esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware Wireless Sensor Networks for Smart Buildings: A Review
###### Abstract  
The design of Wireless Sensor Networks (WSN) requires the fulfillment of several design requirements. The most important one is optimizing the battery's lifetime, which is tightly coupled to the sensor lifetime. End-users usually avoid replacing sensors' batteries, especially in massive deployment scenarios like smart agriculture and smart buildings. To optimize battery lifetime, wireless sensor designers need to delineate and optimize active components at different levels of the sensor's layered architecture, mainly, (1) the number of data sets being generated and processed at the application layer, (2) the size and the architecture of the operating systems (OS), (3) the networking layers' protocols, and (4) the architecture of electronic components and duty cycling techniques. This paper reviews the different relevant technologies and investigates how they optimize energy consumption at each layer of the sensor's architecture, e.g., hardware, operating system, application, and networking layer. This paper aims to make the researcher aware of the various optimization opportunities when designing WSN nodes. To our knowledge, there is no other work in the literature that reviews energy optimization of WSN in the context of Smart Energy-Efficient Buildings (SEEB) and from the formerly four listed perspectives to help in the design and implementation of optimal WSN for SEEB.  
Wireless Sensor Network; IoT; smart buildings; energy efficiency; wireless mesh networks; operating systems +
Footnote †: journal: Sensors and Actuator Networks  
piezoelectric converters, etc. EH techniques also include hybridization that tends to enable the autonomy of power supply in IoT devices.  
This paper tends to make the researcher aware of the energy optimization techniques and opportunities in IoT and WSN for Smart Energy-Efficient Buildings. Therefore, the paper presents different approaches, methods, and technologies based on the layered architecture of the IoT device. Hence, it guides the researcher to design energy-aware IoT devices for Smart Energy-Efficient Buildings.  
The rest of the paper is organized as follows. Section 2 presents a literature review of WSN design challenges. Section 3 discusses the hardware resources and presents different examples that can be used. The different operating systems and embedded systems' alternatives are delineated in Section 4, and we list the various data acquisition techniques and their impact on energy consumption optimization. In Section 5, we list data acquisition techniques at the application layer. Then, in Section 6, we emphasize the network and data link protocols that are energy-aware, along with a comparative study of relevant network topologies. In Section 7, we present different battery types that can be used to power the WSN nodes. Last but not least, we conclude by presenting a framework recommending optimal components to adopt when designing a static WSN for smart buildings.
## 3 System Model and Methodology  
Figure 1 represents the studied system model's overall view. The receiver block receives the data after they have been created and transmitted to the transmitter block and channel. In the next section, we provide a detailed explanation of each block. By comparing the computed BER with the actual data that are transmitted to the channel, the biterr function from MATLAB is used to calculate the BER at the receiver.
## 4 Discussion
### Extracorporeal Blood Purification Capabilities  
CytoSorb(r) is certified and indicated for the extracorporeal removal of multiple molecules directly from whole blood. It can be integrated into any extracorporeal circulation system to efficiently eliminate inflammatory mediators, including pro- and anti-inflammatory cytokines, bilirubin, in cases of liver dysfunction and hyperbilirubinemia, myoglobin, and to prevent acute kidney injury in rhabdomyosis [25,26,27]. Furthermore, asmentioned above, it can remove Ticagrelor, a potent P2Y\({}_{12}\) inhibitor used in cardiovascular patients, allowing for its rapid removal in urgent surgical settings, and rivaroxaban, a direct factor Xa inhibitor, enabling anticoagulation reversal in cases of severe bleeding or emergency interventions [5,6]. By combining high biocompatibility, broad adsorption capacity, and prolonged treatment duration, CytoSorb(r) serves as an effective and versatile hemoadsorption therapy in critical care and extracorporeal blood purification. The adsorption process relies on multiple physicochemical interactions: hydrophobic interactions and Van der Waals forces. The polymer is inherently hydrophobic, which facilitates the adsorption of hydrophobic and amphipatic molecules. Non-covalent Van der Waals forces further stabilize the interaction between the target molecules and the polymer surface: electrostatic interactions, dipole-dipole forces, and weak electrostatic interactions occur between the sorbent's surface and certain biomolecules, enhancing the retention of polar and charged compounds. These interactions contribute to the selectivity of the device in eliminating inflammatory mediators while preserving essential blood components. The controlled porosity of the polymer facilitates the selective entrapment of medium-sized molecules while allowing smaller molecules (e.g. electrolytes, small peptides) to pass through freely. Larger biomolecules, such as immunoglobulins and albumin, exceed the pore size threshold and are not adsorbed, ensuring minimal interference with physiological functions [28].
