esa-sceva/satcom-chunk-collection huggingface dataset:
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
## 2 Background
### Gold Code  
The gold code generator consists of two \(N\)-bit shift registers with different feedback polynomials and initial values. The two \(N\)-bit shift registers have two different M-sequences of equal length as outputs. Gold codes with lengths up to \(2^{N}-1\) are generated through the bitwise modulo sum of the two output sequences. Signals using gold codes include GPS L1C/A [14], BDS B1I [15], BDS B2I [15], BDS B2A [16], BDS B3I [17], GLONASS L1 OCd [18], Galileo E5a [19], and Galileo E5b [19]. The two LFSR structures of the gold code generator are basically the same as the LFSR structure of the M-sequence generator structure shown in Figure 1, but instead of storing the initial value of the shift register in the ROM, a phase selector circuit that selectively determines the output according to the satellite number may be added [4]. Among the signals that use the gold code, the signals that use the phase selector are GPS L1C/A signals, BDS B1I and BDS B2I signals.  
Figure 2 shows an example of a gold code generator that has the structure of a GPS L1C/A code generator [14]. The gold code generator shown in Figure 2 consists of 10-bit shift registers SR1 and SR2, Feedback Taps1/2, Initial ROMs 1/2, and a phase selector. The feedback polynomial of SR1 is \(f(x)=x^{10}+x^{3}+1\), and the feedback polynomial of SR2 is \(f(x)=x^{10}+x^{9}+x^{8}+x^{6}+x^{3}+x^{2}+1\). Each polynomial is implemented with Feedback Taps 1/2. The initial value information of the two shift registers is stored in Initial ROMs 1/2, and the stored initial values are set in shift registers SR1 and SR2. Furthermore, the phase selector selects the two register outputs determined for each satellite number from shift register SR2 to perform modulo-2 summing. In detail, the phase selector consists of as many multiplexers (MUXs) as shift registers, a Selection ROM that stores the multiplexers' control signals, and exclusive OR (XOR) logic. When a satellite number is input, two register outputs determined for each satellite number are selected due to the control signal information stored in the Selection ROM, and modulo-2 summing can be performed. Finally, the L1C/A code is generated with the last register output of shift register. SR1 and the modulo-2 sum of the output of the phase selector and has a length of 1023 chips.  
Figure 2: Conventional code generator architecture of GPS L1C/A signal [14].
## 2 Theoretical Notes
The data acquired are expressed in form of apparent resistivity (\(\rho_{a}\))  
\[\rho_{a}=\Delta V/IK \tag{5}\]  
where \(\Delta V\) (\(V\)) is the measured potential, \(I\) (\(A\)) the transmitted current, and \(K\) (m) the geometric factor, which depend on the position of electrodes. It is possible to define different configuration: Wenner, Shlumberger, dipole-dipole, gradient arrays, pole-pole, pole-dipole. Moreover, due to new acquisition instruments with a high number of channels, it is possible to make a personal disposal of the electrodes with different regular or irregular geometry [65]. The apparent resistivity is then interpreted in terms of real resistivity and depth by means of inversion software. The aim of the inversion procedure is to compute the 'best' set of resistivity values, which satisfies both the measured dataset and some a priori constraints, in order to stabilize the inversion and constrain the final image [66; 67]. However, the method is affected by limitations mainly due to the resolution and time to perform the investigations. In the past, 2D ERTs were successfully used for archaeological prospecting [68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78]. Furthermore, several authors have effectively performed 3D ERT applied in the archaeological field to detect the excavation of buried structures [1; 79; 80; 81; 82; 83]. Resistivity methods, including ERT, are less used then GPR in archaeological surveys for the limited resolution, but in the presence of conductive context as in the case of soils with high content of water, salt water or clay), they could be a good alternative to the GPR that suffers attenuation problems. Furthermore, an integration and a comparison of the results obtained via the two techniques is a preferable approach to support archaeologists and enhance the quality of the interpretation of the geophysical data as showed by the existing literature [1; 84; 85; 86; 87; 88].
