esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection Performance Evaluation for Autonomous Vehicles in Sandy Weather Environments
## 2 Background and Related Work
### Object Detection  
Detection objects using CNNs can be divided into two approaches: the one-stage approach and two-stage approach. The two-stage approach was initially introduced in 2013 when the Region-Based CNN (R-CNN) model was developed. The first stage of the model is called region proposal, which aims to find the regions in the image that contain objects. R-CNN generates approximately 2000 region proposals. The second stage in the CNN is generally the extraction of features from the proposed regions followed by object classification. Because with every proposed region a different network is used, R-CNN remained slow. A new version of R-CNN was introduced in the following year named Fast R-CNN, which aims to address the slowness of R-CNN. Fast R-CNN does not pass 2000 proposals to the CNN; instead, it passes the entire image, and then a feature map is generated, which significantly improves the speed of detection. Another version of the R-CNN family called Faster R-CNN followed. In 2017, a remarkable improvement was achieved when Mask R-CNN was introduced [20]. Mask R-CNN uses Feature Pyramid Network (FPN) [21] as its backbone and adds a new phase to the process of detection which is a segmentation mask for every object.  
The one-stage object detection approach was first introduced by [PERSON] et al. [22] with the YOLO model. The whole process of detection is encapsulated within a single pass to the CNN network. This model was a remarkable milestone for object detection since it provides both high-speed detection for high-FPS scenes (suitable for real-time detection) and good accuracy. After the first YOLO, a new model (YOLOv2) followed, which was trained on PASCAL Visual Object Classes (PASCAL VOCs) and Common Objects in Context (COCO) datasets and achieved a mAP of 76.8 on VOC 2007. These incremental improvements continued with YOLOv3 where a new backbone called Darknet-53 was proposed [23]. YOLOv4 was developed to improve the average precision and frames per second by approximately 10% and 12%, respectively, compared to the previous YOLO model [24]. YOLOv5 and then YOLOv7 in 2022 [25] followed. The authors of [26] proposed a new one-stage approach called Single-Shot multibox Detector (SSD). When SSD received an image input sized 300 \(\times\) 300, it achieved a mAP of approximately 74.3% on the VOC 2007 dataset. This score improved to 76.9% when the authors increased the size of the image to 512 \(\times\) 512.
## 3 Results
### Spatial and Temporal Variations of Land Use Type in the Sanjiang Plain from 2000 to 2015
#### 3.1.2 Spatial Variations  
The entire study area exhibited relative stability in terms of land use type (Figure 4), and the cultivated land area increased gradually, while the forestland area and wetland area decreased gradually. The northeastern and central regions of the study area were the major areas with decreased wetland, and the area with a larger reduction in forestland was mainly located in the northeastern region (Figure 5). The main spatial variations of land use type were the transformations from wetland to cultivated land and from forestland to cultivated land; the corresponding areas were 0.14 \(\times\) 10\({}^{4}\) km\({}^{2}\) and 0.11 \(\times\) 10\({}^{4}\) km\({}^{2}\), respectively (Table 3). Therefore, exploiting forestland and wetland were the primary methods to develop cultivated land, and cultivated land area increased greatly in the northeastern and central parts of the Sanjiang Plain from 2000 to 2015. Moreover, areas with grassland, water, and residential areas exhibited no obvious variations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Year** & **Cultivated Land (\%)** & **Forestland (\%)** & **Grassland (\%)** & **Water (\%)** & **Residential Land (\%)** & **Wetland (\%)** \\ \hline
2000 & 48.29 & 33.05 & 3.89 & 4.78 & 1.92 & 8.07 \\
2005 & 48.64 & 32.96 & 3.99 & 4.82 & 1.92 & 7.67 \\
2010 & 49.27 & 32.66 & 3.95 & 4.83 & 1.92 & 7.37 \\
2015 & 50.61 & 32.10 & 3.85 & 4.82 & 1.92 & 6.70 \\ \hline \end{tabular}
\end{table}
Table 2: Temporal variation of land use type.  
Figure 4: Spatial distribution of land use type.
