esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present study reports the experimental results of a parabolic trough collector field and an absorption cooling system with a nominal capacity of 5 kW, which operates with the ammonia-lithium nitrate mixture. The parabolic trough collectors' field consists of 15 collectors that are made of aluminum plate in the reflector surface and cooper in the absorber tube, with a total area of 38.4 m\({}^{2}\). The absorption cooling system consists of 5 plate heat exchangers working as the main components. Parametric analyses were carried out to evaluate the performance of both systems under different operating conditions, in independent way. The results showed that the solar collectors' field can provide up to 6.5 kW of useful heat to the absorption cooling system at temperatures up to 105 \({}^{\circ}\)C with thermal efficiencies up to 19.8% and exergy efficiencies up to 14.93, while the cooling system operated at generation temperatures from 85-95 \({}^{\circ}\)C and condensation temperatures between 20 and 28 \({}^{\circ}\)C, achieving external coefficients of performance up to 0.56, cooling temperatures as low as 6 \({}^{\circ}\)C, and exergy efficiencies up to 0.13. The highest value for the solar coefficient of performance reached 0.07.  
parabolic trough collector; absorption cooling system; thermal efficiency; useful heat; ammonia-lithium nitrate +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: instituto de Energias Renovables, Universidad Nacional Autonoma de México, Privada Xochicalco S/N, Colonia Centro, C.P. 62580 Temixco, Morelos, Mexico; [EMAIL_ADDRESS] (Y.R.G.L.); [EMAIL_ADDRESS] (W.R.G.F.)  
2 Centro de Ingeniera y Ciencias Aplicadas, Universidad Autonoma del Estado de Morelos, Av. Universidad no. 1001, C.P. 62209 Cuernavaca, Morelos, Mexico; [EMAIL_ADDRESS]  
3 Catedratico CONACYT, Instituto Mexicano de Tecnologia del Agua, Paseo Cuahunahuac, C.P. 62580 Jiutepec, Morelos, Mexico; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +52-777-249-2189
# Secure D2D Group Authentication Employing Smartphone Sensor Behavior Analysis
###### Abstract  
Nowadays, with rapid advancement of both the upcoming 5G architecture construction and emerging Internet of Things (IoT) scenarios, Device-to-Device (D2D) communication provides a novel paradigm for mobile networking. By facilitating continuous and high data rate services between physically proximate devices without interconnection with access points (AP) or service network (SN), spectral efficiency of the 5G network can be drastically increased. However, due to its inherent open wireless communicating features, security issues and privacy risks in D2D communication remain unsolved in spite of its benefits and prosperous future. Hence, proper D2D authentication mechanisms among the D2D entities are of great significance. Moreover, the increasing proliferation of smartphones enables seamlessly biometric sensor data collecting and processing, which highly correspond to the user's unique behavioral characteristics. For the above consideration, we present a secure certificateless D2D authenticating mechanism intended for extreme scenarios in this paper. In the assumption, the key updating mechanism only requires a small modification in the SN side, while the decryption information of user equipment (UEs) remains constant as soon as the UEs are validated. Note that a symmetric key mechanism is adopted for the further data transmission. Additionally, the user activities data from smartphone sensors are analyzed for continuous authentication, which is periodically conducted after the initial validation. Note that in the assumed scenario, most of the UEs are out of the effective range of cellular networks. In this case, the UEs are capable of conducting data exchange without cellular connection. Security analysis demonstrates that the proposed scheme can provide adequate security properties as well as resistance to various attacks. Furthermore, performance analysis proves that the proposed scheme is efficient compared with state-of-the-art D2D authentication schemes.  
Device to Device communication (D2D); certificateless authentication; security and privacy; human activity recognition; continuous authentication +
Footnote †: journal: Accepted: 28 July 2019; Published: 1 August 2019
## 3 Experimental Results  
### Data Selection  
The 1572 selected orthoframes were partitioned into segments each having dimensions of 224 \(\times\) 224 pixels, which is the size many transfer learning architectures take as a default input [84; 86]. Smaller and larger dimensions could also be considered, but there is a trade-off for both of these cases. Smaller segment sizes allowed us to capture more of the road, leaving fewer blind spots. However, it is more difficult to make predictions on smaller segments due to missing context. Conversely, larger segments provide more context for better accuracy, but at the cost of leaving more blind spots at the edge of the road (assuming non-overlapping segments).  
