esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Reliability Based Rescue Resource Scheduling Model
### The Optimization Model  
1. Analysis of constraints  
The sum of emergency materials, equipment or personnel provided by each rescue center shall meet the demands of the accident location.  
\[\sum_{r\in R}EM_{ir}\geq ED_{i} \tag{10}\]  
In addition, the number of emergency materials, equipment or personnel provided by each rescue center shall be less than or equal to its rescue capacity.  
\[EM_{ir}\leq EN_{ir} \tag{11}\]  
The rescue center \(r\) can reach the accident location within the maximum tolerable time by selecting the rescue route \(k_{rs}\) with confidence level \(\alpha\).  
\[R_{k_{rs}}=P\big{\{}t2_{k_{rs}}\leq RT_{i}\big{\}}\geq\alpha \tag{12}\]Constraint (13) ensures that route \(k_{rs}\) must not have a loop.  
\[\sum\limits_{v\in V}\delta_{p}^{k_{rs}}\leq 1 \tag{13}\]  
The flow balance constraint for each node in a transportation model network is as follows.  
\[\sum\limits_{n\in V:t_{nm}\in\mathbb{E}}\delta_{nm}^{k_{rs}}-\sum\limits_{n\in V :t_{nm}\in E}\delta_{nm}^{k_{rs}}=\left\{\begin{array}{ll}1,&m=s\\ -1,&m=r\\ 0,&\text{otherwise}\end{array}\right. \tag{14}\]
2. The objective function  
For accident rescue, the arrival time of emergency materials, equipment and personnel and the travel-time reliability of the rescue route are the main concerns. Once a dangerous goods accident occurs, all rescue centers will respond at once. Therefore, the arrival time can be expressed by the sum of the expected value of the route travel time of emergency materials, equipment and personnel of each rescue center.  
\[Z_{1}=\sum\limits_{i\in I}\sum\limits_{r\in R}[EM_{ir}\cdot E(t2_{k_{rs}})] \tag{15}\]  
In terms of reliability, the total reliability of various emergency materials, equipment and personnel arriving at the accident location is used to represent the reliability of the rescue centers relative to the accident location, which can be expressed as follows.  
\[Z_{2}=\sum\limits_{i\in I}\sum\limits_{r\in R}(EM_{ir}\cdot R_{k_{rs}}) \tag{16}\]
3. The multi-objective rescue resource scheduling model  
According to the above analysis, by considering the factors such as the rescue capacity, rescue demand and response time, the multi-objective scheduling model of rescue resources based on travel time reliability is constructed in order to minimize the total arrival time of rescue resources and to maximize total reliability.  
\[\begin{array}{ll}\text{min}Z_{1}=\sum\limits_{i\in I}\sum\limits_{r\in R} \big{[}EM_{ir}\cdot E(t2_{k_{rs}})\big{]},\\ \text{max}\;Z_{2}=\sum\limits_{i\in I}\sum\limits_{r\in R}(EM_{ir}\cdot R_{k_{ rs}}),\\ \text{s.t.}\\ \sum\limits_{r\in R}EM_{ir}\geq ED_{i},&i\in I,\\ EM_{ir}\leq EN_{ir},&i\in I,r\in R,\\ R_{k_{rs}}=P\big{\{}t2_{k_{rs}}\leq RT_{i}\big{\}}\geq\alpha,&i\in I,r\in R,\\ k_{rs}\in K_{rs}\in K_{rs},\\ \sum\limits_{n\in V:t_{nm}\in E}\delta_{nm}^{k_{rs}}-\sum\limits_{n\in V:t_{nm }\in E}\delta_{nm}^{k_{rs}}=\left\{\begin{array}{ll}1,&m=s\\ -1,&m=r\\ 0,&\text{otherwise}\end{array}\right.,&k_{rs}\in K_{rs},\\ EM_{ir}\in N,\delta_{mn}^{k_{rs}},\delta_{p}^{k_{rs}}\in 0,1,&i\in I,r\in R,k_{rs}\in K_{rs} \end{array} \tag{17}\]
# Investigation of High-Sensitivity pH Sensor Based on Au-Gated AlGaN/GaN Heterostructure
## 2 Materials and Methods
### Measurement Setup  
The transfer (I\({}_{\text{DS}}\)-V\({}_{\text{Ref}}\)), output current-voltage (I\({}_{\text{DS}}\)-V\({}_{\text{DS}}\)) curves, and the real-time (I\({}_{\text{DS}}\)-t) response curves were measured using two Keithley 2450 source meter instruments at 25 \({}^{\circ}\)C.  
