esa-sceva/satcom-chunk-collection huggingface dataset:
# UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering
## 3 Adaptive Anti-NLOS Error KF Algorithm
### Anti-NLOS KF Algorithm and Its Improvement  
In actual UWB positioning, the measured range can be easily affected by the NLOS and multi-path effects, thus inducing abnormal points and leading to poor filtering performance, deviations and even converge failure. An anti-NLOS KF algorithm proposed in the literature [26] mainly compares the triple innovation variance with innovation to determine the existence of NLOS errors. If there are NLOS errors, they will be decreased by zooming in the innovation at a fixed ratio. The anti-NLOS KF algorithm is mainly illustrated below:  
Assuming the innovation \(\mathbf{\Delta}_{k}\) and its variance matrix \(\mathbf{D}_{k}\) at the moment \(k\) are expressed as Equation (15) and (16), respectively:  
\[\mathbf{\Delta}_{k}=\mathbf{z}_{k}-\mathbf{H}_{k}^{\mathbf{\wedge}}\mathbf{x}_{k/k-1} \tag{15}\]  
\[\mathbf{D}_{k}=E\Big{\{}\mathbf{\Delta}_{k}\mathbf{\Delta}_{k}^{T}\Big{\}}=\mathbf{H}_{k}\mathbf{P }_{k/k-1}\mathbf{H}_{k}^{T}+\mathbf{R}_{k} \tag{16}\]  
According to the triple variance theory and combined with Equations (15) and (16), a method to determine whether a measured value is affected by NLOS can be obtained in Equation (17):  
\[\Big{|}\mathbf{\Delta}_{k}^{i}\Big{|}\leq 3\sqrt{\mathbf{D}_{k}^{i}} \tag{17}\]  
where \(\mathbf{D}_{k}^{i}\) is the element \(i\) on the diagonal of matrix \(\mathbf{D}_{k}\), and \(\mathbf{\Delta}_{k}^{i}\) stands for the element \(i\) of \(\mathbf{\Delta}_{k}\). If Equation (17) was not met, Equation (18) would be used to correct its innovation:  
\[\mathbf{\Delta}_{k}^{i}=\alpha\mathbf{\Delta}_{k}^{i},\alpha\in[0,1] \tag{18}\]  
where \(\alpha\) is the correction factor of innovation.  
It can be seen from the above algorithm that the inhibitory effect of the KF algorithm on the NLOS errors is mainly determined by the fixed correction factor of innovation. In an actual UWB mobile positioning scene, the NLOS changes. Hence, the fixed value of \(\alpha\) has a limited inhibitory effect on the NLOS errors.  
Based on the literature [27], an adaptive anti-NLOS KF algorithm was proposed in this paper, where the value of \(\alpha\) was automatically adjusted as per the changes in the difference between innovation and innovation variance, so as to further improve the anti-NLOS error effect. The automatic adjustment method for \(\alpha\) designed in this paper is expressed in Equation (19):  
\[\begin{cases}\alpha=1-(\mathbf{\Delta}_{k}^{i}/(3\sqrt{\mathbf{D}_{k}^{i}})-1)\times b &,\,3\sqrt{\mathbf{D}_{k}^{i}}\leq\mathbf{\Delta}_{k}^{i}\leq 6\sqrt{\mathbf{D}_{k}^{i}} \\ \alpha=1-b&,\,\,\mathbf{\Delta}_{k}^{i}>6\sqrt{\mathbf{D}_{k}^{i}}\end{cases} \tag{19}\]  
where \(b\) is the maximum
# Photonic Integrated Circuits for NGPON2 ONU Transceivers (Invited)
## 4 PIC for Next-Generation PON  
Photonic integration can provide advantageous solutions to PON by limiting the number of optical and electrical interconnections and thus overcoming some of the bulk packaging restrictions, such as bandwidth, power dissipation, and cost [65, 74]. Nevertheless, complexities can arise with the use of this technology, namely fiber interfacing difficulties, number of electrical connections, and possible high-density of components per device, which demands a high power dissipation  
Figure 3: Diagram describing a PON architecture with three coexisting technologies.  
management [70]. The development of PIC solutions for access networks has already been reported, e.g., with an InP monolithic transceiver PIC for NGPON2 applications [75], the proposed layout has the potential to be implemented as an OLT and with filter redesigning as an ONU. A photonic integrated apparatus for tunable multi-wavelength transmission was patented in 2017 [76], the time and wavelength division multiplexing (TWDM)-PON transmitter system proposed is tailored to support current and next-generation access technologies. Monolithically integrated dual electro-absorption modulated laser (DEML) solutions with application in PON and claims of small footprint and low-cost external-modulator-free Tx can likewise be found in the literature [46]. More recently, a theoretical analysis of polarization-independent receiver (PI-Rx) based in a 3 \(\times\) 3 symmetric coupler with applications for access networks was reported [77]. An example of a BOSA transceiver solution implemented in ONU side of a PON with a representation of a 10 Gbit small form-factor pluggable (XFP) evolution from discrete optical component to PIC technology is depicted in Figure 4.  
