esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cell Proliferation of Raw 264.7 Cells  
Macrophages are immune cells that are found throughout the human body and serve as the primary defense mechanism for innate immunity. These cells are capable of phagocytosis and play a crucial role in the immune response to antigens by secreting cytokines and immune modulators to find and eliminate bacteria and foreign substances [28]. The RAW 264.7 cell line, a type of macrophage, was induced with LPS in this study and the production of cytokines TNF-\(\alpha\), IL-1\(\beta\), and IL-6 were measured to assess the cell's ability to proliferate and respond to antigens. The results showed that ethanol extracts from various parts of the ice plant had no toxic effect on the survival of the RAW 264.7 macrophages, with concentrations up to 5-300 \(\mu\)g/mL not having any detrimental impact. In this study, RAW 264.7 cells, a macrophage cell line, were induced with LPS, macrophage-related cytokines, tumor necrosis factor-alpha (TNF-\(\alpha\)), IL-1\(\beta\) (IL-1\(\beta\)), and IL-6(Figure 2).  
Figure 2: Effect of ethanol extract of the ice plant (_M.crystallinum_) on cell viability in lipopolysaccharide-charide (LPS)-treated RAW 264.7 cells (* \(p<0.5\), ** \(p<0.01\), *** \(p<0.001\) compared to the control by ANOVA).  
_M.crystallinum_, also known as the Ice plant, has been studied for its potential health benefits, including its anti-inflammatory properties. This study investigated the effect of an ethanol extract of the Ice plant on cell viability in lipopolysaccharideacharide (LPS)-treated RAW 264.7 cells, which are a type of immune cell often used in studies of inflammation. LPS is a component of the outer membrane of certain types of bacteria, and it is commonly used in studies to induce an immune response and inflammation. In this study, the researchers treated RAW 264.7 cells with LPS to induce inflammation, and then they treated the cells with different concentrations of the ice plant extract to determine its effect on cell viability. The results showed that the ice plant extract was able to increase cell viability in a dose-dependent manner, meaning that higher concentrations of the extract led to greater increases in cell viability. The researchers also found that the Ice plant extract was able to decrease the production of pro-inflammatory cytokines, which are molecules that promote inflammation and can contribute to cell damage and death. Based on these findings, concluded that the ethanol extract of the ice plant may have anti-inflammatory and cytoprotective effects on immune cells, which could potentially have therapeutic benefits in treating inflam-matory conditions. However, more research is needed to fully understand the mechanisms un-derlying these effects and to determine the optimal dosages and delivery methods for potential therapeutic use.
## 3 Experiments and Analysis
### Experimental Evaluation Indicators
#### 3.2.3. Fruits Counting Experiment  
In fruit counting, this paper adopts an average counting precision \(A_{cp}\) as the evaluation index of citrus counting effect. The larger the average counting precision, the more accurate the counting performance of the algorithm in this paper. The formula is:  
\[A_{cp}=\frac{\sum_{1}^{n}\left(1-\frac{|S-G|}{G}\right)}{n} \tag{11}\]  
where \(S\) denotes the number of fruits in the counting video obtained by the algorithm of this paper, and \(G\) denotes the number of fruits in the manual counting citrus video, and \(n\) denotes the number of videos.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Actual Case Analysis
#### 4.2.3 Case 3  
A fault occurs at \(\rm T_{1}\). \(\rm Tp_{1}\)m operates, and then \(\rm CB_{44}\) and \(\rm CB_{51}\) trip. However, the timestamp of the \(\rm CB_{51}\) alarm message is incorrect due to transmission delays. The alarm messages are briefly described as \(\rm Tp_{1}\)m(0), \(\rm CB_{44}(25)\), and \(\rm CB_{51}(70)\). The fault diagnosis process is as follows:  
1. \(\it CBS=\{\rm CB_{44},\rm CB_{51}\}\) can be obtained from the alarm message. Traverse \(CBS\) and search for suspected faulty components. There is a subset \(COM_{44}=\{\rm B_{4},\rm T_{1},\rm B_{5},\rm L_{CXI}\}\), a subset \(COM_{51}=\{\rm B_{5},\rm T_{1},\rm L_{CXI},\rm B_{4},\rm L_{CXI},\rm L_{CXI},\rm L_{CXI},\rm L_{CXI}\}\), and a total set \(COMS=\{\rm T_{1},\rm B_{4},\rm B_{5}\}\).
