esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Load Variation on the Accuracy of Gait Recognition from Surface EMG Signals
## 5 Conclusions  
This paper mainly demonstrates that the load variation can significantly affect the accuracy of gait recognition. At the same time, a much higher accuracy in gait recognition was found when the BPNN data was taken from an intra-load than when the data was taken from inter-loads. Furthermore, we arbitrarily combined the data from the four loads at each speed to simulate possible loads that the exoskeleton (or prosthesis) would face in real-time environments. The results reveal that when the training set data is taken from more types of loads, a better level of performance is achieved in gait recognition. This implies that an exoskeleton (or prosthesis) control system that is trained in a single load or part of a load does not work well in the face of multi-load applications, and that an effective method of improving the performance is to add training set data from more types of loads. The results could be useful, not only in exoskeleton or prosthesis myoelectric guidance, but also in complementing evidence showing that loads that heavily influence lower limb muscular activity can be useful for deepening the extent by which muscular variability occurs during walking, which appears to be related not only to walking speed, but also to gender and age.  
Conceptualization, X.Z; methodology, Z.T.; data curation, C.L.; writing-- original draft preparation, X.Z.; project administration, S.S. This research was funded by the National Natural Science Foundation of China (No. 61562072), the Key Research Plan of Jiangxi Province (No. 20161 BBE50058) and the National Natural Science Foundation of China major research program (No. 91748127). The authors declare no conflict of interest.
## 8 Conclusions
The latter is required to avoid overutilization of a few high quality routes, as the Path-DR metric does. While the ZigBee specification does not foresee an alternative RTO algorithm for the end-to-end reliability mechanism, we could show that the overall PDR can be increased by using certain alternatives, at the cost of slightly higher delays and less energy efficiency. As a balanced alternative to the default algorithm, the cross layer algorithm presented is a simple to implement and effective solution. The evaluations show that PDR, delay and energy efficiency can be improved significantly by altering the default stack configuration or by implementing alternative mechanisms at each layer. Two recommended stack configurations were derived from the results obtained from the evaluations of the mechanisms and parameter settings of the ZigBee layers. The Recommended-Compliant configuration of the ZigBee stack defines a higher number of MAC layer retries and uses the PLF routing metric. This configuration provided an improvement of up to 31.5% in terms of PDR, a delay drop of up to 60.4% and an improvement of the energy efficiency of up to 46.5% for specific traffic conditions compared to the default ZigBee configuration for the investigated scenarios. Finally, the Recommended-Unrestricted configuration requires the modification of the maximum number of MAC layer retransmissions and a modification of the APL layer RTO algorithm. By resorting to the PLF as the routing metric, allowing up to 11 MAC layer retries and applying the cross layer RTO algorithm, a PDR improvement of up to 33.6%, a delay reduction of up to 66.6% and an improvement of energy efficiency of up to 48.7% were achieved compared to the default ZigBee configuration for the investigated scenarios. It could be shown that the two recommended stack configurations also outperform the default settings in three different network topologies for two different transmit power settings. In summary, by tuning a subset of the available ZigBee settings, an important performance improvement is achievable, and the addition of alternative settings and mechanisms may further increase it. Based on the findings of this paper, we recommend the ZigBee Alliance and the IEEE 802.15 Working Group to reconsider the default configuration and/or relax the allowed range of settings for the investigated parameters and mechanisms. Our findings also provide useful guidelines for the configuration and design of non-ZigBee, low-power, wireless networks, which are, however, based on IEEE 802.15.4. This work has been supported by the Spanish Government's Ministerio de Economia y Competitividad through project TEC2012-32531, as well as by the Comissionat per a Universitats i Recerca del DIUE from the Generalitat de Catalunya, the Social European Budget (Fons Social European) and FEDER. [PERSON] wrote the TinyOS code for the sensor nodes, the test and evaluation scripts and acquired and processed data from the experiments. All of the authors have participated in designing, writing and revising the intellectual content of this article. The authors declare no conflict of interest.
