esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Finite Element Analysis Tests  
Before building the test bench, some requisites were verified by means of finite element analysis (FEM) tests. In particular, it was verified that the platform meets the minimal requisites of resistance. It was proven that the structure and the hydraulic actuators were over-dimensioned.  
The structure should withstand around 300 kg, including a standard ATV-Quad and a dummy of 100 kg. In fact, the structure is able to successfully resist a weight up to 600 kg.  
The verification of the resistance of the structure was performed by means of FEM analysis implemented in the software [PERSON] \({}^{\text{\textregistered}}\) v. 2016 (MSC Software Corp., Los Angeles, USA). In particular, the upper part of the test platform was analyzed, since it is the most critical part of the structure, as it directly supports both the ATV and the dummy. The FEM analysis was developed with standard steel as the material, with a density \(\rho_{sted}\) = 7850 kg/m\({}^{3}\) and a yield strength \(\sigma_{y,sted}\) = 275 MPa. The boundary conditions imposed fixed displacement in axis X, Y, and Z, in the inner surfaces of the holes of the hinges, around which the mobile platform rotates, as well as the lower surfaces of the mechanical elements that stand on the fixed surface. A distributed load of 2940 N was applied to all the profiles composing the mobile platform, since it corresponds to the static force applied by a quad, weighing 200 kg and a driver of 100 kg.  
The obtained results showed that the maximal displacement took place in the inner square profile, with a value of \(d_{max}\) = 1.0941 mm, as it can be seen in Figure 3. On the other hand, Figure 4 shows the maximal value of the equivalent von Mises stress, whose value is \(\sigma_{VM,max}\) = 29.82 MPa. This value is reached in the inner corner of the mechanical element that stands on the fixed platform and is closer to the profile that withstand all the weight of the vehicle and its driver.  
From the results that have been obtained in the FEM analysis, it can be stated that the test bench is appropriately designed for withstanding the weight and inertial forces that are originated during the vehicle rollover tests. This conclusion has been obtained from the fact that the yield strength of the steel, used as the building material of the bench, has not been reached at any position of the structure. In fact, the safety factor is:  
\[SF=\frac{\sigma_{y,steel}}{\sigma_{VM,max}}=\frac{275\text{ MPa}}{29.82\text{ MPa}}=9.22 \tag{1}\]  
Figure 2: Dimensioned lateral view of the designed rollover test bench.
# Preliminary Study on Greywater Treatment Using Nonwoven Textile Filters
###### Abstract  
The objective of the study was to evaluate the usefulness of novel nonwoven textile filter technology for greywater treatment. This technology had already been used on a lab scale for septic tank effluent treatment; however, this study is the first time it has been used for greywater treatment. The set-up period with septic tank effluent (STE) feeding was significantly shorter than that of greywater feeding. The average capacities of both filter types were practically the same: 1.0-1.4 cm d\({}^{-1}\). The relatively high efficiencies of chemical oxygen demand (COD\({}_{\text{CT}}\)) and biochemical oxygen demand (BOD\({}_{5}\)) removal (58.8-71.6% and 56.7-79.8%, respectively) were obtained thanks to the relatively low filtration velocity and effective diffusion of atmospheric air into the greywater. The relatively high efficiency of total suspended solids (TSS) removal (67.0-88.4%) was obtained by reducing the effective pore size of the filtration layer due to high biomass concentration and accumulation of suspended solids. Thanks to hydrostatic pressure, the filters can work practically with very low energy consumption. The pollutants removal efficiencies were satisfactory in respect to simple construction and maintenance, low investment and operational costs.  
