esa-sceva/satcom-chunk-collection huggingface dataset:
# Decentralized Blind Spectrum Selection in Cognitive Radio Networks Considering Handoff Cost
## 2 System Model  
The channel model of a cognitive radio network with \(C,C\geq 2\) independent and orthogonal channels that are licensed to a primary network following a synchronous slot structure is illustrated in Figure 1. We model the channel availability \(W_{i}\) as an i.i.d. Bernoulli process with mean value \(\beta_{i}\in\text{B}\): \(W_{i}\sim B(\beta_{i})\), in which \(W_{i}(t)\) denotes the \"free\"(denoted by 1) or \"busy\"(denoted by 0) state at time \(t\) of channel \(i\). The SUs can access the free slot, which will yield a handoff delay if they access a channel that they did not access in the previous slot.  
Figure 1: Slotted channel model.  
The cognitive radio network is a composite of \(M\) secondary users. They access the channels in a decentralized way, i.e., there is no centralized entity collecting channel availability and channel state information (CSI) and then dispatching channels to SUs. In a slot cognitive radio network, a centralized entity will heavily impact the network performance. Regardless, we assume each SU has a pre-allocated rank that is dispatched by the network when the network forming or SU joins the network. For simplicity, we assume the priority of \(SU_{j}\) is ranked by \(j\), i.e., the priority of \(SU_{p}\) is higher than \(SU_{q}\) if \(p<q\) for either type of application. This rank is prior to learning and transmission processes and will not be changed afterwards.  
The SUs behave in a proactive way to access the channels: They record past channel access histories and then utilize them to make predictions on future spectrum availability following a given policy. In addition, because of the inclusion of the handoff delay, SUs have to make the choice of either staying foregoing spectrum with relatively low availability or handing off to a spectrum with higher availability and tolerating the handoff delay. We denote the proportion of the handoff delay to the entire slot as fixed handoff cost \(H\) for simplicity.  
The cognitive radio frame structure is shown in Figure 2. At the beginning of the frame, an SU chooses a channel to sense. Once the sensing result indicates that the channel is idle, the SU transmits pilot to receiver to probe CSI. The CSI is fed back through a dedicated error-free feedback channel without delay. The length of the data transmission is scalable and will be adopted by a transceiver according to the handoff delay or the data length in this scheme. At the end of the frame, the receiver acknowledges every successful or unsuccessful transmission as \(Z_{i,j}(k)=0\) for a collision that occurs; otherwise, it is 1.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 2 Technology Enablers for the Internet of Verticals
### Wide Area Deterministic Networks  
When a network can provide end-to-end ultra-reliable packet transmission with bounded small values of latency/jitter, it is said to be a deterministic network. This concept is illustrated in Figure 1. In some industry applications, the deterministic networks are also required to provide precise end-to-end time synchronization. One of the most important features of 5G is Ultra Reliable Ultra Low Latency (URLLC) communications, e.g., for smart and fully automated manufacturing. As an example, consider mobile robots such as Automatic Guided Vehicles (AGVs), which have numerous applications in the future factories. They are usually monitored and controlled by a guidance control system. The mobile network is the most promising communication technology due to the large-scale mobility of the vehicles. The communication in some mobile robot applications may require the transmission latency to be between 1 to 10 msec and jitter to be less than 50% of latency. Furthermore, the reliability is required to be above six nines (99, 9999%). Another yet more stringent example in industry is the motion control, which is responsible for controlling the moving and/or rotating parts of the machines in a critical manner.  
With respect to the 5G mobile networks operating on top of the TCP/IP suit of protocols, the application scenarios described above can only be realized for localized networks. The queuing in forwarding nodes may introduce large latency and jitter. The theoretical reliability of a single device is usually below six nines, which, even without packet loss, cannot meet the required reliability of the end-to-end path. For URLLC local area networking (e.g., inside a single factory plant) the Time Sensitive Networking standardized in IEEE 802.1 [17] and industrial Ethernet technologies can be used. Furthermore, staring with the Release 16, the 3 GPP is progressing a new study on the NR Industrial Internet of things (IIoT), targeting the integration of the 5G and TSN networks.  
