esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Simulation of New Routines  
Routine evaluation and simulation features are implemented through a REST API. The Python _FastAPI_ ([[https://fastapi.tiangolo.com/](https://fastapi.tiangolo.com/)]([https://fastapi.tiangolo.com/](https://fastapi.tiangolo.com/)) accessed on 1 May 2024) library is the selected tool for the development. Table 4 shows all the available API's endpoints. Four groups of functionalities can be identified:  
* _/consumption_. The endpoints belonging to this group provide insight into the appliances' energy consumption. It is possible to ask for the energy consumption of a specific time range and/or a specific appliance; \begin{table}
\begin{tabular}{c l c c} \hline \hline
**Appliance Type** & **Mode Name** & **Power (W)** & **Duration (s)** \\ \hline Radio & Stand-by & 1 & \(\infty\) \\  & On & 37 & 4435 \\ \hline Dishwasher & Intensive & 1400 & 10,380 \\  & Daily & 810 & 10,260 \\  & Eco & 700 & 6660 \\  & Express & 350 & 1680 \\  & Delicate & 800 & 7080 \\  & Pre-wash & 10 & 660 \\ \hline \multicolumn{2}{l}{Fridge w/freezer} & On & 208 & 6304 \\ \hline Television & Stand-by & 18 & 2514 \\  & On & 110 & 1833 \\ \hline Microwave & Stand-by & 5 & \(\infty\) \\  & Microwave & 750 & 300 \\  & Crisp & 500 & 300 \\  & Grill & 500 & 300 \\  & Grill + microwave & 500 & 300 \\  & Steam & 350 & 300 \\  & Jet defrost & 160 & 300 \\ \hline Lamp & On & 19 & 587 \\ \hline Washing machine & Cotton 90\({}^{\circ}\) & 2000 & 8700 \\  & Cotton 60 ECO & 950 & 7680 \\  & Cotton 60\({}^{\circ}\) & 1200 & 7200 \\  & Cotton 30\({}^{\circ}\) & 550 & 6900 \\  & Synthetic 30\({}^{\circ}\) & 900 & 5400 \\  & Delicate 30\({}^{\circ}\) & 500 & 3600 \\  & Wool & 450 & 3300 \\ \hline Desktop & Stand-by & 4 & \(\infty\) \\  & On & 41 & 12,412 \\ \hline Boiler & Holiday & 33 & 300 \\  & Comfort & 57 & 525 \\  & Auto & 101 & 2187 \\ \hline Air conditioning & Cool & 336 & 3337 \\  & Heat & 656 & 30,176 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of operation modes identification. The _off_ mode is not reported, as it is assumed to have 0 Watt consumption and indeterminate duration for all appliances. _/appliance_. This group provides two methods: one to obtain data from all the appliances of the house and one to obtain information on a specific appliance;
* _/routine_. There are two endpoints for this group as well. One lists all the user-created routines saved in the system, and the other provides the user with data on a specific routine;
* _/simulate_. The endpoints of this group are the ones responsible for the simulation of routine addition. The procedure will be discussed in more detail in the following section. To simulate the addition to the Digital Twin of a new routine created at runtime by the user, it is necessary to have a data structure that represents the state of the appliances
## 1 Introduction
### Required Improvements in the EMS Development for Isolated Microgrids  
As shown above, EMSs for isolated microgrids are commonly designed without analyzing their dynamic behavior. The primary and secondary control layers are responsible for stabilizing the microgrid after disturbance, but the EMS must consider their necessities to perform the operation properly. This issue has been previously solved by incorporating power reserves constraints in the optimization problems of the EMSs [14; 19]. Nevertheless, few little dynamic considerations have been performed when designing EMSs. In addition to the power up/down regulation capacity, there are dynamic aspects that should be considered by the EMSs, which are not studied yet.  
Utility grids are usually characterized by incorporating many rotating machines and, consequently, have large inertia. During power imbalances, and until the primary and secondary controls react, the required energy is obtained from the rotating machines leading to frequency variations. Due to the big inertia, these frequency variations are usually small. Accordingly, in grid connected microgrids, it can be assumed these deviations are not relevant [10]. In contrast, grid isolated microgrids present low inertia, and even lower when large amount of photovoltaic power is installed. Accordingly, these assumptions can no longer be accepted. Power reserves will determine whether the inner control loops can compensate the microgrid imbalances. However, due to the low inertia, the transient frequency deviations can reach unacceptable levels collapsing the system. Even though the study performed in [19] considers the up/down regulation and analyzes the dynamic response, a required inertia to ensure the frequency does not exceed the acceptable limit is not studied. Hence, in this case, the EMS developed in [19] disconnects to match the rotating machines, and the system stability could be compromised. Similarly, if frequency transients present overshoots, the stability of the system is not ensured by the proposed methods in [20; 21].  
