esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 2 Related Work
### Semantic Segmentation  
Semantic segmentation information can be acquired from the fully convolutional network (FCN) [25]. Typically, the convolutional neural network has several fully connected layers after the convolution layers. The feature maps generated from convolution layers are mapped to feature vectors by fully connected layers. For example, the 1000-dimensional vector output by the ImageNet model in [26] indicated the probability that the input image belongs to each class. The FCN classifies the object's class at the pixel level. The FCN uses the deconvolution layer to upsample the feature map and restore it to the input image size. After the deconvolution process, a prediction is made for each pixel classification and the spatial information in the input image is preserved. The FCN fused features from different coarseness layers to refine the segmentation using spatial information. Finally, the loss of the softmax classification was calculated pixel by pixel, which was equivalent to one training sample per pixel. The FCN architecture is shown in Figure 2.  
Based on the FCN, DeepLab-V2 [27], containing atrous spatial pyramid pooling (ASPP), was proposed. In ASPP, parallel atrous convolution with different rates were applied in the input feature map and fused together. As objects of the same class may have different scales in the image, ASPP helps to account for different object scales that can improve the accuracy.  
Figure 1: (**a**) Single shot multibox detector (SSD) architecture. From the convolutional layers C1 to C9, a number of bounding boxes and their corresponding confidence are inferred. (**b**) Fixed size default bounding box on different resolution feature maps. The fixed size default box captures different-sized targets in different resolution feature maps. This architecture allows the SSD to capture both large and small targets in one shot.  
Figure 2: (**a**) Fully convolutional network (FCN) architecture. Including convolutional and deconvolution layers. (**b**) Overlay of the input image and FCN processing results.  
Usually semantic segmentation is treated as a special feature extraction process integrated into the convolutional neural network. The feature map will first be enlarged and then reduced during the inside feature extraction process as shown in Figure 3. During this process, noise is introduced and the detection results are affected. Therefore, the proposed algorithm uses the outside mutual correction (OMC) algorithm to fuse semantic segmentation outside the multibox. Through the proposed algorithm, the pixel-level classification of semantic segmentation can be more effectively utilized to improve the detection accuracy. We used SSD and SSTD as the basic multibox text detectors. DeepLab-V2 was used to generate semantic segmentation information.
## 5 Geophysical Technique Used: Time-Domain Electromagnetic Sounding (TDEM)  
According to [PERSON] [18], time-domain electromagnetic sounding, commonly referred to as TDEM, consists of injecting a constant current into a loop or transmitting coil (TX), generating a constant primary magnetic field. When the current flowing through the transmitting coil (TX) is suddenly interrupted, the primary magnetic field is no longer constant and decreases in value over time until reaching zero. According to [PERSON]'s law, when a body is exposed to a time-varying magnetic field, a series of electromagnetic inductions of electric currents occur in the subsurface (Figure 4). These currents flow in closed paths through the subsurface, migrating in depth and laterally, decreasing in strength over time and, in turn, generating a decreasing transient secondary magnetic field at the surface [19].  
Figure 3: Liceras control piezometer (**left**) and piezometric evolution (**right**) [17].  
Figure 2: Definition of the Almazán S. groundwater boundaries and location of study area.  
This secondary field induces a time-varying voltage in the receiving loop (RX). The way the voltage decays contains information about the resistivity of the subsurface since the magnitude and distribution of the induced currents depend on the resistivity of the medium [21]. The depth migratory character, i.e., short time voltages, provide information on the resistivity of shallow layers, while long time voltages provide information on the resistivity of deeper layers [22].  
In more detail, the process of an electromagnetic survey would be as follows:  
1. A current is circulated through the transmitting loop (Tx), which creates a time-varying primary magnetic field (Figure 5).
2. According to [PERSON]'s law, a rapid decrease of the transmitted current and thus of the primary magnetic field induces an electromotive force (emf.) in the subsurface (Figure 6).  
Figure 4: General arrangement of an electromagnetic sounding [20].  
Figure 5: Transmitter current and primary magnetic field vs. time [23].  
