esa-sceva/satcom-chunk-collection huggingface dataset:
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 4 Experiments  
### Experiment Settings  
We use a three-dimensional laser scanner from Longruan Co., Ltd., Beijing, China. It consists of a handheld LiDAR and an intrinsically safe power supply, for data acquisition in this study, as shown in Figure 12. The spherical component represents the LiDAR, which is connected to a motor responsible for controlling the rotation of the radar. Below the motor is the handheld grip, and at the bottom is the base, designed for convenient point marking. This device is equipped with a 16-line LiDAR, meaning it can simultaneously emit 16 laser beams in the vertical direction, with 16 corresponding laser receivers to capture the reflected laser beams. This feature ensures high precision and efficiency during the scanning process. The main performance indicators and parameters of this instrument are listed in Table 1.  
This paper selects point cloud data of tunnels collected from four different mines to demonstrate the effectiveness of the algorithm, as shown in Figure 13. The tunnels' attribute information is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Performance** & **Parameter** & **Performance Indicator** & **Parameter** \\ \hline \multirow{6}{*}{General Parameters} & Handheld & \multirow{2}{*}{L204 \(\times\) W130 \(\times\) H385} & \multirow{2}{*}{Operating Temperature} & 0 \({}^{\circ}\)C\(\sim\)40 \({}^{\circ}\)C \\  & Dimensions & & & \\ \cline{1-1}  & Battery Box & \multirow{2}{*}{L210 \(\times\) W188 \(\times\) H75} & \multirow{2}{*}{Helative Humidity} & \(\leq\)95\% (+25 \({}^{\circ}\)C) \\ \cline{1-1}  & Dimensions & & & \\ \cline{1-1}  & Handheld & \multirow{2}{*}{3 kg} & Atmospheric & \multirow{2}{*}{80 kPa\(\sim\)106 kPa} \\ \cline{1-1}  & Weight & & & \\ \cline{1-1}  & Protection & & & \\ \cline{1-1}  & Rating & & & \\ \cline{1-1}  & Port & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & Voltage & 12 V & & \\ \cline{1-1}  & Storage & \multirow{2}{*}{256 GB} & \multirow{2}{*}{Measurement Distance} & \multirow{2}{*}{120 m} \\ \cline{1-1}  & Capacity & & & \\ \cline{1-1}  & Single Battery & \multirow{2}{*}{4 h} & \multirow{2}{*}{Scanning Field of View} & \multirow{2}{*}{280\({}^{\circ}\)\(\times\) 360\({}^{\circ}\)} \\ \cline{1-1}  & Working Time & & & \\ \hline Point Cloud & Relative & \multirow{2}{*}{1 cm} & Absolute & \multirow{2}{*}{\(\leq\)5 cm} \\ \cline{1-1} Parameters & Accuracy & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of LiDAR and point cloud.  
Figure 12: The three-dimensional laser scanner used in this study.  
This paper utilizes the Point Cloud Library (PCL) [47] for the core point cloud processing tasks and employs CloudCompare [48] for result visualization.
## 3 Results
### Velocity 3.9-5 m/s
#### 3.4.4 Changes in the Values of Selected Parameters  
Bubble charts prepared in R Studio (version 4.3.3) illustrate quantitative changes for the most commonly analyzed parameters (SL, StL, ST, FT, CT, SF) (Figures 2-4). The charts include studies that provide exact numerical results and assume a constant speed for all subjects [29; 30; 31; 32; 33; 34; 35; 37; 38; 39]. For SL, the highest value, 2.48 m, was observed at a 0% incline [29]. The lowest value, 1.99 m, was recorded by [PERSON] and [PERSON] [30] at a 15.8% incline and a speed of 3 m/s. For StL, as for SL, the lowest value (0.99 m) was noted for a steep incline (17.6%) and a speed of 2.8 m/s. [PERSON] et al. [34; 35] observed as much as a 71.7% (1.70 m) higher value on a 0% slope. This test was conducted at a much higher speed (5 m/s). For similar speeds, the numerical values of SL and StL were lower at high slope values (Figure 2).  
The longest ST was observed for negative gradient values at low speeds. In all measured cases, however, it oscillated between 450 and 470 ms. The FT time lengthened with increasing speed and shortened with increasing slope [30]. In the study, FT decreased by 22% when the slope increased to 8% compared to \(-\)8% at a speed of 2.8 m/s. In the [PERSON] et al. study [34; 35], the largest observed difference was 22.8% at a speed of 5 m/s (Figure 3).  
