esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Characteristics of DP
#### 4.3.3 Characteristics of Monthly DP  
The monthly DP and RDP variation trends were consistent with monthly sand-moving wind frequency (Figure 9). Overall, the DPs of Qiemo and Ruoqiang showed parabolic fluctuations on the monthly scale. The DP of Qiemo peaks (22.4 VU) in May, and its RDD and RDP/DP were 223.93\({}^{\circ}\) (SW) and 0.608 (medium ratio). In June, its RDP/DP ratio was extremely low (merely 0.094), suggesting that sand-moving wind is highly variable in this region; its RDP and RDD were 2.10 VU and 191.46\({}^{\circ}\), respectively, which are much different from the overall manifestations. The monthly DP of Ruoqiang peaked (16.06 VU) in May, after which it first dropped considerably, then increased slightly in October, and finally declined gradually. According to Figure 9, the monthly fluctuation amplitude of RDP/DP in Ruoqiang was higher than that in Qiemo. The RDP/DPs of the two counties dropped drastically in June and December, suggesting that the sand-moving wind direction is disorderly in both months. On the whole, Qiemo has a more stable sand-moving wind  
Figure 8: Characteristics of seasonal DP: (**a**) spring; (**b**) summer; (**c**) autumn; (**d**) winter.  
direction, which means a greater threat from sand hazards on the upwind side of the railway. By contrast, in Ruoqiang, both sides of the railway are threatened by sand hazards under the action of constantly changing sand-moving wind.
## 2 Materials and Methods
### Data Processing  
In this paper, the model is tested on two ways of recognition, hence the dataset was processed twice. First, we performed preprocessing by randomly selecting 3 complete videos from the 45 video datasets to evaluate the frame-wise accuracy of online recognition. For the remaining 42 videos, we segmented them according to predefined boundary frames and manually extracted action segments of each gesture category, known as gesture instances. Subsequently, we divided the gesture instances of these 10 gesture categories into training, validation and test sets in a ratio of 0.64:0.16:0.2. For each gesture instance, we uniformly sampled at least 32 frames. If the number of frames for a gesture instance was less than 32, we opted to duplicate the last frame to fill the sequence to 32 frames, rather than discarding these instances. Ultimately, the numbers of instances in the training, validation and test sets were 1948, 491 and 615, respectively.  
For the offline recognition task, we processed the gesture instances into image sequences with a height H = 171, width W = 128 and sequence length T = 32 as input to the model. For the online recognition task, we further processed the gesture instances to obtain fine-grained segment representations. To ensure the model accurately recognizes gesture transitions, we applied overlapping processing to the extracted 32-frame gesture instances using a sliding window approach with a step size of 2 and sequence length of 16, i.e., moving 2 frames backward each time. There was an overlap of 14 frames between adjacent sequences. Finally, the numbers of overlapping segments obtained for the training,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gestures** & **Gesture Description** & **Boundary Frame** \\ \hline G1 & Clamping the needle & Forceps just touching the tissue \\ G2 & Forceps pick up the tissue & The holder has just started to move \\ G3 & Positioning needle & The needle has just made contact with the tissue \\ G4 & Pushing the needle through tissue & The needle tip has penetrated the tissue \\ G5 & Clamping the needle through tissue & The needle has just departed from the tissue \\ G6 & Pulling suture with holder & The holder loosens the needle or moves out of view \\ G7 & Hand pulling suture & The needle holder approaches the vicinity of the suture line \\ G8 & Knotting the suture & The scissors are not visible in the field of view \\ G9 & Cutting the suture & The scissors are removed from the field of view \\ G10 & Other & The holder regraps the needle, \\  & & Actions other than the nine surgical gestures (G1–G9), Other scenarios \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vocabulary of Surgical Gestures.  
Figure 2: Distribution of surgical gestures in a sample video.  
validation and test sets were 31,708, 8496 and 10,103, respectively. Table 3 shows the data related to surgical gestures in the dataset.
