esa-sceva/satcom-chunk-collection huggingface dataset:
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 5 Research Methodology
### Requirement Validation for the AIRDROP Platform
#### 5.2.2 Data Collection  
The questionnaire was accessible to responders for a period of two months, i.e., from the 1 st of July to the 31 st of August 2023. Particularly in studies based on online surveys, choosing a suitable data collection timeframe is an essential component of the study methodology. The two-month period of time was chosen to guarantee an adequate number of responses. The sample size is a crucial factor in survey-based research for providing statistically significant results. The AIRDROP questionnaire, due to its length resulting from the wide range of HL topics covered, demanded a large time and energy commitment from respondents, which may have reduced the number of prospective responders. In order to prioritize the quality and depth of responses over the number of participants, the research team decided to select a relatively small sample size of 20-30 people. As a general guideline, researchers are urged to identify the minimum necessary sample size according to the particular research objectives [82; 83; 84; 85; 86]. The selected sample size is also aligned with a similar study that validated requirements for a comparable HL robot-assisted platform, which demonstrated the suitability of 24 participants to obtain diverse and in-depth insights [87].  
Overall, there were a total of 26 responses to the AIRDROP survey during the course of the two-month data gathering period, a response rate deemed satisfactory, falling within the expected range. Reaching this goal sample size allowed the study to collect a diversified dataset, encompassing a wide variety of nuanced viewpoints from respondents with varying backgrounds and roles, thus ameliorating the findings' representativeness and bolstering their overall robustness.  
Several measures were taken during the development of the questionnaire and data collection, with a view to ensuring the validity of the data collected. First, the participants were informed of the confidentiality of their responses, thus creating a safe environment for them to express themselves. Second, a pre-test was carried out with a small group of individuals (4) to collect feedback on the questionnaire's overall coherence and comprehensibility. This step allowed for the identification of any potential ambivalences or intelligibility issues, making sure that the questionnaire was easy to understand for all participants. After this preliminary test, the team decided to limit the technical jargon to the minimum extent possible and rephrase or eliminate several requirements, deemed confusing or ambiguous. Moreover, clear instructions were given to guide the respondents on how to complete the questionnaire accurately and effectively.
# Interaction of Machining Parameters on MRR of Sintered NdFeB Processed by EDM-Milling
## 4 Interaction of Machining Parameters on the MRR
### Interaction of Pulse off Time and Current on the MRR  
The interaction curve of current and pulse off time on MRR is shown in Figure 12. The horizontal axis at the top is the value of pulse off time. The vertical axis on the right side is the change in MRR due to the change in current when the pulse off time is set at the corresponding value shown by the horizontal axis. The curve is marked as the influence of current. Using the same method, the curve representing the change in MRR with variations in pulse off time under a certain value of current is drawn and shown in Figure 12, marked as the influence of pulse off time.  
The \(\triangle_{\rm{Toff}}(I)\) is 26.87 mg/min when the pulse off time is set at different values, and the \(\triangle_{I}\) (\(T_{\rm{off}}\)) is 17.18 mg/min when the current is set at different values. Therefore, there is  
Figure 11: Interaction curve of current and pulse on time on MRR.  
Figure 12: Interaction curve of current and pulse off time on MRR.  
no interaction between current and pulse off time on MRR. Both \(\triangle_{\mathit{Toff}\,I}\) and the \(\triangle_{\mathit{1\,Toff}}\) are 0 mg/min, as listed in Table 2.  
The reasons can be explained as follows. When the current is set at a certain value, pulse off time influences the discharge frequency and then the discharge energy per unit time. On the other hand, pulse off time influences the deionisation time and then the utilization of the pulse discharge energy. The above two will result in the variation of MRR, which will be opposite to each other. Specifically, the discharge frequency increases with the decrease in pulse off time, which will be favourable to the improvement of MRR. However, more residual heat and corrosion products remain in the discharge gap. The deionisation ability of the inter-polar dielectric deteriorates and there will be more secondary discharges. As a result, the utilization of energy will be decreased, which will be unfavourable to the increase of MRR. Conversely, the discharge frequency decreases with the increase of pulse off time, which will be unfavourable to the improvement of MRR and the utilization of energy will be increased, which will be favourable to the increase of MRR. Thus, there is no obvious change in MRR with changes in pulse off time. Namely, the significance of the effect of the pulse off time on MRR is not influenced by current.  
