during end-to-end (E2E) ASR training. To the best of our knowledge, this approach has not been explored in previous studies.
## 3 Architecture of AV Components and AV Categorization
### Sensor Fusion and Perception  
The integration of multiple sensors stands as a cornerstone in achieving robust perception systems across various technological domains. In fields like autonomous vehicles, robotics and industrial automation, the fusion of sensors such as LiDAR, radar and cameras is essential for understanding the environment.  
By combining data from diverse sensors, these systems can compensate for individual sensor limitations, providing a more comprehensive and accurate understanding of the surroundings. This synergy not only improves the reliability of perception but also contributes to heightened safety and efficiency in complex operational scenarios. The seamless integration of multiple sensors represents a key advancement, ushering in a new era of intelligent and adaptive systems across various technological applications. In the article [72] by [PERSON] et al., the authors evaluate the functionalities and efficiency of sensors commonly utilized in autonomous vehicles and explores sensor fusion techniques.  
The interaction between sensors and a controller in a machine vision system is shown in Figure 11.  
Sensors, such as cameras, LiDAR and other specialized devices, capture raw data from the surrounding environment. These data are then relayed to the controller, a central processing unit tasked with interpreting and deciphering the incoming signals. Employing sophisticated algorithms and programming, the controller extracts meaningful insights,  
Figure 10: Typical autonomous vehicle concept.  
facilitating real-time analysis and response. This dynamic exchange between sensors and the controller is crucial in applications like object recognition, tracking and automation.  
The fusion of data from cameras, LiDAR and other sensors represents a cutting-edge approach to enhancing perception in various technological domains. By integrating information from diverse sources, fusion techniques create a more comprehensive and accurate understanding of the environment. In applications such as autonomous vehicles, robotics and surveillance systems, this multi-sensor fusion enables a more robust and adaptive perception system. The synergy of camera, LiDAR and other sensor data not only improves object recognition but also enhances overall system reliability, paving the way for more sophisticated and capable technologies.
## 3 Objectives
### Program and Mission Objectives
#### 3.1.2 Open Orbiter Mission Objectives  
The OpenOrbiter mission seeks to achieve goals relevant to the research objectives of the University of North Dakota and the John D. Odegard School of Aerospace Sciences. These include technical objectives as well as developing technologies that would be relevant to local and regional development. As the demonstration mission for a project with an international scope, OpenOrbiter also seeks to advance the objectives of the OPEN program, through demonstrating the OPEN designs and facilitating the creation and refinement of development and testing plans.  
Primary:  
To demonstrate the capability of UND and other participating institutions to design and develop a functional small spacecraft. This spacecraft shall test the technology and validate the performance of the Open Prototype for Educational NanoSats designs.  
Secondary:  
1. To develop a remote sensing payload for the visible portion of the electromagnetic spectrum suitable for Earth science, planetary science, intelligence, space situational awareness, and other applications
2. To demonstrate and test an innovative structural design, layout and electrical configuration for the CubeSat form factor.
3. To demonstrate the capabilities of North Dakota educational institutions and program team members to design, develop and operate a small satellite in preparation for larger missions
4. To provide educational opportunities related to spacecraft design, aerospace project management, engineering and computational sciences for students and others associated with the project.
# The Seepage and Soil Plug Formation in Suction Caisons in Sand Using Visual Tests
## 3 Test Results and Discussion  
### Visual Seepage Paths  
The seepage field can be visualized where the carmine stain flows along streamlines in the soil around the suction caisson. Figure 3 shows the visualization of the seepage flow at point B as \(h=15\) cm and \(S=0.5\) kPa. It was observed that the visual tests achieved good results to study the seepage of sand around a suction caisson during suction penetration. It took 500 s for the carmine stain to move from point B to the mudline, and the length of the seepage path was 3.21 cm. Tests revealed that the seepage paths of soil with different \(S\) in suction caissons had the same motion path. The results indicated that the seepage path of soil is unrelated to the \(S\) applied in the caisson and is affected by the penetration depth during the installation of the foundation. The seepage paths of the carmine stain in sand are plotted with a plane coordinate system, shown in Figure 4. It can be seen that there is an obvious trend: the streamline dyed by carmine stain moves toward the wall of the caisson model. Additionally, the larger the penetration depth, the more obvious was the streamline-adherent trend. It was observed that the seepage path lengths were 1.95 and 1.34 \(h\) when the penetration depths were equal to 5 and 15 cm, respectively. The distance between the carmine stain in the mudline and the inner wall of the caisson model was 0.375 times the diameter of the suction caisson, for \(h=0.42\), 0.83, and 1.25 \(L\), respectively.  
Figure 3: The visualization of seepage flow at point B with \(h=15\) cm and \(S=0.5\) kPa.
