esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Example
### Step 2. Define Test Cases  
Test cases are defined by the BESS control parameter, which is the power limit that the load is allowed to consume from the grid (\(P_{LIMT}\)). Hence, as \(P_{Limit}\) decreases, the battery will increase its contribution. Scenarios are divided between summer scenarios (S) and winter scenarios (W). SB and WB correspond to the base case scenario for each season, which is the current scenario at the real world facility, with a contracted power of 90 kW. Steps of 10 kW have been selected, so the different scenarios are defined in Table 3.  
Figure 8: Cumulative energy consumed by the load in summer and winter scenarios.  
Figure 7: Industrial consumption profiles for summer and winter scenarios.  
As expected, the lower the \(P_{LIMIT}\), the higher the contribution from the BESS. Since the BESS deals with high demand peaks, it follows a similar distribution as the top part of the industrial load profile for the corresponding scenario. Scenarios SB and WB, i.e., when the grid delivers as much power as demanded by the load, implies no contribution from the BESS.  
For \(P_{LIMIT}\) values below 30 kW, BESSs are not able to recharge the same amount of energy delivered during the high-demand period, both for summer and winter scenarios. It must be noticed that \(P_{LIMIT}\) also affects the maximum power available for recharging, i.e., for a \(P_{LIMIT}\) value of 20 kW, the maximum power available for recharging is 20 kW since that recharging power is coming from the grid, which is not sufficient for the case with \(P_{LIMIT}\) values below 30 kW.  
Based on the BESS power profiles, the power and energy requirements (\(P_{BESS-req}\) and \(E_{BESS-req}\)) for the valid scenarios are shown in Table 4.  
Once the requirements are calculated, the most suitable BESS configuration must be selected for each scenario (number of cells connected in series and in parallel). Commercial three-phase inverters for 400 V grids demand 48 V in the DC side. Hence, based on the standard cell parameters, the number of cells in series so the DC side voltage reaches 48 V equals:  
\[\frac{48V_{DC-side-voltage}}{3.7V_{Nominal-cell-voltage}}\approx 13 \tag{15}\]  
The number of cells in parallel depends on the BESS energy requirements. Given that one branch of 13 cells accounts for:  
\[13\,\text{cells}\cdot 3.7\,\,\text{V/cell}\cdot 55\,\,\text{Ah}=2.64\,\, \text{kWh} \tag{16}\]  
The following table can be filled:  
Scenarios S6 to S4 and W8 to W6 can satisfy their energy requirements with just one BESS branch in parallel. It must be noticed that, given the BESS configuration and the power demand shown in Table 4, the BESS in scenario S4 will be more demanded than in scenarios S5 and S6, and the BESS in scenario W6 will also be more demanded than scenarios W7 and W8, i.e., it will work with higher C rates.
## 3 Service Delivery in 5G-Enabled D2D Communications Network
### Service Access and Delivery  
With content access and retrieval process, the original named data object is published by the content provider/data owner using a Uniform Resource Identifier (URI) of the data as the data identity (ID) or data name which is advertised on the MNO/SP network. Thus, for service discovery, the UE searches for specific data by data ID and some contact information from the owner's routing table are requested, which are in search tolerance of the data ID [40]. The UE must learn the ID/key mapping that associates a UE's ID with the data to be able to request data objects on the network. Additionally, the UE which has been granted access to the data can publish the data based on the content owner and SP security policies. The named data object and UE's ID are mapped together into logical/node address. The UE interested in the data uses the logical address to send interest messages to the UE in possession of the data.  
The process of content dissemination and service delivery starts with the UE generating an interest message which is forwarded to BBU through radio heads. BBU starts a CCN forwarding process to match the request content name [15; 37]. If the targeted content exists in BBU pool content store, the content is sent back to the UE, fulfilling the initial request. During content forwarding, the involved entities can choose to cache a content replica or not, which might also depend on the security parameters such as cache authorization. The content retrieval process of D2D communications involves cache satisfaction at BBU, UE and 5 GC. Two applications of content retrieval are considered: (1) where the content is being matched at the 5 GC; and (2) where the content is being matched locally by another UE in proximity, as shown in Figure 1.  
Figure 1: System model.  
However, if the content request is not matched at the UE locally or from the UE in coverage or BBU, the interest is forwarded to 5 GC. If it is matched in the content store of 5 GC, then the content is pushed backwards to the UE. The traffic burden is reduced on backhaul if the request does not go to the 5 GC and the content retrieval is achieved by using fronthaul and D2D links. BBU pool discovers cached content by associated devices and content transmission is performed using D2D communications. To achieve this process, the security requirements for accessing network and service, data caching and sharing must be achieved. We assume that initial authentication of each UE with its HN has been performed and the two HNs have already agreed on the roaming service agreement. Before the establishment of secure D2D communications, each UE sends a verification request to its gNB or another UE. Then, the authentication and authorization procedure is initiated, the UEs exchange security vectors and they verify each other using the proposed protocols for a secure data transmission.  
