esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing Demand with Respect to Offer of Mobility
## 5 DORAM Tool  
Considering the requirements, discussed in Section 2 and above model, DORAM performs the analysis of demand/offer of mobility, taking into account:  
* **Multi-modality**: transportation modalities are considered in the model: train, tram, and bus;  
Figure 8: Morning \(W_{mng}\) and afternoon \(W_{aft}\) weight vectors examples.  
* **Multi-operativity**: It manages the offers of different mobility operators. In the city of Florence, we have 13 main mobility operators (e.g., ATAF, Trenitalia, GEST, etc.). The distribution of their services is not uniform.
* **changing the offer and the demand** in the scenarios of analysis by creating new scenarios, changing the offer and also the services in the maps, and knowledgebase. More details are reported in Section 6.
* **computing KPIs** and related evidence information to perform the PTS analysis and provide support for decision makers about mobility policies in the city. The most relevant KPIs include the number pick-ups and drop-offs at a given stop over time. Moreover, nearby POIs (shopping centers, offices, shops, tourist attractions) along with each vehicle line, the number of vehicle lines and trips in each time interval are the examples of provided information to support the scenario analysis.
* **exploiting a range of data sources**, from the rich Km4 City knowledge base [41; 42; 44] which leads to a more robust computational model, more complete scenario.  
On the DORAM home page (see Figure 9), the Stop panel on the right reports the analysis of the bus stops. It presents the most crowded stops in the city in decreasing order of the sum of daily pick-ups and drop-offs. For each stop in the Stop panel, mobility operator(s) that provide services are shown. Moreover, it is possible to search for stops by text and by clicking in an area. On the bottom, general information regarding the service offer (e.g., # of stops, mobility operators) and the mobility demand (e.g., daily commuters, residential buildings) are reported.  
Figure 9: _Cont_.  
Figure 9: DORAM homepage.  
DORAM also provides KPIs support the decision maker/user to understand in which extent the demand is met by the offer, (see Figure 10) in terms of pick-ups and drop-offs over time. In addition, it is possible to analyze KPIs in different time intervals defined by the user, using the combo boxes on the top of the Stop panel (see Figure 9).  
Evidence information, it is used to support KPIs for each stop. To mention a few; (1) total daily drop-offs, pick-ups and vehicle trips, in the Stop panel (see Figure 9), (2) drop-offs, pick-ups, and vehicle trips, (for instance, see Figure 11a) for each line in each time interval, by clicking on Daily Drop-offs, Daily Pick-ups, and Daily Vehicle Trips in the Stop panel, (3) line information, by clicking on the line direction (see Figure 11b), (4) nearby services (e.g., offices, stores), by clicking on Services on the route button (see Figure 11c), passing vehicle lines and their directions.  
Figure 11: _Cont._  
Figure 10: An example of the DORAM KPIs (pick-ups and drop-offs in each time interval) for a stop.
## 4 Review of the Routing Protocols
### Cluster-Layer-Based Routing Protocol Comparison  
Cluster-based routing algorithms are those that divide nodes in WBANs into different clusters and assign a cluster head for each cluster. Data are routed through the cluster heads from the sensors to the sink. This class of routing protocols aims to decrease the number of direct transmissions from the sensors to the base station. However, the huge overhead and delay relative to cluster selection are the main drawbacks of these protocols. Table 11 shows a comparison between cluster-based routing protocols; the comparison parameters include congestion avoidance, mobility, scalability, energy efficiency and the used methodologies.  
The aforementioned cluster-based routing protocols highlight the following aspects:  
* Most of them are scalable.