Keywords:nonwoven textile filter; greywater; efficiency; removal +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Data Collection and Pre-Processing
#### 2.2.2 Image Resolution Degradation and Enhancement
The network is updated using the Mean Square Error loss, defined as:  
\[L(\theta)=\frac{1}{N}\sum_{i=1}^{N}\left\|Y_{i}-\hat{Y}_{i}\right\|^{2} \tag{1}\]  
where \(N\) is the number of images in the mini-batch used for gradient descent network updating, \(Y_{i}\) is the original HR image and \(\hat{Y}_{i}\) is the reconstructed output, calculated as the weighted sum of all recursions:  
\[\hat{Y}=\frac{1}{D}\sum_{d=1}^{D}w^{(d)}(F_{rec}^{(d)}+X) \tag{2}\]  
where \(D\) is the number of recursions, \(w^{(d)}\) is the weight associated with the output of the \((d\text{th})\) recursion \(F_{rec}^{(d)}\) and \(X\) is the LR input. Since our model utilizes residual blocks in the _embedding_ subnetwork and uses _supervised recursions_, we named it DRESNet - Deep Residual Embedding and Supervised-recursions. The weights of all residual and recursive blocks are shared, what allows for a significant depth increase while keeping the number of parameters constant. The final configuration of DRESNet was experimentally verified by us in [43], where we tested configurations with different number of residual and recursive blocks. In-depth analysis showed that the best results are achieved for 3 residuals in the _embedding_ subnetwork and 9 recursions in the _mapping_ subnetwork. Hence, this configuration was used in further experiments. For the network training, we utilized data from first 15 volunteers of both the SC3000 and the Lepton dataset. Due to memory limits of the training hardware, only 4 images per volunteer from the SC3000 database and 20 images per volunteer from the Lepton database were randomly selected. We were able to select more images from the Lepton dataset, because each image was 4 times smaller than images in the SC3000 dataset. As a result, we built four sets: SC3000-8 with 60 8-bit images, SC3000-16 with 60 16-bit images, Lepton-8 with 300 8-bit images and Lepton-16 with 300 16-bit images. The proposed SR NN was trained on each of these sets separately, 10% of data was used for validation. As previously mentioned, to train the network we have to feed LR data and compare the reconstructed output to the original HR image. Thus, at first we generated synthetic LR inputs by down-scaling and then up-scaling original HR images with inverse scales. In this study, we performed experiments using 2 settings: scale 1/2 and 2; scale 1/4 and 4. Hyperparameters for model training were tuned using the random search approach and final values were set as: 41 \(\times\) 41 patch size, patch stride of 21, Adam optimizer, momentum 0.9. In this study we used the weight decay approach with a decrease of 0.0001 and the learning rate decay approach with an initial value of the learning rate set to 10\({}^{-2}\) and a decrease of 0.1 every 5 subsequent epochs, for which a validation error hasn't decreased. Trained SR models were used to enhance all remaining thermal sequences (25 8-bit and 25 16-bit sequences from the SC3000 dataset; 16 8-bit and 16 16-bit sequences from the Lepton dataset) and verify how it affects accuracy of the RR estimation.
# Theoretical Framework (Module) for Short-Sea Shipping System Evaluation
###### Abstract  
Short-sea shipping, abbreviated SSS, is the transportation of goods by sea over relatively short distances, in contrast to intercontinental ocean and deep-sea shipping. Short-sea shipping (SSS) is important for cargo transportation in some regions of the world with many ports and well-developed liner shipping. The development and improvement of SSS systems is an important scientific and practical task. This article presents theoretical and experimental results of the development and optimization of SSS. A methodology for connecting and evaluating SSS and other transport chains was developed and tested by experimental studies, with the help of which it is possible to assess the efficiency of SSS and other transport chains, e.g., in terms of economy, freight transportation time, and environmental impact. The developed SSS methodology includes sea and land transport corridors, their assessment, and possible ways of optimizing transport chains using a comparative method and can be applied to various transport and logistics chains. The basis for the development and verification of the SSS methodology was the theoretical and experimental results of real short-sea shipping operations. The use of a comparative method based on which transport and logistics chains are assessed allows one to search for the most optimal SSS routes and possible factors that allow optimizing transportation costs and reducing transportation time and environmental impact.  
SSS; logistics chains; ports; maritime transport of freight; intermodality; combined transport; transportation systems +
Footnote †: journal: Applied sciences  
Article
## 3 Materials and Methods  
### Data and Variables  
This research covers all 33 provinces in Indonesia for the period of 2009-2018, integrating a new section in North Kalimantan with East Kalimantan, due to its relatively new formation, to facilitate calculation. With Indonesia's vast territory, each province represents the market price value obtained from the average market rate in each region. The data used in this study were obtained from various sources, a summary of sources and variables is shown in Table 11. The macroeconomics data are from the Central Statistics Agency (BPS), the Ministry of the Agriculture Republic of Indonesia and the International Monetary Fund (IMF), Rainfall from the Indonesian Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG), Climate indices sources available online from the National Oceanic and Atmospheric Administration (NOAA), Japan Agency for marine-earth science and technology (JAMSTEC), and the Australian Bureau of Meteorology (BOM).  