Figure 3: Bubble chart for swing time and flight time.  
Figure 2: Bubble chart for stride length and step length.  
Step frequency increased with increasing slope. The largest percentage difference was noted by [PERSON] et al. [34; 35], which was 14.58% (5 m/s, 0% to 7% slope). For the same inclinations, at 3.1 m/s, [PERSON] et al. [39] noted a difference of only 1.07%. CT values ranged from 250 ms to 330 ms for speeds of 2.8 to 3.2 m/s [30; 31; 32; 33; 37]. Significantly lower CT values were observed at high speeds (3.9-5 m/s), which ranged from 186 ms to 152 ms, according to [PERSON] et al. [34]. Regardless of inclinations, CT was slightly shorter at negative gradients and lower speeds [30; 31]. For speeds of 3.9-5 m/s and inclinations of 0% to 7%, the differences became more pronounced as the speed increased. For a speed of 4.4 m/s, the increase in CT at a 7.19% versus a 0% gradient was 7% (Figure 4).
## 3 Case Study  
A half-through concrete-filled steel tube truss arch bridge is shown in Figure 6, whose main clear span is 190 m, the rise-span ratio is 1/4.5, the arch axis is hingeless catenary, and the arch axis coefficient \(m=1.167\). An arch rib is a concrete-filled steel tube truss structure of the uniform section, and the cross section adopts \(\varphi\)820 \(\times\) 12 mm and \(\varphi\)820 \(\times\) 14 mm. The steel tubes form the top and bottom chord bar of the arch rib, and the section is 4.3 m high and 2.0 m wide. There are 27 hangers on each side of the main bridge, with an equal spacing of 5.1 m. The bridge was opened to traffic in December 2003, and there were some conditions such as a damaged sheath of the hanger, stagnant water at the anchor head, and a large deviation of the cable force of part of the hanger after 13 years of operation.  
The bridge hangers were replaced in 2016 with the pocket hanging method and the upper and lower hangers were adopted as a pair to replace at the same time, which are composed of two \(\varphi\)60 mm steel-core wire ropes (6 \(\times\) 37 S + IWR) symmetrically mounted on the arch rib, two 136b I-shaped steel pocket hanging beams, four sets of tension jacks, and QMV.DHM15-6 loose prevention anchorage with a low retraction anchor belt.  
A vertical force of 1 KN was applied to the lower end of the hanger to be replaced (hanger #10), and the displacement of the bridge deck at the lower end of hanger #10 with different hangers on both sides of hanger #10 was calculated by the force method, as shown in Figure 7. The displacement of the bridge deck at the lower end of hanger #10 varied with the number of reserved hangers on both sides of hanger #10 as listed in Figure 7. It was found that when two or more hangers were left on both sides of hanger #10, the displacement of the bridge deck at the lower end of hanger #10 was basically unchanged through calculation. Therefore, only two hangers on each side of the hanger to be replaced can meet the process needs in practical application.  
Figure 6: Bridge elevation.  
This study aimed to implement the hanger replacement procedure of pocket hanging described by a new method of displacement control, and the precise application procedure on the actual bridge is given as follows: Figure 8a shows the bridge deck test site at the displacement of the lower end of the hanger and Figure 8b shows the cable force test of the hanger. The influence of temperature is usually a difficult factor in the construction control. The temperature is changeable, which has an important influence on the stress and linearity of the bridge structure. Especially for the concrete-filled steel tubular arch bridge, the influence of temperature on the displacement is very significant. In this hanger replacement engineering application, temperature sensors were set to monitor the temperature of the bridge cross section, and the temperature simulation was carried out in the form of integral heating and uniform distribution in the finite element model.
## 3 Experimental Setup  
### Datasets  
The Wuhan University building (WHU) CD dataset (WHU-CD) [48] is a commonly used public available dataset. The dataset includes 12796 independent buildings extracted from aerial images. The image size is 32,507 \(\times\) 15,354 and the pixel spacing is 0.2 m. It covers 20.5 km\({}^{2}\) of the Christchurch area in New Zealand, in 2012 and 2016. The WHU-CD is used to benchmark different BE and CD network choices and experimental setups. The entire image is first cropped into 512 \(\times\) 512 patches, and the whole dataset is then randomly split into three parts with a ratio of 7:1:2 for training, validation, and testing, respectively.  
