esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Near-Surface Defect Detection and Identification
### Attention Mechanism  
After the feature extraction module in the convolution neural network, the attention mechanism can dynamically weigh the features via autonomous learning and then focus on more useful information for classification. During feature extraction, the channel attention mechanism (CAM) assigns the corresponding weight coefficient based on the importance of the feature channel, while the spatial attention mechanism (SAM) performs an information space transformation in the image space domain (height, width) to extract the key feature information for classification. To obtain more useful information in the space domain and channel simultaneously, the two are serially combined to form a lightweight CBAM module.  
As illustrated in Figure 11, the CAM module is responsible for attention weight on the feature channel, while SAM is responsible for attention weight on the feature space. In the CAM module, the input feature map (8 \(\times\) 8 \(\times\) 166) is subjected to maximum and average pooling to obtain two 1 \(\times\) 1 \(\times\) 166 feature maps. These feature maps are subsequently input into a two-layer Multilayer Perceptron (MLP) to add and multiply each element. After activating the sigmoid function, the channel attention weight M\({}_{\text{C}}\) is obtained, which changes the weight of each channel. The channel attention weight is multiplied by the input feature map to obtain the input of the SAM module.  
In the SAM module, the multiplied feature map is first pooled by maximum and average, resulting in two 8 \(\times\) 8 \(\times\) 1 feature maps. A 3 \(\times\) 3 convolution is then used to further reduce the dimension of the feature map channel, resulting in an 8 \(\times\) 8 \(\times\) 1 feature map. The spatial attention weight M\({}_{\text{S}}\) is obtained after activation of the sigmoid function. Finally, the spatial attention weight is multiplied by the initial input feature map to obtain the feature map strengthened by the CBAM module.
## 4 Settings, Results and Discussion
### Ablation Studies
#### 4.2.2 Effectiveness of GU  
To verify the effectiveness of generalization uncertainty in the reduction of language bias, the authors of this paper conduct ablation experiments on VQA-CP v2. Two debiased methods, including Reweight (non-ensemble) and LMH (ensemble), are selected for verification. As shown in Table 4, the results show that, compared with LBSD without the generalization uncertainty constraint, LBSD with the generalization uncertainty constraint improves the performance by 0.27% and 0.63%, respectively, on Reweight and LMH. For the question types \"YES/NO\" and \"Other\" that are highly dependent on language bias, the generalization uncertainty constraint can be added to reduce the language bias of these question types.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Overall** & **Yes/No** & **Num** & **Other** \\ \hline Reweight & 40.06 & – & – & – \\ Reweight * & 40.02 & 45.09 & 12.30 & 44.96 \\ +**LBSD** & **40.99** & **46.34** & 12.55 & **45.98** \\ without GU & **41.28** & **47.07** & 12.30 & **46.20** \\ \hline LMH & 52.05 & 69.81 & 44.46 & 45.54 \\ +**LBSD** & **53.64** & **75.44** & 40.19 & **45.91** \\ +**LBSD** with & **54.27** & **75.49** & 44.02 & **45.96** \\ \hline \hline \end{tabular}
\end{table}
Table 4: VQA-CP v2: Ablation experiments of the generalization uncertainty method on the VQA-CP v2 dataset. * indicates the results from our reimplementation using officially released codes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Overall** & **Yes/No** & **Num** & **Other** \\ \hline UpDn [56] & 39.74 & 42.27 & 11.93 & 46.05 \\ +**LBSD** & **39.99** & 42.76 & 12.36 & 46.12 \\ \hline Bias Product & 39.93 & – & – & – \\ Bias Product * & 39.86 & 41.96 & 12.59 & 46.25 \\ +**LBSD** & **40.47** & 44.28 & 12.28 & 46.21 \\ \hline Reweight & 40.06 & – & – & – \\ Reweight * & 40.02 & 45.09 & 12.30 & 44.96 \\ +**LBSD** & **41.28** & 47.07 & 12.30 & 46.20 \\ \hline LMH & 52.05 & 69.81 & 44.46 & 45.54 \\ +**LBSD** & **54.27** & 75.49 & 44.02 & 45.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: VQA-CP v2: Ablation experiments of the LBSD method on the VQA-CP v2 dataset. * indicates the results from our reimplementation using officially released codes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Overall** & **Yes/No** & **Num** & **Other** \\ \hline UpDn [56] & 37.87 & 42.58 & 14.16 & 42.71 \\ +**LBSD** & **38.55** & **43.29** & 12.90 & **44.13** \\ \hline Bias Product * & 38.81 & 42.96 & 13.34 & 44.91 \\ +**LBSD** & **39.39** & **45.07** & 13.08 & 44.32 \\ \hline Reweight * & 41.46 & 61.52 & 13.02 & 32.94 \\ +**LBSD** & **43.66** & **66.63** & 12.23 & **33.45** \\ \hline LMH & 55.27 & 76.47 & 26.66 & 45.68 \\ +**LBSD** & **55.93** & 75.43 & **34.17** & 45.28 \\ \hline \hline \end{tabular}
\end{table}
Table 3: VQA-CP v1: Ablation experiments of the LBSD method on the VQA-CP v1 dataset. * indicates the results from our reimplementation using officially released codes.