## 4 Proof-of-Concept Background and Setup
### Proof-of-Concept Motivation  
The P4 program we aimed to create is one that would enable a switch to translate between BPv6 and BPv7. This task was chosen for two reasons. First, it was sufficiently complex to explore all aspects of the BP protocols, the P4 language, and networking hardware. A translator would need to be able to parse all BP fields, understand the semantics behind these fields, be able to modify fields as needed, properly update checksums, and send bundles forward; these are all tasks that a SDDTN node should also be able to do. Secondly, it would provide a potential real-world application for deployment on the ISS, which currently uses the ION software with BPv6. Updating software on the ISS can be a complicated endeavor and ION does not have compatibility between BP versions, so this translator could serve as a middlebox in communication with future BP nodes. Therefore, the task of creating a translator serves as an exploration in parsing BP in hardware via P4 programming, and could potentially aid the communications between legacy, current and future DTN nodes.
## 4 Experimental Results
According to Figure 10, it is difficult to distinguish between OBS and IOS, as well as between NS and IS, using high-order sliding digital twin. In addition, based on Figure 11, the challenging area in sliding digital twin are between NS and IS, between BS and OS, and between IBS and OBS. Thus, in the challenging area we have overlapping. According to Figures 9-11, the difference between signal levels in different classes in the proposed smooth sliding digital twin (Figure 9) is much better than in the high-order sliding digital twin (Figure 10) or sliding digital twin (Figure 11) because in this method the signals are much more recognizable in different states (faults) than the other two techniques. Thus, based on these figures, the performance of RMS residual signal classification in the proposed smooth sliding digital twin is better than the other two methods. To test the power of the classification accuracy in the proposed algorithm, the proposed smooth sliding digital twin (proposed algorithm), high-order sliding digital twin, and sliding digital twin are compared. Based on Figure 4, the SVM technique is used for anomaly bearing RMS (indirect) signal diagnosis and crack size identification. In this paper, 25% of the RMS residual signals for the proposed smooth sliding digital twin, high-order sliding digital twin, and sliding digital twin are used for testing, while 75% are used for training. Details about training and testing for crack diagnosis and crack size identification are presented in Table 3.  
Figure 10: Residual RMS signal using high-order sliding digital twin. (**A**) all conditions, (**B**) zoom view for four conditions.
# GNSS Based Low-Cost Magnetometer Calibration
## 2 Magnetometer Calibration Based on GNSS
### GNSS Error  
GNSS is a well-known system. We can determine the position of the receiver based on measuring the distance between the receiver and the satellites in the sky while knowing their position. The pseudo-range is based on Formula (4), where \(t_{r}-t_{t}\) is the total transmission time which we multiply by the speed of light to get our distance from the satellite. Knowing the position of the satellite, we can substitute in Formula (5), where \(x_{s}\), \(y_{s}\), \(z_{s}\) are the coordinates of the satellite, \(x_{r}\), \(y_{r}\), \(z_{r}\) are our positional coordinates, and \(X\) is the distance. The GNSS system must be connected to at least four satellites where it calculates four equations with four unknowns--position \(x\), \(y\), \(z\), and time \(t\)[19].  
\[P=(t_{r}-t_{t})\times c \tag{4}\]  
\[X=\sqrt{(x_{s}-x_{r})^{2}+(y_{s}-y_{r})^{2}+(z_{s}-z_{r})^{2}} \tag{5}\]  
The error of GNSS receivers is dependent on several variables. In general, the GNSS error can be removed through Equation (6), where the UERE (User Equivalent Range Error) is defined as the accuracy of pseudo-range measurements, and DOP (Dilution of Precision) describes the geometry of the satellite constellation [20]. In general, we can define that the error can be caused due to: ephemeris error, satellite clock error, ionospheric error, tropospheric error, and multipath and receiver errors. Several algorithms suppress individual GNSS error increments. The parameters with which we describe the accuracy of position estimation are called HDOP (Horizontal DOP) and VDOP (Vertical DOP). In open-sky scenarios, inexpensive GNSS receivers have an accuracy of the order of one meter. In the case of precise localization, we can use a dual-band differential system with support for all currently available GNSS systems (GPS, GLONASS, BeiDou, Galileo).  