* Efficient algorithms are used for cluster-head selection and for optimising end-to-end path selection.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline \multirow{2}{*}{**Protocols**} & \multicolumn{2}{c}{**Comparison Parameters**} \\ \cline{2-3}  & **Methodology** \\ \hline WASP [139] & \(\bullet\) & The distributed tree is used for channel access and multi-hop routing \\  & \(\bullet\) & Parent and child nodes share information \\ \hline CICADA [140] & \(\bullet\) & A distributed approach using a tree-based algorithm with a TDMA-based mechanism \\ \hline TICOSS [141] & \(\bullet\) & Multi-hop communication by dividing the network into time zones in IEEE 802.15.4 \\  & \(\bullet\) & V-scheduling is used for collision avoidance \\ \hline BIOCOMM [142] & \(\bullet\) & MAC layer and routing layer coordination of neighbour tables; the MAC layer keeps updating the routing layer for neighbour status \\ \hline Tree-based energy-efficient routing [143] & \(\bullet\) & Tree-based approach \\  & \(\bullet\) & Provides better energy consumption in two ways: (a) establishing an energy-efficient end-to-end path, (b) adaptive transmission power mechanism for the nodes according to distance \\ \hline Optimising transmission reliability, energy efficiency, and lifetime in body sensor networks [144] & \(\bullet\) & Proposed a cross-layer energy-efficient algorithm that utilises different characteristics of different layers, including the physical layer, media access control (MAC) and the network layer \\  & \(\bullet\) & The proposed structure also uses optimal power control on a single link to reduce power consumption, which, in turn, prolongs the overall network lifetime \\ \hline Thermal-aware routing protocol [145] & \(\bullet\) & To control the temperature of the sensor nodes, two thresholds are defined for the avoidance and recovery of heat-up devices. \\  & \(\bullet\) & Once these thresholds are reached, the node is declared a hot-spot node and its usage is temporarily blocked. After the cooling procedure, the node will once again participate in the data routing procedure. \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparative analysis of cross-layer protocols.
## 3 Results
### Impact of CNCORS on BDS/GPS Joint POD
#### 3.1.1 Overlapping Arcs Precision
These results show that the CNCORS strongly influence the orbit precision of these two types of satellites, especially IGSO satellites, because the main coverage regions of the GEO and IGSO satellites are near the longitude in China. Given that the orbit of the IGSO satellites is close to the shape of a figure of \"8\" and the southern hemisphere has many stations, the station distribution in the whole orbit arc of the IGSO satellites is even, and its precision accordingly experiences the most improvement after adding the stations. However, MEO satellites operate globally, and after adding the CNCORS, the precision is improved by only 5.1%. Therefore, the CNCORS exert a limited influence on the MEO orbit precision.
# Area-Efficient Universal Code Generator for Multi-GNSS Receivers
###### Abstract  
Although conventional global navigation satellite systems (GNSS) receivers were originally designed for single signals, studies on multi-signal receiver design have recently been actively conducted to achieve high accuracy, precision, and reliability. However, in order for a multi-signal receiver to support various codes, the receiver should support the generation of individual codes. Therefore, the resulting problem of increased complexity must be solved. This paper proposes a hardware structure for an area-efficient linear feedback shift register (LFSR)-based multi-frequency universal code generator. Whereas the existing universal code generators were configured so that feedback polynomials, output registers, and initial values can be selected by placing read-only memories (ROMs), multiplexers (MUXs), and exclusive ORs (XORs) by register bit, in the case of the proposed universal code generator; the circuit was implemented by applying the hardwiring technique to those register bits that have fixed values. According to the results of field programmable gate array (FPGA) implementation, the proposed LFSR-based universal code generator can improve look up table (LUT) by up to 37% and register by up to 78% when compared to conventional code generators, and LUT by up to 36% when compared to the previous universal code generator. Therefore, the proposed universal code generator is a good candidate for implementing multi-frequency receivers to achieve high precision and high reliability.  
global navigation satellite system; universal code generator; linear feedback shift register; hardwiring technique +
Footnote †: journal: 2021  
Area-Efficient Universal Code Generator for Multi-GNSS Receivers  
Footnote †: journal: 2021  
10
## 5 Conclusions
### Future Perspectives  
The technology evolutions have had a steady impact on the range of audio effects available; however, there seems to be little work encompassing the latest growth in machine learning technologies to the use of audio effects. There is use of these technologies to model and represent existing audio effects [170; 171; 172] to represent the entire mixing process [165] or to improve the ability for audio cleanup technologies [175]. However, although we have identified some examples in this article, there has been limited use of this technological advancement to produce new types of audio effects or to facilitate the ability for mix engineers to interact with audio in a completely new way. The scope of technological advancements are consistently in a position to produce more interesting and diverse creative approaches [173; 176].  