However, the wide area deterministic networking for evolved vertical applications, including the distributed cloud manufacturing and wide-area smart grids, would require  
Figure 1: Latency performance of the traditional IP networks (**left**) is contrasted with the performance of future wide area/large-scale deterministic networks (**right**).  
expanding the capabilities of TCP/IP [18; 19]. The ITU-T Focus Group Technologies for Network 2030 (FG NET-2030) [18] and The ETSI Industry Specification Group on the Non-IP Networking (NIN) [19] have identified key technical issues with the current TCP/IP-based networking, which prevent it from delivering the required levels of deterministic services in a wide area and mobile networks, and are currently developing the approaches of TCP/IP evolution to address these.
# Identifying Mis-Configured Author Profiles on Google Scholar Using Deep Learning
## 4 Comparison between Reliable Author Profiles and Mis-Configured Author Profiles
### Features of an Author Profile
#### 4.1.2 Features of Evaluation Metrics  
* H-index, i10-index [14]: H-index and i10-index are indicators of the number of high-quality papers, which are important to quantify the actual productivity and apparent scientific impact of a scholar. Scholars with higher h-index are considered to have greater impact. They tend to pay more attention to their author profiles. However, h-index can also measure the productivity of the scholar. Higher h-index means more papers, which may lead to a higher possibility of mis-configuration.
* #citations: The feature is the total number of citations earned by all the papers in the paper list. The impact of this feature is similar to h-index.
* citation-std: The citation of each paper can reflect its quality to some extent. So, we calculate the standard deviation of the numbers of citations of all papers. Intuitively, the quality of the papers written by a scholar may be similar.
## 3 Materials and Methods
### GC-MS Analysis of Volatile Arona Compounds  
Three grams of EVOO were put into a 20 mL glass headspace vial, and then spiked with 30 \(\upmu\)L of internal standard solution (menthol at 0.057 mg/g; _w/w_ in seeds oil). The headspace in the vial was equilibrated at 40 \({}^{\circ}\)C for 5 min. and the volatile aroma compounds were extracted at 40 \({}^{\circ}\)C for 30 min. The headspace was sampled using 2 cm divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 \(\upmu\)m fibre, purchased from Supelco (Bellefonte, PA, USA). The volatile aroma compounds were desorbed in the GC inlet at 250 \({}^{\circ}\)C for 4 min. in splitless mode, and the fibre was reconditioned for 7 min. at 270 \({}^{\circ}\)C, prior to each analysis. Measurements were made while using a Thermo Trace GC Ultra gas chromatograph coupled to a Thermo Quantum XLS mass spectrometer Thermo Scientific (Milan, Italy), which was equipped with a PAL combi-xt (CTC, Zwingen, Switzerland) autosampler with a SPME option. A VF-wax capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Agilent Technologies) was used. The GC oven temperature gradient was starting from 40 \({}^{\circ}\)C for 4 min., 6 \({}^{\circ}\)C/min. up to 250 \({}^{\circ}\)C, and held for 5 min. Carrier gas was helium at the constant flow rate of 1.2 mL/min. The transfer line and the MS ion source were both set at 250 \({}^{\circ}\)C. Electron ionization was applied at 70 eV with an emission current of 50 mA. Mass spectra were recorded in centroid full scan mode at a scan time of 0.200 s from 30 to 350 _m/z_. Thermo Excalibur software (2.2 SP1, 48, Thermo Scientific) was used for all acquisition control and data processing. Figure S1 reports representative chromatograms. Volatile aroma compounds were identified by comparing the retention times and mass spectra with those of standards, and with mass spectra from NIST 2.0, Wiley 8, and FFNSC 2 (Chromaleont, Messina, Italy). Linear retention indexes (relative to C7-C24 _n_-alkanes) were calculated and then compared to those from the literature. Semi-quantitative analysis was carried out and the concentrations of EVOO volatile aroma compounds were expressed as equivalents of the internal standard menthol in mg/kg of oil.