## 2 System Model
### Network Model  
The local network of VLC-UDNs is shown in Figure 1b, which reflects the overlapping relationship among cells. It consists of four regularly arranged cells and indicates the characteristic of high AP density. The coverage radius of a single AP is \(R_{c}=L\tan(\varphi_{FOV})\). The four APs form the square \(O_{1}O_{2}O_{3}O_{4}\) and \(O\) is the center. Furthermore, \(D_{c}\) denotes the distance between APs. The design of the proposed algorithm is based on the high AP density scenario, which satisfies \(D_{c}\leq R_{c}\). When the condition \(D_{c}\leq R_{c}\) is satisfied, the seamless illumination coverage is achieved in the VLC-UDNs and each terminal would receive an interference signal.
# Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection
## 3 Parametric Study
### Effect of the Length w\({}_{6}\)  
The effect of variation of w\({}_{6}\) on reflection coefficient is shown in Figure 3. The value of w\({}_{1}\) is fixed to 11.5 mm. In 2 mm increments, the value of w\({}_{6}\) is changed from 2.5 to 8.5 mm. The higher resonant frequency is clearly reliant on w\({}_{6}\), whereas the lower resonant frequency is not. Hence, the factor of tuning comes into picture where one factor (one dimension) at a time is taken into consideration during optimization. The higher resonant continues to degrade as the value of w\({}_{6}\) increases from 2.5 mm to 8.5 mm, until it is completely lost. As a result, the final optimized w\({}_{6}\) value obtained is 2.5 mm.  
Figure 3: Return loss variation versus frequency for different values of w\({}_{6}\).  
Figure 2: Return loss variation versus frequency for different values of w\({}_{1}\).
## 2 Materials and Methods  
### The Sample  
In this retrospective study, pre-treatment and post-treatment cephalometric variables were measured on lateral skull radiographs from 20 SDB pediatric patients (nine males and 11 females) with mixed dentition, aged between 6 and 9 years (mean age 7.61 \(\pm\) 0.6), treated with rapid maxillary expander. All patients were treated in the private orthodontic practice of one of the authors (F.M.S., Teramo, central Italy). Statistical data were compared with a control group of 20 patients without SDB (nine males and 11 females) with mixed dentition, aged between 6 and 11 years (mean age 8.4 \(\pm\) 0.5), treated with rapid palatal expansion for their malocclusion, in the same private practice.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (or Ethics Committee) of University of L'Aquila (protocol code DR206/2013, dated 10 January 2014). Informed consent was obtained from all subjects involved in the study.  
The case inclusion criteria were as follows: (1) clinical history of SDB (episodes of sleep apnea, habitual snoring, and/or states of restlessness during sleep, as reported by parents); (2) patients who achieved considerable clinical improvements of SDB after the orthodontic treatment of maxillary expansion; (3) patients who initially showed a malocclusion with necessity of maxillary expansion (subjects with cross bite or palatal constriction); (4) patients with mixed dentition with erupted permanent central incisors before the beginning of maxillary expansion; (5) patients with treatment performed as described in the next section; (6) patients who had lateral cephalometric radiographs taken before and after rapid maxillary expansion, obtained using the same technique, with a clear visualization of the airways.  
Exclusion criteria were as follows: (1) patients with full deciduous dentition; (2) patients previously treated by surgical therapy for SDB (adenoidectomy, tonsillectomy, or adenotonsillectomy); (3) patients with obesity; (4) patients with genetic pathologies related to SDB; (5) patients with craniofacial anomalies and malformations; (6) patients with other systemic pathologies, such as cardiorespiratory or neuromuscular disorders, which could affect normal respiration patterns.
