esa-sceva/satcom-chunk-collection huggingface dataset:
# Bistatic Landmine and IED Detection Combining Vehicle and Drone Mounted GPR Sensors
## 2 Methodology  
### Scenario  
Ray-tracing and FDFD methods are used to simulate a 2D GPR scenario, such as the one shown in Figure 2, with a target buried in the soil. In this scenario, there are \(T\) transmitters (TX) placed at positions \(\mathbf{r}_{t}\) (\(t=1,...,T\)) and, for each transmitter, there are \(R\) receivers (RX) at positions \(\mathbf{r}_{t}^{t}\) (where  
Figure 1: Scheme of the novel GPR system combining FLGPR and DLGPR.  
subindex \(r=1,...,R\) denotes the receiver and superindex \(t\) the transmitter). The soil is characterized by its relative permittivity \(\epsilon_{rs}\) and its conductivity \(\sigma_{s}\). The target is assumed to have a circular shape, with radius \(\delta_{tg}\) and centered at coordinates \((x_{tg},y_{tg})\). It is characterized by its constitutive parameters \(\epsilon_{rtg}\) and \(\sigma_{tg}\). The simulation is performed at \(N\) frequencies, assuming either TE (Transverse Electric) or TM (Transverse Magnetic) polarization.
###### Abstract  
The reliability of the communication link is quite common and challenging to handle as the topology changes frequently in vehicular ad hoc networks (VANETs). Another problem with VANETs is that the vehicles are from different manufacturers. Hence, the heterogeneity of hardware is obvious. These heterogeneity and reliability problems affect the message dissemination in VANETs. This paper aims to address these challenges by proposing a robust routing protocol capable of ensuring reliable, scalable, and heterogeneity-tolerant message dissemination in VANETs. We first introduced a hybrid hierarchical architecture based on software-defined networking (SDN) principles for VANETs, leveraging SDN's inherent scalability and adaptability to heterogeneity. Further, a road-side unit (RSU)-assisted cloud-based location-aware hybrid routing for software-defined VANETs (SD-VANETs) that we call RC-LAHR was proposed. RC-LAHR was rigorously tested and analyzed for its performance in terms of packet delivery ratio (PDR) and end-to-end delay (EED), along with a comprehensive assessment of network traffic and load impacts on cloud infrastructure and RSUs. The routing protocol is compared with state-of-the-art protocols, Greedy Perimeter Stateless Routing (GPSR) and Opportunistic and Position-Based Routing (OPBR). The proposed routing protocol performs well as compared to GPSR and OPBR. The result shows that the EED is reduced to 20% and the PDR is increased to 30%. The network reliability is also increased up to 5% as compared to the OPBR and GPSR.  
cloud-based VANET; SDN; SD-VANET; software-defined networking; VANET 202424045. [[https://doi.org/10.3390/s24041045](https://doi.org/10.3390/s24041045)]([https://doi.org/10.3390/s24041045](https://doi.org/10.3390/s24041045))
## 3 Methodology
### Methodology for Classifying MTSA-Related Accidents
#### 3.2.2 Classification of MTSA-Related Accidents Based on Accident Types and Occurrence Locations  
Marine accidents can be broadly categorized into human and environmental categories. MTSA is related to environmental factors, which refer to the physical and systematic conditions required for vessel navigation.  
In this study, we focused on quantitatively deriving the reduction in marine accidents using MTSA. Therefore, accidents related to human elements indirectly associated with MTSA and incidents unrelated to vessel operations, such as equipment damage, were excluded. Consequently, accident types considered amenable to reduction through MTSA were classified into seven categories: collision, minor collision, grounding, buoy fouling, navigation obstruction, submersion, and damage to the steering gear; these are shown in Table 2.  
Among these categories, minor collisions represent the most direct accidents involving collisions related to MTSA resulting in facilities, whereas the remaining six accidents are considered to be relatively indirect.  
However, not all seven types of marine accidents can be classified as MTSA-related. Spatial connections, including the location of marine accidents and the distance from the marine development project site, should be carefully considered. Thus, to identify MTSA-related accidents based on their location relative to the MTSA-assessed project site, the following criteria were established:  
* Initially, the grouping of accidents was based on their distance from the marine development project site or land-based structures.