The significance of the effect of the current on MRR is not influenced by pulse off time. As analysed in Section 4.5, discharge energy increases with the increase in current, which is favourable to the increase of MRR; however, the utilization of energy will be decreased, which will be unfavourable to the improvement of MRR. In summary, there is no interaction between current and pulse off time on the MRR.
## 3 Results
No differences were reported for what concerns the BclI SNP (Figure 3). \begin{table}
\begin{tabular}{c c c c} \hline  & **GG (N = 11)** & **AG (N = 68)** & **AA (N = 93)** \\ \hline Blood eosinophils \textgreater{} 300 cells/uL & 3/10 (30.0\%) & 29/65 (44.6\%) & 38/88 (43.2\%) \\ \hline ICS/day (ug BDP-HFA) & 300.0 \(\pm\) 279.3 & 322.1 \(\pm\) 201.4 & 339.8 \(\pm\) 209.6 \\ \hline LABA & 7/11 (63.6\%) & 54/68 (79.4\%) & 66/93 (71.0\%) \\ \hline LAMA & 1/11 (9.1\%) & 12/68 (17.6\%) & 10/93 (10.8\%) \\ \hline OCS (\(\geq\)6 months/year) & 0/11 (0.0\%) & 1/68 (1.5\%) & 4/93 (4.3\%) \\ \hline Omalizumab & 1/11 (9.1\%) & 7/68 (10.3\%) & 9/93 (9.7\%) \\ \hline Appolizumab & 0/11 (0.0\% & 1/68 (1.5\%) & 3/93 (3.2\%) \\ \hline Osteoporosis & 1/11 (9.1\%) & 3/68 (4.4\%) & 6/93 (6.4\%) \\ \hline GERD & 4/11 (36.7\%) & 11/68 (16.3\%) & 28/93 (30.1\%) \\ \hline AERD & 2/11 (18.2\%) & 15/68 (22.1\%) & 17/93 (18.3\%) \\ \hline Persistent Rhinitis & 10/11 (90.9\%) & 57/68 (83.8\%) & 78/93 (83.9\%) \\ \hline CRSsNP & 2/11 (18.2\%) & 15/68 (22.1\%) & 19/93 (20.4\%) \\ \hline CRSwNP & 1/11 (9.1\%) & 9/68 (13.2\%) & 14/93 (15.1\%) \\ \hline Cardiopathy & 2/11 (18.2\%) & 2/68 (2.9\%) & 10/93 (10.8\%) \\ \hline \end{tabular}
\begin{tabular}{c c c} \hline AERD: aspirin-exacerbated respiratory disease; BDP-HFA: beclometsharone dipropionate hydrofluoroalkane; CRSSNP: chronic rhinusitis without nasal polyposis; CRSwNP: chronic rhinusitis with nasal polyposis; FE phenotypes: frequent exacerbate phenotype \(\geq\)2 severe asthma exacerbations per year with OCS burst; GERD: gastro–esophageal reflux disease; LABA: long-acting beta-agonists; LAMA: long-acting muscarinic antagonists; ICS: inhaled cortico-steroids; OCS: oral corticosteroids; \({}^{*}p\) < 0.05 vs. GG; \({}^{\$}p\) < 0.05; \({}^{\$}p\) < 0.01 vs. AA. \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._Figure 3: Spirometry and plethysmography in asthmatic patients stratified according to BclI polymorphism variations. FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s (FEV1); RV: residual volume; TLC: total lung capacity; FRC: functional residual capacity; PB: post bronchodilation. Boxplots represent data from the first-to-third quartile; whiskers are drawn according to the Tukey method; “+” indicates the mean value; the dotted line represents the pathological threshold. Figure 2: Spirometry and plethysmography in asthmatic patients stratified according to A3669G polymorphism variations. FVC: forced vital capacity; FEV1: forced expiratory volume in 1 s; RV: residual volume; TLC: total lung capacity; FRC: functional residual capacity; PB: post-bronodilation. Boxplots represent data from the first-to-third quartile; whiskers are drawn according to the Tukey method; “+” indicates the mean value; the dotted line represents the pathological threshold; “= \(p\) < 0.05.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Histogram Gradient Boosting  
The Histogram Gradient Boosting (HGB) approach works faster in predicting the crops as it uses the histograms for splitting the crop data. In HGB, the feature points are categorized and stored into buckets. Then, these buckets are used for constructing histograms. During the construction of decision trees, histograms are used to identify the split points. For each split point, they create a tree node and then find the leaf values for each leaf node to reduce the loss function. This approach allows us to train the model faster with less memory usage.  