There has been a recent growth in the use of adaptive audio effects in automatic mixing [106; 152]. As discussed in [163], there are a number of opportunities for the use of machine learning approaches to learn the _direct transform_ of audio, allowing data-driven computational approaches to learning signal processing systems. The advantages of this approach could produce a multitude of new and different audio effects which are less restricted by traditional analogue hardware design and DSP and instead can represent some more intuitive, or perceptual, attributes or high-level transformational space of audio. The scope and flexibility of this approach lends itself well both to recent technological advancements and to the continual trends of audio effects over the years and their ability to grow with the latest technology whilst still maintaining a grounding in the historical implications and definitions of the technology used to create them.  
Conceptualization, T.W., D.M., and A.M.; writing, T.W., D.M., and A.M.; supervision and funding acquisition, M.B.S. All authors have read and agreed to the published version of the manuscript. This research was funded by EPSRC grant EP/L019981/1 Fusing Audio and Semantic Technologies for Intelligent Music Production and Consumption, by EPSRC grant EP/S026991/1 RadioMe, and by the CdT Media and Arts Technology through the EPSRC grant EP/G03723X/1.  
[PERSON] acknowledges the support of the Royal Society as a recipient of a Wolfson Research Merit Award. The authors declare no conflict of interest.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 8 Limitations  
Due to the advancement and applications of modern radio communication technology, the electromagnetic environment has become more complex, which puts forward higher requirements for the analysis and processing of communication signals. Improving the radio modulation recognition efficiency and accuracy has great benefits for various military and civil fields such as electronic detection, spectrum monitoring, etc. By analyzing and researching various methods of radio modulation recognition based on DL, it is found that most methods still have some defects as described below.  
The recognition rate of SNR below 0 dB is low. It can be seen from the literature worldwide that the recognition rate of radio modulation based on DL decreases with the decrease of SNR. Generally, the recognition rate for SNR above 10 dB is higher, about 90% and the recognition rate of SNR above 0 dB can reach 80%. However, the recognition rate of SNR below 0 dB is very low, generally less than 50%. Therefore, further research is needed to improve the recognition rate of SNR below 0 dB.  
It is still difficult to use various models based on DL in practical applications. The signals of the datasets that have been published are ideal, with almost no noise and lossless channels. However, the real channel environment is very complex. Actual noise and other uncertain factors will have a great impact on modulation recognition. The difficulty of modulation recognition far exceeds that of signal recognition simulation. This is also an important research direction in the future.  
There is some difficulty in implementing DL-based AMR methods on real hardware. At the moment most researcher are working on simulation, for example on computer software. However, very few researcher are working on implementation on real hardware. Deploying DL-based AMR to real hardware is very challenging and requires further investigation.
# Classic Discrete Control Technique and 3D-SVPWM Applied to a Dual Unified Power Quality Conditioner
###### Abstract  
The unified power quality conditioners (UPQCs) are useful to correct distortions in voltage and current waveforms in case of problems related to harmonics, flicker, and power factor. The dual topology of the UPQC allows a loop control in the inverters less demanding in terms of switching functions; however, the control techniques proposed in the literature have some disadvantages as large computation time and some calculation delays. Therefore, this paper presents the application of a classic discrete-time control model for a three-dimensional space vector pulse width modulation (3D-SVPWM) to be used with the dual UPQC, obtaining a fixed commutation frequency and a low computation cost. The results show that the applied method helps to reduce the harmonics in the source, voltage sags and load current and improve the power factor of the electrical circuit tested. Furthermore, the high frequency created by the switching of elements in the UPQC is filtered by the impedance of the power source and it does not represent a problem for the circuit. Due to the simplicity of the model, simulations demonstrate that the application of this classical control technique is enough to achieve good results to compensate harmonics and power factor with the dual UPQC. The development of the controllers is carried out by discretization of the transfer functions of the control in continuous time and their application with the three-dimensional space vector pulse width modulation (3D-SVPWM) technique.  
