esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Input Interactions
#### 4.1.3 Perceived Workload  
Subjective perceived workload was measured by the National Aeronautics and Space Administration Task Load Index (NASA-TLX), which measures the level of six dimensions (mental demand, physical demand, temporal demand, effort, performance, and frustration) to determine an overall workload rating. Each dimension is evaluated on a scale of 0 to 100 [50].  
The ANOVA test shows no significant differences in overall workload rating in the NASA-TLX, \(F\left(2,62\right)=1.17\), \(p<0.315\) between the three input interactions, G-HI (\(M=49.77\), \(SD=17.83\)), TI (\(M=46.64\), \(SD=16.47\)), and VI (\(M=44.06\), \(SD=13.71\)), as shown in Figure 9.  
We also analysed the six dimensions of the NASA-TLX to determine the most influenced NASA-TLX factor of workload rating for each input. As shown in Figure 10, The ANOVA test shows a significant difference in mental demand \(F\left(1.57,48.96\right)=5.48\), \(p<0.012\), with significant differences shown between G-HI (\(M=10.68\), \(SD=7.83\)) and TI (\(M=6.47\), \(SD=6.16\), \(p<0.02\)), and no significant differences between VI (\(M=7.43\), \(SD=5.91\)) and TI or G-HI. Also our results show a significant difference in frustration, \(F\left(2,62\right)=4.29\), \(p<0.018\). VI (\(M=5.89\), \(SD=8.23\)) shows higher frustration than TI (\(M=2.95\), \(SD=4.88\), \(p<0.03\)) and also G-HI (\(M=7.89\), \(SD=10.51\), \(p<0.05\)) shows higher frustration than TI. On the other hand, the ANOVA test also showed statistical significance in physical demand \(F\left(1.89,58.74\right)=3.98\), \(p<0.02\). where G-HI (\(M=5.208\), \(SD=5.97\)) causes a lower physical load than TI (\(M=9.10\), \(SD=8.47\), \(p<0.03\)) and VI (\(M=5.207\), \(SD=5.94\), \(p<0.028\)) shows a lower workload than TI, as illustrated in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input**} & \multicolumn{1}{c}{**Q1**} & **Q2** & **Q3** & **Q4** & **Q5** & **Q6** & **Q7** & **Q8** & **Q9** & **Q10** \\ \hline \multirow{2}{*}{Gaze-Head} & Mean & 60 & 59 & 68 & 66 & 68 & 52 & 70 & 65 & 57 & 70 \\ \cline{2-11}  & SD & (31.7) & (30.8) & (31.3) & (29.6) & (23.1) & (31.7) & (28) & (30.4) & (33.1) & (27.5) \\ \hline \multirow{2}{*}{Touch} & Mean & 72 & 70 & 79 & 80 & 73 & 75 & 82 & 73 & 78 & 71 \\ \cline{2-11}  & SD & (22.7) & (30.9) & (23.8) & (26.7) & (20.4) & (22) & (19.3) & (26.9) & (23.5) & (33.7) \\ \hline \multirow{2}{*}{Voice} & Mean & 70 & 64 & 73 & 73 & 71 & 70 & 82 & 75 & 67 & 73 \\ \cline{2-11}  & SD & (28) & (29.1) & (24.5) & (27.2) & (21.2) & (26) & (20.3) & (22) & (28.7) & (28.3) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The mean (M) and standard deviation (SD) values for the ten increment items of system usability scale scores for the three input interactions.  
Figure 9: National Aeronautics and Space Administration Task Load Index (NASA-TLX) scores for the three input interactions.
# BVLOS Unmanned Aircraft Operations in Forest Environments
## 5 Taking a Risk-Based Approach to BVLOS Operations in Forest Environments
### Other Considerations  
This sub-section outlines some of the other considerations that should be taken into account when planning BVLOS operations in forest environments. Most of these centre on human factors issues, with deliberate interference being the only issue that does not fit within that domain. These are often important issues for determining how well an operation is performed. However, they are also under-researched in the literature in comparison to more technical considerations.
