esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 NFP-Based Hybrid Placement Strategy
### NFP-Based Hybrid Placement Strategy  
Based on the NFP, the hybrid placement strategy places polygons within a given rectangle to minimize the height of the placed polygons. The input of the algorithm is a list of polygons, each of which can be considered as a closed area composed of multiple continuous line segments [12].  
Firstly, set the width and height of the rectangle and the list of polygons to be placed, and place the first polygon in the bottom-left corner of the rectangle. Each polygon to be placed must be placed within the rectangle, and the current lowest height is updated every time a polygon is placed. When placing a polygon, first, obtain the inner fit rectangle (IFR) of the polygon, and then place the polygon inside the rectangle. In order to ensure that the polygons do not overlap, this algorithm uses the NFP algorithm to obtain the non-intersecting area between the polygons, intersects the IFR and all calculated NFPs, obtains the area in which the polygon can be placed, and translates the polygon to the bottom-left corner of the placeable area. Then, calculate the lowest point of the centroid of the area and move the polygon to that point. Finally, check the highest point of each placed polygon to calculate the lowest height within the current rectangle, which is the maximum contour height \(H_{\text{max}}\) of the final packing result. This algorithm combines the bottom-left strategy  
Figure 5: “Downward” strategy and “bottom-left” strategy.  
and the lowest-gravity-center strategy to ensure a compact arrangement of polygons while reducing gaps and waste.  
The pseudocode for the hybrid placement strategy algorithm based on the NFP is shown in Algorithm 1. Among them, n represents the total number of workpieces to be arranged.  
```
1 Initialize the placement process
2 Place the first polygon in the bottom-left corner
3 fori = 1 to n,do
4 Calculate the IFR of the polygon
5 Calculate the NFP between the polygon and the already placed polygon
6 Calculate the intersection of two regions
7 Calculate the lowest point of the center of gravity in the region
8 Move the polygon to the calculated position
9 Calculate the height of all current polygons
10 end
11 Display all polygons and write the results in the text
12 return the maximum height \(H_{\text{max}}\)
```  
**Algorithm 1** Hybrid Placement Strategy Based on NFP
## 2 Materials and Methods
### Development of the Self-Adaptive Transtibial Prosthesis Concept
#### 2.1.2 Conceptualization of the Extensible Prosthetic Module  
The concept of an extensible prosthetic module (EPM) for the prosthetic shaft (see Figure 2) was proposed, incorporating multiple innovative elements aimed at creating a self-adjusting lower-limb prosthesis for the remaining limb (1). The EPM (2), situated between the prosthetic socket (3) and the prosthetic foot (4), facilitates automatic length adjustment of the shaft (optionally or additionally, including the length of the prosthetic foot) based on measurements obtained from the user's body, until it attains a length equivalent to that of the contralateral biological limb.
## 1 Introduction
Whenever the RSU needs to send a message to the destination vehicle, the optimized route is found based on the least RTT to transmit a message to the destination. Minimum RTT implies passing through nodes with the least traffic data. Additionally, the TMR algorithm lessens the amount of data packets to avoid network congestion and data collisions of data packets. Therefore, TMR will speed up the data transmission needed particularly to be less than the vehicle speed and hence avoid any delay of the alert messages. This would certainly alleviate the possibility of vehicle traffic problems. In doing this, a dynamic threshold should be met to find a route; otherwise, RSU should wait a backoff time before repeating the discovery phase, and hence the intermediate vehicles are getting closer with less traffic. Therefore, our proposed routing TMR algorithm can achieve the following contributions:  
* Prioritize ITS emergency messages compared to normal packets. As a result, deliver both emergency warning and data packets in the VANET environment on time to avoid vehicular traffic problems;
* Select the shortest route that has the minimum time delay and, therefore, speed up the data transmission;
* TMR uses the minimum RTT to select the optimized route, and as a result, it can adapt to the topological change;Our protocol reduces the traffic problems by using a threshold value where it should be higher than the RTT value to add its route to the efficient routes array;
* Minimize the data traffic load by reducing the retransmission data packets and therefore enhance the network performance. The analysis of TMR proves promising results and works better under different network performance metrics such as end-to-end delay, packet delivery ratio, packet loss ratio, throughput, and routing overhead compared to other protocols like AOMDV, Energy Efficient Multipath Routing Protocol for Mobile Ad-Hoc Network Using the Fitness Function (FF-AOMDV), Q-learning based Multi-objective optimization Routing protocol (QMR), Efficient Geographic aware Source Routing (EGSR), and Improved Road Segment-Based Geographical Routing (ISR). The remaining sections of the paper are structured as follows: Section 2 presents a literature review. The problem has been stated, and the solution has been proposed in Section 3. Section 4 explains the methodology and Section 5 contains simulation setup, simulation results, and analysis of results. Section 6 is designated to the analysis of the complexity of the algorithm, which is followed by the articulated conclusion in Section 7.