## 4 Results
### Analysis of the Interactive Process Indicators of the Emergency Department
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Variables** & **Total Sample** & **Group A > 4 h** & **RR (>4 h)** & **Group B \(\leq\) 4 h** & **RR (\(\leq\)4 h)** \\  & **N = 94,489 (\%)** & **N = 29,621 (\%)** & **NR (>4 h)** & **NR (\(\leq\)4 h)** \\ \hline Children (0–14 years) & 3480 (3.68\%) & 45 (0.15\%) & **0.04 (0.03–0.05)** & 3435 (5.3\%) & **1.46 (1.45–0.61)** \\ Adolescents (15–19 years) & 4735 (5.01\%) & **940 (3.17\%)** & **0.62 (0.59–0.66)** & 3795 (5.85\%) & **1.18 (1.16–1.20)** \\ Young adults (20–39 years) & 24,256 (25.67\%) & **5195 (3.17\%)** & **0.62 (0.60–0.63)** & 19,061 (29.38\%) & **1.20 (1.19–1.22)** \\ Middle-aged adults (40–49 years) & 13,838 (14.65\%) & **3802 (12.84\%)** & **0.86 (0.83–0.88)** & 10,036 (15.47\%) & **1.07 (1.05–1.08)** \\ Mature adults (50–64 years) & 19,932 (21.09\%) & **6428 (21.7\%)** & **1.04 (1.01–1.06)** & 13,504 (20.82\%) & **0.98 (0.97–0.99)** \\ Older adults (65–69 years) & 5152 (5.45\%) & **1906 (6.43\%)** & **1.19 (1.15–1.24)** & 3246 (5\%) & **0.91 (0.89–0.93)** \\ Old age (70–89 years) & 20,390 (21.58\%) & **9695 (32.73\%)** & **1.77 (1.74–1.80)** & 10,695 (16.49\%) & **0.72 (0.71–0.73)** \\ Very old (=90 years) & 2706 (2.86\%) & **1610 (5.44\%)** & **1.95 (1.89–2.01)** & 1096 (1.69\%) & **0.58 (0.56–0.61)** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Age values analysed with PMApp. Figure 7: General process with attention diagrams and mean of arrival statistics. those with LOS > 4, we found adults with poor general condition [\"Adulto con mal estado general\"] (17.14% vs. 7.13%), abdominal pain [\"Dolor abdominal en adultos\"] (15.47% vs. 6.55%), and limb problems [\"Problemas en las extremidades\"] (10% vs. 32.12%) (Figures 10 and 11, Table 2). Likewise, the LOS > 4 group required a wheelchair or stretcher upon arrival at the ED, with 41.26% (12,223) versus 20.53% (13,320) with a LOS < 4 (Figures 12 and 13,Table 3). There were also differences in the outcome variables, with the admission rate being 22.83% (6762) vs. 6.2% (4025) in the LOS < 4 group. Regarding the percentage of patients who visited the ED five or more times in a year, we also observed differences: 10.18% (3015) vs. 7.74% (5018) in the LOS < 4 group (Figures 14 and 15, Table 4).
## 2 Theoretical Background
### Bi-Lstm  
In the LSTM model, data is transmitted in one direction, conveying historical information [38]. Conversely, the Bi-LSTM model enables bi-directional data flow, transferring information about both the past and future. As a result, Bi-LSTM features two distinct hidden layers that are connected to the same input and output [39]. One of the hidden layers in Bi-LSTM consists of a forward LSTM that transfers historical data from the input sequence to the output sequence. Simultaneously, the other backward LSTM moves future data information from the output sequence to the input sequence. This mechanism enables the model to learn long-term dependencies and enhance its accuracy [40]. Bi-LSTM architecture is given in Figure 2. Here, the hidden state of Bi-LSTM at time \(t\) comprises forward \(h_{t}^{f}\) and backward \(h_{t}^{b}\), while \(H_{t}\) denotes the final hidden output of Bi-LSTM, which results from combining the forward and backward outputs. Technical terms are explained upon their initial usage to ensure clarity [41]:  
\[h_{t}^{f}=LSTM^{f}(h_{t-1},x_{t},c_{t-1}),t\in[1,T], \tag{7}\]  
\[h_{t}^{b}=LSTM^{b}(h_{t-1},x_{t},c_{t-1}),t\in[1,T], \tag{8}\]  
\[H_{t}=\left[h_{t}^{f},h_{t}^{b}\right], \tag{9}\]  
Figure 1: LSTM architecture [32].
# Corrosion Behavior of Zn-Al-Mg-Si Coatings in Sulfur Dioxide-Containing Environment
###### Abstract
However, an important substance polluting the atmosphere, which also has a strong anti-corrosion effect, is sulfur dioxide. [PERSON] et al. [22] studied the initial atmospheric corrosion of ZnAl\({}_{2}\)Mg\({}_{2}\) coatings in humid air containing 80 ppb SO\({}_{2}\). \"In situ\" studies show that Mg-containing sulfite forms as an initial corrosion product in environments containing SO\({}_{2}\), as well as SO\({}_{2}\) and NaCl. Cathodic reduction of oxygen promotes the oxidation of sulfite to sulfate, which, in turn, increases the initial corrosion rate of Zn-Al-Mg coatings. However, such a picture of the impact of SO\({}_{2}\) is incomplete and depends on other environmental conditions in which the research was conducted. The aim of the research in this work is to analyze the corrosion behavior of Zn-Al-Mg-Si coatings in a sulfur dioxide-containing environment. The article presents the results of research on the microstructure of Zn-Al-Mg-Si coatings obtained in a bath with the ZnAl12 Mg3 Si0.3 composition and their corrosion behavior in a sulfur dioxide environment with general moisture condensation. This will allow for the prediction of the impact of SO\({}_{2}\) content in a corrosive environment on the durability of coatings.