## 3 Results and Discussion
### Line Treatment Effects on the Soil Compressibility  
For a quantitative assessment of the mechanical improvement induced by lime addition, some results of 1D-oedometer tests are herein presented and discussed in terms of compressibility properties; the obtained results (axial strain \(\varepsilon_{\mathrm{v}}\) vs. vertical stress, \(\sigma_{\mathrm{v}}\)) are reported in Figure 8. The initial state of raw samples is characterized by voids ratio in the range \(\mathrm{e_{0}}=0.86\div 0.88\), similar to the other values estimated for pyroclastic soils from Central Italy [21]. The initial response of intact samples of this material on first loading (\(<100\) kPa) is moderately stiff. Large volumetric strains occur for vertical stresses higher than about 200 kPa. Nevertheless, the maximum stress level upon loading is low from reaching a single normal compression line. First loading induces a gradual transition (yielding) between reversible to irreversible behaviour of the sample with the increase of vertical stresses, with relevant volume changes even before major yield. The influence of curing time is very clear. As already observed for other pyroclastic soils [3], the effect is mainly due to the development of stable bonds between aggregates deriving from the products of pozzolanic reactions. The increase of one dimensional stiffness becomes relevant longer curing times (at least 7 days), but a certain improvement is observed also for treated samples cured for short time (24 h after compaction).  
Therefore, lime addition undoubtedly leads to an improvement in the mechanical response within the first month, by reducing the compressibility and increasing the yield stress. This mechanical response can be then interpreted based on the results obtained from the investigation at the microstructural level. In fact, the microstructural analyses of the treated pyroclastic soil showed the high reactivity of the system to lime addition and the rapid development of pozzolanic reactions.  
Figure 7: Pore size distribution evolution for raw and lime treated OR soil at increasing curing time (adapted from [PERSON] et al. (2018) [20]).
# Analysis of Lower Limb Performance Determinants in Sport Climbing
## 1 Introduction  
Sport climbing is a multifaceted discipline encompassing various modalities practiced both on rock and in indoor settings [1, 2, 3]. The inclusion of climbing in the Tokyo 2020 Olympic program [4], along with the global proliferation of indoor climbing gyms, has drastically accelerated the sport's development and the rise in participation rates [3, 5, 6]. Moreover, recent years have seen a significant increase in both the scope and performance levels of recreational and competitive climbing, which has sparked growing interest in sports science, particularly regarding performance factors, physiology, and injuries [3].  
Performance in sport climbing is primarily determined by strength and endurance during intermittent isometric contractions of the finger flexor muscles [7, 8, 9]. However, performance is not solely limited to physiological factors related to finger flexors and the upper body; it also relies on a combination of technical skills, flexibility, coordination, psychological, and genetic factors [10, 11, 12].  
In recent years, competitive climbing has increasingly incorporated more coordination-intensive styles that require greater involvement of the lower limbs [13, 14, 15], partly contributing to a rise in injuries in this body region, particularly in disciplines such as bouldering [16, 17, 18]. While traditionally injuries have been concentrated in the upper extremities--such as shoulders and fingers [19, 20]--recent studies suggest that up to ~55% of bouldering injuries affect the lower limbs, compared to 25-35% in the upper limbs. These injuries, often caused by falls, jumps, or extreme movements [16, 17, 18, 21], frequently require emergency care, in contrast to hand injuries, which are often managed in physiotherapy clinics or do not require medical attention [15].  
This phenomenon has fueled growing interest in research on lower limb strength and flexibility, variables that could be linked to climbing performance or injury prevention [2, 10, 13, 22, 23, 24, 25]. In this regard, although research is still in its early stages, it is reasonable to assume that lower limb strength training--similarly to what has already been demonstrated for the upper body [23]--could enhance performance by improving stability, postural control, and efficiency in coordination-based movements, in addition to reducing the risk of associated injuries.  
Therefore, the main aim of this study was to describe and analyze lower limb flexibility and strength variables in recreational climbers, considering sex and climbing level, as well as the differences among these groups. Based on previous research and the physiological and technical demands of climbing [3, 11], we hypothesize that male climbers and those with higher climbing levels would exhibit greater values in lower limb strength variables compared to female climbers and those with lower levels. This difference is considered attributable to the physiological characteristics and specific technical demands of each group.
# A Common Framework for Developing Robust Power-Flow Methods with High Convergence Rate
## 1 Introduction
### Literature Review  
PF solutions in ill-conditioned cases have been widely studied since [PERSON] and [PERSON] developed a robust step size-based solution methodology in [3]. For each iteration of this methodology, an optimal factor is calculated that modifies the Newton's increment vector so that it evolves, reducing the sum of squares of the residual. This approach, although quite robust, is very inefficient because the solution is approached very slowly [4]. In addition, this methodology is only suitable when the PF equations are formulated in rectangular coordinates [3]; however, polar formulations have been explored [5].  