In the volatility model, factors that act as determinants are considered to determine Indonesia's food price volatility. These variables are then referred to as independent variables that affect rice prices, while rice price volatility becomes dependent. The independent variables to be measured include planting area, harvested area, inflation rate, farm wages, fuel oil prices, farmer exchange rates, exchange rate (rupiugh rates against the US dollar), GRDP, GPP, global prices, bank interest rates, rice import and export rates, rainfall data, and climate indices. This study uses five global indices of atmosphere dynamics around the research area that influence regional climatic conditions: southern oscillation index (SOI), oceanic Nino index (ONI), Nino 3.4, El Nino Modoki, and Indian Ocean dipole (IOD).
## 3 Processing Strategies
Considering the similar processing time and resulting clock precision between \(T_{obs}\) of 4 h and 6 h, in this study, \(T_{obs}\) is set to the longer processing arc of 6 h for safety reasons, with \(T_{process}\) (including the determination and the prediction) equal to 18 to 20 s using a processing unit described above. When calculating the STDs, a 4.42-sigma outlier exclusion was performed. In addition to \(T_{obs}\) and \(T_{process}\), other time points in Figure 5 are also important. \(\Delta T\), for example, can be set to 60 to 300 s depending on the real-time precision needed. A short \(\Delta T\) leads to a short prediction time needed to match the real-time window [\(D,D+\Delta T\)], and delivers a higher precision of the real-time clocks of the LEO satellite. In summary, the real-time determination process of LEO satellite clocks in this study is as follows:  
1. At the current time point \(T\), acquire observational data in [\(T-T_{obs},T\)], and obtain real-time GNSS products selected according to Section 2.1;
2. Based on the strategies outlined in Table 2, determine the LEO satellite clocks in near-real-time;
3. According to the prediction method detailed in Section 2.2, predict the LEO satellite clocks over \(T_{pre}\). 4. Perform a second-order polynomial fitting using the predicted clocks, and broadcast the fitted polynomial coefficients to users; \begin{table}
\begin{tabular}{c c c c} \hline \hline \(T_{obs}\) **(h)** & \(T_{process}\) **(s)** & **STD (vs. K) (ns)** & **STD (vs. R) (ns)** \\ \hline
24 & 24 & — & — \\ \hline
12 & 21 & 0.015 & 0.19 \\ \hline
8 & 19 & 0.015 & 0.19 \\ \hline
6 & 18 & 0.015 & 0.20 \\ \hline
4 & 17 & 0.018 & 0.20 \\ \hline
2 & 17 & 0.048 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The average processing time (\(T_{process}\)) and the precision (STD) of the near-real-time clock estimates using different processing arc lengths (\(T_{obs}\)). (K denotes Kalman-filter-based daily solution using the kinematic model and the CNES real-time products, RD denotes the batch least-squares reduced-dynamic solutions using the CODE final products).
# Treatment and Effective Utilization of Greywater:
## 3 Results and Discussion
### Biological Parameters
#### 3.3.3 Residual Chlorine Check  
A residual chlorine check is one of the most effective methods for checking microbial contaminations in wastewater streams [23; 24; 25; 26]. Chlorine is one of the effective chemicals in destroying a variety of organisms such as bacteria, viruses, and protozoa, including _Salmonella_, _Shigella_, and _Vibrio cholerae_. This method of chlorination is widely practiced in wastewater treatment methods in order to reduce microbial contamination. This also decreases the risk of exposure to diseases due to microbial populations. By-products are formed due to the addition of chlorine or any chemical disinfectant to wastewater. The presence of ammonia and the prior condition of the sewage have a considerable impact on the by-product formation. Dechlorination of excess chlorine is highly essential prior to utilization in order to conserve aquatic life and also to reduce the impact of the intensity of disinfection by-products. The residual chlorine content in treated water after chlorination was 0.1-0.2 mg/L. It was within the permissible limit of 1 mg/L [16; 17] ([PERSON] and [PERSON], 2018; [PERSON] et al. 2017). It indicated that the water was free from excess residual chlorine content.  
