esa-sceva/satcom-chunk-collection huggingface dataset:
[28] proposed an efficient architecture based on CNN named CrackNet for detecting cracks on 3D asphalt surfaces. With the emergence of the you only look once (YOLO) network, two automated pavement crack analysis systems were presented based on the YOLOv2 and YOLOv3 framework, respectively, in [29; 30]. Whereas in [31], three models based on a very deep convolutional network (VGGNet), GoogLeNet, and DenseNet were developed as classifiers, and the VGGNet with 19 layers achieved the highest level of performance among the three models. [PERSON] et al. [32; 33] developed two road damage detection models based on the single shot multibox detector (SSD) algorithm and generative adversarial network (GAN), whose datasets were derived from smartphone images and artificial images, respectively. A computer vision model was proposed using the YOLOv5 algorithm for detecting and classifying pavement distress of nine classes in [34]; a sensor-based model has also been investigated in this study. While researchers have made continuous progress in detecting and classifying cracks and other pavement distress based on deep learning, there are still some issues that need to be taken into consideration. In most studies, images in a dataset were captured parallel to the pavement, with the crack area occupying the majority of the image space and no background other than the pavement visible. Particularly, some studies used datasets that consist solely of images of cracks from a single category. However, pavements images in practical situations generally exhibit several cracks of various types, affected by illumination, shadows, and other objects that have an impact on crack detection. Regarding this aspect, the multi-target and multi-class detection of cracks with a complicated background remains a challenging task [35]. For dealing with this task, an intelligent model to detect cracks with a complicated background using an improved deep-learning network is proposed in this paper. The remainder of this paper is organized as follows. Section 2 provides a description of the built dataset of cracks, including image acquisition, image preprocessing, and crack distribution information, and the pavement crack detection model based on the improved deep-learning network is also elaborated in detail from four perspectives. A comparison of the experimental results for the original network and the proposed network is presented in Section 3. Section 4 compares the proposed method with previous related methods. Finally, the conclusion of this study is drawn in Section 5.
## 1 Introduction
### TTN--The Things Network  
Part of the success of LoRaWAN is due to the growth of a public community network known as The Things Network (TTN) [4]. TTN enables low power end-nodes to leverage existing gateways to connect to an open-source network which enables access to the user's applications. It is a decentralized and collaborative network, counting thousands of gateways around the world, used by tens of thousands of developers and businesses to build valuable use cases. Developers can use existing gateways, or add new ones where additional coverage is needed. Gateways have to be connected to the Internet to be able to access the TTN server that runs the required MAC functions. There are many servers worldwide, based on several cloud technology platforms. While there are over 6000 gateways up and running worldwide, there are less than 100 in Africa, hindering access to this open and free network where it is mostly needed.
## 3 System Structure of USV  
### Communication System of USV  
The two approaches to acquire data from unmanned vehicles in the leader-follower swarm-control method are the centralized and decentralized methods [12]. The centralized method is easier to implement in a swarm-control algorithm than the decentralized method. However, it requires substantial infrastructure, such as a server room, and an immobile ground control station. However, the decentralized method is marginally more complex to implement in a swarm-control algorithm than the centralized method. However, it offers the advantages of lower communication overhead, shorter information-sharing time among unmanned vehicles, and flexibility in not requiring a fixed ground control station [13]. Therefore, in this study, a leader-follower swarm-control method was implemented using the decentralized method. Figure 2 illustrates the communication circuit between the ground control station and the leader and follower USVs.  
Communication between the ground control station and each USV was established via wireless RF TCP/IP equipment known as an access point (AP) bridge. The control inputs transmitted from the ground control station were relayed to each USV via the AP Bridge. The transmitted commands were segregated and delivered to the respective components within the USV through a switch hub installed on the board. Each component had a unique IP address to prevent conflicts among the information transmitted from the ground control station and ensure stable operation.