* Within each group, to identify MTSA-related accidents, a detailed examination of the accident locations was conducted based on the criteria outlined below.  
Further details on this process are provided in Section 4.2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classification** & **Discrimination Criteria** \\ \hline Common & The distance from the marine development project site or \\  & land structures is within 3 NM \\ \hline Collision & The accident occurred in front of the project site \\ \hline Minor collision & Direct contact with the project site or nearby \\  & navigational marks \\ \hline Grounding & The accident occurred owing to non-dredging shallow \\  & waters near the project site \\ \hline Buoy fouling & The accident occurred because of unremoved hazards near \\  & the project site \\ \hline Navigation obstruction & The accident occurred because of non-dredging shallow \\  & waters near the project site \\ \hline Submersion & Submersion occurred at or near the project site \\ \hline Damage to steering gear & There was steering gear failure at or near the project site \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification of MTSA-related accidents based on the location of marine accidents.
## 2 Therapeutic Applications of Peptides
### Oral Administration of Peptide-Based Medicines  
Finally, for peptide therapeutics, a zealous approach to the synthesis of orally administered peptide medicines was performed using a multilamellar delivery system for the co-administration of an anti-diabetic peptide, glucagon-like peptide-1 (GLP-1), along side another molecule, dipeptidyll peptidase 4 inhibitors (DPP4i) [60]. Currently, the only administration route for GLP-1 is parenteral, which leads to lower patient medicine adherence [61]. The natural degradation time for GLP-1 post-administration is approximately 2 min [62] due to rapid degradation by the enzyme dipeptidyl peptidase 4. Upon formulation into the dual-action functionalized NPs, hypoglycemic effects of the GLP-1 could still be observed 6 h after administration.  
The synthesis of this formulation is complex, requiring multiple stages and multiple vessel materials: An initial NP was fabricated from PLGA and mesoporous silicon (PSi). This NP had additional surface functional groups of chitosan (CS) (to aid mucoadhesion) and cell-penetrating peptides (CPP), which assist the delivery of NP contents through the lipid bilayer of a cell. This NP contained the API GLP-1 and can be seen in Figure 4. MFs were then employed to further encapsulate this NP within an enteric coating material, hydroxypropylmethylcellulose acetyl succinate (HPMC-AS). The DPP4 was only added upon final encapsulation alongside the HPMC-AS, which protected the two APIs against the inhospitable conditions exhibited upon oral administration. As research into orally administered peptide-based medicines is developed, a wide range of new patient-friendly, peptide-based medicines could begin to emerge. According to a 2019 study, parenterally administered medicines consistently had greater non-adherence rates than oral formulations [61], potentially leading to a lower quality of life.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Application Areas
#### 4.2.2 Surgical Services and Operating Rooms  
Operating rooms pose distinct logistical challenges that DES has proven effective in addressing. [PERSON] et al. [38] documented implementations aimed at optimizing surgical scheduling, managing recovery room capacity, improving staff allocation, and enhancing equipment utilization. Their analysis of a major surgical department revealed that DES-based scheduling strategies increased operating room utilization by 18% while reducing overtime by 32%, demonstrating the potential for simulation to enhance both efficiency and cost-effectiveness in surgical services.
# A Review of Transformer-Based Approaches for Image Captioning
## 5 Vision-Language Pre-Training in Captioning
### VLP in Image Captioning
#### 5.2.5 Scaling Up Vision-Language Pre-Training for Image Captioning  
In this work, [PERSON] et al. [52] study the scaling behavior of vision-language pre-training for image captioning. They develop a large-scale image captioner, called LEMON, based on the transformer architecture and scale the size from 13 million parameters all the way to 675 million parameters. To carry out the training, they develop ALT200M, a large-scale dataset that contains up to 200 million image-text pairs and that is based on the _all_ attribute of images from the internet. Pre-training is carried out on ALT200M followed by fine-tuning on MS COCO. They use subsets of ALT200M of various sizes to study the effects of scaling the dataset. The reference model they use is VinVL [39]; as such, they do not develop any new model. They evaluate the model using Nocaps [57], which assesses the effectiveness of the approach in regard to out-of-domain objects. They also test their model on the MS COCO Karpathy test split [27] as well as Conceptual Captions [59].  
