esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Road Traffic Situation Assessment Model
### Urban Road Monitoring Network
#### 2.1.3 Determination of Road Traffic Situation Level  
The definition of the traffic situation is the overall road condition state and operation trend in road traffic. Traffic situation assessment is to analyze and extract many parameters, use some algorithms to evaluate and analyze the road network condition and give some guiding suggestions. In the traffic industry, the traffic situation is an indicator that can comprehensively reflect the road traffic situation. If the traffic situation can be accurately evaluated and predicted, it will be of great help to future road traffic facilitation and congestion alleviation.  
In 1994, [PERSON] proposed a situation recognition method based on fuzzy logic, combined with traffic flow and vehicle speed, and divided the traffic situation into six levels [31]. Therefore, in the evaluation methods proposed later, most scholars use the method of set  
Figure 2: The setting of side weights in the road monitoring network.  
ting threshold ranges for grade division. The characteristic traffic parameters are used as parameters to set the threshold, such as traffic flow, average vehicle speed, delay time, road occupancy rate, etc. [32]. Therefore, the problem of evaluating and predicting the traffic situation is transformed into the issue of assessing and predicting the above parameters. Therefore, when predicting the traffic situation, the mainstream method is to predict the traffic parameters and then assess and divide by the threshold range.  
However, there is no definite standard for classifying road traffic status. The urban road traffic conditions in different countries are quite different, which leads to different thresholds for each indicator for each research object, so it is not easy to divide the situation level by a specific indicator. The United States divides the traffic situation into grades through some traffic service indicators and quantifies the degree of congestion according to the indicators [33]. Germany divides the road traffic situation into five stages through road traffic flow density indicators [34]. Japan, through the analysis of traffic flow, identifies three levels of traffic service. New Zealand assesses traffic congestion levels by quantifying travel time as a congestion index. In China, the Ministry of Public Security uses the average travel time of road vehicles as an indicator to reflect the degree of traffic congestion on the road network [35]. It divides the degree of congestion into four levels.  
Given the existing traffic congestion and posture classification levels, this paper completes the classification of traffic posture according to the traffic congestion level table in GB\(\backslash\)T 33171-2016 Evaluation Specification for Urban Traffic Operation Condition (hereafter referred to as the evaluation specification). It classifies the urban road traffic posture level into five levels: unobstructed state, slightly unobstructed state, mild congestion, moderate congestion and severe congestion. Detailed traffic situation levels are described in Table 1.
## 3 Motivation
### Motivation for Context Awareness  
As we have discussed in Section 1, clustering techniques are required and beneficial even for highly dynamic IoT systems. With 3 GPP standard infrastructure, the deployment for IoT becomes more flexible and the connectivity problem can be solved straightway. When migrating IoT systems to 5G networks, several critical problems in clustering still need to be addressed.  
The first challenge comes from the fundamental nature of IoT systems--the high degree of diversity. The things in the field are highly heterogeneous and some of the nodes can be extremely advanced. Comparing with traditional WSN, IoT systems are more complex and comprehensive. Traditionally the sensors in a WSN are mainly in charge of environment sensing, data collection and transmission. However, the applications in IoT systems are no longer just designed for those simple tasks. For example, the smart cameras deployed in intelligent monitoring systems are required  
Figure 4: Non-uniform distributed WSN.  
Figure 3: Distribution of nodes still alive after 450 rounds. (**a**) LEACH; (**b**) HEED.  
to transmit high quality video stream to the control centre. Nowadays, in advanced IoT systems, we need to consider more complicated scenarios and user cases. The requirements for the network have raised in terms of network bandwidth and network delays. Besides, if MIMO is realised, each device will have multiple choices on communication interfaces for data transmission. Being aware of the high level application requirements and the low level hardware capabilities is essential to provide QoE supported services.  
The second challenge is the cost for transmission. The energy cost is still a critical concern in IoT systems when deployed in 5G networks. In practice, LTE can be used as the default communication means. If possible (the CH is in range and the user requirements can be satisfied), some of the devices can switch to Bluetooth or ZigBee, which are much more energy efficient solutions.  
