esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Assessment of Urease Activity  
Urease activity of the cultured bacterial cells was assessed at different pH and temperature levels following the indophenol method [21; 22] where ureolysis occurred directly by the bacterial cell in a solution. The collected sample solution (1 mL) from the cell culture was added to 100 mL of the stratum solution containing 0.3 M urea and 0.1 M EDTA (Basic Local Alignment Search Tool) buffer with phenol-nitroprusside. The reaction composition was stirred using a water bath and kept at a steady temperature and pH. At the sampling point, 10 mL solution was sampled and 0.1 mL of 10 M NaCl was added to the solution to prevent quick enzymatic hydrolysis. The composed solution was incubated subsequently for 10 min at 50-60 \({}^{\circ}\)C. The urease activity test was carried out to evaluate the effect of the reaction solution conditions, by 48 h cultured cells at distinctive temperatures ranging from 10 to 60 \({}^{\circ}\)C, and the pH was 6, 7, and 8 (adding EDTA buffer solutions at 30 \({}^{\circ}\)C). The urease activity of the discharged urease from the culture supernatants was also investigated. Cultured bacteria cell culture (1.2 mL) was centrifuged by 10,000 rpm for 5 min at 10 \({}^{\circ}\)C, and then the supernatant (1 mL) was used to examine urease activity as per the following method. Urease activity of the sampled supernatant would correspond to the discharge of urease. The urease activity of bacterial cell pellets and their relationship to bacterial cell growth was also evaluated.  
To remove the supernatant, a 40 mL sample was collected from the bacterial cell culture tube and centrifuged by 10,000 rpm, for 5 min at 10 \({}^{\circ}\)C. To get a solution from the resuspended bacterial cell, the cell pellet was washed by 0.1 M Tris and HCl buffer for repeated times. The urease activity assessment  
Figure 1: Map of Greece showing the sample collection site (Porto Rafti: N 37\({}^{\circ}\) 52\({}^{\prime}\) 57.1\({}^{\prime\prime}\), E 24\({}^{\circ}\) 00\({}^{\prime}\) 51.1\({}^{\prime\prime}\) and Loutraki: N 37\({}^{\circ}\) 57\({}^{\prime\prime}\) 02.3\({}^{\prime\prime}\), E 22\({}^{\circ}\) 57\({}^{\prime\prime}\) 37.7\({}^{\prime\prime}\)).  
was conducted considering different OD\({}_{600}\) values. The urease activity of the cell lysate, soluble and insoluble portion of the cell lysate, and finally the whole bacterial cell was also investigated.  
After 48 h culture of the cell, 10 mL sample solution was collected and centrifuged by following the same procedure as mentioned earlier. The sample solution was resuspended to remove the culture supernatant with 500 mL sonication buffer (0.1 M NaCl, 20 mM Tris/HCl, and 1 mM EDTA) and was named as the whole-cell. The soluble cell lysate was prepared using the disrupted bacterial cell using ultrasonication for 5 min with 130 W, and time interval was 1 min. Remaining bacterial cell-debris was also collected by centrifugation for 20 min at 4 \({}^{\circ}\)C with 8000 rpm, which was insoluble to the solution. The urease activity assessment was also conducted for both soluble and insoluble cases, followed by the indophenol method.
## 9 Discussion  
The average annual solar radiation at the proposed site on the horizontal surface is about 1598 kWh/m2 (as per NASA-SSE satellite data) [42], which is adequate for the installation of the solar PV ground-mounted utility scale system. Annual expected generation for the entire solar PV project is 510 MWh/year, where the evaluation is based on a probabilistic approach for the interpretation of the simulation results over several years.  
The 380 kW grid-connected PV system consists of 810 PV solar modules grouped into 20 \(\times\) 81 strings with each string containing 10 modules in series. The output of the strings is pooled in the array junction box through 4 mm2 photovoltaic DC cables. The output from the junction box is fed to the three grid-tied inverters. The grid-connected inverter is used as a power conditioning unit. DC and AC distribution cabinets contain protective components for the safety of the system.  
