esa-sceva/satcom-chunk-collection huggingface dataset:
# The State-of-the-Art of Sensors and Environmental Monitoring Technologies in Buildings
###### Abstract  
Building energy consumption accounts for 30%-45% of the global energy demand. With an ever-increasing world population, it has now become essential to minimize the energy consumption for the future of the environment. One of the most crucial aspects in this regard is the utilization of sensing and environmental monitoring technologies in buildings as these technologies provide stakeholders, such as owners, designers, managers, and occupants, with important information regarding the energy performance, safety and cost-effectiveness of the building. With the global sensors market value predicted to exceed $190 billion by 2021 and the number of sensors deployed worldwide forecasted to reach the '1 Trillion' mark by 2025, a state-of-the-art review of various commercially-viable sensor devices and the wide range of communication technologies that complement them is highly desirable. This paper provides an insight into various sensing and environmental monitoring technologies commonly deployed in buildings by surveying different sensor technologies, wired and wireless communication technologies, and the key selection parameters and strategies for optimal sensor placement. In addition, we review the key characteristics and limitations of the most prominent battery technologies in use today, different energy harvesting sources and commercial off-the-shelf solutions, and various challenges and future perspectives associated with the application of sensing and environmental monitoring technologies within buildings.  
sensors; communications; selection; placement; batteries; energy harvesting; buildings 1
## 2 Modeling and Methods
### Vehicles' Movement  
The motion of a vehicle is described through a modified NaSch model, based on the extensions proposed in References [8; 21; 31], that captures the deceleration effects of a vehicle approaching a raised crosswalk. Thereby, the motion of a vehicle is described by the following four rules:  
1. Acceleration: \[v_{i}=\min(v_{i}+1,v_{max})\] (5)
2. Deceleration: \[v_{i}=\begin{cases}\min(g_{cur},g_{i})&\text{if }g_{cur}<v_{i},\\ \min(1,g_{i})&\text{if }g_{cur}=0\text{ \& the crosswalk is empty,}\\ 0&\text{if }g_{cur}=0\text{ \& the crosswalk is occupied,}\\ \min(g_{i},v_{i})&\text{otherwise,}\end{cases}\] (6)  
Figure 2: Pedestrian’s neighborhood.  
Randomization:  
\[v_{i}=\max(v_{i}-1,0),\text{ with a breaking probability }P_{b}, \tag{7}\]
4. Movement:  
\[x_{i}=x_{i}+v_{i}, \tag{8}\]  
where \(v_{i}\) is the current speed of vehicle \(i\) at position \(x_{i}\); \(v_{max}\) is the vehicle maximum speed; \(g_{i}\) is the gap towards the next vehicle \(i+1\); and \(g_{cw}\) is the distance between vehicle \(i\) and the crosswalk.  
For this work, the open-boundary condition strategy has been adopted. Each vehicle is inserted at the lane's first position with probability \(\alpha\) if and only if such a position is empty. Vehicle \(i\) starts with speed \(v_{i}=0\) and accelerates as it moves through the lane. When the vehicle's new computed position exceeds the lane length, such a vehicle is deleted with probability \(\beta\). This strategy is intended to reproduce the dynamic of vehicles when transiting across urban intersections.
# Current Promising Strategies against Antibiotic-Resistant Bacterial Infections
## 2 Discovery of New Antibiotics
### Identifying New Antibiotics from Small-Molecule Libraries
BamA is a large \(\beta\)-barrel conserved protein in gram-negative bacteria, implying that BamA inhibitors could have a broad bactericidal activity. The ABC transporter LoICDE captures mature lipoproteins from the inner membrane, which are then transported by LoIA to LoIb where they are finally translocated into the outer membrane [38]. Many inhibitors of the LoICDE complex have been identified in specific library assays but their development into commercial antibiotics was not possible due to the lack of appropriate physical properties and antimicrobial spectrum required for clinical use [39; 40; 41; 42]. The small-molecule compound MAC13243 presented a potent activity against _Escherichia coli_ by directly inhibiting LoIA and MreB [43; 44]. Antibiotic treatments usually lead to microbiome imbalance, whereas MAC13243 had only a limited impact on the commensal gut microbiota [45], exhibiting a strong specificity. Sec machinery provides a major pathway of protein translocation from the cytosol across the cytoplasmic membrane in both gram-negative and -positive bacteria [46]. One component of this pathway is SecA, which is a widely conserved protein and has been utilized as a druggable target. Several reports indicated that SecA inhibitors (such as SCA107, thiouracil derivatives, and HTS-12302 derivatives) are promising broad-spectrum antimicrobials that could increase membrane permeability by inhibiting the synthesis of membrane proteins, reduce bacterial pathogenicity by suppressing virulence factor production, and overcome multidrug resistance by blocking efflux pumps [47; 48; 49].
