esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Wind Speed Forecasting Using Statistical and Machine Learning Methods
## 2 Models
### Benchmark Models  
#### 2.2.1 Stochastic Gradient Boosting (SGB)  
The gradient boosting machine and gradient tree-boost were the terms previously used for gradient boosting when it was implemented newly by [PERSON] [30]. Gradient boosting is a machine learning model used for classification and regression problems [31]. It stage-wisely builds weak predictive models generalised by optimisation of an arbitrary differentiable function. The statistical framework of gradient boosting describes it as an optimisation problem which minimises the loss in a model by a stage-wise addition of weak learners to the models using a gradient descent procedure [30; 31]. Gradient descent traditionally minimises a set of parameters such as coefficients of covariates or ANN weights through loss or error calculation and weight update [32]. The weak learners are organised in substructures or decision trees that replace the parameters. Parameterised tree is added to the model, thereby reducing the error and the residual losses using the parameters of the trees following the direction of the gradient [32]. The gradients spot the error in the weak learners.  
The major drawback to the gradient boosting is that it is a greedy algorithm that can easily over fit training data [32; 33]. One of the variants of gradient boosting is the stochastic gradient boosting (SGB) formed by taking a random sample of the training dataset without replacement [30; 32]. Its general formulation is given in Equation (4).  
\[F(x)=\sum_{m=1}^{M}\beta_{m}h(x;\gamma_{m}), \tag{4}\]  
where \(h(x;\gamma_{m})\in\mathcal{R}\) are functions of \(x\) with parameters \(\gamma_{m}\) and \(\beta_{m}\) which limit over fitting [32; 33].
# Multivariate Analysis of Laser-Induced Tissue Ablation: Ex Vivo Liver Testing
###### Abstract  
A number of laser parameters are often regulated to enhance ablation efficiency during laser surgery. As one of clinical treatments, laser removal of benign prostate hyperplasia has been well accepted by surgical urologists. However, due to complex interactions of the surgical parameters, the procedure is still lengthy and dependent upon the surgeon's skill and experience. The aim of the current study is to evaluate the feasibility of response surface method (RSM) to comprehend ablative interactions of multi-parameters and to identify the optimal ablation rate (AR). As a surrogate model in the feasibility study, bovine liver tissue was utilized for ex vivo ablation testing. Three laser parameters pertinent to laser prostatectomy were selected: power (P), treatment speed (TS), and beam spot (BS). As a three-level fractional factorial RSM, Box Behnken design (BBD) was employed to identify the range of each parameter for achieving the optimal AR. The results showed that regardless of TS, AR was linearly contingent on both P and BS due to high irradiance. TS of 6-7 mm/s induced the maximal AR when P of 180 W and BS of 0.4 mm\({}^{2}\). The corresponding volumetric density energy yielded an ablation volume of 80 mm\({}^{2}\), which was close to a transition to volumetric saturation. The BBD-based model showed a good agreement with the experimental data in terms of ablation volume. The proposed multivariate parametric analysis can be an efficient design method to identify the optimal conditions for laser therapeutics. Further investigations will be performed on prostatic tissue to validate the proposed approach and to explore various optimization processes.  
ablation rate; Box-Behnken design; volumetric energy density; response surface method +
Footnote †: journal: Applied Soft. 2017
## 3 Cooperative Perception Information-Sharing
### Cooperative Perception Information-Sharing Network
#### 3.1.1 DSRC  
One of the keys to enabling wireless communication technology standards designed specifically for vehicular communication is DSRC [61]. The DSRC communication protocol in the United States is based on the IEEE 802.11p and the IEEE 1609 series standards, which are collectively known as wireless access in vehicular environments (WAVE) standards [62].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methodology** & **Advantages** & **Disadvantages** & **Conclusion** \\ \hline Image fusion & High resolution, richer perception information, good target classification effect, mature algorithm, low deployment cost. & The depth information of the target is insufficient, and it is greatly affected by light and adverse weather. & \\ \hline Point cloud fusion & High spatial resolution, rich 3D information, wide detection range, good positioning, and ranging effect. & Poor target classification effect, a large amount of data, easily affected by adverse weather, expensive. & The image–point cloud fusion scheme has the best effect. \\ \hline Image–point cloud fusion & Realizes the complementary advantages of images and point clouds, high resolution, high perception accuracy, richer information, and strong anti-interference. & A large amount of data and a complex algorithm. \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the advantages and disadvantages of three different fusion methods.  
