esa-sceva/satcom-chunk-collection huggingface dataset:
# Shared Logistics--Literature Review
## 3 Substantive Analysis
### Modeling of Shared Logistics  
Visions of the future may sometimes be detached from reality, which is why papers in which attempts were made to model shared logistics are particularly valuable. [PERSON] et al. [67] model a common supply chain, taking into account economic and environmental goals, different logistic costs, and different CO\({}_{2}\) emissions, social needs, external costs, and vehicle load factor. The article demonstrates the modeling results on an example with the participation of four cooperating suppliers. Cooperation between logistics companies is a simple and effective way to increase the efficiency of logistics operations. A study by [PERSON] et al. [47] design the optimal cooperation strategy through collaborative delivery using eco-packages. [PERSON] et al. [54] designed a mechanism for sharing logistics cooperation for express deliveries in conditions of stochastic demand. The authors took into account different operating and risk-sharing models. They model the sharing logistics scheme in the conditions of stochastic demand, excess supply, and shortage of supply, taking into account all aspects of the functioning of this business model. [PERSON] et al. [31] created a model that allows the management of shared storage and shared vehicles. The authors of the model assumed the use of electric cars and additionally planned charging points to optimize the route and minimize costs. [PERSON] [11], using game theory, indicates how great benefits can be brought by a common logistics platform. [PERSON] et al. [12] proposed a digital platform project for the B2B and B2C market based on a survey conducted among companies; therefore, the proposed marketplace conceptual framework takes into account the needs and concerns of companies. [PERSON], et al. [48] compared two models dedicated and shared in packaging management in automotive parts logistics, and showed that total costs, transportation costs, and inventory costs are lower in the shared mode.
# Cost-Effective CNNs for Real-Time Micro-Expression Recognition
## 2 Related Works
[27] proposed a two-stream convolutional network DTSCNN (Dual Temporal Scale Convolutional Neural Network) to address two aspects: the overfitting problem caused by the small sizes of existing ME databases and the use of high-level features. We can observe four characteristics of the DTSCNN: (i) separate features were first extracted from ME clips from two shallow networks and then fused; (ii) data augmentation and higher drop-out ratio were applied in each network; (iii) two databases (CASME I and CASME II) were combined to train the network; (iv) the data fed to the networks were optical-flow images instead of raw RGB frames. [PERSON] et al. [28] studied two variants of an Enriched LRCN (Long-Term Recurrent Convolutional Network) model for ME recognition. Spatial Enrichment (SE) refers to channel-wise stacking of gray-scale and optical flow images as new inputs to CNN. On the other hand, Temporal Enrichment (TE) stacks obtained features. Their TE model achieves better accuracy on a single database, while the SE model is more robust against the cross-domain protocol involving more databases. [PERSON] et al. [29] designed a Shallow Triple Stream Three-dimentional CNN (STSTNet). The model takes input stacked optical flow images computed between the onset and apex frames (optical strain, horizontal and vertical flow fields), followed by three shallow Convolutional Layers in parallel and a fusion layer. The proposed method is able to extract rich features from MEs while being computationally light, as the fused features are compact yet discriminative. Our objective was to realize a fast and high-performance ME recognition pipeline for embedded vision applications under several constraints, such as embeddability, limited memory and restricted computing resources. Inspired by existing works [27; 29], we explored different CNN architectures and several optical flow representations for CNN inputs to find cost-effective neural network architectures that were capable of recognizing MEs in real-time.
## 4 Aerodynamic Design Method Investigation
### Influence of Key Section Airfoils on the Aerodynamic Performance of the Configuration
#### 4.3.1 Kink Airfoil Fore-Loading Design  
Owing to the presence of the inlet and engines bumps, the fuselage surface of the initial configuration is complex. This increases the difficulty of designing the fuselage section airfoils. Therefore, the first consideration is the airfoil at the kink section. The kink airfoil is the Section 4 airfoil, shown in Figure 10. The airfoils of the other sections are unchanged.  
Based on the airfoil design method, three fore-loading configurations--Kink-load1, Kink-load2, and Kink-load3--are obtained. Figure 16a,b compare the shape of the three fore-loading and initial airfoils at the kink position, and the camber increments of the corresponding airfoils.  
