esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 2 Visual Tag Process  
Visual tags serve as objects in the image that provide key reference points and measurement points, and they have a known size and shape. Common visual tags include ARTag [28], AprilTag [29], ArUco [30], and Stag [31], as shown in Figure 1. Ref. [32] conducted an experimental comparison of these four tags, evaluating them comprehensively in terms of accuracy, recognition rate, and computational cost. The experimental results showed that ARTag has a low computational cost but performs poorly in the detection rate of a single marker; AprilTag, Stag, and ArUco exhibit excellent performance in terms of direction and position; however, AprilTag has a high computational cost and is sensitive to motion blur; Stag and ArUco are sensitive to smaller marker sizes and larger distances; and the computational cost of ArUco increases with the number of markers. Given the diversity of visual tags and their differences in characteristics, it is necessary to weigh the selection of tags based on the needs and scenarios of the actual application.
###### Abstract  
The primary outcome was SkM PCr recovery time following exercise, comparing patients with diagnosed HF and healthy controls and reported as standardised mean difference (SMD). **Results:** Of 465 identified studies, 6 met the inclusion criteria and were conducted from 1987 to 2021, comprising 162 participants (N = 86 HF, N = 76 healthy controls). HF patients (mean age 55.1 \(\pm\) 4.16 years, 49 (56.9%) male) were reasonably matched to healthy controls (mean age 50 \(\pm\) 8.9 years, 54 (71%) males). Two studies did not report patients' ejection fractions (EF); the mean EF among patients from the remaining six studies was 24.8%. No studies specifically included participants with HFPEF and none characterised sarcopenia. HF patients exhibited impaired SkM energetics compared to healthy controls, which were characterised by a significantly increased PCr recovery time (SMD: \(-\)1.35, CI: \(-\)2.11, \(-\)0.59). **Conclusions:** PCr recovery is significantly impaired in patients with HFPEF. Females were under-represented, no HFPEF studies were identified, and no studies linking abnormal SkM energetics directly to sarcopenia were identified.  
heart failure; \({}^{31}\)Phosphorus magnetic resonance spectroscopy; sarcopenia +
Footnote †: journal: Medicalof creatine and is related to maximal mitochondrial oxidative capacity. Hence, PCr recovery time is a surrogate measure of mitochondrial function [5]. An increase in PCr recovery time may therefore be indicative of dysfunctional oxidative phosphorylation and reduced SkM quality.  
Although there is an abundance of current research focussing on the diverse manifestations of HF, a critical gap exists in the place of \({}^{31}\)P MRS in the investigation and understanding of SkM energetics in HF. Systematic reviews and meta-analyses in patients with HF have investigated the prevalence of sarcopenia and the impact of interventions and treatments on sarcopenia in relation to HF. Frailty in relation to sarcopenia, biomarkers, and clinical outcomes have similarly been investigated in the context of HF [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17]. Only one review [18] has suggested the benefits of using \({}^{31}\)P MRS to aid the investigation of sarcopenia in HF patients. However, their rationale for using \({}^{31}\)P MRS to diagnose sarcopenia was based on two studies, neither of which included HF patients. Instead, they investigated older people with sarcopenia [19] and the pre-frail elderly [20]. To date, no review has addressed systematically the assessment of SkM energetics in patients with both HF and sarcopenia. Furthermore, there may be important differences in SkM energetics between populations with heart failure with preserved (HFpEF) and reduced (HFrEF) ejection fraction, given their different pathophysiology [21].  
The aim of this review is to quantitatively summarise the available literature on \({}^{31}\)P MRS performed in the SkM of patients with HF (HFrEF and HFpEF) at rest and during post-exercise recovery compared to healthy controls. We hypothesised that PCr recovery time following exercise would be longer in patients with HF.
# Regional Manufacturing Industry Demand Forecasting: A Deep Learning Approach
## 4 Experimental Results
### Results
#### 4.3.3 Manufacturing Industry Demand Forecasting  
In this step, first of all, we need to predict the auto-regressive time of each _Xi_ factor. Then, based on the forecast factors, LSTM is used to forecast the manufacturing demand in the next three years.  
We used LSTM, BP, and GM models to predict the future values of these factors through historical data. It is worth noting that the reason for choosing these three models is that LSTM and BP had excellent results in the previous comparative experiments. The GM (1,1) model is the classic model in the field of auto-regression.  