3. These induced currents generate, in turn, a secondary magnetic field whose variation over time is measured in the receiving coil (Rx) (Figure 7).
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy
## 4 Experiment and Discussion  
### Experimental Settings  
Existing microgravity simulation methods are mainly classified into five categories, namely, parabolic flight, drop tower, suspension method, neutral buoyancy, and air-bearing suspension methods [8; 19]. Compared with the other methods, the suspension method has the advantages of low cost, relatively simple structure, unlimited simulation time, freedom from the influence of liquid resistance and fluidity on the simulation accuracy, and realization of 3-dimensional motion simulation [20; 21; 22]. Therefore, the suspension method was used to simulate the space microgravity environment in this experiment.  
An experiment was designed to verify the correctness of the mathematical models of the relationship between fit clearances and rotation angles. We simulated the optical unit and frame with square steel tubes, and the optical unit was fixed on the frame by positioning mechanisms. Two crown blocks were fixed on the suspension frame, and a hanging wire was passed through the crown blocks. One end of the hanging wire lifted some lead blocks that were the same weight as the optical unit model in total, and the other end was fixed at the center-of-mass position of the optical unit model. Two perpendicular plane mirrors were fixed on the optical unit model. One was parallel to the \(z\)-\(x\) plane and the other was parallel to the \(x\)-\(y\) plane. Two theodolites (Leica TM6100A-from Leica Geosystems AG, Heerbrugg, Switzerland: resolution is 0.1'' when angle display mode is 360'''', and accuracy is 0.5'') were placed on the central axis of the two mirrors respectively. Figure 7 shows the experimental system.
# Security Risk Analysis of LoRaWAN and Future Directions
## Security Challenges of LoRaWAN v1.1
### Roaming-Related  
Roaming support is one of the innovative features introduced in LoRaWAN v1.1. However, it brings extra challenges:  
* Both versions of LoRaWAN are susceptible to bit-flipping attacks (please refer to next section for details of this attack type) happening in between servers. The inclusion of handover-roaming in v1.1 makes the situation worse.
* As also stressed in [34], handover-roaming enables more possibilities for a MITM attack.
* Handover-roaming can cause a fall-back when the back-end (\(sNS\)) that serves the roaming \(ED\) runs an older version of LoRaWAN, i.e., v1.0 [34].
## 4 Methods
### Link Prediction
#### 4.5.2 Joint Loss Function  
The computations are shown in Equations (19)-(22):  
\[\mathcal{L}_{\mathrm{BCE}}=-\frac{1}{\mathrm{N}}\underset{(i,j)\in(\mathbb{E},\mathbb{R})}{\sum}\Big{[}y_{\bar{\mathrm{i}}}\mathrm{log}(\mathrm{s(i,j)})+( 1-\mathrm{s(i,j)})\mathrm{log}(1-\mathrm{s(i,j)})\Big{]} \tag{19}\]  
\[\mathcal{L}_{\mathrm{REC}}=\frac{1}{\mathrm{N}}\underset{i=1}{\sum}(x_{i}- \hat{x}_{i})^{2} \tag{20}\]  
\[\mathcal{L}_{KL}=-\frac{1}{2}\underset{i}{\sum}\Big{(}1+log(\sigma_{i}^{2})- \mu_{i}^{2}-\sigma_{i}^{2}\Big{)} \tag{21}\]\[\mathcal{L}_{\text{total}}=\lambda_{1}\cdot\mathcal{L}_{\text{BCE}}+\lambda_{2} \cdot\mathcal{L}_{\text{REC}}+\lambda_{1}\cdot\mathcal{L}_{\text{KL}} \tag{22}\]  
where \(y_{i,j}\in\{0,1\}\) represents the true positive or negative label for the pair of users \(u_{i}\) and \(u_{j}\), and the datasets for positive and negative samples are denoted as \(\mathbb{P}\) (positive sample dataset) and \(\mathbb{N}\) (negative sample dataset). \(N\) represents the sample size, \(\mathcal{L}_{BCE}\) represents the cross-entropy loss, \(\mathcal{L}_{REC}\) represents the reconstruction loss, and \(\mathcal{L}_{KL}\) represents the KL divergence loss. \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\) represent their respective weights.