When there is a large dataset with more dimensional values, this method is useful.  
Equation (19) shows \(F(x)\) the overall prediction for input \(x\), \(M\) number of models, \(m=1\) to \(M\) represents the boosting iterations, \(v\) is the learning rate, and \(T(x)\) is a prediction of the new tree, and finally, the summation of all the trees is represented.  
\[F(x)=\sum_{m=1}^{M}v\cdot T(x) \tag{19}\]
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 3 Experiments and Analyses
### Neural Network Training  
#### 3.2.1 Sample Collection  
We traversed 3 levels of granularity for satellite 6 to satellite 10; different bandwidth usage is shown in Table 5; and target numbers are shown in Table 6.  
We collected \(3^{5}\times 7\times 8\) = 13,608 samples. Negotiation results can be obtained from the _Leader_ node and converted into connectivity probabilities of each level of granularity as the output of the neural network. Remaining communication bandwidth \(Bw\) and the number of targets \(n\) are also the inputs of simulation. The hop number to the _Leader_ node of each satellite Hn can be read from the simulation result.
## 4 Experimental Results and Discussion
### Analysis of Multi-Stream Expansion Method  
We compared the model's performance after multi-stream expansion with the model's performance with the original main and auxiliary encoder structure through experiments, as shown in Table 4. In the performance of vowel reduction detection, the multi-stream model does not exceed the original stream model. When using G2P as the pronunciation prior, it does not even surpass the situation without adding the pronunciation prior input. The phone recognition performance at this time is not as good as the original streaming model. When using the letter sequence as the pronunciation prior and expanding the multi-stream, its performance at detecting weak vowels exceeds that without adding the pronunciation prior. However, we have observed that the most considerable significance of this method is that when the multi-stream expansion model is combined with a priori composed letter sequences, the phoneme recognition performance is improved a lot, which is the best phoneme recognition performance in our models. The reason this method is relatively poor when combined with the G2P input may be that G2P is obtained through a conversion of the letter sequence, and this process introduces errors. These errors make the relationship between it and the acoustic signal no longer direct, which is not conducive to the function of the attention mechanism.  
We also evaluated the time consumption of the multi-stream algorithm. We used 20 threads for CPU decoding under the same experimental configuration and test set. The average time consumption of each thread was calculated, as shown in the right column of Table 4. However, the execution time of the algorithm was affected by many factors such as calculation optimization, computer performance, system status, I/O load, and so on. We mainly focus on the relative significance of the execution time. The results show that the multi-stream algorithm increases time consumption. We suggest that the increased execution time comes from the calculation of the attention mechanism between different inputs. In view of the fact that our method does not add operations with significantly increased complexity, the increased time consumption is relatively small.
# Facile Fabrication of a Low-Cost
## 3 Results and Discussion
### Regeneration of Alg-PAM  
Under acidic conditions, H protons can displace Pb\({}^{2+}\) ions that are attached to the carboxyl, hydroxyl, and amino groups of the Alg-PAM [42]. To avoid introducing other impurity ions, we used HNO\({}_{3}\) as an eluent to regenerate the adsorbent in this study. In the adsorbent regeneration experiment, the Pb\({}^{2+}\)-loaded Alg-PAM was immersed in 40 mL of the 0.05 M HNO\({}_{3}\) solution for 30 minutes and filtered, and the adsorbent was washed successively with ultrapure water, saturated Ca(OH)\({}_{2}\) solution, and ultrapure water. The adsorbent was then dried in an oven at 40 \({}^{\circ}\)C for 4 hours to obtain the regenerated Alg-PAM. It can be seen from Figure 8 that the regenerated Alg-PAM adsorbent has stable chemical properties, and its removal efficiency for Pb\({}^{2+}\) is in the range of \(97\pm 3\%\). In addition, the physical morphology of Alg-PAM also remains stable after five adsorption-desorption cycles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Adsorbents** & **Adsorbate** & **Maximum Adsorption