Similarly, the European Telecommunications Standards Institute (ETSI) has developed the ITS-G5 protocol based on the IEEE802.11p standard to support the high quality of service requirements of autonomous driving applications [63]. [PERSON] et al. [64] compared and analyzed the characteristics of wireless local area networks, WAVE, and 4G networks and proposed using a neural network model to select an appropriate communication method automatically and dynamically according to a vehicle's density, speed, and data volume before data transmission. However, in recent years, studies have found that DSRC is not enough to support reliable and efficient V2X applications, especially in the case of high vehicle density and highspeed vehicle movement, the communication performance will be significantly reduced [65]. To improve its scalability and quality of service and to make up for the lack of performance in high-mobility environments, a new study group called IEEE 802.11 Next Generation V2X was formed in March 2018 [66]. This resulted in the formation of the IEEE Task Group 802.11 bd in January 2019.
# IoT Privacy and Security Challenges for Smart Home Environments
## 3 IoT Application Domains
### IoT and the Smart Home  
This paper deals with a very different environment--the Smart Home. Professional system design, installation and setup may be available when the smart electronics are included as part of a new home build. However, in most cases, Smart Home IoT technology is likely to be retrofitted to an existing home piece by piece as needs arise. Often, there is no ongoing professional support in either the design or operation phases of the IoT deployment in the Smart Home. While there are some reasonably widespread specialized Smart Home standards, such as X.10 powerline-carrier communications, these lack any type of security, and were designed before these home control networks were connected to the Internet [16]. There are now a plethora of networking standards that can be used in a home (Zwave, Insteon, Bluetooth, Zigbee, Ethernet, Wifi, RS232, RS485, C-bus, UPB, KNX, EnOcean, Thread) [17]. Each has its strengths and weaknesses, and expecting a heterogeneous network with many different protocols to be efficiently and securely managed by a non-expert presents significant challenges.  
The Smart Home potentially provides additional comfort and security, as well as enhanced ecological sustainability. For example, a smart air conditioning system can use a wide variety of household sensors and web-based data sources to make intelligent operating decisions, rather than simple manual or fixed-schedule control schemes. The smart air conditioning system can predict the expected house occupancy by tracking location data to ensure the air conditioner achieves the desired comfort level when the house is occupied and saves energy when it is not.  
In addition to enhanced comfort, the Smart Home can assist with independent living for the ageing. The Smart Home can assist with daily tasks such as cleaning, cooking, shopping and laundry. Low level cognitive decline can be supported with intelligent home systems to provide timely reminders for medication. Home health monitoring can signal caregivers to respond before expensive and disruptive hospitalization is needed [11]. However, none of these benefits is likely to be taken up if the Smart Home system is not secure and trusted.
# Radiator Enablers for Wireless Communication Evolution
## 1 Introduction
### Wireless Communication Evolution  
It is well known that after a relatively prolonged period of maturation (refs. [1; 2]) of electromagnetic theory and the associated development of related devices and components lasting nearly two centuries, wireless communications entered a spiral of evolution. This process started in 1980 and is still evolving, resulting in the 1 to 5G wireless communications generation sequence, each one of them exhibiting distinct features (1G: Cellular Concept, Voice, 2.4 Kbps; 2G: Voice, Message, 64 Kbps; 3G, Internet, 2 Mbps; 4G: All IP, 100 Mbps; 5G: extreme mobile broadband (eMBB), Reliable Low-Latency Communication (RLLC), massive Machine Type Communications (mMTC), 1 Gbps), with a nearly 10-year period from generation to generation.  
It is not hard for one to imagine that the increase in the need for information exchange, coupled with the need for free movement (Movable Infrastructure), initiated the introduction and still fuels the evolution of wireless communications. This evolution is at the core of the 4 th Industrial Revolution, where the asset that is transformed (digital via all IP nets) is information. Indeed, gathering-sensing information (sensor networks), processing information (machine learning, AI), communicating information (wired--but mainly wireless--mobile and satellite networks), and last but not least, consuming information (one can place here the various use cases--eMMB, VR, automation, etc.), has revolutionized our technological civilization. While the network nodes that produce, process, and consume information play a crucial role, we focus here on the interconnecting network. The vision for this network is to expand wherever there is a human (or an IoT device) presence and to connect anyone and anything everywhere and at any time. This vision results in anticipated infrastructure distributed geographically, or better, spatially, using heterogeneous networks covering all of the biosphere and more.