By comparison, although the prediction based on the historical data of Y was successful, it did not seem to give a more accurate result. Specifically, the above three methods only used the historical data of Y for auto-regressive prediction, which is significantly lower than the prediction results of LSTM considering multiple factors in Table 4. Therefore, to make an accurate forecast, we should consider the factors that affect the demand.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **LSTM** & **SVM** & **BP** & **AR** & **RF** \\ \hline MAE & 463.4339183 & 2891.278648 & 1074.917976 & 2019.226016 & 1397.206419 \\ RMSE & 874.8308509 & 3840.997444 & 1446.326249 & 2783.584018 & 2125.591786 \\ MAPE & 0.63\% & 3.59\% & 1.28\% & 3.12\% & 2.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average results of different prediction models.  
Figure 6: The LSTM training result.  
Figure 7: The LSTM prediction results.  
From Table 5, both LSTM and BP had excellent performance in auto-regressive prediction. Because the structure of the BP network was simpler, its training speed was faster than LSTM (as shown in Figures 8 and 9). Therefore, it is more reasonable to choose BP to predict the future value of each factor.  
Using LSTM, combined with the predicted values of each factor (see Table 6), this paper predicts the manufacturing demand of GD from 2020 to 2022. In Table 7 and Figure 10, the predicted results were 14,708.04 billion yuan, 14,878.35 billion yuan, and 1497.72 billion yuan, respectively. According to the forecast data, from 2004 to 2022, the demand of GD's manufacturing industry will increase from 2955.492 billion to 14,977.24 billion yuan, with an average annual growth of 8.92%.
## 2 Materials and Methods
### The Mechanical Design and Working Principle of MyFlex-\(\delta\)
#### 2.1.2 Working Principle of MyFlex-\(\delta\) in the Sagitttal Plane  
Concerning the configuration of the foot prosthesis, during the heel strike, the heel portion of the lower blade and the middle blade absorb the impact with the ground. The contribution of the upper blade during a heel strike is minor compared to the contribution of the middle blade and the lower blade. The foot rotates in plantar flexion to direct with respect to the ankle the force exchanged between the foot and the ground (ground reaction force). The plantarflexion movement continues until toe strike occurs (when the toe touches the ground). From this stage onward, thanks to the sum of forces given by the inertia, by the push that the prosthesis user gives with the opposite leg and by the elastic energy previously accumulated by the lower blade and the middle blade, the foot prosthesis returns first in the resting configuration, and then deform in the opposite direction, generating a dorsiflexion of the foot. From toe strike, the foot is loaded at both heel and toe. From the toe strike to the heel-off (when the heel comes off the ground), a load transfer from the heel to the toe occurs. When the force given by the weight of the body and the inertia, is in front of the vertical line that passes through the ankle, the foot begins to flex. The upper blade and middle blade deform by bending, and while bending, they accumulate elastic energy. The elastic energy that is accumulated during this phase, is then released during the push-off phase.  
Figure 1: (a) MyFlex-\(\delta\) parts and subgroups; (b) picture of MyFlex-\(\delta\); (c) shape of the middle blade.
## 3 Results and Discussion
### Statistical Analysis of Discarded Materials Management
From the information gathered by the waste manager, the total quantities collected were accounted for, out of which those selected for this study were segregated, focusing solely on the four types of DiMaL. In Figure 6, graphs illustrating the work carried out can be observed, showing that the DiMaLs studied represent an average of 8.75% of the total discarded material generated by that company, along with the percentage quantities of each. Figure 7a shows the quantities of DiMaL sent to the landfill (W\({}_{\text{L}}\)) by the authorized waste manager and those allocated to the proposed CE alternative, \(W_{\text{ce}}\). In Figure 7b, the incurred costs (C\({}_{\text{i}}\)) in managing the discarded materials over the study months are shown, alongside the average cost in the study set, approximately EUR 1950.80/month (\(\pm\)10.1%). This monthly average cost was calculated according to the management scheme shown in Figure 3. If the cost of sending them to the landfill, EUR 115, is separated, it represents 36.5% of the total costs (EUR 315). The annual cost of wasting the discarded materials, based on the monthly average cost, would amount to EUR 23,409.60/year. Therefore, the cost associated with the studied quantity, 8.75% of the total, is EUR 2048.34/year, of which the landfill disposal accounts for EUR 747.64/year. Although the amount of discarded materials at Artilab depends on the start date of a new campaign initiated by the subcontracted commercial brand, what the study seems to reveal is a consistent trend over time. This trend is noteworthy because the cost savings in EUR represent approximately one-third of the amount of discarded material used as a by-product/raw material for another production process (C\({}_{\text{i}}\) = 3 \(W_{\text{ce}}-1\)). The data in Figure 7a,b, corresponding to the month of March, confirm the start of a new handbag campaign for the Canvas brand, in which larger quantities of the four studied DiMaLs were consumed. An estimation of the material and labor costs for the production of one of the DiMaL crowns was calculated, resulting in EUR 2.17 and EUR 3.16, respectively, totaling EUR 5.33 per unit. Compared to the two references cited in the Introduction section, both valued at EUR 19 per unit, the manufactured 'cabezudo' stopper was almost four times less expensive.