The BE and CD dataset in the Gaofen Challenge (Gaofen-BECD) included the Gaofen-2 and Jilin-1 multi-temporal satellite imagery, and the pixel spacing is smaller than 1 m. All 4000 images with a size of 512 \(\times\) 512 formed 2000 paired bi-temporal images. All images provided were used as the training set. To create a validation set, approximately 10% of the training set was randomly selected and processed by random 0-10 degrees rotation, random flip, and random 0.9-1.1 scaled resize. Our training set contains 2000 paired images, and the validation set contained 200 generated paired images.
## 4 Case Studies: Supply Chain and Public Tender
### Supply Chains  
As discussed in Section 1.1, the supply chain scenario is of paramount importance within the scope of BCT-based IoT. In the described scenario, the off-chain entity in charge of acknowledging the dispatch of the products, namely the distributor company, represents a single point of trust. In fact, in the worst case it may arbitrarily delay its acknowledgement, thus preventing the manufacturing company from being refunded. As a countermeasure, the Tenderfone SC shown in Listing 1 can be exploited. It leverages on Tenderfone time-related features to make the smart contract autonomously refund the manufacturing company as soon as the shipment duration exceeds a given threshold. This removes the need for an off-chain entity in charge of triggering the refunding in case of a late dispatch. In other words, the SC from Listing 1 can be modelled as the timed finite state automaton depicted in Figure 7 where the transition into the \"Late shipment\" state can be provoked by the flow of time, automatically.  
The functioning of the SC in Listing 1 is quite straightforward. By construction, it is assumed to know the identities of both the manufacturer and the distributor, other than the ID of the product being shipped. The expected duration of the shipment is provided to the SC upon deployment. After that, the SC _(i)_ waits for the manufacturing company to initiate the shipping procedure; _(ii)_ as soon as the shipping procedure is initiated, it schedules the automatic refunding for a later moment in time; _(iii)_ then, it waits for the distributor company to acknowledge the product dispatch. The final outcome of the shipping procedure depends on which event occurs first: in case the distributor acknowledgement firstly occurs, then no refund is performed; otherwise, refund is automatically performed and the late acknowledgement is ignored.
# The Importance of Gram-Negative Rods in Chronic Rhinosinusitis
## 2 Materials and Methods
### Biofilm Formation  
To determine in vitro biofilm formation, the crystal violet assay was performed as described previously [32; 33]. Briefly, isolates grown for 24 h on tryptic soy agar (TSA, OXOID, Hampshire, UK) were suspended in a brain heart infusion broth (BHI, OXOID, Hampshire, UK), adjusted to a turbidity of 0.5 MacFarland and diluted 1:100 in BHI. After dilution, 200 \(\upmu\)L of each sample was seeded into 96-well flat-bottom microtiter plates and incubated at 36 \(\pm\) 1 \({}^{\circ}\)C for 20 h. After incubation, the culture medium was discarded from the microtiter plate, and the wells were washed three times with deionized water, then stained with 0.1% crystal violet for 15 min, rinsed with water, and air-dried overnight. The crystal violet from the stained biofilm was eluted in 250 \(\upmu\)L of 95% ethanol. The optical density (OD) of stained adherent biofilm was measured using an Infinite M200 (Tecan, Grodig, Austria) plate reader at a wavelength of 590 nm. Wells containing uninoculated BHI media served as a negative control. Tests were repeated three times.  
The biofilm formation was interpreted according to the previously described criteria [32]:  
ODc = mean OD of negative control + 3 \(\times\) standard deviation (SD) of negative control;  