Clustering techniques, in order to adapt to 5G and more complicated scenarios in IoT systems, the above challenges need to be solved. The corresponding research in those directions should be further investigated. In order to utilise MIMO 5G platform and facilitate communication in advanced IoT systems, BEEM protocol will be enhanced with a context aware feature to adapt to MIMO scenarios.
# Numerical Study of the Toughness of Complex Metal Matrix Composite Topologies
## 3 Materials and Methods
### Materials  
The studied insert material is a Metal Matrix Composite (MMC) composed of Martensitic Steel (MS) and Titanium Carbides (TiC), named MS-TiC, with particle size around 10 \(\upmu\)m. TiC is one of the most resistant reinforcement ceramic [28, 29, 30]. Mechanical tests were performed on MS and MS-TiC specimens. The mechanical properties of both materials are presented in Table 1. Each test method used for the determination of a given mechanical parameter is specified.  
The fracture toughness testing was performed on pre-cracked heat-treated CT specimens of full MS and full MS-TiC. The pre-cracking was carried out by fatigue testing. Constant \(\Delta K\) of 30 MPa\(\sqrt{\text{m}}\) and 15 MPa\(\sqrt{\text{m}}\) were respectively applied to the full MS and the full MS-TiC CT specimens. The crack growth was monitored according to the compliance method. The fatigue test was stopped when an opening crack of 12 mm was measured which corresponds to a crack length of 30 mm by considering the notch. The fracture toughness testing was then performed on the pre-cracked specimen. A displacement speed of 0.06 mm/ min at an ambient temperature of 23 \({}^{\circ}\)C was applied, the target maximum load was 250 kN. The \(K_{Ic}\) values is computed according to the ASTM E1820 standard [17] and the validity of the tests is verified (Section 3.1).  
\begin{table}
\begin{tabular}{l l c c} \hline \hline  & **Test Method** & **MS** & **MS-TiC** \\ \hline Young’s modulus (GPa) & Impulsive excitation (ASTM C1259 [31]) & 203 & 230 \\ Poisson’s ratio & Impulsive excitation (ASTM C1259 [31]) & 0.29 & 0.26 \\ Strength (MPa) & Tensile (steel) / Three-points bending (MMC) & 1560 & 538 \\ Fracture toughness (MPa\(\sqrt{\text{m}}\)) & Pre-cracked CT (ASTM E399 [16]) & 105 & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of the materials used in this study.  
Figure 4: Topological inserts cut according to a CT specimen—Z-orientation gyroid (Gyroid_01) **(a)**, X-orientation gyroid (Gyroid_02) **(b)**, octet-truss (c)**, Z-orientation FBCCZ (FBCCZ_01) **(d)**, X-orientation FBCCZ (FBCCZ_02) **(e)** and Y-orientation FBCCZ (FBCCZ_03) **(f)**.  
The MS-TiC material has low mechanical properties when it is in the form of a homogeneous structure compared to the MS material (Table 1), which is why it is necessary to find a reinforcement structure in order to not decrease performance.
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
###### Abstract  
Orthogonal time-frequency space (OTFS) modulation outperforms orthogonal frequency-division multiplexing in high-mobility scenarios through better channel estimation. Current superimposed pilot (SP)-based channel estimation improves the spectral efficiency (SE) when compared to that of the traditional embedded pilot (EP) method. However, it requires an additional non-superimposed EP delay-Doppler frame to estimate the delay-Doppler taps for the following SP-aided frames. To handle this problem, we propose a channel estimation method with high SE, which superimposes the perfect binary array (PBA) on data symbols as the pilot. Utilizing the perfect autocorrelation of PBA, channel estimation is performed based on a linear search to find the correlation peaks, which include both delay-Doppler tap information and complex channel gain in the same superimposed PBA frame. Furthermore, the optimal power ratio of the PBA is then derived by maximizing the signal-to-interference-plus-noise ratio (SINR) to optimize the SE of the proposed system. The simulation results demonstrate that the proposed method can achieve a similar channel estimation performance to the existing EP method while significantly improving the SE.  
