esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Future Projects of the Production of Green Hydrogen on an Industrial Scale
### GH2 Projects--Future Facilities (2025-2050)
#### 6.1.6 Frontera Project--Magallanes Region  
The Frontera on-shore wind farm is a facility that will be located in the northeast of Punta Arenas, Magallanes region. It will have 350 wind turbines, the owner of which will be Acciona and Nordex. Furthermore, the height of the towers from ground level to the hub will be in the range of 110 m. Each wind turbine will have an installed capacity of 6.0 MW, and considering all the wind turbines, the wind farm will have a total installed capacity equivalent to 2100 MW, with an estimated capacity factor of 0.54. Figure 15 shows a panoramic image of the landscape of the project site, and Table 8 provides some key technical data for this GH2 production facility.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Units** \\ \hline Location & Primavera Commune, Northeast of Punta Arenas, Magallanes Region & - \\ \hline Concession Owner & Acciona and Nordex & - \\ \hline Phase of the Project & Industrial Plant & - \\ \hline Type of Wind Farm & On-shore & - \\ \hline Number of Turbines & 350 & - \\ \hline Diameter of Turbines & 145 & m \\ \hline Hub Height & 110 & m \\ \hline Installed Capacity per Turbine & 6.0 & MW \\ \hline Total Installed Capacity & 2100 & MW \\ \hline Capacity Factor & 0.54 & - \\ \hline Equivalent Operating Hours (EOHs) & 5000 & Hours \\ \hline Source of Water & Sea Water—Desalination Plant & - \\ \hline Water Supply & 2,210,000 & m\({}^{3}\)/year \\ \hline Electrolyzer Capacity & 1700 & MW \\ \hline GH2 Production & 195,500 & Tons/year \\ \hline NH\({}_{3}\) Production & 1,096,500 & Tons/year \\ \hline CO\({}_{2}\) Avoided & 1,955,000 & Tons/year \\ \hline \hline \end{tabular}
\end{table}
Table 8: Frontera GH2 plant main specifications.  
Figure 15: Panoramic view of the landscape in the Frontera project.  
Considering Figure 15, it is evident that the Frontera on-shore wind farm is situated near the desert part of Patagonia, where no contiguous human settlements are present.  
According to Table 8, this GH2 production plant will use seawater from a desalination plant, considering a water supply of 2,210,000 m\({}^{3}\)/year. Also, the plant will have an electrolyzer with 1700 MW of installed capacity. In this sense, the GH2 production plant will generate 195,500 tons/year of GH2, and 1,096,500 tons/year of NH\({}_{3}\). Finally, this facility will save 1,955,000 tons/year of CO\({}_{2}\) from being emitted into the atmosphere.
## 1 Introduction
We consider the possibility for the future 5G dense/ultra-dense node to adapt its radio transmission/reception capabilities to the characteristics of the available FttC-based fronthaul transport capacity. As indicated in the paper, this could be achieved by introducing a programmable PHY/RF protocol layer in the radio node. In this case, the parameters and types of waveforms to be supported by the node could be selected properly in order for the overall required transport capacity to the central processing unit does not exceed the available fronthaul capacity. As shown in this paper, this allows the creation of flexible and programmable 5G dense/ultra-dense network. The considerations that have been made so far motivate this research concerning the adoption of FttC-based fronthaul for 5G future networks. The main contributions of this paper are listed in the following points. * Analysis of the VDSL2 35b technology potentials with and without vectoring to provide a practical and low cost fronthaul solution for future 5G services; performance results are obtained considering the characteristics of the Italian FttC network infrastructure in large, medium and small cities;
* Clarification of the possibility for the radio node to adapt its radio transmission capabilities to FttC fronthaul transport capacity; this is achieved by considering the possibility of protocol stack splitting for the radio node;
* Analysis of the performance improvement in fronthaul capacity by considering optical evolution (OE) strategies for the present FttC network, mainly consisting in gradual Cabinets densification towards the distributors, progressively replacing copper lines to distributors with optical fibers;
* Evaluation of the costs of the considered OE solutions, as compared with the full-fiber solution for fronthauling. The paper is organized as follows. In Section 2 we provide the description of the considered FttC-based fronthaul architecture. In Section 3 we introduce the main parameters used to assess the system performance. In Section 4 we highlight the main advantages of protocol stack splitting and we discuss on one protocol architecture for the 5G access node leading to a programmable dense/ultra-dense access network. In Section 5 we present the main characteristics of the Italian scenario. In the same Section results obtained from computer calculation are also reported and an analysis of related costs is provided. A discussion on the 5G services that can be supported by the FttC-based fronthaul is discussed in Section 6. Finally, in Section 7 conclusions are drawn.
