esa-sceva/satcom-chunk-collection huggingface dataset:
## Vision-Aided RAIM: A New Method for GPS Integrity Monitoring in Approach and Landing Phase  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1 and [PERSON] 2**  
1 School of Electronic and Information Engineering, Beihang University, Beijing 100191, China; E-Mails: [EMAIL_ADDRESS] (L.F.); [PERSON]DDRESS] (R.L.); [EMAIL_ADDRESS] (X.C.)  
2 School of Civil and Environmental Engineering, UNSW Australia, Sydney NSW 2052, Australia; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-135-0138-8080; Fax: +86-10-8233-8282.  
Academic Editor: [PERSON]  
_Received: 19 March 2015 / Accepted: 8 September 2015 / Published: 10 September 2015_  
**Abstract:** In the 1980s, Global Positioning System (GPS) receiver autonomous integrity monitoring (RAIM) was proposed to provide the integrity of a navigation system by checking the consistency of GPS measurements. However, during the approach and landing phase of a flight path, where there is often low GPS visibility conditions, the performance of the existing RAIM method may not meet the stringent aviation requirements for availability and integrity due to insufficient observations. To solve this problem, a new RAIM method, named vision-aided RAIM (VA-RAIM), is proposed for GPS integrity monitoring in the approach and landing phase. By introducing landmarks as pseudo-satellites, the VA-RAIM enriches the navigation observations to improve the performance of RAIM. In the method, a computer vision system photographs and matches these landmarks to obtain additional measurements for navigation. Nevertheless, the challenging issue is that such additional measurements may suffer from vision errors. To ensure the reliability of the vision measurements, a GPS-based calibration algorithm is presented to reduce the time-invariant part of the vision errors. Then, the calibrated vision measurements are integrated with the GPS observations for integrity monitoring. Simulation results show that the VA-RAIM outperforms the conventional RAIM with a higher level of availability and fault detection rate.  
keywords: GPS; RAIM; aviation navigation; approach and landing phase; computer vision; VA-RAIM +
Footnote †: journal: Journal of Sound and Vibration
# Distributed Localization of Non-Cooperative Targets in Non-Coplanar Rendezvous Processes
## 2 Distributed Localization Method
### State Model  
This paper describes the state of the target under the inertial system. The target state under the J2000 coordinate system is set as follows:  
\[\mathbf{X}=\begin{bmatrix}\mathbf{r}&\mathbf{v}\end{bmatrix}^{\text{T}} \tag{1}\]  
where \(\mathbf{r}=[x\quad y\quad z]\) and \(\mathbf{v}=[v_{x}\quad v_{y}\quad v_{z}]\) are the position vector and the speed vector in the J2000 coordinate system, respectively.  
Due to the short maneuver time, only the perturbation of the harmonic term \(J_{2}\), which has the most significant effect on the target state, is considered. The state equation of the target is given as follows:  
\[\dot{\mathbf{X}}=\begin{bmatrix}v_{x}\\ v_{y}\\ v_{z}\end{bmatrix} \tag{2}\]  
where \(\mu\) is the Earth's gravitational constant; \(R_{e}\) is the average radius of the Earth; \(r\) is the geocentric distance of the satellite; and \(f_{x}\), \(f_{y}\), and \(f_{z}\) are the effects of higher-order terms of the earth's non-spherical perturbation, sun-moon perturbation, solar pressure perturbation, and atmospheric perturbation.  
By linearizing the non-linear state Equation (2) at \(\mathbf{X_{k}}\) and discretizing it with \(T\) as the discrete time step, we obtain the following:  
\[\mathbf{X_{k+1}}\mathbf{=}\mathbf{\Phi}\mathbf{X_{k}}\mathbf{+}\mathbf{T}\mathbf{W_{k}} \tag{3}\]  
Figure 1: Multi-LOS cooperative localization scheme.  
