esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Procedures  
The subjects warmed up for 10 min, including treadmill jogging and dynamic stretching of muscle groups before the formal experiment [25]. The subjects wore the same brand of sports shoes (Model s.y.m.B9025, LurngFurng, Inc., Taipei, Taiwan), which were prepared in advance in the laboratory to control the effect of the shoe air cushion on jumping performance. The subject's dominant leg was determined by the foot that is usually used to kick a ball with better accuracy and speed [26].  
The muscle bellies of the rectus femoris, biceps femoris, tibialis anterior, gastrocnemius, and soleus of the dominant leg were shaved and wiped clean with a razor, sandpaper, and alcohol. The electromyography (EMG) sensors were positioned, pasted, and fixed on the muscle belly according to Cram et al. [27]. The subjects were asked to jump slightly to ensure that the EMG sensors on each muscle were secure and the EMG signals were stable. They were also asked to confirm that they would not feel uncomfortable when performing the movements. The position of the EMG sensor on the muscle was judged and the sensor pasted by the same researcher.  
Drop jumps on four different materials were measured on four separate days. The maximum voluntary isotonic contractions (MVICs) of the lower limb muscles of the subjects' dominant leg were measured before each experiment. The MVICs of the rectus femoris and biceps femoris with knee flexion at 90\({}^{\circ}\), as well as the MVICs of the tibialis anterior, gastrocnemius muscle, and soleus upon dorsiflexion and plantarflexion with the ankle joint at 90\({}^{\circ}\), were measured. Each muscle contraction lasted for 3 s, and measurements were made three times, with a 90 s rest between measurements (Figure 1).  
To ensure proficiency in the experimental movements and to avoid sports injuries during the experiment, the subjects were required to practice drop jumping using various combinations of materials and heights at least two days before the formal experiment. Four common material surfaces found on sports fields were used in this study, namely, sand, turf, PU, and wood (Figure 2). The test orders of the material surfaces and platform heights (30, 40, 50, and 60 cm) were randomly determined by drawing lots. Many studies have shown that a height for drop jumping of between 30 and 60 cm is suitable [10; 28; 29; 30]. Three drop jumps were performed at each height with a 30 s rest between each jump. The subjects rested for 2 min between each height. The tests for each material surface were performed at least 1 day apart to ensure that there was no interaction or fatigue.  
Figure 1: Experimental procedures.  
Figure 2: Four common material surfaces on sports fields.  
When preparing for the drop jump, the subjects were required to place their hands next to their waist to reduce the influence of the arm swing movement, and their feet were shoulder-width apart. After hearing the start signal, the subjects were asked to lean their body and leave the platform with their dominant leg in a natural way, land on both feet, and jump off the ground quickly with their best effort.
## 3 Results  
A total of 7648 studies were initially identified. After excluding duplicates, 674 were sieved by title and abstract, and 637 studies were ruled out, since they did not satisfy the eligibility criteria. An additional 20 references were obtained through inverse searches based on the 37 previously selected studies, which were potentially relevant due to the title and abstract. The 57 selected studies by title and abstract were read in-depth: 39 of them were excluded, since they did not satisfy the eligibility criteria. Finally, 18 papers were included in this SR [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42] (Figure 1 and Supplementary Materials).  
\begin{table}
\begin{tabular}{|p{142.3 pt}|} \hline P: adult participants (\(>\)18 years of age) from both genders, diagnosed clinically with stroke and gait and/or balance disorders; \\ I: interventions using commercial video games (Nintendo Wii®, Microsoft Xbox®, Microsoft Xbox® in combination with Kinect® device or a combination of the previous ones) to treat balance and gait after a stroke; \\ C: studies comparing two exercise interventions (one of them was a programme of VR training and the other was a conventional rehabilitation programme) and \\ O: provide balance and/or gait results registered with instrumentation systems and/or observational ratings (posturography, static balance index, postural assessment scale, Berg balance scale, Timed Up and Go test, Functional Reach Test, Falls Efficacy Scale International, 10-m walk test, 6-min walk test and dynamic gait index test). \\ \hline \end{tabular}
\end{table}
Table 2: Eligibility criteria according to Population, Intervention, Comparation and Outcome (PICO).