## 3 Experimental Results and Analysis
### In Contrast to Other Mainstream Models  
By contrasting the performance of the CCS-YOLOv8 model with those of other popular one- and two-stage target detection models, we can further validate the enhanced model's efficacy. To assess the model's complexity in detail, these metrics include the number of model parameters and the quantity of computations. Faster R-CNN utilizes a two-stage computational inference design to effectively enhance detection accuracy. However, Faster R-CNN can encounter challenges with tasks that involve detecting small targets. This is primarily because the model heavily relies on high-dimensional feature mapping for prediction, which can lead to the overlooking of fine-grained feature information. Faster R-CNN's detection performance on the livestock dataset is noticeably worse than those of the one-stage algorithmic models, as Table 3 illustrates. Furthermore, the model has higher training and inference costs due to its large number of parameters and computing needs, which makes it challenging to satisfy the demands of livestock target monitoring. Among one-stage algorithms, the YOLOv8n model exhibits the lowest complexity. However, aside from SSD and YOLOv3 tiny, it does not demonstrate a clear advantage in detection performance. By comparison, exceptional detection results are obtained by the CCS-YOLOv8 model. Meanwhile, the number of parameters and computational volume being low at 3.38 M and 13.7 GFLOPs, respectively, indicate that the model is designed to be lightweight yet high-performing, making it more practical for real applications.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Precision** & **Recall** & \multirow{2}{*}{**[EMAIL_ADDRESS] (\%)**} & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & \multirow{2}{*}{**F1-Score (\%)**} & **Parameters** & **FLOPs** \\  & **(\%)** & & & **(\%)** & & **(\%)** & & **(M)** & **(G)** \\ \hline Faster & 75.3 & 58.3 & 66.9 & 46.1 & 41 & 65.7 & 41.75 & 87.9 \\ R-CNN & & & & & & & & \\ SSD [37] & 75.6 & 65.2 & 75.7 & 45.6 & 43.5 & 70 & 25.12 & 88.2 \\ YOLOv3 tiny & 80.1 & 68.5 & 71.1 & 48 & 44.5 & 73.8 & 12.14 & 19 \\ YOLOv5s & 84.1 & 80.1 & 81.1 & 55.8 & 50.6 & 82.1 & 7.04 & 16 \\ YOLOv3 [38] & 80.5 & 75.8 & 81.3 & 53.5 & 49.6 & 78.1 & 8.97 & 13.4 \\ YOLOv6n [39] & 80.1 & 74.1 & 76.5 & 52.8 & 48.3 & 77 & 4.24 & 11.9 \\ YOLOv7 tiny [40] & 84.1 & 79.6 & 81.7 & 53.2 & 49.3 & 81.8 & 6.03 & 13.2 \\ YOLOv8n & 83 & 74.3 & 78.6 & 53.7 & 48.8 & 78.4 & 3.01 & 8.2 \\ YOLOv8s & 83.9 & 80.2 & 82 & 59.3 & 52.8 & 82 & 11.1 & 28.7 \\ CCS- & & & & & & & & \\ YOLOv8 & 84.1 & 82.2 & 84.4 & 60.3 & 53.6 & 83.1 & 3.38 & 13.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other models.  
Figure 9: **(A)** The training mAP curves: the CCS-YOLOv8 model surpasses the YOLOv8n model on the curves of map0.5, map0.75, and map0.5:0.95. **(B)** The PR curve: The CCS-YOLOv8 model demonstrates superior performance for detecting and capturing most livestock instances with high recall and maintaining high accuracy.
## 3 Results and Discussion
### Odorants in Fresh Shitiake Mushrooms
#### 3.1.2 Odorants Identified by DSE-SAFE  
As shown in (Table 2), 21 odorants were positively and seven compounds tentatively identified in fresh shitiake mushrooms by DSE-SAFE. Three compounds were not identified. Twenty-three odorants exhibited high FD factors (FD \(\geq\)3) by AEDA, indicating that these 23 compounds made major contributions to the overall aroma and were the predominant odorants of fresh shitiake mushrooms. Among these, phenylacetaldehyde (floral), _trans_-4,5-epoxy-(E)-2-decenal (metallic), 1,2,4,5-tetrathiane (sulfury, burnt), 1,2,4,6-tetrathiepane (sulfury) and lenthionine (sulfury, burnt) had the highest FD factors of 729, followed by 3-methyl-butanal (chocolate), 3-(methyl-thio)-1-propanol (cooked potato-like), dimethyl tetra-sulfide (mushroom) with FD factors of 243. The relatively high FD factors of sulfur compounds, like 1,2,4,5-tetrathiane, 1,2,4,6-tetrathiepane, lenthionine, 3-(methyl-thio)-1-propanol and dimethyl tetra-sulfide, could demonstrate that the sulfur compounds also provided major aroma properties to fresh samples. However, [PERSON] et al. [12] reported that dimethyl tetra-sulfide and 1,2,4,6-tetrathiepane were not detected in fresh shitiake mushrooms. This might be due to the different types of GC column and detector. Only the polar column was used in the study of [PERSON] et al. [12], while dimethyl tetra-sulfide and 1,2,4,6-tetrathiepane were identified as the predominant odorants on the nonpolar column in this study. Therefore, the use of columns with different polarities could more comprehensively identify odorants, since there were many types of aroma compounds with different polarities in the food matrix. In addition, 1-octen-3-one and 1-octen-3-ol were also more important odorants in fresh samples with FD factors of 81, which was consistent with the result identified by DFA. Furthermore, only 13 compounds were consistent with the odor-active compounds detected by [PERSON] and [PERSON] [3] in raw shitiake mushrooms. This difference might be due to the different varieties, cultural practices and collection time of shitiake mushrooms, and the distinct extraction and identification methods applied [37].