## 2 Carbon Nanostructures as Templates for Titania Nanomorphologies
### Nanohorns  
Nanohorns can be considered as clusters of nanocones, and they have also been used as templates for titania. The nanocarbon entanglement features a high surface area that templates a large distribution of \"hard-soft\" bimetallic sites, where 1.5 nm palladium nanoparticles were embedded within the titania phase while being electrically wired to an electrode by the nanohorn support. This hybrid electrocatalyst activated carbon dioxide reduction to formic acid nearly at zero overpotential in the aqueous phase, while being able to produce hydrogen thanks to a sequential formic acid reduction [99].  
In another study, nanohorns templated the formation of anatase with nanoflower morphology through a solvothermal method. The photocatalytic degradation of two model dyes (i.e., methylene blue and methyl orange) was combined with the generation of hydrogen fuel upon solar-light irradiation [100].  
Besides the prospective uses in solar cells [180,181], nanohorns are potentially suitable for biological uses thanks to their additional advantageous features relative to other carbon nano-allotropes, such as the ease of oxidation and the high dispersibility in buffer solutions [182]. To this end, they have been used as templates for nanostructured titania with the nanohorn morphology and applied to phosphoproteomics on cancer cell lysates for diagnostics purposes thanks to the well-known binding affinity for titania with phosphorylated peptides (Figure 5) [101].  
Figure 5: TEM images of carbon nanohorns (**a**) without or (**b**) with anatase [101].
## 4 Results and Discussion
### The 1 LCM for the Vehicle with a Driver Assistance System
#### 4.4.2 The Steering Wheel Angle Control System Adopted for the Lane Change Maneuver
Figure 14 shows the results obtained for a maneuver carried out at such a speed, where an assistance system was used, for a maneuver duration time of T\({}_{\rm w}\) = 4 s. As in the previous cases, the curves represent the input applied, i.e., the steering wheel angle \(\alpha_{\rm k}\)(t) in the sinusoidal form controlled by the open system (Sine) and in the form controlled by the assistant system (Assist. S.), and the responses, i.e., lateral displacement y\({}_{\rm O\rm C}\)(t) and yaw angle. The curves include those adopted as the reference (Reference), those obtained as a result in the maneuver controlled by the open system with a sinusoidal input and carried out on a homogeneous road surface (Sine Dry, i.e., on dry concrete, see Figure 14a, and Sine Ice, i.e., ice, see Figure 14b) and carried out on a heterogeneous road surface (Sine) and in the maneuver controlled by the assistance system under consideration (Assist. S.). The maneuver began at the instant of t = 1 s.  
As in the case with the vehicle speed of V = 90 km/h, the extending of the lane change time from 3 s to 4 s and the reducing of the proportional control gain K\({}_{\rm p\psi}\), without changing K\({}_{\rm p\psi}\), led to very satisfactory results, i.e., the vehicle trai  
Figure 14: Results of the simulation of the vehicle motion (steering wheel angle \(\alpha\)k; lateral displacement of vehicle’s centre of mass y\({}_{\rm O\rm C}\) and yaw angle \(\psi_{\rm C}\)) during the 1 LCM where an assistance system was used for a change in the road surface condition: (**a**) the Dry\(\rightarrow\)Ice type, i.e., from dry concrete (right lane) to ice (left lane); (**b**) the Ice\(\rightarrow\)Dry type, i.e., from ice (right lane) to dry concrete (left lane). Vehicle speed V = 140 km/h. Maneuver duration time T\({}_{\rm w}\) = 4 s.  
correctly. A disadvantage of the second of the mentioned changes to the PID controller of the assistance system is an increase in the intensity of changes in the yaw angle \(\psi_{\mathrm{C}}\) and in the steering wheel angle \(\alpha_{\mathrm{k}}\) (which can be seen in Figure 14). This slightly reduces the desirable effects of extending the lane change time \(\mathrm{T_{w}}\). Such functioning of the assistance system may cause some anxiety for the driver. Nevertheless, the system fulfills its task.