## 3 Absorbable Membranes
### Absorbable Membranes of Synthetic Polymers  
A stage in the development of membrane technologies was the artificial design of absorbable materials. These materials include biodegradable aliphatic polyesters: (a) polylactic acid (PLA); (b) polyglycolic acid (PGA) and (c) polycaprolactone (PCL) and their copolymers: poly-lactic-co-glycolic acid (PLGA) or poly-lactic-co-caprolactone acid (PLCL) [84; 85]. PLA can be used alone or copolymerized with PGA. Polyester degradation is determined by hydrolysis and dependent on the hydrophobicity of the polymer [86]; PLA being more hydrophobic than PGA, degradation time is, thus, prolonged.  
Aliphatic polyester membranes are bio-absorbable; the resorption of these membranes depends on the polyester types and ratio [87]. They also have good workability and maneuverability. Major disadvantages include lack of rigidity, stability but also concerns about the interactions of their degradation products with the recipient organism [88]; inflammatory reactions due to macrophages and leukocytes around the membrane were observed during absorption [89].  
Improving mechanical properties using only biodegradable polyester can be difficult, which is why their main clinical indication is for small vertical bone defects, with the association of bone regeneration materials [12]. A combination of different porosity layers was proposed: the less porous side could act as a barrier, preventing epithelial cell infiltration, and the other side in contact with bone defect would allow tissue integration. Such techniques have been used in the Guidor Matrix Barrier (Sunstar Americas Inc., Schaumburg, IL, USA) and Resolut (W.L. Gore and Associates, Newark, DE, USA). Woven fibers technique (Vicryl Periodontal Mesh; Ethicon Inc., Cornelia, GA, USA), or polymer solution dissolved during the surgical procedure and molded in a cassette to form the Atrisorb membrane (Atrix Laboratories, Fort Collins, CO, USA) have also been adopted [55].  
Poly-lactic-co-glycolic acid (PLGA) can be prepared in different copolymer forms depending on the ratio between PLA and PGA; its advantages include biocompatibility and biodegradability, and it is also easy to process [90]. [PERSON] et al. [91] developed a bi-layered PLGA membrane, composed of a dense, thin film that prevents the growth of epithelial cells and a thick, microfiber layer in order to stabilize the blood clot and promote cell colonization; the absorption rate was similar to the monolayer membrane of PLGA, but the rate of bone neo-formation was higher compared to the PLGA membrane [92]. Similar results were obtained by [PERSON] et al. [93] with a bi-layered membrane from PLCL. The authors also observed that the two-layer PLCL membrane reduced bacterial adhesion and prevented bacterial invasion inside the membrane.
## 5 Working Face Abandoned Roadway Filling Technology Process  
### Conveying and Filling Process  
The new inorganic cementitious filling material comprises three components: A material, B material, and foaming agent. During slurry preparation, both A and B materials are mixed thoroughly with the foaming agent. The slurry is pressurized using a filling pump, conveyed via a double-trip high-pressure pipeline, and directed into a mixing device through a slurry pipe for uniform blending. Subsequently, it is filled into the abandoned roadway from bottom to top.  
The abandoned roadway slurry filling technology process mainly consists of three parts: filling preparation, slurry filling, and post-filling cleaning and finishing. The basic process flow is illustrated in Figure 14.  
1. Filling preparation. The unloading worker loads and unloads the grouting material at the material stacking area, while personnel from each work type coordinate operations and prepare the pumping station. Prior to filling, the abandoned roadway should be cleared by removing wooden pallets and erecting temporary containment walls to seal off the section.
2. Grouting and filling. Following the specified ratio of material components, the mixer will dispense the materials into the mixing bucket for thorough blending. Once the slurry is prepared, start the pump and use the conveyor system to transport the materials to the filling location for grouting. During the filling process of the abandoned roadway, at least two monitoring points should be established to observe the slurry condition and filling progress, respectively.