active power filter (APF); series compensator; shunt compensator; three-dimensional space vector pulse width modulation (3D-SVPWM); classic control
## 5 Conclusions
### Limitations of the Study  
#### 5.1.1 Study Design and Absence of a Control Group  
A key limitation is the lack of a control group receiving traditional classroom instruction. This study was designed as a pilot investigation, primarily aimed at assessing the feasibility and effectiveness of \"Parabolic Basketball VR\" as a learning tool for projectile motion. The single-group pre- and post-design was chosen due to logistical constraints and the exploratory nature of the research. However, we acknowledge that a comparative study involving a control group exposed to conventional instructional methods (e.g., lectures, videos, or text-based study) would provide a more robust evaluation of the relative benefits of IVR-based learning. Future research should implement a between-group experimental design, where two groups undergo the same pre- and post-tests, with one group learning through IVR and the other through conventional methods.
## 3 Verification of the Proposed Method
### Specifications in Simulation and Experiment  
In [PERSON] et al. [35], a single straight-tooth end-mill was used, for single tooth could reduce the influence of the cutter runout error. The cutter material is sintered carbide K10 without coating. The heat conductivity \(k\) and thermal diffusivity \(K\) of the material are 67 W/(m\(\cdot\)K) and \(2.2\times 10^{-5}\) m\({}^{2}\)/s, respectively. The details of the tool insert are depicted in Figure 7. A blind hole is drilled near the tool tip to install an optical fiber, which could capture the temperature signal in machining process. The radius of the hole is 0.5 mm, and the position of the hole is also clearly shown in Figure 7. The bottom of the blind hole lies underneath the rake face [0.1, 0.2, 0.3, 0.4, 0.5] mm for different measurement purposes. While the insert is mounted on the toolbar, the rake angle \(\alpha_{r}=0^{\circ}\), clearance angle \(\alpha_{c}=5^{\circ}\), and cutter radius \(R=26\) mm. The insert edge radius \(R_{0}=0.01\) mm. In [PERSON]'s simulation, the cutter is modeled as a rigid and heat transferable body. The material of the workpiece is Ti-6 Al-4V, and its conductivity coefficient and specific heat are 7.955 W/(m\(\cdot\)K) and 612 J/(Kg\(\cdot\)C), respectively. The initial temperatures of the cutter and workpiece are all set to 25 \({}^{\circ}\)C (the environment temperature). The friction coefficient of the given tool-workpiece pair is set to \(\mu=0.6\). The machining parameters used in this verification are presented in Table 2.  
Figure 7: Details of the insert and the blind hole to install optical fiber ([PERSON] et al. [35]).
## 4 Results and Discussion  
In this section, the effectiveness of wavelet transform with various diversity techniques and the effectiveness of various wavelet families for MIMO diversity techniques are explored. Additionally, analytical findings are used to validate the observed performance. Our work was purely a simulation, and experimental work was out of our scope due to the constraints of equipment and cost. In order to validate our simulation work in accordance with theoretical work, we compared our simulation results with the mathematical model described in Section 3.4 so that the parameters of SNR and BER were correctly measured.