## 4 Experiments and Measurement Uncertainty Analysis  
### Standards and Parts to Be Measured  
To verify the performance of the SEPP method and the HDMM, a variety of standards and parts shown in Figure 12 were measured. The two deep and small hole standards were specially designed and used to verify the hole attitude adjustment method, planned measurement process, measurable depth, and depth-to-diameter ratio. The ring gauge standard was calibrated by NIM of China and Physikalisch Technische Bundesanstalt (PTB) of Germany. A tube part from a rocket engine was also measured. To this point, the smallest hole diameter measured with the HDMM was \(\phi\)3 mm, and the maximum hole depth measured was 150 mm.  
Figure 11: Diameter measurement process.
## 3 Hydrogen Production Technologies
### Background Concepts
#### 3.1.5 Biological Hydrogen Production  
Photobiological processes use live microorganisms and sunlight to turn water--and sometimes organic matter--into hydrogen. In photolytic biological systems, microorganisms such as green microalgae or cyanobacteria use sunlight as a mechanism to split water into oxygen and hydrogen ions; these hydrogen ions are later combined through direct or indirect routes and released as hydrogen gas [48]. Some photosynthetic microbes use sunlight as the driver to break down organic matter, releasing hydrogen; this mechanism is commonly known as \"photofermetative hydrogen production,\" and some of its major challenges include a very low production rate and a low solar-to-hydrogen efficiency--something researchers are currently looking at, expecting to make the microbes better at collecting and using energy, thus becoming more suited overall for hydrogen production.
# Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
## 4 Experimental Results
### Performance Breakdown  
We analyzed the performance of range index structures for disaggregated memory, as shown in Figure 8. The performance trends on the Zipfian distribution and uniform distribution were similar. Blink-tree spends the most time on RDMA operations, such as RDMA Read, RDMA Write, and RDMA CAS. Sherman and RF-tree take advantage of their local cache, so the elapsed time for RDMA operation was drastically reduced. As shown in Figure 8, both Sherman and RF-tree similarly experience significant performance overhead due to the CRC (checksum) computation (i.e., 7-50%). Moreover, RF-tree showed less in-memory computation overhead than Sherman because RF-tree stores the search layer in the consecutive memory space, and RF-tree has a sorted layout. Since the in-memory computation overhead is low, RF-tree is more efficient for disaggregated memory. Sherman shows low overhead from RDMA Write operations because it only writes key-value pairs to the target data node instead of the whole data node. More  
Figure 6: Throughput of range index structures for disaggregated memory (Zipfian distribution).  
over, Sherman incurs higher overhead for in-memory overhead, which is inefficient for disaggregated memory.  
In workload E, Sherman batches RDMA Read operations for reading the data nodes. RF-tree traverses data nodes using a sibling pointer. Sherman spends more time on in-memory operation than RF-tree because Sherman has an unsorted data node layout and SkipList-based local cache of the compute server.
## 3 Application Description and Requirements
### Core Features
#### 3.2.2 Document Resource Management  
Users will have the ability to create, delete, and rename their own documents. Users will also have the option to share the document with other users based on a unique identifier that can be shared. Moreover, the owner of a text document can restrict access to the resource by revoking the rights to view and interact with the document of specific users.
## Appendix A Derivation of Expression (45)  
Given the travel cost functions of the two links  
\[K_{1}(F,G)=a+\frac{bF}{\mathbb{Q}G} \tag{11}\]  
\[K_{2}(F,G)=a+\frac{b(1-F)}{Q(1-G)} \tag{12}\]  
we aim to express \(V(F,G)=K_{1}(F,G)-K_{2}(1-F,1-G)\) as a function of \(F\) alone.  