Figure 17 depicts the comparison of the aerodynamic characteristics of the fore-loaded and initial configurations. Figure 18 describes the variation of pitching moment and the lift-to-drag ratio. As shown, the fore-loading of the kink airfoil has a negligible influence on the lift-to-drag ratio and pitching moment characteristics of the aircraft. This is because of two primary reasons: (1) the wing has a large sweep angle resulting in a strong spanwise flow, and consequently, the fore-loading effect of a single profile airfoil is limited; and (2) the location of the modified region is close to the center of gravity, and the nose-up pitching moment generated by fore-loading is too small to alter the pitching moment of the aircraft. Therefore, owing to the limitations of the initial planform shape, a larger fore-loading airfoil cannot provide a satisfactory zero-lift moment and favourable pitching moment characteristic of the aircraft during cruise.  
Figure 16: The fore-loading modification of kink airfoil. (**a**) Airfoils comparison; (**b**) Different camber increments.  
Figure 19 depicts the comparison of the chordwise pressure distribution along the \(Y/B=0.5,0.75, and 0.92 sections of the wing, at various fore-loaded and initial configurations. As shown, in the part near the wing root, the fore-loading effect is quite substantial. However, as the wingtip airfoil is unchanged, the fore-loading effect becomes progressively weaker along the spanwise direction, which further explains why the fore-loading kink airfoil design cannot alter the pitching moment of the aircraft.
## 5 Case Studies
### Results and Discussion
#### 5.2.2. Results for the Second Case Study and Comparison  
The aim of the second case study is to illustrate the performance of the proposed method in the RTS transformation set. First, two images of each state were selected as the temporary set. Then, each image of temporary set was rotated by angels \(\pm 15^{\circ}\) and \(\pm 20^{\circ}\), translated with translation vectors \([-35,\;-25]\) and \([35,\;25]\), scaled with scaling factors of 0.5, 0.8, 1.2 and 1.5. Consequently, a RTS transformation set was obtained including 600 (\(8\times 75\)) images. Some of their pre-processed images are illustrated in Figure 6. Accordingly, their values of radial Chebyshev moment invariants are given in Table 5. It can be observed that the proposed multi-feature vectors are really invariant to rotation, translation and scaling.  
Figure 5. The recognition rate curve of IFA-WSVM model.  
Figure 6. \(Cont.\)Among the 75 images of each sample of temporary set, 20 images were randomly selected as the training set. There were 160 images in the training set, and the remaining 440 images were used as the testing set. In the same way, after the pre-processing, the methods based on HMIs, ZMIs and RCMIs were used to extract the local and global multi-features, and the IFA-WSVM model was utilized to recognize the combustion state. Due to the randomness, the best results obtained by the proposed algorithms are given in Table 6.  
As seen from the results for second case study, the global region moment invariants alone cannot get a good effect of recognition. On the contrary, the combination of region moment invariants and contour moment invariants contributes to achieving the higher recognition rates. Among these methods, the method based on RCMIs can still have the highest recognition rate. Besides this, the operating time further confirms the viability of real-time recognition herein, saving a lot of computational resources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**Region Moments**} & \multicolumn{3}{c}{**Region and Contour Moments**} \\ \cline{2-7}  & **Hu** & **Zernike** & **Radial-Chebyshev** & **Hu** & **Zernike** & **Radial-Chebyshev** \\ \hline Recognition amount & 376 & 391 & 419 & 397 & 412 & 433 \\ Recognition rate & 85,45\% & 88.86\% & 95.23\% & 90.23\% & 93.64\% & 98.41\% \\ Average operating time of a single image & 33.39 ms & 100.90
## 3 Results
### Evaluation of the System with Children Aged 4 to 6  
We conducted a workshop in a robotics laboratory tailored for children aged 4 to 6, despite our datasets including ages up to 8. This age range was chosen due to the developmental differences between younger children and those older than 6, aiming for a more homogeneous group. The workshop was structured into two one-hour sessions, each accommodating 12 students, effectively covering the same content twice in two hours with a brief transition period. The session included an introduction to robotics, emphasizing our robot's operation, followed by interactive tasks where children commanded the robot to perform simple actions. Figure 7 captures a moment when children interact with the robot during the workshop.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**STT Correct**} & \multicolumn{2}{c}{**Gemini**} & \multicolumn{2}{c}{**GPT-3.5 Turbo**} & \multicolumn{2}{c}{**GPT-4**} \\ \cline{2-7}  & **Correct** & **Runtime** & **Correct** & **Runtime** & **Correct** & **Runtime** \\ \hline Total & 184 & 200 & 1.2 s & 192 & 1.1 s & 186 & 2.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: LLM evaluation over Google STT context-aware transcriptions on the VID1S dataset. STT Correct corresponds to the perfectly accurate transcriptions, while the other columns indicate the number of voice instructions that the semantic analysis tools correctly identified along with their runtime. The total number of voice instructions was 311.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Total Attempts** & **Successful Attempts** & **by STT** & \multicolumn{2}{c}{**Errors by Object Detection**} & **by LLM** \\ \hline VID Tasks & 100 & 93 & 7 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: This table provides a breakdown of the total and successful attempts, as well as errors categorized by STT, object detection, and LLM.  