Since NR considers a first-order truncation of the Taylor series truncation of PF equations, its accurate and reliability can be enhanced by using a second-order expansion. This approach arises in the so-called second-order PF solution methods, which have not been widely used in industry applications due to the expensive calculation of the Hessian matrix. The author of [6] proposed an elegant variant of the second-order methodologies that avoids the explicit computation of the Hessian matrix.  
[PERSON] proposed a common framework between the NR-based solution of the PF and the Forward Euler methodology in [1]. On the basis of this analogy, it is established that any explicit numerical method can be adapted for developing robust PF solution techniques. This idea has been widely exploited by the authors in several references [7; 8; 9; 10; 11]. The implicit formulation of this paradigm has been recently proposed in [12].  
In [2; 13], a dynamic solution paradigm for solving the PF was proposed. It takes advantage of the computational integration routines, so that a widely convergent methodology is obtained. However, its computational cost is still very expensive, which limits its application in realistic large-scale systems.  
The Levenberg method has been exploited for PF analysis in several references [14; 15; 16]. Although these techniques might be very efficient and convergence is normally ensured (at least in the least squares sense), the obtained value may not correspond with the explicit solution of the PF equations [14], which might bring accuracy issues.  
Recently, several efforts have been made in order to adapt the Holomorphic Embedding Method to the solution of the PF equations (see e.g., [17] and references therein). The Holomorphic Embedding Method represents an infinite number of formulations, each one with different numerical properties. An alternative point of view was proposed by the authors in [18]. It takes advantage of the homotopy theorem which, in combination with other efficient numerical techniques, arises in a novel family of robust and efficient PF solution methods.
## 2 Results
### Effects of AfsakA Deletion on Osmotic Stress Response in _A. flavus_  
To evaluate the potential functions of _AfsakA_ under extracellular stimuli responses, we analyzed the sensitivity of WT, \(\Delta\)_AfsakA_ and \(\Delta\)_AfsakA::AfsakA_ to osmotic stress agents. To do this, \(10^{6}\) conidia were inoculated onto YES agar media with and without the formerly described osmotic stress supplements. The cultures were then incubated for 4 days at 37 \({}^{\circ}\)C in dark condition. The results indicated that without the stress, WT and complementary strains grew a little better than \(\Delta\)_AfsakA_ mutant but not significant (Figure 3A). Subsequently we analyzed the expression level of _HPS_, _GPD_, _GRE_ and _STL_ genes which are related with osmolality and the result showed that the expression levels of all these 4 genes were significantly decreased in \(\Delta\)_AfsakA_ than that in WT and \(\Delta\)_AfsakA::AfsakA_ strains (Figure 3C). Then growth inhibition rate of WT, \(\Delta\)_AfsakA_ and \(\Delta\)_AfsakA::AfsakA_ strains under D-sorbitol and NaCl were also examined. Surprisingly, all the strains showed almost the same sensitivity to 1.2 mol/L NaCl, whereas \(\Delta\)_AfsakA_ mutant was more sensitive to hyperosmotic stress induced by  
Figure 2: Schematic illustration of _AfsakA_ deletion and complementation strains of _A. flavus_. **(A)** Homologous recombination technique was applied for the deletion of _AfsakA_. The fragments 5\({}^{\prime}\) UTR (untranslated region) (5\({}^{\prime}\)_AfsakA_), _A. fumigatus pyrG_ and 3\({}^{\prime}\) UTR (3\({}^{\prime}\)_AfsakA_) were each amplified with primer pairs of P1/P2, _pyrG_-_F_/_pyrG_-R and P3/P4 and fused together with primer pairs P7/P8. The fusion PCR product was transferred into CA14 (_PyrG_, \(\Delta\)_ku70_) in order to produce \(\Delta\)_AfsakA_ (_Aku70_; _pyrG_; \(\Delta\)_AfsakA_:_pyrG_). **(B)** The results of the PCR analysis of \(\Delta\)_AfsakA_ and \(\Delta\)_AfsakA::AfsakA_. ORF (open reading frame), AP and BP fragments were amplified with a couple of primers P9/P10, P1/P801 and P4/P1020 respectively. **(C)** Verification of _AfsakA_ deletion and complementary strains with reverse transcription-PCR. _Actin_ gene was used as an endogenous control. Lane 1 used gDNA and lane 2 used cDNA as template. **(D)** qRT-PCR analysis of the expression level of _AfsakA_ gene in \(\Delta\)_AfsakA_ mutant, WT (wild-type) and \(\Delta\)_AfsakA::AfsakA_ strains. The line bar in every column in **(D)** indicates standard errors of four replicates and the asterisks show significant difference between the knockout mutant and other strains (WT and complementation strain) (***\(p\leq 0.001\)).  