## 3 Preliminaries
### Inter-Core Crosstalk
Therefore, the setting of the crosstalk threshold is critical and depends on the modulation format, core distribution, and the number of FSs that are on adjacent cores and whether their FSs indexes are the same as those to be allocated to the current request. Different modulation formats can tolerate different amounts of crosstalk, and the tolerance decreases with the increase of the modulation order [8]. To simplify the calculation, BPSK with the highest tolerance is adopted in this paper. Finally, the crosstalk threshold is set to an appropriate value in its value range (related to the distribution of cores) through several experiments.
# A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios
## 5 Performance Evaluation
### The One Simulation Environment  
_The Opportunistic Network Environment_ (The One) software is a well-know simulation tool tailored for the performance evaluation of DTN routing protocols. Written in Java, it is capable of simulating packet routing between nodes with various DTN routing algorithms and sender and receiver types. The One can generate node movements using different mobility models, and provides a graphic visualization of both mobility and packet forwarding progress through a customized interface. The software is available as open source and can be customized as needed through a main configuration file denoting the simulation components.
## 5 Conclusions  
In conclusion, following the theoretical analysis on the hybrid optical CDMA/DWDM system, it was found that the EOPM module was more effective using the CSRZ modulation format compared to the MDRZ and RZ modulation formats. This is due to the phase shift introduced by the EOPM between the pulses. The phase shift is responsible for reducing the intra-channel FWM effect, as the destructive interference can be achieved between different pulses by optimizing the phase shift. The simulation results indicated that the strength of the FWM effect depends on the optical signature code properties, where the longer code is better. The phase deviation of the EOPM module is the key parameter that plays a crucial role in eradicating the effect of intra-channel FWM.  
Conceptualization, N.A. and T.E.; methodology, N.A. and T.E.; software, N.A.; validation, N.A. and T.E.; formal analysis, N.A. and T.E.; investigation N.A. and T.E.; writing--original draft preparation, N.A.; writing--review and editing, N.A. and T.E.; visualization, N.A. and T.E.; supervision, T.E. and M.R.M.; project administration, T.E. and M.R.M.; funding acquisition, T.E. and M.R.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Multimedia University (Malaysia), project SAP ID: MMUI/160092.  
Not applicable.  
This work was supported by theHigher Colleges of Technology, United Arab Emirates, for publication sponsorship.  
The authors declare no conflict of interest.
## 1 Introduction  
A large number of tunnels have been in service for the past decades all over the world. Turnels were mainly supported by concrete lining [1; 2]. The type of concrete lining can be divided into three categories, including shotcrete, prefab and cast-in-place lining. The cast-in-place concrete lining was the most commonly used one and it was studied in this paper. The persistent ageing and seismic activity lead to many problems to the cast-in-place concrete lining, such as cracking, corrosion and leakage [3; 4; 5]. Damage to the tunnel lining will decrease its integrity and subsequently increase the risk of tunnel lining collapse [6]. In order to deal with this situation, there is an urgent requirement for accurate tunnel lining health assessment methods [7; 8; 9; 10].  
Generally, the stability of aged cast-in-place tunnel lining was mainly evaluated based on visual inspection [11]. However, the evaluation standard was qualitative, and the results depended on the experience of different engineers. As a solution of this problem, the Tunnel-lining Crack Index (TCI) was proposed to quantitatively evaluate the severity of tunnel lining cracks [12]. It has been widely applied for the inspecting of surface defects of tunnels in Japan. The stability assessment based on TCI is reasonable to some extent because the mechanical stability of a tunnel is greatly influenced by the severity of cracks in the lining [13]. However, the intersection and distribution of cracks, which could greatly influence the stability of tunnel lining, was not considered in TCI. The uncertainty relationship between TCI and the instability of tunnel lining called for more alternative assessment methods and health indexes.  