2. \(\rm T_{1}\) is selected for fault diagnosis. The \(\mathbf{\alpha}_{p(11)}^{0}\), \(\mathbf{T}_{p(11)}^{0}\), \(\mathbf{\alpha}_{p(12)}^{0}\), and \(\mathbf{T}_{p(12)}^{0}\) of the fault diagnosis model for the \(\rm CB_{44}\) and \(\rm CB_{51}\) subnets are as follows. \[\left\{\begin{array}{l}\mathbf{\alpha}_{p(11)}^{0}=[0.9833,0.2,0.2,0.7756,0.2,0.2,0,0,0,0]^{\rm T}\\ \mathbf{T}_{p(11)}^{0}=[[25,25],[25,25],\varnothing,[0,0],\varnothing,\varnothing, \varnothing,\varnothing]^{\rm T}\\ \end{array}\right.\] (40)\[\left\{\begin{array}{l}\alpha_{p(12)}^{0}=[0.9833,0.2,0.2,0.7756,0.2,0.2,0,0,0,0 ]^{\mathrm{T}}\\ T_{p(12)}^{0}=[[70,70],[70,70],\varnothing,[0,0],\varnothing,\varnothing,\varnothing, \varnothing,\varnothing]^{\mathrm{T}}\end{array}\right. \tag{41}\]
3. The diagnosis results for the CB\({}_{44}\) subnet are \(\alpha_{p10(11)}=0.8797\) and \(T_{p10(11)}=[-40,-10]\). The diagnosis results of the CB\({}_{51}\) subnet are \(\alpha_{p10(12)}=0.8795\) and \(T_{p10(12)}=[-40,40]\). The diagnosis results of T\({}_{1213}\) are \(\alpha_{(1)}=0.8795\) and \(T_{(1)}=[-40,-10]\), and its measured value \(\eta_{(1)}=0.9156\), which is greater than 0.6. Thus, T\({}_{1}\) is a faulty component.
4. Similarly, the measured values of B\({}_{4}\) and B\({}_{5}\) are 0.1857 and 0.1881, respectively, and they are non-faulty components.
###### Abstract  
The knowledge of spatial and temporal variability of soil water content and others soil-vegetation variables (leaf area index, fractional cover) assumes high importance in crop management. Where and when the cloudiness limits the use of optical and thermal remote sensing techniques, synthetic aperture radar (SAR) imagery has proven to have several advantages (cloud penetration, day/night acquisitions and high spatial resolution). However, measured backscattering is controlled by several factors including SAR configuration (acquisition geometry, frequency and polarization), and target dielectric and geometric properties. Thus, uncertainties arise about the more suitable configuration to be used. With the launch of the ALOS Pulsar, Cosmo-Skymed and Sentinel 1 sensors, a dataset of multi-frequency (X, C, L) and multi-polarization (co- and cross-polarizations) images are now available from a virtual constellation; thus, significant issues concerning the retrieval of soil-vegetation variables using SAR are: (i) identifying the more suitable SAR configuration; (ii) understanding the affordability of a multi-frequency approach. In 2006, a vast dataset of both remotely sensed images (SAR and optical/thermal) and in situ data was collected in the framework of the AgriSAR 2006 project funded by ESA and DLR. Flights and sampling have taken place weekly from April to August. In situ data included soil water content, soil roughness, fractional coverage and Leaf Area Index (LAI). SAR airborne data consisted of multi-frequency and multi-polarized SAR images (X, C and L frequencies and HH, HV, VH and VV polarizations). By exploiting this very wide dataset, this paper, explores the capabilities of SAR in describing four of the main soil-vegetation variables (SVV). As a first attempt, backscattering and SVV temporal behaviors are compared (dynamic analysis) and single-channel regressions between backscattering and SVV are analyzed. Remarkably, no significant correlations were found between backscattering and soil roughness (over both bare and vegetated plots), whereas it has been noticed that the contributions of water content of soil underlying the vegetation often did not influence the backscattering (depending on canopy structure and SAR configuration). Most significant regressions were found between backscattering and SVV characterizing the vegetation biomass (fractional cover and LAI). Secondly, the effect of SVV changes on the spatial correlation among SAR channels (accounting for different polarization and/or frequencies) was explored. An inter-channel spatial/temporal correlation analysis is proposed by temporally correlating two-channel spatial correlation and SVV. This novel approach allowed a widening in the number of significant correlations and their strengths by also encompassing the use of SAR data acquired at two different frequencies.  
backscattering; soil water content; surface roughness; leaf area index; sensitivity analysis 20179677; doi:10.3390/rs9070677
## 1 Introduction  
The orbit is a well-designed and complex structure that protects the ocular globes. Orbital wall fractures are the result of traumatic forces being applied to the globe or surrounding bone [1]. The intricate geometry of the bony orbit makes anatomical reconstruction extremely challenging [2]. These traumas to the orbit may cause enophthalmos, globe dystopia, diplopia, or extraocular movement limitation if precise restoration is not achieved [1; 3; 4].  