3. Cleaning and finishing. After completing the filling of the section, seal the grouting holes, activate the pump to flush water through the system, clean the grouting pipeline, organize the equipment, and prepare for the next section of grouting and filling.  
Figure 14: Flow chart of the filling process of the abandoned roadway.
## 5 Criteria Significance Research Methods
### Direct Percentage Weight (DPW) Method  
This method is mostly used in practice. The DPW method is as clear and logical as the method of ranking indicators (criteria) (rank correlation), but its accuracy is much higher. When using the direct method of determining percentage weights, the sum of the weights of all evaluations of the research criteria of each expert object must be equal to 100%. With percentage ratings, they are divided by 100. It is easier to evaluate criteria directly in percentages if they were ranked at the beginning: a lower rank corresponds to a higher weight.  
The sums of percentage weights \(p_{ij}\) of all n experts (\(j=1\), \(2\), \(\ldots\), \(n\)) of each \(i\)-th criterion \(\sum_{j=1}^{n}p_{ij}\) and their normalized weights \(\omega_{i}\) are calculated:  
\[\omega_{i}=\frac{\sum_{j=1}^{n}p_{ij}}{100n}, \tag{11}\]  
where \(p_{ij}\)--expert \(j\)-th assigned the weight given to \(i\)-th the criterion in percentage and \(n\)--expert number.  
When assigning percentage weights to the criteria, it is preferable that there are no identical (duplicate) weights. As a result, they can be given to the nearest tenth of a percent.  
The weights \(\omega_{i}\) of the criteria calculated by the DPW method are nonlinearly correlated with the average ranks of these criteria \(\overline{R}_{i}\). Most often, non-linearity is determined by the increased significance of the most important criteria.
# Nozzle with a Feedback Channel for Agricultural Drones
###### Abstract  
In recent years, small drones have been used in agriculture, for spraying water and pesticides. Although spraying systems affect the efficiency of agricultural drones considerably, research on the spraying system of drones is insufficient. In this paper, a new nozzle with a feedback channel is proposed for agricultural drones. The proposed nozzle was manufactured through 3D printing, and its performance was compared with that of the nozzle used in commercial agricultural drones. Images taken with a high-speed camera were digitally processed, to track the area and location of spray particles, and the spraying characteristics were evaluated based on the size and uniformity of the droplets obtained from the images. The proposed nozzle provided a better performance, as it could spray smaller droplets more uniformly. Commercial nozzle droplets have an average diameter of 1.76 mm, and the proposed nozzle has been reduced to a maximum of 215 \(\upmu\)m. In addition, the full width at half maximum (FWHM) of the commercial nozzle is 0.233, but the proposed nozzle is up to 1.519; the proposed nozzle provided better performance, as it could spray smaller droplets more uniformly. Under the condition of 30 kg, the best performance in the proposed nozzle, the minimum value of the average droplet diameter of the nozzle without feedback channel is 595 \(\upmu\)m and the maximum value of FWHM is 1.329. Therefore, a comparison of the performance of the proposed nozzle with that of a nozzle with no feedback channel indicates that the feedback channel effectively reduces the droplet diameter and improves the spraying uniformity. It is expected that the proposed nozzle can be useful for designing the spraying systems of agricultural drones.  
feedback channel nozzle; agricultural drone; flow visualization; spraying uniformity +
Footnote †: journal: Applied Sciences
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.3 Data Delivery Ratio  
In mobile-sink based WSNs, where the sink changes its location over a period of time and is moving and not providing its location on a regular basis, data packets will be delivered at the sink's outdated location, which will increase the data packet drop ratio and decrease the data delivery ratio.  