In order to classify a segment as defect or not defect, we considered the percentage of defect pixels on the image. If more than 5% of the pixels on a segment were masked by the digitizer, it would be labeled as a defect segment. With this criterion, 15% of all segments would have a defect, and 85% would not. It is known that class imbalance during training reduces the performance of deep neural networks [87]. To balance our dataset, only \(N\) non-defect segments were sampled for each orthoframe containing \(N\) defect segments. This way, ~8 segments per orthoframe were sampled on average.  
For the purposes of neural network training, the obtained 12,728 segments with dimensions 224 \(\times\) 224 pixels were split into training and validation sets, with the ratio of 0.85 and 0.15, respectively. As is typical for deep learning cases, the training set was used to optimize the parameters of the model with respect to the cross-entropy loss function, and the validation set was used to measure if the model was overfit to the training data.  
In addition, a test set consisting of 55 new orthoframes from different roads was used to evaluate how well the model generalized to new conditions. As opposed to the training and validation set, for the test set, we extracted all of the possible segments, so a total of 1007 defect-free and 185 defect segments were obtained.
# Broadening Bandgap Width of Piezoelectric Metamaterial by Introducing Cavity
###### Abstract  
In this research, a semi-analytical model of the adaptive piezoelectric metamaterial, built upon continuum mechanics characterization, was formulated and analyzed to reveal the fundamental features of bandgap with respect to unit-cell parameters under transverse wave. A new mechanism to broaden the bandgap width, was then introduced through geometric cavity synthesis. It was demonstrated that the cavities incorporated into the host structure of the piezoelectric metamaterial can increase the electro-mechanical coupling of the system, which effectively yields broadened bandgap width. Case studies were performed to demonstrate the enhanced performance of the new design, as well as the tunability. Compared with the conventional piezoelectric metamaterial, the metamaterial with cavity synthesis can increase the bandgap width from 45 Hz to 126.7 Hz.  
acoustic metamaterial; piezoelectric transducer; LC shunt circuit; bandgap width; electro-mechanical coupling; geometric cavity +
Footnote †: journal: Applied Physics Letters
## 2 System Model
### Air Interface Model  
The air interface is in charge of signal modulation and transmission over the wireless links. It is a common bottleneck that limits the communication capacity of each user. Our paper considers an orthogonal frequency division multiplexing (OFDM)-based air interface with \(K\) subcarriers. Each subcarrier is an independent channel that adapts \(M\)-quadrature amplitude modulation (\(M\)-QAM). The subcarrier index is denoted by \(k(k=1,2,\ldots,K)\).  
Let us denote \(c_{uk}\), \(a_{uk}^{2}\), and \(p_{uk}\) as the assigned bits of user \(u\) on subcarrier \(k\), the instantaneous channel gain of user \(u\) on subcarrier \(k\), and the transmission energy allocated to user \(u\)'s subcarrier \(k\), respectively. We have \(p_{uk}=f(c_{uk})/a_{uk}^{2}\), where \(f(c)\) denotes the required received energy in the subcarrier for a reliable reception of \(c\) bits per symbol when the channel gain is equal to unity. In addition, we assume that the channel gains are exponentially distributed [25; 26; 27].  
The total transmit power and bandwidth of the BS are denoted as \(P_{T}\) and \(B\), respectively. In the presence of multiple users, the air interface should properly allocate the subcarrier (i.e., band width) and power resource to multiple users. The subcarrier allocation is indicated by a binary variable \(\rho_{uk}\in\{0,1\}\). If \(\rho_{uk}=1\), it means that subcarrier \(k\) is allocated to user \(u\). Each subcarrier can only be allocated to at most one user.  