## 4 Results  
### Wound Segmentation  
#### 4.1.1 Model Selection  
Table 1 reports the performance results of every model with the mean IoU and mean Dice coefficient. This table shows that MobileNet-Unet achieves the best performance of all architectures with the same hyperparameters following the procedure described in Section 3.2.2. The MobileNet base models show a high mean _IoU_, whereas for ResNet50, the metric does not exceed the 60% value. Between segmentation models with the ResNet50 base network, SegNet had a better performance, while Unet showed a poor segmentation result. However, the inverse is noted for MobileNet, in which Unet achieved better scores than SegNet.
## 2 Materials and Methods  
### Directions of the Thrusters During Orbital Manoeuvres  
Figure 1 presents a system of reference vectors for a satellite on the orbit, which is used in orbital mechanics for planning manoeuvres. The \"prograde\" vector (red, top-right) represents the direction of movement of the satellite and is responsible for increasing orbital speed, while the opposite \"retrograde\" vector (red, bottom-left) reduces the speed, lowering the orbit. The \"radial\" and \"anti-radial\" vectors (green) are perpendicular to the movement. The \"radial\" vector points outwards from the central body, while the \"anti-radial\" points towards the centre of the orbit, modifying its shape locally. Furthermore, the \"normal\" and \"anti-normal\" vectors (blue) are perpendicular to the orbit plane and are responsible for the changes in the inclination of the orbit. This system illustrates the main directions of satellite manoeuvres that influence the speed, shape, and orientation of the orbit.
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 2 Related Work
Results concerning single and multiple gateways scenarios are reported, such as the impact of the spreading factor on the performance. However, the paper does not deal with smart city scenarios and does not incorporate realistic channel models based on measurements, as done in this paper. Authors [32] presents results achieved through a small testbed composed of two LoRa devices equipped with different sensors (light, temperature, smoke, humidity, etc. ), which sends measurements to a gateway placed in the university campus. The Authors do not characterize the network performance neither from a link- nor from a network-level viewpoint, but they just show data measured by sensors to demonstrate the feasibility of the testbed. Similar considerations can be done for [33], which reports a PoC deployment addressing the problem of river Lifley monitoring in Dublin city center. They deployed a buoy on the Lifley river in Dublin for a duration of 8 months. Also, in this case, the results consist of measured data in terms of water temperature, atmospheric pressure and water depth. In contrast with the above cited works, in this paper we consider a real, densely built-up, urban scenario, where devices are located inside buildings. First, we derive the path-loss models for two districts of Bologna through in-field measurements, then we use such models in a network simulator allowing to derive the LoRa performance at network-level.
## 2 Materials and Methods
### Spectra Processing and Analysis
#### 2.4.4 Aquagram Calculation  
The absorbance values at specific WAMACS define the water spectral pattern (WASP) [27], which can be visualized by a chart called the aquagram [28]. The 12 most influential wavelengths selected using the Savitzky-Golay second-order-derivative, PCA and PLS methods are used to display the absorption spectral pattern of each water on the aquagram. The aquagram displays the normalized and averaged absorbance values of the sample spectra on a radial axis. The radial axis is defined by the 12 selected wavelengths. The normalized absorbance was calculated using Equation (1).  
\[\mathrm{A}^{\prime}_{\lambda}=\frac{\mathrm{A}_{\lambda}-\mathrm{\mu}_{ \lambda}}{\sigma_{\lambda}} \tag{1}\]  
where \(\mathrm{A}^{\prime}_{\lambda}\) is the normalized absorbance shown on the aqueous spectrum, \(\mathrm{A}_{\lambda}\) is the absorbance after the second-order-derivative treatment, \(\mathrm{\mu}_{\lambda}\) is the mean of all spectra of the treated sample, \(\sigma_{\lambda}\) is the standard deviation of all spectra of the treated sample, and \(\lambda\) is the selected wavelength.