## 1 Introduction
The presented method for determining the optimal position of the relay station can be practically applied in aerospace applications. To better understand the resulting path losses, we observed several ray paths from the rocket to the relay stations. We found that slow envelope variations and fast fluctuations in the received power are due to the transmitting antenna pattern and the phase difference between the direct and indirect reflected rays, respectively. Based on this path loss analysis, according to the rocket's altitude, it is possible to determine the optimal relay position considering the outdoor environment near the rocket launch site.
## 5 Measurements and Results
### Mirrors on Sitall Substrates  
The material sitall CO-115M or \"Astrositall\" refers to the category of \"zerodures\", which are materials with a low coefficient of thermal expansion (\(1.5\times 10^{-7}\)\(1/^{\circ}\)C).  
The other solid-state characteristics are listed in Table 1. The optical properties are characterized by a small difference in the refractive indices of the extraordinary and ordinary rays (\(n_{e}-n_{o}\)) \(\leq\) 0.001 and, accordingly, the small birefringence parameter \(d\)\(\leq\) 10 NM/cm. Due to these properties, Astrositall is used to make the mirrors for large telescopes, including complex aperture synthesis mirrors.  
The indicated characteristics of Sitall CO-115M are confirmed in the temperature range from \(-60\)\({}^{\circ}\)C to \(+60\)\({}^{\circ}\)C. However, reliable data for cryogenic temperatures are unavailable. The results of our measurements of the evolution of the integral characteristics of a FP cavity with mirrors on substrates made of this sitall are shown in Figure 3.  
One can see a rather rapid drop in both parameters when the FP cavity is cooled. The finesse (red line) drops almost seven times as the temperature decreases by 100 \({}^{\circ}\)C. The contrast (blue line) weakens slightly more slowly, by three times when approaching the nitrogen temperature. Measurements at lower temperatures have already lost their meaning and have not been carried out. In the reverse  
Figure 3: Evolution of the finesse and contrast for FP cavity with mirrors on sitall during the cooling process.  
course, during warming up, the parameters were restored (not shown), coming to the initial state with some hysteresis (a slight vertical shift for the same temperature). Thus, the observed changes were reversible.
## 6 Purpose-Based Encryption
### Implementation  
_(1) ABE Encryption:_ We have implemented the ABE scheme [55] by using the jPBC [72], a Java API that wraps the pairing-based cryptosystems written in C [73]. The ABE scheme requires an access policy in the form of a Linear Secret Sharing Scheme (LSSS), but our disclosure policy needs a mixed formula of Boolean expressions, simple thresholds, and numerical range. Therefore, we first convert our policy string to a Boolean formula and then to an LSSS. To convert a numerical range to a Boolean formula, we borrowed the idea of a segment tree [74]. To convert a Boolean formula to an LSSS, we followed the algorithm of [PERSON] in [75].  
_(2) Ciphertext serialisation:_ So far, we have described that we use ABE for encryption. Actually, we do not use ABE to encrypt PII directly but combine it with the Advanced Encryption Standard (AES) [76]. With AES, we can encrypt arbitrarily large data without affecting the performance. In the first step, we generate an ephemeral key and use it as a symmetric key in AES to encrypt the PII. In the second step, we use ABE to encrypt the ephemeral key. For embedding the ciphertext in a SOAP message as well as in an HTTP POST, we encode the ciphertext in a base64 string format.