The _E. coli_ check and residual chlorine check suggested that the greywater is suitable for non-potable reuse after treatment. However, quantitative microbial risk assessment can be included to check the microbial risk involved in the non-potable reuse of greywater [27]. Furthermore, economic analysis can be planned in the future [28]. For possible water reuse in the agricultural sector, the retention of nutrients (i.e., total nitrogen and orthophosphate) must also be investigated [29]. In addition, the overall treatment of greywater can also be improved suitably by using a combination of treatment processes (such as membranes and coagulation) to obtain high-quality treated water for irrigation purposes [30].
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
## 7 Performance Analysis
### Storage Overhead  
In our designed D2D communication model, the UEs are resource-limited devices with constrained storing capacity and computing ability. Hence, it is not efficient to store massive key information in the UE storage. Furthermore, additional storage overhead is required for authentication as well. In the contrast, considered as the major component of D2D network, SN is assumed to have adequate storing ability for key information recording and generation towards all the participated devices. Therefore, the analysis here emphasizes on the storage overhead in UE side, while the SN is not included due to the aforementioned design. The state-of-the-art D2D authentication and key agreement schemes including SeDS [19], LRSA [32], GRAAD [12], and PPAKA [9] are compared with the proposed scheme in order to prove its efficiency on storage overhead. Similarly, the storage overhead for D2D device is taken into consideration.  
As for the UE in the proposed protocol, the public generators \(g,w\in\mathbb{G}\) are previously stored in UE side. During the offline registration phase, the confidential key information \(\langle X_{i},\mathcal{U}_{i},\mathcal{V}_{i}\rangle\) is stored with its own identity \(ID_{i}\). Accordingly, we define the length of key information including \(X_{i}\), \(\mathcal{U}_{i}\), \(\mathcal{V}_{i}\) and is 160 bits, the identity \(ID_{i}\) is 32 bits. At this point, the storage overhead for each UE is \(32+160\times 5=(832)\) bits. Similarly, in the subsequent authentication phase, the length of public key \(PK\), the validation result \(\eta_{i}\), and the self-generated partial secret key \(\theta_{i}\in\mathbb{Z}_{p}\) are 160 bits each. Hence the storage cost of UE in authentication phase can be calculated as \(160\times 6=(960)\) bits. In the following group key distribution phase, the received packet \(\langle\delta,\wp,a_{0},\ldots,a_{t-1}\rangle\) is stored as well, where \(t\) denotes the number of participating UEs. The length of the final group key \(\gamma\in\mathbb{Z}_{p}\) is defined as 32 bits. In this way, the total storage cost can be summarized as \(832+960+160\times 3+16\times t+32=(2304+16t)\) bits. Obviously, the storage cost is related to the number of validated UEs. The comparison results on storage overhead with the four existing D2D authentication scheme are illustrated in Table 3. Apparently, minor storage cost is required in our authentication scheme on the resourced-constrained UEs.
