esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Verifying Performance using 5G NR Measurement Data  
The proposed deep-learning-based path loss model was further applied to a real non-standalone 5G NR system in the urban environment of Taipei City. As shown in Figure 16, the measurement environment was located at latitude 25.044773\({}^{\circ}\) N and longitude 121.539456\({}^{\circ}\) E, and covered an area of 1 \(\times\) 1 km\({}^{2}\). Nine base stations operating in the 2.1 GHz frequency band were included in the measurement area. Measurement equipment was placed in a vehicle driving along selected routes. The measurement reports include the reference symbol received power (RSRP), E-UTRAN cell identity (ECI), and the vehicle's GNSS coordinates. Then, the path losses between the Tx-Rx pairs were calculated according to the link budgets.  
Figure 15: Prediction performance of the proposed deep learning model and the explanatory model.  
Figure 17: Prediction performance of the proposed path loss model and the conventional models in measurements.  
Figure 18: The cumulative distribution of the absolute error of path loss prediction in simulations.
## 3 Results  
### Study Selection  
The PubMed and Cochrane Library database search identified 373 articles: 156 were duplicate articles, while 217 studies were screened for title, abstract and language. Seventy-two full-text articles were finally selected for further evaluation. Of the 72 papers, 49 were excluded: 4 case reports, 13 case series with fewer than 10 patients, 3 letters, 1 review article, 1 paper in Spanish language, 21 papers reporting other kinds of flap than FAMM or i-FAMM, 5 papers not reporting flap viability outcomes and 4 papers not meeting the inclusion criteria. The selection process identified 20 articles as eligible for inclusion in the review: 12 retrospective studies, 5 case series and 3 case-control studies (Figure 1).
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements
#### 3.2.2 Sensor Response in N\({}_{2}\) Atmosphere  
Under N\({}_{2}\) atmosphere the sensor showed a lower baseline resistance than in air (\(\sim\)3 M\(\Omega\)). This result is compatible with the reduction of the density of oxygen species bound to the surface and then the increment of free carrier electrons into the conduction band that reduce the space-charge region at SnO\({}_{2}\) grains boundaries. The response to 0.4 mg/L diacetyl exposure confirms a typical n-type sensing, with a resistance decrement due to the interaction of diacetyl molecules with the surface. In addition, sensors tested under N\({}_{2}\) conditions shows a response comparable in terms of shape and magnitude with that observed under air, even if the sensor in N\({}_{2}\) atmosphere do not show a complete recovery. A similar trend was already observed with a SnO\({}_{2}\) sensor in argon atmosphere [43]. Measurements performed in air and N\({}_{2}\) appeared with the same shape. Previews works indicated that despite a traditional n-type response was observed, in absence of oxygen the sensing mechanism is related to the direct adsorption of VOC on the MOS surface [43].  
The sensor response in N\({}_{2}\) is 1.60, but both response and recovery time result longer than in air measurements cause the poor interaction between N\({}_{2}\) carrier and the sensor surface.
## 2 Materials and Methods
### Recognition of Appliance Operation Modes
A detailed scheme of the procedure can be found in Figure 3. Firstly, the activations of each appliance are standardized, thus helping with the consequent training process by enhancing its convergence speed and stability and by setting the same scale for each activation. The standardized activation is then fed to the Encoder in order to produce the latent states. Next, the K-means algorithm is used to cluster the latent states into K different groups. The optimal value of K for each appliance category has been obtained by running a grid search procedure. The grid search's solutions have been evaluated using the _silhouette score_ proposed in [38], choosing the one with the highest value. Figure 2: Architecture of the implemented autoencoder. The last step of the approach requires identifying the appliances' modes of operation, given the cluster labels, thus computing their mean energy consumption and duration. This procedure is presented in Figure 4. All the appliance activations are assigned to the closest cluster centroid. Once the assignment is done, it is possible to obtain the estimated energy consumption of each cluster by computing the average energy consumption of all the activations belonging to that cluster and by rounding it to the nearest integer. The same can be done for the activations' duration, producing a table that specifies the average consumption and duration for each cluster representing one mode of operation. Finally, names are assigned to each mode of operation by comparing the extracted data with the information provided by the appliances' manuals. Table 3 summarizes the results of the identification procedure for all the appliances included in the hypothetical smart home. Figure 4: Operation modes identification procedure. Figure 3: Procedure for clustering the operation modes of appliances.