# Digital Cloud Environment: Present Challenges and Future Forecast
## 5 Monitoring System and Prediction Models  
Although VM types in the public cloud can be scaled up and down very quickly, it should be done during planned maintenance to avoid service outage. For the purpose of capacity planning and performance evaluation, the monitoring system is needed. In this paper, Zabbix enterprise-class system is considered as one of the leading monitoring solutions in the world, having many built-in possibilities including [23; 24; 25]:  
* Open-source code, allowing customization and implementation of new data models (prediction models in our case).
* Enterprise-class product, supporting the server pool of up to 15,000 remote hosts with a total rate of 500 monitoring values per second.
* Free software to install from scratch and free for annual upgrades without affecting the customized code.
* Built-in templates with system metrics like CPU, RAM, disk I/O, etc., allowing monitoring of new hosts in \"one click\".
* Enhanced reporting web user interface with graphs and analysis tools.  
Zabbix supports a wide scope of built-in system metrics and triggers to monitor CPU, memory, disk space, etc., but business specific items should be implemented manually. Figures 4 and 5 show examples of such business items, measuring the incoming and outgoing user traffic at RingCentral and Genesys Companies accordingly [16; 17].  
User workload is usually cyclic but cannot be predicted using polynomial forecasting models due to [PERSON]'s problem of oscillation for polynomial models of higher degrees [26] (Figure 6). Instead, a linear model is applied for capacity evaluation where only maximum values in peak time are taken into account. As a result, the prediction looks more stable and precise.  
The same approach can be applied to any monitoring tool other than Zabbix. The purpose of the prediction data model is to analyze the workload trend and verify whether VM downscale is possible or not. If the workload is stable (Figure 4) or slowing down (Figure 5), the downscale is reasonable, otherwise it is risky and possible that upscaling will be needed soon.
## 3 Monitoring and Detection Technology of SF\({}_{6}\) and Its Decomposition Components
### Photoacoustic Spectroscopy (PAS)
#### 3.2.2 Quartz-Enhanced Photoacoustic Spectroscopy (QEPAS) Method  
Quartz-enhanced photoacoustic spectroscopy technology has a strong anti-interference ability against external noise and has a small size, low cost, and fast response, making it the first choice for trace gas detection in various complex situations. The quartz tuning fork (QTF) can detect the sound waves generated when the gas absorbs and modulates the light [29]. After the resonant frequency of the laser beam bending mode of the QTF in-plane modulation occurs, the resonant tube produces a standing-wave vibration mode, and the tip deflects. This causes the appearance of electrical signals, with the signal being proportional to the concentration of the absorbed analyte [30]. Laser sources of different wavelengths can detect a wide range of trace gases, from ultraviolet to terahertz, up to parts per billion [31].  
The QEPAS measurement system is widely used for transient gas measurement in various complex scenarios. [PERSON] et al. [32] reported a quartz-enhanced photoacoustic SF\({}_{6}\) sensor for ventilation studies using a 10.5 \(\upmu\)m continuous-wave distributed feedback quantum cascade laser (QCL). The SF\({}_{6}\) sensor locks the QCL wavelength at the absorption peak of SF\({}_{6}\) by detecting sound waves induced by gas absorption, with a detection limit of 4.6 ppb. This sensor can capture transient changes in SF\({}_{6}\) concentration during ventilation. [PERSON] et al. [33] studied a fast response SF\({}_{6}\) measurement system based on quantum cascade lasers and compared it to commercial instruments for instantaneous SF\({}_{6}\) tracer gas measurement. The QEPAS system can be used for transient tracer gas measurement. With a data acquisition interval of 0.4 s, the QEPAS system can record the SF\({}_{6}\) concentration in real time after rapid changes and can successfully capture the concentration peak, while commercial instruments have mostly failed to do this.  