Capacity (mg/g)** & **References** \\ \hline Polyampholyte hydrogel & Pb\({}^{2+}\), Cd\({}^{2+}\) & 216.1(Pb\({}^{2+}\)), 153.8(Cd\({}^{2+}\)) & [28] \\ Nanohydroxapatite & Pb\({}^{2+}\) & 200.0 & [29] \\ GO \({}^{1}\) & Pb\({}^{2+}\) & 178.5 & [30] \\ GO/chitosan/FeOCH & Pb\({}^{2+}\) & 111.1 & [31] \\ Thiol-functionalized GO & Pb\({}^{2+}\) & 200.0 & [32] \\ Dithiocarbamate-functionalized GO & Pb\({}^{2+}\) & 132.0 & [33] \\ Xanthate-modified thiourea & Pb\({}^{2+}\) & 188.1 & [34] \\ chitosan sponge & Biochar & Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\) & 153.1(Pb\({}^{2+}\)), 28.1(Cd\({}^{2+}\)), 34.2(Cu\({}^{2+}\)) & [35] \\ Amino-functionalized wood flour & Pb\({}^{2+}\) & 189.9 & [36] \\ GO & Pb\({}^{2+}\) & 125.0 & [37] \\ Chitosan-poly(acrylic acid) & Pb\({}^{2+}\) & 138.9 & [38] \\ composite beads & Pb\({}^{2+}\) & 70.0 & [39] \\ Titanium-Tin oxide nanocomposite & Pb\({}^{2+}\) & 99.8 & [40] \\ Nitrogen-doped carbon & & & \\  & & & \\  & & \\  & & & \\  & & \\  & & & \\  & & \\ \end{tabular} \({}^{1}\) GO: graphene oxide. \({}^{2}\) Alg-PAM: sodium alginate-polyacrylamideamide.  
\end{table}
Table 2: Adsorption capacities of the reported adsorbents for Pb\({}^{2+}\).  
Figure 7: Maximum adsorption capacity of the Alg–PAM for Pb\({}^{2+}\).
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 2 Recent Works  
Since MEC and NFV appeared, many researchers in the industry and research community have worked on how to provide low-delay and reliable network services for mobile users by deploying SFCs and BVNFs on edge servers. We review the recent work about the deployment of P-SFCs and BVNFs as the joint deployment of SFC and BVNFs with parallelization or not.
# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 7 Virtual Resource Allocation Algorithms
### Selected Papers
Even though profit generation was always a factor in performance optimization for network operators, the shift in the architecture with network virtualization makes dynamic revenue gains an apparent performance criteria for resource allocation. This shift transforms the network into a market interaction between different stakeholders that aim to profit from the available physical and virtual resources [93,94] in a dynamic way. In [94], a VNE problem is analyzed for FiWi hybrid nodes that have abstracted physical optical and wireless resources. The main objective is to maximize profit by considering the gains of service requests and costs of the physical plane including networking and edge computing servers' resources. Virtual resource allocation in a FiWi network is combined with an energy saving perspective in [93]. FiWi network resources are monitored globally to put the low-load devices to sleep mode. The results show that the algorithm managed to provide low energy consumption, high network service profit, and high network link utilization. The integration of network virtualization technologies creates the incentive to dynamically reconfigure both the physical and virtual resources of converged optical and radio networks. For this reason, the joint optimization of both radio, optical and virtual resources is studied in the literature. The radio, optical, and fog resources are controlled with SDN in a cross-layer architecture for a fog-computing-based radio over fiber network in [92], in which the controller selects MEC nodes and establishes paths with spectrum and modulated radio frequency allocation. A mmWave 5G C-RAN pooling gain solution over an ARoF fronthaul design is presented in [95], in which both virtual and physical resources are allocated with a resource pooling algorithm.
# A New Design of Codebook for Hybrid Precoding in Millimeter-Wave Massive MIMO Systems
## 4 Simulation Results  
### Simulation Setup and Explanations  
In this section, we simulate the proposed algorithm, varying with different SNR and RF chains, and compare the simulation results with some existing algorithms in terms of system spectral efficiency. The channel model we adopted is the discrete-time narrowband channel model and there are \(N_{\text{cl}}=4\) clusters where \(N_{\text{ray}}=20\) paths in each cluster. For the convenience of simulation, we assume that all clusters have the same power, the same azimuth angular spreads of AoDs and AoAs, and the angle expansion is \(6^{0}\). The number of channel samples \(N_{\text{tr}}\) is 5000 and the size of analog precoding codebook size \(N_{1}\) is equal to the size of the digital precoding codebook \(N_{2}=64\). The detailed simulation parameters are shown in Table 1.  