Their findings show that the performance of captioning models generally improve with an increase in the model size as well as the dataset size. More specifically, they show that larger models benefit more when the dataset size is beyond a certain threshold. In their experiments, they find that with only 3 million image-text pairs for pre-training, the performance plateaus early even as the model size increases. This plateauing effect is not observed (for the model sizes they look at) when there are more than 10 million image-text pairs for pre-training, i.e., with sufficient data, the performance keeps improving as the model size increases. This implies that the model capacity is the performance bottleneck, which suggests that training even larger models could push farther the limits of VLP for captioning tasks.
## 5 Results and Discussion
### Effect of Adding a Constant
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**MDS** & **Stability** & **MMD** & **UMD** & **BMD1-m** & **BMD1-av** & **cBMD1** \\ \hline mMDS & STvr & 3.04 & 2.64 & 5.87 & 11.6 & 4.89 \\ mMDS & STov & 0.97 & 1.12 & 2.98 & 6.70 & 0.60 \\ mMDS & STma & 0.41 & 1.0 & 6.03 & 8.48 & 2.65 \\ nMDS & STvr & 0.01 & 0.01 & 0.65 & 1.25 & 0.46 \\ nMDS & STov & 0.11 & 0.08 & 0.61 & 0.94 & 0.42 \\ nMDS & STma & 0.27 & 0.18 & 1.31 & 2.27 & 0.62 \\ PCoA & STvr & 0.23 & 0.21 & 1.41 & 3.07 & 0.28 \\ PCoA & STov & 0.55 & 0.65 & 0.06 & 0.78 & 0.34 \\ PCoA & STma & 0.8 & 0.67 & 1.06 & 1.83 & 0.54 \\ \hline \end{tabular} Key: BMD1-m and BMD1-av denote values calculated when the added constant is the minimum and the average distance, respectively.  
\end{table} 
Table 2: As in Table 1 for the dataset NO1. Figure 6: Cluster probabilities obtained from the confidence ellipses of mMDS, nMDS, and PCoA using the distance measure MD1 and the WHI dataset. The added constant to the distance values is equal to 0 (\(\bullet\)) and the average of the MD1 values (o). From all these tests, we obtained the following: In the datasets we studied, when the added constant is the minimum of the distances, the effect on the uncertainty in MDS is overall small irrespective of the MDS technique used. With the increase in the value of the added constant from the minimum value to the average value of the distances, alterations in the confidence ellipses may be detected. However, in this case, the effect of the added constant depends on the MDS technique used. In particular, this is almost insignificant for the nonmetric nMDS technique; it is rather small for the PCoA and becomes significant when using the metric mMDS. Note that the corMDS is not affected by adding a fixed value to the input distances. Noting that for the application of MDS with corrected Euclidean and Mahalanobis distances, as well as when using MMD and UMD, we add the absolute minimum value of the distance, we conclude that, in general, we can use any of the MDS techniques to construct confidence ellipses. However, since we cannot rule out the possibility of an extreme negative value, choosing nMDS, corMDS, or even PCoA, where the latter runs via the _cmdscale()_ function of R, is safer.
## 4 Experimental Verification
### Case Study II: According to Different Fault Location  
#### 4.2.1 Data Introduction  
To further prove the effectiveness of the proposed method in distinguishing different fault parts, the previously measured test data were regrouped according to the different fault locations. The general idea is that different degrees of failure of one of the two fault locations of each part at different speeds were combined with normal trouble-free data, which resulted in two datasets (Table 3) with 10 health conditions each. For example, we can make a group of three sets of crack data on the right side of the smaller rotor, three sets of fault data of the inner ring of the bearing, and three sets of fault data of the planet wheel and normal fault-free data. The remaining three sets of crack data on the left side of the larger rotor, three sets of failure data of the bearing outer ring, three sets of failure data of the sun wheel and normal trouble-free data are made into the other group. Again, the normal failure-free data is Shared by both sets of data, so there are still 19 different health conditions. The selection of parameters is the same as before.
