esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance traveled in warehouses and distribution centers where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. We developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave), and Pennylane locally or remotely. The proof of concept, when run in the above stated quantum environments, showed the execution time of our algorithm with different public access simulators on the market, computational results of our picking and batching algorithm, and analyze the quantum real-time execution. Our findings are that the behavior of the Amazon Braket D-Wave is better than Gate-based Quantum Computing over 20 qubits, and that AWS-Braket has better time performance than Qiskit or Pennylane.  
quantum computing; machine learning; picking problem; batching problem; quantum robotics; Raspberry Pi4; docplex +
Footnote †: journal: Accepted: 23 June 2021  
quantum runs that are less susceptible to the cumulative effects of noise and work well in current devices.  
Recently, the scientific community has begun researching the real implementation of quantum computing algorithms in mobile platforms because implementations are not here yet [10].  
In this article, we demonstrate that we can implement this system in a well-known, widely used in robotics fields, computer system, like raspberry-pi, exploring the performance of a quantum picking and batching model. A hybrid system is proposed to effectively replace the current ones and open the doors to quantum computing in robotics. In addition, we are analyzing the results obtained with different public access simulators on the market: IBMQ, Amazon Braket (D-Wave), and Pennylane. As far as the authors know, this is the first time this type of implementation has been done.  
After Section 1, the document is organized as follows; Section 2 shows previous work on both assembly techniques and approaches to picking and batch management systems; then, Section 3 presents the quantum fundamentals needed from this era to solve this problem; next, the implementation of the proposed strategy and the creation of the qRobot performed in Section 4 are explained; to continue, Section 5, which shows the results of our experimental analysis, and Section 6, in which some open problems are summarized, compared, and presented; and, finally, Section 7 concludes the previous results and describes the future work.
## 1 Introduction
Nevertheless, the social and financial interests could not be aligned [3]. The technology transfer professionals in universities mentioned that the commercialization pressure linked with licensing revenue has been augmented in the last 15 years [4]. Additionally, it was presented in [5] that technology transfer offices that adjust the portfolio over their aspirations performance archive better results regarding the productivity of the technology transfer office. The question of \"value\" generated for some TTOs tends to focus more on public perception regarding the volume of patents or various national and peer rankings. These conditions do not promote the transfer of innovation. The question of financial return seems to be a lower level \"value\" generator. Thus, little quantitative effort has been made to understand and improve the odds that a TTO will be self-sufficient. Technology transfer activities can also generate income called technical income, and this income is a valuable reference for assessing a TTO. The activities can be classified as technology trade, testing, consulting, etc. [6]. However, very little work has been performed to produce TTO income models, and long-term tracking of TTO income has yielded minimal insights. One of the first papers to explore financial returns identified artificial intelligence as a sweet spot in terms of income and patent volume [7]. Except for Professor [PERSON]'s paper [7], little effort has been made to understand the underpinnings of income generated by TTOs. Understanding the financial performance of TTOs has been hampered by a lack of basic income generation models, leaving open the questions:  
* Is it luck? * Is it random? * Is it a lottery? * Do more \"irons in the fire\" increase the chances of greater financial returns? * Is there a characteristic statistical distribution? If one could tease out an underlying probability distribution from historical income data, one could explore modeling income and strategies to improve a TTO's financial performance. Reviewing selected historical TTO income data yields statistical distributions similar to those underlying historical venture capital return, which can be used to model TTO income. To gain insight into TTO IP portfolios' potential, we developed a model based on historical income, power-law distributions [8], and Monte Carlo techniques [9]. This model, primarily based on historical data, is the first step to understanding the income potential of TTOs and can help define and evaluate a portfolio's investment strategy. This model does not forecast future income but the statistical probability of income generation based on a TTO's historical results. This kind of modeling/simulation enables probabilistic assessment but not point predictions. Roughly speaking, the model does not predict next year's income or shorter time horizons; without a detailed cash flow history, estimating next year's income is highly unlikely to be an accurate or reliable prediction. However, this model can illustrate the portfolio's probability of returning future positive cash flows (or not).
