esa-sceva/satcom-chunk-collection huggingface dataset:
# Production of Photovoltaic Electricity at Different Sites in Algeria
## 4 Conclusions  
Algeria is initiating a green energy dynamic by launching an ambitious programme to develop renewable energy and energy efficiency. This vision of the Algerian government is based on a strategy focused on the development of inexhaustible resources, such as solar power, and their use to diversify energy sources and prepare Algeria for the future.  
Figure 5: Monthly evolution of (**a**) solar radiation and (**b**) power PV at different sites in Algeria (January 2022).  
Figure 6: Monthly evolution of (**a**) solar radiation and (**b**) power PV at different sites in Algeria (July 2022).  
Figure 7: Monthly PV efficiency at different sites in Algeria in (**a**) January 2022 and (**b**) July 2022.  
Through the combination of initiative and intelligence, Algeria is embarking on a new sustainable energy era.  
This research was carried out across the Algerian territory where we selected five sites of different geographical situations: Ouargla, Algers, Bechar, Sidi Bel Abbes and Batna. It focused on the study of the influence of the geographical site on photovoltaic production.  
The study showed that when climatic conditions are different, photovoltaic production will also be different. Significant daily and monthly fluctuations in production were observed.  
The results showed that the site of Ouargla, located in the south-east of Algeria, has favorable conditions of temperature, radiation, and geographical coordinates for good photovoltaic production, especially in summer during the month of July, considered as the warmest month. Studies of the production of photovoltaic electricity are necessary to see how the behavior and adaptation in conditions of sites, as well as the climatic and social condition, effects on working and profitability for a long time. This photovoltaic system faces many problems of adaptation, and to be reliable and competitive optimizes their cost.  
Conceptualization, K.B., S.E.L., N.C., A.B., F.M. and M.R.O.; methodology, K.B., A.B., N.C., G.A.M.A., H.M.M., M.M.R. and F.M.; validation, K.B., A.B., S.E.L., N.C., H.M.M., M.M.R. and F.M.; investigation, A.B., G.A.M.A., F.M. and N.C.; resources, K.B., A.B., H.M.M., M.M.R. and F.M.; data curation, K.B., A.B., G.A.M.A., F.M. and N.C.; writing--original draft preparation, K.B., S.E.L., N.C., A.B., M.R.O., G.A.M.A. and F.M.; writing--review and editing, A.B., N.C., G.A.M.A., H.M.M., M.M.R. and F.M.; supervision, A.B., S.E.L., N.C., F.M. and M.R.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Practical Mobility Management in Modern Scenarios
### Possible Issues and Solutions When Dealing with Mobility in VLANs  
One of the main issues when dealing with VLAN in mobile environments is the management of the IP addresses. In general, DHCP servers are responsible for IP address assignments, but the huge increase in available mobile devices induces great challenges related to DHCP performance. One countermeasure is represented by reconfiguring the IP lease times and the address pools dynamically, adapting them to the WiFi user behavior [46]. In the first case, the lease times are set on the basis of the historical time pattern to reclaim IP addresses, while in the second case the IP addresses are migrated across VLANs on the basis of the spatial-temporal mobility correlation.  
Regarding the address pools, for a higher number of clients, DHCP messages (generally broadcast messages) will consume more bandwidth. The main solution is to split the overall network into a subset of VLANs, without implementing a DHCP server for each VLAN. It is possible to exploit the advantages of using a DHCP relay agent [47] for each VLAN, able to change the properties of the DHCP packet broadcasted by the client in its broadcast domain and forward it to DHCP server (Figure 6). In this way, most of the traffic is cut from the network, increasing the overall throughput.  
Regarding the IP lease time, there are three main states for leasing an address:  
1. [label=()]
2. Initializing: the IP address is acquired after a set of message exchanges (discover message from client C to broadcast B, offer message from server S to C, unicast request message from C to S and the acknowledgement from S to C);
3. Renewing: in this state, a message is sent by the client (if it is still present in the network) to the server, asking for the extension (in time) of the lease; it is a periodical message (the period is equal to the half of the lease time);
4. Releasing: this state occurs if the client sends a clear release message to the server (because it wants to leave the network) or if the periodical renew message has not been sent.  