According to the above issues, for designing a reliable EMS, it is still necessary to incorporate dynamic constraints into the problem formulation. Particularly, in addition to the power reserves, the minimum grid inertia to ensure an stable operation should be considered on the tertiary control layer of isolated microgrids.
## 6 VLC-IoT: Existing and Proposed Approaches
### VLC-IoT: Existing and Possible Network Topologies  
The VLC technology is still an evolving technology. As a result, it has not fully adjusted to IoT requirements of low-cost, low power, low complexity, and small devices. Presently, the VLC technology is implemented using field-programmable gate arrays (FPGAs) or digital signal processors (DSPs) [59; 60]. However, FPGAs or DSPs do not match IoT massive-connectivity requirements in terms of low power consumption, low cost, and small size. For the purpose of communication, IoT relies on wireless communications majorly comprising of nodes and gateways commonly referred to as wireless sensors networks (WSNs) [60]. These nodes perform specific tasks, e.g., temperature and humidity sensing whereas the gateways function as the interface between the nodes and the internet. Nodes in WSNs can operate in different network topologies depending on routing algorithms and applications.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 4 The Proposed Scheme
### System Initialization  
The TA is required to generate system parameters of VANETs during system initialization. The details are depicted as follows:  
1. The TA chooses an additive group and a multiplicative group \(G_{1}\), \(G_{T}\), respectively, where the prime order is \(q\) and the generator is \(P\).
2. A bilinear pairing e: \(G_{1}\times G_{1}\to G_{T}\), and four hash functions \(H_{0}\), \(H_{1}\):\(\{0,1\}^{*}\to G_{1}\), \(H_{2}\): \(\{0,1\}^{*}\to Z_{q}^{*}\), and \(H_{3}\): \(\{0,1\}^{*}\times G_{1}\to Z_{q}^{*}\) are selected.
3. TA chooses \(SK_{TA}\in Z_{q}^{*}\) as the master key and \(PK_{TA}=SK_{TA}P\) as the public key. In addition, \(K\in\{0,1\}^{n}\) is selected as a secret key.  
TA broadcasts the parameters _param_=\(\{G_{1}\), \(G_{T}\), \(e\), \(q\), \(P\), \(PK_{TA}\), \(H_{0}\), \(H_{1}\), \(H_{2}\), \(H_{3}\}\) to all entities in VANETs.
# Fault Diagnosis Approach of Main Drive Chain in Wind Turbine Based on Data Fusion
## 2 Fault Features Extraction of Wind Turbine Main Drive Chain Based on Data Fusion  
The entire process of the proposed data-fusion based method is given in Figure 1. The whole process can be divided into two steps: fault features extraction, and fault diagnosis based on data fusion. Details of each step are described separately in Sections 2 and 3.  
Figure 1: Flow chart of fault diagnosis approach of main drive chain in wind turbine based on data fusion.  
This section demonstrates the method of fusing two types of real-time monitoring systems to extract the features of main drive chain faults. This method takes advantage of the globality of the wind turbine SCADA system and the pertinence and depth of the main drive chain vibration fault diagnosis system for the real-time monitoring of the wind turbine main drive chain.
## 2 State of the Art
### Greedy-Based Routing Protocols  
This set of protocols searches the path with the least number of hops between nodes through the Greedy Forwarding approach [47], minimizing message delay. One of the most prominent protocols of this category is Geographic Position Mobility-Oriented Routing (GPMOR) [48]. GPMOR was designed considering some characteristics of FANETs and bases its operation on the predictive approach. Based on the Gauss-Markov mobility model [1] and the geographical positions of the nodes, GPMOR predicts the node positions and selects the next closest forwarding node to the receiver optimally, following the Greedy approach. Other greedy-based routing protocols are Mobility Prediction-Based Geographic Routing (MPGR) [49], Geographic Load Share Routing (GLSR) [50], Geographic Greedy Perimeter Stateless Routing (GPSR) [51], Greedy-Hull-Greedy (GHG) [52], Greedy-Random-Greedy (GRG) [53], Greedy Distributed Spanning Tree Routing 3D (GDSTR-3D) [54], and UAV Search Mission Protocol (USMP) [51].
# Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
## 2 Materials and Methods
### Image Segmentation  
This processing step plays an important role in image classification. Segmentation is the separation of ROIs (lesions, masses, and microcalcifications) from the background of the image.  
In traditional CAD systems, the tasks of specifying the ROI, such as an initial boundary or lesions, are accomplished with the expertise of radiologists. The traditional segmentation task in DM can be divided into four main classes: (i) threshold-based segmentation, (ii) region-based segmentation, (iii) pixel-based segmentation, and (iv) model-based segmentation [3, 78]. Furthermore, US image segmentation includes several techniques: threshold-based, region-based, edge-based, water-based, active-contour-based, and neural-network-learning-based techniques [141, 142].  
The accuracy of the segmentation affects the results of CAD systems because numerous features are used for distinguishing malignant and benign tumors (texture, contour, and shape of lesions). Thus, the features may only be effectively extracted if the segmentation of tumors is performed with great accuracy [106, 142]. This is why researchers are using DL methods, especially CNNs, because these methods have shown excellent results on segmentation tasks. Furthermore, DL-CAD systems are independent of human involvement and are capable of autonomously modeling breast US and DM knowledge using constraints. Two strategies have been utilized for full image sizes for training CNNs for DM and US instead of ROIs: (1) high-resolution [143] and (2) patch-level [144] images. For example, recent network architectures that have been used to produce segmented regions are YOLO [145], SegNet [146, 147], UNet [148], GAN [149], and ERFNet [150].
# Methodology for Simulating 5G and GNSS High-Accuracy Positioning
## 1 Introduction
The proposed methodology and the snapshot approach are applicable to outdoor use cases with high-accuracy positioning requirements at periodic or triggered events, such as the location-based services, emergency and mission critical applications or road-related uses cases defined in [1]. Thus, this contribution is especially convenient for the standardization of 5G NR positioning, in order to provide realistic and reproducible hybrid assessments. The structure of the paper is as follows. The novel scenario definition is described in Section 2, with the relation between network density and GNSS visibility. The GNSS, cmWave and mmWave observables are characterized in Section 3. Then, the positioning algorithm is defined in Section 4. The simulation results are discussed in Section 5. Finally, the conclusions and future work are drawn in Section 6.
## 3 Research Design
### Data Preprocessing and Learning Model  
The PROSTATEx dataset [24] was utilized in this paper, which comprises 330 lesions from 204 subjects. The dataset includes T2 transverse, sagittal, and coronal MRI images, as well as ADC, BVAL, and KTRANS. In this section, the T2 images were preprocessed by cropping them to 120 \(\times\) 120 pixels around the prostate centroid and then normalizing them. The dataset also provides lesion centroid coordinates and a label indicating whether the lesion is clinically significant. Recently, ground truth masks for the lesion and prostate zones [25] have been made available, and these were used during the learning phase (as illustrated in Figure 1) before the experiment to identify whether there is a lesion. The dataset was split into training and testing sets in a 70/30 ratio, with the training set used to train the model and the testing set used to assess performance and provide images for the paper cohort during the experiment. During the learning phase, MRI images with and without lesions were shown to the radiologists with randomized explanation methods. The classification was not provided to prevent biased results.  
To map T2-weighted MRI images to a binary variable that indicates the presence or absence of a lesion in the image, a Convolutional Neural Network (CNN) was trained. The model was based on the VGG architecture [26], and transfer learning with ImageNet weights was employed. Grid search, a hyperparameter selection method that tests various combinations of hyperparameters and selects the best parameters based on the model's performance, was employed to determine the optimal parameters for the model. The training set discussed previously was utilized to train the model using Python 3.6 and PyTorch 1.8.0, and the resulting model was evaluated on the testing set, achieving an AUC of 0.87, sensitivity of 0.85, and specificity of 0.88, which are results quite similar to results in the literature [27; 28; 29].
# Intersection Routing Based on Fuzzy Multi-Factor Decision for VANETs
## 3 Intersection Routing Based on Fuzzy Multi-Factor Decision
### Intersection Decision  
In this section, we will present routing over an SN (intersection node) level. Based on the information stored in the memory about the neighbor vehicle and the link connection information, we also employ other features for selecting the next best SN to forward data. The features include a 2-hop neighbor, the count of the vehicle in a whole link, the link lifetime, and the vehicle density on the segmented road between two SNs. The three features are described as follows:  
1. 2-hop neighbor: each SN keeps the information around itself in 2-hop. If a vehicle has a 2-hop neighbor, we consider the SN between them as the best candidate vehicle.