Orbital wall fractures are commonly managed through the implementation of implants to restore the integrity of the bony structure. Conventional implants are shaped and bent manually during the surgical procedure. However, accurate bending of orbital implant for achieving precise reconstruction of the orbital wall is time consuming and is highly dependent on the experience of the surgeon [5]. Manual molding and trimming of the implant according to a three-dimensional (3D) fracture shape can be significantly challenging when these procedures are based on two-dimensional (2-D) computed tomography (CT) images, especially for large fractures or when performed by inexperienced surgeons. Inaccurate positioning of implants may also induce the above-mentioned complications [6].  
Three-dimensional printing technology has recently come into the spotlight [7; 8; 9]. This technology enables surgeons to design orbital implants that closely conform to the shapes and surfaces of individual fracture sites [10]. This can result in better functional and aesthetic outcomes and may reduce the risk of surgical complications, with the latter being particularly important for surgeons with less experience [11; 12]. The actual time spent in the operating room could also be reduced since the preoperative planning would be more accurate and detailed, and the implants would be customized to the patient. This can reduce risks associated with prolonged surgeries and anesthesia [13].  
In our previous study, we introduced customized 3D-printed orbital implant templates and intraoperatively applied them to patients with orbital wall fractures [8]. Our study provided quantitative evidence demonstrating the highly effective reconstruction of anatomical contours using these templates. The authors of the present study measured orbital volumes to compare the efficacy of customized orbital implants using 3D-printed templates with conventional manual-bending implants in orbital wall reconstruction.
## 3 Attitude Controller Design  
For the NTAUV, which is heavier than water, the attitude control is the essential function for maneuvering control. In general, an attitude controller is designed as the inner loop of a higher level controller, such as path following or trajectory tracking.  
In this section, an adaptive I&I attitude controller is designed. I&I is a nonlinear controller design method, and it yields a stabilizing scheme that counters the effect of the uncertain parameters adopting a robustness perspective [29; 30; 31]. The stability of the controller is proved in Section 3.3.
# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 2 Methods
```
Data: Video frame set \(F\), initial tracking state set \(T_{t-1}\) Result: Updated tracking state set \(T_{t}^{*}\), lost target set \(L\) Input :\(D_{t}\), \(P_{t}\) Output :\(T_{t}^{*}\), \(L\)
1\(F_{t}\) // Current frame \(D_{t}\leftarrow\texttt{Detect~{}Objects}(F_{t})\) // detection boxes \(D_{h},D_{l}\leftarrow\texttt{Detect~{}Objects}(F_{t})\) // High scoring detection boxes \(D_{h}\), low scoring detection boxes \(D_{l}\)
2\(P_{t}\leftarrow\texttt{Predict~{}Tracks}(T_{t-1})\) // Predict the target bounding boxes in the next frame
3\(OF\leftarrow\texttt{Calculate~{}OF~{}Vectors}(D_{t},P_{t})\) // Calculate the average optical flow vectors to represent the temporal information of the target
4\(OF_{d}\leftarrow\|((D_{t},D_{t}+1)\text{ and }(P_{t},P_{t}+1))\|\) // Calculate the Euclidean distance of the optical flow vector for consecutive frames
5\(IoU\leftarrow\texttt{Calculate~{}IoU}(D_{t},P_{t})\) // Calculate the Intersection over Union (IoU)
6\(IoU_{d}\gets 1-IoU\) // \(IoU_{d}\) is an element in the \(IoU\) distance matrix
7\(IoU_{d},OF_{d}m\leftarrow\texttt{Calculate~{}Feature~{}Matrix}(IoU_{d},OF_{d})\) // Calculate the feature similarity matrices of \(IoU_{d}\) and \(OF_{d}\) separately
8\(S_{f}\gets a\times IoU_{d}m+(1-\alpha)\times OF_{d}m\) // \(S_{f}\) is the similarity matrix obtained by fusing \(IoU\) and \(OF\) features
9\(S\) // \(S\) is the feature similarity matrix obtained by fusing \(S_{f}\) and ReID