where \(\mathbf{\Phi}=\mathbf{I}+AT\), \(\mathbf{\Gamma}=T(\mathbf{I}+A\frac{\mathcal{T}}{\mathcal{I}})\mathbf{G}\), and \(\mathbf{G}=[\mathbf{0}_{3\times 3};\mathbf{I}_{3\times 3}]\). In this paper, \(T\) was set as 1 s. \(A\) are the Jacobi matrices of the state equations, specifically:  
\[\mathbf{A}=\begin{bmatrix}\mathbf{0}_{3\times 3}&\mathbf{I}_{3\times 3}\\ \frac{\partial\mathbf{v}}{\partial\mathbf{r}^{T}}&\mathbf{0}_{3\times 3}\end{bmatrix} \tag{4}\]
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 5 Settings for Performance Assessment
### Performance Metrics
#### 5.1.3 E2E  
The average E2E latency is depicted in Figure 10 for various node counts. Since the proposed protocol is based on the compaction of the communication region, when the node density is low, the number of nodes participating therefore also becomes very limited and even no node participates in the communication process, which leads to an increase the latency time. It can be seen that with the rise in nodes count, the E2E latency for all protocols increased but due to the dependable route selection, the CAEL took roughly the same amount of time on average to transport the data packet as the RGoV and DyTE. In contrast, CAEL easily outperformed the remaining COOP, NCA(MPR), and CACA protocol because CAEL works on the principle of minimizing the communication region of a node and also selects the best route based on the availability of the link before its expiration.  
Figure 10: End-to-end delay against various number of nodes.  
Figure 9: Routing overhead against various number of nodes.  
COOP, NCA (MPR), and CACA are solely based on improving the MPR selection to reduce the network overhead, whereas DyTE and RGoV control overhead packets using the time and distance information of the nodes, and RGoV also adds a clustering technique on top of that to improve the performance by adding a reliability factor. The proposed protocol (CAEL), on the other hand, not only minimizes the communication region but also checks the link expiration of each connected route to determine the best possible way to reach the destination. The results show that the overall performance of CAEL is far more effective than that of COOP, NCA (MPR), CACA, DyTE, and RGoV.  
It is observed from the results that the performance of the proposed protocol is superior to the other mentioned protocols, but in some scenarios, the performance of CAEL may deteriorate and affect the computation performance. Those multiple scenarios are when very few nodes are identified in the trilateral region and those nodes appear to reside on the border of the trilateral region, which means that they can leave of the trilateral region very quickly. Secondly, when the calculated LETs between all nodes are very low, and third, due to the large number of HCs, since RF is based on RET and HC, if HC is very high, the reliability would be affected.
## 3 Experiments and Evaluation  
This section evaluates the proposed methods experimentally. An introduction to the datasets, parameters, and evaluation metrics is first provided in Section 3.1, followed by the overall results in Section 3.2. Local details and analysis on the precision and efficiency are provided in Section 3.3.
# Detection of RINEX-2 Files With Mixed GPS L2P(Y)/L2C Carrier Phase Observations
###### Abstract  
Presently, the global positioning system (GPS) satellite constellation consists of 40% older Block IIA and IIR space vehicles and 60% newer IIR-M and IIF satellites. Only newer GPS satellites are capable of transmitting the L2C signal which is in quadrature to the legacy L2P(Y) signal being broadcast by all satellites. The data format RINEX-2 is not prepared to contain carrier phase observations of both L2 signals, but should contain either one or the other. If a mix of unaligned L2P(Y) and L2C carrier phase observations are stored in a RINEX-2 file, the quarter cycle bias causes the file to be defective and not usable for precise positioning purposes. Algorithms that detect such files are presented in this study. They are mainly based on the analysis of widelane fractional ambiguities and were applied to RINEX-2 files of 2624 reference stations. Seventy-two station files (2.7%) were found to be defective since they contained mixed and unaligned L2P(Y) and L2C carrier phase observations. If such files are used for precise positioning with ambiguities being fixed to integer values, resulting coordinate errors in long baselines can reach centimeter levels. Unaligned L2 observations often prevent ambiguity fixing, especially in short baselines.  