1. Non-biofilm producer (N): OD \(\leq\) ODc;
2. Weak-biofilm producer (W): ODc < OD \(\leq\) 2 \(\times\) ODc;
3. Moderate-biofilm producer (M): 2 \(\times\) ODc < OD \(\leq\) 4 \(\times\) ODc;
4. Strong-biofilm producer (S): OD > 4 \(\times\) ODc.
# Development and Evaluation of Topical Formulation Containing _Agrimonia pilosa_ Extract
## 1 Introduction  
Natural products can be developed as medicines or used as cosmetics. And the target market is large with a high growth rate [1; 2]. Natural products contain numerous ingredients that show various effects. Although, each of these components exhibits pharmacological effects, synergistic effects usually appear when they exist together in various components. However, it has been difficult to provide clarity on this point [3; 4]. In ancient times, medicines were developed from natural products based on traditional knowledge. As separation, extraction, and formulation technologies have developed, the strategies for developing natural medicines have also developed systematically [5; 6; 7; 8; 9; 10; 11]. Many ingredients have shown excellent efficacy in both in vitro and in vivo experiments, but in many cases, stability is not guaranteed or bioavailability in the human body is poor. Therefore, it can be difficult to achieve the desired effect [12; 13]. To reduce unexpected side effects and ensure expected effects, formulation and stability evaluation should be conducted for all products, including pharmaceuticals and cosmetics, and expiration dates must be set [14; 15; 16]. Recently, this importance has been gradually recognized and evaluations on the stability of natural products have been reported [17; 18; 19]. However, most did not analyze the ingredients and evaluate the stability.  
_Agrimonia pilosa_ is distributed throughout East Asia and Central Europe and is a widely used traditional medicinal herb owing to its anticancer, antioxidant, and anti-inflammatoryproperties and beneficial effects on skin inflammation. The herb is reported to contain flavonoid, isocumarin, triterpene, and tannin components and has been used for hemostasis and to treat branch trichomoniasis and malaria [20; 21; 22; 23; 24]. Apigenin-7-O-glucuron (A7 OG) and luteolin-7-O-glucuron(L7 OG), a representative flavonoid glucuronides present in _A. pilosa_ (Figure 1), have shown many pharmacological effects, such as anti-inflammatory properties [25; 26; 27; 28; 29]. Thus, _A. pilosa_ exhibits potential for use as a medicine. In this study, we manufactured formulations that could be utilized and evaluated for their physicochemical properties and biological stability. The optimal formulation was selected by comparing physicochemical stability among the preparations. We selected A7 OG and L7 OG as an indicator ingredients because they not only possess anti-inflammatory efficacy but can also be found in a sufficient amount for analysis. The selected formulation was evaluated under long-term and accelerated stability conditions. The physicochemical stability, flavonoid glucuronide content, and biological stability were evaluated to observe the occurrence of contamination, and the stability was evaluated comprehensively. In this experiment, we aimed to validate the analysis method for flavonoid glucuronide, which is an indicator component develop an optimized formulation containing _A. pilosa_ and evaluate the stability comprehensively.
# A Non-Fungible Token and Blockchain-Based Cotton Lint Traceability Solution
## 6 Discussion
### Smart Contract Security Analysis  
In this study, we explore the security analysis process of the proposed solution. Ensuring the security of the smart contract code is crucial, given that smart contracts written by Solidity can be at risk of potential attacks, flaws and vulnerabilities when executed on the blockchain and cannot be changed once deployed. Relying on the Remix IDE for essential syntax error detection is insufficient to secure smart contracts. For this purpose, we employ a specialised smart contract vulnerability detection tool called Oyente, which operates directly against the EVM bytecode and can identify and report potential security issues, such as DAO attack-like vulnerabilities. To ensure the supply chain's (SC) security, we use Oyente to perform several security checks on the smart contracts, including checks for re-entry attacks, integer underflow, integer overflow and timestamp dependency. Figure 10 shows the results of the smart contract security analysis. All the security check results of the inspection NFT smart contract indicate the absence of a security risk, and its code coverage is 92.6%; higher coverage means that more code is scanned and verified. These smart contracts successfully avoid the effects of known attack methods and vulnerabilities through multiple security analyses and corresponding code revisions.  
Our solution is built around the blockchain technology and is designed to provide a decentralised, tamper-proof and transparent means of managing information and data. Data integrity, as one of the vital security attributes of this scheme, provides a solid foundation for the trust mechanism between participants. All participants involved in the system, including the seed cotton owners, manufacturers, lint inspectors and end customers, interact through the Ethernet network. Whether for a small-scale auction transaction of seed cotton or a large-scale record of the flow of cotton lint, all transactional activity is stored in hash form on the blockchain ledger.