## 3 Proposed Methodology
### Data Restoration Enhancement Module
This design emphasizes the interaction of spatial information on different channels in spatiotemporal data, helping to capture data features more effectively. Additionally, the introduction of the inverted bottleneck block makes the hidden dimension of the residual block four times wider than the input dimension. By increasing the hidden dimension, the model can better learn complex representations of the data without introducing too many parameters. At the same time, the ConvNeXt model uses a large 7 \(\times\) 7 convolutional kernel. The advantage of this decision is that it can capture a wider range of contextual information during the training process of spatiotemporal data, thus better capturing global background features. Following the 7 \(\times\) 7 large convolutional kernel, using two 1 \(\times\) 1 convolutional kernels helps to capture more local information, improve model representation, enhance the ability to express different sizes, and enable the network to have a multi-scale receptive field. In addition, the ConvNeXt model also makes several adjustments at the micro level. The ConvNeXt model replaces the ReLU function with the GELU function and reduces the number of activation functions. Furthermore, the ConvNeXt model uses fewer normalization layers and replaces BatchNorm with LayerNorm. Following the downsampling design of the Swin Transformer, it employs a 2 \(\times\) 2 convolution with a stride of 2 for spatial downsampling. Therefore, through these micro and macro designs, the ConvNeXt model better adapts to the characteristics of spatiotemporal data in its application, enhancing sensitivity to data features and capturing capabilities, thus providing a powerful tool for the application of deep learning in scientific data compression. In summary, the restoration enhancement module uses a deep learning network to correct the compressed data. Specifically, on the decompressed data, we conduct a series of feature extraction operations through the ConvNeXt network, representing a combination of various feature operations. By designing a loss function, we fit the decompressed data to the original data through unsupervised pre-training. The overall structure is illustrated in Figure 3.  
In terms of the operations of the ConvNeXt network specifically, we describe them through the following formula:  
\[x_{i}=g(d_{i}) \tag{7}\]  
Here, \(d_{i}\) represents the reconstructed input \(i^{th}\) and \(g\) indicates a series of feature extraction operations applied to the network input. Unsupervised pre-training can be applied to the network with the aim of minimizing the following formula:  
\[E(\theta)=\sum_{i=1}^{m}{(x_{i}-d_{i})^{2}} \tag{8}\]  
Through this restoration enhancement module, we combine the efficient compression capability of JPEG with the powerful feature extraction capability of the ConvNeXt network in the data compression and reconstruction process. This improves reconstruction accuracy while maintaining data compression rates, especially achieving better results in complex scenarios such as oceanic spatiotemporal data.