## 2 Theory
### An Accelerated 3D Fast Inversion Algorithm for Gravity Data
We can do the same processing with \(M\times M\) diagonal square \(W_{d}\):  
\[{W_{d}}^{M\times 1}=diag\Big{(}SS^{T}\Big{)}^{\frac{1}{2}} \tag{24}\]  
To convert the two weighted matrices \({W_{d}}^{M\times M}\) and \({W_{m}}^{N\times N}\) into their vector forms, simply let them serve the same purpose as the original two matrices. In fact, we can analyze this from the perspective of matrix transformation. In Equation (15), \(S_{w}\) essentially performs a two-step transformation: initially applying a row transformation to the matrix \(S\). To be more specific, it multiplies each row of the \(S^{M\times N}\) matrix by \({W_{d}}^{M\times M}(i,i)\)\((i=1,2,\ldots M)\). Subsequently, it conducts a column transformation by multiplying each column with \({W_{d}}^{N\times N}(j,j)\)\((j=1,2,\ldots N)\). This process yields the resulting matrix \(S_{w}\). Therefore, rewriting the weighted matrix in the algorithm into vector form can also achieve the purpose of Equation (15). We only need to multiply the \(i\)th row of matrix \(S^{M\times N}\) by the \(i\)th component \({W_{d}}^{M\times 1}(i)\)\((i=1,2,\ldots M)\) of vector \({W_{d}}^{M\times 1}\) accordingly, and then multiply the \(j\)th column of the obtained matrix by the \(j\)th component \({W_{m}}^{N\times 1}(j)\)\((j=1,2,\ldots N)\) of vector \({W_{m}}^{N\times 1}\) to get \(S_{w}\). Other processing methods involving the diagonal matrix to participate in the calculation are similar to Equation (15). In this way, we can rewrite the large matrix involved in the algorithm, especially \(W_{m}\), into vector form, effectively releasing computer memory and improving running speed. (c) In each iteration of the traditional algorithm, the long vector \(r_{k}\), \(p_{k}\) with dimension \(N\) are calculated and stored several times, especially in the step (9), where two large vectors \(r_{k}\) and \(r_{k-1}\) must be stored simultaneously; however, this is completely unnecessary. In fact, in further algorithm improvement, we only need to store the inner product value of the negative gradient vector calculated at each iteration and use it in the next iteration stage. In this way, the simultaneous storage of the two long vectors \(r_{k}\) and \(r_{k-1}\) can be avoided, releasing more computer memory space, and making the algorithm run more efficiently. In this paper, we make several improvements to give the optimized conjugate gradient method, which improves operation efficiency and achieves the goal of fast inversion. The improved algorithm is as follows. Under the condition of the same equipment, the acceleration algorithm proposed in this paper is faster than the traditional algorithm and can handle a larger data scale. It is worth noting that the new algorithm is consistent with the traditional method in calculating the sensitivity matrix, so it is limited by the size of the sensitivity matrix like the traditional method. In other words, due to the limited performance memory of the computer device used, the size of the storage sensitivity matrix exceeds the limit, so the method in this paper will also be ineffective.
## 1 Introduction
For instance, [27] assessed the precipitation distribution in the high altitude region of Hindu-Kush-Himalaya basins by using different precipitation products. The study reported the better performance of ECMWF Re-Analysis (ERA-Interim) product at high catchments as compared with WATCH Forcing Data Methodology (WFDEI) and APHRODITE products. Using different gridded precipitation products, [28; 29] reported the significant errors in different gridded product; however, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product performed better in the high catchments of the Indus basin. On the contrary, [30] reportedthe overestimation of TMPA products against reference data using the simple statistical metric approach over the complex topography of Pakistan. The present study is first of its kind, which aims to bridge the gap of knowledge with a detailed multiple-scale assessment of the spatiotemporal uncertainties of selected global precipitation products that are generated by different sources by using different statistical metrics, trends evaluation and comparison approach over the Punjab province, Pakistan. It is worth mentioning that Punjab province is highly important in the perspective of agriculture and irrigated farming as it produces major agriculture commodities of the country and it is highly vulnerable to changes in most of meteorological parameters with high frequency events and, hence. is highly prone to climate change [31]. In this study, we aim to assess the quality and differences of the different GPPs generated from multiple sources, i.e., Gauge Based (GB) products, Global Precipitation Climatology Centre (GPCC), Center for Climatic Research, University of Delaware (UDel), Asian Precipitation--Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Climate Prediction Centre (CPC), Climatic Research Unit, University of East Angelia (CRU) and Satellite Based (SB) products, Tropical Rainfall Measuring Mission (TRMM, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS), Global Satellite Mapping of Precipitation (GSMap) and Climate Hazard Group Infrared Precipitation with Station Data(CHIRPS) during the period of 1979-2017 and 2003-2017, respectively, over the Punjab province in Pakistan. Secondly, to evaluate and compare the changes in temporal trends and abrupt turning points in selected GPPs against the reference data for the study region. We perceive the usefulness of this study as multi-directional, because the findings of the study could be used as baseline for the selection of potential GB and SB GPPs over multiple time scales for different hydro-meteorological studies.