D2D discovery and communication are performed during content dissemination. Content discovery is based on CCN interest and data object messages, which is associated with the data name and UE's ID.
## 1 Introduction
### Control Algorithms  
Control algorithms for the heat pump cycle can be categorized into conventional, advanced, and smart. Conventional control algorithms are the most widely used type of control algorithm for heat pumps. They rely on fixed set points and simple on/off control logic, making them easy to implement and maintain. However, they may be less efficient and unable to adapt to changing conditions.  
Advanced control algorithms are more complex but offer a wider range of features and functionalities. They can consider more variables, such as outdoor and indoor temperature and system operating conditions. They can also use more sophisticated control techniques, such as fuzzy model predictive control (MPC). Advanced control algorithms can improve the efficiency and performance of heat pumps, but they may be more expensive and complex to implement. [PERSON] et al. [19] compared the performance of on/off control and proportional-integral (PI) control for air-to-air heat pumps. The results show that PI control can improve the system's efficiency and stability compared to on/off control. [PERSON] et al. [20] implemented MPC on an air-to-air heat pump and demonstrated its ability to optimize energy consumption while maintaining comfort levels. [PERSON] et al. [21] presented a model-based predictive control strategy to optimise the performance of phase change material (PCM) ceiling panels coupled to a multi-stage air-to-air heat pump. [PERSON] et al. [22] applied fuzzy logic control to a ground source heat pump system and showed its effectiveness in improving system performance and user comfort. [PERSON] and [PERSON] [23] presented the effectiveness of a PLC control system and optimized operation strategies in improving a desiccant-coated heat exchanger-based heat pump fresh air system's efficiency (COP) and dehumidification rate. The study primarily used PID control methods. A PLC control system was developed to manage the system's components. Optimized operation strategies were established for the electronic expansion valve, compressor frequency and switchover period. The results showed an increase in COP of 25% in summer and 78% in winter compared to a non-optimised system.  
Artificial intelligence (AI) control algorithms are the most advanced type of control algorithm for heat pumps. They can learn from data and adapt to changing conditions in real-time. They can also integrate with other smart home devices and systems to optimize energy use and comfort. AI control algorithms have the potential to significantly improve the efficiency, performance, and convenience of heat pumps; however, they are still in their early stages of development. [PERSON] et al. [PERSON] et al. (2018) investigated using deep reinforcement learning (Deep RL) for optimal control of air-source heat pumps, highlighting its potential for significant energy savings.
# An Indoor Smart Parking Algorithm Based on Fingerprinting
## 1 Introduction  
Indoor localization [1] is the process of detecting positions in indoor environments. The big challenge is about not to rely on a global positioning system (GPS), due to the interruption of connection with satellites. In outdoor settings, typically handled through vehicular ad hoc networks [2], localization is an issue from several points of view: from routing [3] to data dissemination [4], but also smart parking [5]. See [6; 7; 8] for interesting proposals on localization.  
In this work, it has been decided to focus on the last challenging aspect: the smart parking problem. When speaking about the _parking problem_, we mean the problem of handling, in an efficient manner, the process in which drivers compete for a shared resource, namely the available parking slots.  
While in outdoor environments the smart parking problem has been widely investigated, the management of such an issue in indoor contexts seems to be still an open issue. For this reason, in this work, the smart parking problem is formalized in an indoor scenario and solved through fingerprinting. The considered scenario is an airport with a car park to be handled. Typically, people approaching the car park simply try to get as close as possible to their target, which in the case of an airport is a terminal, most likely. To the best of my knowledge, there is not any system able to guide us inside a car park in a way that is similar to how GPS guides us along the streets of our cities.  
In this work, a fingerprinting localization technique is used. This has been proven to be simple and reliable and it works in two phases: a training offline phase, and an online localization phase [9]. Precisely, fingerprinting is used in this work to measure the received signal strength from any access point, placed in the offline phase, to infer the position of some points of interest, that in the specific airport scenario are the parking slots and the terminals.  