The pH values of four pH standard buffered solutions were 1.68, 4.0, 6.86, and 9.18 at 25 \({}^{\circ}\)C. Then, we dropped deionized (DI) water or pH solution between the sensing membrane and the quasi-reference electrode using a micro-pipette. A 30 \(\upmu\)L solution can sufficiently cover the whole sensing membrane and the exposed quasi-reference electrode region.
# Effect of Processing on Volatile Organic Compounds Formation of Meat--Review
## 1 Introduction  
The meat quality is considered in terms of the following characteristics: color, flavor, texture, tenderness, etc. [1; 2; 3]. It is considered that aroma is an important factor influencing customers' decision to purchase a meat product [4]. The main features of meat products depend on raw material (genetic conditions, breed, sex, feeding type, welfare, muscle's type, etc.), processing techniques (whole pieces, mincing degree, smoking process, etc.), and drying parameters (time, temperature, or humidity) [5; 6]. These features lead to the formation of the typical meat and meat products flavor [7]. The VOCs which cause the characteristic aroma of cooked meat are obtained from reactions that occur during different processes. Some researchers found that precursors of aroma-effect compounds have a significant impact on the final flavor of cooked meat and on some of the volatile products of the flavor-creating reactions [7; 8]. Meat flavor generated during heat treatment results from the interactions of precursors located in the raw meat. These actions contain pyrolysis of peptides and amino acids, degradation of ribonucleotides, and also sugar, Maillard reactions, Strecker degradation, lipid oxidation, degradation of thiamine and lipids, as well as microbial metabolism [9; 10; 11]. The Maillard reaction is significant due to the formation of a range of urranthols and distiludes that have a distinctive'meaty' aroma and characterize very low odor thresholds [12].  
The classes of organic compounds which are responsible for the formation of meat aroma are aldehydes, hydrocarbons, alcohols, esters, ketones, carboxylic acids, furans, ethers, pyrazines, pyridines, pyrroles, oxazoles and oxazolines, thiazoles and thiazolines, thiophenes, and other sulfur-containing compounds [13; 14]. The origin of meat in combination with a specific cooking technique is one of the features which influences the perception of meat and final meat products quality [15].  
Instrumental analysis is considered more practical for the detection of VOCs compared to sensory analysis. Gas chromatography (GC) is widely used to analyze and separate volatile aroma-contributing substances in meat [16]. Headspace solid-phasemicroextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) and headspace-gas chromatography-ion mobility mass spectrometry (HS-GC-IMS) are more often used in liquid products [17]. GC-IMS is a new technique for hot gas phase separation detection [18]. Both methods allow us to describe the molecular level of a single volatile compound. An electronic nose based on ultrafast gas chromatography is a non-destructive and trustworthy method for VOCs analysis. This equipment has already been widely applied in both the field of quality control and science analysis [19; 1; 20; 21].  
Therefore, we decided to review the impact of various factors affecting the formation of VOCs in meat. Non-thermal and thermal aspects of VOCs formation were considered.
###### Abstract  
In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was introduced for practical testing. The proposed system's communication reliability was tested in two different scenarios: a mountainous forest with sloping areas and trees and a watercourse, which referred to environmental and flooding monitoring applications. Wireless network performances were evaluated through the received signal strength indicator (RSSI) level of each wireless link, a packet delivery ratio (PDR), as the successful rate of packet transmission, and the end-to-end delay (ETED) of all data packets from the transmitter to the receiver. The experimental results demonstrate the success of the multi-hop WSN deployment and communication in both scenarios, where the RSSI of each link was kept at the accepted level and the PDR achieved the highest result. Furthermore, as a real-time response, the data from the source could be sent to the sink with a small ETED.  
multi-hop; IEEE802.15.4; implementation; mountainous forest; watercourse 2024 5 774-793. [[https://doi.org/10.3390/signals5040043](https://doi.org/10.3390/signals5040043)]([https://doi.org/10.3390/signals5040043](https://doi.org/10.3390/signals5040043))
# AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
## 2 Related Work
### Intrusion Detection and Prioritization  
Intrusion detection and prioritization are commonly used for investigation; after alert identification, operators will only address alerts in the top 10% of the list. A detection and prioritization model to design an alert correlation system [19; 36] based on the extended Dempster-Shafer theory was investigated to identify the top alerts for the analysis listed in ascending order of importance. The study in [37] leveraged a deep neural network [38] to prioritize and respond to intrusion alerts using an automated alert-triaging process. Important events in the logs were extracted and passed to security operators. Moreover, in [39; 40], an unsupervised learning approach for alert prioritization was studied by adopting an isolation forest and day-forward-chaining analysis to detect anomalies and high-priority alerts. Recently, provenance graphs [41; 42; 43; 44] have also been used for alert prioritization by performing automated attack triage using historical information to assign threat scores to the alerts.  