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 2 Background and Related Work
Our work presents an architecture composed of tiled baseband processor blocks that can be turned on/off and whose operation can be customized at run-time according to communication demands. While previously-published works focused on one or a few communication features and scenarios, our design combines enhanced flexibility, upgradeability, cost-effectiveness, and energy-efficiency into a versatile baseband processing architecture suitable for downlink transmission in 5G scenarios such as: dynamic carrier aggregation, centralized multi-mode baseband processing, multi-waveform, and multi-service coexistence. Regarding UFMC baseband modulation, our approach considers a sporadic short-packet transmission scenario using few filtered sub-bands and aims to reduce the utilization of FPGA-embedded DSP blocks in order to fit the design in cost-optimized FPGA devices.
## 6 Proposed Scheme
### User Registration  
The \(VU\) chooses \((ID_{v},PW_{v})\) and submits \(ID_{v}\) to TA via a secure channel. TA generates a random number \(y_{v}\) and timestamp \(t_{v}\) for each \(VU\) and TA computes \(PID_{v}=E_{K}[ID_{v}\|y_{v}\|t_{v}]\), \(A_{v}=h(ID_{v}\|K)\) and responses \((PID_{v},A_{v})\) to \(VU\) via a secure channel. \(VU\) inputs their biometric information \(BIO_{v}\) on the reader to obtain \(\{BSK_{v},\epsilon_{v}\}=GEN(BIO_{v})\), and computes \(C_{v}=h(ID_{v},PW_{v},BSK_{v})mod~{}n\), \(PPID_{v}=PID_{v}\oplus h(PW_{v},ID_{v},BSK_{v})\), \(PA_{v}=A_{v}\oplus h(PW_{v},BSK_{v},ID_{v})\), where \(n\in\left(2^{4},2^{8}\right)\), and stores \(<G,P,\epsilon_{v},C_{v},PPID_{v}\), \(h(\cdot)\), \(PA_{v}>\) into a Smart Card (SC).  
**Remark 5**.: _The reason for taking the modulus \(n\) in the verification equation \(C_{v}=h(ID_{v},PW_{v},BSK_{v})mod~{}n\) is to resist side channel attacks. Even if the attacker's guessed identity and password can pass verification, when \(n=256\), there are \(2^{32}\) pairs of identities and passwords, and the attacker cannot know which pair is correct. Meanwhile, it can achieve three-factor secrecy. On the other hand, the advantage of using the Fuzzy Extractor algorithm is that even if the user inputs a small difference in biometric information compared to the previous one, the same biometric key can still be restored.
# Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data +
## 1 Introduction
To evaluate the effectiveness of this approach, we conducted experiments on the UNSW-NB15 dataset [29], a widely used benchmark for evaluating intrusion detection systems. Our model achieved an F1 score of 83% for the \(T=1\) case, which is similar to a real-time IDS classification. Moreover, it attained an F1 score of 91% for predicting the existence of an attack in the next \(T=20\) packets. Our main contributions can be summarized as follows:  
* We extend and improve existing intrusion detection systems by implementing proactive prediction. * We compare our approach with state-of-the-art current methodologies. * We experiment with the configuration parameters (e.g., size of the input window) of our method thoroughly to provide insights. * We propose a novel methodology combining deep learning components. The current manuscript is an extension of the previous work presented at the 2023 International Symposium on Distributed Computing and Artificial Intelligence (DCAI) [30], providing a more complex and robust architecture with more experiments, as well as an ablation study to validate the proposed architecture. The structure of the remaining sections is as follows: Section 2 provides an overview of the previous approaches on IDSs based on machine learning as well as multivariate time series prediction state-of-the-art methodologies. Section 3 provides an overview of the IDS dataset with the relevant prediction features. In Section 4, we delve into the methodology behind the proactive IDS, the pre-processing steps we conducted, as well as the architecture of the model. Section 5 presents the results of our experiments, which evaluate the performance of our approach in different scenarios with many alternative hyperparameter settings. In Section 6, we perform an ablation study to validate the architectural choices of the model. Lastly, in Section 7, conclusions are drawn, and potential future directions for further research are outlined.