Figure 9 shows the data delivery ratio of VGRQ and QDVGDD. The data delivery ratio of QDVGDD is low in comparison to VGRQ because in QDVGDD, the sink travels along the boundary of the sensor network to acquire the data. Boundary cell headers are responsible for sending the data of non-boundary cell headers apart from their own. Thus, with the increase in sensor nodes, overhead on boundary cell headers increases, which reduces the data delivery ratio. On the other hand, in VGRQ, the sink moves randomly in the sensor field. Therefore, the load of data transmission is distributed over all the cell headers, which improves the data delivery ratio. The data delivery ratio of QRRP is lower as compared to VGRQ, due to congestion around ring nodes. This reduces the data delivery ratio of QRRP. However, random mobility of QRRP makes it perform better than QDVGDD.  
Figure 8: Data delivery delay.  
Figure 9: Data delivery ratio.
## 5 Implementation and Evaluation  
### Experimental Setup  
To implement the proposed LSTM-BIGRU-based activity recognition and localization. One computer equipped with an Intel Wi-Fi 5300 NIC is used as the receiver, an IEEE 802.11n protocol-based TP-Link Archer C1200 AC1200 Wireless Dual Band Gigabit Router as the transmitter, and a Toshiba portage R930 laptop as the receiver. The transmitter sends packets continuously at the 2.5 GHz frequency band using its three antennae (\(N_{t}\) = 3) following the protocol of IEEE 802.11n. The receiver, which uses the PicoScene Wi-Fi sensing platform tool, collects and stores CSI for 10-80 subcarriers for each packet according to specific activities with its two antennas (\(N_{r}\) = 2). Additionally, the PicoScene platform was used to collect the Wi-Fi signal data, which was then preprocessed to extract CSI features. These features were used as input in our proposed hybrid neural network model for activity recognition and localization.  
Overall, Figure 3 shows that the experimental setup for this study involved collecting Wi-Fi signal data using the PicoScene Wi-Fi sensing platform tool at 10 different locations and four different activities--standing, sitting, bending, and walking. The collected data were then preprocessed and used to train and evaluate a hybrid neural network for activity recognition and localization. As shown in Figure 3, we undertake a series of experiments aimed at substantiating the correlation between the amplitude of CSI and phase information. Volunteers are engaged in the task of pushing a sleek plate across the designated link, with varying speeds ranging from slow to fast. Through systematic experimentation, we seek to establish a clear connection between the observed CSI and the diverse phase value at which the participants move the target.
## 4 Discussion
For the reconstruction of the postoperative cases, the postoperative anterior-posterior radiograph was used to derive the alignment of the femoral stem and the acetabular cup, which is less accurate than a 3D assessment of the postoperative alignment using x-ray stererophotogrammetric analysis or postoperative CT imaging [61]. However, this would expose patients to additional radiation. Regarding the measurement error of our method, there is a difference between the radiologic and anatomic inclination and anteversion of the acetabular cup. The difference between anatomic and radiologic anteversion can be neglected in our study because the measured radiologic anteversion was lower than 25\({}^{\circ}\) for most of the patients, which would lead to a deviation between anatomic and radiologic anteversion of less than 10% [58; 79; 80]. A further limitation is that due to the necessities of the treatment of real patients, no real parameter study was conducted, where systematically only one parameter was changed at one time. In our study, more than one design parameter was changed for each patient, committing the limitation of cross-interference between distinct parameters. To overcome these limitations, further studies where only one parameter will be changed need to be conducted. Due to the fact that the analyses were only conducted for one distinct implant design, our results may not be fully applicable for other implant designs developed by other manufacturers. Further studies, therefore, investigating other implant designs should follow. Furthermore, total hip impingement represents a multifactorial process within the musculoskeletal system. Our new framework provides a convenient alternative to estimate the patient-specific ROM in a preoperative manner, where neither forces and torques nor complex constraints are of interest and the result is the geometric interference [33; 38]. Therefore, it may be beneficial to consider the active muscle forces and real contact behavior of the implant components, which was previously performed on the example of the hip and knee joint, using a robot-assisted test method based on a musculoskeletal multibody model that can also provide data with respect to resulting joint forces [27; 81].