After these considerations, it is clear that the lease time should be set adequately: if it is too high, the address pool could exhaust soon, while if it is too low, DHCP servers could  
Figure 5: A logical representation of the VPN architecture considered in [37].  
be overloaded. So, one of the best solutions is to evaluate users behavior in terms of DHCP requests, and then adapt the lease time to the network as needed in terms of online time pattern to reclaim IP in time.
## 5 Simulation Results
### Cluster Performance for Mobile Networks  
We now consider mobile networks for which the coordinates of UAVs are updated once every second according to the group reference points. In the following simulation, the speed of UAVs varies in the ranges of \([3,5]\) m/s, \([5,8]\) m/s, \([8,10]\) m/s, and \([13,15]\) m/s separately while the group is fixed as \(M=20\).  
The bar graph of Figure 14 shows the number of cluster adjustments performed per interval of 2s with our group-oriented clustering algorithm under different velocities. The cluster adjustment numbers are vast at the initialization clustering phase. After the initial adjustment period, the cluster adjustment numbers gradually stabilize and fluctuate within a small range, illustrating that our clustering algorithm has certain stability in mobile networks. Furthermore, the UAV swarm with higher speed will have more cluster adjustments.  
The total number of cluster adjustments during the whole clustering process under different velocities is shown in Figure 15. Our algorithm has the least number of adjustments than the COG and CMPOG algorithms, which indicates that our algorithm is more stable for dynamic network clustering.  
In addition, the number of groups will influence the stability of dynamic network clustering. Assuming the speed of UAVs varies in the range of \([8,10]\), Figure 16 displays the cluster adjustment numbers when the group numbers are 5, 10, and 20.  
Figure 12: The average number of clusters corresponding to one group under different numbers of groups.  
Figure 13: The total inter-cluster communication link correlation for UAV swarms with different numbers of groups.  
The smaller the number of groups, the fewer cluster adjustments during the moving process, indicating that dividing the nodes belonging to the same group into one identical cluster can make the cluster structure more stable. Additionally, the clustering result is more stable when the group size is closer to the maximum number of UAVs in one cluster.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 3 Results
### Testing Single-Image, Super-Resolution Configurations and Designing a Multi-Scale Adaptive Model
Notably, however, the geostrophic current field based on altimetry only suffers minor evolutions after three days (Figure 9F), while dADR-SR actually appears to have smeared out most of the features observed previously. Current intensities are also significantly and unrealistically reduced on the 3 rd of July, especially in the intense, anticyclonic meander observed along the Algerian coast at 6\({}^{\circ}\)E (Figure 9H). Figure 8: Impact of cloud cover on satellite SST interpolated data. Interpolated field; (**A,B**), related nominal interpolation error; (**C,D**), MODIS Terra pseudo-true colour images; (**E,F**) from NASA Worldview ([[https://worldview.earthdata.nasa.gov](https://worldview.earthdata.nasa.gov)]([https://worldview.earthdata.nasa.gov](https://worldview.earthdata.nasa.gov)), last accessed on 14 January 2022). On the first date, clear-sky conditions (**E**) lead to extremely clear and distinct SST patterns and low interpolation errors in all the Mediterranean (**A,C**). Three days later, clouds arriving from Morocco (**F**) prevent the reconstruction of small-scale dynamical features in the SST field and lead to increased interpolation errors (**B,D**). The thin cyan box identifies the area zoomed in Figure 2:0  
Several similar cases can be picked up by looking at the entire time series, but our qualitative understanding of the power and limitations of the model and observations analysed in this first work are fully confirmed also by the successive quantitative assessment. This latter exercise was carried out by matching the independent estimates of surface currents  
Figure 2:1 Impact of the smoothing introduced by SST data interpolation on the dynamical reconstruction (zoomed from Figure 8). (**A**,**B**) original altimeter-based surface geostrophic currents; (**C**,**D**) super-resolved surface geostrophic currents; (**E**–**H**) SST L4 field with overplot of the eddy contours identified through AMEDA detection algorithm (red = cyclonic, blue = anticyclonic, black dots stand for automatically detected eddy centres) applied to original altimeter currents (**E**,**F**) and to super-resolved field (**G**,**H**). Current vectors are overplot in (**A**–**D**) plots. obtained from in situ drifting floats with co-located/concurrent data-driven modelling and altimeter data. Actually, the error associated with the super-resolved and standard altimetric estimates (assessed here as the absolute value of the difference with respect to drifter data) does not display a unique behaviour along the individual drifters' trajectories, with alternating improvements and degradations of the current velocity components, which do not present marked geographical characterization (Figure 10). Overall, though, SR fields seem to provide slightly more accurate values in some (mostly offshore) areas of the western basin, while, on average, they seem to perform worse in the easternmost part of the Levantine basin (close to Israel/Lebanon coasts).