The humidity situation determines the degree of failure of the insulated equipment. In order to obtain the humidity content of the monitored equipment and ensure the normal operation of the equipment, [PERSON] et al. [22] used a commercially available near-infrared distributed feedback laser (DFB) to generate feedback and design a gas sensor system using QEPAS technology. According to the linear response of 0.14-2.37% water vapor concentration and optical signal, the fitting degree was good, and there was a linear correspondence to the water content. The final detection limit of the sensor was 0.49 ppmv.  
[PERSON] et al. [9] designed a QEPAS gas sensor to detect a trace CO gas in an insulating device (Figure 3b). The center wavelength of the quantum cascade laser was 4.61 \(\upmu\)m, and the spectral characteristics of the CO were obtained. The minimum detection limit was able to reach 10 ppb, and the sensor response time was 3 min.
## 3 Results and Discussion
### Physical Characterization of CMSC
#### 3.8.2 Moisture Content  
CMSC showed the ability to absorb moisture from the air, where the amount of moisture absorbed and the rate of absorption is dependent upon the initial moisture content of CMSC. The moisture content of CMSC was approximately 8.11% (\(w/w\)). An explanation is that in the case of CMSC, water is more tightly bound within the superstructure and not so easily dehydrated under the conditions used (oven, 105 \({}^{\circ}\)C, 2 h). This tight association may be due to interactions between water molecules and ionic groups with counter-ions or hydroxyl groups functioning as sorption sites for the water molecules.
## 4 One-Dimensional-Two-Dimensional Drainage Overflow Modeling
### Comparison of Simulated Data and Observed Data  
The scenario of historic drainage overflow was first modeled, referred to as scenario 1, in which the results were used as the reference for validation and comparison. Figure 7 shows the simulation of flooding in Kampung Melayu Subang, displayed in a bird's eye view via the 3-D viewer in Google Earth Pro. Notably, the simulation corresponds to flooding scenarios in the historic flood event on 31 March 2015. The total precipitation for a 2-h storm duration (05:00 PM-07:00 PM) is 52.2 mm, while the rainfall hyteograph is given in Figure A3.  
As shown in Figure 7, flooding typically occurs in the northern and southeastern areas of Kampung Melayu Subang. It is observed that the major affected areas are along Resak Road, Merbau Road, Jati Road, and Chempaka Road, which is in accordance with the previous report showing the flood vulnerability of Kampung Melayu Subang [9]. For a better insight into the flood scenario, the flood depths at the four roads mentioned above are presented in Figure 8. Drainage overflows occur within a few minutes to one hour after the rainfall. The flood depths in the southeastern areas (Jati Road and Chempaka Road) pick up earlier than that in the northern areas (Resak Road and Merbau Road). Unlike the southeastern areas, a gradual increase in flood depth is noticed for the northern areas, until it achieves its maximum depth. The maximum flood depth varies from a minimum of 0.11 m to a maximum of 1.04 m (Figure 8). The results closely match the historical flooding, with maximum depth ranging from 0.3 to 1.0 m [8]. There is a discrepancy in flood depth minima between the simulation and observation, which may be due to the lower grid resolution used in the model, as mentioned by [PERSON] and [PERSON] [46].  
Figure 6: Direct connections between 2-D mesh (black lines) and 1-D drainage channel (yellow lines).