###### Abstract  
Mobile mapping systems are part of modern data collection in geodesy. It is one of many surveying methods where field collection is performed in a short time. Among their advantages are cost savings and better visualisation than classic surveying methods. This article is focused on accuracy determinations in urban built-up areas of mobile laser scanning using the Riegl VMX-2 HA system. These areas, where there is a combination of dense housing and trees, are an integral part of cities. Their diversity and complexity make surveying by other surveying methods time-consuming and complicated. In particular, the GNSS RTK method encounters problematic locations where sky obscuration by surrounding elements reduces measurement accuracy. Data collection was performed on a test base in the city of Pilsen, Czech Republic. The base includes 27 control points and more than 100 checkpoints. Two sets of coordinates were created for the points; the first set is calculated using tied net adjustment and the second one is determined by RTK GNSS measurements. Point cloud calculations were processed in RiPROCESS software from Riegl, using different configurations and qualities of the control points. Each point cloud was analysed including the determination of point cloud deviations. This article is also dedicated to the identification of problematic spots, where measurement can be degraded. The results presented in this paper show the influence of the quality and different spacing of the control points on the point cloud, its accuracy compared to the precise points, and the global and local deformation of the point cloud. This work can be used as a basis for replacing classical surveying methods with a more efficient mobile laser scanning method.  
mobile mapping system; mobile laser scanning; point cloud; Riegl VMX-2 HA  
2024143387. [[https://doi.org/10.3390/app14083387](https://doi.org/10.3390/app14083387)]([https://doi.org/10.3390/app14083387](https://doi.org/10.3390/app14083387))
## 4 Performance Evaluation
### Impacts of UTs on System Availability  
As a UT is designed to demodulate the signal of the HS with the interference of all UT signals, BER performance should be guaranteed when using the spectrum-saving scheme. To give instructions on the proposed method, we provide the definition of system availability as follows.  
**Definition 1**.: _Assume that UT \(1\) has the worst channel condition. With a given threshold \(\Gamma\), the system availability of the spectrum-saving scheme is defined as \(P_{awa}=\mathbb{P}(SINR_{0,1}\geq\Gamma)\)._  
In this case, the system availability can be calculated as  
\[P_{awa} =\mathbb{P}(SINR_{0,1}\geq\Gamma)\] \[=\mathbb{P}(\frac{|h_{1}|^{2}|g_{0}|^{2}\beta}{|h_{1}|^{2}G\alpha \beta+(2|h_{1}|^{2}+1)\tau B}\geq\Gamma)\] \[=1-\mathbb{P}(\frac{|h_{1}|^{2}|g_{0}|^{2}\beta}{|h_{1}|^{2}G \alpha\beta+(2|h_{1}|^{2}+1)\tau B}<\Gamma)\] \[=e^{-\frac{1}{L_{1}}\frac{\tau B\tau}{|g_{0}|^{2}\beta-G\alpha \beta-2\tau B\Gamma}}. \tag{41}\]  
We can find that the system availability degrades with the number of UTs.