## 3 Results and Discussion  
### Characterization of SiC Nanoparticles  
The XRD pattern presented in Figure 1 shows strong and sharp diffraction peaks, indicating the crystalline structure of the product. The diffraction peaks at approximately 35.81, 41.41, 60.21, 71.91, and 75.61, can be indexed as (111), (200), (220), (311), and (222) planes of \(\beta\)-SiC (Joint Committee on Powder Diffraction Standards (JCPDS) card No. 29-1129), respectively. The crystallite size of the nanoparticles was estimated using the Debye-Scherrer equation:  
\[D=\frac{k\lambda}{\beta cos\theta}\]  
where \(D\) is the diameter of nanoparticles, \(k\) = 0.94 is the dimensionless shape factor, \(\lambda\) (wavelength of X-rays used) = 1.5406 \(\times\) 10\({}^{-10}\), \(\beta\) is the full width at half-maximum, and \(\theta\) is the Bragg angle. Its value was found to be 25 nm.  
The morphology and structure of the as-prepared SiC products were characterized by SEM and TEM in the present study. Figure 2A presents the TEM image of SiC nanoparticles. Numerous nanoparticles with semi-spherical morphology were observed from the TEM image. The particles are approximately 10-40 nm in size. Their diffraction patterns indicate three perfect diffraction rings, which correspond to the (311), (220) and (111) planes of cubic SiC. Figure 2B shows typical scanning electron micrographs of SiC nanoparticles. Single-phase primary particles with almost spherical-shaped nanocrystallites were observed. According to this figure, it can be observed that after the removal of the volatile materials, precursor particles form a nearly nanosized structure with a semi spherical morphology.  
Figure 1: X-ray diffraction pattern of SiC nanoparticles.  
The nitrogen adsorption and desorption isotherms for the SiC nanoparticles were also determined. The total pore volume and specific surface area were 0.06 cm\({}^{3}\cdot\)g\({}^{-1}\) and 39.43 m\({}^{2}\cdot\)g\({}^{-1}\), respectively.
# BIZON-UGV for Airport Pavement Testing: Mechanics and Control
## 3 Modelling and Control
### Control System and Autopilot Design
The output of a PID-FF controller is calculated in the time domain from the feedback heading angle and its reference error as follows:  
\[u_{\psi}=\text{sat}\bigg{\{}k_{P}e+k_{I}\int edt+k_{D}\frac{de}{dt}+k_{FF}\psi_ {ref};\;u_{\psi,min},u_{\psi,max}\bigg{\}}, \tag{13}\]  
where \(k_{P}\), \(k_{I}\), \(k_{D}\) are proportional, integral, derivative gains, and \(k_{FF}\) is feedforward gain, as a compromise between reference and process gains. Feedback control does not provide predictive control action to compensate for the effects of known or measurable disturbances. In that case, feedforward controllers are capable of achieving perfect control if it is physically achievable. However, an approximate model should be available to use feedforward control effectively. In particular, it is important to know how the controlled variable responds to changes in both the disturbance and manipulated variables. Hence, the quality of feedforward control depends on the accuracy of the plant model. Working with a feedback PID controller, with good UGV dynamics modelling or approximation, may be very useful and effective. The PID-FF controller output must be bounded by the implementing subsystems that introduce saturation, because of the stability problem. In practice, it is obvious that all quantities have upper and lower limits. In addition to improving the disturbance rejection task, a dedicated low-pass filter (LPF) is desirable. As shown in Figure 9, the wheel torque is only scaled and limited by the saturation subsystem, commonly due to the range of drive converter frequency. Then, merging \(u_{T}\) within the turn control signal \(u_{\psi}\) in the mixer, the controller allows the system to output left and right wheel control signals \(u_{LW}\) and \(u_{RW}\) (Figure 8).
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 5 Validation, Evaluation and Obtained Results
### Best Effort
#### 5.1.1 Multiple Clients  
The solution is intended to deal with several client connections. Hence, it is important to analyse the resultant impact of growing devices in the gateway networking environment. In order to do so, the same protocol comparison was made where each client was imposing a bit rate of 1 Mbit/s. It should be mentioned that only one slice was considered, and the tests below were not performed simultaneously. Figure 14 shows the total bit rate of the TCP and UDP protocols resulting from a growing client number.  
Figure 13: Maximum achievable bit rate when utilising TCP and UDP protocols.  
TCP clients suffer and cannot keep up with the requested bandwidth, which at roughly 70 clients, begins to deteriorate. Understanding the performance of the system resources is important and might reveal some limitations of an SDN implementation. Hence, usually, software performance is directly limited by hardware, and the Iperf3 CPU utilisation reports should be taken into account.  