# On the Vaneless Space Vortex Structures in a Kaplan Turbine Model Operating at Speed No Load
## 3 Results
### Precessing Frequencies of the VSV Structures  
Once \(z_{D}\) is calculated with Equation (8), the precession frequency, \(f_{0}\), of each structure can be derived from Equation (5) or Equation (6). As seen in Figure 5, the computed reduced precession frequencies, \(f_{0}^{*}\), corresponding to 2V and 3V follow approximately lineal and parallel trends when plotted as a function of \(Q_{11}\). The 3V presents lower precession frequencies than the 2V, suggesting that the higher the number of vortices per structure is, the lower \(f_{0}\) is.  
Figure 3: Spectra of the guide vane torque (TGV), the pressure (PVS\({}_{\rm A}\)), the blade strain (SB\({}_{\rm R}\)) and the shaft torque strain (SS\({}_{\rm T}\)) for \(\alpha\) equal to 3.98\({}^{\circ}\).  
Figure 4: Spectra of (**a**) TGV and of (**b**) SB\({}_{\rm R}\) at all SNL conditions.  
The 4V and 6V\({}_{\rm{GV}}\) follow the same trend as the 2V and 3V when plotted as a function of \(Q_{11}\) but presenting lower precession frequencies, which confirms that the structures with higher number of vortices precess slower. On the contrary, the 6V\({}_{\rm{B}}\), apparently closer to the runner blades than the guide vanes, follow a different trend to the 2V and 3V, suggesting that the location of the VSV structures influences the frequency evolution.  
Based on these two distinct frequency evolutions, one followed by 2V, 3V, 4V and 6V\({}_{\rm{GV}}\) and another one followed by 6V\({}_{\rm{B}}\), it can be concluded that the precession frequencies of all the VSV structures are primarily governed by the SNL condition, and in turn by \(Q_{11}\), and \(z_{D}\). Similarly, other authors also found that the frequency of the VSV structures depended on the blade opening [27]. Moreover, per equal \(z_{D}\), the 6V\({}_{\rm{B}}\) apparently closer to the runner blades presents always higher precession frequencies than those of the 6V\({}_{\rm{GV}}\) apparently closer to the guide vanes.
## 2 Materials and Methods
### Footprint, Weight, and Waste Reduction Analysis  
In order to evaluate the specific dimensions and weight of the MD/MCr unit, footprint and weight analysis was performed. These parameters were also evaluated in terms of the corresponding process intensity metrics, including productivity to size (P/S) and productivity to weight (P/W) ratios provided in the literature [9], which are defined mathematically as following:  
\[P/S=\frac{\text{Productivity}\left(\frac{kg}{h}\right)}{\text{Size}(m^{3})} \tag{1}\]  
\[P/W=\frac{\text{Productivity}\left(\frac{kg}{h}\right)}{\text{Weight}(kg)} \tag{2}\]  
The conversion of wastewater into valuable products (freshwater and salt crystals) was monitored according to the following correlation:  
\[Waste\;Intensity=\frac{\text{Total waste}(kg)}{\text{Mass of product}(kg)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material** & Polypropylene \\
**Type** & Hollow fiber \\
**No. of fibers** & 19 \\
**Length of fibers (cm)** & 42 \\
**Inner fiber diameter (mm)** & 1.8 \\
**Outer fiber diameter (mm)** & 2.7 \\
**Membrane thickness (mm)** & 0.45 \\
**Average pore size (\(\mu\)m)** & 0.2 \\
**Porosity (\%)** & 73 \\
**Surface area (cm\({}^{2}\))** & 45.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of membrane and membrane module used in membrane distillation/crystallization (MD/MCr) experiments.  
Figure 1: Membrane distillation and crystallization setup used in the experiments.  
For the calculation of P/S and P/W metrics, each hollow fiber membrane module was assumed to have a 0.201 m diameter, one-meters length, 34 m\({}^{2}\) of area, and 16 kg of weight according to the information provided in the literature [8]. For the calculation of the size of the MD/MCr unit, 50% space density between the modules was assumed. The calculations were based upon a wastewater volume of 10 m\({}^{3}\) per week.