# UAV Communication Recovery under Meteorological Conditions
## 3 Methods
### UAV Coverage Radius with Elevation Angle Considering Gaseous Attenuation Only  
In this section, we analyze the relationship between the coverage radius of the UAV and the UAV elevation angle under the influence of the atmosphere only. We remove the specific attenuation of weather (\(\gamma\)) in Equation (10), and then the corresponding implicit function of the coverage radius of the UAV with respect to the elevation angle under the influence of the atmosphere only can be obtained. The ambient temperature is assumed to be 15 degrees Celsius.  
The UAV coverage radius and the elevation angle in four distinct regions, considering gas-only environments, are depicted in Figure 5. We can observe the following trends. First, the coverage radius of the UAV operating at 28 GHz (solid line) is generally larger than that at 71 GHz (dashed line) under the same number of reflections in each region. Second, for a given frequency, the coverage radius of the UAV decreases as the number of reflections increases. Lastly, the coverage radius of the UAV in the four selected regions (suburban, urban, dense urban, and high-rise urban) decreases sequentially. Specifically, based on Figure 5, we summarize the maximum coverage radius of the UAV in Table 3. By analyzing these maximum coverage radius data, we find some interesting regularities. For example, in Region 1, under the two frequency conditions, the maximum coverage radius based on two reflections is reduced by 15% compared with that of one reflection, and the maximum coverage radius based on three reflections is reduced by 25% compared with that of one reflection. The reduction percentages of the maximum coverage radius based on two and three reflections compared to that of one reflection in the four regions are summarized in Table 4. It can be seen that the reduction percentage of the maximum coverage radius for different numbers of reflections compared to one reflection is independent of the frequency from Table 4. In addition, under the two frequency conditions, compared with the maximum coverage radius based on one reflection in Region 1, the reduction percentages in the other three regions are 13%, 24%, and 35%, respectively.  
Figure 5: UAV coverage radius versus elevation angle under gases in four regions.
# Experiencing LoRa Network Establishment on a Smart Energy Campus Testbed
## 4 Experiments for LoRa PHY
### Preliminary  
The testbed consists of a LoRa node, a LoRa gateway, and a data server. Experiments run in a typical IoT communication pattern; that is, a node periodically sends data packets to a gateway that forwards it to a database server for further analysis.  
A LoRa node is implemented using the MultiTech's mDot(tm) MTDOT-XIP-SMA [23] equipped with an 8-inch omnidirectional dipole antenna with a gain of 3 dBi. It uses Semtech's SX1272/73 LoRa communication module [24]. The node radiates signals at a transmission power of 11 dBm, and the SF value is set to 7 by default. The node moves around on the campus and transmits 100 data packets with a 1-byte payload length to a gateway once it stands in a fixed position. No retransmission policy is applied; the node does not check whether packets are successfully received after sending them out, and thus, some packets may be lost. A LoRa gateway is implemented using the MultiTech's Conduit(tm) MTCDT-247A [25]. It has the same communication module and antenna and can thus communicate with the LoRa node. Upon receiving data, it forwards the data to a data server for further processing via the Ethernet. The gateway is installed on the roof of a five-story building about 15 m high (Building #7 in Figure 4) with no obstacles nearby, as shown in Figure 6.  
Throughout the experiments, the gateway stays in the same position. Instead, we change the node's location and conditions for communication so that as communication distance varies, the node is either on the inside or outside of a building, may be surrounded by a crowd (moving), and may have an insecure line of sight. By having these variables in the experiments, we can assess how well data signals are successfully received and decoded by the gateway. To this end, the gateway measures and records the Received Signal Strength Indication (RSSI) of all incoming signals.