The workshop yielded several important observations. Firstly, the activity was highly engaging, with children enthusiastic about interacting with the robot. The students issued 39 voice commands, of which, 15 required repetition due to the system's initial failure to interpret the instructions accurately. Despite these challenges, all participants could successfully command the robot by repeating the command when needed.  
Most failures stemmed from the children's inability to formulate instructions within the given time, resulting in incomplete commands. Additionally, developmental differences in speech skills, especially among four-year-olds, presented challenges. This highlights a significant challenge for future improvements: enhancing the system's robustness to accommodate the diverse characteristics of younger children and making the system more adaptable to their unique needs.  
These findings are encouraging, demonstrating the system's adaptability and its practicality for use by children in this age range. The option to retry commands significantly boosts the system's utility, ensuring that despite initial miscommunications, children can successfully interact with the robot as intended.
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 3 Hybrid Representation Approach
### CNN-Based Image-like Representation of Sensor Measurements  
The main idea in this representation variant is to treat sensor measurements as an image, which is subsequently analyzed by a CNN. Figure 2 illustrates examples of such 'images', derived from sensor measurement samples of the datasets used in this work (see Section 4.1) and associated with different driving behaviours. It could be observed that for each type of driving behaviour there is a distinctive visual element. Taking into account that each time slice label is derived from the label of the containing route (e.g., normal, semi-aggressive or aggressive), the absolute classification accuracy is not a primary concern at this stage. The intuition is to generate a time-series encoding which represents frequency of occurrence of driving patterns and is distinctive of each driving style.  
The CNN architecture used in this work consists of three convolutional layers filtering their input with 32, 64 and 64 kernels of size 1 \(\times\) 1, 1 \(\times\) 1 and 3 \(\times\) 3, respectively. A flatten layer is used to transform the output of the last convolutional layer to a vector. This vector is the input to a dense layer with 100 units. Finally, a fourth dense layer classifies each time slice. The end product of this component in the context of the proposed approach is the  
Figure 1: Summary of the main stages of the proposed approach.  
Figure 2: Examples of image-like representations of sensor measurements for different driving behaviours: (**a**) 50 \(\times\) 3 image for normal driving behaviour in UAH dataset, (**b**) 50 \(\times\) 3 image for aggressive driving behaviour in UAH dataset, (**c**) 50 \(\times\) 22 image for normal driving behaviour in MOTIF 1 dataset, (**d**) 50 \(\times\) 22 image for semi-aggressive driving behaviour in MOTIF 1 dataset, (**e**) 50 \(\times\) 22 image for aggressive driving behaviour in MOTIF 1 dataset.  
histogram encoding the frequency of occurrence of each type of driving behaviour, in the time slice level (Figure 1). Convolutional and dense layers employed the ReLU activation function, except for the last dense layer that employed the sigmoid activation function. Sparse categorical cross-entropy is employed as a loss function and stochastic gradient descent is employed for optimization. This CNN-based approach has been presented in detail in our preliminary work [10].
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 3 Circuit Implementation and Simulation Results  
We use a charge packet technique akin to digital coding to generate spikes in the spike-coded FPA system, and its working principle will be described in this section. In addition, we have designed this encoding circuit to be configurable, relying on the control of the feedback signal to select the encoding mode for the pixel luminance information.
## 4 Modeling and Analysis
### Modeling for General Relay-Node Selection on Intersection  
To evaluate the selection of the relay-node, we consider the scenarios that there is at least one node in the Intersection Region and in partition range on every branch respectively. In addition, it is assumed that the Hunter locates at the point \(R/2\) from C to analyze the lower bound of the performance since this case has the biggest partition range in the Intersection Phase. In the following, based on the results in the previous subsection, we will first explore the analytic model for the metrics (i.e., \(T_{\text{part}}\), \(T_{\text{con}}\), \(\beta\) and PDR) in Intersection Phase and Branch Phase, respectively.