## 4 Discussion
### The Role of CBCT in Paleoimaging  
Intraoral radiographic techniques, such as periapical and occlusal radiographs, have long been used for diagnostic purposes in dentistry. However, a correct diagnosis is sometimes impossible to achieve by using these techniques, mainly due to superimposing structural components. To overcome these shortcomings, more recently CBCT has been extensively used [31; 51; 52].  
CBCT has been previously used in assessing archaeological samples [53; 54], or for forensic purposes [55], mostly for the study of bone and dentition [56].  
CBCT in studying ancient human remains has been limitedly used so far, despite the important role of diagnostic imaging (paleoimaging) in paleopathology and anthropology [57; 58]. Multi-Slice Computed Tomography (MST) has proved its place in Paleoimaging, whereas CBCT, despite its advantages, is still not widely used as an imaging technique for the study of ancient bone remains [59].  
According to available literature data, to date, several papers reported the use of CBCT for paleopathological studies, describing diseases or malformations.  
The study of [PERSON] et al. [47] documented a case of hyperdontia using CBCT, namely a mandibular supernumerary distomolar in the mandible of a Chalcolithic male from Spain.  
[PERSON] et al. [60] used CBCT to provide a differential diagnosis of an exostotic bony lesion within the left maxillary sinus of a Romano-British (3 rd-4 th centuries AD) adult male from Newport, Lincoln. The conclusion was that, more likely, the lesion was of odontogenic origin.  
The case of dwarfism-related skeletal dysplasia in a Late Joseon Dynasty (South Korean) individual was reported by [PERSON] et al. [61]. In this case, CBCT was used to scan the available cranial fragments, in order to obtain a 3D reconstructed image.  
[PERSON] et al. [62] explored the skeletal remains of an individual, dating to the 10 th-12 th century AD from the parish church of S. Pietro in Pava, Siena, Italy, presenting multiple osteomata on the skull and long bones. CBCT confirmed that the lesions consisted of compact bone, supporting the diagnosis.  
[PERSON] et al. [63] reported the use of CBCT in assessing the Italian mummy of [PERSON], dating from 18 th century. The scan revealed that he suffered from atherosclerosis, affecting the abdominal arteries. For this reason, the arteries presented multiple calcifications, allowing to validly employ CBCT.  
[PERSON] et al. [59] used CBCT for studying an ancient child mandible discovered in an archaeological site in Northern Italy and made a comparison with MSCT in paleomaging, emphasizing its role in imaging studies of ancient bone remains and encouraging its broader application in studying archaeological samples.  
Based on the available literature data, CBCT represents a useful paleoimaging tool, for assessing mineralized human remains.
## 2 The Configuration of SFRM
### Ballast Tank and Attitude Adjusting System  
Our SFRM contains forward, aft ballast tanks, and an attitude adjusting system that allows the variation of mass, and movement of mass inside the model as shown in Figure 2. The designs of ballast tanks were maximised within the constraints of the hull geometry. The forward and aft ballast tanks are of the same design, approximately equal distance from midships, but each one can be independently controlled. The dimension of each ballast tank is 0.084 m \(\times\) 0.084 m \(\times\) 0.084 m, and the weight of the full tank is 600 g. In addition, both the inflow rate and the outflow rate are set to be 0.0002 m\({}^{3}\)/s. For the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SFRM** & \\ \hline Full length [m] & 2.335 \\ \hline Diameter [m] & 0.273 \\ \hline Bow length [m] & 0.47 \\ \hline Midsection length [m] & 1.26 \\ \hline Stern length [m] & 0.532 \\ \hline Total height [m] & 0.383 \\ \hline Sail length [m] & 0.197 \\ \hline Sail width [m] & 0.0358 \\ \hline \multicolumn{2}{c}{**Propeller**} \\ \hline Diameter [m] & 0.15 \\ \hline Number of blades & 4 \\ \hline Hub to diameter ratio & 0.292 \\ \hline Pitch to diameter ratio & 1.32 \\ \hline \multirow{2}{*}{Revolution speed [RFM]} & 370 at \(U=0.5\) [m/s] \\  & 785 at \(U=1.0\) [m/s] \\  & 1210 at \(U=1.5\) [m/s] \\ \hline \multirow{2}{*}{Effective Power [W]} & 0.17 at \(U=0.5\) [m/s] \\  & 1.51 at \(U=1.0\) [m/s] \\  & 4.44 at \(U=1.5\) [m/s] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Principal particulars of the SFRM and the propeller.  
