esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Implementation Details  
THUMOS14: In order to extract spatial-temporal features from THUMOS14, we used a two-stream inflated 3D ConvNet (I3D) [26] module pre-trained on the Kinetics-400 [27] dataset. We sampled 16 consecutive RGB and optical-flow with an overlap rate of 75% as clips, which were respectively input to the I3D network, and extracted features of dimension 512 \(\times\) 2 at the first fully connected layer. Then, the output features of the two-stream network were concatenated to obtain the input features of our model. According to the evaluation method, the mean average precision (mAP) with an IoU threshold [0.3, 0.4, 0.5, 0.6, 0.7] was the evaluation metric used on THUMOS-14. We used Adam [46] to optimize the network and set the batch size, initial learning rate, and total epoch number as 2, 10\({}^{-4}\), and 50, respectively.  
ActivityNet 1.3: We adopted an R(2+1)D model to extract features from ActivityNet 1.3 pre-trained on the TSP [47] dataset. We sampled 16 consecutive frames with a stride of 16 as clips (i.e., non-overlapping clips). Following [10,18], the length of the sequence was rescaled into a fixed length of 128 using linear interpolation. According to the evaluation method, a mAP with IoU threshold [0.5, 0.75, 0.95] and an average mAP [0.5:0.05:0.95] were the evaluation metrics used on ActivityNet 1.3. We used Adam to optimize the network and set the batch size, initial learning rate, and total epoch number as 16, 10\({}^{-3}\), and 15, respectively.  
Our method was implemented based on PyTorch 1.1, Python 3.8, and CUDA 11.6. We conducted experiments with one NVIDIA GeForce RTX 3090 GPU, Intel i5-10400 CPU, and 128 G memory.
## 3 Results and Discussion
### Numerical Analysis of Spatiotemporal Effect
#### 3.2.2 Horizontal Displacement of Diaphragm Walls  
Based on the lateral displacement of the diaphragm walls, as shown in Figure 9, when excavating to the bottom slab, excavation caused the obvious deformation of the retaining structure. The last excavation had no support at the bottom, and the support stiffness above the excavation face was large, which led to the horizontal deformation developing downward, causing large deformation, and the maximum deformation position was shifted downward [3]. From the point of maximum lateral displacement, the lateral displacement of the diaphragm walls gradually decreased both upward and downward. To analyze the rationality of the parameters used in this simulation, the simulated horizontal displacement of the diaphragm walls was compared with the on-site monitoring data, as shown in Figure 10. Figure 10a shows that the lateral displacement of the diaphragm walls increased and then decreased along the depth, with the maximum lateral displacement occurring about 5 m below the base slab of the pit, with both simulated and monitored lateral displacements exceeding 60 mm. The simulation data fitted well with the monitoring data, with a maximum difference of about 5 mm, accurately reflecting the lateral deformation pattern of the diaphragm walls. Figure 10b indicates that both the simulation and monitoring data reflected the deformation pattern of the diaphragm walls' lateral horizontal displacement increasing with the progress of excavation and the extension of construction time. This was because when excavation was shallow, the pressure difference between the soil inside and outside the wall was very small, leading to minimal changes in lateral displacement. As the depth of excavation increased, the pressure difference also increased, leading to the greater lateral displacement of the walls. Additionally, the excavation range was permeated with flow plastic mucky soil, causing the deformation values to increase and develop over time, with the numerical simulation also considering the rheological characteristics of soft soil. The simulation data and monitoring data generally matched well. However, during the base slab pouring process, the monitoring data show the continued lateral deformation of the diaphragm walls, whereas the simulation data show the lateral displacement beginning to converge. This discrepancy may be due to the presence of large machinery around the site during the actual base slab pouring, which was not considered in the simulation.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 5 Signal Technologies
### Ultrasound-Based Systems  
Ultrasonic systems are most commonly used in short-range measurement [51]. The signals have several advantages, such as a slow propagation speed, a negligible penetration in walls, and a low cost of the transducers. They do not interfere with electromagnetic waves and have a relatively short range. Instead, they use building material and air as a propagation medium. The accuracy achieved by ultrasound-based systems is typically a few centimeters. These characteristics are attractive for use in indoor positioning systems. The distance between the beacon and the target node can be calculated by measuring the time of flight (TOF) of the ultrasonic signal, while the Friis formula can be used to evaluate the power budget and the SNR at the target. Using ultrasonic signals, the measurement of time is more accessible than in the case of radio waves due to the lower speed of the acoustic waves and, therefore, the lower temporal resolution required for the measurement itself. The TOF measurement requires a correct temporal synchronization of the network nodes [52].  