Approaches using deep learning techniques for intrusion detection [15] have been proposed. Deep learning is a type of AI that uses multiple layers of nonlinear processing units to learn from data with automatic feature learning and is scalable for large volumes of data. These intrusion detection approaches are based on convolutional neural networks (CNNs) [45; 46; 47] and CNNs with feature extraction [48] using methods such as principal component analysis and auto-encoders for dimension reduction. Other approaches are based on deep neural networks (DNNs) [49], few-shot learning with a combination of a CNN and a DNN [50] for feature extraction, and DNNs with deep stacked auto-encoders [51]. Deep learning approaches are used for network traffic classification into normal or attack classes or multi-class classification of various network attack types.
## 4 Overview of Object Detection in AVs  
Most OD algorithms primarily adhere to a common framework, as shown in Figure 7. In OD, we have considered the following three issues:  
1. Vehicle Detection: Vehicle detection is the process by which AVs identify and locate other vehicles on the road. This capability is crucial for AVs to make informed decisions about their own movement, such as maintaining safe distances, changing lanes, or responding to traffic situations.
2. Pedestrian Detection: Pedestrian detection involves the recognition and tracking of people walking near or crossing the road. This is a vital safety feature for AVs as it enables the vehicle to anticipate and react to the presence of pedestrians, preventing collisions and ensuring the safety of both the vehicle's occupants and those outside.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Ref.** & **Year** & **Dataset** & **LiDAR** & **Radar** & **Camera** & **Adverse Weather** \\ \hline (37) & 2020 & LIBRE & ✓ & - & ✓ & Rain, fog, Daytime \\ \hline (51) & 2013 & Kitti & ✓ & - & ✓ & - \\ \hline (54) & 2021 & ACDC & - & - & ✓ & Rain, fog, snow \\ \hline (55) & 2020 & A2D2 & ✓ & - & ✓ & Rain \\ \hline (56) & 2020 & Waymo & ✓ & - & ✓ & Rain, night \\ \hline (57) & 2020 & NuScenes & ✓ & ✓ & ✓ & Rain, night \\ \hline (58) & 2020 & BDDI00K & - & - & ✓ & Daytime, night \\ \hline (59) & 2020 & ApolloScape & ✓ & - & - & Rain, Night \\ \hline (60) & 2016 & SYNTHIA & - & - & - & Snow \\ \hline (61) & 2016 & NCLT & ✓ & - & ✓ & Snow \\ \hline (62) & 2021 & CADCD & ✓ & - & ✓ & Snow \\ \hline (64) & 2020 & End of the earth & ✓ & - & ✓ & Snow \\ \hline (65) & 2020 & nulmages & - & - & ✓ & - \\ \hline (66) & 2019 & Argoverse & ✓ & - & ✓ & - \\ \hline (67) & 2019 & Astyx & ✓ & ✓ & ✓ & - \\ \hline (68) & 2020 & DENSE & ✓ & ✓ & ✓ & Rain, snow, fog, night \\ \hline (69) & 2018 & Foggy Cityscape & - & - & - & Fog/haze \\ \hline (70) & 2020 & Berkley DeepDrive & - & - & ✓ & Rain, fog, snow, night \\ \hline (71) & 2017 & Mapillary & - & - & ✓ & Rain, fog, snow, night \\ \hline (72) & 2019 & EuroCity & - & - & ✓ & Rain, fog, snow, night \\ \hline (73) & 2017 & Oxford RobotCar & ✓ & ✓ & ✓ & Rain, snow, night \\ \hline (74) & 2020 & A* 3D & ✓ & - & ✓ & Rain, night \\ \hline (75) & 2021 & 4 Seasons & - & - & ✓ & Rain, night \\ \hline (76) & 2018 & WildDash & - & - & ✓ & Rain, fog, snow, night \\ \hline (77) & 2018 & KAIST multispectral & - & - & ✓ & Daytime, night \\ \hline (78) & 2021 & Radiate & ✓ & ✓ & ✓ & Rain, fog, snow, night \\ \hline (79) & 2020 & EU & ✓ & - & ✓ & Snow, night \\ \hline (80) & 2022 & Boreas & ✓ & ✓ & ✓ & Rain, snow, night \\ \hline (81) & 2020 & DAWN & - & - & - & Rain, snow, fog, sandstorm \\ \hline (82) & 2021 & PVDN & - & - & ✓ & night \\ \hline \hline \end{tabular}
\end{table}
Table 2: Publicly available AVs datasets.  