## 4 Test Results and Discussion  
### Failure Mode  
At the beginning of loading, all panel specimens performed elastically, with no concrete cracking. After the concrete cracked, the panel specimens entered a nonlinear deformation stage, with the continuous development of multiple cracks at the inner side of the curved section, as shown in the red marks in Figure 10a. Upon reaching the ultimate bearing capacity, a principal crack penetrated the cross-section of the curved section, as depicted by the red marks in Figure 10b, signaling the failure stage. Subsequently, fibers began to pull out from the concrete (UHPC) or rupture (GRC), causing a consecutive decrease in bearing capacity.  
Figure 10c-f, respectively, show the failure modes of UHPC and GRC panel specimens, as well as the tension specimens. It is evident that steel fibers or glass fibers were interlaced within the cracks, constraining the concrete cracks. The failure mode of UHPC panel specimens was consistent with that of UHPC tension specimens (Figure 10c,e), namely, both were steel fibers pulling out from the concrete. As shown in Figure 10f, in the cracks of GRC tension specimens, glass fibers were densely distributed and ruptured when the ultimate load was reached. However, the failure mode of GRC panels primarily involved the detachment of the glass fibers from the concrete, with only a small portion of glass fibers rupturing, as shown in Figure 10d. Furthermore, due to the larger volume fraction and greater length of glass fibers, quite a few glass fibers were distributed in the thickness direction of the panel, making it easier to detach at the curved section.
# A Light Signaling Approach to Node Grouping for Massive MIMO IoT Networks
## 7 Numerical Evaluation
In the absence of transmission power control, fair performance--in the form of equal SINR--cannot be achieved exactly; however, power partitioning comes as close as possible using only partitioning. If partitioning were to then be combined with transmission power control, power partitioning would then provide the best conditions for carrying out max-min fair power control, since the users in each group will have as similar received power levels as possible before applying the power control algorithm. For these reasons, power partitioning provides a good comparison algorithm making use of the full CSI for our use case, in which the goal is to maximize the minimum SINR, rather than provide the maximum sum rateas in other user selection algorithms (see Section 3). Finally, we computed the SINR achieved for each node for the partitions produced by each of the algorithms. Here, the interfering nodes are only those that were placed in the same group, as it is these nodes that will transmit in the same coherence block. We then took the minimum SINR obtained by any node in any group as our primary performance metric; however, we also examined the average and maximum SINR across all the nodes.
## 4 Experimental Results and Analysis
### Selection of the Number of Layers  
In this section, we evaluate the impact of the number of CNN and LSTM layers on classification performance by using the grid search strategy. As shown in Table 6, we present the classification performance when the number of layers for CNN and LSTM is set to 1, 2, and 3. The experimental results indicate that the optimal performance is achieved when the number of layers for both CNN and LSTM is set to 2. Additionally, we present the grid search results for the CNN output channels and LSTM hidden states in Table 3 of Appendix B, where the best results are obtained when both are set to 128. In practical applications, an appropriate network architecture can be selected based on the hardware situations and performance requirements. Additionally, increasing the training data size allows for a more complex model to capture more intricate patterns within the data, thereby enhancing its identification performance and generalization capability.