Several steps and guidelines for PIC implementation in PON are addressed in this section, along with the technology impact on reach (Section 4.1); the adopted band and wavelength selection criteria (Section 4.2); the integrated photonics packaging (Section 4.3); and the issues related with the control of complexity, power dissipation, and form factor (Section 4.4).
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 4 Full-Wave Electromagnetic Simulation Results
### Single-Beam Beamforming
We observe that a significant reduction (more than 8 dB) in the passband ripple can be with the proposed transmit beamformer with similar stopband attenuation compared with the nonsparse [39; 40] and sparse [32] filter designs based on the windowing technique. Furthermore, the average side-lobe level of the proposed beamformer is lower than 7 dB, and the peak side-lobe level is lower than 4 dB compared with these filters. Compared with the nonsparse optimization-based design [41], the proposed transmit beamformer provides a more than 85% reduction in DSP complexity without substantial degradation of the accuracy of the beam pattern. This confirms the superior performance of the proposed transmit beamformer compared with the transmit beamformers that employ 2-D FIR filters designed using previously proposed techniques [32; 39; 40; 41]. Figure 9: Magnitude frequency response of the 2D FIR trapezoidal filter \(\left|H(e^{i\omega_{x}},e^{i\omega_{ct}})\right|\) for DOD = 30\({}^{\circ}\) using (**a**) windowing method [39; 40] (nonsparse), (**b**) optimization (nonsparse) [41] (**c**) windowing method [32] (sparse), and (**d**) proposed (sparse). Polar plots of the simulated beam patterns obtained using the filter made using (**e**) windowing method [39; 40] (nonsparse), (**f**) optimization [41] (nonsparse), (**g**) windowing method [32] (sparse), and (**h**) proposed (sparse).
## 4 Solutions to Two Basic Subcases  
### Case 1: Absorption Only  
In the case with absorption only when \(c=0\), using the result in Equation (18), we obtain  
\[\frac{d\delta^{2}}{dt}=D_{0}\frac{(n+1)(n+2)}{m+1}+\delta^{2}b\frac{(n+1)(n+2 )}{(np+1)(np+2)}=A+B_{A}\delta^{2} \tag{29}\]  
where \(B_{A}=\frac{(n+1)(n+2)}{(np+1)(np+2)}=bM_{a}\).  
Equation (29) has the same structure as Equation (18), with a solution of  
\[\delta_{A}=\sqrt{\frac{A}{B_{A}}}\sqrt{e^{B_{A}t}-1}=\sqrt{\frac{A}{b}}\sqrt{ \frac{N}{M_{A}}}\sqrt{e^{M_{A}F_{0}}-1} \tag{30}\]  
From Equation (30), defining \(\delta_{A}\), the approximate solution becomes  
\[u_{a(Abs)}=\left(1-\frac{x}{\sqrt{\frac{D_{0}}{b}}\sqrt{\frac{N}{M_{A}}}\sqrt {e^{M_{A}F_{0}}-1}}\right)^{n}=\left(1-\frac{x}{\sqrt{\frac{N}{M_{A}}}\sqrt{e ^{M_{A}F_{0}}-1}}\right)^{n}=\left(1-\frac{\eta}{\sqrt{\frac{N}{M_{A}}}}\right) ^{n} \tag{31}\]  
Figure 4: Solution (Equation (28)), with \(p=1\) and \(q=2\), and \(k=3/2\Rightarrow m=1/2\): (**a**) As a function of \(X\) for short times (\(0<Fo<1\)); (**b**) As a function of \(X\) for long times (\(Fo>1\)); (**c**) As a function of the similarity variable \(\eta\).  
This solution defines again the spatial dimensionless variable \(X=x/\sqrt{D_{0}/b}\) and the similarity variable \(\eta_{A}=\frac{x}{\sqrt{\frac{D_{0}}{b}}\sqrt{e^{M_{A}F_{0}}-1}}\). The only difference with the general solution Equation (28) is the term \(M_{A}\). Thus, the front is defined by \(X_{front}=\sqrt{\frac{N}{M_{A}}}\sqrt{e^{M_{A}F_{0}}-1}\) or by \(\eta_{front}=\sqrt{\frac{N}{M_{A}}}\). The approximate solution as a function of the dimensionless spatial variable \(X\) is shown in Figure 5 for various times (represented by the Fourier number \(Fo\)) and with effects of the exponent \(m\).  