The target coordinates can be estimated by multilateration concerning some beacons distributed in known places. These systems are called acoustic measurement systems because the systems function using sound waves. Ultrasound is stealthy for the human ear compared to the sound wave. Additionally, this system is associated with RF technology to fulfill the synchronization requirement [53]. Ultrasound has, in addition, several advantages over electromagnetic waves when the application of implantable devices in healthcare is considered. The presence of water in the human body produces attenuation of the RF waves; therefore, it is necessary to increase the transmission power. This increase can be translated into soft-tissue heating because of absorption. This factor increases the size and weight of an implantable sensor. On the other side, the choice of ultrasound technology allows for the creation of implantable sensors at the level of micro-dimensions and low energy consumption [54] due to the relatively low attention to transmitting power to implanted sensors encountered in the human tissues. Furthermore, they are not affected by interference, in the transmission of data for patient monitoring, due to the presence of other electromagnetic devices.
## 1 Introduction
The reason for this is that there is no need for solutions of differential equations (as in standard OCP methods), nor backward/forward integrations in order to work out the optimal speed profiles (as in standard QSS methods). The proposed method exploits the knowledge of the curvature of the trajectory. The curvature is worked out through a rigorous approach, with the benefit that it allows for the use of trajectories defined with respect to a generic curve parameter and not necessarily the arc length. Simulations are carried out along a circle, an ellipse, and a combination of straight lines and a hairpin bend. The remainder of the paper is structured as follows. Section 2 discusses the modelling of the vehicle and its performance limits. Section 3 describes the proposed optimisation approach. The simulation results are in Section 4. The main conclusions are drawn in Section 5.
# Blocking Cyclic Job-Shop Scheduling Problems
## 2 Blocking Cyclic Job Shop Scheduling  
In this section, we define the blocking cyclic job shop scheduling problem with sequence-dependent setups. We first introduce the terminology and notation that will be used throughout the remainder of this paper. We then discuss alternative ways of defining blocking during setups. To illustrate the problem and the differences between the two setup constraints, we provide a detailed example.  
The problem can be defined as follows. Let \(N=\{1,2,3,\ldots,J\}\), where \(J\) is the total number of jobs in an MPS. Each job \(j\in N\) contains a set of \(O_{j}\) operations where \(I_{j}=\{1,2,3,\ldots,O_{j}\}\). For each job, the sequence of operations is given in advance. Moreover, each operation is assigned to a machine \(M\) where the set of machines are \(M=\{1,2,3,\ldots,K\}\). The sequence \(F_{j}=\{f_{1},f_{2},f_{3},\ldots,f_{(O_{j})}\}\) specifies the machines that the operation \(i\in I_{j}\) of job \(j\in N\) will be processed on. Thereupon, \(f_{i}=m\) denotes that the \(i^{th}\) operation would be processed on machine \(m\in M\). Moreover, the processing time of the \(i^{th}\) operation of job \(j\in N\) is given by \(P_{j,i}\). Each job has at most one operation on each machine. Let \(J_{m}\) be the set of jobs that have an operation on machine \(m\) and \(o(j,m)=i\) if \(f_{i}=m\) for job \(j\).  
The assumptions are as follows:  
* Each machine has a set of operations to execute and can execute only one operation at a time.
* The operations of a job are linked by precedence constraints.
* Each job has its own unique path through the machines, independently of the other jobs.
* Each operation is assigned to one particular dedicated machine and executed with no interruption and without pre-emption for a fixed processing time.
* There are no buffers between machines, so jobs continue to occupy a machine after the end of an operation until the next machine is available.  