# The Effect of Hydrolysis on the Antioxidant Activity of Olive Mill Waste
## 2 Materials and Methods
### Determination of Ferric Reducing Antioxidant Power Assay  
Ferric reducing antioxidant power (FRAP) was determined using a spectrophotometric method previously described by [PERSON] and [PERSON] (2018). The FRAP assay is based on the ability of phenols to reduce Fe\({}^{3+}\) to Fe\({}^{2+}\). This assay is conducted in an acidic medium in order to maintain iron solubility. At low pH, the ionization potential that drives the hydrogen atom transfer decreases, whereas the redox potential increases. When the Fe\({}^{3+}\) to Fe\({}^{2+}\) reduction occurs in the presence of 2,4,6-trypyridyl-s-triazine (TPTZ), it forms a colour complex of Fe\({}^{2+}\) which produces an intense blue colour ([PERSON], 2016). A standard calibration curve was made with ascorbic acid (Sigma-Aldrich) with an R\({}^{2}\) value of 1.0 and a regression of y = 0.0005x + 0.399. For this assay, the FRAP reagent was prepared by mixing 25 mL of 300 mmol/L acetate buffer, 2.5 mL of 10 mmol/L TPTZ solution dissolved in 300 mmol/L HCL and 2.5 mL of 20 mmol/L FeCl\({}_{3}\) solution in a 10:1:1 ratio. An amount of 10 \(\mu\)L of extract at a dilution factor of 25 was mixed with 200 \(\mu\)L of FRAP reagent, and the contents were mixed vigorously. Ferric tripyridyltriazine (FeIII-TPTZ) complex is reduced to ferrous tripyridyltriazine (FeII-TPTZ) form in the presence of antioxidants and develops an intense blue colour, with an absorption of 630 nm.
## 4 Outlook and Future Directions  
From the above discussion we have seen a broad range of applications where bioinspired principles and methods can be applied to address the problems of scalability and the computing resource scarcity of individual nodes in a networked sensor system. Bioinspired methods are best developed and analyzed in the context of a multidisciplinary conceptual framework that provides for biological models and well-founded analytical principles. The framework is based on the stages of probing the biological systems, the subsequent modeling of biological behavior--often including simplification--and algorithm development [4]. In addition, such framework shall be extended to take into account the implementation, validation and deployment aspects in order to address the many presumptions of the behavior of the large-scale network model. Subsequently we will be able to explicitly exploit the conceptual framework, in order to develop, analyze and validate novel and more sophisticated bioinspired computational schemes including those inspired by complex processes within networking.  
Bioinspired models have proven to be a useful to solve complex problems in a collaborative/competitive way such as route optimization, clustering, time synchronization, localization and for system protection. We have referenced a number of examples of reports on such studies. However, many of the studies of bioinspired principles in networking are solely based on simulations and quite often also for networks of a modest size. Furthermore, much attention has been paid to the performance of the central algorithm at the expense of looking at the entire system in a realistic environment. In the future, the transformation of existing simulations into practical real-world sensor network application is desired. Inevitable, this will fuel the further development of commercial engineering products based on bioinspired principles.  
A recent, very promising and largely unexplored research field of nanoscale communication networks has emerged from looking at bioinspired principles in communication. The term \"nanonetwork\" has been coined [41]. A nanonetwork is defined as the interconnection of multiple nanomachines using molecular communication. The propagation of communication signals in nanonetworks is different from the ordinary communication networks based on electromagnetism most notable by the fact that nanonetworks use molecules as signals. Other characteristics are a low propagation speeds and very low energy consumption required to establish communication [41]. Nanonetworks are expected to expand the capabilities of nanomachines both in terms of complexity and range of operation by allowing them to coordinate, share and exchange information. Inevitably, this will open a door to new applications in biomedicine, environmental research, industrial manufacturing, military technology, and for consumables.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 3 Routing Implementation
### Routing Determination
#### 3.3.2 An Illustrative Example  
To clearly demonstrate how to discover the \"routing + coding\" processes, we present a simple example within the topology given in Figure 2b. Assuming that Flow 1 (S1-R1-R2-R3-D1) is an existing flow, we want to find a path for new Flow 2 (S2-D2). The process proceeds as follows.  
1. In the initiation of the discovery process, Node S2 broadcasts the RREQ message.
2. When Node R3 receives the RREQ message, it first checks whether the RREQ message has already arrived. If so, Node R3 needs to discard the redundant RREQ message to prevent the loop. If not, Node R3 will temporally store the \" who-can-overhear\" information (i.e., Nodes R2 and R4 can overhear the flow from Node R3) and the upper path (i.e., the upper path is S2-R2). Node R3 then updates its overhear information (i.e., adding Nodes R2, R4, and D1 into the neighbor list) and broadcasts the RREQ message.