Based on the results obtained in PVsyst tools, one day of energy production by the PV system plant was measured and compared with the total electricity demand in one day, as  
Figure 6: CO2 emission reduction vs. time.  
shown in Figure 2. From the figure, it can be seen that only 16% of the electricity demand is covered. This is due to the restricted rule of NEM that only allows 75% of the MD of electricity to be connected to the utility grid.  
Although the PV system plant would only cover 16% of the total electricity demand, the reduction in the electricity bill would be significant since the solar PV plant produces energy during the on-peak hours. The electricity rates are MYR 0.365/kWh and MYR 0.224/kWh during on-peak and off-peak hours, respectively. Moreover, the MD point during the day can also be covered by the PV plant or can be reduced significantly. Since this plant is small compared with the electricity demand of the building, it will not export electricity except for on the weekends. It is expected that the electricity will be used on a daily basis, as shown in Figure 7.
# Universally Composable Oblivious Transfer with Low Communication
## 1 Introduction
The latticed-based cryptographic protocols have received more attention owing to their provable security and lower asymptotic complexity. In 2019, [PERSON] proposed a framework for the OT protocol [20] under the UC security model based on key exchange protocol and symmetric encryption. Based on the [PERSON]'s framework, [PERSON] constructed a UC-secure OT protocol [21]. In addition, [PERSON] proposed an efficient UC-secure OT protocol [22] based on the NewHope key exchange protocol [23] on the lattice. [PERSON] proposed a two-round UC-secure OT protocol [24] under the CRS model. It achieved statistical sender security and statistical receiver security under the LWE assumption. The errors in the LWE difficult problem are not deterministic, which must be added. However, the errors in the LWR difficult problem are generated by scaling and rounding. Besides, you can solve the LWE problem with an LWR RO machine under special parameter settings. The OT protocol is one of the most used fundamental cryptographic primitives in secure multi-party computation. The main problems of today's oblivious transfer protocols are as follows:  
1. Most existing OT protocols are based on the number theory and cannot resist quantum computer attacks. 2. Most existing OT protocols can only achieve full or half simulation security in the stand-alone model. 3. The efficient MPC protocols with low communication costs [25] have become the research focus.
# C-Legged Hexapod Robot Design Guidelines Based on Energy Analysis
## 2 Kinematic and Energy Model of a Single C-Leg System
### Energy Model  
#### 2.3.1 Energy Consideration  
Even in a simplified single C-legged system like the one being considered up to now in this article, there are many factors that contribute to the total energy consumption, and therefore, affect the power demand.  
On the one hand, there are kinetic energy (KE) and gravitational potential energy (GPE) variations, which ultimately involve energy consumption and are dependent on the system's kinematic model. It is worthwhile pointing out that this factor can be a big contributor to the total energy consumption, especially in legged robots, due to their complex kinematic motion, which produces non-linear trajectories. For example, a wheeled vehicle moving on a leveled, flat surface with the motors running at constant rpms will have no velocity changes, and therefore, no KE and no GPE changes. Let us now consider, a system consisting of a single C-leg shape moving on the same leveled, flat surface with the motor again set to constant rpms. In this case, the system will not be moving at a constant velocity or in a straight path, and therefore, will give rise to KE and GPE variations.  
On the other hand, one can find the classic sources of energy consumption that appear in every moving system: Friction losses, elastic deformations, drag, motors internal resistance, etc.  
In this subsection, the energy consumption and power demand, due to the motion of the system will be obtained using the Kinematic equations for a single C-Leg developed in the previous subsection. The Y- and X-directions will be considered separately. In the former, there are KE and GPE variations, while in the latter, only KE variations exist. Throughout this energy and power analysis, the system parameters that will be used for the graph representations will be a dimensionless system (\(l_{c}=0\)) with r = 0.2, m = 1 kg, \(\omega=60\) rpm.  
Afterwards, the total energy consumption and power demand will be calculated by adding the horizontal and vertical contributors, and the impact of the mass, and the leg radius and leg angular speed on the energy and power will be graphically analyzed.