## 1 Introduction  
Cu-Sn-Ti alloys are commonly used as brazing alloys for diamond [1; 2; 3; 4; 5; 6]. The composition of the microstructure of Cu-Sn-Ti alloys can be easily determined. However, the crystal structures of Cu-Sn-Ti alloys, especially those related to ternary intermetallic compounds, have not been thoroughly studied. For example, Cu-15 wt.%Ti-10-wt.%Sn alloys have various binary intermetallic compounds with different morphologies, such as Cu-Sn, Cu-Ti, and Sn-Ti intermetallic compounds, and unidentified phase domains obtained based on compositional analysis [4]. Cu\({}_{2}\)SnTi was the first found for the ternary intermetallic compound and other ternary alloys have not yet been discovered in the Cu-Sn-Ti alloy [7]. X-ray diffraction (XRD) patterns of unidentified phase can be determined but not be found in JCPDS (Joint Committee on Powder Diffraction Standards) cards. The ternary intermetallic compound of CuSnTi (Phase H) was identified using the XRD patterns [8]. In addition, the ternary intermetallic compound of CuSn\({}_{3}\)Ti\({}_{5}\) (Phase B) and its crystal structure were resolved by the XRD patterns [9; 10; 11]. Furthermore, the ternary intermetallic compound of CuSnTi\({}_{3}\) (Phase E) and its crystal structure were resolved by the XRD patterns and the selected area diffraction (SAD) patterns generated by transmission electron microscope (TEM), which matches bulklike InNi\({}_{2}\) structure (space group: 194 \(P6_{3}/nmc\)) [12]. Recently, the Cu-Sn-Ti diffusion-triple annealed at 823 K for 60 min was extended to demonstrate that CuSnTi is in equilibrium with the stable binary compounds of Sn\({}_{5}\)Ti\({}_{6}\)Sn\({}_{3}\)Ti\({}_{2}\), Cu\({}_{3}\)Sn, Cu\({}_{4}\)Ti\({}_{3}\), Cu\({}_{41}\)Sn\({}_{11}\), and Cu\({}_{4}\)Ti [13]. More recently, a new approach based on the Monte Carlo simulation has been extensively used to demonstrate the processes of the sintering/coalescence of Cu-Ni nanoparticles [14]. This technique enables the development of a new generation of polymetallic nanomaterials for catalysts, sensors, and other applications. As of now, ternary intermetallic compounds Cu\({}_{2}\)SnTi, CuSnTi, CuSn\({}_{3}\)Ti\({}_{5}\), and CuSnTi\({}_{3}\) as well as binary intermetallic compounds, such as Cu-Sn, Cu-Ti, and Sn-Ti [15], are currently known crystal structures. We believe that other ternary intermetallic compounds copper, tin and titanium should still exist. Therefore, the purpose of this study is to study new ternary intermetallic compounds in Cu-Sn-Ti alloys.
## 3 Results and Discussion
### N/A: Not Available  
Based on the data mentioned above, it can be concluded that a product selling price of $10/L is the minimum selling price able to fulfil all the given parameters. The hand sanitiser product with a $10/L price produces a 9.8% IRR, will break even at 6.1 years, and has an NPV of $108,000 at the end of the plant's lifetime. With a gross margin of 21%, the product has a lower value according to the average gross margin obtained by speciality chemical industries, which have gross margin values at 31.16% [44]. Therefore, we need to choose the product selling price that can fulfil this parameter. The price point of $12/L produces a gross margin value of 34% with a higher IRR and NPV compared  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material/Utility** & **Unit Cost (S)** & **Annual Amount** & **Reference Unit** & **Annual Cost (S)** \\ \hline Air & 0.00 & 5,914,956 & kg & 0 \\ Cellulose & 2.50 & 4058 & kg & 10,146 \\ Citric Acid & 0.63 & 2866 & kg & 1791 \\ Glycerol 98\% & 0.60 & 471 & kg & 283 \\ H\({}_{2}\)O\({}_{2}\) 3\% & 0.40 & 1356 & kg & 542 \\ OPEFB & 0.01 & 80,000 & kg & 488 \\ Sodium Citrate & 0.76 & 5431 & kg & 4127 \\ Sodium Hydroxide & 0.43 & 80,000 & kg & 34,000 \\ Water & 0.01 & 4600 & MT & 46 \\ Yeast & 1.95 & 8158 & kg & 15,907 \\ Standard Power & 0.10 & 588,067 & kW-h & 58,807 \\ Low Pressure Steam & 3.34 & 1983 & MT & 6624 \\ High Pressure Steam & 5.78 & 1119 & MT & 6468 \\ Cooling Water & 0.05 & 186,277 & MT & 9314 \\ Chilled Water & 0.19 & 78,573 & MT & 14,536 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A detailed list of materials and utilities cost.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Product Price** & **IRR** & **PBP** & **Gross Margin** & **ROI** & **NPV** \\