# Multi-Condition Constrained Pedestrian Localization Algorithm Based on IMU
## 2 Methods
### Zero-Velocity Detection and Interval Constraint Correction
Upon analysis, these misclassifications are likely due to sensor noise or vibrations and impacts from the pedestrian's foot, which cause sudden changes in the test statistics. Based on extensive data analysis of pedestrian gait characteristics, a low-cost, feasible periodic phase threshold constraint algorithm is proposed, which can be expressed as follows:  
\[\left\{ \begin{aligned} \text{Z\_high}(\text{i},\text{i}+\text{N}( \text{m}))&=0,\text{ N}(\text{m})<\text{L\_min}\\ \text{Z\_high}(\text{i})&=1,\text{ others}\end{aligned} ,\right. \tag{6}\]  
where Z represents the zero-velocity state of the pedestrian at each moment, obtained from Equation (5). It is an array composed of high and low levels, with Z_high being the collection of the high-level segments in Z. N is the collection of sequence lengths of each Z_high segment. L represents the range of ideal zero-velocity interval sequence sizes, and it is expressed by the following formula:  
\[\left(\text{L\_min},\text{L\_max}\right)=\frac{\left(\text{R\_min},\text{R\_ max}\right)}{\text{Ts}}, \tag{7}\]  
where R represents the reasonable zero-velocity time range for a healthy adult during walking, as mentioned earlier. The above constraint algorithm can make reasonable adjustments to the abnormal zero-velocity intervals caused by misdetection in traditional zero-velocity detection, thereby constructing a new zero-velocity constraint algorithm that is more resistant to interference and relatively simple. Figure 4 shows the zero-velocity detection results after being corrected by the above constraint algorithm. By comparing and analyzing Figures 3 and 4, it can be observed that all the abnormal zero-velocity intervals have been identified and eliminated, zero-velocity detection results with higher accuracy are obtained. This has significant importance for the final accuracy of the localization results.
# Improving Deep Learning-Based Recommendation Attack Detection Using Harris Hawks Optimization
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.5.1 Comparison with Original Methods  
The performance comparison of multiple detection methods when detecting the test sets injected with random, average, and bandwagon attack on Movielens 10 M dataset are shown in Figures 6-8, respectively. As shown in Figures 6-8, the four detection methods have good detection performance overall. Through further observation, it can be found that the DL-DRA, CNN-SAD-HHO, and DL-DRA-HHO methods maintain high detection performance on most of the test sets. The strong learning and recognition ability of deep learning are well developed and demonstrated in these methods. The comparison results show the success of the proposed approach.  
Figure 6: Performance comparison of the four detection methods when detecting the test sets injected with random attack on Movielens 10 M dataset.  
In terms of recall and AUC, CNN-SAD and CNN-SAD-HHO have similar performance. However, in terms of precision the performance of the improved approach CNN-SAD-HHO is better than that of the original method CNN-SAD. This is because although both methods can effectively identify attack profiles, more genuine profiles are identified as attack profiles in the original method.  
The performance comparison of multiple detection methods when detecting the test set on Amazon dataset are shown in Table 6.  
Figure 8: Performance comparison of the four detection methods when detecting the test sets injected with bandwagon attack on Movielens 10 M dataset.  
