esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Problem Formulation  
As shown in Figure 1, we considered a UAV-assisted maritime IoT system where \(U\) UAVs with the same fixed flight height \(H\) were used as the aerial base stations to execute the data collection-offloading mission in the target area. The mission of the UAVs is to collect the hydrometeorological data sensed by \(M\) buoys with a random distribution in the target area and offload all the collected data to the OBS. The sets of UAVs and buoys are denoted as \(u\in\mathcal{U}=\{1,2,\ldots,U\}\) and \(m\in\mathcal{M}=\{1,2,\ldots,M\}\), respectively. The total mission completion time of the UAVs is divided into multiple equal time slots and is denoted as \(T_{\text{total}}=K\delta\), where \(K\) is the number of time slots, \(k\in\mathcal{K}=\{\infty,\in,\ldots,\mathcal{K}\}\) is the set of time slots, and \(\delta\) is the duration of each time slot. Note that, at each time slot, the UAVs need to choose the appropriate operation mode between data collection and data offloading according to the current network states. In each time slot, the UAV \(u\) cannot collect and offload data at the same time.  
The horizontal coordinate of UAV \(u\) at time slot \(k\) is denoted as \(q_{u,k}=\big{\{}x_{u,k},y_{u,k}\big{\}},\forall u\in\mathcal{U},k\in\mathcal{K}\). The flight direction of UAV \(u\) is limited by an angle of \(\psi_{u,k}\in[0,2\pi)\) and a velocity of \(V_{u,k}\in[0,V_{\max}]\), where \(V_{\max}\) is the maximum flight velocity of the UAV. The coordinates of UAV \(u\) can be expressed as \(x_{u,k}=x_{u,0}+\sum_{k^{\star}=1}^{k}V_{u,k^{\star}}\delta\cos(\psi_{u,k^{ \star}})\) and  
Figure 1: The UAV-assisted maritime IoT system.  
\(y_{u,k}=y_{u,0}+\sum_{k^{*}=1}^{k}V_{u,k^{*}}\delta\sin(\psi_{u,k^{*}})\), respectively. Considering the limitation of the flight range, \(x_{u,k}\) and \(y_{u,k}\) are respectively constrained by  
\[0 \leq x_{u,k}\leq x_{\max},\forall u\in\mathcal{U},k\in\mathcal{K} \tag{1}\] \[0 \leq y_{u,k}\leq y_{\max},\forall u\in\mathcal{U},k\in\mathcal{K} \tag{2}\]  
where \(x_{\max}\) and \(y_{\max}\) are the length and width of the target area, respectively. Moreover, the distance between UAV \(u\) and UAV \(u^{*}\) at \(=\) time slot \(k\) can be expressed as \(d_{u,u^{*},k}=\sqrt{\left(x_{u,k}-x_{u^{*},k}\right)^{2}+\left(y_{u,k}-y_{u^{ *},k}\right)^{2}}\). In order to avoid a collision, it is necessary to ensure the minimum safety distance \(d^{\min}\) between UAVs, which is expressed as  
\[d_{u,u^{*},k}\geq d^{\min}.\forall u,u^{*}\in\mathcal{U},u\
eq u^{*},k\in \mathcal{K} \tag{3}\]  
Denote the set \(i\in\mathcal{I}=\{0,1,\ldots,M\}\) as the set of OBS and buoys, where \(i=0\) means the OBS. The horizontal coordinate of buoy \(m\) and the OBS is \(\left(x_{i},y_{i}\right)\). Hence, the distance of the buoy-UAV and UAV-OBS at time slot \(k\) can be given by \(d_{u,i,k}=\sqrt{\left(x_{u,k}-x_{i}\right)^{2}+\left(y_{u,k}-y_{i}\right)^{2} +H^{2}}\).
