esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurements and Testing of the Measuring System  
### Measurement Protocol  
The developed adaptive data transmission algorithm was tested in terms of energy efficiency and network load. For this purpose, a measurement protocol was designed, which consisted of 26 measurement cases, P1 to P26. The measurement protocol controlled the data transmission from 16 sensors, which allowed to simulate the practical application of the algorithm. In individual measurement cases, data from sensors were transmitted with predefined frequencies\(f_{T}\). The tested measurement cases are presented in Table 2. The measurement protocol includes data transmission from all 16 sensors both at the highest frequency\(f_{T}\) of 60 Hz (P1) and the lowest frequency of 5 Hz (P26). The cases from P2 to P25 represent selected intermediate transmission states of the sensor network.  
For example, in the measurement case P21, the data are transmitted for 15 sensors with the frequency\(f_{T}\) equal to 5 Hz, and for one sensor with the frequency \(f_{T}\) equal to 5 Hz, 15 Hz, 30 Hz, and 60 Hz (for the sensor, for which data are transmitted at a given frequency \(f_{T}\), data are also transmitted with all available frequencies below \(f_{T}\)). Measurements of each case from P1 to P26 were recorded for 20 s. During this time, the MEMS sensors made continuous measurements, and their parameters \(d_{i}\) (the parameter controlling the transmission filter) were set in such a way as to ensure the frequency of data transmission \(f_{T}\) from individual sensors in accordance with the developed measurement cases.
# A Motion Control of Soft Gait Assistive Suit by Gait Phase Detection Using Pressure Information
## 3 Gait Phase Detection Method
### Relation between Calf Back PARM Variables  
A relation between the CB PARM variables for the gait phase detection is derived as follows. First, the following equation is derived from the state equation of gas:  
\[P(dV/dt)+(dP/dt)V=(dW/dt)R\theta+WR(d\theta/dt) \tag{1}\]  
where \(V\) (m\({}^{3}\)) is the PARM volume, \(W\) (kg) is the mass of air, \(R\) (J/(kg K)) is the gas constant, \(\theta\) (K) is the air temperature, and \(t\) (s) is the time. In Section 3.2, the pressure \(P\) is represented as absolute pressure. We consider Equation (1) during the pre-swing phase. We assumed that air charge to or discharge from the CB PARMs is zero, since the CB PARMs are the closed chambers. We also assumed that the air temperature in the CB PARM is constant since the pre-swing phase is generally short time under 1 s. These assumptions are represented as:  
\[dW/dt\simeq 0,\ \ \ d\theta/dt\simeq 0. \tag{2}\]  
Then, Equation (1) could be written as follows:  
\[dP/dt=-(P/V)(dV/dt). \tag{3}\]  
Equation (3) shows that the relation between the pressure derivative and the volume derivative is negative proportional. In addition, we consider the deformations of the CB PARMs in the pre-swing phase as shown in Figure 6. The left figure is a stretched state, and the right figure is a contracted state of the PARM. The bold line on the PARM is a wire whose length is constant. The relation between the pressure derivative and the length derivative is as follows:  
\[dP/dt=-(P/V)(dV/d\ell)(d\ell/dt) \tag{4}\]  
where \(\ell\) (m) is the length of the PARM. \(dV/d\ell\) is a negative value since when the PARM length becomes long, the PARM volume becomes small.  
Therefore, the relation between the pressure derivative \(dP/dt\) and the length derivative \(d\ell/dt\) is proportional. Under the assumptions of Equation (2), at the start of the pre-swing phase, the CB PARM is stretched and its length is long. After that, the stretched PARM contracts in a short time,  
Figure 5: Gait phases in one cycle motion. A horizontal axis represents a gait percentage in the one cycle motion. The gait phases of a white color leg are classified in a stance phase, a pre-swing phase, and a swing phase, and an opposite side leg is also classified in the same manner.  
and its length becomes short. Therefore, a sign of the length derivative \(d\ell/dt\) will become positive to negative in the pre-swing phase, and also a sign of the pressure derivative \(dP/dt\) will become positive to negative. As a result, the pre-swing phase will be be detectable from the pressure information of the CB PARMs.