## 2 Related Research and Motivational Challenges
In any case, designing a model with such scalable and flexible computing capabilities for supporting efficiently all the aforementioned objectives involves several challenges by means of the following:  
* The real-time acquisition of remote raw data using agricultural sensors and other wireless devices as well as the process and categorization of these heterogeneous data into appropriate context is by any means a challenging task. * Integrating data sources and distributed components is complicated since PF facility systems generate raw data sets in a variety of forms such as analog signals, binary data streams, or even multimedia content (i.e., images and video streaming). As a result, accurately processing various forms of data and classifying them into context in order to trigger the corresponding operations or services in a short amount of time is a challenging issue which becomes more complex in the cases that these data derive in great volumes from several distinct PF facility systems. * The distribution of data migration and storage is a main matter of challenge as due to the nature of agricultural activities the data sets and their respective services are geographically allocated. * A challenging issue in PF facility systems is the real-time interaction with the end user (farmers, equipment, etc.) in terms of choosing and delivering the most appropriate service from those offered for a specific context. Introducing a framework which employs a cloud context-aware middleware for integrating agricultural WSAN's into the IoT is expected to be an alternative solution for overcoming these challenges by using the computational and storage resources offered into the cloud in order to facilitate the process, analysis and storage of sizeable amounts of raw data converted into context and reduce the computing load of sensing devices employed in various distinct PF facility systems. In such manner the framework acts in the backend as a DSS providing finally intelligent services which optimize agricultural production, minimize the cost, and assist all agricultural stakeholders in making decisions for the benefit of sustainable growth.
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 3 Methods
### Selection of PHC  
PMHRP selects one PHC responsible for sharing the geographic position information across the network. Unlike geographic-based routing protocols, which use the flooding mechanism that increases the normalized load of the network, PMHRP implements three methods suitable for different network conditions and the UAV's speed (i.e., static, random, and centroid).
# One-Class SVM Model-Based Tunnel Personnel Safety Detection Technology
## 3 Tunnel Operator Safety Status Prediction and Early Warning Workflow  
The workflow of OCSVM-based workforce safety status prediction and early warning is shown in Figure 3. It is mainly divided into three modules: data collection, model building and personnel safety detection.  
Figure 3: Tunnel work safety inspection and alarm process.  
Figure 2: OCSVM model algorithm flow chart.  
Data collection: The data sources are sent to the upper end for collation by deploying relevant sensor devices at the work site through data transmission, and the process is shown in Figure 4. It is mainly divided into the data collection layer, data transmission layer and data application layer. The main roles are described below.  
* Data acquisition layer: The discrete data required for the experiment are obtained through the body temperature and heart rate sensors, relative position feedback devices, and temperature and humidity sensors. The data are sent to the data transmission layer to complete the transmission.
* Data transmission layer: This layer mainly consists of a microprocessor-integrated communication and data transmission device. The former has the advantage of higher integration, which can increase the rate of data transmission, and then the data are sent to the upper computer model through WiFi, Zigbee, etc.
* Data application layer: The OCSVM model acquires the data sent over and provides real-time feedback to complete the detection of staff safety status as well as early warning functions. The model can also be updated in real time using the data transmitted over.  
Model building: The process is shown in Table 1.  
Personnel security status detection: The basic process is shown in Table 2.  
In the OCSVM model built in this paper, there are two types of model outputs, i.e., normal and abnormal cases. In terms of the output warning value, this paper sets a warning threshold of 0.78 (the threshold is set according to the statistical analysis of tunnel  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Steps** & **Measures** \\ \hline
1 & Normalize the training set \(X\in R\) before modeling, as described below \\
2 & Find the model optimization parameters on the normalized training set \(X\in R\) \\
3 & Build an OCSVM model for personnel safety detection based on the found optimization parameters [21] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model building steps.  
Figure 4: Data collection hardware framework.  
construction monitoring data and relevant expert experience, and can be further optimized according to the actual situation). If the output accuracy value exceeds 0.78, it indicates an abnormal situation, and the closer to 1, the more obvious the abnormal situation. That is, an output value in the range of (0-0.78) means that the situation in the tunnel is normal; (0.78-1) means that there is an abnormal situation in the tunnel.
## 4 Case Study
### Results and Discussion
#### 4.2.4 The Impact of the Types of Input Parameters on Prediction Accuracy  
Six more cases were designed to investigate the impacts of different types of input parameters on the prediction accuracies of BPNNs. In each case, one type of influence factor in Table 1 was removed from the input data, and a new BPNN was trained to establish the relationship between the other five types of factors and ground settlements. The prediction results at these five settlement observation points were shown in Table 3. Compared with the prediction results of the optimum BPNN in Section 4.2.2, neglecting time-dependent influence factors significantly increased the prediction errors of BPNNs. Specifically, the average absolute error \(\overline{E_{a}}\) increased by 54.4% if construction stages were neglected, and \(\overline{E_{a}}\) increased by 49.1% if weather conditions were neglected. Accordingly, it can be concluded that time-dependent influence factors regarding construction stages and weather conditions should be taken into consideration in BPNN-based ground settlement prediction models. Additionally, neglecting previous monitoring data led to a dramatic increase in \(\overline{E_{a}}\) of the optimum BPNN, from 0.57 mm to 16.98 mm, indicating that previous monitoring data are critical input parameters in establishing an accurate BPNN-based ground settlement prediction model.  