10\(MT_{h},UD_{h},RT_{h}\leftarrow\texttt{Match}(D_{h},P_{t},S)\otimes\theta_{1}\) // The relationship between first stage matching judgment and \(\theta_{1}\)
11\(T_{t}^{*}\leftarrow\texttt{Update~{}Tracks}(MT_{h},T_{t-1})\) // Update tracking states
12\(MT_{l},UD_{l},RRT\leftarrow\texttt{Match}(D_{l}\cup RT_{h},P_{t},S_{f}) \otimes\theta_{2}\) // The relationship between second stage matching judgment and \(\theta_{2}\)
13\(T_{t}^{*}\leftarrow\texttt{Update~{}Tracks}(MT_{l},T_{t}^{*})\) // Update tracking states
14 foreachtrack\(\in\)RRTdo
15\(i\gets 0\)
16 while\(i<30\)and\(track\
otin L\)do
17\(D_{t+i}\leftarrow\texttt{Detect~{}Objects}(F_{t+i})\)
18\(MT,UD,remainingRRT\leftarrow\texttt{Match}(D_{t+i},P_{t+i},S_{f})\)
19 if\(track\in remainingRRT\)then
20\(T_{t}^{*}\leftarrow\texttt{Update~{}Tracks}(MT,T_{t}^{*})\)
21\(i\gets 30\) // If the track is matched within 30 frames, update \(i\) and break the loop
22 end if
23 else
24\(i\gets i+1\) // If the track is not matched within the next 30 frames, add it to the lost track set \(L\)
25 end if
26 if\(i=30\)then
27\(L\gets L\cup\{track\}\)
28 end if
29
30 end if
31 end while
```  
**Algorithm 1**IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm.
# Fog Computing for Smart Cities' Big Data Management and Analytics: A Review
## 8 Challenges and Open Research Issues
### Characterizing and Mapping Smart City Applications  
Applications have diverse requirements. Some of them have low data rates and/or low latency requirements, while others like medical imaging or video surveillance have very high data rates. Therefore, IoT applications need to be characterized to determine the key issues in mapping them to an edge device, fog, and cloud hierarchy; then, allocation and scheduling algorithms need to be created to map workloads on top of the edge, fog, and cloud systems [114]. Furthermore, user and vehicle mobility are scenarios that need to be characterized to allocate required resources in the edge-cloud continuum [115; 116]. Mobility may involve moving the state of an application from one service to another or moving an entire application and its dependencies [117].  
Smart city services are typically composed and executed as dependent task workflows. These composite services often experience delays if they rely on cloud solutions. In [118], the authors proposed personalized multimedia services delivery by composing service-specific overlays (SSOs) to take advantage of mobile edge computing's recent advancements. They presented a workflow network-based approach to mobile edge nodes' cooperation in a fog-cloud hierarchy to form guaranteed SSOs.
## 5 Changes in Rock Properties with Increasing Freeze-Thaw Cycle
### Changes in Physical and Mechanical Properties of Rocks
#### 5.3.5 Changes in P-Wave Velocity  
Similar to the method used to examine absorption changes with increasing freeze-thaw cycle, P-wave velocities of the rocks were measured every 150 cycles up to 900 cycles. As shown in Figure 11, the P-wave velocity showed a clear decreasing trend in all rocks with increasing number of freeze-thaw cycle. The baseline P-wave velocities of rocks from Locations A and B were 6136 m/s and 4231 m/s, respectively. The mean P-wave velocity of the rocks from Location A after 900 freeze-thaw cycles was 5035 m/s, which was a 17.9% decrease compared to baseline, while that of the rocks from Location  
Figure 10: Absorption changes with increasing freezing-thaw cycle of locations A and B.  
B was 4051 m/s, which was a 4.2% decrease. In average, the rocks from Locations A and B showed decreases of 1.1223 m/s and 0.1398 m/s per cycle, respectively. Thus, the change in P-wave velocity with increasing freeze-thaw cycle was approximately eight times faster for the rocks from Location A than for those from Location B. Such behaviour results from increase of absorption due to increased micro cracks on rock surface with increasing freeze-thaw cycles of absorbed water within the rocks. Therefore, the increased micro cracks induced a reduction of P-wave velocity.  