Figure 1: (a) Side, and (b) rear view of the submarine free-running model (SFRM).  
attitude adjusting system, the trim angle and the heel angle of the SFRM can be controlled suitably by fine-tuning the locations of the lead weights. Overall speaking, the total mass, center of gravity (CG), trim angle and heel angle can be regulated suitably by using ballast tanks and the attitude adjusting system.
## 4 Results
### Targeting a Single Object Moving in a Specific Area on a Particular Path  
Take the scenario where the target audience is commuters traveling between specific two places as an example. With 5G data used, advertising can undoubtedly select the target conditions to be delivered accurately. Still, if you use GPS data to analyze the target, this delivery will be similar to random delivery as discussed in the previous cases. Relatively speaking, the probability of accurately hitting the target will drop significantly. We can discuss the case according to the situation of the target population and the total population in different proportions:  
CASE1: When the correct audience accounts for 10% of the total population  
Since this case is to designate commuters traveling between specific two places, the total number of audiences delivered under this requirement is generally unlikely to be very high. We assume that only 10% of people meet such conditions. Focusing on these targets is not practical. It is challenging to spend ten times the budget for total advertising exposure; even if the budget is not a problem, it will cause trouble to the other 90% of the non-audience.  
CASE2: When the correct audience accounts for 1% of the total population  
When the audience accounted for only 1% of the total population, only some business owners in some specific industries could use random placement for advertising exposure. It means that the profit ratio of the business is very high and can cover the high advertising budget by the vast profits from customers. Even for business owners who can have such profit, they generally do not use random placement for advertising exposure. There are other ways in many practical applications.  
When the ratio of the audience to the total population is too low, the owners will often advertise by filtering the area. Due to the use of the area as a filtering condition, the total number of audiences that need to promote is less, so the accuracy is improved. Although the final hit ratio is still the same as the full-scale delivery, this change saves the advertising budget and does not bore too many audiences. Hence, it becomes a compromise that can be implemented. Regardless of the ratio of the audience to the total population, there is always a significant performance gap for audience analysis between the use of GPS data and 5G data (see Table 21).
# Study on Mechanism of Series-Flow of Pollutants between Consecutive Tunnels by Numerical Simulation
## 2 Mathematical Model  
### Governing Equations  
The aim of the research is to reveal the innate law of flow field between consecutive tunnel portals. So in this paper, the influence of both natural wind and traffic wind on air motion and pollutant dispersion between tunnel portals were ignored, and the airflow temperature at all inlet and outlet boundaries was assumed to be 300 K.  
The airflow between consecutive tunnels portals can be seen as a steady turbulent flow of incompressible fluid [15]. The Reynolds time-averaged equations (i.e., RANS equations) [20] were used to describe the turbulent flow. Since RANS equations are highly non-linear, with more independent variables than the dimensions of the system, the Standard \(k\)-\(\varepsilon\) turbulence model was used to close the equation system. Its expression is as follows:  
\[\frac{\partial}{\partial x_{i}}(\rho ku_{i})=\frac{\partial}{\partial x_{j}} \Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{k}}\bigg{)}\frac{\partial k}{ \partial x_{j}}\Bigg{]}+G_{k}-\rho\varepsilon \tag{1}\]  
\[\frac{\partial}{\partial x_{i}}(\rho\varepsilon u_{i})=\frac{\partial}{ \partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{\varepsilon}} \bigg{)}\frac{\partial\varepsilon}{\partial x_{j}}\Bigg{]}+C_{1\varepsilon} \frac{\varepsilon}{k}G_{k}-C_{2\varepsilon}\rho\frac{\varepsilon^{2}}{k} \tag{2}\]  
where both \(i\) and \(j\) range from 1 to 3 independently; \(x\) stands for the space coordinates; \(k\), \(\varepsilon\), \(\rho\), \(u\), and \(\mu\) are the turbulent kinetic energy, turbulent dissipation rate, air mass density, air velocity, and dynamic viscosity coefficient, respectively; \(\mu_{t}=\rho C_{\mu}k^{2}/\varepsilon\), is turbulent viscosity; the model coefficients \(C_{\mu}\), \(\sigma_{k}\), \(\sigma_{\varepsilon}\), \(C_{1\varepsilon}\) and \(C_{2\varepsilon}\) are 0.09, 1.0, 1.3, 1.44 and 1.92 respectively [21]; \(G_{k}\) refers to the turbulent kinetic energy caused by the average velocity gradient, its expression is:  
\[G_{k}=\mu\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{j}}\bigg{)}\frac{\partial u_{i}}{\partial x_{j}} \tag{3}\]The species transport equation was used to describe the pollutant dispersion. Considering that the concentrations of pollutant components are scalars and have almost a same normalized distribution. So, CO was used as tracer gas. The species transport equation ignoring buoyancy-driven effects caused by density difference of varied gas components is shown as Equation (4):  
\[\frac{\partial(\rho c_{s}u_{i})}{\partial x_{i}}=\frac{\partial}{\partial x_{j}} \bigg{[}D_{s}\frac{\partial(\rho c_{s})}{\partial x_{j}}\bigg{]}+S_{s} \tag{4}\]  
where \(S_{s}\) is the production rate, i.e., the mass of \(s\) produced by chemical reaction per unit time and volume, \(D_{s}\) and \(c_{s}\) represent the diffusion coefficient and volume density of \(s\), respectively. RANS equations and Equations (1), (2) and (4) were the governing equations for the air motion and pollutant dispersion between the consecutive tunnels.
# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 3 Results
### Benchmark Evaluations
#### 3.2.1 Validation on the Full-Reference Dataset LOL  
The LOL dataset contains real captured images, encompassing 485 pairs of normal-light and low-light images for training, as well as an additional 15 image pairs for testing. We used 15 low-light test images stemming from the LOL dataset for validation. For the quantitative assessment of our methods, we employed PSNR, SSIM [31], and LPIPS [37] as evaluation metrics. The numerical evaluations, along with the visual results, are presented in Figure 4 and Table 1 for a comparative analysis.  
As depicted in Figure 4, our method exhibited reduced noise and color shifts, yielding superior visual outcomes when juxtaposed with the contemporary SOTA zero-reference method, namely, Zero-DCE [13]. Furthermore, our method showed comparable performance with the SOTA full-reference method in terms of visual quality.  
In Table 1, an upward arrow (\(\uparrow\)) signifies that the larger value is indicative of superior quality, while a downward arrow (\(\downarrow\)) suggests the opposite. The bold highlights the best result among the full-reference methods, and the red designates the best result among the zero-reference methods. As shown in Table 1, our method achieved a more advanced performance compared with existing zero-reference image-enhancement methods. Compared with the SOAT zero reference image-enhancement method Zero DCE, our method achieved a PSNR of +4.596 gain, an SSIM of +0.224 gain, and an LPIPS of \(-0.100\) gain. Higher PSNR values indicate that our method can suppress artifacts and better reflect color information. Higher SSIM values indicate that our method better preserves structural information with high-frequency details. As for LPIPS, our method also achieved better performance. However our method failed to achieve better performance compared with the partial full-reference methods KinD++ [38] and LLFlow [39], which rely on a large amount of pairwise data and reference images to train the model. Our method has a wider range of application scenarios than the above methods, which rely on full-reference image data for training.  
To further demonstrate the advantages of our method in other aspects, we compared it with other advanced non-full-reference methods, namely, RetinexNet [31], EnlightenGAN [10], and Zero-DCE [13], in terms of runtime, and the results are shown in Table 2.  
According to the results in Table 2, our method had a faster running speed compared with other non-full reference methods. Although it ran slower than the SOAT zero-reference method Zero-DCE, our method achieved a balance between enhancement and running speed.
## 2 Materials and Methods
### Density and Porosity Test  
A hydrostatic balance was used to apply the Archimedes thrust principle, collected by the ISRM [3]. The dry mass (\(m_{dry}\)) was determined after having the specimens in an oven at 105 \({}^{\circ}\)C for 24 h, and the saturated mass (\(m_{sat}\)) after 48 h immersed in water, while the immersed mass (\(m_{sub}\)) was obtained by immersing them already saturated to consider the specimen as a solid. The dry density (\(\rho_{dry}\)), saturated density (\(\rho_{sat}\)), and effective porosity (\(n_{eff}\)) were calculated from Equations (33)-(35), respectively:  
\[\rho_{dry}=\frac{m_{dry}\,\rho_{w}}{m_{sat}-m_{sub}}, \tag{33}\]  
\[\rho_{sat}=\frac{m_{sat}\,\rho_{w}}{m_{sat}-m_{sub}}, \tag{34}\]  
\[n_{\rm eff}=\frac{m_{sat}-m_{dry}}{m_{sat}-m_{sub}}100, \tag{35}\]  
where \(m_{dry}\) is the dry mass (g), \(m_{sat}\) is the mass of the saturated specimen (g), \(m_{sub}\) is the mass of the immersed specimen (g), and \(\rho_{w}\) t is he density of water (g\(\cdot\)cm\({}^{-3}\)).