GPS; L2C; quarter cycle phase shift; RINEX +
Footnote †: journal: Sensors
## 2 Biology of the Stigonematales  
Members of the Stigonematales are classified as heterocystous (_i.e._, nitrogen-fixing) cyanobacteria, and within this classification, specifically distinguished as \"true branching\" filamentous cyanobacteria (Figure 1). Heterocysts form within filaments, particularly during nitrogen starvation, and are able to enzymatically (via nitrogenases) utilize dinitrogen (_i.e._, N\({}_{2}\)). Taxa which produce heterocysts have been shown [6] to represent a monophyletic group (based on 16s rDNA) within the cyanobacteria previously referred to as the orders Nostocales and Stigonematales, but classified by more recent taxonomic nomenclature as Section IV and V, respectively. That said, the Stigonematales have been shown, in fact, to represent a polyphyletic group sub-classified as \"T-branching\" and \"Y-branching\" groups [7; 8]. In the current review, however, we will employ the classic nomenclature of Order Stigonematales.  
In addition to being heterocystous, the Stigonematalean cyanobacteria are also capable of producing hormogonia (motile trichomes) and akinetes (thick-walled dormant cells) adding, thereby, to their ecological success. In fact, the Stigonematales represent the most complex prokaryotes, and were originally thought to be directly ancestral to eukaryotes [9]. Moreover, the order represents an ecologically diverse group with representatives isolated from a wide range of terrestrial and freshwater habitats, including numerous thermophiles, as well as notable epiphytic species.  
Figure 1: Photomicrograph of _Fischerella_ 52-1 as a representative of the branched filamentous habit of the Stigonematales.  
With respect to bioactive metabolites, a relatively limited number of taxa within the order, however, have been investigated. And, to-date, only six genera representing only three (of the eight described) families of the order, namely Fischerellaceae (_Fischerella_, _Westelliopsis_), Mastigoladaceae (_Hapalosiphon_, _Westiella_, _Mastigoladus_) and Stigonemataceae (_Stigonema_), have been specifically studied with respect to bioactive metabolites. Arguably, this pursuit has been inherently hindered by inabilities to culture representative species of other taxa, as well as lack of clear links to either toxicity or drug potential which might, otherwise, fuel research in this regard. As one noteworthy example, in this regard, a novel species from the order was only recently isolated [10] as an epiphyte of the plant, _Hydrobilla verticillata_, and linked to intoxication of waterfowl (see Section 8, Relevance as Harmful Algae, below). However, the relevant toxic metabolites, in this case, remain to be identified.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 4 Human Activity Signatures by Using a Distributed MIMO FMCW Radar System  
The lack of the multi-perspective illumination of SISO radar systems is their major limitation, preventing the realization of a direction-independent HAR system (as described in Section 3). To overcome this limitation, we propose to utilize a MIMO radar system and distribute its antennas in an indoor environment to realize a direction-independent HAR system. We have distributed two pairs of collocated transmitter-receiver antennas to illuminate the indoor environment from different perspectives. For the sake of simplicity, we have limited the number of antennas to four, but the proposed approach can be straightforwardly scaled for a larger number of antennas. A separate radar signal preprocessing chain (as described in Section 2) has been adopted for each pair of collocated transmitter-receiver antennas. Thus, we have deployed a \(2\times 2\) MIMO radar system, which consists of two radar subsystems denoted as Radar\({}_{1}\) and Radar\({}_{2}\). Radar\({}_{1}\) comprises a transmitter antenna \(A_{1}^{T_{x}}\) and a receiver antenna \(A_{1}^{R_{x}}\). Analogously, Radar\({}_{2}\) consists of a transmitter antenna \(A_{2}^{T_{x}}\) and a receiver antenna \(A_{2}^{R_{x}}\). We propose to position the two radar subsystems such that their boresight axes are orthogonal to each other, which enables the \(2\times 2\) MIMO radar system to effectively capture the scatterer motion in the horizontal \(xy\)-plane.  