## 2 Materials and Methods  
### Study Area and Scope  
The study area is located in the southeast of Togo, in West Africa (Figure 1), with 35,296 habitants as per the 2022 census data. The focal point of the study was the ecovillage of Donomade. It is located about 130 km from Lome, the capital of Togo. The geomorphology of the Donomade zone consists of a peneplain covered by infertile soils degraded by intensive agriculture and slash-and-burn cultivation [22]. The ecovillage is included in the transboundary Mono River biosphere [23; 24]. The climate is subequatorial Guinean with 1000 mm.y\({}^{-1}\) annual average rainfall and an average temperature of 27.6 \({}^{\circ}\)C [25]. Five (05) land cover types comprise the landscape components: croplands, fallows, and parklands (72.64%), followed by woody savanna and open forest (21.70%) and build area (3.80%), are the most important land cover features in the study area. The main crops grown are maize, cassava, and groundnut. New agroecological practices have spread from the Donomade model farm (FeMoDo) to the surrounding area in recent years.  
A zoning approach was adopted to assess the availability of plant materials and their accessibility for mulching purposes. A series of concentric buffer zones with radii of 200 m, 2 km, 5 km, and 10 km were designed, with the model farm at the center, using the geoprocessing tools in QGIS software (Figure 1).
## 4 Three-Dimensional Modelling by the SfM-MVS Approach
### Multiple View Geometry  
In a pin-hole model, a 3D point \(X\) is projected to an image point \(x\) as:  
\[\lambda x=PX, \tag{8}\]  
Figure 8: From a collection of stereoscopic images to a 3D model.  
where \(\lambda\) is the scalar that represents the inverse depth of the 3D point, and \(P\) is called the camera matrix and can be decomposed as:  
\[P=K[R|t], \tag{9}\]  
where \(R\) is the rotation matrix relative to the camera orientation, \(t\) represents a 3D translation vector relative to the position of the camera centre, and \(K\) represents the intrinsic calibration matrix representing the projection properties of the camera [80].  
Thus, epipolar geometry describes the geometric relationships that exist between the stereo images and that depend only on the internal parameters of the cameras and their poses [81].  
Considering point \(X\) in three spaces imaged in two views, at \(x_{1}\) in the first and at \(x_{2}\) in the second, it is necessary to stabilise the relationships between these two points. Figure 9 shows a representation of epipolar geometry; the distance between the two camera centers, \(C_{1}\) and \(C_{2}\), is called baseline; the intersection of the baseline with the image planes generates the epipoles, \(e_{1}\) and \(e_{2}\). The lines \(l_{1}\) and \(l_{2}\) are called epipolar lines, and the plane containing the baseline and the epipolar lines is called the epipolar plane.  
Two-view geometry can always transform the cameras with homography to simplify the treatment; moreover, the homographic transformation is a rigid translation. Therefore, one approach is to place the origin of the reference system and the axes in the first camera. In this condition, it is possible to write the epipolar constraint equation:  
\[x_{2}^{t}\;F\;x_{1}=0, \tag{10}\]  
where \(F\), the fundamental matrix, is \(k_{2}^{-t}\;S_{t}R\;k_{1}^{-t}\). \(S_{t}\) represents the skew-symmetric matrix.  
The camera matrices, if the internal calibration parameters (\(k_{1}\) and \(K_{2}\)) are not known, are definable only up to a projective transformation (angles and distances not respected); otherwise, with a known calibration, the reconstruction will be metric.  
In the latter case, the fundamental matrix for the coordinates normalized by the calibration is called the essential matrix and is usually denoted \(E\) instead of \(F\) to emphasize that it is the calibrated case; the image coordinates are normalized, and the equation of the epipolar constraint becomes \(x_{k_{2}}^{t}\;E\;x_{k_{1}}=0\).  
Figure 9: Fundamentals of epipolar geometry.
# Intersection and Complement Set (IACS) Method to Reduce Redundant Node in Mobile WSN Localization
## 5 Evaluation
### Computation Cost  
The computation cost is the cost to process valid sample production, as has been elaborated in Section 4.3.2. The overall computation cost of the localization process increases for every valid sample taken from each blind node's neighbors and is used for the weightage calculation of the blind node samples. Measurement of computation cost is stated in the following equation:  
\[comp\ unit=\sum_{i}^{n}s_{q_{i}} \tag{10}\]  
\(n\) represents the total number of the first and second hop neighbors of the blind node, while \(s\) is the number of samples of each neighbor node \(q\). One computation unit represents one valid sample point obtained from a neighbor node for the blind node sample's weightage calculation.  