Notice that the indoor smart parking problem is nothing but a particular case of indoor localization since it requires finding the position of available slots in an indoor environment. For this reason, the problem presents the same challenges and difficulties as the standard indoor localization problem. It has been decided to focus on the indoor smartparking problem instead of simple indoor localization since the latter has been already investigated deeply.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Model Assessment and Implementation
#### 3.2.2 Disentanglement Validation  
Using the encoder sub-architecture, we analyzed the disentanglement of the latent representation based on the features' dependency. To perform this in an unsupervised way [36], we used the Pearson correlation between all the features on the representation to determine how orthogonal the generated features are. We report the average over all the values (**Pcorr**) and the average over the absolute values (**Pcorr-A**). The mutual information (**MI**) between the continuous features was also measured, including a normalized version (of a value in the range \([0,1]\)) that is obtained by dividing on the entropy of every feature (**N-MI**). The calculation of MI is a discrete approximation of the real continuous space calculation, based on the \(k\)-neighbors' implementation [57].  
Here, we compare the high-level specialized features included in Kepler's metadata with the features learned by RAE\({}_{t}\)[10] and the PCA features extracted over the frequency domain (spectrum) representation of the time series (_Fourier followed by PCA_ or F-PCA) [20].
# Streetlight Control System Based on Wireless Communication over DALI Protocol
## 3 System Architecture
### Application for the Maintenance of the Lighting Network  
A graphic application for mobile devices has been developed to manage maintenance tasks for lighting networks. The application obtains information from the database created by the SCADA  
Figure 5: View of one of the windows of the local SCADA.  
Figure 6: Detail of the information window of a lamp.  
system. Once the application has been launched and the user has been identified, the streets allocated to the operator will appear. If any failure has happened, the application will inform the user with an interface such as the one shown in Figure 7. Each streetlamp is identified by a unique identifier (nodeID), which is set by hardware when the node is installed, as mentioned in Section 3.1.  
From this moment, the application includes several screens, such as the one shown in Figure 8, which helps the technician to determine the location of the streetlight. This system is particularly useful in small towns or villages where just one company is responsible for maintaining the whole lighting system, preventing unnecessary driving around to control the status of the streetlamps.  
This is the case, for example, in some areas of Andalusia (Spain), which has scattered small towns and villages where having an onsite maintenance technician is impossible. Once the technician has solved the problem, the system detects it and the database is updated accordingly.  
Figure 8: App screenshot GIS location of an incident.  
Figure 7: App screenshot with information on incidents for the maintenance technician.
## 7 Conclusions  
In this study, a database of 47 stocky RC columns was used to extract the modeling parameters for constructing column envelope curves. The extracted values were then compared with the modeling parameters values calculated using the ASCE 41-17 equations. The comparison results showed that the error between the measured values and the values calculated with the ASCE 41-17 equations was significant for the columns with height-to-depth ratios (\(a_{v}/h\)) less than 2.0. In this study, a calibration factor was proposed for RC columns with \(a_{v}/h\) less than 2.0 to improve the accuracy of the values of modeling parameters \(a\) and \(b\) calculated from the ASCE 41-17 equations. The following conclusion can be drawn.  
1. The ASCE 41-17 equations for deformation modeling parameters produce erroneous predictions for stocky RC columns, because these equations do not consider the effect of \(a_{v}/h\) of RC columns. The application of the proposed calibration factor significantly improved the accuracy of the calculated values of modeling parameters \(a\) and \(b\) for columns with \(a_{v}/h\) less than 2.0.
2. After applying the calibration factor to the ASCE 41-17 equations, errors in the calculated values of modeling parameters \(a\) and \(b\) significantly decreased. For parameter \(a\), the error reduced from 87.7% to 41.4%, and for parameter \(b\), the error reduced from 71.1% to 42.3%.
3. The envelope curves for the stocky RC columns were accurately constructed using the modeling parameters with the proposed calibration factor, which match the measured cyclic curves.  
Conceptualization, S.W.H.; methodology, C.S.L.; investigation, C.S.L., M.A.P.Z., K.L.; writing of the original draft preparation, S.W.H., M.A.P.Z.; writing of review and editing, S.W.H.; visualization, C.S.L.; supervision, S.W.H.; project administration, S.W.H.  
This research was funded by the Korea Agency for Infrastructure Technology Advancement (grant number 19 CTAP-C152179-01).  
The research was supported by a grant from the Korea Agency for Infrastructure Technology (19 CTAP-C152179-01). The valuable comments of four anonymous reviewers are greatly appreciated.  
The authors declare no conflicts of interest.
## 3 Proposed APF-IRRT\({}^{*}\) Algorithm
### Procedure of Hybrid Algorithm  
The basic process of the hybrid APF-IRRT* algorithm is described as follows:  
Step 1: Establish a grid map based on the workspace of the mobile robot, including the position of obstacles, starting point \(x_{start}\) and target point \(x_{tar}\).  
Step 2: Set the number and neighborhood radius of iteration. Set step size of I-RRT* algorithm.  
Step 3: Using the RRT algorithm to plan the initial path, calculate the path length \(c_{best}\) and the Euclidean distance \(c_{min}\) between the starting point and the target point.  