# A Dual-Adaptive Approach Based on Discrete Cosine Transform for Removal of ECG Baseline Wander
## 2 Methods
### The Proposed Dual-Adaptive Scheme
#### 2.2.3 Reconstructing BW and Subtracting It from the Original ECG  
The following equation was used to reconstruct BW from those DCT coefficients before the optimal cut-point.  
\[bw(n)=\sum_{k=1}^{I_{OCP}}w(k)y(k)\cos(\pi(2n-1)(k-1)/2N,\;n=1,2,\ldots,\;N \tag{11}\]  
then, a BW-free ECG could be restored by subtracting the estimated BW from the original ECG. It is shown as follows.  
\[x_{r}(n)=x(n)-bw(n) \tag{12}\]  
where \(bw(n)\) is the estimated BW, \(x_{r}(n)\) is the'retrieved' ECG after BW removal, and \(x(n)\) is the original ECG contaminated with BW.
## 3 Results and Discussion
### Comparison of AC@SCF with Other Adsorbents for Cr(VI) Adsorption  
Table 4 presents the maximum adsorption capacity (ascending values) of various adsorbents for Cr(VI) removal. As shown in this table, the adsorption capacity of AC@SCF was much higher (109 mg/g, 24 h experimental value; 137 mg/g, theoretical prediction for equilibrium stage at pH 3) than other adsorbents reported elsewhere.  
Micropore size, micropore volume fraction, and the specific surface area seemed to have greater influence on the adsorption capacity of Cr(VI) in the AC@SCF sorbent. The surface area of the activated carbon derived from \"spent coffee\" pyrolysis process was highly noticeable and the abundant microporous structure probably increased the probability of adsorption of Cr(VI) at available active surface sites.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**adsorbent** & **S\({}_{\text{BET}}\) (m\({}^{2}\)/g)** & **Pore Diameter (nm)** & **Solution** & **Maximum** & **Reference** \\
**Activated Carbon (AC)** & **S\({}_{\text{BET}}\) (m\({}^{2}\)/g)** & **(micro/meso)** & **pH** & **Acapacity (mg/g)** & **(**)** \\ \hline AC coconut tree sawdust & — & — & 3.0 & 3.46 & [58] \\ \hline AC groundnut husk & — & — & 3.0 & 7.0 & [59] \\ \hline AC mango kernel & 490.43 & — & 2.0 & 7.8 & [60] \\ \hline AC coconut shell & 5–10 & — & 4.0 & 10.88 & [16] \\ \hline AC peanut shell & 95.51 & 11.0 & 2.0 & 16.26 & [61] \\ \hline AC _Casuarina equisetifolia_ & 156–735 & — & 3.0 & 17.2 & [62] \\ \hline AC bael fruit shell & — & — & 2.0 & 17.27 & [63] \\ \hline AC pineapple leaves & 374.9 & 3.66 & 2.0 & 18.77 & [64] \\ \hline Spent coffee & — & — & 2 & 22.75 & [27] \\ \hline AC _Terminalia arjuna_ nuts & 1260 & — & 1.0 & 28.43 & [65] \\ \hline AC longan seed & 1511 & 0.48 & 3.0 & 35.02 & [66] \\ \hline AC _Hewen brasiliensis_ & 1674 & — & 2.0 & 44.05 & [29] \\ (rubberwood) sawdust & 1674 & — & 2.0 & 58.50 & [67] \\ \hline AC tires & 832 & — & 2.0 & 70 & [68] \\ \hline AC tamarind hull & 431 & — & 2.0 & 70 & [68] \\ \hline AC olive bagasse & 718 & 0.4-2.0/3.0-4.1 & 2.0 & 88.59 & [69] \\ \hline AC _Posidonia acanica_ & 1148 & 0.57/1.38/6.38/35.3 & 3.0 & 118 & [57] \\ \hline AC _Ziziphus jujuhu_ cores & 608.31 & 1.0 & 2.0 & 124.25 & [70] \\ \hline Spent coffee AC@SCF & 1372 & 0.67/1.28/1.6/27 & 3.0 & 137 & This work \\ \hline Spent coffee & — & — & — & 156.7 & [28] \\ \hline AC@SCF & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The maximum adsorption capacity of literature reported adsorbents which were used for removal of Cr(VI) ions from aqueous solution.