## 3 Theoretical Foundation
### 6G Enabled Tactile Internet Framework for Healthcare  
The theoretical foundation of wireless communication is based on fundamental principles from communication and information theory, signal processing, detection, and estimation. The 6G framework is an emerging network technology, and distinct devices characteristically evolve to the subsequent paradigm value-added services and are achieved through the development of a distributed service 6G enabled Tactile Internet framework, as shown in Figure 2, that encourages tactile apps for the healthcare industry.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.2 MODIS Lai  
The MODIS collection 6 (C6) LAI product (MCD15A3H) with 500 m spatial resolution is retrieved from combined Terra MODIS and Aqua MODIS every 4 days [40]. We used only the images with good quality (main algorithm with or without saturation), i.e., bit 0 of the quality control layer equals to 0. The main algorithm generating MODIS LAI product is based on a three-dimensional radiative transfer model that is used to generate look-up-tables (LUTs) [45]. The algorithm only uses the red and NIR daily surface reflectance data (MOD09 GA, 500 m) as input data because of high uncertainties in other bands [46]. The biome map is another important input, in which global vegetation is classified into eight biomes with different canopy and soil patterns [8; 47]. For the observed bi-directional reflectance factors in the red and NIR bands at a specific set of solar and view zenith angles, the algorithm finds the best LAI estimate from biome-specific LUTs. Compositing is done by selecting the daily retrieved value corresponding to maximum LAI over the 4-day compositing period.
# The Big Picture: An Improved Method for Mapping Shipping Activities
## 7 Conclusions and Future Work
\begin{tabular}{l l} AIS & Automatic Identification System \\ TER-AIS & Terrestrial-based AIS \\ SAT-AIS & Space-based (satellite) AIS \\ TDMA & Time Division Multiple Access \\ SOTDMA & Self Organized Time Division Multiple Access \\ CSTDMA & Carrier Sense Time Division Multiple Access \\ SAR & Synthetic Aperture Radar \\ CFAR & Constant False Alarm Rate \\ SUMO & Search for Unidentified Maritime Objects \\ AI & Artificial Intelligence \\ LSTM & Long Short-Term Memory \\ NNs & Neural Networks \\ RNN & Recurrent Neural Networks \\ SoG & Vessel Speed-over-Ground \\ CoG & Vessel Course-over-Ground \\ MMSI & Maritime Mobile Service Identity \\ A* & A-star algorithm \\ FastDTW & Fast Dynamic Time Warping \\ TCI & True Color Image \\ R-SWIR & Red Band and the Infrared Band \\ ESA & European Space Agency \\ EMFF & European Maritime and Fisheries Fund \\ CREXDATA & Critical Action Planning over Extreme-Scale Data \\ \end{tabular}
# Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset
## 3 Burapha-TH Dataset Construction
### Data Preparation Process
#### 3.3.1 Image Deskewing  
It is challenging to ensure that all paper is in the correct position for the scanning process. When scanning, some images are skewed, as shown in Figure 4(a). Algorithm 1 illustrates the pseudo-code for deskewing an image to overcome this problem. We used three OpenCV libraries [25] from a GitHub webpage [26] to implement this part. The libraries are DatasetService, DeskewService, and GraphicsServices. The procedure's input is an original handwritten form image file (dm), and a deskewed handwritten glyph from an image file (ddm) is the output result of the deskewing process. In this algorithm, the main procedure is deskewing (straightening) text in image form (line 7) by calling the deskew function from DeskewService (DeskewService().deskew) that returns deskewedimage and guessedAngle. The guessedAngle value is used to check for the proper angle at \(-20\) (line 8). We will call the rotate image function from the GraphicsService library in the skewed case, adding 90 to the guessedAngle (line 9). The output of the Image_deskewing algorithm is depicted in Figure 4(b).  