## 3 Results
### Validation of the Models
#### 3.1.2 Simulation of PAR Experiments
Figure 9b,c are the modeled geometry and computational mesh for the SP8 and SP9 analyses. In the experiment, after the hydrogen injection was completed and the concentration of hydrogen measured in the pressure vessel was uniformly distributed, the gate installed at the entrance of the PAR was opened through an electric signal. In Figure 10, the remaining amount of hydrogen and the hydrogen recombination rate of the PAR obtained by the test and the simulation are compared over time. The time of the gate opening is denoted in the figure. It is seen that hydrogen available in the vessel is  
Figure 8: Hydrogen mass remaining in the test vessel and recombination rate of: (**a**) the HR2 test simulation with the AREVA correlation; (**b**) the HR14 test simulation with the NIS correlation. Figure 9: SPARC SP8 and SP9 test: (**a**) KNT-40 PAR installed in the test vessel; (**b**) geometry modeling; (**c**) mesh generated for simulation. quickly reduced by the PAR operation. Through this comparison, it can be seen that the PAR model implemented in the contain3D code well simulates the hydrogen recombination characteristics of the KNT PAR except for a short period of the gate opening.
## 2 Materials and Methods
### Cloud Detection and Atmospheric Conditions  
The issue of cloud identification in satellite observations has been tackled using various cloud masking techniques for optical sensors over the years (e.g., [57; 58; 59; 60; 61; 62]), with  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Yield (q/ha)** \\ \hline
2018 & 57 \\
2020 & 79 \\
2022 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Annual wheat yields of the investigated field (q = metric quintal; ha = hectare).  
Figure 1: Investigated field (red polygon).  
recent improvements driven by ML advancements [62; 63; 64; 65]. However, all cloud masks have technical limitations, leading to omission and commission errors [66]. Additionally, optical band imagery (used for vegetation monitoring) can be contaminated not only by the presence of tropospheric clouds but also by the presence of cirrus clouds and mist. Mist, in particular, can alter the vegetation indices due to its variable impact on red and NIR bands based on vapor characteristics (droplet size and density) [67]. Then, the ideal cloud mask depends on the use case (the bands you want to mask and the content of the ground cells you want to observe). Thinking from this perspective, in 2017, the s2 cloudless algorithm was developed [68]. Since 2020, this has been integrated into the Google Earth Engine (GEE) as Sentinel-2: Cloud Probability [69; 70], allowing users to set a threshold to convert the cloud probability into a mask. The authors recommend applying a CP threshold of 0.4 (40%) [66], but customization is suggested for optimal results based on the intended application.  
To manage cloud cover, we propose an alternative solution practiced by several authors (e.g., [71; 72; 73]), which is the Maximum Value Composite (MVC) procedure [74]. In a predetermined time period (e.g., 1 month), MVC selects for each pixel the greatest VI value. Considering that vegetation indexes (like NDVI) assume values around zero in the presence of clouds, such a process is expected to automatically filter out cloud recurrences in the time interval for which the computation is applied. However, in humid regions, such as the Po River Valley, this approach is also not risk-free, as the agricultural field, or part of it, may be cloudy and/or foggy during the entire predetermined time interval. To address this issue, we implemented a specific cloud detection method, the NDVI-based clear pixel procedure (NDVI-CPP), in this study.
## 3 Results and Discussion
### Adsorbent Characterization
#### 3.2.6 SEM and EDX Analyses  
Surface morphologies of ADAV and TTAV300 were investigated using SEM. Micrographs shown in Figure 8A,B indicate that ADAV and TTAV300 contained a number of pores with different radii. These data confirmed the loss of some volatile and organic matter at 300 \({}^{\circ}\)C and the formation of carbonaceous mass with an advanced pore structure. These findings are in alignment with the BET surface area and the TGA analyses of other thermally treated adsorbents [14; 15; 16]. On the other hand, energy dispersive X-ray spectroscopy (EDX) analysis was used to study the elemental compositions of ADAV and TTAV300. Obtained results show that the TTAV300 was mainly composed of carbon (92.75%) in comparison to 72.55% for ADAV (Figure 8C,D). Other elements such as Na, Mg, P, Cl, Ca, and Ca were also present, and their total concentration was less than 2%. These results confirm the formation of carbonaceous material following the thermal treatment.  
Figure 8: \(Cont.\)  
Figure 7: Nitrogen adsorption–desorption isotherms at 77 K for ADAV (**A**) and TTAV300 (**B**) and the relation between pore volume and diameter for both ADAV (**C**) and TTAV300 (**D**).
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 5 Problems and Optimization
### Radius Limit Value  
In the algorithm reasoning from the previous section, sufficient conditions were provided to meet condition (2), but they are not necessary conditions. This condition is satisfied by preserving all edges from paired nodes to their adjacent paired nodes. To fully leverage the long-distance transmission advantages of laser communication and to explore the maximum radius that the algorithm can extend while satisfying condition (2), this is indeed a research-worthy question. In the section on planarity, a method was proposed to divide the range of virtual nodes into two parts for consideration. Below, we will further analyze and expand on this method.  