\[Error=\text{DOP}\times\text{UERE} \tag{6}\]  
For more accurate positioning, modern approaches are used to guarantee satellite navigation accuracy. A common approach is the use of service areas of satellite-based augmentation systems (SBAS), which use geostationary satellites. Another approach is the Ground-Based Augmentation System (GBAS) which is used in air transport when the  
Figure 1: Effect of magnetometer error.  
aircraft approaches the airport. This service is only local (area near the airport) [21]. The Real-time kinematic positioning (RTK) approach uses multiple receivers with carrier phase determination at multiple frequencies. However, such an approach requires one static station. Another possibility is the PPP approach, which can aim the point with an accuracy below centimeters. It can do this based on the knowledge of satellite orbits and time from GNSS reference stations. However, the initial focus takes up to tens of minutes and requires constant access to GNSS reference stations [22]. We will focus only on cheap sensors that use only SBAS, whose position estimation error in open space is around 2 m.
## 2 Automatic Train Operation  
Automatic train control system (ATC) plays an important role in improving the automation level of urban rail transit operation and ensuring the safe and reliable operation of trains. ATC consists of three subsystems: automatic train protection (ATP), automatic train operation (ATO), and automatic train supervision (ATS). The main function of ATO is to adjust the traction or brake force of the train in real time according to different train operation conditions to ensure the control of train operation speed so that the train can track the desired train operation curve smoothly and accurately [24; 25].  
Operating principle of ATO: under the supervision of ATP, ATO automatically calculates the desired curve of train operation in combination with route conditions, signal state, time adjustment command, and train performance, and then uses ATO controller to replace train driver to apply traction or brake control command to the train [26; 27]. On the premise of ensuring the safety of train operation and not triggering ATP emergency brake, ATO controller can automatically adjust the operating speed of the train to meet the performance indexes such as the operating interval between trains, parking accuracy of a train arriving at the station, passenger comfort, etc. The composition principle of ATO system is shown in Figure 1.  
Because the train operates continuously for a long time, its load, operation environment, and performance will change with time, which makes the train motion characteristics have strong uncertainty and external interference. In addition, the sensor time delay, traction, or brake actuator fault and control input saturation of the train exist for a long time. Therefore, the good performance index of the train operation control system can only be achieved by improving and perfecting the ATO control algorithm.
## 3 Classical Routing Protocols
### Modification of LEACH Based on CH Selection
#### 3.2.12 Modified-LEACH  
LEACH protocol is modified for a heterogeneous network in [86], where CHs are selected from advanced nodes only. The network has two categories of nodes called normal and advanced, where the latter has higher energy than the former. After nodes are deployed, the threshold value for CH selection is set as  
\[T(n)=\left\{\frac{p\times a}{1-a\times p[r\text{mod}(\frac{1}{p\times a})]},n\in G\right. \tag{14}\]  
If the node is normal, then \(a=1\) and 5 otherwise. Simulation results with a network with 10% advanced nodes show that the network lifetime is considerably increased. The paper does not discuss other parameters such as stability period, throughput, and energy dissipation.
# Enhanced Matching Game for Decoupled Uplink Downlink Context-Aware Handover
## 5 Simulation and Results
### Simulation Environment  
The validation was directed in a heterogeneous system comprising LTE and Wi-Fi technologies. Additionally, the simulation scenario ran under a similar system arrangement to a previous work [26], where media-independent handover and proxy mobile Internet protocol version 6 administrations were empowered. The adopted handover trigger mechanism is a velocity- and QoS-aware which we detailed in our last study [27].  
For our simulation, we considered 5 macro BSs (MBS) of 500 m and a bandwidth of 20 MHz. In those BSs, N UEs and M femto BSs (FBS) were uniformly deployed. Figure 1 illustrates the topology used to demonstrate our approach. The transmit power of each femto BS m was \(p_{m}=22\) dBm, and transmissions were affected by distance-dependent path loss and shadowing. The minimum SINR required by each UE was \(SINR_{min}=9,56\) dB, and the noise level was \(\sigma=-121\) dBm. The traffic rate was dependent on the QoS requirements of UE, assuming a standard packet size of 2000 bytes. The rest of the simulation parameters are outlined in Table 3.  