In this section, we mainly emphasize the impact of the direction of human activities on the TV radial velocity distribution \(p_{ii}(v,t)\) of the link from \(A_{i}^{T_{x}}\) to \(A_{i}^{R_{x}}\) for \(i\in\{1,2\}\). First, we discuss the measurement setup for the proposed \(2\times 2\) MIMO radar system. Second, three different experimental scenarios are illustrated in which the human activities are performed and analyzed. Third, we discuss the radial velocity distribution \(p_{ii}(v,t)\) and mean radial velocity \(\overline{v}_{ii}(t)\) derived from measurements of the \(2\times 2\) MIMO radar system. Finally, the implications of different directions of human activities on the performance of an RF-based step counter are discussed.
## 4 Proposed Scheme
### Relay-Node Selection and Rotation
Total number of cluster heads \(\rho\) and total number of sensor nodes \(N\). Output: A policy for the selection of relay node for each cluster head to transmit its cluster data to the base station in an energy efficient and stable manner. Provides balanced energy transmission between nodes through timely and energy efficient rotation of relay nodes. 1:\(d_{0}^{min}\leftarrow\sqrt[]{\frac{\varepsilon_{e}(a)}{1-2^{-\frac{1}{2}}}}\)
2:for\(i=1\) to \(\rho\)do
3:\(d_{CH_{i}-BS}\gets d\)
4:if\(d_{CH_{i}-BS}>d_{0}^{min}\)then
5: Compute the radius '\(A\)'
6:for\(j=1\) to \(N\)do
7:if\(d_{ij}<A\)then
8:\(\mathcal{R}_{i-candidate}\gets j\)
9: While\(E_{(CH_{i},j)}\leq E_{\text{arg}}(j)\) && \(E(j)\geq\alpha*\max(\mathbb{E})\)do
10: Continue to transmit data via relay node '\(j\)'
11:\(\mathbb{E}(j)\leftarrow\mathbb{E}(j)-\varepsilon_{j-BS}\)
12:else
13: Rotate relay role to next candidate
14: Break
15:endif
16:endfor
17:else
18: Transmit data via direct communication between '\(CH_{i}\)' and '\(BS\)'
19:\(E_{Direct}=\left\{ \begin{aligned} E_{ELEC}*l+\epsilon_{fs}*l*d^{2} if\ d\leq d_{o}\\ E_{ELEC}*l+\epsilon_{mp}*l*d^{4}\ otherwise\end{aligned} \right\}\)
20:\(\mathbb{E}(i)\leftarrow\mathbb{E}(i)-E_{Direct}\)
21:endif
22:\(rnd\gets rnd+1\)
23:endfor
```  
**Algorithm 3**Relay-Node Selection & Rotation Policy
## 4 Testbed and Experiments
### Experiments
The results obtained with traffic-light based control algorithms have been incorporated as well; particularly, a fixed time control (FX) system with different cycle lengths (30, 60, and 90) and an intelligent advanced traffic light control technique (iREVD) [2] that proactively adapts the traffic lights to reduce congestion. The fixed traffic light control system assigns a fixed green time for each branch of the intersection where the passing priority (green light) is given. This passing priority is cyclically passed through all branches with a fixed cycle time (in our case, 30, 60, and 90 s were analyzed).
# Radiator Enablers for Wireless Communication Evolution
## 2 Challenges, Enabling Technologies, and Antenna Developments
### Antenna Evolution to Provide New Degrees of Freedom
#### 2.2.1 MIMO Technology  
One of the notable paradigms already mentioned in the previous paragraphs that supports antenna contribution to wireless communications is MIMO technology, with the accompanying ever-increasing number of antenna ports. This increase explicitly affects the capacity and the SINR of a wireless system. A greater capacity means more users and more telecommunication load can be served. The capacity is increased via the increase in the multiplexing gain, that is, the extra degrees of freedom (spatially parallel independent channels) provided by the MIMO (see Equation (1)). The SINR is affected by the increase inthe desired signal power due to the beamforming gain provided by the directional array antenna and by concurrently reducing the interference due to more directed radiation (see Equation (2), parameter H, and SINR). In turn, the SINR increase reduces the FER, thus increasing reliability (see Equation (4)). Also, by affecting SINR, one can increase coverage or alternatively reduce transmitted power. This, together with the optimized spectral efficiency, improved energy efficiency, and longer battery life for IoT devices, is achieved (see Equation (6)).  