Consider any pair of nodes \(u\) and \(v\), with their corresponding virtual node \(m\). Due to the symmetry of the paired nodes, we only need to consider the semicircle where node \(u\) is located. Consider the extreme case where a node \(m^{\prime}\) connected to the virtual node \(m\) is located at the boundary of \(m\)'s range. Since the paired nodes are symmetric with respect to \(m^{\prime}\), the maximum distance between \(u\) and its paired node is achieved when the  
Figure 4: Node edge crossing.  
line connecting the paired nodes is perpendicular to \(um^{\prime}\). Let us assume the length of this maximum distance is \(r_{m}\), as shown in Figure 5. It can be easily derived that  
\[r_{m}^{2} =r_{2}^{2}+{r^{\prime}}^{2}\] \[=r_{2}^{2}+{(\sqrt{r^{*2}-h^{2}}-{r_{1}})}^{2}+h^{2}\] \[=r_{2}^{2}+r_{1}^{2}+{r^{*}}^{2}-2r_{1}\sqrt{r^{*2}-h^{2}}\]  
It is evident that \(r_{m}\) is directly proportional to \(h\). When \(h\) takes its maximum value \(r^{*}\), \(r_{m}\) reaches its maximum value \(\sqrt{r_{2}^{2}+r_{1}^{2}+{r^{*}}^{2}}\). Since \(r_{1},r_{2}\leq r/2\), it follows that \(r_{m}\leq\sqrt{r^{2}/2+{r^{*}}^{2}}\). Because the point of maximum distance should be within the maximum communication range of node \(u\), that is, \(\max r_{m}\leq 1\), it follows that \(r^{*}\leq\sqrt{1-{r^{2}}/2}\).  
In the previous section, it was determined that a sufficient condition to meet requirement (1) is \(r^{*}\geq 2r\). Based on the arguments presented above, it can be concluded that \(r\leq\frac{\sqrt{2}}{3}\), and, consequently, \(r^{*}=\frac{2\sqrt{2}}{3}\).
## 3 Materials and Methods
### Sample Preparation and Thermal Treatment  
The next step was to produce laboratory samples similar to sugarcane bagasse through the industrial process. Thus, the canes were processed using a 4-roller Roskamp mill, simulating the industrial process. Then, the canes were washed several times with running water, to eliminate as much sugar as possible, and cut into small pieces with an average size of \(20\times 10\times 5\) mm, which were dried in an ECOCELL forced air oven at a temperature of 90 \({}^{\circ}\)C for 24 h. After this initial drying period, the material was washed to extract the sugar by placing in water for 12 h, after which the water was renewed. This process was repeated 3 more times, until it was found that the water was clean and colorless. After the washing process, the pieces of sugarcane were dried for an additional 12 h at 90 \({}^{\circ}\)C. Figure 2 shows the sequence from raw material to sugarcane pieces used in the torrefaction test.  
In this work, a torrefaction protocol described by [PERSON] et al. (2018) was used [PERSON] et al. (2018). The samples were torrefied and pyrolyzed in the muffle furnace, which has a built-in controller with the ability to program 4 temperature thresholds and the residence time. There is a cavity on the top of the device to facilitate the extraction of torrefaction gases. Then, the muffle furnace was programmed to the desired temperature (\({}^{\circ}\)C) and residence time (minutes), with each level corresponding to a torrefaction phase. Sample preparation started by weighing approximately 0.5 kg of material. Preparation was aided by conventional aluminum foil with the objective to wrap the sugarcane bagasse in a cylindrical shape. During this study, torrefaction tests were carried out in triplicate to guarantee their reproducibility (Figure 3).  
The particular parameters that can be changed during the thermal process are temperature and residence time, which were changed in order to understand the best set of parameters to obtain high-quality samples. Table 3 defines the parameters used for each test performed. It should be noted that the selection of parameters described in the table was done in a sequential manner, taking into account the results obtained for each torrefaction type. That is, the results obtained in one process always influenced the choice of parameters used in the next one. Obtained materials are shown in Figure 4.  