The front length measured as a function of the Fourier number Fo for various values of the exponent m is shown in Figure 6. Profiles of the approximate solution as a function of the similarity variable \(\eta_{A}\) are shown in Figure 7. We have to take into account that any changes in \(M_{a}=f(n,p)\) result in changes of the prefactor of \(Fo\) in the definition of \(\eta\). In general, the increase in \(m\) yields shorter fronts and increases the profile convexity, which  
Figure 5: Solution with absorption only (\(b=1\)) as a function of the dimensionless spatial variable \(X\), with simultaneous effects of the exponents \(m\) and the Fourier number \(Fo\). An example with \(p=2\). Panels (**a**–**c**) for times less than the characteristic time scale; panels (**d**–**f**) for times greater than the characteristic time scale.  
is already a known effect. At the same time, with stipulated \(m\), the increase in \(p\) has the opposite effect, i.e., it extends the front. Recall, in all these simulations, \(n=1/m\).
## 3 Data Analysis
First, the Global Indicator of Economic Activity (IGAE) variable is used to track the Mexican economic cycle. Second, the real U.S. gas price is used to measure its influence in border-crossing fluctuations. The gas price difference between the U.S. and Mexico is a relevant variable for passenger vehicles crossings, especially across the Texas border. Moreover, the difference tends to be positive, influencing the crossing of Mexican citizens to the U.S. to look for cheaper gas, when such price differences are significant. The data on the exchange rate between the Mexican peso and the U.S. dollar are retrieved from Banco de Mexico (Mexican Central Bank), which publishes a monthly real exchange rate index based on a weighted basket of numerous currencies. The gas price is retrieved from the U.S. Energy Information Administration. In this study, the monthly retail gasoline prices of all grades of formulations in the U.S. are used. The data are adjusted in real terms. The monthly IGAE indicator and the real exchange rate from 2004 to February 2020 are displayed in Figure 3. The Mexican economy experienced an upper trend during this period, which was caused by trade liberalization and other economic reforms. However, the figure reveals that during the 2008 global financial crisis, the Mexican economy suffered a slowdown pattern, with the Mexican peso depreciating significantly. As a result, from 2016 onwards, the economy and the real exchange rate experienced a sideways trend, reducing the economic growth rate as compared with that of the previous years. A dummy variable is included in the econometric model to capture the exogenous events that impacted the U.S. and may influence border crossings. The information about the U.S. economic recessions is retrieved from the National Bureau of Economic Research (NBER). Regarding the D_US_CRISIS variable, from December 2007 to June 2009 (including the 2008 global financial crisis) is assigned a value of 1. Figure 2: Northbound PoE’s annual pedestrian and personal vehicle crossings by regions, from 2004 to 2019 (million). Note: The annual data are from 2004 to 2019 and are in millions. In Figures (**a**) and (**b**), the crossings for other are on the right vertical axis. Source: Own estimations using data from the Bureau of Transportation Statistics. Table 3 presents the correlation matrix of the dependent and the main independent variables. The real exchange rate's negative sign regarding pedestrians and personal vehicles is as expected. Hence, there is some initial evidence that northbound border crossings decline when the Mexican peso depreciates. In the case of pedestrians and personal vehicles, the IGAE coefficient is negative, although the correlations are small. Furthermore, as projected, the real gas price is negative for personal vehicles. Lastly, the correlation between the two Google Trend variables is 0.408.
# Multiservice-Based Traffic Scheduling for 5G Access Traffic Steering, Switching and Splitting
## 1 Introduction
### Our Contribution  
In this study, we considered and investigated an optimization problem related to traffic scheduling in NWA systems. The novelty of this study is highlighted by the following facts: (1) Our algorithm breaks the existing strategy, which offers users the possibility to connect to only one type of network (3 GPP or non-3 GPP) so that the transmitted data can be dynamically transmitted over one or two networks simultaneously, based on the service type; (2) The data stream transmission corresponding to each service can avoid high-load paths and choose alternative low-load paths; (3) Through distributed power- and bandwidth-allocation strategies, the effective capacity of the system can be maximized, while satisfying the specific service delay requirements of different users.  
Additionally, this study makes the following contributions:  
1. A smart mode selection module was employed to choose between 3 GPP and non-3 GPP access. A model for the selection of the transmission path, based on the utility function, was developed.  