Figure 1: Simulation topology.
# Mdp1 _sciences_
## 3 Results & Discussion  
### Detection of Incompliance Events for Bi-Normal Joint Pdfs  
As described above, some intuition can be built with regards to the impact of (a) forecast accuracy and (b) correlation between changes in the primary variable (wind speed) and the forecast error by studying bi-normal pdfs (Equation (15)). In Section 2.2, a case was made for the importance of minimizing these correlations in the forecasting process, in addition to the obvious requirement of minimizing the rms forecast error. Using Equation (15), the absolute fractions of _true positives_ and their rates relative to the true number of events (the detection rate for true positives) can be calculated as a function of the correlation coefficient \(\rho\) and the ratio \(\kappa=\sigma_{y}/\sigma_{y}\) of the standard deviations of the wind speed changes and the forecast error, respectively. Some representative results are shown in Figure 4. The results of the numerical simulations obtained with two normally distributed time series with \(T=5\times 10^{6}\) time steps and a given correlation coefficient are shown in open circles, whereas the results obtained from the integration of Equation (12) using Equation (15) are shown as continuous lines. It can be seen that the theory and the simulation produce identical results.  
As expected intuitively, the probability of forecasting a large negative change (an incompliance event) decreases with the size of the event (i.e., the tolerable negative slope), and the overall behavior is similar for joint pdfs with negative (Figure 4a) or zero (Figure 4b) correlation, albeit with a varying sensitivity with respect to the standard deviation ratio. Given that the probability of occurrence of such events decreases, as well (not shown for brevity), it is more relevant for the present context to inspect the detection efficiency, again as a function of the size of the event (the tolerable slope). From the comparison of Figure 4c,d, it can be seen that the case with negative correlation (Figure 4c) not only has an overall smaller detection efficiency for a given standard deviation ratio, but also decays much faster as a function of the tolerable slope, i.e., incomplace events with large negative wind speed changes are more difficult to detect, compared to the uncorrelated case. This is a fully consistent with the geometric interpretation offered in Figure 2. The practical implication is that, for detecting small wind speed changes, it is important to reduce the overall forecast error, whereas, for larger incompliance events, it is more important to have a small correlation coefficient. Evidently, this is the more relevant case for practical applications.
_Applied_  
_sciences_  
_Received_  
**ECOAL Project--Delivering Solutions for Integrated Monitoring of Coal-Related Fires Supported on Optical Fiber Sensing Technology**  
**[PERSON] 1,2**, [PERSON] 3,4, [PERSON] 3, [PERSON] 5, [PERSON] 5, [PERSON] 5, [PERSON] 6, [PERSON] 6,**  
**[PERSON] 1,2**, [PERSON] 6,7, [PERSON] 5, [PERSON] 8, [PERSON] 8, [PERSON] 8, [PERSON] 8, [PERSON] 1,2, [PERSON] 1,9, [PERSON] 5, [PERSON] 3,4, [PERSON] 9, [PERSON] 6, [PERSON] 5, [PERSON] 3,10, [PERSON] 1,2,**  
* [1] Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; [EMAIL_ADDRESS] (J.R.); [EMAIL_ADDRESS] (L.D.); [EMAIL_ADDRESS] (A.C.T.); [EMAIL_ADDRESS] (J.A.G.)
* [2] Institute of Earth Sciences, Pole of University of Porto, 4169-007 Porto, Portugal; [EMAIL_ADDRESS] (D.V.); [EMAIL_ADDRESS] (J.F.); [EMAIL_ADDRESS] (J.L.S.); [EMAIL_ADDRESS] (J.M.B.)