2. The number of the vehicle in the effective link: the hop count recording starts whenever a link is created. Finally, the hop count of a link is recorded in the SN as a factor of an SN that can further choose the next hop.
3. The link lifetime: longer link lifetimes are better for data forwarding. We assume that the link lifetime is the shortest lifetime between two vehicles which are in the same link as the standard. Each vehicle calculates and keeps the shortest lifetime and forwards it to the SN.  
Before the selection of the SN, several steps are taken into account as shown in Figure 3.  
DSN is the destination static node which is the two SNs located at both ends of the road segment where the destination vehicle is located.  
If the SN has only one 2-hop neighbor, the data packet is forwarded along with the link to the next SN. If the SN has more than one 2-hop neighbors, the SN selects the road segment with the highest degree of connectivity and the one which is geographically closest to the destination. The degree of connectivity is calculated based on the other two features using the following equation:  
\[\text{Deg}_{\text{i}}=\text{w}_{1}\text{N}_{\text{i}}+\text{w}_{2}\text{T}_{ \text{i}},\text{ i}=1,2\ldots\text{n}, \tag{11}\]  
where \(\text{N}_{\text{i}}\) is the number of vehicles in the effective link and \(\text{T}_{\text{i}}\) is the link lifetime.  
If the SN has no 2-hop neighbor, but its neighbor SN is located on the destination segment which is also the destination vehicle, then the packet is forwarded to the neighbor SN. Otherwise, the packet is forwarded back to the last SN with more than one 2-hop neighbors and a suboptimum SN is chosen for forwarding the packet.  
Figure 2: (**a**) Single link path is establishing; (**b**) Two link paths are establishing from different SNs.  
When the packet arrives at the SN located at the destination segment and the packet cannot be forwarded to the destination immediately, the packet is forwarded toward another DSN.  
The pseudocode of the intersection decision is described in Figure 4.
## 2 Data and Methods
### Energy and Water Balance  
This section briefly introduces the energy and water-balance equations normally used in land surface models (LSMs). An LSM of the surface-atmosphere interaction begins with down-welling solar radiation and incoming precipitation, which are the most critical drivers at the terrestrial surface. The important processes are the total amount of absorbed solar energy (also known as the net radiation \(R_{n}\)) and its partitioning into sensible and latent heat fluxes. In order to estimate each major flux, the model must solve the balance equation [47] (Figure 2a). The energy balance equation with the main elements can be written as follows:  
\[R_{n}=ET+SH+\mathcal{G}+\mathcal{L}_{m}, \tag{1}\]  
where \(ET(\mathrm{W}\cdot\mathrm{m}^{-2})\) and \(SH\) are the latent and sensible heat fluxes, respectively, \(G\) is the ground heat flux, and \(C_{m}\) describes the minor energy exchanges, including the metabolism of energy for photosynthesis and storage flux in the canopy air space [48; 49]. ET in the energy balance is normally written as \(\lambda\)\(\lambda\)\(E\). However, we unify \(\lambda\)\(E\) into ET for convenience. The unit conversion between \(\mathrm{W}\cdot\mathrm{m}^{-2}\) and \(\mathrm{kg}\cdot\mathrm{m}^{-2}\cdot\mathrm{s}^{-1}\) (\(\mathrm{mm}\cdot\mathrm{s}^{-1}\)) is made through the latent heat of vaporization (\(\lambda\approx 2.454\times 10^{6}\) J \(\cdot\) kg\({}^{-1}\)). \(G\) and \(C_{m}\) were omitted in this study because their contributions are minor and because the roles of \(ET\) and \(H\) are easily contrasted. based on the nine models in Table 2, the correlation coefficient between \(R_{n}\) and  
Figure 1: Taylor diagram for model validation. PR, TAS, ET, SH, and SSM data were compared with ERA5 data from 1981 to 2010, and SSM data were compared with FLUX-MTE data from 1982 to 2007.  
\(ET+H\) was \(R^{2}=0.98\) According to the energy budget, the water vapor flux (\(\sim\)\(ET\)) is a vital component and is directly linked to the water budget (Figure 2b).  