1.2 mol/L D-sorbitol than the WT and complementary strains strain (Figure 3A,B). These finding suggested that _AfsakA_ is involved in responses to some hyperosmotic stress in _A. flavus_.
## 5 Low-Power Cellular Technologies for IoT
### Lte-M  
LTE-M stands for long-term evolution for machines and is predominantly designed for low cost, deep coverage, and long-lasting battery-powered IoT devices. It is an industrial IoT technology designed for enhanced machine-type communication (eMTC) specified in the 3 GPP studies provisioning different LTE categories: Cat-0 in release 12, Cat-M1 in release 13, and Cat-M2 in release 14. It is one of the cellular IoT connectivity options that was first introduced in the 3 GPP release 13 standardisation. Similarly to NB-IoT, LTE-M was introduced to support massive IoT deployments of low-complexity devices with deep coverage for UEs in the hard-to-reach locations. The summary of the improvement in the subsequent releases includes positioning accuracy, multicast support, higher data rate, reduced overhead for UEs and control channel signalling, longer battery life, quality channel, coexistence with other radio technology, enhanced use cases, and quality reporting to the network. LTE-M relies on the existing LTE infrastructure and design actions such as a software upgrade in the base station. LTE-M data rates are higher than LoRa and NB-IoT networks for both the uplink and downlink channels. LTE-M may be more widely adopted with more connectivity opportunities for smart city applications in developing countries. In most implementations, LTE-M and NB-IoT support data transmission at a maximum coupling loss (MCL) of 156 dB in a 1.4 MHz bandwidth and 164 dB in a 200 kHz bandwidth, respectively, [50]. Previous mobile technologies before 3 GPP standardisation are not suitable for massive machine-type communication (mMTC) because of the high cost and power consumption of IoT modules. One deployment strategy of NB-IoT and LTE-M networks is by a software upgrade to an existing GSM or LTE-based station. As shown in Figure 6, this opportunity is only being taken in Europe, the United States, and other countries with a few trials happening in Africa. Vodacom deployed the 3 GPP release 13 cellular NB-IoT in South Africa through the Vodaworld campus laboratory to facilitate the development of NB-IoT-related applications. Vodacom launched NB-IoT (LTE Cat-NB1) on the B8(900 MHz) band in November 2017 [69]. In Nigeria, the mobile telephone networks (MTN) launched NB-IoT (LTE Cat-NB1) on the B8(900 MHz) band in the 2018 trial. With Huawei's CloudAIR 2.0 spectrum sharing innovation, MTN overlapped NB-IoT and LTE in the 900 MHz band [70]. In Kenya, prepared gas services have been made possible by Safaricom NB-IoT (LTE Cat-NB1) deployed on the B20 (800 MHz) band [71].
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 5 Experiments
### Benchmark Datasets  
We use the E2006-tfidf dataset [47] to predict the volatility of stock returns based on the SEC-mandated financial text report, represented by tf-idf. It was collected from thousands of publicly traded U.S. companies, for which data from different companies are inherently non-identical and the privacy consideration for financial data demands federated learning. The RCV1 dataset [48] is used to predict categories of newswire stories recently collected by Reuters, Ltd. The RCV1 can be naturally partitioned based on the news category and used for federated learning experiments since readers may only be interested in one or two categories of news. Our model training process mimics the personalized privacy-preserving news recommender system where we use the K-means method to partition the datasets, respectively, into 10 clusters. Each device randomly selects two of the clusters for use in the learning. We run t-SNE to visualize the hidden structures found by K-means as shown in Figures 4 and 5, respectively, for the E2006-tfidf dataset (sparse linear regression) and the RCV1 dataset (sparse logistic regression). For the MNIST images, there are 10 digits that automatically serve as the clusters.  
For all datasets, the data in each cluster are evenly partitioned into 20 parts, and each client randomly picks two clusters and selects one part of data from each of the clusters. Because the MNIST images are evenly collected for each digit, the partitioned decentralized MNIST data are balanced in terms of categories, whereas the other two datasets are unbalanced.  
Figure 4: Visualization of 10 K-means clusters for E2006-dfidf using t-SNE.  
Figure 3: The comparison of different algorithms in terms of the objective function value vs. communication rounds (**a**,**b**) and for regression (**a**) and classification (**b**). Note that the Distributed-IHT is the baseline method that communicates every local update (so the number of rounds equals the number of iterations) and may be the best scenario for reducing the objective value.  