One job of each type has to be completed per cycle. The objective is to minimise the cycle time. Until now, this has not considered the question of setup times.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Data Collection  
GNSS measurements were collocated at 1 Hz using different Android smartphones. According to GNSS measurements, we define \(\mathbf{G}\) as follows:  
\[\mathbf{G}\triangleq\left\{g_{i}\left|i=0,1,\cdots,n\right\},g_{i}\triangleq \left\{ctype_{i},vid_{i},cnr_{i},azi_{i},ele_{i}\right\}\right. \tag{1}\]  
Figure 3: Algorithm framework of our proposed IO detection.  
Figure 2: Four types of complex indoor and outdoor scenarios. These four types of scenarios cover campuses, office buildings, shopping malls, overpasses within the city.  
where \(\mathbf{G}\) denotes a set of n visible satellites smartphones recorded in a second. \(g_{i}\) denotes visible satellite information, including the constellation type, ID in the constellation, Carrier Noise Ratio(CNR), azimuth and elevation when available.  
The datagram information (including $GPGGA, $GPGSA, $GPGSV, and other datagrams) in the NEMA-0183 protocol was obtained through the interface provided by the Android system. We define \(\mathbf{S}\) as follows:  
\[\mathbf{S}\triangleq\left\{pdop,hdp,vdop\right\} \tag{2}\]  
where \(\mathbf{S}\) contains the value of Position Dilution of Precision (PDoP), Horizontal Dilution of Precision (HDoP), and Vertical Dilution of Precision (VDoP) of the current environment by parsing the $GPGSA datagram from NEMA-0183 protocol.
## 3 Materials and Methods
### Phenolic Compound Analyses  
Non-anthocyanin phenolic compound analyses were performed on an Agilent 1260 Infinity Quaternary LC system coupled with an Agilent 6460 triple quadrupole mass spectrometer. The analysis conditions were set according to a previous published paper [61]. An Agilent Poroshell 120 EC-C18 column (3.0 \(\times\) 50 mm, 2.7 \(\upmu\)m) was used for the separation of the compounds. The wine sample was filtered through 0.22 \(\upmu\)m membrane filters (Syringe Filter, PTFE, Supelco, Bellefonte, PA, USA), and 2 \(\upmu\)L was then directly injected into the LC-MS system with a flow rate of 0.3 mL/min. The mobile phase was (A) water + 0.1% formic acid and (B) acetonitrile. The elution program was as follows: 0 to 1 min, 5% B; 1 to 3 min, 5% to 15% B; 3 to 15 min, 15% to 20% B; 15 to 18 min, 20% to 40% B; 18 to 20 min, 40% to 60% B; 20 to 20.1 min, 60% to 5% B; 20.1 to 30 min, 5% B. The column was maintained at 25 \({}^{\circ}\)C during the elution program. A total of 32 non-anthocyanin phenolic compound standards were collected based on the precursor to product ion transition using multiple reaction monitoring (MRM) mode under a negative electrospray ionization. These compounds in the wine sample were identified by comparing their mass spectrum and retention time with the external standard, and quantified using the external standard curve. Since the external standards, _cis_-plecid and _cis_-resveratrol, were not available, their content was quantified using their trans-isomer according to a published method [62].  
The anthocyanin analyses were also carried out using the same apparatus with an Agilent Poroshell 120 EC-C18 column (3.0 \(\times\) 100 mm, 2.7 \(\upmu\)m) according to a previous method with minor modifications [62]. The wine sample was filtered through 0.22 \(\upmu\)m membrane filters and 3 \(\upmu\)L was injected. The flow rate was set at 0.3 mL/min and the column was maintained at 25 \({}^{\circ}\)C. The mobile phase was comprised of (A) 0.1% formic acid (\(v/v\)) in water and (B) 0.1% formic acid (\(v/v\)) in acetonitrile. The elution was programed as follows: 0 to 2 min, 8% B isocratic; 2 to 5 min, 8% to 15% B; 5 to 15 min, 15% to 25% B; 15 to 25 min, 25% to 70% B; 25 to 32 min, 70% to 8% B. A positive electrospray mode was used with the gas temperature of 330 \({}^{\circ}\)C, gas flow rate of 10 L/min, nebulizer pressure of 35 psi, and capillary voltage of 3500 V on the mass spectrometer. The mass spectrum analysis was conducted in multiple reaction monitoring (MRM) mode under a positive electrospray ionization. The quantitation of anthocyanins with the standard available was carried out using their external anthocyanin standards. The rest of the anthocyanins in the wine samples were quantified using the external standard that had the same anthocyanin aglycone.