## 3 Materials and Methods
### Location and Climate Conditions  
The city of Guayaquil (2\({}^{\circ}\)11\({}^{\prime}\)21.89\({}^{\prime\prime}\) S, 79\({}^{\circ}\)53\({}^{\prime}\)20.64\({}^{\prime\prime}\) W) is the largest and the most populated city of Ecuador, with an estimated area of 2494 km\({}^{2}\) and about 2.72 million people in 2020, based on projections of the National Census Institute (INEC) [46]. Guayaquil has a tropical climate, corresponding to the Aw group from the Koppen-Geiger classification [44], with two well-defined seasons: wet (January-April) and dry (May-December). The climate conditions of Guayaquil are depicted in Figure 1. The monthly average temperatures vary between 23.4 to 26.5 \({}^{\circ}\)C. The monthly minimum temperatures are above 18.0 \({}^{\circ}\)C, while the monthly maximum temperatures are below 33.2 \({}^{\circ}\)C. The relative humidity values are higher during the wet season, while the annual average is about 70%. The monthly average global radiations are between the 3.5 and 5.3 kWh/m\({}^{2}\).  
Figure 2 shows the distribution of the annual climate conditions of Guayaquil (temperature and humidity) plotted on the psychometric chart based on a Typical Meteorological Year (TMY) file. As can be observed, during almost all the year, the city presents high outdoor temperatures and humidity. It means that around 86% of the year people could experiment indoor discomfort. Therefore, efficient air-conditioning systems and/or passive energy saving strategies are required to reduce the energy demand for cooling in these climates. Some strategies suggested by the Climate Consultant v. 6.0 tool [48] for the studied climate are dehumidification, sustainable cooling, and solar shading.
## 4 Experiments and Analyses  
### Datasets and Metrics  
This study employs extensive experiments on two benchmark datasets, FUNSD [7] and XFUND [23], and the Chinese medical examination report dataset (CMERD) gathered from real medical scenarios. FUNSD contains 199 real, fully labeled scanned images and is divided into 149 training sets as well as 50 test sets for many types of KIE tasks. XFUND is a multilingual dataset for KIE tasks. It contains form data across 7 languages. Each language contains 199 form-data instances and is divided into 149 training sets and 50 test sets. XFUND statistics are presented in Table 1, where each number indicates the number of units in each category. CMERD contains 657 images from different medical institutions, covering different types, departments, diseases, etc. Text block content and its spatial coordinates within the images are labeled by DB-Net++ [14], while text recognition is facilitated by CRNN [15]. For key information extraction, 11 entity categories are denoted, including the test unit, the full Chinese name of the test item, the test result, reference range, hint information, etc. The number of categories presented in the training and test labels is counted in Figure 9. Due to the complexity and variety of entities in the patient identification table header (application department, clinical diagnosis, specimen type, etc. ), the key-value is adopted for annotation. The key is defined as a question, and its corresponding value is defined as the answer entity, with all remaining information categorized as Other. The final result is that each piece of information on the MER image exists in the form of a dictionary. Each message contains \"transcription\", \"label\", \"points\", \"id\", and \"linking\" segments. Specifically, the \"points\" segment corresponds to the coordinates \((x1,y1,x2,y2)\), and the value of the \"linking\" segment is [question id, answer id]. If no key-value information is present, the \"linking\" remains empty. Moreover, this paper labels the \"Body\" with the smallest bounding rectangular box that encompasses all the entities in each row, and employs its position as the training label for the associated entity detection. The 657 images from CMERD are randomly divided into a training set (70%) and a test set (30%). A visual representation of some results is provided in Figure 10. An example of a MER document image with annotations and an explanation is shown in Figure 11.  
Figure 10: Some results of Chinese medical examination report dataset (CMERD) annotations; (**a**) is the annotated medical examination report (MER) image sample; (**b**) is the label sample of the MER image; (**c**) denotes semantic entity recognition (SER) label results; (**d**) denotes relationship extraction (RE) label results; (**e**) is the sample of the minimum bounding rectangular box for each row of entities for additional annotations; (**f**) denotes a sample of associated entity detection training labels. Figure 9: Statistical chart of the number of entities in the 11 categories in the training and test sets.
## 4 Results
### First Case Study: Prang Structure  
The models derived from the UAV and models obtained from TLS were compared with total station data to make more detailed assessments. The results listed in Table 3 indicate that the RMSE horizontal of the Prang structure for TLS-calculated data from 20 checkpoints of ground control is 0.068 m, and that of the UAV is 0.066 m. The RMSEs of the vertical Prang structure were 0.030 and 0.054 m for TLS and UAV, respectively.