From (11) and (12), we have  
\[V(F,G)=\frac{b}{Q}\bigg{(}\frac{F}{G}-\frac{1-F}{1-G}\bigg{)} \tag{13}\]  
According to the Logit-like signal control policy  
\[G=H(F)=\frac{\exp\left(\frac{\gamma F}{\mathbb{Q}}\right)}{\exp\left(\frac{ \gamma F}{\mathbb{Q}}\right)+\exp\left[\frac{\gamma(1-F)}{\mathbb{Q}}\right] }=\frac{1}{1+\exp\left[\frac{\gamma(1-2F)}{\mathbb{Q}}\right]} \tag{14}\]  
so that  
\[\frac{F}{G}=F\bigg{\{}1+\exp\left[\frac{\gamma(1-2F)}{\mathbb{Q}}\right] \bigg{\}} \tag{15}\]  
From (14), we obtain  
\[1-G=\frac{\exp\left[\frac{\gamma(1-2F)}{\mathbb{Q}}\right]}{1+\exp\left[\frac {\gamma(1-2F)}{\mathbb{Q}}\right]} \tag{16}\]  
so that  
\[\frac{1-F}{1-G}=(1-F)\left\{\frac{1+\exp\left[\frac{\gamma(1-2F)}{\mathbb{Q} }\right]}{\exp\left[\frac{\gamma(1-2F)}{\mathbb{Q}}\right]}\right\} \tag{17}\]  
We now subtract (17) from (15) and find  
\[\frac{F}{G}-\frac{1-F}{1-G}=F\left[\exp\left(\frac{\gamma(2F-1)}{\mathbb{Q}} \right)+\exp\left(\frac{\gamma(1-2F)}{\mathbb{Q}}\right)+2\right]-\exp\left( \frac{\gamma(2F-1)}{\mathbb{Q}}\right)-1 \tag{18}\]  
which, after multiplication by \((b/Q)\), yields expression (45) in the main text.
# Line Clipping in 3D: Overview, Techniques and Algorithms
## 3 Line-Clipping Algorithms in Three-Dimensions
### Three-Dimensional Cohen-Sutherland Line Clipping  
The Cohen-Sutherland algorithm is considered a classic line-clipping algorithm in two dimensions. The three-dimensional analogue is very similar to its two-dimensional version but with a few modifications. For an orthogonal clipping region, the three-dimensional  
Figure 8: Implementation of three-dimensional viewing.  
Figure 7: A frustum-shaped clipping region is computational expensive.  
Figure 9: Applying clipping using a normalized canonical clipping region.  
space is divided again into regions but this time a 6-bit region code, instead of a four-bit one, is used to classify the lines' endpoints. These six bits are as in Figure 10.  
The regions are related to the space that is in front of the near plane, the space between the near and the far plane, and the space behind the far plane; see Figure 11.  
The testing strategy is virtually identical to the two-dimensional case. The bit codes can be set to true (1) or false (0), depending on the test for these equations as follows:  
Bit 1: endpoint is in front of the view volume;  
Bit 2: endpoint is behind the view volume;  
Bit 3: endpoint is above the view volume;  
Bit 4: endpoint is below the view volume;  
Bit 5: endpoint is right of the view volume;  
Bit 6: endpoint is left of the view volume.  
To clip a line, the classification of its endpoints with the appropriate region codes is required. The line is as follows:  
Visible: : if both endpoints are 000000;  
Invisible: : if the bitwise logical AND is not 00000;  
A clipping candidate: : if the bitwise logical AND is 000000.  
All lines with both endpoints in the [000000] region are trivially accepted. All lines whose endpoints share a common bit in any position are trivially rejected. The clipped line is derived from the parametric equation of the line in three-dimensional space using homogeneous coordinates and by using boundary coordinates whenever it is necessary.
# Aerodynamics and Motion Performance of the H-Type Floating Vertical Axis Wind Turbine
## 3 The Dynamical Equations of the Floating VAWT
### Hydrodynamic Loads  
Hydrodynamic loads were calculated by using the potential theory and the Morison's equation [27]. The potential theory was used to calculate the wave loads, added mass and radiation damping of the large scale structure (including the upper buoyancy tank, upper mechanical tank, heaving plates, and bottom ballast tank). The first-order wave loads and second-order mean drift forces were considered in this paper.  
The Morison equation was used to calculate the inertial load and viscous drag load of the slender structure (truss structures, \(D/\lambda<0.2\), where \(D\) is the diameter of the structure and \(\lambda\) is the wave length). The transverse hydrodynamic force per unit length can be written as follows,  
\[dF=\rho\pi\frac{D^{2}}{4}\dot{u}_{w}+\rho\pi C_{a}\frac{D^{2}}{4}(\dot{u}_{w}- \dot{u}_{b})+\frac{1}{2}\rho C_{d}D(u_{w}-u_{b})|u_{w}-u_{b}|, \tag{30}\]  
where \(C_{a}\) and \(C_{d}\) are the added mass and quadratic drag coefficients, respectively; \(u_{w}\) is the transverse wave particle velocity; and \(u_{b}\) is the local transverse body velocity.  