Step 4: Randomly generate node \(x_{rand}\), find the neighbor node \(x_{near}\), and calculate the vector \(\alpha\) from \(x_{near}\) to \(x_{rand}\).  
Step 5: Initialize the APF parameters, including the attractive coefficient, maximum attractive force, repulsive coefficient and repulsion distance. Calculate the sum vector \(\beta\) of APF.  
Step 6: Calculate sum vector \(\theta\) of \(\alpha\) and \(\beta\), generate a new node \(x_{near}\), and rewrite the parent node for \(x_{new}\).  
Step 7: Perform collision detection and elliptical domain detection for the path.  
Step 8: Search for a path from the start point to the target point. If found, calculate the path length. If not found, expand the search tree.  
Step 9: Determine whether the convergence goal is achieved or not. If it is not achieved, expand the node of the IRRT* until achieved. If it is achieved, end the algorithm.  
The chart of the hybrid APF-IRRT* algorithm is shown as Figure 3.
# Using Graph Neural Networks for Social Recommendations
###### Abstract  
Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user-item, and user-user relationships but also item-item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item-item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms.  
social recommendation algorithm; graph neural networks; recommender systems; social network; influence diffusion
## 3 Our Approach
### Delay-Resilient Remote Memory Attestation
#### 3.3.1 Step 1: Challenge Generation  
The challenge request will be sent from the verifier to the remote device in order to verify whether the memory is changed by the adversary or not. To avoid an adversary launching replay attacks to a response within a time duration (_i.e._, returning the old response message to the current challenge), a random challenge message is required. Therefore, the challenge message generation consists of the following three steps:  
* Step 1.1: The verifier generates a random key based on the current time of the verifier, which is shown as follows: \[Challenge:key=hash(T_{Verifier})\] (5) where \(T_{Verifier}\) is the current time of the verifier.
* Step 1.2: The message is generated and transmitted: \[m=\{key\|W\}\] (6) where \(W=\{W_{1}\|\cdots\|W_{k}\}\) is the seed for the \(RC4\) function.
* Step 1.3: The verifier sends a challenge message to the remote device with the corresponding message authentication code \(MAC_{n}(m)\) as: \[Verifier\to n:request=\{m\|MAC_{n}(m)\}\] (7)  
Figure 4: The process of delay-resilient remote memory attestation [27].  
where \(m\) is the message sent by the verifier. The verifier records the message when the packet is sent as: \[Ts_{Verifier}=Tcurrent_{Verifier}\] (8) where \(Tcurrent_{Verifier}\) is the current time of the verifier.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 4 Simulation Results and Discussion of C-FSS
### Z Parameter of C-FSS  
Figure 11 indicates the z-parameter of the proposed C-FSS. The z-parameter shows the impedance characteristics of the structure which is crucial to understand the electromagnetic behavior across the specified frequency range. Around 1.8 GHz, the impedance curve shows a sharp change, likely indicating a transition from capacitive to inductive behavior, or vice versa. Around 2 GHz, a rapid transition shows strong reactive behavior. The range of frequencies where z-parameters show behavioral changes indicates the operational bandwidth of the C-FSS.  
Figure 11: Z-parameter of C-FSS.  
Figure 10: Angular stability of C-FSS in TE mode. (**a**) Reflection coefficient. (**b**) Transmission coefficient and angular stability of C-FSS in TM mode. (**c**) Reflection coefficient. (**d**) Transmission coefficient.
# Developing a Novel Design for a Tubular Solid Oxide Fuel Cell Current Collector
###### Abstract
Although a good performance was achieved, a uniform flow distribution was not guaranteed. Additionally, many previous studies [26; 27; 28; 29; 30] have focused on the current collector designs of tubular SOFCs. Previous studies have either focused on the current collection or on achieving a uniform distribution for the species inside the cell from the above literature review. To achieve a higher current collection from the cell, previous studies have focused on increasing the contact active surface area with electrodes, which is considered an obstacle due to the nature of tubular SOFC design, as it means reducing the available area for the fuel and oxygen flow. Therefore, the main objective of the current work is to fulfill the real need to develop and optimize a new current collector that can collect the current efficiently and distribute the species uniformly through the flow channels. In this study, a new current collector design is developed in which the flow channels are divided into small trapezoidal channels. These small trapezoidal channels can guarantee the uniformity of the flow and simultaneously concentrate the species distribution towards the electrodes' surface to boost the electrochemical reactions and make the best use of the fuel. Additionally, this new design increases the active contact area of the electrodes for better current collection in a simple design that can be easily manufactured. The current study develops a numerical model for tubular SOFCs, followed by the validation of the model with available experimental data. Then, the developed numerical model investigates the new current collector design and compares it with the traditional design.