**(USD/L)** & **(\%)** & **(Year)** & **(\%)** & **(\%)** & **(USD)** \\ \hline
2 & N/A & N/A & \(-\)295 & \(-\)26 & \(-\)1,824,000 \\
4 & N/A & N/A & \(-\)97 & \(-\)14 & \(-\)1,278,000 \\
6 & N/A & N/A & \(-\)32 & \(-\)3 & \(-\)732,000 \\
8 & 0.4 & 10.9 & 1 & 9 & \(-\)224,000 \\
10 & 9.8 & 6.1 & 21 & 16 & 108,000 \\
12 & 17.0 & 4.3 & 34 & 24 & 440,000 \\
14 & 23.2 & 3.3 & 44 & 31 & 772,000 \\
16 & 28.7 & 2.6 & 51 & 38 & 1,104,000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effect of hand sanitiser price on some economic parameters in this study.  
to the $10/L price-point, with values of 17% and $440,000, respectively. It also has a relatively short payback period, at about 4.3 years. These economic parameters are from the market's point of view, with the average product selling price of hand sanitiser in Indonesia being $0.87 per 50 mL, or about $17.40/L, the product is economically competitive compared to the existing products in Indonesia.
## 2 Study Area
### Leyte Province  
The Province of Leyte is located on the island of Leyte in the Eastern Visayas region. The province is bounded by Carigara Bay in the north, the San Juanico Strait and Leyte Gulf in the east, the Visayas Sea and Ormoc Sea in the west, and the province of Southern Leyte, also located within Leyte Island, in the south.  
The province and the whole of Leyte Island are bisected by the 140-km segment of the Philippine fault. Along the fault trace, features such as west-facing fault scarps and narrow linear valleys with displaced stream channels are present [30]. These topographic expressions contribute to the overall geomorphology of the province.  
The regional geomorphologic units of Leyte province were classified based on geology, elevation, slope gradient, and curvature. The province is divided into seven terranes: Lowlands, Footslopes, Upper and Lower Denudational Slopes and Hills, Denudational Mountains, Volcanic Denudational Mountains, and Karstic Environment (Figure 2).  
Lowlands and Footslopes are characterized as landforms with low elevation, low slope gradient, and low curvature. These units are composed of thick deposits of Quaternary unconsolidated alluvium.  
Upper and Lower Denudational Slopes and Hills are mostly composed of Late Miocene to Pliocene agglomerate, tuff, calcareous, and tufaceous sandstone, conglomerate, shale, and clastic limestone. In the central portion of the province, the slopes are made of more recent Pliocene to Pleistocene agglomerate, tuff, conglomerate sandstone, shale, and local limestone.  
Denudational mountains comprise the central mountain range that runs from north to south of the province found along the Leyte segment of the Philippine Fault. The mountains are comprised Late Miocene to Pliocene coralling limestone underlain by shale, sandstone, and local conglomerates. The Volcanic Denudational Mountains are differentiated to recognize the more recent Pliocene to Pleistocene volcanic rocks of andesite and basalt lava flow, shallow intrusives, agglomerate, and tuff located in the higher elevations.  
Figure 1: (**A**) Geological and (**B**) geomorphologic map of GMMA, as well as the location of the ReMi and 3 CMT survey sites depicted by red points. Maps adapted from [PERSON] et al. (2022) [5]. Geologic map modified from BMG (1983) [28] and geomorphologic map adapted from MMEIRS (2004) [29]. The red points represent the survey sites considered for the HVSR Inversion.  
Karstic environments refer to the Pliocene to Pleistocene coralline limestone underlain by minor sandstone, shale, and agglomerate, particularly located in northwestern coastal municipalities of the province, such as Isabel, Merida, and Palompon.  
Representative points across all geomorphologic units in the province were selected as study sites. A total of 27 refraction microtremor and three-component microtremor pair sites and 136 stand-alone three-component microtremor sites were considered in the Vs30 estimation.