## 4 Research on Image Acquisition and Theoretical Methods
### Depth Image Acquisition
The use of specialized equipment like the explosionproof searchlight helps to maintain consistent lighting conditions, which is crucial for obtaining clear and precise images necessary for depth analysis. (2) Proper positioning of the camera is crucial. Two camera positions, as shown in Figure 4, should be selected for capturing images. These positions should be at the one-third and two-thirds points of the tunnel cross-section, at a straight-line distance of 6-8 m from the palm face, with the camera height set between 1.5 and 1.7 m. In Figure 4, the blue arrows represent the direction and angles of capture, while the red dot indicates the camera's position and height. Correct camera placement ensures optimal angle and distance for capturing detailed images of the tunnel's profile surface and palm face. This precise positioning is key to achieving high-quality depth images that are essential for constructing an accurate 3D point cloud data model. Properly positioned cameras help minimize distortions and capture comprehensive details, thereby enhancing the overall effectiveness of the experiment [37; 38]. (3) Adjustments to camera settings and lighting equipment are essential; the environmental conditions within the tunnel, characterized by dust and dim lighting resulting from explosions and construction operations, significantly increase the complexity of image acquisition, particularly when compared to outdoor photography. Therefore, it is necessary to finetune the camera parameters and lighting equipment before shooting to accommodate these unique tunnel conditions. Proper adjustment of camera settings, such as aperture and shutter speed, is crucial to ensure that the images captured are of high quality and suitable for subsequent analysis. Additionally, adjusting the lighting equipment to provide adequate illumination despite the challenging conditions will help in reducing shadows and enhancing the clarity of the captured images. This careful preparation is vital for obtaining accurate and reliable data from the tunnel environment. (4) Image Acquisition Methodology. Seven photographs are taken from each of the two designated shooting positions on both the profile surface and palm face. Each image must overlap by at least 30% with the adjacent image to ensure accurate feature point matching during image processing (see Figure 5) [39]. During image capture, the camera lens should be perpendicular to the tunnel surface, maintaining a steady angle and avoiding any tilt or rotation. This precise alignment is crucial for minimizing distortions and ensuring that the  
Figure 4: Camera shooting position schematic. captured images can be accurately stitched together during postprocessing. By adhering to these guidelines, the integrity of the feature points across the images is preserved, facilitating the creation of a reliable and detailed 3D point cloud model of the tunnel's interior. Proper overlap between images also helps in reducing errors during the stitching process, ensuring a seamless and accurate representation of the tunnel surfaces.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 3 A Crossroad Passing Model
### Convoy Following Criterion  
Suppose that the first vehicle in the convoy \(F_{(x)i}\) arrives at the crossroad at \(T_{(x)i,arrive}\), and the entire convoy \(F_{(x)i}\) passes the crossroad stop line at \(T_{(x)i}\). Let \(\Delta T_{guard}\) be the guard time interval when changing to another crossroad direction to pass.  
1. When \[T_{i+1,arrive}-T_{(x)i}\leq\Delta T_{guard}\] (2) the convoy \(F_{i+1}\) will pass the crossroad together with the convoy \(F_{(x)i}\) and get the passing order of \((x)\).
2. When \[T_{i+1,arrive}-T_{(x)i}>\Delta T_{guard}\] (3) there are two conditions. 1 If \[r_{(x+1)}<T_{i+1,arrive}-\Delta T_{guard},\] (4) after the convoy \(F_{(x)i}\) has passed the crossroad, it turns directly to the road direction whose passing order is \((x+1)\), which is generally more efficient. So, the convoy \(F_{i+1}\) gets the passing order of \((y+1)\). 2 If \[r_{(x+1)}\geq T_{i+1,arrive}-\Delta T_{guard},\] (5) after the convoy \(F_{(x)i}\) has passed the crossroad, the convoy with passing order \((x+1)\) is further away from the crossroad than the convoy \(F_{i+1}\). And it is more efficient to wait for  
Figure 2: Initial smart contract process for a new convoy to obtain a passing order.  
the entire passing of the convoy \(F_{i+1}\), which also obtains order \((x)\), to pass together. So, when convoy \(F_{(x)i}\) passes the crossroad, let the convoy \(F_{i+1}\) continue to pass. Moreover, it is better for convoy \(F_{i+1}\) to accelerate to the crossroad after receiving an order \((x)\), which improves road efficiency and saves energy.
## 2 Materials and Methods  
The methodology adopted to identify changes in the landscape of the semi-arid region of Pernambuco, caused by the presence of mining areas, consisted of the following steps: data acquisition, processing, and results generation (Figure 1).  
In the first step, data acquisition was carried out with the selection of scenes, considering factors such as broad coverage, absence of clouds, periods of low rainfall incidence, and availability of data sharing via cloud platforms. Additionally, as the vegetation of the Caatinga biome, present in the study area, is sensitive to rainfall [45], it is necessary to analyze the response of spectral indices concerning vegetation, considering the effects of precipitation. In this regard, precipitation data for the study region during the image acquisition period was obtained from the Pernambuco Water and Climate Agency (APAC).  