# A Low-Brightness Image Enhancement Algorithm Based on Multi-Scale Fusion
## 4 Experiment and Analysis
### Subjective Evaluation of Experimental Results  
In this section, we subjectively analyze the effect of the proposed method on different images and compare it with other methods. The performance of our method was tested on the VE-LOL dataset [20], which is a large low-light image dataset with rich materials, including indoor and outdoor scenes. Due to space limitations, we selected eight representative images, namely \"bookshelf\", \"cupboard\", \"doll\", \"classroom\", \"swimming pool\", \"hall\", \"gym\", and \"wardrobe\". Figures 3-10 show the experimental results of the different methods.  
The enhanced images obtained by our method are brighter and more natural, as shown in Figure 3. In comparison, the images obtained by the MF, NPE, and SRIE methods lack brightness and detail, making it difficult to clearly see the dolls on the bookshelf. In addition, these methods suffer from noise and artifacts. It is obvious that the MSR method increases the image brightness excessively.  
In Figure 4, the scene in the cabinet has a clear bottle of wine after image enhancement. In the more complex scene shown in Figure 5, our method demonstrates its ability to enhance the pattern with satisfactory contrast, allowing clear visualization of dolls, fruits, and costumes. Figure 6 clearly shows the artifacts surrounding the bright areas when [PERSON]'s algorithm is applied.  
In the swimming pool scene shown in Figure 7, the MF method produces excessive noise and lacks noticeable contrast. In contrast, our method excels in preserving rich and natural details, allowing for a more realistic image restoration. Figure 8 shows the presence of brightness distortion in the LIME method. Figure 9 shows a gym scene. In addition, Figure 10 shows that our method excels in both brightness balance and detail rendering, outperforming other algorithms.  
The detailed analysis of the experimental results reveals several problems with the different image enhancement methods. The enhanced images of Dong have too much noise; their details are blurred. The enhanced images of LIME are visually pleasing, but the details of the dark region in the enhanced image are not clear. The enhanced images of MSRCR show excessive overall brightness enhancement. A detailed analysis shows that the overall enhancement effect of MF is not clear, and they look unnatural. The enhanced images of NPE are relatively natural, but the image is noisy, and the color saturation is slightly lower. The SRIE method makes the enhancement effect weak and does not show the details of the image. Our method achieves a balanced enhancement of image brightness without excessive enhancement.  
Figure 4: Enhanced effect of “cupboard” image.  
Figure 3: Enhanced effect of “bookshelf” image.  
Figure 5: Enhanced effect of “doll” image.  
Figure 6: Enhanced effect of “classroom” image.  
Figure 8: Enhanced effect of “hall” image.  
Figure 7: Enhanced effect of “swimming pool” image.
# Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field
## 4 A Prototype Network-Based Risk Level Identification Model for Lane Change  
Metric learning is a notable example of a meta-learning algorithm that measures the degree of similarity between two instances through a distance-based method, such as a prototype network [44], a matching network [45], and a relation network [46]. The prototype network calculates each instance class prototype center by projecting instances into space, and it analyzes the category of objects by comparing the distance of objects to each instance-class prototype center during the classification process. As shown in Figure 6, the query set of the instances to be tested after embedding the network is represented in red. Furthermore, the class prototype centers for each class are represented as \(C_{n}\), and the distance between the sample under testing and each class prototype center is represented as \(D\).
# Spatiotemporal Evolution of Gas in Transmission Fluid under Acoustic Cavitation Conditions
## 2 Methods
### State Equation of Gas-Liquid Mixtures  
The homogeneous flow model requires the addition of the state equation of the gas-liquid mixture to make the set of equations closed. In this study, the fluid is considered as a homogeneous mixture of pure liquid and a certain amount of gas, so the gas-liquid mass flow ratio is introduced as:  
\[q=\rho Au \tag{13}\]  
\[\mu=\frac{q_{g}}{q_{l}} \tag{14}\]  
where \(\mu\) is the gas-liquid mass flow rate ratio and \(q_{g}\) and \(q_{l}\) represent gas and transmission liquid mass flow rates.  