## 2 Design Method of Conventional Switched Type Attenuators
### Conventional \(\pi\)-Type Attenuator  
As can be seen from (4), as attenuation increases, \(Rp\) diminishes, and the return loss of the T-type structure deteriorates. Notably, when the attenuation reaches 8 dB, there is a marked deterioration in the return loss, indicating that the T-type structure is not suitable for large attenuation bits. In contrast, the \(\pi\)-type structure shows greater suitability for large attenuation bits. Figure 4 shows the conventional \(\pi\)-type structure and its equivalent circuits.  
Figure 4: (**a**) Schematic diagram of the \(\pi\)-type attenuation structure and its equivalent circuits. (**b**) Reference state. (**c**) Attenuation state.  
Figure 3: (**a**) Schematic diagram of the simplified T-type attenuation structure and its equivalent circuits. (**b**) Reference state. (**c**) Attenuation state.  
In the reference state, transistor \(M\)s is on, and transistor \(M\)p is off. The insertion loss is determined by the on-resistance of transistor \(M\)s and the series resistance \(R\)s. Consequently, the corresponding transmission \(S\)-parameter can be expressed as follows:  
\[S_{21,REF-\pi}=1-\frac{R_{s}R_{s,on}}{2(R_{s}+R_{s,on})Z_{0}+R_{s}R_{s,on}} \tag{7}\]  
In the attenuation state, transistor \(M\)s is off, and transistor \(M\)p is on; the insertion loss is determined by the T-type attenuation network and the on-resistance of transistor \(M\)p. Consequently, the corresponding transmission \(S\)-parameter can be expressed as follows:  
\[S_{21,ATT-\pi}=\frac{2Z_{0}(R_{p}+R_{p,on})^{2}}{(2Z_{0}+R_{s})(R_{p}+R_{p,on} )^{2}+(2R_{p}+2R_{p,on}+R_{s}){Z_{0}}^{2}} \tag{8}\]  
The relative attenuation \(\Delta S21\) can be expressed as follows:  
\[\Delta S_{21}=\frac{S_{21,REF-\pi}}{S_{21,ATT-\pi}} \tag{9}\]  
The S11 of attenuation state can be expressed as follows:  
\[S_{11,ATT-\pi}=\frac{R_{s}(R_{p}+R_{p,on})^{2}-Z_{0}^{2}(2R_{p}+2R_{p,on}+R_{ s})}{(R_{s}+2Z_{0})(R_{p}+R_{p,on})^{2}+2Z_{0}(R_{s}+Z_{0})(R_{p}+R_{p,on})+R_{s}{Z_ {0}}^{2}} \tag{10}\]  
Similarly, when the value of S11,\(ATT\)-\(\pi\) = 0, it allows for the computation of the following.  
The relative attenuation \(\Delta S21\) can be expressed as follows:  
\[R_{s}=\frac{Z_{0}(1-S_{21,ATT-\pi}^{2})}{2S_{21,ATT-\pi}} \tag{11}\]  
\[R_{p}=\frac{Z_{0}(1+S_{21,ATT-\pi})}{(1-S_{21,ATT-\pi})}-R_{p,on} \tag{12}\]  
Based on (7), (8), (11), and (12), we can calculate \(R\)s and \(R\)p under the optimal impedance matching condition.