The P-wave velocity showed an apparent decreasing trend in all rocks with more freeze-thaw cycles because additional micro fissures or cracks developed inside the rocks during weathering. As the size of surface pores becomes larger with increasing freeze-thaw cycle, resulting in increased time for waves to be transmitted through the specimens, or an increase in discontinuities, leading to a decrease in P-wave velocity.  
Rocks from Location A relatively have low porosity and high P-wave velocity compared to those of rocks from Location B. The main difference in porosity and P-wave velocity of rocks from Locations A and B results from different mineralogy. From the results from the experiments in this study, porosity and P-wave velocity of rocks from Location A increased by 1.4 times higher and decreased by 8 times lower than that from Location B with an increasing number of freeze-thaw cycles. Therefore, it is inferred that calcite and dolomite dominantly found from rocks of Location A are more susceptible to freeze-thaw weathering compared with quartzite dominantly found from rocks of Location B. A more significant weathering trend of rocks from Location A induced higher R\({}^{2}\) values, compared to that for rocks from Location B. Even relatively small scatters of data from the fitted trend line having small gradient could exhibit small R\({}^{2}\) value.
## 5 Mapping
### Loop Closure  
Incremental frame tracking could cause drift accumulation, and after a small drift was transmitted through many keyframes, it could produce unacceptable results. Loop closure and global optimization were considered effective methods to solve this cumulative drift. The bag of binary words (BoW) [37] was applied for loop closure detection in the proposed approach. Loop closure detection was carried out after the new keyframe was added to the map. Once the loop was detected, the global BA was implemented to achieve the global consistency of the map.
## 4 Experimental Results
### Evaluation Metric  
The surrogate model is evaluated by using the following metrics [37]. The root means squared error (RMSE) and the squared correlation coefficient (\(R^{2}\)). The root means square error is the average of mean error between the actual and predicted value.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})} \tag{5}\]  
\[R^{2}=1-\sum_{i=1}^{n}(y_{i}-\hat{y_{i}}) \tag{6}\]
# Reducing Safety Risks in Construction Tower Crane Operations: A Dynamic Path Planning Model
## 1 Introduction
These methods incorporate multiple physical constraints to achieve safe and efficient material handling in challenging offshore environments [17; 18; 19]. In construction sites, autonomous tower cranes have implemented offline trajectory planning algorithms to enable precise payload movement along straight lines while efficiently navigating through obstacles [20; 21]. To further enhance safety and efficiency in tower crane operations at construction sites, researchers have developed various intelligent algorithms for path planning optimization [22; 23; 24]. [PERSON] et al. [25] compared and summarized three intelligent algorithms, including the node generation-based A-star (A*) search algorithm, the sampling-based Rapidly Exploring Random Tree (RRT), and the heuristic Genetic Algorithm (GA). [PERSON] et al. [26] studied the optimization of tower crane path points based on construction crane operation standards and proposed an improved RRT Connect algorithm. By introducing heuristic information into the path search, the efficiency and quality of the algorithm were enhanced. [PERSON] et al. [27] proposed an automated lift path planning method for tower cranes based on environmental point clouds, utilizing an optimized A* algorithm to identify executable paths. However, in real-world crane operations, the dynamic environment is complicated by the unpredictable movement of ground workers. Traditional path planning methods may not provide effective decision support to mitigate the risk of falling loads. To address these limitations in tower crane operations, this study aims to develop an effective method for mitigating the risk of falling loads to ground workers during tower crane operations, thereby providing operational support to tower crane operators and ensuring safety. We propose a dynamic path planning model for tower crane operations that integrates computer vision and path planning algorithms. This model includes three key modules: a path information collection module, a path safety risk evaluation module, and a path planning module. It identifies on-site information necessary for the dynamic operation of tower cranes, perceives safety risks, and proposes path planning solutions. The instance segmentation capability of the You Only Look Once version 8 (YOLOv8) framework is demonstrated through a case study, and the effectiveness of the improved Dynamic Window Approach for tower cranes (TC-DWA) algorithm for path planning is validated via simulation experiments. The study also involves the construction of a safety monitoring platform for tower crane operations, which revealed a significant number of unsafe conditions on construction sites. This platform provides driving reminders and suggestions for adjusting the operation paths of tower crane operators.