## 4 Simulation Experiments and Discussion
### Experiment 2: Analysis of the Reflector TDE Impacts on ABP Accuracy
#### 4.4.1 Analysis for Thermal Misalignment Errors
Among the thermal misalignment errors, \(\psi_{\text{SSR},ma}\), \(\psi_{\text{FSR},ma}\), and \(\psi_{\text{MR},ma}\) had no impact on the thermally induced BPE. For the remaining thermal misalignment errors, \(\varphi_{\text{MR},ma}\) and \(\theta_{\text{MR},ma}\) had the highest impacts on the BPE, followed by \(\varphi_{\text{FSR},ma}\) and \(\theta_{\text{FSR},ma}\), while \(\varphi_{\text{SSR},ma}\) and \(\theta_{\text{SSR},ma}\) had the lowest impacts. Specifically, the impacts of \(\varphi_{\text{MR},ma}\) and \(\theta_{\text{MR},ma}\) were by one order of magnitude substantially higher than those of \(\varphi_{\text{FSR},ma}\), \(\theta_{\text{FSR},ma}\), \(\varphi_{\text{SSR},ma}\), and \(\theta_{\text{SSR},ma}\) at the same level. 2. Once the scan azimuth of the ECF (i.e., \(\phi_{q}\)) was specified, there was an approximately positive linear relationship between the thermal misalignment error and the resulting BPE (i.e., as the thermal misalignment error increased, the resulting BPE tended to increase as well). On the other hand, once the thermal misalignment error level was  
Figure 6: The impact of the roll misalignment angle \(\varphi_{\text{PSR},ma}\) of the FSR on the BPE. (**a**) The variation of the BPE with respect to the ECF scan azimuth at some fixed levels of \(\varphi_{\text{FSR},ma}\). (**b**) The variation of the BPE with respect to \(\varphi_{\text{FSR},ma}\) at some fixed ECF scan azimuths. specified, most thermal misalignment errors (except for \(\varphi_{\text{MR},ma}\)) led to sinusoidal BPEs over a scan period of the ECF. The fluctuation range of the BPEs was generally in the magnitude of \(\sim\)10\({}^{-1}\)\(\upmu\)rad with a small thermal misalignment error; however, as the thermal misalignment error increased, the fluctuation of the BPEs would gradually become pronounced, and the fluctuation range could reach the magnitude of \(\sim\)10 \(\upmu\)rad. Figure 7: The impact of the roll misalignment angle \(\varphi_{\text{MR},ma}\). of the MR on the BPE. (**a**) The variation of the BPE with respect to the ECF scan azimuth at some fixed levels of \(\varphi_{\text{MR},ma}\). (**b**) The variation of the BPE with respect to \(\varphi_{\text{MR},ma}\) at some fixed ECF scan azimuths.
# A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage
## 3 Defect Detection Method
Due to the small defect, the magnetic dipole model can be introduced to simplify Equation (2) [18]:  
Figure 1:3  
Take the derivative of Equation (8) to \(x\) and set it equal to 0:  
Figure 1:4  
Equation (9) is solved, and it is obtained that \(H_{z}(x,\ z_{0})\) is the minimum when \(x=\sqrt{a^{2}+z_{0}}^{2}\) and \(H_{z}(x,\ z_{0})\) is the maximum when \(x=-\sqrt{a^{2}+z_{0}}^{2}\). Therefore, if the auxiliary sensors 3 and 4 are distributed in front of and behind the main sensor and the distance is \(\sqrt{a^{2}+z_{0}}^{2}\), the signal amplitude difference between the main sensor and the auxiliary sensor is the largest when the main sensor is directly above the defect. From the above derivation, the optimal distance from the auxiliary sensor to the main sensor is related to the size of the measured defect. In the actual measurement, the size of defects is different. Since the signal of small defects is usually weak, to ensure the detection effect, the distance between sensors is calculated according to the minimum defect size that should be detected. In this paper, defect identification is carried out in two steps. First, the RMS of the main sensor signal is calculated as the threshold. The signal of the main sensor greater than the threshold is preliminarily determined as a defect. Otherwise, many sampling points need to be considered, and the amount of calculation is large. Then, the defects detected in the first step are further considered by the relative value of the sensors signals. A defect is considered to exist at \(l_{0}\) if conditions 1 and 3 are satisfied, or conditions 2 and 3 are satisfied; Otherwise, there is no defect at \(l_{0}\). **Condition 1.**\(H_{Z}(l_{0})=0\). **Condition 2. **\(H_{x}(l_{0})\) is the maximum value. **Condition 3. **\(H_{x}(l_{0})>H_{x1}(l_{0}-l_{1}),H_{x}(l_{0})>H_{x2}(l_{0}+l_{1}),H_{z}(l_{0})<H_{ z3}(l_{0}-l_{2}),H_{z}(l_{0})>H_{z4}(l_{0}+l_{2})\). \(l_{1}=a+\sqrt{bz_{0}+z_{0}}^{2}\) is the distance from auxiliary sensors 1 and 2 to the main sensor, and \(l_{2}=\sqrt{bz_{0}+z_{0}}^{2}\) is the distance from auxiliary sensors 3 and 4 to the main sensor. Due to the random interference in the actual detection, signals without defects may meet the above conditions, resulting in misjudgment. To improve accuracy and reduce the computation, a threshold is set. Root mean square (RMS) can not only represent the significance of the average, but also indicate the dispersion degree of the data sample. In the absence of defects, the signal amplitude is almost the same, and the RMS is small. If the signal dispersion of defects is large, the RMS is large. Therefore, RMS is used as a threshold to eliminate the influence of the noise. Signals greater than the threshold can be initially identified as defects, and then use the above conditions to determine defects further.