Therefore, the computation cost depends on the number of neighbor nodes and the number of samples used for the localization process. MSL* requires a high computation cost due to the usage of all nodes in the network but is outperformed by MCL in terms of convergence speed and sampling [4]. LCC then manages to decrease the computation cost by reducing the number of nodes being used for localization. Thus, implementing IACS will further decrease the computation cost.
# Enhanced LoRaWAN Adaptive Data Rate for Mobile Internet of Things Devices
## 5 The Adaptation of Proposed Schemes in a LoRaWAN Deployment  
The typical ADR of LoRaWAN requires the operation of both the NS and the ED. In general, to deploy the new ADR in the ED, the so-called firmware update process must be performed. These firmware updates are usually performed through a wired connection, such as serial communication, to the target ED. However, because of the nature of the massive LoRaWAN, this method is challenging. Therefore, STM, one of the LoRa Mote makers, supports firmware update over the air (FUOTA) [43; 44]. FUOTA is an excellent approach to deploy the ADR algorithm to the ED. However, our proposed methods (i.e., G-ADR and EMA-ADR) are the improved ADRs on the network server-side and do not require ED update technology like FUOTA. Therefore, the proposed ADRs can be deployed by updating the server itself as part of its maintenance work.
## 2 Materials and Methods
### Material Characterization
#### 2.2.1 Orthotropic Yield Locus  
The Lankford value (r-value) [37] is a material parameter widely used to evaluate the out-of-plane plastic anisotropy of sheet metal. It is defined as the ratio of the plastic strain in the ortogonal direction of the applied load to the plastic strain in the thickness direction (out-of-plane) while performing a uniaxial tensile test [38].  
Quasi-static tensile tests at a strain rate of 0.004 s\({}^{-1}\) is performed in \(r_{0^{\circ}}\), \(r_{45^{\circ}}\) and \(r_{90^{\circ}}\) to the rolling direction for 0.8 mm blank thickness. From this experimental work-hardening test, the planar orthotropy of the yield strength and Lankford coefficients are extracted, as shown in Figure 2.  
The resulting parameters for the material selected are listed in Tables 1 and 2.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & \multicolumn{1}{c}{**Thickness**} & \multicolumn{1}{c}{**Youngs**} & \multicolumn{1}{c}{**Yield**} & \multicolumn{1}{c}{**Tensile**} & \multicolumn{1}{c}{**Uniform**} \\  & **Thickness** & **Modulus** & **Stress** & **Strength** & **Elongation** \\
**Test** & **(mm)** & **(GPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(\%)** \\ \hline
0\({}^{\circ}\) & 0.8 & 167 & 358 & 718 & 46.9 \\
45\({}^{\circ}\) & 0.8 & 158 & 341 & 657 & 53.4 \\
90\({}^{\circ}\) & 0.8 & 152 & 337 & 678 & 55.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of material properties.  
The yield strength in equibiaxial tension is also an input for building the yield locus. To fullfill the yield locus, a modified Miyauchi test [39] with grooves in the shear deformation zone is used. [PERSON] suggested a simple shear test with two shear zones. As shown in Figure 3, the specimen has three bars that are all fixed or clamped to the universal machine.  
These three bars are connected by the regions that will be analyzed. When the middle bar is loaded, the connecting regions will be deformed by shear deformation. One of the advantages of this test is that this geometry reduces the rotation of the shear zone during loading. From this test, the yield limit and flow stress in pure shear are obtained together with the yield strength in shear.  
A tensile test with a wasted specimen (plane strain tension in the center of the specimen) is used to iteratively calibrate the size of the yield locus in plane strain tension (mainly by changing the exponent of the yield locus). In Figure 4, experimental results are shown together with the yield locus described by Equation (2).