## 4 Discussion
Laser surface modification did not show improved behavior regarding microgap formation compared with the control group. It has been postulated that laser surface modification may provide enhancement in the bonding of RBC CAD/CAM blocks [45; 46; 47]. Nonetheless, the laser parameters of the treatment such as average power, repetition rate, duration of irradiation, laser tip/surface distance, and the size of the irradiated area may induce differences in surface topography [48]. As SEM observations revealed in the current study, the laser-treated surfaces showed large, intact areas among the craters, which means that the surfaces were not uniformly modified to increase the micromechanical interlocking of resin cement materials in their irregularities. This might have an impact on the bonding between the material's surface and the luting agent, resulting in the formation of large microgaps comparable to those of the control group. Presumably, different laser parameters (i.e., increased irradiation time) may offer improved surface modification and bonding performance. The mechanism of action of the Er,Cr:YSGG laser involves high absorption of laser energy from water molecules, and as a consequence microexplosions, which lead to the removal of the particles from the surface of the restorative, especially inorganic components, and the creation of microporosities, as observed in the SEM images of this research [46]. The Er,Cr:YSGG laser beam has the advantage of the minimum generation of a thermal damaged layer, which is attributed to its hydrokinetic structure and the absence of smear layer formation [47]. Moreover, this laser wavelength is considered a less hazardous micromechanical treatment in comparison with sandblasting, due to the fact that Al\({}_{2}\)O\({}_{3}\) aerosols have been implicated in inducing detrimental effects on the human body [49]. Although the findings of this investigation revealed larger micropgap formation following laser treatment than when using the other mechanical surface treatments, in a previous report [50], the aforementioned treatments did not exhibit significant differences regarding bond strength. Additionally, it has been noted that erbium lasers are also capable of removing the glass phase of CAD/CAM materials, unlike sandblasting [51]. Some of the drawbacks of this in vitro research include the evaluation of only one restorative material, environmental conditions (temperature and humidity) that did not coincide with the oral conditions, the testing of only one laser protocol for the surface treatments, and the sectioning of the specimens for evaluating microgap formation using SEM. Presumably, a non-destructive microscopic technique such as microcomputed tomography could make more accurate measurements.
## 2 Research Progress in In Situ Road Health Monitoring Technology  
Strain is one of the important parameters of mechanical analysis and evaluation systems in the civil engineering field. At present, asphalt pavement damage (such as rutting, cracking, potholes, etc.) is caused by a variety of factors, and a few causes can be identified through an intuitive observation and analysis of the surface, but most distresses originate from the coupling of various forces on the surface layer and substructure. Repeated traffic loads cause large tensile strain at the bottom of the asphalt layer, leading to an overall decrease in the pavement bearing capacity of fatigue. The repeated compression of vehicles  
Figure 1: Road structure health monitoring.  
leads to the rutting of pavements, which causes permanent deformation. Therefore, it is necessary to understand the mechanical parameters, such as the mechanical response and strain under a combined action of various factors, of pavement structures. The assumptions made during pavement designs can be verified by monitoring and analyzing the response of different layers of the pavement under moving loads over time [13; 14]. At the same time, this may predict the occurrence of fatigue or rutting so as to provide empirical support for the effective evaluation of the service condition of pavements, early damage detection, estimations of the remaining working life, and the design of pavement maintenance [11; 15; 16].  
Over the past many years, researchers have made great efforts to monitor the strain status of asphalt pavements. The harsh construction process of asphalt pavements leads to the easy inactivation of ordinary sensors during installations or loads. The sensors need to be able to withstand high temperatures (up to 150-170 \({}^{\circ}\)C), humidity, high pressure, and repeated overloading. It must be noted that most conventional sensors used in other civil structures cannot be used directly for the interior structures of pavements [16]. In recent years, scholars have carried out a lot of research on optical fiber Bragg grating sensors (FBGs), strain gauges, piezoelectric sensing elements, embedded vibrating string strain gauges, and other sensors in the field of mechanical property monitoring for pavement structures. Different sensors have various abilities for sensing physicochemical parameters related to the health of civil structures. At present, sensors used for deformation monitoring are generally designed based on the principles of strain measurements.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### High-\(k\) Dielectric and Metal Gate (HKMG)  
In order to satisfy the aggressive down-scaling of metal oxide semiconductor (MOS) transistors a large effort was devoted to use new gate materials to solve the unendurable integrating problems involved in producing chips [58; 59; 60; 61]. Therefore, both new high-\(k\) gate dielectric and metal gate electrode materials were sought. The exploration for new high-\(k\) dielectrics was focused on resolving boron penetration and polysilicon depletion issues. The other important concern was also compatibility of high-\(k\) dielectrics with new metal electrodes which were required to replace the traditional polysilicon gate electrode used in nMOS and pMOS transistors [62; 63; 64].  