3. When one or more RREQ messages arrive at destination Node D2 in a limited time, D2 sorts all received RREQ messages, then automatically chooses the available paths with the best link quality, and inserts the inverse of the path into the RREP message.
4. When Node R2 receives these RREP messages, it will compare the path with its temporarily-stored information. Moreover, according to the constrained coding conditions, Node R2 has the coding opportunity in which Flow 2 can be encoded with existing Flow 1, and the RREP message will mark the node as a coding node.
5. As the RREP message returns to the source Node S2, based on the information of potential coding opportunities and routing coding benefit (which will be described in Section 3.4 in detail), the source node will decide the final route.  
For example, the routing information of Flow 2 is shown in Table 3. It is stored in the RREP message to help the sources determine the best route. Moreover, the size of each part in Table 3 can be modified according to different situations. In this experiment, the total storage overhead is 184 bits in this table, which is large enough to store the route information.
## 3 Proposed Methodology
### Proposed GATransformer
#### 3.2.3 GAT Module
When fully enlarged, the attention mechanism's coefficients, as shown in Figure 8a, become:  
\[\varsigma_{j,k}=\frac{\exp(L(w^{T}[\omega\xi_{j}\oplus\omega\xi_{k}]))}{\sum_{ i\in\eta_{j}}\exp(L(w^{T}[\omega\xi_{j}\oplus\omega\xi_{k}]))} \tag{3}\]  
\(\oplus\) represents the concatenation operation, and T represents transposition. To generate output features for each node, the normalized attention coefficients are utilized to compute a linear combination of features corresponding to them, optionally with a nonlinearity \(\sigma\):  
\[\xi_{j}^{\prime}=\sigma\left(\sum_{k\in\eta_{j}}\varsigma_{j,k}\,\omega\xi_{j }\right) \tag{4}\]  
Following the method adopted by [30], we discovered that multi-head attention stabilizes self-attention learning. \(B\) separate attention mechanisms convert Equation (4) and concatenate their features to produce the following output feature representation:  
\[\xi_{j}^{\prime}=\prod_{b=1}^{B}\sigma\left(\sum_{k\in\eta_{j}}\varsigma_{j,k }^{b}\,\omega^{b}\xi_{j}\right) \tag{5}\]  
\(\prod\) denotes concatenation function, \(\varsigma_{j,k}^{b}\) are normalized attention coefficients from the bth attention mechanism, and \(\omega^{b}\) is the weight matrix of the input linear transformation. The final output \(\xi^{\prime}\) will have \(Bd^{\prime}\) characteristics instead of \(d^{\prime}\). Concatenation is no longer sensible if we execute multi-head attention on the network's final (prediction) layer. Instead, we use averaging and postpone applying the final nonlinearity (typically a softmax or  
Figure 8: (**a**) The proposed attention mechanism; (**b**) Overall process of multi-head self-attention with three heads by one node. logistic sigmoid for classification problems), calculated as the following equation. In contrast, Figure 8b shows multi-head graph attentional layer aggregation. \[\xi_{j}^{\prime}=\sigma\left(\frac{1}{B}\sum_{b=1}^{B}\sum_{k\in\eta_{j}}\xi_{j,k }^{b}\,\omega^{b}\xi_{j}\right) \tag{6}\]  
The GAT attention mechanism determines channel weight associations. The network prioritizes each node's most essential channel neighbors above all channel neighbors. This approach can consider the extent to which channels affect one another and the connections between channels across several layers.