# Flying Ad Hoc Networks: A New Domain for Network Communications
## 2 State of the Art
### Positioning Protocols
The proposed algorithm dynamically adjusts the position of the nodes and the radius of the UAVs according to the demand of the users at ground level, increasing the performance of the network in terms of the probability of coverage and reduction of the delay. Inspired by the typical movements of bacteria (which move according to their attraction to attractors or away from repellents, aka chemotaxis), the authors in Reference [47] obtained the optimal position of a set of nodes of a FANETs by setting the height of each node, since vertical movements require a greater amount of energy than horizontal movements. In Reference [41], the authors analyzed the optimal deployment of a FANET obtaining the probability of coverage for the UAV-ground link. Then, based on the circle packing theory, the number of available nodes, and the gain and bandwidth of the antennas, they obtained the position and the optimal altitude for each node in an optimal deployment. From a different perspective, [PERSON] et al. [48] investigated the minimum number of UAVs needed to provide coverage in an area, ensuring that each terrestrial node in the ground has coverage with at least one UAV node. To do so, UAVs were placed following an inward spiral path until all the terrestrial nodes were covered. The authors assumed that the UAV-ground channel presented LoS and they fixed the transmitted power of the UAV to obtain the minimum signal-to-noise ratio (SNR) in the receivers. Another solution was introduced with Self-Deployable Point Coverage (SDPC) [49]. SDPC studies the optimal positioning of FANETs nodes in coverage expansion tasks in cases of disasters, looking for the optimal position to cover the largest number of people per UAV, and maintaining a connection between each UAV. Whereas SDPC can be used with RW-UAVs nodes, its application with FW-UAVs is limited because it does not consider smooth trajectories and progressive curves. The authors in Reference [50] proposed an algorithm to find the optimal position of a UAV based on fine-grained LOS information in a highly urban area. Finally, [PERSON] and [PERSON] [51] obtained an expression of the coverage probability for the UAV-ground link modeling the base stations as a uniform binomial point process, and in Reference [52], [PERSON] et al. employed a neural-based cost function to deploy multiple UAVs in an area with full coverage. Their results showed that they were able to achieve higher capacity than the existing cellular network. Despite the notable results accomplished in positioning, none of these studies contemplated the impact that the UAVs themselves can have on the embedded communication devices in charge of providing coverage, and therefore likely affecting their outcomes.
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 2 Types of Smart Sensors in Precision Agriculture
### Crop Health Sensors (NDVI cameras, chlorophyll meters)
The agrochemical spraying process uses a Pulse-Width Modulation (PWM)-based fluid flow control system, ensuring precise application of the required agrochemical quantities guided by the vision-based feedback system. The results of several field tests confirm the effectiveness of the proposed vision-based agrochemical spraying framework in achieving real-time precision.
## 3 Results
### Method Validation
The developed method was found to be selective for the generated peak of the derivatized OATD-02 from other reaction reagents (related substances). The peak was well separated from the other eluting peaks. Moreover, the peak of OATD-02 was symmetrical with an acceptable theoretical plate (N > 3000) and tailing factor (<1.5). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature** & **Retention Time, Min** & **Efficiency** & **Tailing Factor** \\ \hline
30\({}^{\circ}\)C & 8.61 & 29,238 & 1.47 \\ \hline
35\({}^{\circ}\)C & 8.38 & 30,358 & 1.50 \\ \hline Flow, mL/min & Retention time, min & Efficiency & Tailing factor \\ \hline
0.4 & 9.80 & 28,664 & 1.50 \\ \hline
0.5 & 8.61 & 29,238 & 1.47 \\ \hline
0.6 & 7.83 & 30,013 & 1.46 \\ \hline Fluent composition & Retention time, min & Efficiency & Tailing factor \\ \hline
0\% B & 8.61 & 29,238 & 1.47 \\ \hline
5\% B & 8.18 & 27,328 & 1.49 \\ \hline Column S/N\# & Retention time, min & Efficiency & Tailing factor \\ \hline
1573018118510 & 8.61 & 29,238 & 1.47 \\ \hline
1733216418402 & 8.55 & 35,545 & 1.50 \\ \hline Flow rate of alizarin, mL/min & Retention time, min & Efficiency & Tailing factor \\ \hline
0.4 & 8.60 & 29,183 & 1.48 \\ \hline
0.5 & 8.61 & 29,238 & 1.47 \\ \hline
0.6 & 8.62 & 29,706 & 1.48 \\ \hline Wavelength \(\lambda\)ex/\(\lambda\)em & \% change in peak area & Efficiency & Tailing factor \\ \hline
468/580 & 1.6 & 29,305 & 1.48 \\ \hline
470/580 & - & 29,238 & 1.47 \\ \hline
472/580 & 1.7 & 29,299 & 1.48 \\ \hline
470/578 & 1.9 & 29,353 & 1.47 \\ \hline
470/582 & 1.9 & 29,412 & 1.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The robustness results for OATD-02.