# Hyperspectral Imaging to Evaluate the Effect of Irrigation Water Salinity in Lettuce
## 3 Results and Discussion
### Effect of Salinity on Spectral Features
#### 3.2.1 Non Preprocessed and SNV Reflectance Spectra
Many authors have related the changes in the visible region of the spectra to the decrease of pigment content in the leaves, primarily of chlorophyll [20; 32; 33]. When the chlorophyll content decreases, there is an increase of reflectance mainly in the blue and red regions of the spectra. This effect causes the spectra to flatten, resulting in a decrease in the height of the peak near 550 nm. Some research indicates that the total chlorophyll and carotenoid content decreases in plant leaves under salt stress [34]. [PERSON] et al. [35] introduced a carotenoid reflectance index (CRI\({}_{550}\)) (Equation (3)), based on the reflectance values at 510 (R\({}_{510}\)) and 550 (R\({}_{550}\)) nm, to follow the evolution of the carotenoid content in the leaves, and that decreases with the decreasing of carotenoids. \[\text{CRI}_{550}=(1/\text{R}_{510})-(1/\text{R}_{550}) \tag{3}\]  
Applying the CRI\({}_{550}\) index to our spectra, a decrease in the carotenoid content could be observed as the salinity increased. The values of CRI were 0.78, 0.74, 0.65 and 0.55, respectively, for our salinity treatments from Ct to S3. Chlorophyll absorption bands at 480 and 680 nm bands, suffered changes in the reflectance level showing a progressive increase with increasing salt concentrations (Figure 3C). This could indicate a slight loss of chlorophyll in the leaves when the salinity increased. Different views have been reported regarding the observed changes in the visible range with increasing salinity levels. Some studies related to vegetative growth under salinity conditions showed a decrease in the peak at 550 nm and in the visible region with increased salinity in the plants or soil [14; 15], which is consistent with our findings. However, other studies showed either an increase in reflectance in the visible region, primarily near 550 nm [26; 36], or no differences in the visible range under salt stress conditions [37].
# Dynamic Modeling and Simulation of a Robotic Lander Based on Variable Radius Drums
## 5 Discussion
### Case (2): Tople Simulation  
In the following, the results of an inclined surface sample impact simulation with an inclination of 1:5 are discussed. We disregard the ratchet structure joint rotation. Differently from Case (1), in which the state of the lander was changing only along the vertical axis, in this simulation the state of the lander changes along the other two directions as well. In Figure 11a, the displacement is reported of the lander center of mass along the three directions. In Figure 11b, we can see the speed of the lander along the three directions. Figure 11c shows the acceleration of the center of mass of the lander along the three directions; this has been slightly filtered in order to remove the high-frequency high spikes. While the main component of motion remains along the vertical axis, in this case the others components cannot be neglected. The lander impacts with a vertical velocity \(v\approx 3\,\mathrm{m}\,\mathrm{s}^{-1}\), and its structure is subjected to peaks of acceleration having modulus \(|a|\in[2.1,3]\) g. From the figures the bounces of the lander can be observed before stabilizing.  
In Figure 12a, the rotations are reported of each of the three leg structure revolute joints. Contrary to _Case (1)_, the joints rotation have a different behavior between them; the joint relative to the first leg, that is the upstream leg does not rotate at all. On the other hand, the downstream legs exhibit the maximum rotation; that is, \(\vartheta\approx 42^{\circ}\). This reflects also on the reactive torques generated by the lander, as reported in Figure 12b, as the exerted torque is a function of the relative joint rotation.  
Moreover, in Figure 12c the behaviors are reported of the contact forces exerting between the soil and each of the the leg pads. From this figure a high peak of force can be observed for the leg 1; this corresponds to the instant when the upstream leg impacts with the soil and acts as a pivot, transforming the translational kinetic energy into rotational kinetic energy. The peaks responsible for the dissipation of most of the kinetic energy can be seen in Figure 13; however, as the force peaks are shifted in time, the dissipation of the kinetic energy has a lower rate. Finally, as we expected, the modulus of these contact forces is not the same. Leg 1 has a lower force, due to the fact that it is the one that is upstream and impact first; in fact, the transformation of its energy from translational to rotational means that less energy goes in the spring. More precisely the force is \(F\approx 5\,\)N, whereas for the other two legs which are located downstream the force is higher, reaching \(F\approx 10\,\)N.
## 4 Frequency Analysis and Vibration Evaluation  
### Analysis of Vibration Frequency  
We carried out a brief analysis of the frequencies with relatively concentrated energy in the spectrum maps. However, the origin of these frequencies remained unknown, so the question was further analyzed.  