2. A method for determination of the suitable service network, based on the network load, which quickly and effectively discards high-load networks, was developed. This method comprises three steps: (a) determination of the service network candidate set; (b) elimination of the poor link quality network; and (c) elimination of the high-load network, based on a sigmoid function.
3. An effective network capacity maximization problem was formulated under specific DQoS, statistical bandwidth and power constraints. Using the Lagrangian function and the sub-gradient algorithm, the original problem was solved and an optimal resource allocation solution for 3 GPP access and non-3 GPP was derived.  
The rest of this paper is organized as follows. Section 2 introduces the system model, including the network and service utility components, as well as the calculation of the effective network capacity. In Section 3, we describe the proposed resource optimization scheme, which is divided into three steps: (1) determination of the number of connection links; (2) selection of the transmission path; and (3) distribution of resource allocation. In Section 4, we show the convergence of the link adaptations and provide simulation results corresponding to our scheme. Finally, Section 5 presents the conclusions drawn from the simulation results.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
## 4 Problem Formulation
### Cooperative Caching and Resource Allocation Problem
#### 4.1.3 Communication Model  
For the content service of user \(u\), the service delay can be composed of the transmission time between user \(u\) and access satellite \(v_{u,a}\), the routing time for ISLs, the transmission time between cloud center \(dc\) and access satellite \(v_{dc,a}\), and the sending time of data. The sending time of content \(w\) for user \(u\) can be given as  
\[t_{u,w}^{snd}=\frac{x_{u,w}\cdot s_{w}}{b_{u,w}}, \tag{6}\]  
the total transmission time of satellite-terrestrial links, i.e., user \(u\) and access satellite \(v_{u,a}\) and cloud center \(dc\) and access satellite \(v_{dc,a}\), for the content service of user \(u\) can be given as  
\[t_{u,w}^{tr}=\frac{x_{u,w}\cdot k_{u}}{c}+\frac{x_{u,w}\cdot a_{u,dc}\cdot k_ {dc}}{c}. \tag{7}\]  
The routing time between different ISLs for the content service of user \(u\) can be indicated as  
\[t_{u,w}^{isl}=x_{u,w}\cdot\sum\limits_{e\in E}q_{u,e}^{w}\cdot t_{e}. \tag{8}\]  
Then, the total service delay time of retrieving content \(w\) for user \(u\) can be given as:  
\[t_{u,w}=t_{u,w}^{snd}+t_{u,w}^{tr}+t_{u,w}^{isl}. \tag{9}\]
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Lossless Compression
#### 6.2.3 Combined Files  
The dataset provided by Fugro has been excluded from the previous two sections, as it contains both MRZ (and SKM) and MWC datagrams. In the five files, the datagrams distribution is approximately: 70% MWC datagrams, 20% MRZ datagrams and 10% SKM datagrams.  
Once again, FAPEC achieves the highest compression ratios. In this case, it is even more significant, as it shows at least 50% better performance than _bzip2_, the second-best option. _Zstd_ and _gzip_ exhibit a very similar performance, and in this case, also similar to _bzip2_.  
Regarding the throughput, FAPEC is three times faster than _gzip_ and seven times faster than _bzip2_. However, _Zstd_ is slightly faster than FAPEC, as can be seen in Figure 13.  
Note that this dataset is especially interesting because high ratios are obtained in the presence of mostly MWC datagrams. These results strengthen our observation from Section 6.1, where we remarked that the low ratios in Figure 8 are scene-specific.  
It is worth mentioning that we also carried out these tests on a Raspberry Pi 400 platform, featuring an ARM processor running at 1.8 GHz, to evaluate the compression speeds of the various solutions. Here, FAPEC is undoubtedly the fastest data compressor, either in these combined files or in the separate ones, reaching compression throughputs of 35 to 75 MB/s, whereas the other compressors can barely reach 10 MB/s.  
Figure 12: Comparison of lossless compressors showing that FAPEC performs better both in time and ratio than _gzip_, _Zstd_ and _bzip2_ on KMALL files.  
Figure 13: Lossless compression ratio (**left** panel) and lossless compression throughput (**right** panel) for the tested KMALL combined files.