To harvest these gains, technology needs to provide antenna systems with at least a thousand antenna ports, not elements. Thus, relatively dense structures need to be delivered. The real challenge is for those systems to provide a broad bandwidth. For example, for a system working in FR3, it is advantageous, for the hardware part (antenna and beamformer), to cover, at once, the whole band, of about 17 GHz. This should happen primarily on the BS side. Thus, the MIMO evolution is not only toward using more elements but also exhibiting this advantage for the whole allowable frequency band. Thus, hardware-transparent gMIMO is the goal and the key enabler of the next step of the evolution of wireless communication. It is important to note that the relative rf front end should include both the radiating aperture and the respective feeding--beamforming network (ref. [22]).
# Research on the Correlation Filter Tracking Model Based on the Deep-Prunned Feature Network
## 4 Experiment
### Evaluation Metrics  
In this paper, the OTB2013 dataset was used as the tracking dataset, so the curve evaluation method OPE (One-Pass Evaluation), which is common to the OTB dataset, was used in measuring the tracking performance of the model. OPE refers to initializing the first frame with the target location annotated in the dataset and then running the tracking algorithm to obtain the average accuracy and success rate. The OPE contains the average pixel error (APE) and the average overlap rate (AOR), which are the average of the precision rate and the success rate, respectively. Where the precision rate is the percentage of video frames where the distance between the center of the target box estimated by the tracking algorithm and the center of the target box manually labeled is less than a given threshold; the success rate is the percentage of video frames where the overlap rate (OR) between the predicted target box and the manually labeled target box obtained by the tracking algorithm is greater than a given threshold.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 14 Enhancements
#### 2.2.4 New Transport-Block-Size Support  
Contrary to Release 13 where NB-IoT supports relatively low data rates (\(\sim\)250 kb/s and \(\sim\)226.7 kb/s in downlink and uplink, respectively), 3 GPP Release 14 introduces a new NB-IoT device category which supports the improved data rates by enhancing the Transport Block Size (TBS) to 2536 bits. These data rates can be reached thanks to the ability to support a second Hybrid Automatic Repeat Request (HARQ) process. This second HARQ is useful for enhancing the reliability of the link for the UEs that experience favorable channel conditions. Implementation of this optional second HARQ process results in throughput gain as it reduces the overhead caused by NPDCCH scheduling gaps.
# Automated Destination Renewal Process for Location-Based Robot Erands
## 3 Methods  
The process of automating location-based robot errand tasks consists of two stages, as follows: data collection of indoor space and robot positioning based on object recognition (see Figure 1).  
In the first stage, a map of the indoor space is created, and data collection for that space is carried out. Using CAD drawings, the robot generates a grid map of navigable areas and initial destination nodes. As the robot explores these nodes, it updates sensor-based obstacles. Additionally, the grid map is segmented based on obstacles, allocating each space and performing classification based on the information recognized within each space.  
The second stage manages nodes representing positions where objects can be manipulated and performs a hierarchical organization of indoor environment information. Based on the location of objects obtained through exploration, the robot creates a workspace and estimates the optimal position, considering the surrounding environment. This position is then automatically managed for destination creation and the relocation of nodes through GVG thinning. Ultimately, node management is performed by consolidating and hierarchically organizing the information obtained from the previous stage.  
Figure 1: The overall flowchart of the automated destination renewal process.
## 3 Conventional Positioning Solutions in 5G Networks  
In positioning solutions, the potential of using Machine Learning (ML) models is generally undisputed; however, in some cases, these models are not a viable solution for various reasons (e.g., computational resources, training/testing time, and data quantity and quality). Fortunately, a plethora of 5G positioning methods has already been devised [31] and most of them are capable of achieving high-quality results even without machine learning models; therefore, in this section, we present some of the most prominent solutions without ML for positioning in 5G networks. The experiments to be mentioned are divided into two groups depending on whether 5G is assisted by other technologies or not.