orthogonal time-frequency space modulation; channel estimation; superimposed pilot; perfect binary array +
Footnote †: journal: _entropy_  
0023
# A Hard Example Mining Approach for Concealed Multi-Object Detection of Active Terahertz Image
## 2 Related Work  
### Object Detection in Terahertz Image  
Due to poor imaging quality and immature level of object detection technology, earlier work paid more attention to object segmentation in terahertz images. [PERSON] [6] proposed a multi-level threshold segmentation algorithm to model radiation temperature using a Gaussian mixture model, which used an anisotropic diffusion algorithm to remove noise. [PERSON] [5] also used the Mixed Gaussian model to estimate object boundary. Due to the complexity of terahertz imaging, the above methods can only obtain rough segmentation results. In our previous work [7], we proposed a deep-learning-based method--Mask Conditional Generative Adversarial Nets (Mask-CGANs)--to segment objects in a terahertz image of poor quality.  
Convolutional neural networks have excelled in a wide range of tasks, and they have been applied to terahertz image object detection. [PERSON] [12] used a sliding window to slide on the terahertz image to obtain the sub-images and obtained the probability of the existence of the object in each sub-image. Then, the probabilities of each sub-image were accumulated to obtain the probability map of the whole image. Finally, the location and the bounding box of the object were obtained by threshold filtering. [PERSON] [13] improved on [PERSON]'s work by using a two-step search method instead of an exhaustive search to reduce the computational complexity. All the methods detected a single hidden object but ignored the situation of multiple objects in practical application.  
In addition to the above approaches specifically designed for terahertz image object detection, [PERSON] [14; 15] verified the performance of three universal object detectors(i.e., SSD [16], R-FCN [17], and YOLOv3 [8]) on terahertz image dataset. It was demonstrated that SSD and YOLOv3 have faster detection speeds, while R-FCN has a higher detection rate. [PERSON] [18] proposed an improved Faster R-CNN [19] to detect terahertz images. The input image needs to pass through the Faster R-CNN and human body threshold segmentation branch to detect the object and the human body.  
While there has been research work dedicated to deep learning terahertz image object detection, the lack of publicly available terahertz datasets due to the sensitive nature of terahertz imaging technology and privacy protection has limited research in this direction. We hope that our proposed terahertz multi-object detection dataset facilitates the development of this research.
## Materials and Methods
### Introduction of the EEMD-CSO-SVM Model  
Based on the above-mentioned methods, the EEMD-CSO-SVM model is proposed for wind speed forecasting. Taking one of the datasets as an example, the main procedures of the proposed model is shown in Figure 2. Firstly, the EEMD noise reduction technique is applied to the raw wind speed data in order to obtain a stationary time series. In this paper, the raw data is decomposed into 10 IMFs, among which the first IMF is considered as noise and removed. The rest are recombined for the forecasting. Then, each set of data from the four wind turbines are used to perform three-step forecasting to testify the validity of the proposed model. The de-noised wind speed data is applied to train the SVM model whose key parameters (\(\gamma\), C) are optimized by the Cuckoo Search algorithm. The results of forecasting are recorded for further analysis.
# Experimental Demonstration of MmWave Vehicle-to-Vehicle Communications Using IEEE 802.11 ad
## 3 GiV2V Communication Architecture
### GiV2V Communication Radio Range  
In this section, we introduce a well-known pathloss model of IEEE 802.11 ad to compare the theoretical outcomes with the measurement results. Based on this model, we calculated the theoretical coverage value with the given system parameters of a commercial IEEE 802.11 ad module (Section 4.1) and then compared it with measurement results (Section 5). [PERSON] et al. [54] presented an indoor pathloss model of 60 GHz WLAN (i.e., WiGig) for the LOS environment based on the measurement study, as presented below.  
\[L_{d}(dB)=A+20 log_{10}(f_{MHz})+10\alpha log_{10}(d), \tag{2}\]  
where the \(A\) is 32.5 dB and without a shadow factor. \(d\) is the distance between the transmitter and receiver (\(km\)) and \(\alpha\) is a pathloss exponent of LOS (e.g., 2).  