As the distribution of cracks in tunnel lining is quite complex, it is difficult to make a precise description through conventional mathematical methods. Fortunately, fractal theory is used to analyze the distribution of complex graphics. It has been widely used in art, astronomy, geography, biology, fluid dynamics, probability theory, chaos theory, and pure mathematics in the past few decades [14; 15; 16; 17; 18; 19]. In recent years, the fractal theory was used to describe the propagation of cracks in rock or concrete specimens in laboratory [20; 21; 22; 23; 24; 25]. These studies confirmed that the cracks in concrete have fractal properties [21]. Therefore, it is possible to describe the distribution of cracks and evaluate the states of tunnel lining by fractal theory. Moreover, the development of machine vision-based method makes it possible to collect the images of tunnel lining and to calculate the dimensions of the cracks efficiently [26; 27].  
In this study, the fractal dimension of cracks in tunnel lining was taken as a new health assessment index. A series of comparative tests and field tests were conducted to evaluate the validity of this new method. In addition, the quantitative correlation between fractal dimension and TCI of cracks in tunnel lining was studied.
# Revisiting the Analysis of Radiative Mid-Range Wireless Link for Pacemakers
## 2 Materials and Methods
### Mid-Range Wireless Link Formulas
#### 2.2.2 Chu Formula  
An approximation of the Friis formula has been derived based on the fundamental mode of Gaussian beams, which is called \"Chu formula\". The formula is a function of the distance \(R\) and effective aperture area \(A_{e}\), which is also relevant to the far-field gain \(G\). The Gaussian beam is used to include both approximation in the far-field region and gain reduction effect in the radiative near-field region.  
\[\times \frac{\frac{P_{r}}{P_{t}}=\frac{\lambda^{2}}{16\pi^{2}R^{2}}G_{t}G_{r}}{1+ \left(\lambda^{2}/(16\pi^{2}R^{2})\right)\left(\left(\sqrt{F(G_{t})}G_{t}+ \sqrt{F(G_{r})}G_{r}\right)/2\right)^{2}} \tag{7}\]  
In contrast to the separable asymptotic gain reduction factors for transmitting and receiving antennas, the formula includes a combined form of the gain reduction factors of the two antennas. It is worth noting that the switching function \(F(G)\) is incorporated with the antenna gain in the correction term, except for the Friis formula.
# Coordinated Control after Grasping the Space Targets Using Controllable Damping Mechanism
## 5 Conclusions  
In this article, a full-dimensional controllable damping mechanism (FDCDM) with gyroscopic structure is introduced into the joint of the space robot, and the whole-body dynamic model of SMS with full-dimensional damping mechanism (SMS-FDCDM) is established using the Kane method. Then, we focus on the contact process during the capturing and the stabilization of the complex system after capturing non-cooperative space targets. Therefore, a novel backstepping non-singular fast integral terminal SMC (BNFITSMC) is proposed to optimize the momentum distribution and impact absorption. The impact force is buffered by six-dimensional force damping, and the disturbance to the system is greatly reduced, so the attitude of SMS is quickly stabilized after capture. The simulation results verify the advantages of the proposed method, such as (1) low steady-state error, (2) quick convergence time, (3) smooth control torque without chattering, and (4) robustness with uncertainties and disturbances. Therefore, the numerical simulation of collision experiment demonstrates the superior performance of the proposed approach. However, it should be noted that the introduction of this mechanism will make the joint become a variable stiffness joint. Therefore, stiffness estimation is necessary considering the controller, and the stiffness estimation usually requires accurate estimation of the flexible force. This will be the focus of our future research.  
Conceptualization, R.C. and M.C.; methodology, R.C.; software, R.C.; validation, R.C., M.C., Q.J. and X.Z.; formal analysis, R.C.; investigation, M.C.; resources, R.C.; data curation, R.C.; writing--original draft preparation, R.C.; writing--review and editing, M.C.; visualization, Q.J. and X.Z.; supervision, Q.J. and X.Z.; project administration, M.C.; funding acquisition, M.C. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the National Natural Science Foundation of China (Grant No. 51875046), Natural Science Foundation of Beijing Municipality (Grant No. 3202021) and Major Project of the New Generation of Artificial Intelligence, China (No. 2018 AAA0102905). The authors would like to thank anonymous reviewers for their valuable comments to improve the quality of this paper.  
Not applicable.  
The authors declare no conflict of interest.