According to relevant research findings, pressure pulsations generated within the vaneless area predominantly propagate upstream as pressure waves while running the units. These pressure pulsations primarily correspond to the passing frequency of the water pump turbine runner blades, with a majority exhibiting dominant frequencies that are integer multiples of the lobe frequency. The specific calculation formula for this phenomenon is as follows:  
\[f=\frac{n_{H}\times Z_{1}}{60}K,K=1,\;2 \tag{1}\]  
Figure 14: The spectrum maps of units 1# and 4#, and units 1# and 5# under power generation conditions. (**a**) P1—upper reservoir; (**b**) P3—upper inclined shaft; (**c**) P8—horizontal flat tunnel; (**d**) P11—lower flat tunnel.  
Figure 15: The root mean of vibration of units 1# and 4#, and units 1# and 5# under power generation conditions.  
where \(f\) is the primary frequency. The rotational speed of the units, denoted as \(n_{H}\), was a crucial parameter in this study. \(Z_{1}\) represents the number of rotor blades, and \(K\) is the integer coefficient of the ratio of rotor blades to guide blades [20], which is rounded off to obtain the value of \(K\).  
The frequencies for units 1#, 2#, 3#, and 4# were  
\[f_{1}=83.33\ (\mathrm{Hz}),f_{2}=166.67\ (\mathrm{HZ}) \tag{2}\]  
For units 5# and 6#, the frequencies were  
\[f_{3}=110\ (\mathrm{Hz}),f_{4}=220\ (\mathrm{Hz}) \tag{3}\]  
In these results, the frequency was within 100 Hz, and the energy was primarily concentrated at 60 Hz and 70 Hz. From 100 Hz to 200 Hz, the energy was primarily concentrated at 120 Hz and 170 Hz, whereas the energy in units 5# and 6# was concentrated at 220 Hz. These frequencies were different from the previous results. Based on the conclusions in Sections 3.3 and 3.4, there was mutual interference while running adjacent pipelines and some discrepancies between different types of units. We suspect this is why the test results were inconsistent with the theoretical calculation results. To prove this conclusion, we recommend setting up measuring points in the volute to directly measure the frequencies in the units which were running.
## 2 High Data Rate Modulator Design Description
### SCCC Encoding
The systematic puncturing algorithm uses the number of survival systematic bits, i.e., \(S_{sur}\), to specify a puncturing pattern on each 300-bit sequence. The variable \(S\) represents the number of systematic bits that are divided into 300-bit sequences to puncture each sequence accordingly. After puncturing, each 300-bit sequence will contain exactly \(S_{sur}\) surviving bits. The parity puncturing  
Figure 4: Convolutional code block diagram for CC1 and CC2. Figure 5: CC1 parity decimation puncturing scheme. algorithms defines a rate-matching algorithm which deletes a total number of \(\Delta\) parity bits from the total stream of \(P\) parity bits. Further details of both algorithms are specified in [2]. The row/column interleaver is a block memory of size \(N=8100\times m\). The number of rows--8100--is the number of symbols generated from one code block; and the number of columns--\(m\)--is the modulation order which varies according to the current ACM format, as explained earlier (see the table in Figure 11). The write/read procedure is illustrated in Figure 6. When reading the row/column interleaver, each row contains \(m\) bits that modulate a symbol from the constellation mapping block.
# Beyond the Horizon, Backhaul Connectivity for Offshore IoT Devices
## 2 Methodology
### Link Budget for Availability and Capacity  
For proposed link budget analysis to estimate availability and capacity in ED, we have considered constant parameters of a standard off-the-shelf Digital Microwave Radio (DMR) as described in Table 2. At 5.8 GHz frequency, W-Fi equipment installed on the seaport has maximum 1 Watt transmit power available. Horn antennas with wide beam width can provide gains up to 20 dBi. Insertion losses including cable and connector losses can be minimized to 2 dB. For a standard 20 MHz bandwidth with minimum receiver sensitivity of \(-94\) dBm, a link budget in ED can be predicted. Link budget is based on a fade margin including both average path loss and additional fade margin due to climate-induced EDH variations. Table 3 shows the additional fast fading margin required on top of slow fading in the proposed link budget. The fast-fading margin for a standard Wi-Fi is based on Multiple Inputs Multiple Outputs (MIMO) equipment which has at least 2 \(\times\) 2 diversity as in [21].