3. Road Lane Detection: Road lane detection is the ability of AVs to identify and understand the position and orientation of road lanes. This information is essential for the vehicle to navigate within its designated lane, follow traffic rules, and make correct turns, ensuring a smooth and safe driving experience.
# Nickel (Ni\({}^{2+}\)) Removal from Water Using Gellan Gum-Sand Mixture as a Filter Material
## Materials and Experimental Preparation
### Experimental Process
#### 2.2.2 Adsorbate Preparation  
Nickel concentration in groundwater and municipal tap water in polluted areas was in the range of 100-2500 mg/L. Conversely, a nickel concentration greater than 1000 mg/L was found in the water boiled in electric kettles [19]. In this study, to mimic adverse conditions due to environmental pollution, an initial concentration of 1000 mg/L of NiCl\({}_{2}\).6H\({}_{2}\)O [20; 21] was used.
## 2 Multi-Objective Operation Model
### Dynamics Simulation Model  
In this study, a time step-based simulation model is employed to describe the operational state of a railway catenary maintenance vehicle [9]. The acceleration \(a_{i}\) is set as a constant value within each time step \(\Delta t\). The kinematic equations for the \(i\)-th time step can be expressed as follows:  
\[\left\{\begin{array}{l}v_{i+1}=v_{i}+a_{i}\Delta t\\ s_{i+1}=s_{i}+v_{i}\Delta t+\frac{1}{2}a_{i}\Delta t^{2}\end{array}\right.,\
## 3 System Model
In this figure, we define the discrete values of the time and frequency axes, where the minimum resolution of the frequency is \(W/(2^{N_{SF}})\), and the frequency bins are assigned to the label as \(k=1,2,\ldots,2^{N_{SF}}\). In addition, the minimum time resolution is \(T_{s}/2^{N_{SF}}=T\), and each time span is assigned a label of \(n=1,2,\ldots 2^{N_{SF}}\). In the next time span after \(n=2^{N_{SF}}\), the label returns to \(n=1\); therefore, the label of the time span is periodic. We define \(X_{n,k}\) as the partial frequency spectrum of the \(k\)th channel and \(n\)th time span. We also define the group of all channels and time span as \(\mathbf{V}\), which is defined as follows:  
\[\mathbf{V}=\Big{\{}(1,1),(1,2),\ldots,(n,k),\ldots,(2^{N_{SF}},2^{N_{ SF}})\Big{\}}. \tag{2}\]  
The chirp pattern is modeled as a combination of partial frequency spectra. We define \(\mathbf{\phi}_{I}\) as the spectral combination of the \(l\)th chirp pattern, where \(l=1,2,\ldots,L=2^{N_{SF}}\). In the first modulated symbol of Figure 3, the chirp pattern is \(l=2\) and its spectrum combination is  
\[\mathbf{\phi}_{I=2}=\{(1,2),(2,3),(3,4),(4,1)\}. \tag{3}\]  
In the demodulation, the power of the partial spectrum, \(|X_{n,k}|^{2}\), in accordance with the combination of the chirp pattern, \(\mathbf{\phi}_{I}\), is combined. The combined results for the partial frequency spectrum in \(\mathbf{\phi}_{I}\) are given as  
\[\eta_{l}=\sum_{(n,k)\in\mathbf{\phi}_{I}}|X_{n,k}|^{2}. \tag{4}\]  
In the first modulated symbol of Figure 3, the combined results for the partial frequency spectrum are  
\[\eta_{l=2}=|X_{1,2}|^{2}+|X_{2,3}|^{2}+|X_{3,4}|^{2}+|X_{4,1}|^{2}. \tag{5}\]  
The receiver estimates the chirp pattern number selected by the transmitter \(l^{\star}\) as  
\[l^{\star}=\arg_{l}\max\eta_{l}, \tag{6}\]  
Figure 3: Frequency spectrum of chirp modulation in LoRa. Carrier frequency cyclically increases or decreases. In accordance with the relationship between the chirp pattern number \(l\) and the information data, the receiver specifies the transmitted data. We assume a packet access scheme in which the payload of a packet comprises multiple data bits and a cyclic redundancy check (CRC). The CRC is effective for detecting error bits. In the assumed wireless sensor networks, if the CRC detects any error bits from the packet, the packet is deleted. The packet is not re-transmitted to compensate for the deleted packet. Even if CCI occurs, the received signal to noise plus interference power ratio (SINR) is greater than the required level for demodulation, and the demodulation is successful owing to the capture effect [11].
# Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems
## 3 Modeling of the Milling Machine
### Finite Element Model of Milling Machine  
Figure 4 presents the finite element model of milling machine's. Each structural component of the system was meshed with ten-node tetrahedral elements, with a total of 124,648 elements and 224,175 nodes. The components of the feeding mechanism, such as ball screw/nut, linear roller guides, and sliding guideways were included in the machine model. For linear roller guides, the overall structural stiffness \(K_{n}\) in normal direction was 500 and 850 N/um for normal and medium preloaded roller guides. Regarding to the sliding guideway with Turcite-B antifriction layer, the contact stiffness was measured as 750 kN/um per unit area in meter [23]. The overall contact stiffness at ball groove was estimated as 1.62 kN/um according to the specifications of the ball screw [24]. In order to obtain a whole analysis model of a milling machine, the spindle-bearing system created in the above section was incorporated in the spindle ram, as shown in Figure 4. Also, in finite element analysis, the materials used for structural components are made of gray cast iron with an elastic modulus \(E\) = 660 GPa, Poisson's ratio \(\mu\) = 0.3, and density \(\rho\) = 7200 Kg/m\({}^{3}\). The materials of linear rolling components have an elastic modulus \(E\) = 210 GPa, Poisson's ratio \(\mu\) = 0.3, and density \(\rho\) = 7800 Kg/m\({}^{3}\).The vibration mode shapes associated with the frequencies of the milling system were obtained by implementing the modal analysis into the finite element computation.  
Figure 3: Solid model and finite element model of spindle unit.  
The harmonic analysis was performed to measure the frequency response at the end of the cuter. In the finite element governing equation for harmonic analysis, the damping matrix was assumed to be proportional to the structural stiffness matrix \([K]\) according to the relationship \([C]=\beta[K]\). The value \(\beta_{mr}\), representing the structural damping factor, is calculated from 2 \(\xi_{mr}/w_{r}\), where \(\xi_{mr}\) is the modal damping ratio for spindle dominant vibration mode, about 2.5%.
## 3 Application of the TGF_N_V for Solving Roll Damping of Hull Sections with Bilge Keels
### Hull Section Under Small Amplitude Roll
The study also confirms the possibility of employing TGF_N_V to estimate the  
Figure 4: Equivalent roll damping using different methods (TGF [25], TGF_N [25], TGF_N_V ) as compared with experimental fluid dynamic (EFD) data [13; 26]. (**a**) Equivalent roll damping related to the first viscosity distribution approach; (**b**) Equivalent roll damping related to the second viscosity distribution approach. Figure 3: Two viscosity distribution approaches for TGF_N_V. (a) \(\epsilon=0.25\) for segments \(16\leq i\leq 60\) and \(101\leq i\leq 145\), while \(\epsilon=0\) for the rest segments; (b) \(\epsilon=0.17\) for all segments. viscous effects as well as nonlinear effects of a hull section with bilge keels rolling on the free surface. In contrast, the inviscid methods TGF and TGF_N are not appropriate for evaluating the roll damping of the hull section with bilge keels.
# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
###### Abstract  
Multiple unmanned aerial vehicles (UAVs) are organized into clusters in a flying sensor network (FSNet) to achieve scalability and prolong the network lifetime. There are a variety of optimization schemes that can be adapted to determine the cluster head (CH) and to form stable and balanced clusters. Similarly, in FSNet, duplicated data may be transmitted to the CHs when multiple UAVs monitor activities in the vicinity where an event of interest occurs. The communication of duplicate data may consume more energy and bandwidth than computation for data aggregation. This paper proposes a honey-bee algorithm (HBA) to select the optimal CH set and form stable and balanced clusters. The modified HBA determines CHs based on the residual energy, UAV degree, and relative mobility. To transmit data, the UAV joins the nearest CH. The re-affiliation rate decreases with the proposed stable clustering procedure. Once the cluster is formed, ordinary UAVs transmit data to their UAVs-CH. An aggregation method based on dynamic programming is proposed to save energy consumption and bandwidth. The data aggregation procedure is applied at the cluster level to minimize communication and save bandwidth and energy. Simulation experiments validated the proposed scheme. The simulation results are compared with recent cluster-based data aggregation schemes. The results show that our proposed scheme outperforms state-of-the-art cluster-based data aggregation schemes in FSNet.  
clustering: data aggregation; dynamic programming; flying sensor network; internet of things +
Footnote †: journal: Future Internet