Figure 2: Sequence of procedures to obtain sugarcane pieces used in torrefaction tests: (**a**) raw material as bought; (**b**) sugarcane after being processed with the roller mill; (**c**) sugarcane pieces; and (**d**) pieces being washed.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 2 Materials and Methods  
### Preparation of Test Specimens  
Experimental denture base resin specimens were prepared by adding TiHA (3%, 6%, or 9% [\(w/w\)]) to the polymer powder of polymethyl methacrylate (PMMA)-based, self-curing denture base resin (DUPE RESIN, dupe pink, GC Co., Tokyo, Japan). The resin without TiHA was used as a control. The polymer powder and resin monomer were mixed in a powder-liquid ratio of 5 g/3.5 mL at 25 \({}^{\circ}\)C, in accordance with the manufacturer's instructions, poured into putty-type silicon rubber molds, and pressed by glass plates. After polymerization for 30 min, all specimens (\(\varphi\) 10 \(\times\) 1 mm) were polished with four kinds of waterproof abrasive papers in the sequence #600, #800, #1000, and #1500 (Riken Corundum Co., Ltd., Konosu, Japan). This was followed by 24 h soaking in distilled water to remove the residual monomer. For experiments with bacteria, the specimens were first disinfected using 0.01% (\(v/v\)) sodium hypochlorite, and then rinsed with sterile distilled water three times.
## 2 Methodology
### Components of the CTCR Methodology
#### 2.1.2 Progress Measurements  
By obtaining the data from measuring the physical progress of each activity, costs, and durations executed from the timeline, it is possible to measure the total physical progress of the project in a given period. This is done using Equation (4).  
\[A_{physical\ real\ i}=k1\times\frac{\sum_{i=1}^{n}A_{Ri}\times C_{i}}{C_{T}}+k2 \times\frac{\sum_{i=1}^{n}A_{Ri}\times T_{i}}{T_{T}}+k3\times\frac{\sum_{i=1}^ {n}A_{Ri}\times RC_{i}}{RC_{T}}+k4\times\ \sum_{i=1}^{n}A_{Ri}\times R_{i} \tag{4}\]  
where \(A_{physical\ real\ i}\) is the real physical progress of the project in period \(i\) (in %) and \(i\) is the period of time of the evaluation. \(K1\), \(2\), \(3\), \(4\) are non-dimensional coefficients to provide weightings for the time, cost, criticality, and risk criteria. These coefficients are weighted on the basis of a multicriteria analysis that is developed below. \(A_{R}\) is the real progress of the work in period \(i\). \(C_{i}\) is the planned cost accumulated in period \(i\). \(C_{T}\) is the total planned cost. \(T_{i}\) is the accumulated effort in time periods of all of the activities planned to be executed up to period \(i\). \(T_{T}\) is the total effort resulting from the summation of the planned time periods for all of the activities that comprise the project (it is not the project period). \(RC_{i}\) is the accumulated effort in time periods of all of the critical path activities planned to be executed up to period \(i\). \(RC_{T}\) is the total effort resulting from the summation of the planned time periods for all of the critical path activities that comprise the project. \(R_{i}\) is the risk accumulated in period \(i\), and n is the total number of periods at the evaluation date.  
With the real budgetary execution, it is possible to calculate the periodical budgetary progress using Equation (5).  
\[A_{real\ budget_{i}}=\sum_{j=1}^{n}C_{Rj} \tag{5}\]  
where \(A_{real\ budget\ i}\) is the progress of the project's real budget in period \(i\) (monetary value), \(i\) is the evaluation period, and \(j\) is the number of activities involved in the evaluation period \(i\). \(C_{R}\) is the total real cost of the activities in period \(i\) (monetary value), and \(n\) is the total number of activities planned for the period \(i\) (units of time).  
With the measurements of the physical progress of each activity on the timeline's critical path, it is possible to calculate the progress of the critical path by applying Equation (6).  
\[A_{real\ critical\ pathi}=100\times\frac{A_{Ri}\times R_{i}}{\sum_{j=1}^{n}R _{j}} \tag{6}\]  
where \(A_{real\ critical\ pathi}\) is the real progress of the completion of the critical path in the evaluation period \(i\) (in %), \(i\) is the evaluation period, and \(j\) is the number of critical path activities involved in evaluation period \(i\). \(A_{R}\) is the real progress of the work activities on the critical path in period \(i\). \(R\) is the planned time of the activities on the critical path (units of time), and \(n\) is the total number of activities on the critical path of the project timeline.