# Measurement and Analysis of Radar Signals Modulated by the Respiration Movement of Birds
## 3 Results
The calculated respiration rate is approximately 0.7 Hz in Figure 6a,b. In this experiment, we recorded the data after the duck flapped its wings for a while, and we could even hear the quick and loud sound of the duck's breathing; hence, the respiration rate of the duck was a little higher \begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & & \multicolumn{2}{c}{**LRRP**} & \multicolumn{2}{c}{**HRRP**} \\ \cline{3-6}  & & **Dead Duck** & **Living Duck** & **Dead Duck** & **Living Duck** \\ \hline \multirow{2}{*}{**S-band**} & **Mean (dB)** & \(-\)70.6949 & \(-\)74.9089 & \(-\)82.665 & \(-\)74.3358 \\  & **Scope (dB)** & 0.0425 & 0.3765 & 1.0122 & 0.9605 \\ \multirow{2}{*}{**X-band**} & **Mean (dB)** & \(-\)73.146 & \(-\)73.965 & \(-\)84.528 & \(-\)99.519 \\  & **Scope (dB)** & 0.057 & 3.053 & 0.2246 & 4.062 \\ \multirow{2}{*}{**Ku-band**} & **Mean (dB)** & \(-\)79.720 & \(-\)83.146 & \(-\)98.167 & \(-\)98.777 \\  & **Scope (dB)** & 0.137 & 2.621 & 0.235 & 3.772 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Some statistics results of signal fluctuations in Figures 4 and 5. Figure 6: The comparison of signal fluctuations produced by respiration. (**a**) Radar signals in time series and spectrum of a duck (head-view, HRRP), (**b**) Radar signals in time series and spectrum of a duck (tail-view, HRRP). than the normal state, and the value approached the upper limit of the reference rate (i.e., 30-50 breaths/min (0.50-0.83 Hz) [18; 24]). In addition, the comparison in Figure 6a,b indicates that the signal fluctuation modulated by respiration is very weak, and there were no signal fluctuations or respiratory frequencies when the radar wave shot on the back of the duck (i.e., tail-view) (Figure 6b). This extraction required that the chest of the duck face the radar wave (i.e., head view) (Figure 6a).
###### Abstract  
A tunable demultiplexer with three output channels infiltrated by liquid crystal (LC) is presented, which is based on a metal-insulator-metal (MIM) waveguide. The operating frequencies of the three output channels can be tuned simultaneously at will by changing the external bias electric field applied to the LC. By analyzing the Fabry-Perot (FP) resonance modes of the finite-length MIM waveguide both theoretically and numerically, the locations of the three channels are delicately determined to achieve the best demultiplexing effects. Terahertz (THz) signals input from the main channel can be demultiplexed by channels 1, 2 and 3 at 0.7135 THz, 1.068 THz and 1.429 THz, respectively. By applying an external electric field to alter the tilt angle of the infiltrating LC material, the operating frequencies of channels 1, 2 and 3 can be relatively shifted up to 12.3%, 9.6% and 9.7%, respectively. The designed demultiplexer can not only provide a flexible means to demultiplex signals but also tune operating bands of output channels at the same time.  
liquid-crystal devices; demultiplexer; terahertz; metal-insulator-metal waveguide +
Footnote †: journal: Applied Physics Letters
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 3 State Representation
### Hybrid Strategies  
For some approaches, the RL agent is not used as the optimizer or solution generator. Instead, it takes an **auxiliary role** to other optimization strategies, using information about the state of the optimization itself, not the solution. Large-scale and uncertain resource scheduling problems are solved in [261], with an agent making pre-selections and order solving to simplify the problem a linear programming model must solve next. In [254], an RL agent complements a constraint programming algorithm. The state representation includes information on the instance solving, the current model and statistics from the past solving operations.  
The RL agent can often be paired with **metaheuristics**[253; 262], such as ant colony optimization [263] or particle swarm optimization [259]. Agents can read population metrics [203; 257; 260] or directly use algorithm optimization **parameters** as the state [252]. In [253], where the agent is used to control the parameters of a Cuckoo search metaheuristic, the state describes the population diversity and the tracks if there is diversification of intensification of solutions. In [259], the state is the current population levels of a particle swarm optimization algorithm.  
The set of optimization parameters, which are used to generate circuit designs, are the state of [252]. A fuzzy rules-based approach is used in [258], where the conditional part of the fuzzy rules is the state.  
Some agents read population metrics [203; 253; 257; 260], such as average fitness and diversity or population improvement [260]. In [253], where the agent is used to control the parameters of a Cuckoo search metaheuristic, the state describes the population diversity and the tracks if there is diversification of intensification of solutions. The authors of [254] complement the RL agent with a constraint programming algorithm. The state representation includes information on the instance solving, the current model and statistics from the past solving operations.  
The set of optimization parameters, which are used to generate circuit designs, are the state of [252]. In [259], the state is the current population levels of a particle swarm optimization algorithm. The agent will only work on the number of levels. This level-based learning approach forces particles to learn from upper levels only. Lower-level particles will focus on exploration and higher-level particles on exploitation.