## 2 Related Works
### Multihop Communication in LoRa Networks  
Multihop communications are used to extend the connection distance over the communication range of single-hop communications. To enable coverage extension in IoT LoRa Networks based on single-hop communications, many studies have been published in IoT LoRa networks. The first multihop LoRa network was investigated in [32]. As a result, when a data packet was broadcast, all neighbouring end devices checked its origin in terms of hop distance and relayed the messages if they were closer to the sink. The algorithm allowed the use of beacons for time synchronization as well as for the communication distance between each end device and the GW. However, it caused a lot of packet redundancy and delay, which slowed down network performance. [PERSON] et al. [19] investigated the use of multihop LoRa topologies to enable energy-efficient connectivity in smart city applications. In this study, packet reception rates for various source-to-destination distances, SFs, and transmission powers were used to evaluate single-hop and multihop LoRa topologies. Then, the results of the study demonstrated that two-hop networks could significantly outperform single-hop networks in terms of range. However, that study did not go into detail about the best relay placement to influence range extension for distant end devices and the powerful impact. In [33], the authors proposed a multihop communication scheme called FSRC based on a selective relay operation. By controlling communication parameters, the relay control strategy of the FSRC maximized both coverage probability and minimum success probability for all SF regions. Like other similar schemes [34; 35], they introduced a programmed e-node and an implicit relay node to act as a transparent range extender with an overhearing operation, respectively. With the primary goal of replicating the same packet sent by distant end devices, they received it and relayed it to the GW by overhearing. However, these implementations generated a large amount of network traffic from broadcasts, resulting in performance degradation.  
In [36; 37], an energy-efficient multihop communication scheme called e2 MCH and a LoRa mesh networking system for large-area monitoring of IoT applications were studied. The research presented in these papers introduced multiple relay devices for collecting data from IoT sensors spread across a large geographical area and forwarding them to a GW in close proximity. The research, however, was vulnerable to packet collisions, which reduced the reliability and energy efficiency of LoRa networks. The proposed SFPCR scheme employs a centralized optimum relay assignment scheme that iteratively chooses the best relay LoRa device from the candidate set of devices in the LSFZ based on both packet success probability and residual energy, to transmit a data packet from the subspace clusters in the HSFZ. Consequently, the proposed multihop approach offers a practical framework for reducing the transmission distance and transmission power required by distant nodes with large spreading factor values.
## 4 Proposed Model Description
### Priority Conditions  
As explained so far, the model lacks an ability to create scenarios such as priority right/left conditions, giving it to crossed lines while turning on a junction, or conditions required for operating a roundabout junction. For this purpose, the \"crossed\" lane object has been developed.  
The \"crossed\" property may be assigned to any lane type object, along with the specification of another lane, which traffic is considered superior to the said \"crossed\" lane. Any lane can comprise multiple \"crossed\" lanes. Then, the traffic transferred by this lane is only allowed to proceed if all the \"crossed\" lanes are empty and its signal is green, if assigned.  
Using the crossed lane feature in the model, it is possible to construct all the discussed scenarios in a systematic manner. The use of the crossed lane feature is illustrated in Figure 4. On the very left, the utilization as a priority right condition is shown, see Figure 4a. The cars traveling on the red lane are allowed to leave it (if these intend to continue straight ahead) only if there are no cars traveling on the green lanes (assigned as crossed lanes to said lane). The example depicted in the middle, see Figure 4b, illustrates the same condition as one for modeling priority to vehicles on the crossed lane when turning left.  
On the far right, see Figure 4c, the last mentioned example captures the construction of a roundabout. In this case, cars on the red lanes give priority to cars traveling on the green lanes, since the European legislation dictates priority left.  
Further, the priority conditions shall be applied only to sections of the road which, when conducting traffic, generate a possibility of an accident. There is, however, no reason to give priority rights to vehicles which are too far to become a threat.
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 6 Clock and Data Recovery (CDR) Circuits
### CDR Examples
Therefore, the loop latency introduced by the digital filter becomes tens of multiples of the bit period, since the latency of the digital filter is a few multiples of the digital clock period. In [89], a direct path without the filtering process, which provides only a phase adjustment, is separated from the main loop. As a result, fast phase response is maintained while the  
Figure 44: Dual loop CDR with integration-based front-end presented in [78]. drawbacks of analog loop filter are overcome. The block diagram of the receiver presented in [89] is shown in Figure 45. The receiver fabricated in 65-nm CMOS achieves a data rate of 26.5 Gb/s while consuming 254 mW. Notably, the all-digital approach becomes more effective as CMOS technology continues to scale down.