# Highly Doped Phosphate Glass Fibers for Compact Lasers and Amplifiers: A Review
## 3 CW Phosphate Fiber Lasers and Amplifiers
A different approach devoted to the fiber laser power scaling, which can overcome the above limitations, is indeed to employ phosphate glass instead of silica as fiber material. The higher solubility of RE ions in phosphate glass hosts allows the increase of RE ions concentration and thus to generate the necessary amplification over a much shorter fiber length as compared to silica fibers. The onset of the non-linear phenomena described above is then considerably limited. In addition, the higher photodarkening threshold in phosphate glasses eliminates the risk of power degradation over time in high power applications [15]. Another important advantage of phosphate glasses in power scaling of fiber lasers is related to the possibility to easily adjust the refractive indices [41]. Thus, DC optical fibers with a reduced core numerical aperture (NA) and hence a much larger single-mode core are feasible. Furthermore, thanks to a large glass forming region, it is possible to manufacture glasses with considerably large refractive index differences and thus realize a high NA pump waveguide with a glass based outer cladding instead of a low-index polymer coating, as widely used in DC silica fibers. These all-glass DC optical fibers offer a high degree of immunity to heat loading [8; 42]. A typical configuration used to realize high power fiber lasers is the master oscillator power amplifier (MOPA) system: the high power laser is obtained by splitting it into a low-power signal generator (master oscillator) and an optical power amplifier that boosts the output power. Thus, the high-spectral qualities of the signal are preserved while simplifying the manufacturing constraints of the seed-laser. For this kind of device, an all-fiber structure without the use of free-space components is a better solution in order to simplify configurations while improving compactness and reliability. Phosphate glasses main features, such as extremely high doping level, low SBS gain coefficient and absence of photodarkening, make them an ideal host to develop a MOPA system.
###### Abstract  
Frequent coal mine gas disasters pose significant threats to the safety of miners and the continuity of coal mining operations. Understanding and mastering the patterns of gas occurrence is the foundation for controlling gas outbursts. This study, drawing on previous theories, research, and practical coal mine production data, analyzes the structural characteristics of the Kaiping syncline, with particular emphasis on the structural differentiation at its northeastern uplifted end. The study examines how gas generation and storage are influenced by progressively layered structures and their effect on coal mine gas management. The results indicate that the Kaiping syncline has a NE-SW axial orientation, which gradually shifts to an asymmetric syncline with a nearly EW trend, rising towards the northeastern end. At the turning end, the strata on the northwest limb are steep--locally vertical or overturned--gradually transitioning into the gentler southeast limb with dips of 10\({}^{\circ}\) to 30\({}^{\circ}\), further complicated by a series of sub-parallel secondary folds. The gas formation process in coal seams has undergone multiple stages, regulated by structural burial and thermal evolution. The current gas storage characteristics result from the combined effects of these structural factors. The Kaiping syncline can be divided into two gas zones: a high-gas zone in the northwest limb and a shallow low-gas zone paired with a deep high-gas zone in the southeast limb. At the turning end, structural differentiation results in significant variations and gradations in the gas storage conditions of the coal seam. This differentiation directly causes a transition from coal and gas outburst mines in the northwest limb to low-gas mines in the southeast limb, highlighting the significant influence of structural factors on gas generation, preservation, and mine gas emissions. This study integrates theoretical analysis with measured data to enhance the understanding of structural evolution and its influence on gas storage. It offers guidance for preventing coal seam gas disasters and ensuring the safe production of coal mines in the Kaiping coalfield.  
Kaiping syncline; syncline uplifted end; structural differentiation; structural burial-thermal evolution history; mine gas +
Footnote †: journal: applied sciences
## 4 System Model
### Multi-Player Computation Offloading Algorithm  
In this subsection, we present the design of our multi-player computation offloading algorithm which provides comprehensive processes for deriving the optimal computation offloading decision of the constrained optimization problem in Equation (16) in an efficient manner.  
First, all VR devices are initializing their offloading decision \(\alpha_{i,j,0}=1\), that means local execution. Then, each device uploads the computation tasks' requirements which includes \(\{a_{i,j},bi,j,c_{i,j},\tau_{i,j},p_{i}^{T},p_{i}^{R},\zeta_{i}\}\) and the local computation capabilities \(f_{i}^{L}\) to the edge server. Afterwards, the edge server calculates the uplink and downlink data rate for each VR device based on the current number of VR players. In addition, the edge server finds the optimal execution place for each computation task (i.e., local, edge or cloud) through solving the optimization problem in Equation (16). Finally, each VR device receives the execution place for their computation tasks from the edge server, thereby minimizing the sum cost of the entire system in terms of time and energy.  
Algorithm 1 provides the detailed process of the multi-player computation offloading algorithm in which \(\mathcal{O}(NM)\) is the time complexity where \(N\) and \(M\) denote the total number of VR devices and their tasks, respectively.