# Land Cover Classification Using a KOMPSAT-3A Multi-Spectral Satellite Image
## 1 Introduction
A review of these algorithms can be found in [PERSON] and [PERSON] [8]. In this study, we perform the land cover classification of high resolution KOMPSAT-3A multispectral data using four supervised classification methods: MahD, MinD, ML and SVM around the Buan area of Jeollabuk province, Republic of Korea. The challenge with the KOMPSAT 3A data is that it has only four i.e. Red, Blue, Green and NIR (RGB-NIR) bands of very high resolution. A secondary objective, to examine the ability of the derived ratio and indices bands in improving classification of KOMPSAT-3A, was also investigated. The study will explore the potential of KOMPSAT multispectral data for classification of land cover and compare the performance of the results among the methods. The classification study is the first in the Buan area using KOMPSAT-3A data.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 2 Experimental Procedure
### Catalysts Characterization  
The samples of the reduced catalysts--Pd as Pd\({}^{0}\)--were characterized using conventional techniques: X-ray diffraction, Inductively-coupled plasma atomic emission spectroscopy (ICP-AES), N\({}_{2}\) and Ar adsorption isotherms, NH\({}_{3}\) TPD (Ammonia temperature programme desorption), and Transmission electron microscopy (TEM). High-angle XRD patterns were taken with a Philips X'PET MPD diffractometer (Philips, Eindhoven, The Netherlands) using Cu-K\(\alpha\) radiation in order to ascertain the crystallinity of the samples. X-ray diffraction patterns were measured within the 5-75\({}^{\circ}\) range employing a step size of 0.1\({}^{\circ}\) and a counting time of 10 s. Inductively-coupled plasma atomic emission spectroscopy (Varian, Palo Alto, CA, USA) was employed to determine the Pd content and Si/Al atomic ratio of the samples with a Varian VISTA-AX CCD spectrophotometer. Previously, the samples were digested in a H\({}_{2}\)SO\({}_{4}\)-HF mixture.  
Nitrogen adsorption-desorption isotherms at 77 K were carried out with a Micrometrics TriStar 3000 instrument (Micromerities, Norcross, GA, USA). The surface areas were calculated by the BET (Brunauer, Emmett, Teller) method using relative pressures in the range 0.05-0.15. Total porosities were determined as the total volume of pores at P/P\({}_{0}\) = 0.98. Argon adsorption isotherms at 87 K were performed with a Quantachrome AUTOSORB instrument (Quantachrome, Boynton Beach, FL, USA). Previously, the samples were outgassed at 300 \({}^{\circ}\)C for 3 h under vacuum (P \(<\) 10\({}^{-5}\) mmHg).  
The cumulative pore volumes and pore size distributions were determined by the non-local density functional theory (NLDFT) model, assuming cylindrical pore geometry.  
Ammonia TPD measurements were performed in a Micromeritics Autochem 2910 apparatus (Micromeritis, Norcross, GA, USA). Initially the samples were heated to 550 \({}^{\circ}\)C under a 50 Nml min\({}^{-1}\) helium flow, holding this temperature for 30 min. Then, the samples were cooled to 180 \({}^{\circ}\)C, and subsequently, a 35 Nml min\({}^{-1}\) ammonia flow was circulated for 30 min. Then, the ammonia flow was stopped and a He flow of 50 Nml min\({}^{-1}\) was passed through the samples for 90 min. Then, the chemisorbed NH\({}_{3}\) was removed by increasing the temperature to 550 \({}^{\circ}\)C with a ramp rate of 15 \({}^{\circ}\)C min\({}^{-1}\). The ammonia concentration in the effluent gases was monitored by means of a thermal conductivity detector (TCD).  