## 5 Practical Application Research and Discussion of Results
### Discussion  
The conducted analysis of the decision-making process using standard methods--specialist recommendations and specialized textbooks--compared with the application, revealed that errors in decision-making in the diagnosis and treatment of nephron diseases--specifically, focal segmental glomerulosclerosis with nephrotic syndrome--were identified in 17 out of 127 patients. Although the number of cases and clinical significance of the detected errors were rather insignificant, a comparative analysis of the outcome measures between the group of patients who had errors and those who did not was not conducted, mainly due to the small number of these patients and the lack of statistical significance regarding the achievement of outcome measures. Traditional methods were associated with a higher error rate (13.4%) in comparison to the potential improvements observed during application-assisted decision-making, emphasizing the tool's capability to reduce errors and enhance diagnostic and therapeutic efficiency. The study showed that the use of the application could assist the physician in the diagnostic and therapeutic process, reduce the time needed for accurate diagnosis and treatment, and improve the efficiency of the verification process of treatment effectiveness for kidney diseases. Further prospective studies with detailed analysis of the treatment process in groups with and without the application are required.
## 2 Materials and Methods
### Simulations  
The performance of coded excitation imaging scheme was first investigated in silico through the simulation software Field II [23; 24] based on the phased array described at the beginning of this section. To benchmark, an SLT imaging scheme, 2, 4, and 6-MLT schemes using a burst excitation (1.45 cycles square wave at 2 MHz) were included. For all imaging schemes, the transmit focal point was maintained at a depth of 70 mm and a dynamic apodization with # of two was adopted in reception. As previously proposed, a Tukey (\(\alpha\) = 0.5) -Tukey (\(\alpha\) = 0.5) (TT) apodization scheme on transmit and receive were also tested on different MLT schemes (coded and non-coded) for crosstalk suppression [8; 9].  
To appreciate the spatial resolution as well as the crosstalk artifacts, the point-spread-functions (PSFs) of different imaging schemes were simulated. In particular, four point scatterers were positioned at depths from 30 mm to 90 mm with an equal axial interval of 20 mm. In the azimuth direction, they were positioned at 10 mm offset of the transducer symmetry axis to better appreciate the potential crosstalk. A given PSF profile was evaluated by the \(-\)6 dB beam width in both radial and lateral directions. The crosstalk was quantified by calculating the intensity difference between the PSF of a  
Figure 4: Frequency response of the two chirp codes.  
Figure 5: Illustration of the interleaving arrangement of two orthogonal codes on a given MLT imaging scheme. Code 1 and code 2 are mutual orthogonal codes.  
given MLT scheme and that of the SLT imaging scheme (_xtalkpsf_). For details on the definitions of these parameters, please refer to Formulas (3), (5), and (7) in Reference [9].
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Femtosecond Laser Radiation
The chirped pulse amplification (CPA) technique often produces pre-pulses containing a significant amount of energy [94]. A high contrast ratio of 100 or more is required between the energy of the main pulse and any pre-pulses to make the effects of pre-pulses on ablation negligible [105]. An additional source of a pre-pulse is amplified spontaneous emission (ASE) from the laser amplifiers. To eliminate ASE, the laser design typically includes successive stages of amplification interspersed with spatial filters. Another important effect produced by fs-radiation is filamentation, which is the consequence of self-focusing of the laser beam while travelling from the laser output. The high-intensity central part of the wave front would see a higher index of refraction in the transfer medium than the low-intensity edge due to a nonlinear increase of the index of refraction. Thus, the light wave propagates slower through the central part of the lens than through its edges, resulting in the curvature of the wave front toward the axis of propagation. Sufficiently intense femtosecond laser pulses undergoing self-focusing in any transparent optical medium (including air) will collapse to the generated plasma. During filamentation of a femtosecond laser pulse, some fundamental nonlinear processes are involved in the propagation and interaction, including nonlinear pulse phase change [106]. A recent review on fs-LIBS discussed filamentation effects in the context of this spectroscopic method [38].