# A Reinforcement Learning Approach to Speech Coding
## 8 Control Policy  
A large part of this investigation, and one that highlights the power of the reinforcement learning approach to speech coding, is the selection of a Control Policy. We impose an average rate constraint of 1.5 bits/sample during active voice regions and code the Silence regions with a much simplified, relatively standard approach. The active voice regions are thus of primary interest in this paper. The bit rate constraint corresponds to an average energy or average power constraint somewhat directly, since an increased speech coding bit rate requires a higher transmitted bit rate over the channel. Because of this direct mapping, rather than bring in the modulation and communications ideas, we focus on speech compression since many different techniques can be used for transmission over a physical channel.
# Developing the Smart Sorting Screw System Based on Deep Learning Approaches
## 1 Introduction
The main idea of the AAE model is to generate fake images that are similar to the original input image through the process of minimizing the differences between the input and output image, in order to detect the defective region. Although numerous anomaly deep learning networks have achieved remarkable success, the existing studies mainly focus on the performance of deep learning networks in relation to defect detection applications. Yet, seldom research has considered the application of anomaly network algorithms in defective screw systems. Therefore, the proposed smart sorting screw system employs anomaly deep learning networks of two classical models and IoT technology to detect the defective screws. The research contribution can be summarized as follows:  
1. A template matching algorithm is utilized to expand the curved screw surface images into panoramic images, which can comprehensively and automatically detect the defective surface of the spiral screw. 2. A novel anomaly detection method running on convolutional autoencoder and adversarial autoencoder networks is utilized to automatically recognize the defective areas without the benefit of defective images for model training. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Approach** & **Method** & **Strengths** & \multicolumn{2}{c}{**Weaknesses**} \\ \hline \multirow{4}{*}{Supervised-based classifier} & Classification network: CNN & Fast training and inference & 1. & Requires numerous abnormal \\  & & Fast training and inference; & & and normal images for training \\  & & high performance of & 2. & Hard to collect abnormal \\  & & defect localization & & images in practical situations \\  & & Object detection network: & Identify the defect with the & 3. & Tedious manual \\  & YOLO, Faster R-CNN & bounding boxes & & annotation work \\ \hline \multirow{3}{*}{Unsupervised-based classifier} & Convolutional Autoencoder; & Model training without & 1. & Imprecise defect localization \\  & Adversarial Autoencoder & annotation; requires only & & with poor reconstruction \\  & Adversarial Autoencoder & positive datasets for & 2. & A large amount of clean normal \\  & & model training & & data are needed to obtain \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The comparison between supervised learning and unsupervised learning. To improve the process of deep learning training, the IoT technology is introduced to the defective screw detection system, which can upload images to a cloud platform for more efficient model training. The remainder of this research is organized as follows. The proposed method is presented in Section 2, which contains the structure of the proposed system, an image stitching technique, and two anomaly detection techniques. The experiment and discussion in this study are illustrated in detail in Section 3. Finally, the conclusions drawn from of this study are provided in Section 4.