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 6 Open Issues
### Controller Placement Problem  
The controller placement problem is crucial in minimizing delay in SDN-based VANETs. By installing local controllers in ideal locations and distributing the workload, individual controllers can handle the traffic efficiently. Using queueing models, delays over wired and wireless links can be assessed and minimized while controllers are located. In order to describe the finite queue of each vehicle OBU and RSU, the packet arrival rates follow a Poisson distribution with a mean packet arrival rate. The controller placement problem in an SDVN is complicated by various factors, and the trade-off between the controller number and latency is one of them. If too few controllers are used, the delay experienced by OBUs located far away from the controllers may increase, which is undesirable in delay-sensitive VANET applications. At the same time, deploying too many controllers may increase the overhead and result in increased latency due to the communication delay among the controllers.
# All-Optical Modulation and Ultrafast Switching in MWIR with Sub-Wavelength Structured Silicon
## 2 Methods and Samples  
A femtosecond laser system was used to measure the time-resolved reflectivity of the optically-excited samples. The system delivered 60-fs pulses with a 2.7-W average power at an 800-nm wavelength and 1-kHz repetition rate. The pulses were nearly of a Gaussian shape as measured by the intensity autocorrelator [21]. The beam was then split into a ratio of 10/90 by a beam splitter. The low-intensity part was used as the pump beam, while the high-intensity part was directed into an Optical Parametric Amplifier (OPA) to generate the signal beam with a tunable wavelength in the range between 4 and 6 \(\upmu\)m. According to the manufacturer (Coherent), the pulse duration of MWIR signal was about 100 fs. A retro-reflector on a motorised linear stage was used to control the pump arrival time at the sample surface with respect to the signal pulses. The pump beam was set to be \(p\)-polarised while the signal beam was \(s\)-polarised to avoid any possible interference. The incident angles of the signal and pump beam at the sample surface were 20\({}^{\circ}\)and 30\({}^{\circ}\), respectively. The reflected MWIR beam was directed to a home-built monochromator, providing a resolution of 10 nm, to monitor the spectral response. A Mercury Cadmium Telluride (MCT) detector was placed at the exit of the monochromator equipped with a pre-amplifier and a lock-in amplifier to collect the IR signal with a relatively low signal-to-noise ratio of less than 3%.  
The sub-wavelength structured silicon membrane was used as the modulator material in this work. It was made by photoelectrochemical etching of silicon in Hydrofluoric (HF) acid using a procedure similar to that described elsewhere [22; 23]. In this procedure, an ordered hexagonal 2D array of holes was etched through the bulk silicon membrane. The thickness of the sample was about 15 \(\upmu\)m, with 0.5 \(\upmu\)m holes separated by 1.5 \(\upmu\)m, as shown in the insert to Figure 1b. Bulk silicon (similar membranes without holes) was used as a reference to compare the results with the structured membranes.
## 3 Results and Discussion
### Low-Frequency Noise Exposure and Its Main Health Effects  
Table 1 presents a synthesis of the 39 articles based on three of the categories listed in the methodology section. These categories were as follows: cases of low-frequency noise studies in a small population, in a large population, and in a population of animals.  
Of the 39 articles that were included in the evaluation of this article, it was observed that the main effects on human health are more prevalent in aspects such as sleep disorders (11.7%), discomfort, sensitivity and irritability to noise (10%), annoyance (13.3%), stress (6.7%), hearing loss (8.3%), reduced performance/fatigue (5%), heart rate/cardiovascular diseases (10%), tension and blood pressure (6.7%), anxiety (1.7%), depression (3.3%), imbalance (3.3%), and mental performance (6.7%).  
There were also other effects on human health but with an incidence in very specific aspects (13.3%), such as the frequency of chromosomal aberrations in bone marrow cells, excess bilirubin, peptic ulcers (gastric and duodenal), effects on the cerebral blood barrier, haemodynamic events, irreversible imbalance with structural damage to the otocomial membrane, tinnitus and sound reconversion therapy, and vocal disorders and effort.  
Only the effects on human health related to sleep disturbance, noise discomfort, annoyance, hearing loss, and cardiovascular disease were analysed, as these were the themes where a greater number of articles were observed, thus allowing a better comparison and evaluation between the various articles.