Despite not being a surprise, it is clear that the CPU usage is damaging the TCP scenario performance as shown in Figure 15. As was anticipated, the bit rate degradation starts when the machine enters full processing power, causing the total clients' bandwidth not to go further than 75 Mbit/s. Natural intuition would blame the protocol characteristics, which contrary to UDP, is connection oriented and focuses on reliability.  
Figure 14: Total bit rate of the TCP and UDP protocols resulting from a growing client number.  
Figure 15: CPU utilisation when gradually increasing clients in the TCP and UDP protocols.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 4 Optimization Problem of the Proposed System
### Mathematical Model of the Constraints
#### 4.2.1 Energy Balance Constraints  
The electric, heating, and cooling loads should be met at each time interval, where the energy balance equations are as follows:  
* Electric Energy Balance Constraint \[\sum_{t=1}^{T}\left\{P_{MT}^{t}+P_{FC}^{t}+P_{DE}^{t}+P_{W}^{t}+P_{PV}^{t}+P_{ SB-dis}^{t}+P_{U-p}^{t}=P_{L-f}^{t}+P_{SB-ch}^{t}+P_{U-s}^{t}+P_{EC}^{t}+P_{ EH}^{t}\right\}\] (34) where \(P_{W}^{t}\) and \(P_{PV}^{t}\) are the supplied wind and solar power and \(P_{U-p}^{t}\) and \(P_{U-s}^{t}\) are the buying and selling power from/to the upstream system.
* Heat Energy Balance Constraint \[\sum_{t=1}^{T}\left\{H_{MT}^{t}+H_{GB}^{t}+H_{EH}^{t}+H_{HS-dis}^{t}+H_{U-p}^{ t}=H_{L}^{t}+H_{HS-ch}^{t}+H_{U-s}^{t}+H_{AC}^{t}\right\}\] (35) where \(H_{U-p}^{t}\) and \(H_{U-s}^{t}\) are the exchange in heat power with the upstream system and \(H_{L}^{t}\) is the heat load in kW.
* Cooling Energy Balance Constraint \[\sum_{t=1}^{T}\left\{C_{EC}^{t}+C_{AC}^{t}=C_{L}^{t}\right\}\] (36) where \(C_{L}^{t}\) is the cooling load in kW.
# Directed Clustering of Multivariate Data Based on Linear or Quadratic Latent Variable Models
## 5 Algorithms Required for Directed Clustering  
Given a multivariate dataset \(x_{i}\in\mathbb{R}^{m}\), \(m>1\), \(i=1,2,\ldots,n\), we first need to decide whether to fit the data with the linear model or the quadratic model. The model selection process is carried out with Algorithm 1. The algorithm requires the specification of a maximum considered number of components, \(K_{max}\). We suggest taking \(K_{max}=10\), as in practical applications there will rarely be a need to use more than 10 components. This is similar to the 'nonparametric maximum likelihood estimation' of standard mixture models; the monograph by [PERSON] et al. (2017) discusses this approach extensively.  
```
(i) Choose an integer \(K_{max}\) and fit the linear model (1) and the quadratic model (2) separately to the data, for all \(K=2,3,\ldots,K_{max}\). (ii) For each model type, (1) and (2), select the best-fitting model by identifying the value of \(K\) yielding minimum BIC values. (iii) We compare the BIC values from the best-fitting linear model and the best fitting quadratic model. The model with the minimum BIC value will be our chosen model.
```  
**Algorithm 1**Model selection  
From the best-fitted model according to Algorithm 1, we obtain the posterior probability matrix \(W=(w_{ik})_{1\leq i\leq n,1\leq k\leq K}\) and the estimated mass points \(\hat{z}_{1},\ldots,\hat{z}_{K}\). Each mass point \(\hat{z}_{k}\) has a corresponding mass \(\hat{\pi}_{k}\), and each pair of mass points and its mass has a matching column in the \(W\) matrix. The directed clustering of \(m\)-dimensional non-linear data \(\{x_{i}\}\) will be obtained according to Algorithm 2.  