# Design of Wireless Sensor Network for Agricultural Greenhouse Based on Improved Zigbee Protocol
## 2 Design and Implementation of the Wireless Sensor Network
### Routing Protocol Design
\(Q_{csum}\) is the sum of the proportions of all node cached packets experienced by the RREQ packet from the source node to the receiving or forwarding node, and \(E_{csum}\) is the sum of the energy consumption proportions of all nodes experienced by the RREQ packet from the source node to the receiving or forwarding node. Also, L_sum is the sum of the link quality of all nodes experienced by the RREQ packet from the source node to the receiving or forwarding node, and Hop_countis the total number of hops experienced by the RREQ packet from the source node to the receiving or forwarding node [21]. \(\cos t\) is the energy consumption, queue buffer score, and link quality of the RREQ packet from the source node to the receiving or forwarding node. The smaller the average energy consumption ratio and the average cache packet occupation ratio in the path are, the higher the link quality is, and the lower the \(\cos t\) value is, indicating a better comprehensive performance of the path. At this time, the established path will be relatively stable, and can effectively alleviate network congestion. Finally, this research improves the path establishment method of the Zigbee routing protocol, and finds an effective path for the source node to reach the destination node by RREQ packets during the routing discovery process [22]. Upon receipt of a RREQ packet, the destination node initiates the cache timer and sends the RREP (Residential Real Estate Project) packet back to the source node in the order of their arrival [23]. After the first RREP packet hits the source node, the source node will cache all the RREP packets that arrived during this period before the timer expires, and sorts these packets according to certain priority rules. The path with the highest priority is defined as the primary path, and the path with the second priority is defined as the backup path. When the main path is successfully established, the data are preferentially transmitted to the destination node through this path. When the primary path cannot continue to transfer data due to congestion, the previously established backup path is selected to successfully transmit the data to the destination node. The improved algorithm first compares the comprehensive performance index cost value of each feasible path in the path establishment process, and selects the path with the lowest cost value as the main path during data transmission [24]. As maintaining multiple backup paths would consume more network resources, this article will select only one backup path from other paths. Figure 3 shows the specific flow of the algorithm. Figure 3: The specific flow of the algorithm.
###### Abstract  
In this study, a grasping motion simulation method based on finite element analysis was developed for the virtual evaluation of gripping comfort while gripping a cylindrical object. The validity of the grasping motion simulation was verified by comparing the contact pressure distribution generated on the palm of a hand using a finite element model with the measured result obtained via experiments on a human subject. The mean absolute difference between the simulation and experimental results at 23 regions was 7.4 kPa, which was considered to be significantly low and an acceptable value for use in assessment of the gripping comfort score. Furthermore, topology optimization was introduced into the simulation to propose an easy method for obtaining a rough shape of the gripping part of a product that is comfortable to grip. An objective function during the optimization process was defined to minimize the contact pressure concentration level, and this was observed to have a negative correlation with the gripping comfort. The optimization result indicated low element density at the parts in contact with the tips of the index and middle fingers as well as high element density at the parts in contact with the proximal part of the palm. The method allows a designer to evaluate the gripping comfort of a product during the design process and aids in developing a shape that can provide better gripping comfort. Additionally, the method can also be used to reevaluate the gripping comfort of existing products.  
gripping comfort; finite element analysis; hand model; grasping motion; contact pressure; topology optimization; product design +
Footnote †: journal: Applied Sciences
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Derivation of the Motion Differential Equations
#### 3.3.2 Motion Differential Equation for the Planet Gear, \(mi\)  
The support force of the bearing acting on the planet gear, \(mi\), is as follows:  
\[\left\{\begin{array}{l}F_{bxmi}=k_{bxm}x_{mi}+c_{bxm}\dot{x}_{mi}\\ F_{bymi}=k_{bym}y_{mi}+c_{bym}\dot{y}_{mi}\\ F_{bzzmi}=k_{bzzm}z_{mi}+c_{bzzm}\dot{z}_{mi}\\ M_{b\theta zmi}=k_{b\theta zm}\theta_{zmi}+c_{b\theta zm}\dot{\theta}_{zmi}\end{array} \right., \tag{12}\]  
where \(k_{bxm}\), \(k_{bym}\), \(k_{bzzm}\), and \(k_{b\theta zm}\) represent the support stiffnesses of the bearing in the respective directions, and \(c_{bxm}\), \(c_{bym}\), \(c_{bzzm}\), and \(c_{b\theta zm}\) represent the support dampings of the bearing.  