Now, considering the transmission in a single subcarrier, the required received power \(f(c)\) can be written as a function of the target BER \(P_{e}\) and bits per symbol \(c\) as [20; 28]\[f(c)=\frac{N_{0}}{3}\left[Q^{-1}\left(\frac{P_{e}}{4}\right)\right]^{2}(2^{c}-1), \tag{6}\]  
where \(N_{0}\) is the power spectrum density of white Gaussian noise and \(Q^{-1}(\cdot)\) is the inverse \(Q\)-function [29]. For the \(u\)-th user, the bits per symbol duration aggregated over multiple subcarriers is given by  
\[C_{u}=\sum_{k=1}^{K}c_{uk}\times\rho_{uk}\qquad\text{(bits/symbol)}. \tag{7}\]  
Similarly, the sum energy per symbol duration for user \(u\) across multiple subcarriers is  
\[P_{u}=\sum_{k=1}^{K}p_{uk}\times\rho_{uk}=\sum_{k=1}^{K}\frac{f(c_{uk})}{\alpha _{uk}^{2}}\times\rho_{uk}. \tag{8}\]  
Because the symbol rate scales linearly with the system bandwidth \(B\), the total power (per second) allocated to user \(u\) can be written as  
\[P_{u}^{total}=B\times\sum_{k=1}^{K}p_{uk}\times\rho_{uk}. \tag{9}\]  
Similarly, the bit rate (i.e., bits per second) of user \(u\) is  
\[R_{u}=C_{u}\times B=\sum_{k=1}^{K}c_{uk}\times\rho_{uk}\times B\qquad\text{( bits/s)}. \tag{10}\]
## 4 Application of the Design Method
### Selection of the Isolator Types (LRB or NRB)  
The torsion effect of the seismically isolated tall buildings should be controlled strictly. To avoid the torsion, the rigid center of the isolation system is required to be approximately identical to the mass center of the superstructure, which is specified in the technical specification for seismic-isolation with laminated rubber bearing isolators (CECS126:2001) [40]. To achieve this, LRB isolators are usually adopted for the columns and the corner of the core tube in RC frame-core tube tall buildings. In addition, an index named eccentricity of the isolation system is introduced to quantitatively evaluate the potential torsion effect.  
Based on the above, LRB isolators are adopted for the isolators under the columns and the corner of the core tube, while NRB isolators are adopted for other isolators as shown in Figure 14. The yield ratio calculated has a value of 2.3%, which lies in the proposed rational range of the yield ratio (i.e., 2-3%), indicating that there is no need to add other LRB isolators. Then, the eccentricity of isolation system is calculated to have the values of 0.0043% and 0.0007% in the \(X\) and \(Y\) directions, respectively, which are both lower than the limit of 3%.
# Mass Processing of Sentinel-1 Images for
## 3 Methods  
This section describes a number of tools and platforms that have been developed in JRC through the years and that have been used in this study. SUMO (ship detection software) and the image metadata extraction process were run on JEODPP (platform for storage and processing of Earth Observation data), Blue Hub (maritime surveillance platform) and the recurrent target analysis were run outside JEODPP.
## 3 Method
### Model Architecture  
#### 3.3.1 Model Overview  
The architecture of CeT is demonstrated in Figure 2(a). Once inputs enter CeT, it first uses the angle between direction vector \(\phi_{\zeta}\) and azimuth \(\phi^{t}_{\zeta,i}\) to decompose the spot speed \(v^{t}_{\zeta,i}\) and then converts it into the vehicle motion vector \(x^{t}_{\zeta,i}\) \({}^{\prime}\in\mathbb{R}^{d_{model}}\), where \(d_{model}\) represents the feature dimension. Meanwhile, CeT converts vehicle-type attribute \(s_{\zeta,i}\) into vehicle-type embedding \(s_{\zeta,i}\) \({}^{\prime}\in\mathbb{R}^{d_{model}}\) and fuses \(x^{t}_{\zeta,i}\) \({}^{\prime}\) and \(s_{\zeta,i}\) \({}^{\prime}\) to \(\epsilon^{t}_{\zeta,i}\in\mathbb{R}^{d_{model}}\). In this way, CeT reshapes and projects \(\epsilon^{t}_{\zeta}=\{\epsilon^{t}_{\zeta,1},\epsilon^{t}_{\zeta,2},\ldots, \epsilon^{t}_{\zeta,N^{t}_{\zeta}}\}\) to \(\epsilon^{t}_{\zeta}\in\mathbb{R}^{d_{model}}\), and the lane segment state at time step \(t\) can be expressed as \(X^{t^{\prime}}=\{\epsilon^{t}_{1},\epsilon^{t}_{2},\ldots,\epsilon^{t}_{N_{ls }}\}\). Then, CeT converts the \(t\)-th temporal attribute \(T^{t}\) and spatial attribute \(TM^{t}\) into the temporal embedding \({TE^{t^{\prime}}}\in\mathbb{R}^{N_{ls}\times d_{model}}\) and spatial embedding \({SE^{t^{\prime}}}\in\mathbb{R}^{N_{ls}\times d_{model}}\), which guide the temporal self-attention (TSA) and spatial self-attention (SSA) computation on \({X^{t^{\prime}}}\). In this way, CeT can capture the encoding sequence \(X_{en}\in\mathbb{R}^{T_{H}\times N_{ls}\times d_{model}}\), which is input into the decoder. In the decoder, CeT takes \(Y^{\prime}=\{Y^{T_{H}}\}\in\mathbb{R}^{N_{ls}}\) as initial input and utilize TSA and SSA to convert \(Y^{\prime}\) into the decoding sequence \(Y_{de}\in\mathbb{R}^{N_{ls}\times d_{model}}\). Subsequently, CeT utilizes the temporal encoder-decoder attention (TEDA) and FNN to translate \(X_{en}\) and \(Y_{de}\) into the traffic \({Y^{T_{H}+1}}^{\prime}\) prediction at time step \(T_{H}+1\). Afterward, CeT updates the input of the decoder to \(Y^{\prime}=\{Y^{T_{H}},Y^{T_{H}+1}}\in\mathbb{R}^{2\times N_{ls}}\) and conducts the next round of decoding. After \(T_{P}\) iterations of decoding, the final prediction of \(Y\) is generated as \(Y^{\prime}\in\mathbb{R}^{T_{P}\times N_{ls}}\).