Figure 7: Performance comparison of the four detection methods when detecting the test sets injected with average attack on Movielens 10 M dataset.  
As shown in Table 6, most detection results of the improved methods are better than those of the original methods on the dataset of real scene. The DL-DRA-HHO method has the best detection performance. The experiments show the success of the proposed approach.  
To illustrate the cost of time, we record the training time of the multiple detection methods with the key hyperparameter optimization process as shown in Table 7. The computer configuration for running the experiments is as follows: NVIDIA GeForce RTX2080, 32 G RAM, Win 10 OS. Python 3.7 ([[https://www.python.org/](https://www.python.org/)]([https://www.python.org/](https://www.python.org/)) (accessed on 15 July 2022)) and Tensorflow 2.4 ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)) (accessed on 15 July 2022)) are employed for coding.  
As shown in Table 7, CNN-SAD method has the longest training time. DL-DRA method has the shortest training time. The training time of the proposed methods CNN-SAD-HHO and DL-DRA-HHO is at the middle level.
# Energy-Efficient Multi-User Routing in a Software-Defined Multi-Hop Wireless Network
## 4 Strategy of Communication Path Selection
### Lagrange Relaxation-Based Aggregated Cost
#### 4.1.2 Algorithm Description  
In the LARAC algorithm, \(Dijkstra(s,t,c)\), \(Dijkstra(s,t,d)\) and \(Dijkstra(s,t,c_{\lambda})\) denote, respectively, [PERSON]'s shortest path algorithm using link costs, link delays, and aggregated link costs with respect to the multiplier \(\lambda\).  
In the process of solving the target path, the first step is to set \(\lambda=0\) and use the algorithm to calculate the shortest path \(p_{c}\) of the original cost \(c\). Once the found path meets the delay constraint,the algorithm terminates with the success of finding the optimal solution. If not, the algorithm saves and records the path \(p_{c}\) as the best path that does not satisfy \(\Delta_{delay}\). In order to check whether there is an appropriate solution, the shortest path \(p_{d}\) of the delay \(d\) is calculated. If the obtained path exceeds \(\Delta_{delay}\), there is no suitable path from \(s\) to \(t\) that satisfies the delay requirement, and the algorithm stops with failure. Otherwise, it indicates that a feasible solution exists, and we store the path \(p_{d}\).  
The steps of the path selection algorithm under the delay constraint are introduced indetail in Algorithm 2:  
```
0: A graph \(G=(V,E)\), each edge \(e\) with energy consumption \(c(e)\) and delay \(d(e)\), a given delay constraint \(D\in Z^{+}\).
0: A c-minimal path between the nodes \(s\) and \(t\).
1:procedureLARAC (\(s\), \(t\), \(c\), \(d\), \(\Delta_{delay}\))
2:\(p_{c}\) = [PERSON](\(s\), \(t\), \(c\))
3:if\(d(p_{c})\leq\Delta_{delay}\)then
4:return\(p_{c}\)
5:endif
6:\(p_{d}\) = [PERSON](\(s\), \(t\), \(d\))
7:if\(d(p_{d})>\Delta_{delay}\)then
8:return \"There is no solution\"
9:endif
10:loop
11:\(\lambda=\frac{c(p_{c})-c(p_{d})}{d(p_{d})-d(p_{c})}\)
12:\(r\) = [PERSON](\(s\), \(t\), \(c_{\lambda}\))
13:if\(c_{\lambda}(r)=c_{\lambda}(p_{c})\)then
14:return\(p_{d}\)
15:elseif\(d(r)\leq\Delta_{delay}\)then
16:\(p_{d}=r\)
17:else
18:\(p_{c}=r\)
19:endif
20:endloop
21:endprocedure
```  
**Algorithm 2** Lagrange Relaxation based Aggregated Cost  
Next, we need to calculate \(\lambda\) from Formula (10) and get the aggregated cost \(c_{\lambda}\). With this value of \(\lambda\), we can find a new \(c_{\lambda}\)-minimal path \(r\). By comparison, if \(c_{\lambda}(r)=c_{\lambda}(p_{c})=c_{\lambda}(p_{d})\), we will obtain the optimal \(\lambda\) as proven in [27]. Otherwise, set \(r\) as the new \(p_{c}\) or \(p_{d}\) according to whether \(r\) is infeasible or feasible.  