Figure 6 shows that our proposed Fed-HT and FedIter-HT can significantly reduce the communication rounds required to achieve a given accuracy. In Figure 6a,c, we further notice that federated learning displays more randomness when approaching the optimal solution. This may be caused by dissimilarity across clients. For instance, the three different algorithms in Figure 6c reach the neighborhood of different solutions at the end, where the proposed FedIter-HT obtains the lowest objective value. These behaviors may be worth exploring further in the future.
# Tunable Fano Resonances in an Ultra-Small Gap
## 3 Results and Discussion
### Effect of Nanocube Dielectric Environment on Fano Resonance  
The dark mode in the Fano resonance refines the spectrum and increases the refractive index sensitivity of the LSPR. Some recent studies have been working to improve refractive index sensitivity [29; 30; 47]. Here, silver nanocubes were tested in different dielectric environments, including air (\(n=1.0\)), water (\(n=1.33\)), alcohol (\(n=1.362\)), and dimethylformamide (DMF, \(n=1.428\)). In the scattering spectra plotted in Figure 5a, the dip position is red-shifted when the refractive index of the surrounding dielectric environment increased. When the size of metal NPs is much smaller than the wavelength of incident light, the calculation model of metal NPs can be replaced by the model of spherical particles, and the LSPR frequency is defined as  
\[\omega_{LSPR}=\frac{\omega p}{\sqrt{2_{\varepsilon_{d}}+1}} \tag{1}\]  
where \(\omega_{p}=\sqrt{\frac{N_{e}^{2}}{\varepsilon_{m\varepsilon}}}\) is the resonance frequency of plasmon, \(N\) is the free electron concentration, \(e\) is the electronic charge, \(\varepsilon_{0}\) is the dielectric constant of free space, \(m_{e}\) is the effective electronic mass, and \(\varepsilon_{d}\) is the dielectric constant of the surrounding environment. Therefore, as the refractive index increased, the LSP resonant frequency decreased, resulting in a spectral red-shift. A linear fit (red line) of the Fano resonance shift (black circles) as a function of refractive index \(n\) is shown in Figure 5b. The resulting sensitivity is about 140 meV/RIU(refractive index unit), with a FOM value of the Fano resonance of 2.4. These data can provide a reference value for a sensor based on Fano resonance refractive index sensitivity.
## 3 Numerical Modeling and Validation
### Numerical Model Development
#### 3.1.2 Parameter Determination for Numerical Modeling  
Three distinct cases were established for numerical validation, with key parameters including the uniaxial compressive strength of intact rock (\(\sigma_{\text{ci}}\)), rock density (\(\rho\)), the Geological Strength Index (\(GSI\)), and intact rock material constant (\(m_{\text{i}}\)), as detailed in Table 1. The disturbance factor \(D\) was assigned a value of 0 according to the criteria specified in Table 2 [27]. The elastic modulus (\(E\)) and Poisson's ratio (\(\
u\)) of the surrounding rock were calculated using Equations (5) and (6), respectively.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 4 Performance Evaluation
### Results and Discussions  
The performance of the proposed SDNPS is compared with that of a conventional SDN with either static or dynamic routing. In the conventional SDN, the monitoring period with dynamic routing is fixed at 3 s; no monitoring period is used with static routing.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Simulator & Mininet 2.5 \\ SDN controller & Ryu Controller \\ \hline Switch & OpenFlow or P4 \\ \hline Link bandwidth & 20 Mbps, 30 Mbps, 60 Mbps \\ \hline Packet size & 1470 Byte \\ \hline Queue size & 50 packets \\ \hline URLLC flow & 5 Mbps, 17 Mbps \\ \hline mMTC flow & 50 Kbps \\ \hline Number of mMTC devices & 100, 250, 500 \\ \hline Packet transport protocol & UDP \\ \hline Packet generation tool & Iperf \\ \hline Flow paths for URLLC slice & \#1. S1-S3-S5-S6\#2. S1-S3-S4-S6 \\ \hline Flow paths for mMTC slice & \#1. S1-S2-S5-S4-S6\#2. S1-S2-S4-S5-S6 \\ \hline _Ts_ & 0.5 s \\ \hline _Hd_ & 1 millisecond \\ \hline _Hq_ & 80\% \\ \hline _Hu_ & 80\% \\ \hline Simulation time & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameter settings.  
Figure 10: SDN topology used in the simulation.  
In this subsection, we obtain and discuss the average throughput, packet loss ratio, and packet delay for the mMTC and URLLC network slices in the conventional SDN and the proposed SDNPS.