# Man-in-the-Middle Attacks in Mobile Ad Hoc Networks (MANETs): Analysis and Evaluation
## 4 Environment of Simulation  
### Setup of Simulation  
The main goal of our experiment was to investigate the performance of the MANET in the presence of harmful nodes launching MITM assaults. To simplify our simulation, this paper made use of the INET framework [29] and NETwork Attacks (NETA) [30], as depicted in Figure 4.  
The INET framework is an OMNeT++ model [31]-based open-source suite for MANET. NETA is a framework devised to imitate assaults in MANET by utilizing OMNeT++ and the INET framework. OMNET++ describes several modules (PHY layer and MAC layer, application layer) to satisfy the actual system manner.  
* Scenario one: Assailants are dispersed randomly throughout the system;
* Scenario two: Assailants are gathered in a fleet configuration.
## 1 Introduction
At the same time, with the promotion of the strategy of Large-scale Development of the Western Region and the Belt and Road, urbanization is accelerating in western China, bringing a problem; the scarcity of land resources is becoming more and more serious. As a new type of public transportation, the metro not only has the advantages of speediness and convenience but also can effectively use the underground space and make reasonable use of land resources. Therefore, it is inevitable to construct large-scale metro facilities in collapsible loess areas. The metro depot is the management center of parking, inspection, maintenance, operation, and repair of metro vehicles. It is a necessary facility to ensure the regular operation of the metro line and is also an indispensable part of the urban rail transit system. Due to its functional needs, a single metro depot construction covers a colossal area, usually 15-40 hm\({}^{2}\)[22]. As urban land resources are increasingly scarce due to population growth, the development and utilization of properties on the metro depot is the key to achieving the goal of efficient use of land resources, and it is also an important measure to increase the economic benefits [23; 24; 25]. Nowadays, many cities combine the exploitation of the metro depot with residence and commercial districts; the parking maintenance function is guaranteed under the cover plate, while dwelling or commercial constructions are developed on the cover plate, forming a transit-oriented development (TOD) mode [26; 27; 28]. Because of the functional requirements of the metro depot, the pile base span is large, usually about 12-24 m. The pile bases bear the load transferred by the superstructure, and the space between the pile bases is only for stopping trains whose load is small. Therefore, the large load is concentrated on the pile bases. This phenomenon will seriously affect the bearing capacity of the pile base. Most of the existing research on metro depots focuses on the influence of vibration produced by train operation on the surrounding buildings and measures of shock absorption [29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39]. The above research revealed the law for the bearing capacity of pile bases, which may prompt the innovation of pile base technology and contribute to the construction of infrastructures. Meantime, the innovation of pile base technology can improve the bearing capacity of buildings, which is conducive to the sustainable development of cities and can reduce the loss caused by climate change such as earthquakes, typhoons, and floods. However, there are few studies on the mechanical characteristic of adjacent pile bases under the metro depots in a collapsible loess region. Therefore, based on model tests, two adjacent pile bases under a metro depot are taken as an object to study their mechanical properties in collapsible loess stratum.
# Mixed Reality: A Reconsideration Based on Mixed Objects and Geospatial Modalities
## 4 MR Demonstration: Mixed Objects and Geospatial Modalities
### Geospatial Output Linking Modalities
#### 4.2.1 Object Output Languages  
1. Geovisualization Frameworks (Three.js). Geovisualization frameworks reside on top of 3D Graphics APIs and undertake the task of creating 3D digital worlds (scenes) with spatially referenced objects [6]. The Three.js Javascript library built on top of WebGL is employed for rendering the 3D Scene that hosts digital objects, mixed or virtual ones.