## 2 Test Site  
The test field is an alluvial plain of the Han River in Seoul, South Korea. This area can be classified as granitic rocks of the Precambrian period, and granites of the Mesozoic period. The results of the site investigation revealed that deposits at the site (Figure 2a) were landfill at the depths of 0-8 m (sandy silt, _SM_ classified by ASTM D2487-17e1 ([PERSON] and [PERSON] (2011)), a sedimentary layer at the depths of 8-16 m (Clay with low plasticity, _CL_), weathered rock at the depths of 16-21 m (poorly graded sand, _SP_), and a soft rock at the depths deeper than 21 m. A standard penetration testing (SPT) showed average measured \(N\)-values (\(N_{\text{measured}}\)) of 6.3 and 11.5 blows per 30 cm for the landfill and sedimentary layers, respectively. Details of the \(N\)-values are not shown, as the landfill and sedimentary layers were excavated and out of the research target. The weathered rock and soft rock layers showed high penetration resistances as \(N_{\text{measured}}\) = 50 blows per 5-25 cm. The rock quality designation (RQD) and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Location** & **Features** & **Results** & **Reference** \\ \hline \multirow{3}{*}{1996} & Johnson, & & A ground surface was disturbed & \\  & Tennessee & Irregular and poor rock layers & significantly due to drilling. Accordingly, & \\  & & beneath soft to firm clay layers & soil softening and localized & \\  & & & subsidence occurred. & \\ \hline \multirow{3}{*}{2004} & Richmond-San & High capacity micropile; Grout & Failed to field test. Post grouting was & \\  & & thixotropy when the mix water, & attempted twice, but failed due to rapid & \\  & Rafael Bridge, CA & ambient air, the extreme & strength gain with strong & \\  & & temperature of bagged cement, etc. & lateral confinement. & \\ \hline \multirow{3}{*}{2005} & Knoxville, & Battered micropiles, thick grout & The central reinforcing bar failed to install & \\  & Tennessee & mixture, poor quality control, & to the bottom of the drilled hole; therefore, & \\ \cline{1-1}  & & tight-fitting centralizers, etc. & not passed the field tests. & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Historical failures associated with micropiles.  
total core recovery (TCR) of the soft rocky layer indicate that the rock quality of the layer is poor in general (average RQD = 37% and TCR = 83%). Using a borehole image profile system (BIPS), the evidence of the fractured zone was identified with major and minor faults (Figure 2b). The fault zone and weak zone showed variable thicknesses from a shallow thickness (3-4 m) to a heavy thickness (8-10 m).  
The groundwater table exists at EL. \(-\)5.5 m. An excavation and cutoff walls were required for underground construction. The groundwater should be pumped during the construction. The construction site is a nearby urban area. A deep foundation system may cause construction difficulties and civil complaints. This situation exactly corresponds to those depicted in Figure 1. Therefore, micropiles were selected as the foundation system of the underground structure.
## 5 Proposed Model
### Density-Based Clustering
#### 5.2.5 Cluster Leader Selection  
From the clustered WSN nodes, the optimal cluster leader (CL) is selected based on metrics such as high residual energy (\(\mathrm{res}^{\mathrm{ene}}\)), less mobility (\(\mathrm{mob}\)), less distance (\(\mathrm{dis}\)) from the local gateway, and high trust score (\(\mathrm{tru}\)) (i.e., provided by the neighbor nodes and local gateway based on its behavior). Overall, the clustering of WSN nodes overcomes the issues of unwanted energy consumption and complexities in the SDWSN environment. Algorithm 2 provides CL selection method.
# Penetration Overload Prediction Method Based on a Deep Neural Network with Multiple Inputs
## 2 Related Work  
In the past, domestic and foreign scholars used traditional mechanical filtering or original signal filtering methods according to a single cut-off frequency [10; 11] to filter penetration signals. Such methods lack effective theoretical support and have difficulty meeting the requirements of high accuracy. Reference [12] used singular value decomposition to reduce the signal noise, and the reconstructed penetration overload curve could better reflect the penetration process. Reference [13] was able to better decompose multichannel mixed signals by using underdetermined blind source decomposition and feature extraction. In reference [14], a multilayer perceptron (MLP) was used to predict the number of penetration layers, but without considering the impact beyond the time sequence, the losses of training set and test set were large. References [15; 16; 17; 18] performed signal preprocessing on experimental data and simulation data based on empirical mode decomposition [19] and variable mode decomposition [20], respectively, which can clearly decompose the intrinsic mode functions (IMFs). References [21; 22] carried out box-filter processing on the original penetration signal and dynamic threshold algorithm processing on the signal envelope. A new identification method was proposed in reference [23]; this approach uses a short-time Fourier transform to filter multilayer penetration signals and obtain layer number information according to the signal frequency components. References [24; 25] conducted filtering analysis on the penetration overload signals of different types of warheads, such as flat-nosed projectiles and earth-penetrating projectiles, to study their internal mechanisms. References [26; 27] studied the penetration overload mechanism at high and low penetration speeds.