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## Materials and Methods
### Synthesis of Triblock Copolymers  
The facile and controlled synthesis of the ionic triblock copolymers PLLA-PEI-PLLA (MW: 1700-2000-1700) using an amine protection/de-protection approach can be found in our previous report [31]. Briefly, the PEI precursor \(\alpha\),\(\omega\)-dihydroxy poly(2-methyl-2-oxazoline) (PMOx) was prepared by the ring opening polymerization (ROP) of 2-methyl-2-oxazoline (2-MeOx) using the bifunctional initiator _trans_-1,4-dibromo-2-buttene. Linear PEI was obtained through base hydrolysis of the amide functional groups of PMOx. The secondary amines of PEI were then fully reacted with excess di-tert-butyl dicarbonate to obtain \(N\)-Boc protected PEI (PEI-N-Boc). The hydroxyl end-capped homobifunctional PEI-N-Boc, was used as a macroinitiator for the living ROP of L-lactide using the organo-metallic catalyst Sn(Oct)\({}_{2}\) to yield the triblock copolymer PLLA-(PEI-N-Boc)-PLLA. The N-Boc protecting groups were then deprotected using TFA to yield the desired ionic triblock copolymer PLLA-PEI-PLLA. The amphiphilic triblock copolymers, PLLA-PEG-PLLA and PDLA-PEG-PDLA were prepared by the ROP method, using the macroinitiator PEG-diol and the monomers L-lactide and D-lactide, respectively. The complete synthesis procedure for the preparation of the amphiphilic triblock copolymers can be found in our previous report [29].
## 2 System Model
### Downlink JP-CoMP  
JP-CoMP exchanges data and channel state information (CSI) among the cooperating base stations concurrently. These data exchanges and CSI shared in JP-CoMP are connected by a backhaul link. The downlink in JP-CoMP will increase the transmission throughput to each user. The cooperating base stations will transmit the downlink signal to each user in the cluster. Each user will receive signals from different base stations. The JP-CoMP scheme typically provides higher sum capacity levels when compared to other schemes by mitigating interference signals from neighboring cells. This coordination scheme also has a simple configuration. However, JP-CoMP requires a high backhaul bandwidth and low latency due to the data exchanges among cooperating base stations.
## 4 Compaction Quality Evaluation System and Its Indices  
### Evaluation System for Compaction Quality  
The three-level \(i\)-AHP evaluation system for compaction quality is established as illustrated in Figure 2 for hybrid evaluation of compaction quality and deriving the _HCI_. Based on Table 1, the compactness (\(K\)) and dry density (\(\rho_{d}\)) for gravel-mixed cohesive soil at the core wall, porosity (\(n\)) and dry density (\(\rho_{d}\)) for rockfill area are selected as the physical indices [3]. The dynamic elastic modulus (\(E_{\rm vd}\)), deformation modulus (\(E_{0}\)) and subgrade modulus (\(K_{30}\)) are selected as the mechanical indices.  
### Evaluation System for Compaction Quality  
To obtain the real-time physical and mechanical indices, two groups of independent variables are selected as the prediction inputs, respectively. The compaction parameters (i.e., rolling speed \(N_{s}\), compaction passes \(N_{p}\), vibration state \(N_{v}\) and compaction thickness \(N_{t}\)) and the material parameters  
Figure 1: Research framework.  
Figure 2: \(I\)-AHP system for compaction quality evaluation.  