# A Look at the Importance of Chirality in Drug Activity: Some Significative Examples
## 6 Non-Steroidal Anti-Inflammatory Drugs (NSAIDs)  
Ibuprofen (**30**) is a chiral NSAID, often taken as an example of chiral inversion: (\(S\))-ibuprofen is far more active than (\(R\))-ibuprofen in inhibiting cyclooxygenase 1 (COX-1)enzyme. When administered as the racemate, the ibuprofen has proven to undergo unidirectional enantiomerization being converted in vivo into the (_S_)-ibuprofen, thus, the (_R_)-ibuprofen behaves as a pro-drug for the (_S_)-enantiomer [95]. Ibuprofen was the first chiral drug of the NSAIDs class to be switched to the single-enantiomer version in 1994, then the ketoprofen (**31**) was submitted to chiral switching and marketed as the (_S_)-(+)-enantiomer in 1998. In this case, the metabolic enantiomerization from (_R_) to (_S_) in vivo, and _vice versa_, was shown to be negligible in humans [26]. Other chiral NSAIDs are naproxene, carprofen, fenoprofen, flurbiprofen, and indoprofen, and for all of them, the (_S_)-enantiomer is the eutomer. Finally, the chiral NSAIDs pyranoindoles etodolac (**32**) and pemedolac exhibit anti-inflammatory and analgesic activities [96]. Etodolac has also shown anticancer activity, with the (_R_)-etodolac being a more potent inhibitor of the Wnt signaling than its enantiomer [97].
# Parametric Analyses of the Response of Masonry Walls with Reinforced Plaster
## 3 Numerical Simulations
### Retrofitted Masonry--Plaster Strength  
In order to investigate the influence of the plaster strength on the in-plane behavior, three different models were compared by changing the mechanical properties of the plaster, according to the concrete types reported in Table 5. For the nonlinear analyses, the N7 anchor configuration was considered (Figure 20) with 30-mm plaster. The nail diameter was 10 mm, while its length was 200 mm. The mechanical and interaction properties of the other materials, loading conditions, and mesh sizes were those defined in Sections 2 and 3.  
Figure 26 shows the load vs. displacement curves for the different plaster strengths. It is evident that the increase in the strength was beneficial in terms of the maximum load. Furthermore, the initial stiffness of the system was changed significantly by changing the resistance of the plaster, and it was higher for higher strength. Figures 27 and 28 show the damage pattern of the tension and compression for the different configurations at the peak load. The crack distribution was clearly influenced by the strength of the plaster, so that, for lower compressive strength, the damage tended to be localized in the area close to the loading points, with evident crushing. This crushing was not evident in the fc40 configuration, in which the damage was limited to the central area of the panel, with multiple small cracks developing along the diagonal.  
Figure 29 shows the load vs. displacement curves for the different configurations. The increase in the plaster thickness increased both the collapse load and the stiffness of the retrofitted wall. In the 50-mm configuration, a first peak was evident before the maximum load was reached, even though, as in the other configurations, a slight change in slope was detected at the same displacement value. Figure 30 shows the damage at the peak for the different thicknesses. For 20 mm, the damage was spread over a wider area, and consisted in multiple thin diagonal cracks, which often started in the anchor positions. By increasing the thickness, the damage to the plaster was more limited, and for the 50-mm configuration,  
Even though increasing the plaster thickness may be beneficial in terms of collapse load, it should be always remembered that increasing the mass of the wall is not recommended in seismic areas, where the plaster thickness should always remain limited.