```
(i) Fit the data \(\{x_{i}\}\) according to the best model identified by Algorithm 1. (ii) Order the estimated mass points \(\hat{z}_{1},\ldots,\hat{z}_{K}\) in an ascending order. (iii) Reorder the columns of \(W\) according to the ascending order of mass points. (iv) Perform the clustering rule (e.g., MAP rule, \(\hat{k}(x_{i})=\max_{k}w_{ik}\)) on the rows of the reordered \(W\) matrix.
```  
**Algorithm 2**Directed clustering  
Following the strategy above, each observation \(x_{i}\) will be assigned to a cluster \(k\), which is ordered along the curve \(g(z,\hat{\theta})\) according to the latent variable, represented by the values of the mass points \(\hat{z}_{k}\).
## 2 Materials and Methods
### Data Preparation and Basic SVM Prediction Model
#### 2.1.2 SVM Prediction Model  
It can be seen that the input features of Figure 4 are not linearly separable, because some data points of the stable and unstable samples of the voltage curves intersect with each other. SVM is able to map a linearly inseparable data in low-dimensional space to a linearly-separable high-dimensional space through kernel functions. Figure 5 shows the mapping process visually.  
The basic principle of SVM is shown in Equation (1).  
\[\left\{\begin{array}{l}\underset{w,b,\zeta}{\min}\big{(}\frac{1}{2}\big{)}w^ {T}w+C\sum\limits_{i=1}^{N}\zeta_{i}\\ s.t.\big{(}w^{T}\varphi(X_{i})+b\big{)}yi\geq 1-\zeta_{i}\\ \zeta_{i}\geq 0,i=1,\ldots N\\ f(X)=\sum\limits_{i=1}^{N}\alpha_{i}y_{i}\varphi(X_{i})^{T}\varphi(x)+b\\ \end{array}\right\}, \tag{1}\]  
where \(\omega\) is the weight vector of the hyperplane; \(b\) is the threshold value; \(\zeta\) is the relaxation variable; \(C\) is the penalty factor for the relaxation variable; \(N\) is the number of training samples; \(\varphi(.)\) is the mapping function from low-dimensional space to high-dimensional space, the kernel function is chosen as radial basis function (RBF); \(X_{i}(i=1,\ldots,N)\) are the support vectors; \(y_{i}\) is the output of \(i_{th}\) sample; \(f(.)\) is the fitted model of SVM, where \(\alpha_{i}\) is the coefficient of \(i_{th}\) sample.
## 3 The Design and Implementation of the AR Enhanced Game \"Exploring Ancient Greece\"  
The serious game \"Exploring Ancient Greece\" is an educational Android application aimed at supplementing the teaching of history and making it more enjoyable. Through AR, students will have the opportunity to visit archaeological sites throughout Greece and, in doing so, learn about the life and culture of the ancient Greeks. It is designed for Primary school students from the 4 th grade and up, who, based on their school curriculum, have been taught the history of Ancient Greece.  
The game is divided into three levels (Figure 1). In the first level, the student explores archaeological sites from various places in Greece through AR, receiving useful information with the help of audiovisual material. The second level is a trivia game, consisting of text and AR graphics, with multiple-choice questions, where the knowledge gained from the first level is tested. The third part consists of an escape room in which the player is trapped in an archaeological site and tries to solve riddles to find the exit. Escape rooms are games that can be used for various purposes and, in addition to knowledge, require numerous additional skills such as collaboration, observation, and multitasking [5]. In education, escape rooms are widely used in subjects such as chemistry, physics, and history because they have been proven to enhance students' motivation and interest in learning, develop critical thinking and analytical skills, and contribute to their social relationships [5].  
Figure 1: The start menu of the game consisting of (starting from the top): the title of the game “Exploring Ancient Greece”; the buttons for the three levels, namely “Learning through traveling”, “Escape Room”, and “Quiz game”, and the “Exit button”.
## 4 Experiments and Results
### Average Number of Fragments by File Size  
In this section, we show how the average number of fragments varies according to the size of the file.  
Typically, larger files are split into more fragments. Analyzing how the number of fragments varies with file size can help to understand and to optimize file system behavior, improving data management efficiency and reducing potential bottlenecks. Figure 6 illustrates the average number of fragments (on the \(y\)-axis) as a function of the size of the original file (on the \(x\)-axis). To calculate the average value and establish a reference statistic, 1000 tests were conducted for each specified file size. The different colors of the curves correspond to the six distinct hardware systems used for testing.