For the convenience of numerical analysis, we give a brief description of the algorithm proposed in this paper and some other algorithms compared with it.  
\((a)\)**Hybridprecoding algorithm with quantization [1]:** This algorithm is proposed in reference [1]. The algorithm is based on the OMP algorithm for analog codebook design, their digital codebook design directly uses the Grassmannian codebook, and does not use some clustering algorithms to reduce codebook space.  
\((b)\)**OMP-based Codebook [4]:** This algorithm is proposed in reference [4]. Firstly, the algorithm was based on the improved OMP algorithm for analog codebook design; then, they used the dictionary learning algorithm for digital codebook design; finally, Lloyd clustering algorithm was used for codebook space reduction.  
\((c)\)**Proposed Codebook:** The algorithm is proposed in this paper. We have elaborated on it in Section 3.  
\((d)\)**Optimal unconstrained Precoding:** This algorithm is directly based on SVD decomposition of channel \(\mathbf{H}\) to get the optimal unconstrained precoder \(\mathbf{F}_{\text{opt}}\). Although the algorithm can get the best spectral efficiency theoretically, it is impossible to achieve the optimal precoder without considering the constraints in practice.
Sensors_**2014**, _14_, 23697-23723; doi:10.3390/s141223697  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Deployment-Based Lifetime Optimization Model for Homogeneous Wireless Sensor Network under Retransmission**  
**[PERSON] 1,2,*, [PERSON] 1,3, [PERSON] 4 and [PERSON] 1,2**  
\({}^{1}\) School of Reliability and Systems Engineering, Beihang University, No.37 Xueyuan Road,  
Haidian District, Beijing 100191, China; E-Mails: [EMAIL_ADDRESS] (X.L.);  
[EMAIL_ADDRESS] (N.H.)  
\({}^{2}\) Science and Technology on Reliability and Environmental Engineering Laboratory,  
No.37 Xueyuan Road, Haidian District, Beijing 100191, China  
\({}^{3}\) China CEPREI Laboratory, Guangzhou 510610, China  
\({}^{4}\) Department of Systems and Industrial Engineering, University of Arizona,  
Tucson, AZ 85721, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-10-8233-8651.  
External Editor: [PERSON]  
_Received: 23 July 2014; in revised form: 21 November 2014 / Accepted: 27 November 2014 / Published: 10 December 2014_  
**Abstract:** Sensor-deployment-based lifetime optimization is one of the most effective methods used to prolong the lifetime of Wireless Sensor Network (WSN) by reducing the distance-sensitive energy consumption. In this paper, data retransmission, a major consumption factor that is usually neglected in the previous work, is considered. For a homogeneous WSN, monitoring a circular target area with a centered base station, a sensor deployment model based on regular hexagonal grids is analyzed. To maximize the WSN lifetime, optimization models for both uniform and non-uniform deployment schemes are proposed by constraining on coverage, connectivity and success transmission rate. Based on the data transmission analysis in a data gathering cycle, the WSN lifetime in the model can be obtained through quantifying the energy consumption at each sensor location. The results of case studies show that it is meaningful to consider data retransmission in the lifetime optimization. In particular, our investigations indicate that, with the same lifetime requirement, the number of sensors needed in a non-uniform topology is much less than that in a uniform one. Finally, compared with a random scheme, simulation results further verify the advantage of our deployment model.  
keywords: wireless sensor networks; sensor deployment; lifetime optimization; retransmission; energy consumption +
Footnote †: journal: Journal of the Acoustical Society of America
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
## 5 Modeling Device-to-Device Communications in SimuLTE
### Modeling Air Transmissions of D2D MAC PDUs
Another issue to be considered at the receiving side is that a UE might be the intended target of multiple simultaneous and overlapping D2D transmissions. This may happen, for instance, when ARS allocation is in place and two or more members of the same multicast group attempt to use the same RBs simultaneously. In this case, the receiver will attempt to decode only the MAC PDU with the  
Figure 15: D2D peering map (DM: direct mode, IM: infrastructure mode). highest received power, according to the capture effect. All the other simultaneous transmissions are instead treated as interference. SimuLTE models the capture effect by postponing decoding to the end of the TTI: during a TTI, UEs just store all the messages received during a TTI, along with their received power, without performing decoding operations. At the end of the TTI, the MAC PDU with the highest received power is selected and passed as an argument to the error() function, whereas the other ones are simply discarded.