In outdoor GiV2V communication, additional attenuation from water vapor (\(L_{vap}\)), oxygen (\(L_{O_{2}}\)), and rain (\(L_{R}\)) are considered [8]. Those atmospheric parameters (\(dB/km\)) for further loss are assumed as a constant for the relatively short communication period in this study.  
\[L_{a}(dB)=d(L_{vap}+L_{O_{2}}+L_{R}).\]  
Accordingly, the total pathloss can be \(PL(d)=L_{d}+L_{a}\). For simplicity, we assumed that the pathloss, \(L_{a}\), owing to the atmospheric condition was static during the short communication period. Therefore, the pathloss was only determined by the distance, \(d\), at a given operational frequency (e.g., 60 GHz).  
In the LOS environment, the radio range can be determined by the following outage probability with the sensitivity level, \(T\), of the target modulation coding scheme (MCS).  
\[P(P_{r}\geq T)=P(PL(d)\leq P_{t}+G_{R}+G_{T}-T-IL), \tag{3}\]  
where \(IL\) is the implementation loss, such as that from cables. The maximum values of antenna gain of the transmitter \(G_{T}\) and receiver \(G_{R}\) are assumed to be the same when both have beam directions toward each other and use same number of antenna array.  
The maximum GiV2V coverage, \(d\), can be derived as \(PL^{-1}(P_{t}+G_{R}+G_{T}-T-C)\). Therefore, the reachable probability between vehicles can be defined as \(P(D\leq d)\) with a random variable, \(D\), indicating the distance between a transmitter and receiver. In the above equation, the effective range is decided from only the antenna gain of the transmitter and receiver (i.e., beam forming factor) while the  
Figure 2: Beam direction and gain of linear array antenna (ULA).  
other component is the constant loss, \(C\). In this study, we assumed no transmitter power control was achieved between vehicles. Using Equation (3), the maximum range, \(d\), can be expressed as follows:  
\[d=\Big{(}\frac{P_{t}G_{R}G_{T}}{TC}\Big{)}^{1/\alpha}. \tag{4}\]
# Convolutional Sparse Modular Fusion Algorithm for Non-Rigid Registration of Visible-Infrared Images
## 5 Experimental Methods
### Ablation Study
The removal of the information entropy loss significantly degraded the mutual information (MI) metrics of fused images while slightly increasing root mean square error (RMSE) values. These findings demonstrate that incorporating information entropy loss is critical for training feature extraction modules, as it enhances feature extraction efficiency. Similarly, eliminating the self-supervised registration loss reduced MI metrics of fused images and markedly elevated RMSE values. This experiment confirms that integrating self-supervised registration loss is essential for accelerating the training of registration modules.
## 3 Results and Analysis
### Optimization of Enzyme Production Conditions for Strain Fermentation
#### 3.3.3 Validation Test  
Optimizing the process conditions with the response surface methodology revealed the optimum production conditions. Our results showed that the fermentation time was 1.86 min, and the rotation rate and temperature were 180.36\(\times\)\(g\) and 25.74 \({}^{\circ}\)C, respectively. We obtained the highest value of CMC enzyme activity at a value of 4.527 U/mL. To verify the model's reliability, we changed the optimized fermentation enzyme production conditions to reflect the actual situation at a fermentation time of 2 d, a rotation rate of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Fermentation** & **Rotation Rate** & **Temperature** & **Enzyme** \\  & **Time (d)** & (\(\times\)\(g\))** & (\({}^{\circ}\)C) & **Activity (U/mL)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 5: JZ2 response surface experimental design and results.  
\(180\times g\), and a temperature of 26 \({}^{\circ}\)C. Validation tests were conducted under these conditions. The enzyme activity of strain JZ2 was 4.625 U/mL, close to the model's predicted value. Therefore, the model parameters optimized by the response surface analysis method were reliable and accurately optimized for the enzyme production conditions of the strain. According to the literature, [PERSON] et al. screened out the cellulase-producing bacillus from distillery lees, and its enzyme production activity was 1.2 U/mL [37]. The cellulosic strains screened in this paper had relatively high enzyme production.