# An Approach to Dynamic Sensing Data Fusion
## 1 Introduction
The existing research includes the Delta model [10], Markov model [11], Shirai model [12], and data detection model [13]. The process of dynamically adjusting the acquisition timeintervals is as follows. According to the current changing degree of collection data, adaptively adjust the acquisition time intervals, i.e., designate a basic acquisition time unit in advance, and the practical collection timeinterval is K times the basic acquisition time unit (K is an integer).When the collection timeinterval needs to be increased, appropriately increase the value of K; otherwise appropriately decrease the value of K acquisition time. The existing research includes [14; 15; 16]. In order to verify the method of dynamic sensing data acquisition, we used the W5 experimental system [17], which has common sensor types. This is an experimental system for monitoring, image acquisition, data analysis, meteorological prediction, and pesticide spraying. It is simple and direct and was therefore chosen as the experimental platform in this study. The quality and efficiency of sensor data acquisition are of great significance to Internet of Things equipment, namely, (1) improving the quality of Internet of Things equipment because accurate sensor data is the scientific basis for improving the quality of equipment; (2) real-time understanding of the status of equipment because staff can understand the status of the system in real time through sensor data acquisition so as to monitor the equipment; (3) measuring the quality of Internet of Things equipment. In practice, the quality and efficiency of sensor data collection are always regarded to be an important factor to measure the quality of Internet of Things equipment. The aim of this study was to introduce the dynamic change judgment model of sensor data and adjust the acquisition frequency in real time according to the change in sensor data. The change insensor data was calculated and judged according to data collected in the recent period, and the amount of data collected depended on the performance of the system. Feedback control was used to deal with the change in environment and make the model adaptive. We mainly investigated the acquisition timeinterval algorithm and the data changing degree judgment algorithm. The rest of this paper is organized as follows. Section 2 discusses the dynamic acquisition algorithm of sensor data, including judgment of the degree of data change, adjustment of acquisition time interval, dynamic acquisition model of sensor data, and the method of data fusion. Section 3 outlines the experiment, and Section 4 concludes the work.
## 5 Simulation Results  
In this section, the performance of the IFO estimation schemes is evaluated in the ISDB-T system, with transmission parameters given in Table 1. The carrier center frequency was set to \(f_{c}=600\) MHz, and the sampling time was \(T_{s}=63/512\,\mathrm{\SIUnitSymbolMicro s}\)[4]. Let us consider the six-path Typical Urban (TU) and Hilly Terrain (HT) channels with a maximum excess delay of \(5\,\mathrm{\SIUnitSymbolMicro s}\) and \(17.2\,\mathrm{\SIUnitSymbolMicro s}\), respectively [25]. The SNR is defined by the ratio of the received average signal power to the noise power, including the NSCs. To make a parallel between (10) and (14), we consider the situation where the CP-based objective metric (10) uses the same number of explicit pilot symbols as the TMCC, that is, \(N_{p}=N_{t}\), although the number of usable CPs is small when compared to that of TMCC subcarriers. In order to apply (10) to the ISDBT system, it is thus assumed that the TMCC information has been perfectly decoded. In the following, the effectiveness of the IFO estimation schemes is evaluated in terms of the probability of erroneous estimation and the computational complexity resulted from the estimation of the IFO.
###### Abstract
This study presents an improved version of the Federated Random High Local Performance (Fed-RHLP) algorithm, building upon the initial iteration introduced in [12]. The primary objective of this improvement is to refine the process of robust client selection by identifying a more suitable and reduced subset of clients, thereby addressing the challenges posed by significant Non-IID issues. The improved Fed-RHLP algorithm prioritizes the selection of clients with three key characteristics: a substantial number of samples, demonstrated label diversity, and high local model performance across both local updates and global aggregation. Concurrently, the algorithm allows for the inclusion of lower-quality clients, enabling them to contribute their local datasets during the training and aggregation phases. The implementation of this algorithm results in accelerated convergence and improved accuracy, particularly in scenarios characterized by high Non-IID conditions. Moreover, it ensures fairness by facilitating proportional participation from the majority of clients.