The meshing force acting on the planet gear, \(mi\), as a result of its mesh with the ring gear, \(d\), is as follows:  
\[\left\{\begin{array}{l}F_{dmiL}=k_{dmiL}\delta_{dmiL}+c_{dmiL}\dot{\delta}_{ dmiL}\\ F_{dmiR}=k_{dmiR}\delta_{dmiR}+c_{dmiR}\dot{\delta}_{dmiR}\end{array}\right., \tag{13}\]  
where \(k_{dmiL}\) and \(k_{dmiR}\) represent the mesh stiffnesses of the gear pairs between the left and right ring gears, \(d\), and planet gears, \(m\); and \(c_{dmiL}\) and \(c_{dmiR}\) represent the mesh dampings.  
The motion differential equation for the planet gear, \(mi\), is as follows:  
\[\left\{\begin{array}{l}m_{m}\ddot{x}_{mi}-\left[(F_{amiL}+F_{amiR})\sin\alpha _{ami}-(F_{dmiL}+F_{dmiR})\sin\alpha_{dmi}\right]\cos\beta_{b}+F_{bxmi}=0\\ m_{m}\ddot{y}_{mi}-\left[(F_{amiL}+F_{amiR})\cos\alpha_{ami}+(F_{dmiL}+F_{dmiR}) \cos\alpha_{dmi}\right]\cos\beta_{b}+F_{bymi}=0\\ m_{m}\ddot{z}_{mi}+(F_{amiL}-F_{amiR}-F_{dmiL}+F_{dmiR})\sin\beta_{b}+F_{bzzmi}=0 \\ J_{m}\ddot{\theta}_{zmi}+(F_{dmiL}+F_{dmiR}-F_{amiL}-F_{amiR})r_{bm}\cos\beta_ {b}+M_{b\theta zzmi}=0\end{array}\right., \tag{14}\]
## 3 Experimental Results
### Conventional Cascaded Design of the Microwave Amplifier-Phase Shifter
Moreover, minimum return losses (\(S_{11}\) and \(S_{22}\)) of 16.2 dB and a minimum gain (\(S_{21}\)) of 13.28 dB are achieved. Figure 10: Conventional reflection-type phase shifter: (**a**) PCB layout with physical dimension; (**b**) phase shift range; and (**c**) \(S\)-parameters. Circuit dimensions: \({W_{1}}^{\prime}=2.38\); \({W_{2}}^{\prime}=3.95\); \({W_{3}}^{\prime}=0.8\); \({L_{1}}^{\prime}=2\); \({L_{2}}^{\prime}=23.2\); \({L_{3}}^{\prime}=4\); \({L_{4}}^{\prime}=11.87\) [unit: millimeter] (PSR: phase shift range).
## 2 Materials and Methods
### Field Experiments
#### 2.4.1 GNSS Test Platform  
Two testbeds were designed for testing the internal GNSS and external DGNSS receivers. The first testbed was built for static positioning tests in which the receiver maintained a fixed position. Because the user in the RHEA scenario must supervise the entire robotic fleet, the user's position will change often and will remain constant for a variable amount of time. Thus, we were interested in how the positioning accuracy changed with the time that the user stayed at a single location. Therefore, tests were performed at three locations at which the user stayed 1, 2 and 5 min. The user's position was tested at the same locations with both receivers.  
In the second set of tests, positioning errors were calculated from the coordinates of the user's position along an ideal straight path (Figure 3). First, a straight line was marked on the ground (the ideal line) by stretching a 20 m rope between points (A and B). The GNSS average coordinates for reference points A and B were determined from a GNSS antenna mounted on a 2 m survey pole for 10 min (Figure 4). After the coordinates were determined, the user walked from point A to point B along the stretched rope twice, once carrying the internal GNSS module and once carrying the external DGNSS module. The results from the two modules were compared based on the error between the straight line and the actual trajectory. In both cases, the measurements were sampled at a frequency of 1 Hz.  
Figure 3: Configuration for measuring errors between the ideal and actual paths.