The TEM micrographs were taken in a Philips TECNAI 20 microscope (Philips, Eindhoven, The Netherlands) that operated with a LaB\({}_{6}\) filament under an accelerating voltage of 200 kV. Previously, the samples were prepared by dispersion in acetone, followed by stirring in an ultrasonic bath and final deposition over a carbon-coated copper grid. This technique allows determining the morphology of the support particles as well as the size of the Pd nanoparticles.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### Static PPP Results  
The positioning accuracy, the convergence time, and the accuracy of ZTD estimates with the PPP processing strategy are analyzed.
# Data Balancing Based on Pre-Training Strategy for Liver Segmentation from CT Scans
###### Abstract
Recently, focal loss, which could automatically adjust the contribution of easy-negative samples in training process and rapidly focus on hard examples in every batch training process, has achieved great success in one-stage detector objection [16]. However, focal loss failed to change the imbalance between hard samples and easy samples in training dataset, the contribution of hard slices may still be overwhelmed in the training process. In order to solve or alleviate this imbalance problem, we introduce an online hard example enhancement method to increase the proportion of the hard samples in the training dataset. First, we use partial slices in the whole training dataset to train a pre-training model according to the needs of segmentation task, and then the pre-training model is used to distinguish hard samples and easy samples in the rest slices of the whole training dataset, i.e., adding the identified hard samples to the training datasets used in the pre-training processes. Second, the hard slices identified by pre-training model are selected and enhanced by flipping, and then these slices are added to the dataset used in pre-training process to enhance the ratio of hard slices in training dataset, and improve the contribution of hard slices in training process. Therefore, the basic purpose of pre-training strategy is to get a sample classifier, which could distinguish hard/easy slices according to actual task need. To demonstrate the effectiveness of the proposed method, we adopt a classical 2D FCN model based on VGG-16 [17] and 2D U-Net [3], as shown in Figures 13 and 14 respectively, for the task of the liver segmentation, kidney segmentation and spleen segmentation from Computed Tomography (CT) scans.
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 4 Proposed Method  
The semantic segmentation thread implemented by SegNet is the most time-consuming part of the DS-SLAM algorithm and needs to be accelerated. In [9], SegNet runs on GPU but its high power consumption is not suitable for actual deployment on battery-powered mobile robots. Therefore, this paper will accelerate the inference of semantic segmentation network on FPGA to improve the real-time performance and energy efficiency of the DS-SLAM system.
# A Review of Deep Learning Applications for Railway Safety
## 2 Overview of Deep Learning Approaches
### Tasks  
#### 2.2.1 Classification  
Classification is a sort of supervised learning that is the process of identifying the category relationship of existing data and determining the category of newly observed data by itself. In the field of images, it is used to assign an appropriate label (or class) to objects in a given image as input. For example, a classification model can be trained to recognize a number in handwritten images.  
There are various types of image classification models. Visual geometry group (VGG) is a relatively early classification model developed to determine how the depth (number of layers) of neural networks affect performance [19]. VGG has a structure that combines convolutional layers for feature representation and fully connected layers for classification. Filters of 3 \(\times\) 3 are used to reduce the number of model weights that require learning to efficiently increase the depth of the model.  
Residual Net (ResNet) [20] is a deep learning network with 152 layers. For ResNet, a new concept called residual block was introduced. Unlike previous networks, which aimed to generate output values as similar as possible to the correct answer, ResNet was designed to minimize the residual (the difference between the output and input values). This approach makes it possible to preserve previously learned information and to consider only additionally learned information. DenseNet [21] is similar to ResNet, but it uses the operation of the concatenation of the output of the previous layer with the next layer.  
Res2 Net [22] is a structure that combines ResNet with DenseNet and is a classification model that leads to performance improvement by configuring hierarchical residual-like connections in a bottleneck residual block. Res2 Net is also characterized by segmentation by increasing the range of receptive fields in each network layer rather than expressing multi-scale layer-wise features.  
Finally, Inception is a neural network structure designed to address problems that arise when classification models of deep and wide structures learn [23]. The number of channels was reduced while maintaining the input form using a convolutional layer of a 1 \(\times\) 1 filter and matrix operations were densely performed to increase the computational efficiency. In addition, Inception uses an auxiliary layer that delivers backpropagation by calculating the intermediate learning error, to convey information to the deep layer during learning, and batch normalization, to prevent overfitting that frequently occurs in deep learning.