## 3 Results
### Comparative Analysis of PPP and Static Relative Methods in Urban Areas
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Station** & \begin{tabular}{c} **Orthometric** \\ **Height with** **PPP(m)** \\ \end{tabular} & \begin{tabular}{c} **Orthometric** \\ **Height with Static** **Relative (m)** \\ \end{tabular} & \begin{tabular}{c} **Absolute** \\ **Differences** \\ \end{tabular} & \begin{tabular}{c} **Ellipsoidal** \\ **Height with PPP** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Ellipsoidal Height** \\ **with Static (m)** \\ \end{tabular} &
\begin{tabular}{c} **Ellipsoidal Height** \\ **Difference** \\ \end{tabular} \\ \hline Est1 & 39.33 & 39.38 & 0.05 & 11.72 & 11.77 & 0.05 \\ Est2 & 38.15 & 38.24 & 0.09 & 10.53 & 10.62 & 0.09 \\ Est3 & 39.00 & 39.04 & 0.04 & 11.40 & 11.44 & 0.04 \\ Est4 & 39.54 & 39.56 & 0.02 & 11.94 & 11.96 & 0.02 \\ Est5 & 39.42 & 39.44 & 0.02 & 11.81 & 11.83 & 0.02 \\ Est6 & 38.78 & 38.80 & 0.02 & 11.17 & 11.19 & 0.02 \\ ISL1 & 39.76 & 39.18 & 0.58 & 12.15 & 11.56 & 0.59 \\ ISL2 & 38.41 & 38.03 & 0.38 & 10.80 & 10.42 & 0.38 \\ ISL3 & 38.18 & 38.18 & 0.00 & 10.56 & 10.57 & 0.01 \\ LCSF & 38.77 & 37.90 & 0.87 & 11.16 & 10.29 & 0.87 \\ LCDF & 40.37 & 40.38 & 0.01 & 12.76 & 12.76 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Heights obtained using the GGM10 model and the absolute differences. Figure 13: Absolute coordinates differences between the static relative and the PPP methods.
## 5 Experimental Investigations  
This section encompasses experimental evaluations and a comprehensive analysis of the proposed methodology in the paper. We commence by presenting a detailed account of the experimental setup (Section 5.1), encompassing an overview of key components and configurations employed for the evaluations. Following this, baseline results are introduced (Section 5.2), serving as reference points for the enhancements achieved by the proposed approach. Subsequently, a concentrated focus is placed on experiments conducted on the CADS dataset (Section 5.3), showcasing the effectiveness of the Auto-HPIF model within the context of a Chinese in-vehicle environment. Section 5.4 extends the analysis to encompass public datasets, demonstrating the generalizability of the Auto-HPIF model across diverse domains. Furthermore, an ablation study is conducted (Section 5.5), dissecting the impact of the primary innovations proposed in this paper on the model's performance. Lastly, an error analysis (Section 5.6) is conducted to gain deeper insights into failure cases and identify potential directions for future improvements.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 4 Experiments
### Energy Consumption and Latency
Additionally, variations in the channel conditions can introduce further challenges, potentially reducing the reliability of PURs and necessitating more frequent fallbacks to the RAP. These factors must be considered when evaluating the effectiveness of PUR schemes, particularly in networks with varying loads and device types.