###### Abstract
[PERSON] [28] used rotation speed, welding speed, downward force, tool hardness, shoulder diameter, and pin diameter as input variables, and tensile strength, yield strength, hardness, and tensile strength of notch as output variables to estimate FSW parameters for AI7075-T6 aluminum alloy using a neural network model with the back propagation algorithm. Previous studies using machine learning to predict the mechanical properties of FSW mainly used rotation speed, welding speed, shoulder diameter, etc., as input data. In this case, there was a limitation that various parameters such as pin diameter, pin height, plunge depth, pin thread, shoulder pattern, tilting angle, etc., were not considered. Accordingly, the temperature gradient and maximum temperature during the FSW process, which are representative of the FSW process parameters, were selected as new input data for the present study. First, in order to obtain the input variables required for ANN training, specimens were fabricated using copper and aluminum to perform FSW experiments under various process conditions. The temperature data for each process condition were then collected using thermocouples. The maximum temperature and temperature gradient were determined by correlating the collected temperature data with the results of the heat transfer analysis of the finite element analysis. The data were subsequently quantified and employed as input variables for the ANN. In order to train the ANN, the tensile strength obtained from the tensile test subsequent to FSW was employed as the output variable. The artificial neural network (ANN) model was constructed using the Python programming language, and the Hyperband tuner was employed to optimize the ANN's hyperparameters. The overall process flow of this study is shown in Figure 1. Finally, to assess the efficacy of the constructed ANN model, the validation data were applied to the trained ANN model and the predicted tensile strength was compared with the actual tensile strength of the validation data.
## 2 Material and Methods
### Determination of Antioxidant Activity  
The scavenging activity of leaf extracts on ABTS**+** radicals was determined according to the method of [PERSON] et al. (1999) [20]. The plant extracts were mixed with the ABTS**+** solution (0.03 mL) diluted with distillated water to an absorbance of 0.7. After 6 min, the absorbance was measured at 734 nm. The results were expressed as Trolox Equivalent (mmol TE/g dw).  
The CUPRAC test was conducted by a spectrophotometric method described by [PERSON] et al. (2006) [21]. The plant extract was mixed with 10 mM copper chloride (1.0 mL), 7.5 mM neocuproine (1.0 mL) and 1 M acetate buffer (1.0 mL). After 30 min, the absorbance was measured at 450 nm. The results were expressed as Trolox Equivalent (mmol TE/g dw).  
The chelating ability of ferrous ions was assessed according to the method described by [PERSON] et al. (2022) [19]. The plant extract was mixed with 0.1 mM iron II sulfate (0.2 mL) and 0.25 mM ferrozine (0.4 mL). After 10 min, the absorbance was measured at 562 nm.  
Superoxide radical scavenging activity was measured based on the method described by [PERSON] and [PERSON] (1988) [22]. The plant extract was mixed with 150 \(\upmu\)M NBT (1.0 mL), 468 \(\upmu\)M NADH (1.0 mL) and 60 \(\upmu\)M PMS (1.0 mL). After 5 min, the absorbance was measured at a wavelength of 560 nm.  
Hydroxyl radical scavenging activity was evaluated by the method of [PERSON] et al. (2022) [19]. The plant extract was mixed with 0.2 mM 2-deoxyribose, 1.0 mM iron ammonium sulphate, 1.04 mM EDTA, 1.0 mM ascorbic acid, 0.1 M perhydrol, 2.8% trichloroacetic acid, 1% thiobarbituric acid. After being heated to 100 \({}^{\circ}\)C for 15 min and cooled to room temperature, the absorbance was measured at 532 nm.  
The results of ferrous ion chelating capacity, superoxide activity and hydroxyl radical scavenging activity were calculated according to equation  
\[\text{I}\left(\%\right)=\left[\text{[A0}-\text{A1]}/\text{A0}\right]\times 100\]  
where A0 is the absorbance of the control sample and A1 is the absorbance of the test sample. The IC\({}_{50}\) values (mg/mL), calculated from the regression curve, indicate the extract concentration inducing 50% radical scavenging.