\[\lambda=\frac{c(p_{c})-c(p_{d})}{d(p_{d})-d(p_{c})} \tag{11}\]
## 3 Dataset and Methodology  
In this work, active landforms associated with periglacial dynamics have been identified and inventoried using the DInSAR technique with Sentinel-1 data in combination with optical data (Spot images) [5; 60; 61]. The Sentinel-1 differential interferograms were used to detect active areas. Later, they were classified according to landform type with the support of optical images from the 6 and 7 SPOT satellites. The obtained inventory was then compared with previous inventories made using optical images and based on geomorphological criteria to establish the degree of activity of the landforms. Last, a statistical analysis was carried out to evaluate the influence of topographical characteristics (sites and elevations) on rock glacier behavior.
## 3 Analytical Methods and Applications
### Analysis of Surfaces and Geostatistics  
Analysis of surfaces and geostatistics deal with interpolation of surfaces and kriging. Spatial interpolation is the process of using various points with known values to estimate values at other unknown points, creating a continuous surface of values. Interpolation methods are directly based on the surrounding measured values or on specified mathematical formulas that determine the smoothness of the resulting surface. Various deterministic methods exist for performing spatial interpolation [62], such as inverse distance weighting (IDW), a natural neighbor technique, a trend-based method, and the spline technique. Shepard interpolation was used for estimating the Grand Canyon height map and a high-risk flooding map [63]. An example of spatial interpolation is provided in Figure 8, where point altitude meters (i.e., black dots) are used to create an elevation surface of some area.  
Interpolation has been used for diverse topics such as studying vector-borne diseases [64] (i.e., composite regression with variable selection and thin-plate spline algorithm) and malaria [65] (i.e., Bayesian interpolation), for estimating the level variations of the sand layer of sandy beaches or dunes, for creating dynamic 3D maps of the area under study in real time [66], as well as for understanding fish-school characteristics around artificial reefs [67] (i.e., IDW algorithm).  
On the other hand, kriging belongs to a family of interpolation methods using geostatistical techniques, based on statistical models that include autocorrelation, i.e., the statistical relationships among the measured points. In the context of IoT, kriging has been used for estimating zinc concentrations, in order to assess contamination at the sidements of the Lake Ontario [68], and for considering irrigation water suitability [69]. It has also been used for the modeling of air temperature and precipitation [70].  
Both interpolation and kriging techniques were applied in [71] to assess contamination of soil by heavy metals, using IDW, local polynomial, ordinary kriging and radial basis functions (RBF). Ordinary kriging and RBF were more accurate than the other methods. Both interpolation and kriging were also used in [72], for deploying site-specific soil fertility maps, employing IDW and ordinary kriging. Prediction efficiency showed that ordinary kriging outperformed IDW. Therefore, in both cases [71; 72], findings show that kriging is a more accurate technique than interpolation.
###### Abstract  
In this work, we report the preparation of nanostructured electrodes based on dense arrays of vertically-aligned copper (Cu) nanowires (NWs) to be subsequently covered by cadmium telluride (CdTe) thin films, with great potential to be used within \"substrate\"-type photovoltaic cells based on Al\({}^{\Pi}\)-PVI heterojunctions. In particular, the multi-step preparation protocol presented here involves an electrochemical synthesis procedure within a supported anodic aluminum oxide (AAO) nanoporous template for first generating a homogeneous array of vertically-aligned Cu NWs, which are then further embedded within a compact CdTe thin film. In a second stage, we tested three deposition methods (vacuum thermal evaporation, VTE; radio-frequency magnetron sputtering, RF-MS; and electrochemical deposition, ECD) for use in obtaining CdTe layers potentially able to consistently penetrate the previously prepared Cu NWs array. A comparative analysis was performed to critically evaluate the morphological, optical, and structural properties of the deposited CdTe films. The presented results demonstrate that under optimized processing conditions, the ECD approach could potentially allow the cost-effective fabrication of absorber layer/collecting electrode CdTe/Cu nanostructured interfaces that could improve charge collection mechanisms, which in turn could allow the fabrication of more efficient solar cells based on Al\({}^{\Pi}\)-BVI semiconducting compounds.  
supported nanoporous alumina (AAO) templates; copper (Cu) nanowire arrays; cadmium telluride (CdTe) thin films; electrochemical deposition (ECD)