```
0:\(d_{m}\) #a original handwritten form image
0:\(d_{dm}\) #a deskewing handwritten from image
0:\(\text{DeskewingImage}(d_{m})\)
1:begin
2:from services.DatasetService import DatasetService
3:from services.DeskewService import DeskewService
4:from services.GraphicsService import GraphicsService
5:imageCV = GraphicsService().openImageCV(dm)
6:imagePath = dm.ImagePath()
```  
**Algorithm 1**Pseudo-code of Image_deskewing.  
Figure 4: Data Collection Sheets: (**a**) depicts three parts of collected information (**b**) depicts the case of recording the characters, vowels, and digits and the case of rewriting any incorrect writing.
## 2 Problem Statement and Preliminaries
### Station Neighbourhood and Criticality  
Rebalancing operations are fundamental to manage or potentially avoid in advance critical situations in which some stations are critically empty whereas some others are critically full. The entire framework leverages on the definition of critical station that takesinto account the occupancy status of neighbouring stations. To this aim, we first define the occupancy rate \(OR\) of a station \(s\) at a given timestamp \(t\) as:  
\[OR_{s}(t)=\frac{used\_slots_{s}(t)}{used\_slots_{s}(t)+free\_slots_{s}(t)}\]  
A station is critical if its occupancy rate is either \"much higher\" or \"much lower\" compared to the average occupancy rate of its neighbouring stations. A station in one of those two critical situations can be, or quickly become, full or empty and hence unavailable. To identify stations in a critical situation, we define the critical rate of a station \(s\) at timestamp \(t\) and we compare it with a user defined threshold \(cr\). The critical rate of a station \(s\) at timestamp \(t\) is defined as follows:  
\[CR_{s}(t)=OR_{s}(t)-\frac{1}{|N(s)|}\sum_{s_{i}\in N(s)}OR_{s_{i}}(t)\]  
As can be seen from the previous equation, the critical rate \(CR_{s}(t)\) can be either positive or negative, depending on the fact that the occupancy rate of station \(s\) at time \(t\) is higher or lower than the average occupancy rate of its neighbourhood, respectively. Given a criticality threshold \(cr\), if \(CR_{s}(t)>cr\) we say that the station, at time \(t\), is positively critical (i.e., it hosts, with respect to its capacity, \"too many\" bicycles compared to its neighbouring stations), otherwise if \(CR_{s}(t)<-cr\) we say that the station is negatively critical (i.e., \"too few\" bicycles are present in the station compared to the average occupancy rate of its neighbourhood). The criticality threshold was introduced to let the system identify critical situations and compensate/react before the bike stations become either completely full or completely empty, i.e., the system performs a sort of \"predictive maintenance\" operation and tries to act when the stations are already in a critical situation but before the stations become completely unusable.  
As we will describe in Section 4, the proposed methodology addresses positively and negatively critical situations and applies rules to balance the number of bicycles stored in those stations, trying to flatten the local occupancy rate using a single vehicle for each unbalanced neighbourhood such that all the critical stations belonging to a subset of a neighbourhood achieve (almost) the same occupancy rate and hence either completely full or completely empty statuses are avoided or at least limited.