(i.e., gradation \(M_{g}\) and moisture content \(M_{m}\)) are chosen to predict the distribution of material physical indices [1; 4; 13], and the drum vibration acceleration during roller travel is chosen to predict the distribution of material mechanical indices [19]. The SVR with CFA is employed to establish the compaction quality prediction model, and its efficiency and accuracy have been demonstrated in Reference [13]. Through the above process, we can obtain the real-time physical and mechanical indices.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 5 Machine Learning Models for Identification of the LOS, NLOS, and MP Conditions
### Multi-Layer Perceptron Classifier for the UWB Localization System  
MLP is a type of deep feedforward artificial neural networks, which contains at least three layers (an input layer, a hidden layer, and an output layer) in a single network [47]. Typically, the neurons in the hidden and output layers of the MLP use nonlinear activation functions such as sigmoid, ReLU, and Softmax. The term deep is usually applied when there is more than one hidden layer in the network. MLP utilizes the backpropagation algorithm [48] for training the network. In this paper, the MLP classifier is configured using the rectified linear unit (ReLU) as the activation function for the hidden layers, the Softmax function as the output layer, and the Adam (adaptive moment estimation) as an optimization algorithm. The maximum number of epochs was set to 500, allowing early stopping if the training loss had not improved for 10 consecutive epochs.  
The evaluations of MLP were conducted for up to 6 fully-connected hidden layers in two conditions using 50 and 100 neurons in each hidden layer (Table 4). The results showed that there was a significant increase in the overall accuracy by adding a second and third hidden layer to the network. However, the improvement was meager when more than 3 hidden layers were used for a network. In terms of the number of neurons per layer in the network, the use of 100 neurons in each hidden layer constantly beat the use of 50 up to four consequent layers. However, the difference could not be clearly distinguished when more than 4 layers were used in the network.  
In terms of the processing time (training and test times), adding more hidden layers and more neurons in the network had a negative impact on the performance (the last two columns in Table 4), i.e., the lower the processing time, the better the performance. Therefore, a trade-off between the accuracy and processing time was necessary to have an efficient performance. The results in Table 4 suggest that the use of 3 hidden layers in which each contained 100 neurons seemed a good choice for solving the evaluated UWB-based multi-class classification problem.
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 3 Proposed Architecture
### Conceptual Architecture
It also ensures isolation and traffic control for each client, ensuring that the traffic and policies of one slice do not affect others. To meet these requirements, it is essential to have agreements between the clients and the neutral host. Implementing techniques to share network performance indicators, monitoring tools, and application programming interfaces (APIs) is crucial to ensure network visibility for clients. In this architecture, roaming plays a crucial role in the authentication and verification of users belonging to operator clients. The LBO model, chosen in our approach, allows for user traffic to be locally routed to the Internet, unlike the HR model, where traffic is directed to the HPLMN. Despite the latter being seen as maintaining more visibility over the traffic for the MNOs, by providing a specific network slice for each client, together with an effective approach in terms of APIs and performance indicators availability, the proposed architecture allows them to maintain visibility and control over their users, QoS policies,  
Figure 4: Flowchart of the authentication procedure. and services. Roaming is fundamentally involved only in terms of authentication and authorization. To maintain control over their users, the operator clients themselves ensure the authentication within the neutral host's network, thus leveraging the standardized security and reliability for roaming. For non-operator clients' users, authentication is performed on the neutral host's network as is typical in traditional mobile networks, given that they do not require roaming agreements. For operator clients in roaming service, the rationale is to allow the neutral host to use the same PLMN ID used by the operator client's mobile network to provide connectivity to its users. This approach simplifies the switching process between the VPLMN (neutral host network) and HPLMN (home network) for the user's device, as the devices maintain a constant connection to the same PLMN ID. This ensures faster and more transparent network switching for the user, unlike what typically occurs in international roaming, where prolonged service losses and roaming notifications are common, consequently improving the user experience when using the neutral host network.