The Wadam [28] was used to calculate the hydrodynamics in this paper. The wind turbine was in the parked state and the wind loads and mooring system were not considered in the hydrodynamic calculation, while the mass and shape of the blade were considered.
## 3 Results and Discussion  
In this section, the characteristics of pore pressure and seismic and acoustic signals due to wave erosion of the slope are discussed. The results of the Baseline Case are discussed in detail. Two types of wave erosion are defined: impacting and swashing. Impact-type erosion involves wave breaking and splashing, whereas swash-type erosion involves only water moving up and down without breaking and splashing.  
The pore pressure signals might indicate how the internal structure of the slope is affected by the waves. An increase in pore pressure can signify that water is being pushed into the slope, potentially destabilizing it and causing material to become loose or displace. Seismic signals can reveal the intensity and the effect of the waves reaching the slope. Different erosion types might show distinctive seismic patterns. The acoustic signals generated by the erosion processes can provide additional insights.  
Impact-type erosion with wave breaking and splashing is likely to produce stronger and more abrupt seismic signals compared to the swash type. Impact-type erosion might generate louder and sharper sounds due to the breaking and splashing, while swash-type erosion might have a more consistent and softer soundscape. The evidence is provided in the following. By analyzing these signals and comparing the two types of erosion (impacting and swashing), researchers can gain a deeper understanding of the erosion mechanisms and the effects of different wave actions on slope stability. This analysis is crucial for designing and implementing measures to protect slopes from erosion.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## 5 Solving a Pseudo-Static Scenario
### Checking the Fitness of Individuals: Check Function
#### 5.2.1 Signal Coverage Evaluation (Associate Function)
The Signal-to-Interference-plus-Noise-Ratio (\(SINR_{ij}\)) can be calculated by subtracting every noise and co-channel interferences to the received signal strength. To do so, let us declare \(\mathcal{Z}_{j}\) as the collection of drones that may cause interferences (channel planning is out of the scope of this paper) to the network deployed by UAV \(j\). Then, the SINR (in dB) can be expressed as:  
\[SINR_{ij}=RSSI_{ij}-F-N-\sum_{k\in\mathcal{Z}_{j}}(RSSI_{ik}) \tag{19}\] \[N=-174+10\log_{10}(C_{BW}) \tag{20}\]  
where \(N\) is the thermal noise, \(F\) accounts for the receiver's noise figure, and \(C_{BW}\) represents the signal bandwidth, which depends on the IEEE 802.11 standard revision under consideration.
## 2 Materials and Methods
### Uncertainty Analysis  
The uncertainty analysis method introduced by [PERSON] and [PERSON] [21], which is based on careful specifications of the uncertainties in the various primary experimental measurements, was applied to the measurement results. The method aims to estimate the total uncertainty of the measured or calculated values, such as thermal conductivity, porosity, compressive strength, tensile strength and water absorption ratio.  
According to this method, let the quantity that needs to be measured in the system be \(R\), and the \(n\) independent variables affecting this quantity be \(x_{1}\), \(x_{2}\), \(x_{3}\), \(\ldots\), \(x_{n}\). In this case, it can be written as \(R=f\left(x_{1},x_{2},x_{3},\)\(\ldots\), \(x_{n}\right)\). If the uncertainty of each independent variable is \(w_{1}\), \(w_{2}\), \(\ldots\), \(w_{n}\), and the uncertainty of the magnitude \(R\) is \(w_{R}\), the uncertainty was calculated as follows according to [PERSON] and [PERSON] [21]:  
\[w_{R}=\sqrt{\left(\frac{\partial R}{\partial x_{1}}w_{x_{1}}\right)^{2}+\left( \frac{\partial R}{\partial x_{2}}w_{x_{2}}\right)^{2}+\cdots+\left(\frac{ \partial R}{\partial x_{n}}w_{x_{n}}\right)^{2}} \tag{4}\]  
The uncertainty of each variable was estimated with the help of the instruments' catalogs. Then, the total uncertainties for each independent variable were found for thermal conductivity, porosity, compressive strength and water absorption ratio as \(\pm\)4.7%, \(\pm\)6%, \(\pm\)4% and 3%, respectively.