## 3 Results and Discussion
### Relationships between CO2 Adsorption Capacity and Nanopore Structure
As indicated in Figure 9, when the pressure is smaller than 3.5 MPa, CO\({}_{2}\) is mainly adsorbed in larger pores (23-200 nm) of coals, and the pore volume and specific surface area of larger pores (23-200 nm) of granulitic coal are larger than those of crumple coal, which indicates the larger adsorption capacity of granulitic coal as compared to crumple coal. As the pressure increases (3.5-7 MPa stage), CO\({}_{2}\) is adsorbed in smaller pores of coals (2-23 nm); at this stage, the pore volume and specific surface area of crumple coal are larger than those of crumple coal, which indicates a larger adsorption capacity of crumple coal. Figure 8: Relationships between absolute adsorption capacity and pore volume (**a–d**) and specific surface area of coals (**e–h**): (**a**) Absolute adsorption capacity versus pore volume of macropore; (**b**) Absolute adsorption capacity versus pore volume of mesopore; (**c**) Absolute adsorption capacity versus pore volume of micropore; (**d**) Absolute adsorption capacity versus pore volume of total pore; (**e**) Absolute adsorption capacity versus pore area of macropore; (**f**) Absolute adsorption capacity versus pore area of mesopore; (**g**) Absolute adsorption capacity versus pore area of micropore; (**h**) Absolute adsorption capacity versus pore area of total pore.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 3 Methodology
### Routing Approaches Considered for the Analysis  
As previously mentioned, the routing approach, reactive or proactive, is a factor that could affect the dissemination of information packets in ad hoc IoT networks. The reactive and proactive approaches are implemented in this work using the routing protocols considered as the de facto standards for each approach, namely AODV [51] and OLSR [52]. This section presents the main features of the base experiment for our evaluation. Network, mobility, and routing algorithm parameters are provided in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Configuration** \\ \hline Transport layer & Simple Transport Layer (Not ACKS or \\ Retransmissions mechanisms enabled) \\ Network layer & OLSR and AODV for routing. \\ Link layer (MAC) & Simple MAC. \\ Physical layer (PHY) & 750 kbps with 1500 km of radio range \\ \hline
**Orbit Parameters** & **Configuration** \\ \hline Max altitude & 2000 km (LEO) \\ \hline
**Parameter** & **Value** \\ \hline Number of nodes (density) & 20,240 \\ Number of groups with 20 and 240 nodes & 30 and 30 \\ Number of trials per parameter set and satellite group & 10 \\ Starting date for all trials & Monday, 3 May 2021 \\ Days and hours for ad hoc IoT network communication attempts per trial & Monday, Wednesday, and Thursday at 12 a.m. \\ ad hoc IoT Network communicationSimulation time per attempt & 600 s \\ Number of packets sent per communication attempt. & 100 \\ Satellite Model & ONION CubeSat Small platform \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulator parameters.  
Figure 2: Simulation scenario with the selected coordinates for a buoy and urban center.
## 2 Dynamic Modeling
### State-Space Model  
By using the orthogonality conditions of \(\varphi_{mn}(x,y)\), substituting Equation (2) into Equation (1), multiplying \(\varphi_{mn}(x,y)\) and then integrating over the entire area of the plate, the usual state-space representation is obtained as:  
\[\left\{\begin{array}{l}\dot{\mathbf{x}}=\mathbf{A}\mathbf{x}+\mathbf{B}_{F} F(t)+\mathbf{B}_{u}y(t)\\ e=\mathbf{C}\mathbf{x}\end{array}\right., \tag{5}\]  
where \(\mathbf{x}=\left[\dot{T}_{11},\omega_{11}T_{11},\dot{T}_{12},\omega_{12}T_{12 },\cdots\dot{T}_{MN},\omega_{MN}T_{MN}\right]^{\mathrm{T}}\); \(\mathbf{A}=\mathrm{diag}(\mathbf{A}_{mn})\), \(\mathbf{A}_{mn}=\left[-\eta_{p}\omega_{mn}-\omega_{mn}\omega_{mn}\omega_{mn} \right]\);\(\omega_{mn}^{2}=D_{p}/\rho_{p}h_{p}\int_{0}^{L_{x}}\int_{0}^{L_{y}}\int_{0}^{ L_{y}}\left[(\partial^{2}/\partial x^{2}+\partial^{2}/\partial y^{2})^{2}\varphi_{mn}(x,y) \right]\varphi_{mn}(x,y)\mathrm{d}x\mathrm{d}y\); \(\mathbf{B}_{\mathrm{F}}=1/\rho_{p}h_{p}[\varphi_{11}(x_{F},y_{F}),0,\varphi_{ 12}(x_{F},y_{F}),0,\cdots,\varphi_{MN}(x_{F},y_{F}),0]^{\mathrm{T}}\); \(\mathbf{B}_{u}=-\Delta_{u,x}\Delta_{u,y}\chi_{a}/\rho_{p}h_{p}\)\([\phi_{11}(x_{x},y_{a}),0,\phi_{12}(x_{a},y_{a}),0,\cdots,\phi_{MN}(x_{a},y_{a}),0]^{\mathrm{T}}\); and \(\mathbf{C}=-\Delta_{s,x}\Delta_{s,y}\chi_{a}/\rho_{p}h_{p}[0,\phi_{11}(x_{s},y_ {s}),\)  
Figure 1: Schematic diagram of the smart plate.  