* [4] Department of Physics and Astronomy, Faculty of Science at University of Porto, 4169-007 Porto, Portugal
* [5] Departamento de Electronica, Universidad de Alcala, 28801 Alcala de Henares, Spain; [EMAIL_ADDRESS] (A.L.-G.); [PERSON].-L.); [EMAIL_ADDRESS] (H.F.M.); [EMAIL_ADDRESS] (S.M.-L.); [EMAIL_ADDRESS] (O.E.); [PERSON](M.G.-H.)
* [6] Department of Ingenieria Electrica y Electronica, Universidad Publica de Navarra, 31006 Pamplona, Spain; [EMAIL_ADDRESS] (R.P.-H.); [PERSON]ESS] (A.L.-A.); [PERSON]DRESS] (A.P.); [EMAIL_ADDRESS] (M.L.-A.);
* [7] Federal University of Santa Catarina, Centro de Engenharia da Mobilidade (CEM), Florianopolis 89.218-035, Brazil
* [8] CNRS, XLIM--Departement Photonique-Fibre, Faculte des Sciences et Techniques, 87060 Limoges, France; [EMAIL_ADDRESS] (H.B.); [EMAIL_ADDRESS] (R.J.); [PERSON]S.R.); [PERSON].-L.A.); [EMAIL_ADDRESS] (P.L.)
* [9] Interdisciplinary Centre of Marine and Environmental Research, University of Porto, 4450-208 Matosinhos, Portugal
* [10] Faculdade de Ciencias Exatas e da Engenharia, Universidade da Madeira, 9020-105 Funchal, Portugal
* [11] Correspondence: [EMAIL_ADDRESS]; Tel.: +351-220-402-490  
**Abstract:** The combustion of coal wastes resulting from mining is of particular environmental concern, and the importance of proper management involving real-time assessment of their status and identification of probable evolution scenarios is recognized. Continuous monitoring of the combustion temperature and emission levels of certain gases allows for the possibility of planning corrective actions to minimize their negative impact on the surroundings. Optical fiber technology is well suited to this purpose and here we describe the main attributes and results obtained from a fiber optic sensing system projected to gather data on distributed temperature and gas emissions in these harsh environments.  
**Keywords:** coal mining; coal waste deposits; spontaneous coal combustion; optical fiber sensing; temperature measurement; gas detection; environmental impact
## 7 Results and Discussion
### Evaluation Metrics
#### 7.1.4 Average End-to-End Delay  
End-to-end delay can be defined as the average delay between the receipt and packet sources at corresponding delivery. Equation (29) shows the average end-to-end delay of data packets per round.  
\[D_{average}=\sum_{i=0}^{\mathit{Pkt}_{red}}\frac{T_{reciected}(i)-T_{send}(i)}{ \mathit{Pkt}_{(received)}}. \tag{29}\]
# Wildlife Monitoring Using a Multi-UAV System with Optimal Transport Theory
###### Abstract
We develop the optimal transport-based multi-UAV monitoring strategy that prioritizes the monitoring areas by reflecting partial information such as GPS trackers. The major contributions of this paper are summarized as follows. Based on the optimal transport theory, we formulate the multi-UAV monitoring problem to increase the detection rate of animal herds, particularly medium-to-large mammals having group behavior like deer. This problem setup includes which area needs to be monitored as well as how a team of UAVs cover the spacious domain collaboratively. The control algorithm for the team of UAVs is then developed under the proposed optimal transport framework. The time-varying nature of the wildlife location is incorporated into the proposed plan, dramatically increasing the detection rate. The proposed scheme has the potential to significantly improve the wildlife detection rate while saving time, costs, and resources by incorporating complementary information (e.g., GPS tracker, camera traps, and acoustic recording devices) into the monitoring plan. The rest of this paper is organized as follows. Section 2 describes the problem to solve for the wildlife monitoring problem with a theoretical background on the optimal transport theory. The main results are provided in Section 3, which includes animal movement modeling, optimal transport-based multi-UAV monitoring strategy, sample point propagation, and other monitoring methods for performance comparison. To support our proposed works, various simulation results are presented in Section 4. Finally, Section 5 concludes the paper.