Figure 9: Impact of the size of training dataset on prediction accuracy.
## 2 Methods
### Introduction to U-Net  
U-Net, a landmark architecture in image segmentation, has displayed exceptional prowess owing to its symmetric encoder-decoder framework coupled with distinct skip connections [28], especially when addressing intricate backgrounds and irregular objectives [29]. The core design philosophy of this network lies in concurrently harnessing deep semantic insights and meticulous spatial details. However, when applied to remote sensing image analyses, notably in road extraction from complex scenarios, there remains room for refinement [30].  
In our presented MixerNet-SAGA model, we judiciously tailored the U-Net in the following ways to adeptly tackle the challenges of road extraction from remote sensing images:  
Integration of the ConvMixer Module to the Encoder: This stands as the principal innovation within the MixerNet-SAGA framework. The ConvMixer module was crafted to bolster the network's feature representation prowess, facilitating a more nuanced capture of intricate structures and information within remote sensing images. Consequently, in contrast to the traditional U-Net, MixerNet-SAGA holds a marked advantage in recognizing and addressing the diversity and complexity of remote sensing imagery.  
Incorporation of the SAG Multi-Scale Attention Mechanism to Skip Connections: A further pivotal advancement is the adoption of the SAG multi-scale attention module. Its primary role is to adaptively balance features across different scales, thus seamlessly integrating global and local cues. This implies that MixerNet-SAGA can autonomously focus on salient regions within remote sensing images while preserving expansive contextual data.  
Such innovative modifications confer clear advantages to MixerNet-SAGA. Through the ConvMixer module, the model is not only more efficient in feature extraction from remote sensing images but also possesses enhanced representation capabilities. Leveraging  
Figure 1: The overall architecture of the MixerNet-SAGA network. In the figure, blue represents the feature map, CMA represents the ConvMixer block, MAG represents the Scaled Attention Gate (SAG) module, and the pink cube represents the feature map processed by the MAG module.  
the SAG module, the model can more discerningly pinpoint and interpret salient and challenging areas within such images.  
Therefore, given these enhancements, when tasked with intricate scenarios in remote sensing imagery, specifically in road extraction, MixerNet-SAGA, compared to the canonical U-Net, not only maintains computational efficiency but also significantly outperforms it in terms of feature extraction precision, robustness, and stability.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 3 The Theory of Shop Scheduling Problems
### Shop Layouts
#### 3.2.3 Flexible Manufacturing System (FMS)  
A _flexible manufacturing system_ (FMS) is a relatively new technology which refers to an automated hybrid of flexible job shop [59] which utilizes an automated material handling system to maneuver jobs between machines [60]. All machine and material handling systems are monitored and controlled by a centralized computer [61].  
The FMS scheduling problem resembles JSSP with some extensions including the movements of the material handling system and its common constraints of blocking and holdup [59].  
Common objectives within the FMS scheduling problem include makespan, mean flowtime, and maximum flowtime [60]. Often additional objectives will be included regarding the travel time within the material handling system [62].
## 2 Fault Diagnosis Method Based on System Characteristics
### Response Signal Selection  
RC has a variety of response signals, such as: vibration signals, pressure signals, acoustic emission (AE) signals, temperature signals, and IAS signals et al. Among them, the pressure signal and temperature signal cannot be measured outside the RC, so it is necessary to remould the RC cylinder structure and embed the sensor for measurement. The fault characteristics will be adversely affected due to the damage to the original structure of the cylinder. The use of AE and IAS signals requires the sensors to have higher sensitivity and measurement accuracy. Additionally, the signal processing methods for IAS are less than those for the vibration signal. Moreover, vibration signal is easily obtained and most widely used in fault diagnosis.  
The number of impacts whose amplitude exceeds 200 in Figure 5a is significantly greater than that in Figure 5b, and the periodicity of the signal waveform in Figure 5a is more obvious than that in Figure 5b, as shown in Figure 5. The vibration signal not only contains a wealth of fault features, but also maintains a system transfer characteristic relationship with the current signal. Therefore, this paper adopts vibration signal as a response signal to analyze the response characteristics of RC valve fault.  
Figure 4: Time-frequency domain waveform of excitation signal of 2 mm leakage valve.  
Figure 5: Time-domain waveform of response signal; **(a)** normal valve, **(b)** 2 mm leakage valve.