2. Image processing (additive blending, blackening). As already analyzed, a Rendered-Transparent Object acts as a 3D transparent mask of a Mapped Object. This is achieved by blackening the Mapped Object and applying the linear dodge blend mode [31], also known as Additive Blending. These simple techniques implement Mixed Object and achieve occlusion as shown in Figure 10, where the real building partially covers the virtual helicopter.
## 2 Wind Tunnel Test
### Data Processing Method  
Wind pressure fluctuations on the surface of models were measured by a multi-pressure measurement system (PSI DTC Intitium, ESP-64 HD, Intitium Inc., Tokyo, Japan). The sampling frequency of the system was 312.5 Hz, and a total of 20,000 samples were collected at each pressure tap. The uncertainty was estimated to be around \(\pm\)3% and \(\pm\)6% for mean and fluctuating wind pressure, respectively. The wind pressure on the models is expressed in the form of a non-dimensional pressure coefficient, as defined by Equation (1):  
\[C_{p,i}(t)=\frac{P_{i}(t)-P_{\infty}}{\frac{1}{2}\rho V_{H}^{2}} \tag{1}\]  
where \(C_{p,i}(t)\) is the wind pressure coefficient of tap \(i\) at time \(t\); \(P_{i}(t)\) is the measured pressure of tap \(i\) at time \(t\); \(P_{\infty}\) is the static pressure; \(\rho\) is the air density; and \(V_{H}\) is the wind speed at the top of models.  
The local force coefficient and base force coefficient \(C_{D,k}(t)\), \(C_{JD}(t)\) (along-wind) and \(C_{L,k}(t)\), \(C_{JL}(t)\) (cross-wind) were calculated based on the wind pressure measurement, as defined by Equations (2)-(5). It should be noted that when \(Re>10^{4}\), the surface viscous friction drag is negligibly small [19; 39]. Considering that the range of \(Re\approx 1.4\times 10^{5}\)-5.7 \(\times\)\(10^{5}\) in the present study, it is acceptable to omit any friction contribution:  
\[C_{D,k}(t)=\frac{\pi}{N}\sum_{i=1}^{N}C_{p,i}(t)\cos(\theta) \tag{2}\]  
\[C_{L,k}(t)=\frac{\pi}{N}\sum_{i=1}^{N}C_{p,i}(t)\sin(\theta) \tag{3}\]  
\[C_{JD}(t)=\sum_{k=1}^{M}C_{D,k}(t)(H_{k}/H) \tag{4}\]  
\[C_{JL}(t)=\sum_{k=1}^{M}C_{L,k}(t)(H_{k}/H) \tag{5}\]  
where \(N\) is the number of pressure taps at elevation \(k\); \(H_{k}\) is the corresponding tributary height; and \(M\) is the number of pressure tap layers.  
Figure 4: The relationship between wind speed and turbulence intensity on the surface of the platform center.
## 4 Discussion
They found that patients with lower bone density, as assessed by CBCT, had a higher probability of successful expansion compared to those with denser bone. This finding is consistent with previous studies suggesting that bone strength in the midpalatal suture region is a critical factor that should be considered before initiating treatment. The study by [PERSON] et al. [18] compares the skeletal and alveolar effects between hybrid and conventional expanders in growing patients, indicating that the use of microimplant anchorage reduces the negative impact on the bone morphology of posterior teeth but has not been investigated in the adult population. [PERSON] et al. [14] also highlight bone strength as a key factor, suggesting that the degree of ossification and the morphology of the midpalatal suture may predict treatment success. This finding is reinforced in both studies by the use of CBCT imaging to assess suture morphology before expansion. In the systematic review by [PERSON] et al. [25], they concluded that there is evidence of midpalatal suture opening after rapid maxillary expansion and long-term skeletal stability. One of the main limitations of this study is the lack of consensus in the scientific literature on the reliability of HU measurement using CBCT. Although CBCT has been used in numerous studies, the accuracy and consistency of HU measurements remain a matter of debate among experts, which may affect the interpretation of results [9,23]. Furthermore, the study was limited by the small sample size, which may restrict the generalisability of the findings to a larger population. Finally, another limiting factor is the lack of a long-term follow-up, which prevents the assessment of the effects and outcomes of interventions or treatments over a prolonged period. The results of this study have a number of important implications for the current practice of orthodontics. One the one hand, the age of the patients plays an important role in the success rate of the MARPE treatment. On the other hand, it was demonstrated that a lower density of the MNS and PNS have a direct impact on the MARPE treatment success. These values can be studied in the CBCT.