The water balance equation is given by the following:  
\[P=ET+O+(I+\Delta S) \tag{2}\]  
where \(P\) is precipitation, \(ET\) is the evapotranspiration, \(I\) is infiltration into soil, \(O\) is the runoff, and \(\Delta S\) is a storage flux term [50]. The units of water balance (\(\text{kg}\cdot\text{m}^{-2}\cdot\text{s}^{-1}\)) are converted to \(\text{W}\cdot\text{m}^{-2}\) for comparison with the energy balance variables. These simple expressions for water and energy balance are the foundation of LSM, connoting various static and dynamic activities and their interactions.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 3 Methodology
### Elements for the Prototype
#### 3.2.3 Implementation of IP Telephony System
To do this, the default hostapd configuration file must be opened with the command in Listing 5, and inside the file, the following line must be added:  
```
DAEMON_CONF=''/etc/hostapd/hostapd.conf',
```  
Listing 5: Default hostapd configuration file. ```
1 subonano/etc./default/hostapd
```  
Listing 6: Command to edit the DHCP server configuration. ```
1 subonano/etc./dhcpcd.conf
```  
Listing 7: Default hostapd configuration file. Figure 6: Access point configuration parameters. ```
5 Dnsmasqconfiguration: In the proposed prototype, _dnsmasq_ is used to manage IP address allocation and provide name resolution services on the wireless network created by the Raspberry Pi. First, the command in Listing 8 is executed to back up the configuration. Then, the _dnsmasq_ configuration file is edited with the command in Listing 9, and the address range is set using the command in Listing 10. ```  
Listing 8: Command to back up the original dnsmq configuration file. ```
1 sudomv/etc./dnsmasq.conf/etc/dnsmasq.conf.orig
2 dncp-range=192.168.4.2,192.168.4.20,255.255.0,24h
```  
Listing 9: Command to edit the dnsmq configuration file. ```
1 sudomano/etc./dnsmasq.conf
```  
Listing 10: DHCP address range configuration for the wlan0 interface. ```
1 interface=wlan0
2 dhcp-range=192.168.4.2,192.168.4.20,255.255.255.0,24h
```  
Listing 11: Command to move and backup the sysctl.conf configuration file. ```
1 sudomv/etc/sysctl.conf
```  
Listing 12: Enabling IPv4 packet forwarding in the system. ```
1 net.ipv4.ip_forward=1
```  
Listing 13: Command to enable NAT and save the iptables configuration. ```
1 sudoiptable-tnat-APOSTROUTING-oeth0-jMASQUERADE
2 sudosh-c''iptables-save>/etc/iptables.ipv4.nat'
```  
Listing 14: Command to edit the /etc/rc.local file.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### The Backward Induction Algorithm for MDP Problem  
The backward induction algorithm is an effective solution to the optimal strategy and value function in the finite-stage Markov decision programming problem [26]. A new function, \(V_{*}^{n}(i)\), was proposed based on the backward induction algorithm, which is formulated as:  
\[\begin{array}{ll}V_{*}^{n}(i)&=\max\limits_{a_{i}\in A_{i}}[r(k,a_{i})+\sum \limits_{j\in s}p(j\big{[}k,a_{i})\,V_{*}^{i+1}(j)]\\ &=r(k,f_{*}^{i}(k))+\sum\limits_{j\in s}p(j\big{[}k,a_{i})\,V_{*}^{i+1}(j)\\ &(k\in s,i=\{N,N-1,N-2,\cdots,0\})\end{array}, \tag{34}\]where \(V_{*}^{N+1}(k)\)\(=0,\forall k\in s\)[26]. According to Equation (34), the optimal value function of the expected total rewards can be calculated as \(V_{*}^{1}=(V_{*}^{1}(1),V_{*}^{1}(2),\cdots,V_{*}^{1}(q))\). Meanwhile, the decision sequence \(\pi^{*}=(\circ_{*}^{1},\circ_{*}^{2},\cdots,\circ_{*}^{n})\) obtained is the optimal strategy.  
With the backward induction algorithm, the number of states required to traverse is \(M\times L^{3}\). The state space may be very large if some of the elements are of large size and may encounter the curse of dimensionality [29]. An effective method to reduce the computational complexity in MDP model is proposed in literature [28]. In this case, we also eliminate some states that do not need to be searched according to the wireless relay network properties in our model by reference [28].  
**Proposition 2**.: _When \(h_{SR}^{i},\ h_{RD^{\prime}}^{i},\ \varepsilon(i)\) are fixed value, and the optimal action is \(a_{i}=