## 4 Performance Analysis
### Theoretical Analysis
#### 4.1.3 Average End-To-End Delay  
As we all know, if the delivery ratio is close to 1, it indicates that most of the messages have been delivered. For intra-clustering routing, according to the formula (7), it is assumed \(T_{N}=9\), the delivery ratio can be reached \(1-(1-0.4)^{9}=0.99\). That is to say, the messages could be delivered within maximum 9 time slots by the intra-clustering routing approach. Therefore, the average delay can be computed as:  
\[D_{intra-clustering}\approx\sum_{i=1}^{9}i*((1-(1-0.4)^{i})-(1-(1-0.4)^{i-1}))= 2.38 \tag{20}\]  
Like the intra-clustering routing, for inter-clustering routing, when the delivery ratio \(P_{inter-cluster}^{\prime}(\Delta t\leq T_{N})\) is close to 1, the average delay for inter-clustering routing can be obtained as:  
\[D_{inter-clustering}\approx\sum_{i=1}^{T_{N}}i*(P_{inter-cluster}^{\prime}( \Delta t\leq i)-P_{inter-cluster}^{\prime}(\Delta t\leq i-1)) \tag{21}\]
# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 1 Introduction
Therefore, the economic structure of Chongqing shows a weak correlation between the urban industry and the rural economy. This relationship is responsible for the movement of many migrant workers into the city [18]. The urban development and layout of Chongqing have benefited from the convenient access to two rivers. The expansion of Chongqing has been constrained by its topography. The formation of a more complex irregular road network represents a spatial leap in the development of Chongqing. Urban areas of Chongqing have been developed in areas with relatively low topography and are relatively fragmented. It is difficult to estimate the accessibility of food in Chongqing due to the imbalance of the tertiary sector based on the industrial economy and the isolated vertical traffic pattern of the city. The aim of the present study was to identify the spatial food distribution pattern of the mountain city of Chongqing at the community level. GIS was used to identify the walking time required to access food under a realistic road network using a Baidu map and considering the temporal factors limiting the accessibility metric. The augmented two-step method was established as the distance between community centers and different types of stores. This method was used to calculate food accessibility indicators reflecting the distribution characteristics of food accessibility/desert in Chongqing. The spatial correlations between socioeconomic indicators based on house price/age and food accessibility indicators reflected the balance of food distribution in Chongqing. These indicators were used to identify the status of and objective reasons for the different distributions of food accessibility. In addition, suggestions are made for the development and planning of the city. The main contribution of the present study is a proposed effective solution for food accessibility analysis suitable for mountain cities. In addition, the present study describes the food distribution of Chongqing and provides some suggestions for development of the city. This paper has the following structure: Section 2 provides a brief description of related work in the field and analyzes the status and limitations of current food access analysis; Section 3 describes the data used and study area; Section 4 describes the methods used in the present study; Section 5 summarizes the experiment results and analysis and suggests development plans for Chongqing; and Section 6 provides a conclusion and discussion of further research needed.
## 4 Solution Problem
### Load Prediction Algorithm (LPA)  
Mobile network congestion in regions with high population density is a problem that increases with population growth and urban modernization, such as digital cities or smart cities that bring the concept of interconnecting everything. This increase in traffic directly affects the mobile network by reducing the QoS of users in the region. If the increase in user density is programmed in the form of concerts, sports games, and demonstrations, the resulting congestion can be resolved by preemptively planning the infrastructure so that congestion does not occur. However, most of the congestion of mobile networks is generated by the high density of users in everyday life, which makes preventive planning difficult due to the randomness of the congested regions. This congestion is based on the temporal and spatial flow of the region, which is affected by the seasonality and the trend that is generated by the regionality in Figure 1. This scenario can lead to planning problems, high deployment costs for utilities, uncontrolled deployment of macrocells that can be harmful to health, visual pollution, a decrease in QoS, and an increase in blocked users in the mobile network.  
Machine learning models are great solutions to nonlinear problems. Provided there is a database to train and test the model, it is possible to perform predictions on nonlinear data. To overcome the problem of nonlinearity of the data used in this study, we used the random forest and GRNN models as a good solution for macrocell load forecasting, as shown in Algorithm 1.  
```
1:Input: week, day, hour, macrocell id
2:\(x\gets\)_week_, _day_, _hour_, _macrocell id_
3:\(y\gets\)_network load_
4:\(train\leftarrow(x,y,random\) 70%)
5:\(test\),\(validation\leftarrow(x,y,random\) 30%)
6:procedure(training)
7:\(normalization(train,test,validation)\)
8:\(initialize\)\(models\)
9:while\(n<iteration\)do
10:\(model\gets training\)
11:endwhile
12:endprocedure
13:\(prediction\gets model(dataset)\)
14:\(result\gets metrics(prediction)\)\(\triangleright\) Equations (26)-(28)
15:Output: trained model\(\triangleright\) selection of the best model for the scenario
```  
**Algorithm 1**_Load Prediction Algorithm_