Next, land use and land cover mapping were developed. Initially, the spectral indices were calculated, followed by class training. At this stage, due to factors such as the sensor's spatial resolution and the diversity of land uses in the area, four classes were chosen: water, vegetation, bare soil, and urban patches (built-up areas). The vegetation class was grouped into herbaceous and shrub vegetation, while the water class included both watercourses and water bodies.  
As a result, the landscape changes caused by mining infrastructure were identified through thematic maps and quantified using class extraction and separability. The classification accuracy relative to the image was established through evaluation parameters: Overall Accuracy (OA), Kappa index, Omission Errors (OEs), and Commission Errors (CEs).
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 3 System Model  
We consider an enterprise supply chain to provide drugs to a hospital. This supply chain consists of a set of drugs \(\mathcal{I}\) managed during period \(T\). To simplify the problem formulation, all the notations are presented in Table 1. We assume that the system works in a time-slotted fashion, spanning time slots \(1,2,\ldots,T\), with an index \(t\) that corresponds to a week or a month defined by the hospital.  
Each drug \(i\in\mathcal{I}\) has a storage capacity \(C_{i}(t)\) and an expired function \(e_{i}(t)\) that is used to measure the amount of expired drug \(i\) at time \(t\). With different types of drugs, the storage capacity units could be different (e.g., box, bottle, etc.) so that our model could flexibly define the capacity unit for each specific drug \(i\). To make a refilling decision, we consider a refilling cost \(p_{i}(t)\), which can be measured or collected by prediction methods. In this work, we simply model this function by the regular (or base) price \(\underline{p}_{i}(t)\) and the penalty price \(\beta_{i}(t)\) due to the emergency demand. The function is calculated as follows:  
\[p_{i}(t)=\underline{p}_{i}(t)+\beta_{i}(t),\forall i\in I,t=1,2,\ldots,T. \tag{1}\]  
Practically, we are unaware of the price in advance and, therefore, cannot make a long-term plan. In this work, we presume that the price parameters are given before making a refilling decision at time \(t\). We also denote the budget \(B_{i}\) that is used to buy drug \(i\) during time \(T\).
## 2 Materials and Methods
### Proposed Assessment Method
#### 2.1.3 Sharpness  
Sharpness represents the aspects of fine details and edge components of an image and it is distributed in the high-frequency band of a Fourier domain [30]. Sharpness features can be extracted from high-pass filtered images, which is outlined in Equations (5) and (6).  
\[Sha_{1}=\frac{1}{k_{1}k_{2}}\sum_{l=1}^{k_{1}}\sum_{k=1}^{k_{2}}\log\biggl{(} \frac{E_{max,k,l}}{E_{min,k,l}}\biggr{)} \tag{5}\]  
The first sharpness feature in Equation (5) is calculated by using \(5\times 5\) sized image patches and \(E_{max}\) and \(E_{min}\) are the maximum and minimum luminance values of the high-pass filtered image patches, respectively. This feature increases as the sharpness adjustment level increases, which is shown in Figure 5a.  
\[Sha_{2}=\frac{1}{NM}\sum_{i=1}^{N}\sum_{j=1}^{M}\sqrt{\sum_{k=-K}^{K}\sum_{l=-L }^{L}w_{k,l}(E_{k,l}(i,j)-\mu(i,j))^{2}} \tag{6}\]  
Figure 4: Comparison graph between the contrast adjustment level and the calculated contrast features. Contrast feature 2 and 1 represent the local and global aspects of the contrast of the image, respectively.  
The second sharpness feature is obtained by using Equation (6). The standard deviation of the high-pass filtered luminance values is used to calculate how widely distributed the high-frequency components were in the images. \(w_{k,l}\) is a relatively large-sized 15 \(\times\) 15 2D circularly symmetric Gaussian weighting function. This feature increases as the sharpness adjustment level increases, which is shown in Figure 5b. Similar to the assessments of the colorfulness and contrast, our proposed method uses both local (Equation (5)) and global (Equation (6)) sharpness features to consider the HVS properties. These feature values are also compared with the HDR sharpness features for the final quality score calculations.