In this part, the material choice of high-\(k\) dielectric and metal gate for different technology nodes are presented and then atomic layer deposition (ALD) of metal gate, which is the main challenge beyond 14 nm technology node due to the much high aspect ratio is discussed.
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 4 Experimental Characterization
### Silicon Nitride Device  
The internal shifters on the fabricated silicon nitride device showed an average I\({}_{\pi}\) of 10.2 mA with an average resistance of 2.8 k\(\Omega\), while the external phase shifters had 1.4 k\(\Omega\) of resistance and an average I\({}_{\pi}\) of 14.5 mA; all were evaluated for operation at 1550 nm. The average P\({}_{\pi}\) resulted to be 296 mW. MZIs have an average loss of 1.5 dB and an average ER of 12.8 dB. After a careful analysis it has been found that both the excess loss and the relatively limited ER are mainly due to the directional couplers, which showed excess losses and unbalancing from the nominal 50/50 splitting ratio. The PIC exhibits a 3 dB bandwidth of about 40 nm, as reported in Figure 7a, showing the normalized transmission on the longest path as a function of the wavelength. In addition, the IL at 1550 nm along the same path is about 7.5 dB plus 3 dB from the two crossed spot size converters. Figure 7b,c report the normalized transmission at 1550 nm of the best and the worst MZI from the ER standpoint (namely, MZI 4 in cross state and MZI 7 in bar state), with an extinction of 16.1 and 7.5 dB, respectively.  
The impact of this silicon nitride processor on the bit resolution of analog signals has been then evaluated by acquiring and analyzing the spectra of the sinusoidal signal crossing the PIC with different configurations. Figure 8 reports the bit resolution reduction as a function of the CW laser power normalized to the reference signal at the maximum laser power. The graph includes the reference curve, together with the ENOB reduction for the best and worst paths in the device, i.e., \(I_{3}\to O_{4}\) and \(I_{4}\to O_{1}\) in Figure 1, respectively. The ENOB reduction for the best path sits in the range 1.3 - 2.4 with respect to the reference curve, while the variation due to different paths is \(<\)1. As expected and similarly to the previous case, the PIC did not introduce spectral distortions, with a THD degradation \(<\)1 dB and the ENOB reduction mainly due to optical losses and related SNR decrease.  
Figure 5: Characterization of silicon-on-insulator-based 4 \(\times\) 4 linear optical processor. **(a)** Optical processor bandwidth on the longest path; **(b)** normalized transmission of MZI 6 in cross state; (c) normalized transmission of MZI 9 in bar state.  
Figure 6: ENOB reduction as a function of laser power for the reference signal, and the best and the worst path in the silicon-on-insulator-based 4 \(\times\) 4 linear optical processor.
## 3 Results  
As mentioned above, the algorithms were selected based on their open-source availability and independence from any additional input information source other than a monocular RGB frame sequence. Following the primary taxonomy described in [9; 21], we selected the classic sparse-indirect system ORB-SLAM [54], the classic dense-indirect system DF-ORB-SLAM [16], the classic dense-direct system LDS-SLAM [29], and the classic sparse-direct method DSO [21]. Then, we added to this study the currently available ML implementations of the ORB-SLAM2 [54], DSO [21], and SVO [13], which are the DynaSLAM [32], CNN-DSO [15], and CNN-SVO [11]. Additionally, the direct proposals derived from the DSO system were included due to the impressive reconstruction results that this method showed during experimental evaluation; thus, the LDSO [30] and DSM [31] systems representing the addition of loop closure and SLAM capabilities for the DSO system were selected. Figures 2 and 3 present some examples of the evaluated algorithms' execution on the outdoor and indoor sequences of the TUM-Mono dataset.