# Impact of Lactic Acid Bacteria on Immunoreactivity of Oat Beers
###### Abstract  
The common contamination of oats with gluten cereals represents a problem for celiacs. One way to reduce the level of toxic peptides may be hydrolysis by lactic acid bacteria (LAB). The study examined the influence of the addition of a LAB starter at the grain malting stage on the immunoreactivity of oat beers using enzyme-linked immunosorbent assays with rabbit antibodies and human sera. Immunoblotting was used to identify proteins involved in the immunoenzymatic reaction. The immune response to QQQP and PQQQ sequences was much higher in barley and barley malt (64-76% in relation to wheat) than in oats (20%) and oat malts (below 26%). In the case of anti-QQQPP peptide antibodies, the differences were not so pronounced, mainly due to the high heterogeneity of the oat malt samples. The remaining immunoreactivity was effectively reduced during the technological process of beer production. The mashing process contributed most to the decrease in immunoreactivity, with the wort produced from oat sour malt having an immunoreactivity level of lower than 4%. In the subsequent stages of the beer production process, the immune response was further reduced to below 2% in the resulting beer. Although the level of immunoreactivity of oat sour malt assessed with rabbit antibodies was comparable to that of the regular one, oat sour beers presented significantly weaker immune responses than barley beers, which was not always the case with regular oat beers. This proves the beneficial effect of LAB on reducing the immunoreactivity of the raw material. The analysis performed with human sera confirmed this tendency. Although the immune response to oat beer was strongly dependent on individual sensitivity, the remaining immunoreactivity in oat beers after simulated digestion was only 0.6-2.0%.  
Keywords:gluten contamination; immunoreactivity; lactic acid bacteria; sour malt; oat beer +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Sensory Analysis  
The results of the sensory evaluation analysis of dried jujube slices are shown in the radar charts in Figure 3. In addition to jujube-ID, roasted sweet, caramel, bitter, and burnt were the primary characteristic flavors of jujube slices formed during drying. These flavors together constituted the unique flavor of red jujube after baking. Additionally, the drying time significantly affected the intensities of the basic tastes (roasted sweet, caramel, bitter, and burnt). With the increase in drying time, roasted sweet and caramel increased first and then decreased, and the bitter and burnt flavor increased. For instance, the scores of the B4 sample were the highest in roasted sweet (9.0) and caramel (8.0) attributes. Both 2,3-butanedione and acetoin showed high content and a roasted sweet smell contributing to the \"roasted sweet\" and \"caramel\" attributes of the B4 sample. The B7 sample had the highest rated value of the \"bitter\" and \"burnt\" descriptors, while the B1 sample showed the lowest sensorial score. This is likely because the adjustment of the treatment drying time resulted in significantly high intensities of roasted sweet and caramel flavors, which was linked to intense drying characteristic aroma. On the other hand, it produced burnt-bitter notes due to excessive heating. At the same time, the range of intensity of the jujube-ID as the important flavor parameter, which represented the flavor of dried jujube, was 9.5-1.0, with a mean value of 4.5; the highest values of this essential attribute were found in dried jujube, and the values then decreased with increasing the time of drying. For instance, samples B6 (30 min) and B7 (45 min) had values of 2.0 and 1.0, respectively. The B1 samples were described more often as having a jujube-ID flavor than the other samples, and acids could perhaps account for the similarity in jujube-ID. Based on the radar charts, the B4 samples were considered caramel and roasted sweet. The overall profiles of the B7 samples were small in the radar charts, given that they were described as bitter and burnt with weak caramel and roasted sweet flavors. The B7 sample showed a strongly bitter note compared to other dried jujube slice samples, mainly because of the high content of 2-acetylfuran (22.40 \(\upmu\)g/kg). The different kinds, relative contents, sensory threshold values of aromaticative compounds, and the mutual synergism among aroma-active compounds determined the sensory characteristics of jujube slices. Furthermore, the sensory analysis also agrees with the results of GC-MS and GC-O.  
Figure 3: Spider chart of sensory evaluation for jujube slices at different microwave drying times: (B1) drying time 0 min; (B2) drying time 1 min; (B3) drying time 2 min; (B4) drying time 2.5 min; (B5) drying time 3 min; (B6) drying time 4 min; (B7) drying time 5 min.