## 3 All-Fiber Lasers Mode-Locked with m-SWCNT Saturable Absorber  
A single m-SWCNT saturable absorber containing 300 nm of the nanomaterial was integrated in three all-fiber lasers to experimentally verify its wideband nonlinear transmission. Erbium, thulium, and holmium-doped active fibers were used as a gain medium. The details of the investigated laser cavities are listed in Table 1. The lasers were operating in an all-anomalous (solitonic) dispersion regime. All three laser cavities employed a similar ring design incorporating a suitable wavelength division multiplexer, an isolator, and an output coupler (Figure 3). Polarization controllers were used to optimize the laser performance, as non-polarization maintaining fibers and components were used.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dopant** & Er\({}^{3+}\) & Tm\({}^{3+}\) & Ho\({}^{3+}\) \\
**Fiber type** & OFS & Nufern & iXblue \\  & EDF80 & SM-TSF-5/125 & IXF-HDF-8-125 \\
**Fiber length** & 30 cm & 17 cm & 194 cm \\
**Output coupling ratio** & 10\% & 10\% & 30\% \\
**Pump wavelength** & 980 nm & 1565 nm & 1940 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Active fiber specification, output coupling ratio, and pump wavelength for the three investigated fiber lasers.  
Figure 3: Schematic of the experimental laser setup. WDM, wavelength division multiplexer.  
Figure 2: Metallic single-walled carbon nanotube (m-SWCNT) thin film deposited on a fiber connector tip (**a**). Nonlinear optical transmission of a 300 nm m-SWCNT saturable absorber fitted with a fast saturable absorber model measured at 1560 nm (**b**) and 1950 nm (**c**).
## 3 System Model and Problem Formulation
### Problem Formulation  
This section is devoted to the investigation of optimizing cache hit efficiency and base station (BS) selection for online training with the aim of maximizing the ratio of cache efficiency to training energy consumption. The total number of federated aggregation epochs is represented by \(T\). To improve the cache performance while minimizing training energy consumption, this study proposes a hit efficiency to energy (HE) gain metric, defined as the ratio of the hit efficiency to the energy consumption of the selected BSs. The objective is formulated as follows:  
\[P1:\text{max}\sum_{t\in\mathcal{T}}\frac{H_{t,system}}{E_{t,system}} \tag{11}\]\(\begin{array}{l}\text{\emph{subject to} :}\\ \mathcal{N}_{1}\subset\mathcal{N}\\ \mathcal{N}_{3}\subset\mathcal{N}\\ L_{n}\geq\ 1\\ f\in\mathcal{F}\\ n\in\mathcal{N}\\ u\in\mathcal{U}\\ x_{B}\text{\emph{and} }x_{c}\in\{0,1\}\\ \sum_{f\in\mathcal{F}}P_{u,f}=1\end{array}\)  
The application potential of energy efficiency ratios, such as the one proposed herein, is considerable. Specifically, the aim of attaining reliable training efficiency while simultaneously reducing energy consumption in the context of online training can be expanded to a broader range of scenarios.
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 4 Experiments
### Baselines  
* **TFIDF + SVM**: This technique uses TF-IDF to make text vectors and SVM [28] as a text classifier.
* **CNN**[29]: This method uses CNNs containing multiple filters and employs softmax as a classifier.
* **RCNN**[30]: The approach fuses RNN and CNN to make a new model and exploits the advantages of both models to improve the performance of text classification.
* **HARNN**[31]: First, the method considers the hierarchical structure of documents; words form sentences and sentences form documents, so the modeling is also performed in these two parts and the attention mechanism is introduced.
* **FLA**[2]: The method proposes an attention-based neural network framework that can perform the task of charge prediction and relevant law articles extraction and show the importance of law articles in the civil law system for judicial decision making.
* **TOPJUDGE**[32]: The approach unifies multiple subtasks of LJP into a single learning framework, builds dependencies between LJP subtasks into a DAG form and enhances trial predictions using prior knowledge. The model can handle any DAG-dependent subtasks.
* **GCN**[12]: This method uses text to construct a graph and then a graph convolutional network to extract node features for classification.
* **MPBFN-WCA**[2]: The approach designs a multi-view forward prediction and backward verification framework to efficiently exploit the dependencies between multiple subtasks. The word matching features of fact descriptions are integrated into the network through an attention mechanism to distinguish cases with similar descriptions but different penalties.
* **LADAN**[8]: The model effectively solves the problem of confusing charges (law articles) in the LJP task. The method takes into account not only the positive effect of the textual definitions of charges (law articles) on the semantic extraction of case fact descriptions but also the negative effect of interrelationships (e.g., similarity relations) between the definitions of charges (law articles) on the semantic extraction.