Figure 2: Basic charge amplifier for the PZT sensor.  
\(0,\phi_{12}(x_{s},y_{s}),\cdots,0,\phi_{MN}(x_{s},y_{s})\)]. The controllability function, \(H_{con}\), and observability function, \(H_{obs}\)[23], are denoted as, respectively,  
\[\begin{split} H_{con}&=\min\tfrac{1}{2}\int_{-T}^{0} \|y(\tau)\|^{2}\mathrm{d}\tau\\ H_{obs}&=\min\tfrac{1}{2}\int_{-T}^{0}\|e(\tau)\|^{2 }\mathrm{d}\tau\end{split} \tag{6}\]  
With Equations (5) and (6), the controllability and observability of AVC is related to sensing signal \(e(t)\) and actuating voltage \(y(t)\). Based on Equations (1) and (3), the modal strains are related to \(e(t)\) and \(y(t)\). Therefore, modal strains at the locations of the PZT actuators and sensors have a direct influence on the controllability and observability of AVC.  
The state-space representation provides a complete and reliable model for the smart plate, which contributes to clarifying the controllable mode in AVC in Section 3.
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Day-Ahead Optimal Scheduling Model  
#### 4.1.1 Low-Carbon Economy Scheduling Objective Function  
To realize low-carbon power, carbon emission cost is introduced into economic dispatch objective function of the power system:  
\[\text{min}F=F_{1}+\sum_{t=1}^{T}C_{\text{tax}}(E_{C,t}-E_{D,t}) \tag{9}\]  
where \(F_{1}\) is the cost function of system operation. \(\$\). \(C_{\text{tax}}\) is the unit carbon treatment cost in the market, \(\$\)/ton. \(E_{C,t}\) is the thermal power unit carbon emissions, ton. \(E_{D,t}\) is the carbon allocation amount of the generator unit in time period \(t\), ton. When the unit's carbon emission is within the range of carbon  
Figure 2: IDR curve.  
allocation, the carbon treatment cost is 0. \(T\) is the number of segments in the scheduling cycle and \(T=24\) in day-ahead scheduling.  
Wind power and photovoltaic power generation are renewable energy sources, so there is no carbon emission in the power generation process. In addition, their carbon emission is not considered in the solving process. The carbon emission in the power system comes from the coal consumption of thermal power units. The carbon emission allocation quota of generators in the time period t is as follows:  
\[E_{\text{D},t}=\sum_{i=1}^{N_{\text{F}}}\eta_{\text{D}}P_{\text{F},i,t} \tag{10}\]  
where \(N_{\text{F}}\) is the number of thermal units, and \(\eta_{\text{D}}\) is the carbon emission allocation of generator's unit active power output.  
The actual carbon emission of thermal power units during the period \(t\)[23] is:  
\[E_{\text{C},t}=\sum_{i=1}^{N_{\text{F}}}\Bigl{(}\alpha_{i}P_{\text{F},i,t}^{2 }+\beta_{i}P_{\text{F},i,t}+\delta_{i}\Bigr{)} \tag{11}\]  
where \(\alpha_{i}\), \(\beta_{i}\), \(\delta_{i}\) are emission factors of thermal power units, respectively.