## 1 Introduction
In normal spinel, A\({}^{2+}\) has low crystal field stabilization energy (CFSE) and occupies tetrahedral sites. B\({}^{3+}\) has high CFSE and occupies octahedral sites. It is represented as (A\({}^{2+}\))tet(B\({}^{3+}\))tetO\({}_{4}\). In inverse spinel, A\({}^{2+}\) has high CFSE and occupies octahedral sites, and B\({}^{3+}\) has low CFSE and occupies some  
Figure 1: Normal spinel structure of Co\({}_{2}\)MnO\({}_{4}\). Component structures, namely, tetrahedral and octahedral, are shown on the right-hand side [22]. Copyright 2010, AIP Publishing. octahedral and some tetrahedral sites. It is represented as (B\({}^{3+}\))tet(A\({}^{2+}\)B\({}^{3+}\))octO\({}_{4}\). The complex spinel is the intermediate between the normal and inverse spinel structure in which both A\({}^{2+}\) and B\({}^{3+}\) cations partially occupy the two various sites [23; 24]. Among the three types, normal spinel structures are generally cubic close-packed oxides with two tetrahedral and four octahedral sites per formula unit. B\({}^{3+}\) ions occupy half the octahedral holes, and A\({}^{2+}\) ions occupy one-eighth of the tetrahedral holes. The tetragonal phase presents the different sets of active lattice planes ready for the O\({}_{2}\) adsorption and desorption process, which differs from the cubic phase. Spinels have gained more attention due to their easy synthesis methods, stability, structural flexibility, and a minimum of two orders of magnitude higher electrical conductivity than the equivalent single-metal oxide and helpful contributions from all A and B ions with multiple valence states (A\({}^{3+}\)/A\({}^{2+}\) and B\({}^{3+}\)/B\({}^{2+}\)) in the crystal structure [25]. Hence, spinel-based materials possess fair electrochemical activity. Particularly, in the key ZAB reactions such as ORR and OER kinetics (Figure 2), the adsorption and reduction of O\({}_{2}\) vastly depend on the d-orbitals of cations placed in octahedral B sites relative to tetrahedral A sites. This phenomenon occurs due to the substantial overlap of the high lying e\({}_{g}\)-3d orbital in the octahedrally coordinated metal cations with an orbital of O 2p and the weak overlap of the 3d orbital in the tetrahedrally coordinated metal cations with an oxygen atom [26]. Based on the molecular orbital theory, the presence of octahedral site active cations in, e\({}_{g}\)orbital accounts for spinel activity [27]. Thus, the structural flexibility and variable valence states of the spinel oxides demonstrate the potential to fine-tune their catalytic performance in zinc-air batteries. To date, several researchers have investigated spinel-based bifunctional oxygen electrocatalysts for ZABs. In this review, we summarize the role of spinel-based compounds as the bifunctional oxygen electrocatalysts for ZABs with a brief introduction and discussion of the batteries' mechanism along with the mechanism in the air cathode.
# Service Life Prediction and Life Cycle Costs of Light Weight Partitions
## 3 Methodology
### Summary  
The method presented in this chapter was developed to predict the deterioration pattern and service life of interior residential building components (in this case, partitions). The method is based on collecting observations through field surveys, systematically rating them according to predefined performance scales, and using statistical means to analyze findings and predict parameters necessary for determining the component's service life expectancy in standard service and failure conditions.  
The method offers notable advantages due to its systematic approach, enabling observation of the deterioration rate of partitions in residential buildings and the prediction of service life expectancy. It also effectively supports life cycle planning and the calculation of life cycle costs for residential construction processes. However, the method requires substantial investments of time and effort into conducting field surveys, gathering data, and establishing an appropriate sample database for each partition type, which is a significant limitation.
# Narrowband Internet of Things (NB-IoT):
## 2 Narrowband-IoT Standard and Releases
### Release 13
#### 2.1.5 Physical Channels and Signals  
NB-IoT adopts the same frame structure as LTE, with 1024 hyper frames, consisting of 1024 frames that contain 10 subframes of two slots with a duration of 0.5 ms each in the time domain. Similarly, in the frequency domain, NB-IoT contains 12 subcarriers of 7 OFDM symbols mapped in each slot. In addition to that, when NB-IoT uses the 3.75 kHz spacing on the uplink, 48 subcarriers are used with a slot duration of 2 ms.  
The following channels and signals are used in the uplink:  
* Narrowband Physical Random Access Channel (NPRACH).
* Narrowband Physical Uplink Shared Channel (NPUSCH).
* Demodulation Reference Signal (DMRS).  
And the following are in the downlink frame:  
* Narrowband Physical Downlink Shared Channel (NPDSCH).
* Narrowband Physical Downlink Control Channel (NPDCCH).
* Narrowband Reference Signal (NRS).
* Narrowband Primary Synchronization Signal (NPSS).
* Narrowband Secondary Synchronization Signal (NSSS).
* Narrowband Physical Broadcast Channel (NPBCH).  