# Analysis of Passive RFID Applicability in a Retail Store: What Can We Expect?
## 1 Introduction
Even when this incident RF power is constant, and the RFID system is stationary and positioned in the environment with no obstacles whatsoever, the system performance depends on power level, frequency of operation, and orientation of the tag with respect to the reader. This is due to chip and antenna characteristics. Here we focus on the variation of power impinging on the tag and a mixture of material properties surrounding the tag. In a retail store there is a wide variety of propagation phenomena due to a complex environment full of furniture, shelves, refrigerators, and products made of materials with different electrical properties. To characterize the retail store as an IoT environment, a lot of measurements and simulations with the help of fundamental theory are needed. It is virtually impossible to calculate and take into account each product's size, material, and propagation effects for every retail store. Therefore, the empirical study is performed to characterize the application of RFID in a retail store from an EM point of view. The contribution of this study is the evaluation of tag responsiveness in a real retail store by EM characterization of a multipath channel and tag antenna radiation properties. RFID signal propagation phenomena in a store simulation model equipped with standard retail furniture (shelves, desks, refrigerators, etc.) are investigated for different scenarios, from empty shelves to shelves with products of different electrical properties. In addition to simulations, measurements performed in a proof-of-concept manner are used to estimate the technology advancement, i.e., tag performance for different generations of chips. The tag attached to different retail store products is analyzed, and the key performance drawbacks are pointed out. In a real retail store, the EM properties are evaluated by the measured number of tag responses in line-of-sight (LOS) and non-LOS area, and with the tag attached to retail products. The first part of the paper, Section 2 deals with the fundamental aspects of the RFID system and points out the main EM effects of retail products on tag antenna radiation properties. Section 3 focuses on a retail store simulation model, different products' materials, different receiver setups, and visualization of EM propagation phenomena. Here, different generations of chips are compared by their sensitivity level to how it affects the reader coverage in the store. The measurement results are given at the end of this section, and tag responsiveness is evaluated by the EM characterized phenomena.
## 3 Model Assumptions
### Energy Consumption Model  
The underwater energy consumption model provided in [52] is employed in the paper. This paper assumes \(P_{0}\) is minimal power that a node needs to receive packets, and minimal transmitting power should reach \(P_{0}A(l)\), where \(A(l)\) denotes an attenuation function. The energy consumption for transmitting and receiving can be calculated by:  
\[E_{t}(l)=T_{t}P_{0}A(l) \tag{1}\]  
\[E_{r}=T_{r}P_{0} \tag{2}\]  
\[A(l)=l^{1.5}a^{l} \tag{3}\]  
\[a=10^{4(f_{c})/10} \tag{4}\]  
\[\alpha(f_{c})=0.11\frac{{f_{c}}^{2}}{1+{f_{c}}^{2}}+44\frac{{f_{c}}^{2}}{4100+ {f_{c}}^{2}}+2.75\times 10^{-4}{f_{c}}^{2}+0.003 \tag{5}\]  
where \(E_{t}\) (\(l\)) is the energy consumption for transmitting, and \(E_{r}\) is the energy consumption for receiving. \(T_{t}\) is the time for nodes to send packets, and \(T_{r}\) is the time to receive packets. \(l\) is the distance between transmitting nodes and receiving nodes. \(\alpha(f_{c})\) is the absorption coefficient in \(\mathrm{dB/km}\) and \(f_{c}\) is the frequency in kHz.
# Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
## 4 Performance Evaluation
### Simulation Setup  
We employed the mmWave ns-3 module and mmWave protocols to evaluate network protocols, such as TCP and SCTP throughout the 5G mmWave environment. The attributes were configured accordingly as needed in the simulation. The details of attributes concerning the mmWave are found  
Figure 3: DT visualization.  
in the file named \"mmWaveAttributesList\", whereas the objects are illustrated in the file named \"MmWaveHelper\", where the method is provided [79; 80]. mmWave was stacked through ns-three endpoints that included \"UEs\" and \"eNBs\". The UEs were connected to E-UTRAN Node B (eNB), where both TCP and SCTP generated traffic throughout connections. The bandwidth uplink was 500 Mbps, and the downlink was 1 Gpbs where the \"UEs\" delay was 1 ms. Then, the message transmission unit (MTU) size was 1500 bytes and the chunk size was 1468 bytes. Next, each eNB was connected to a group of 20-30 UEs. The eNBs were linked to Serving Gateway (SGW) and the core gateway Packet Data Network Gateway (PGW), the bandwidth of the upload link was 5 Gbps, with 5 ms delay. Both bandwidth and delay for the download link had the same upload link. PGW was employed as an HTTP cache server. It was connected to the remote host over a bidirectional connection. The bandwidth connection was 10 Gbps with a 10 ms delay. The drop-tail was used for the queue management scheme and active queue management. The mmWave carrier frequency was 28 GHz, and the mmWave bandwidth was 1 GHz, as presented in Figure 4.
# Automotive Cybersecurity Application Based on CARDIAN
## 4 Case Study
### Three-Step Algorithm  
The intrusion detection methodology consists of three phases and is described in patents No. 102021000009548 and No. EP 22168635.5 (see Figure 2).  
Figure 1: Automotive domain ontology.  
The first phase includes the acquisition of data within time windows of standard duration. Within these windows, it may happen that multiple messages containing the same information may be repeated; in this case, the data that will be forwarded to the second step will be the result of the averaging operation between these. On the contrary, if the data are not present within the time window, they will be replaced by the value 0.  
The second phase concerns the identification of the driving scenario (e.g., urban driving, motorway, etc.). The sequence of data produced by the first phase is analyzed and compared with the predefined scenarios using the Jaccard index. At that point, the scenario that will be used as a reference will be the one whose Jaccard index has the highest score.  
The Jaccard index, also known as the Jaccard coefficient, is defined, for two sets called \(A\) and \(B\), by the following equation:  
\[J=\frac{\left|A\cap B\right|}{\left|A\cup B\right|} \tag{1}\]  
The identification of the scenario allows the selection of the Bayesian network to be used, and more precisely, the values contained in the a priori and conditional probability tables, which describe each node of the network.  
The third phase involves the use of the Bayesian network. The data produced by the first phase are fed to the Bayesian network, which will provide as output the probability that this data set was determined by an attack.
## 4 The Proposed DSB Workflow Scheduling Algorithm
### Proposed Algorithm
#### 4.4.1 Task Prioritization  
In the first step which is called task prioritization, we propose a task ordering strategy for optimizing task assignment. First, the tasks of the workflow are assigned priorities in order to guarantee task dependencies. For assigning priorities to the workflow tasks, their upward ranks [29] are calculated by Equation (6). The priority of tasks calculated using this technique has several benefits, which make it a worthy candidate for the prioritizing phase. Its implementation is simple. It considers the execution time of the workflow tasks, the transfer time between each pair of the tasks and the critical path of the workflow. Besides, this technique ensures that every task of the workflow has a higher priority over its successor tasks. It is calculated recursively for each task, beginning from the ending task to the first task of the workflow. The degree of dependency of each task is calculated by Equation (7). The tasks in the workflow having higher number of inward and outward edges have higher priority than others for scheduling them on the available VMs. Finally, the task scheduling sequence after the first phase, contain tasks sorted in descending order of their priorities. By assigning a priority to each task of the workflow, this method creates tasks with higher priorities earlier than the other tasks.