## 3 The Proposed Routing Protocol--CRD
### Packet Structures  
In this subsection, we present the packet structures as shown in Figures 3 and 4 for clustering and routing process, respectively. The packet structures used for clustering phase can be explained as follows:  
* Information Packet (Info Packet): A node uses an info packet to send its information to its neighbors.
* Neighbors' Information (NBI) List Packet: A node uses a NBI list packet to send its neighbors' information to its neighbors.
* Clusterhead Advertisement (CH ADV) Packet: A cluster head uses a CH ADV packet to send an advertisement packet to its neighbors.
* Join Cluster Packet: A node uses a join cluster packet to send a join cluster request message to a cluster head. The packet structures used for routing phase can be explained as follows:
* RREQ Packet: To find a route to a destination, a source sends a route request (RREQ) packet to its neighbors.
* RREP Packet: If a destination receives a RREQ packet, it replies a RREP packet to the source.
* Data Packet: If a route is established, a source sends data packet to a destination.
# Trapping and Methanation of CO\({}_{2}\) in a Domestic Microwave
## 3 Results and Discussion
### CO\({}_{2}\) Methanation under Continuous Flow Conditions in a DRIFTS Reactor  
The _operando_ study was carried out by introducing the methanation stream (2% CO\({}_{2}\) + 20% H\({}_{2}\) in N\({}_{2}\)) at room temperature over the samples without any pre-treatment to mimic typical CCU operations under which the sample is initially exposed to CO\({}_{2}\) and air. The Ru nanoparticles were thus likely oxidized. No formation of carbonyl of Ru could be observed at room temperature (Figure 4A), indicating that the Ru surface was oxidized or could not activate CO\({}_{2}\).  
Figure 3: (**A**) Product selectivity at a steady-state under MW irradiation during continuous flow experiments. Catalyst masses used for the various runs (1) 450 mg SiC, (2) mixture of 354 mg SiC + 91 mg Fe\({}_{3}\)O\({}_{4}\), (3) mixture of 354 mg SiC + 91 mg Fe\({}_{3}\)O\({}_{4}\) followed by 25 mg Ru/SiO\({}_{2}\) and (4) same as (3). (**B**) Carbon balance, CO\({}_{2}\) conversion and product yields as a function of time under MW irradiation during the second run with SiC + Fe\({}_{3}\)O\({}_{4}\) + Ru/SiO\({}_{2}\). Feed: 2% CO\({}_{2}\) + 20% H\({}_{2}\) in N\({}_{2}\), total flow: 50 mL min\({}^{-1}\), MW power = 800 W.  
No methanation activity could be measured below 100 \({}^{\circ}\)C neither over the fresh nor on the used sample, although bands assigned to Ru\({}^{0}\)-CO carbonyls [32] were clearly observed at 94 \({}^{\circ}\)C (Figure 4A). This indicates that at 94 \({}^{\circ}\)C Ru was partly reduced and able to dissociate CO\({}_{2}\) and store CO. This likely explains the uncomplete carbon balance observed around 90 \({}^{\circ}\)C during the flow experiment (Figure 3B), as the carbonyls formed poisoned the surface at low temperatures.  
The ca. 4-fold lower DRIFTS band area of carbonyls species observed over the used sample as compared to the fresh catalyst (Figure 4B) and ca. 5-fold lower methanation rate indicated by the shifted Arrhenius plots (Figure 4C) suggest that the deactivation was mostly due to sintering of the Ru nanoparticles, likely induced by the high temperature of the neighboring radiating SiC + Fe\({}_{3}\)O\({}_{4}\) when exposed to MW. Sintering is a common process in heterogeneous catalysis and can occur through various mechanisms, e.g., metal particle coalescence and Ostwald ripening [33], depending on the mobility of atoms and thermodynamic stability of the particles with respect to their size. Different strategies can be employed to limit sintering, e.g., using porous supports strongly interacting with the metal particles. The sintering of materials exposed to MW has also been discussed in detail elsewhere [34]  
Note that the apparent activation energy remained unchanged at about 76 kJ mol\({}^{-1}\), in agreement with those reported for highly loaded Ru-based catalysts [35].