To solve the pressure term for gas-liquid two-phase flow, the gas-liquid mixture fluid state equation needs to be combined. Given a gas-liquid mass flow rate ratio \(\mu\), the mixture fluid density \(\rho\), gas density \(\rho_{X}\), and gas void fraction \(\alpha\) are all functions of pressure \(P\) and temperature \(T\).  
Substituting Equation (3) into Equations (13) and (14):  
\[q=[\alpha\rho_{X}+(1-\alpha)\rho_{l}]Au \tag{15}\]  
\[q_{g}=\alpha\rho_{X}Au \tag{16}\]  
\[q_{l}=(1-\alpha)\rho_{l}Au \tag{17}\]  
Combining Equations (13)-(17):  
\[\mu=\frac{\alpha\rho_{X}}{(1-\alpha)\rho_{l}} \tag{18}\]  
\[\rho_{X}=\frac{\rho\mu}{\alpha(1+\mu)} \tag{19}\]  
Substituting Equations (18) and (19) into Equation (6) yields the state equation of the mixture fluid:  
\[\rho=\frac{\rho_{l}(1+\mu)P}{P+\mu\rho_{l}r_{X}T} \tag{20}\]  
The pressure \(P\) is related to the mixture fluid density \(\rho\) by the mixture fluid state equation, for which an expression for the pressure gradient needs to be given since the flow in the tube generates a pressure drop:  
\[\frac{\partial P}{\partial x}=\frac{\mathrm{d}P}{\mathrm{d}\rho}\frac{ \partial\rho}{\partial x} \tag{21}\]
## 2 Materials and Methods
### Critical Appraisal and Overview of Studies Included
The twelve articles selected for the review comprised eleven experiments [46; 47; 48; 50; 51; 52; 53; 54; 55; 56; 57] and one mixed method [49]. Unlike RQ1, RQ2 resulted in three conference papers [46; 52; 57] as well as nine published journals [47; 48; 49; 50; 51; 52; 53; 54; 55; 56]. Although all related to applying vibration or acoustic technologies for remote sensing, only two studies [52; 53] used both vibrations and sounds simultaneously, with an additional one mentioning that the hybrid use of both technologies should be considered. Regarding geographical location, only one was published and conducted in the United Kingdom [49]. Four were based in the US [46; 47; 51; 56]. Two were from Israel [52; 53], one from Italy [48], one from Spain [50], one from China [55], one from Columbia [54], and one was based in an unspecified location in the EU [57]. To ensure relevance with real-life applications, five studies involved human subjects (participants recruited or stunt actors or experimenters themselves) [47; 54; 56; 57], four involved human-mimicking objects (e.g., a doll) [48; 52; 53; 55], and the rest focused on algorithmic simulations that did not involve subjects at the date of publication.
## 4 Proposed System Design Overview
### Dataset Construction  
The RPL-IIoT has a distributed and hierarchical system model that necessitates personalised and distributed security solutions instead of a centralised security model. Hence, the proposed FT-CID model relies heavily on the RPL data collected from the IIoT application domain to build a distributed security protocol. Due to the ethical, technical, and economic complications of modelling a real IIoT network infrastructure with real-time operation, building or generating simulation datasets is vital for enabling intrusion detection in an IIoT environment [33; 34]. Hence, in this study, we designed an IIoT network architecture through simulation on the Contiki-NG OS. The Cooja environment emulates an IIoT network with real CPU and memory power as the host workstation in the simulation design to generate normal and intrusion samples. The simulation design of the IIoT network is based on the considerations of portability and applicability in a real-world scenario. The data collection process is no different than a heterogeneous IIoT network. It generates the same feature space because the hardware and protocols used by all the nodes in the IIoT network remain the same. In the FT-CID model, the data are collected from the IIoT network by involving AMI. However, as the FT-CID builds IIoT datasets with various data samples and similar features, this may result in redundant and non-informative data. Inputting the collected data directly into the learning process escalates the time and maximises the algorithm complexity. Hence, to ensure quality decision making and reduce training time and algorithm complexity, it is crucial to preprocess the data and select optimal feature subsets in the IDS.  
Figure 2: Overview of the design of the proposed FT-CID model.