In general, NPRACH is used by UEs to perform initial access to the network, to request transmission resources, and to reconnect to the base station after a link failure. NPDSCH and NPUSCH are used to carry the downlink and uplink data packets transmissions, respectively. DMRS is used for uplink channel estimation accuracy. The UE acquires Master Information Block (MIB) from NPBCH and System Information Block (SIBs) from the NPDCCH. The defined MIB and SIB are broadcasted once during 640 ms and 2560 ms intervals, respectively. The timing of the remaining SIBs is configured in SIB1-NB. NRS is used for cell search and initial system acquisition. NPSS and NSSS are used by the UE for its frequency and timing synchronization with the base station. Due to overhead scheduling gaps in NPDCCH, the downlink and uplink peak data rates are \(\sim\)250 kb/s and \(\sim\)2267 kb/s, respectively, [34; 40; 41; 42; 43].
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 5 Packet Generators
### Evaluation Technique  
The experiments for the tool comparison in this paper have been carried out using the \(\mathcal{T}_{\text{base}}\) topology with links running at 100 Mb/s. Initially, \(l\) has been set to 64 B (the minimum packet length allowed by the standard) and \(\omega\) has been varied, in a few discrete steps, from 100 us down to 10 us, in order to bring \(\omega\) close to its minimum value, which is approximately 6.7 us for these values of link speed and \(l\). Further experiments performed with larger \(l\) (and not shown here) confirmed that this scenario represents the most critical case for packet generators, because it maximizes the computational and network processing burden put on them. The maximum inter-packet interval was set at \(\omega=100\) us, corresponding to a link load of less than 10% when \(l=64\) B, because lower loads were considered of no interest for DUT evaluation.  
Generators were configured to nominally send \(m_{\omega}=10^{5}\) packets per experiments. The number of packets actually sent may differ, depending on the rate control algorithm used by each generator, but was found to be within 0.07% of the nominal value in all cases.  
If \(t_{M}\) and \(t_{M}^{\prime}\) are the absolute times at which two consecutive packets arrive at the \(n_{M}\) port (\(t_{M}^{\prime}>t_{M}\)), then \(t_{M}^{\prime}-t_{M}\) represents exactly the inter-packet interval, as _TAP_ introduces a constant, albeit unknown, delay. Taking (5) into account, we have \(t_{M}^{\prime}-t_{M}=(u_{M}(t_{M}^{\prime})-c_{M})-(u_{M}(t_{M})-c_{M})=u_{M}(t_ {M}^{\prime})-u_{M}(t_{M})\), meaning that we can ignore the offset \(c_{M}\). The sample mean \(\bar{X}_{\omega}\) and standard deviation \(s_{\omega}\) of the inter-packet interval have been used as estimators of the true mean and standard deviation to evaluate packet generators' performance.
# Different Object Functions for SWIPT Optimization by SADDE and APSO
## 2 System Model
### Antenna Array  
The deployment of the transmitting antenna array is shown in Figure 2 and we apply a beamforming technique to focus the intensity to reduce the path loss in the environment. The circular antenna array is useful for adjustment with multiple receivers, as the deployment is symmetrical.  
The array factor is used to calculate the field and can be derived as [27]  
\[AF(\theta,\phi,f)=\sum_{n=1}^{M}W_{n}e^{jkr[sin\theta sin\theta_{n}cos(\phi- \phi_{n})+cos\theta_{n}cos\theta]} \tag{5}\]  
where \(\theta\) and \(\phi\) are the spherical coordinates system. \(\theta_{n}\), \(\phi_{n}\) and \(r\) are the elevation angle, azimuth angle and the distance of the antenna position respectively. \(M\) is the total number of antennas in the antenna array. \(k\) is the wavenumber. The adjustment of phase delay and magnitude power can be expressed as  
\[W_{m}=I_{m}e^{iD_{m}(f)} \tag{6}\]  
where \(I_{m}\) and \(D_{m}(f)\) are the excitation current and the phase delay. The relation of feed length and \(D_{m}(f)\) can be expressed as  
\[D_{m}(f)=-\frac{2\pi f}{c}\sqrt{\epsilon_{r}}\cdot\ell_{n} \tag{7}\]  
Figure 2: The deployment of antenna array.  
where \(\varepsilon_{r}\) is the relative permittivity of feed line and \(\ell_{n}\) is the feed length used to adjust the phase delay. \(c\) is the light speed.