## 2 Magnetic Mechanism and Control System for a Wireless Operation  
### Principle of Wireless Manipulation for Finger Training  
The proposed wireless manipulation method for finger flexion and extension using a magnetic control system exploits the magnetic force between an electromagnet and permanent magnets. The principles are shown in Figure 1. For finger flexion and extension, a magnet array (M1) is attached to the fingers, and attractive and repulsive forces are generated between the magnets and driving coil according to the coil-generated magnetic field direction. Figure 1a shows the basic configuration, consisting of the driving coil and three magnet arrays: M1, M2, and M3, on the fingers and palm, and in the driving coil, respectively. M1 facilitates the main finger actuation; M2 and M3 allow maximum flexion and extension through the \(F_{M1,2}\) and \(F_{M1,3}\) magnetic forces, respectively (the magnetic forces between M1 and M2 and M3, respectively). Figure 1b illustrates the finger flexion method. The green arrow indicates the magnetic field direction. For +\(H\), repulsive \(F_{M1,E}\) is generated towards M1, corresponding to finger flexion. Hence, M1 comes close to M2 on the palm and an attractive force that enhances the finger flexion occurs between M1 and M2. The proposed multi-link assist device containing multi-magnets (M1 array) maintains a stable posture for four-finger flexion. For a \(-\)**H** field direction at the driving coil, attractive \(F_{M1,E}\) occurs between M1 and the coil. Because of force direction, the device generates finger extension. When M1 is close to M3, the two magnet arrays enhance the finger extension (Figure 1c). Hence, we can define the forces acting to generate flexion and extension as follows: Flexion = \(F_{M1,E}+F_{M1,2}\) and Extension = \(F_{M1,E}+F_{M1,3}\).
## 4 Principle and Implementation
### Water Segmentation Model  
#### 4.2.1 SegFormer-UNet Network Structure  
The SegFormer-UNet is a network architecture developed in this paper specifically for segmenting bodies of water in water gauge images. This architecture incorporates the strengths of various semantic segmentation network structures. By summarizing the previous work [20; 21; 22; 23; 24; 25; 26; 27], it was discovered that a high-performing semantic segmentation network tends to have the following characteristics: firstly, a strong backbone network as an encoder is a prerequisite. The main reason for the performance improvement of Transformer-based networks over traditional convolutional neural networks is that the Transformer has a stronger encoding capability. Secondly, the network structure needs to have the ability to interact with information at multiple scales. Finally, the network needs to have sufficient spatial perception capability. Based on the above, this paper redesigns  
Figure 2: Matching projection point selection.  
an efficient encoder-decoder architecture for semantic segmentation with reference to the architectures of the SegFormer and UNet networks. The resulting architecture, named SegFormer-UNet, is depicted in Figure 3. The SegFormer-UNet architecture incorporates many features from both SegFormer and UNet, including the attention mechanism from SegFormer. The attention mechanism used in the SegFormer encoder allows the model to focus on key feature areas, improving segmentation accuracy. The skip connections from UNet enable the combination of low-level and high-level feature maps, helping the model to better learn features at multiple scales and improve segmentation robustness. Additionally, the SegFormer-UNet architecture includes Transformer Encoder layers in the encoder, which can help the model capture global contextual information and improve segmentation accuracy. Overall, the advantages of the SegFormer-UNet architecture include focused attention on key feature areas, learning features at multiple scales, and the consideration of global contextual information.
## 4 Discussion
### Analysis of Variation of Parameters SIs  
The variation of parameter SI was influenced by parameter range. The SA in 3.1 results showed that smaller parameters were more susceptible to ranges. The narrow range will limit the sensitivity index of the small-value parameters. Therefore, the value range of principle is very important for accurate SI. For this model, the recommended parameter ranges are given in Table 1. Similar to the previous study [19], the SIs of \(\pm\)30% perturbation range was applicable when the specific range cannot be given.  
The above results showed that the most sensitive parameters of GPP were different from that of NEE, and the reason was needed to discuss. GPP and NEE were the main products of photosynthesis and respiration processes, and their physiological processes are expressed by formulas. According to formulas (15) and (16), NEE was a direct result of \(ER\), while \(ER\) was directly calculated with \(ER_{0}\) and \(Q_{10}\). Thus, the two parameters were more sensitive to NEE but insensitive to GPP.