_Applied_  
_sciences_  
_Article_  
**Study of 2,4-D Spectral Characteristics and Its Detection in Zizania Latifolia Using Terahertz Time-Domain Spectroscopy**  
**[PERSON] 1,2,3, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2 and [PERSON] 1,2,*(c)**  
Received: 25 April 2019; Accepted: 29 May 2019; Published: 31 May 2019  
**Featured Application: Identification and detection of pesticide residues in crops.**  
**Abstract: 2,4-Dichlorophenoxyoxyacetic acid (2,4-D) is a common plant growth regulator, which can remain in food and, with long-term consumption, threaten human health. Therefore, it is necessary to propose an effective detection method. Terahertz time-domain spectroscopy technique (THz-TDS) has good advantages in the quantitative and qualitative analysis of most biomolecules due to its rich fingerprint characteristics. In this paper, density functional theory (DFT) was applied to geometry optimization and frequency vibration calculation of 2,4-D, and THz-TDS was used to quantitatively detect 2,4-D in Zizania latifolia. The results showed that there were three characteristic absorption peaks of 2,4-D at 1.36, 1.60, and 2.38 THz, respectively, and the theoretical spectra were in good consistency with experimental spectra, with slight discrepancies. Additionally, the absorption peak at 1.36 THz had the best absorption characteristics and was chosen as the main peak for 2,4-D quantitative analysis. It was demonstrated that the limits of detection (LOD) of 2,4-D in Zizania latifolia were found to be as low as 5%, the absorbance intensity at 1.36 THz showed a good linear relationship (R\({}^{2}\) = 0.9854) with 2,4-D concentration from 5% to 30%, and the recovery was 93.29%-98.75%. Overall, this work enriched the fingerprint database of pesticide molecules on the basis of terahertz spectroscopy and could provide a technical support for the detection of 2,4-D in food by terahertz spectroscopy.**  
**Keywords: Terahertz time-domain spectroscopy; 2,4-dichlorophenoxyoxyacetic acid; density functional theory; quantitative analysis; Zizania latifolia**
## 3 Results
### Cultural Differences by Demographic Variables
#### 3.2.2 Differences by Work Location (Facility/Headquarters)  
Generally speaking, the analysis of survey scales by workplace shows homogeneous results between headquarters personnel and facility personnel (\(\delta<0.20\)). The only relevant difference is found in the hazard perception scale (facility M = 4.38; headquarters M = 3.16; \(\delta\) = 0.76) (Table 8, Figure 9). Within this scale, personnel working at the facility have a higher average score than headquarters personnel.  
However, this workplace-based homogeneity in scores is not present in the three BARS within this study, all of which show differences that are statistically significant and have magnitudes of medium effect (attention to safety: facility M = 4.33; headquarters M = 4.74; t(75.05) = \(-\)4.99; \(p\) = 0.000; \(\delta\) = \(-\)0.58), (formalization: facility M = 3.93; headquarters M = 4.46; t(17.55) = \(-\)2.67; \(p\) = 0.002; \(\delta\) = \(-\)0.67), (resources allocation: facility  
Figure 8: Categorized BARS percentages for resource allocation by company.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Facility (N = 3767)**} & \multicolumn{4}{c}{**Headquarters (N = 473)**} \\ \hline
**Scales** & **N** & **M** & **SD** & **N** & **M** & **SD** & **Cohen’s \$** \\ \hline Hazard perception & 3759 & 4.38 & 1.62 & 473 & 3.16 & 1.54 & 0.76 \\ Safety & 3761 & 5.89 & 0.78 & 473 & 5.89 & 0.68 & 0.00 \\ SCWE & 3759 & 4.66 & 1.42 & 473 & 4.92 & 1.44 & \(-\)0.18 \\ Planning & 3318 & 5.38 & 1.08 & 432 & 5.53 & 1.04 & \(-\)0.14 \\ Adaptive capacity & 3318 & 4.48 & 1.31 & 432 & 4.68 & 1.28 & \(-\)0.16 \\ Overall resilience & 3319 & 4.93 & 1.10 & 432 & 5.11 & 1.08 & \(-\)0.16 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Descriptive statistics of the scales by work location.  
Figure 9: Scales averages by location.  
M = 3.25; headquarters M = 3.84; t(83.48) = \(-\)3.85; \(p\) =0.000; \(\delta\) = \(-\)0.60) (Table 9, Figure 10). Headquarters personnel score higher than facility personnel in terms of emphasis on safety (97.92% vs. 86.77%) (Figure 11), formalization level (100% vs. 86.36%) (Figure 12), and resources allocation (82.86% vs. 46.62%) (Figure 13). It is interesting to mention the BARS for resource distribution in the case of facility personnel is interesting, as only 46.6