# A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution
## 2 The Proposed Method
### The Technique of Grammatical Evolution  
The process of grammatical evolution uses chromosomes that represent the production rules of the underlying Backus-Naur form (BNF) grammar [103] of the objective problem. BNF grammars have been widely used to describe the syntax of programming languages. Any BNF grammar is a set \(G=(N,T,S,P)\) where the following are true:  
* The set \(N\) represents the non-terminal symbols of the grammar. Any non-terminal symbol is analyzed to a series of terminal symbols using the production rules of the grammar.
* \(T\) is the set of terminal symbols.
* The non-terminal symbol \(S\) represents the start symbol of the grammar.
* The set \(P\) contains the production rules of the grammar. Typically, any production rule is expressed in the form \(A\to a\) or \(A\to aB,\ A,B\in N,\ a\in T\).  
The process that creates a valid program starts from the symbol \(S\) and gradually replaces non-terminal symbols with the right-hand side of the selected production rule from the provided chromosome. The rule is selected with the following steps:  
* In the first step, the next element is taken from the current chromosome. Let us denote this as V.
* The next production rule is selected through \[\text{Rule}=V\ \text{mod}\ N_{R}\] where \(N_{R}\) is the total number of production rules for the current non-terminal symbol.  
The BNF grammar for the proposed method is shown in Figure 1. Symbols in \(<>\) brackets denote non-terminal symbols that belong to set \(N\). In every line of the grammar,a production rule is shown for every non-terminal symbol. The numbers in parentheses represent the sequence number of the production rule for the corresponding non-terminal symbol. For example, the non-terminal symbol <op> has four production rules, with each leading to a terminating arithmetic operation symbol. The constant N is the dimension of the input dataset.  
An example that produces a valid expression for the chromosome  
\[x=[10,12,20,8,11]\]  
with \(N=3\) is shown in Table 1. This chromosome represents a series of sequential numbers of production rules from the above grammar. The grammar evolution method takes the elements of the chromosome one by one and finds the corresponding production rule by using the remainder of the division and the number of symbols of each non-terminal symbol. The final expression created is \(f(x)=\sin(x_{3})\).  
Figure 1: BNF grammar of the proposed method.
# Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion
## 4 Experiments
### Quantitative Evaluation
In the visualization comparison, the proposed method outperforms other methods in visual effects, especially in suppressing the interference of the Earth halo and eliminating noise artifacts. The visual comparisons are presented in Figures 19 and 20. It can be observed that some traditional methods, such as gamma correction and CLAHE, fail to effectively enhance fine details, leading to under-enhancement results. Meanwhile, methods based on Retinex theory and deep learning, including SSR, NeRCo, and RetinexFormer, tend to blur details and amplify noise. In contrast, the proposed method not only enhances contrast and reveals finer details more clearly but also effectively suppresses the Earth halo. In the quantitative evaluation, this study processed 10 types of representative artificial satellites, encompassing 3940 satellite images containing the Earth and 51,671 individual satellite images. When assessing images that include the Earth, the proposed method achieved PSNR exceeding 35.61 dB, which is more than 2.92 dB higher than the second-best method (gamma correction with \(\gamma=0.8\)) and 6.18 dB higher than the deep learning-based RetinexFormer method. Additionally, the SSIM reached 0.782, greatly surpassing the scores of both NeRCo and RetinexFormer. For individual satellite images, the proposed method achieved a PSNR of over 50.79 dB and SSIM above 0.95, indicating that the en
## 2 Background
### Traditional Sampling Techniques  
There are many classical and traditional sampling techniques that exist to handle imbalanced datasets, such as Random oversampling (ROS), Random undersampling (RUS), and Synthetic Minority Oversampling Technique (SMOTE). In the ROS technique, we select and duplicate a random set of samples from the minority class to alleviate the imbalance problem. Meanwhile, in RUS, we balance the dataset by removing a set of samples from the majority class in a random manner. SMOTE is a more advanced technique for generating new synthetic samples based on the k-nearest neighbors algorithm. Firstly, it selects a random sample from the minority class. Then, it identifies the k-nearest points and selects one at random. Finally, it creates a new sample between the selected sample and the selected neighbor [8].