esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Wind Trajectories  
BC concentrations were measured for a year (June 2017-May 2018) to investigate (1) the diurnal heterogeneity of BC aerosols; (2) the relative dominance of biomass and fossil fuel sources resulting in BC heterogeneity; (3) the role of long-range and mountain wind transport, and overall meteorological influences on BC pollution; and (4) to compare BC concentrations over the HKH region in Pakistan [29]. Since fine particulate matter is transported from low-lying areas by upslope mountain wind, the concentration of fine particulate matter is observed to be high [30]. Since BC aerosols are primarily restricted in this fine size area of aerosols, this up-slope transport of aerosol particles may be well associated with the up-slope transport of BC aerosols. Thus, upslope transport of BC aerosols by mountain wind is found to compensate for the study period's low anthropogenic activity [29]. During the sampling period, the wind blowing frequency from specific directions was observed as, in Hindukush, overall meteorological conditions were low to quiet, and at other times the wind velocity remained around 2 m/s or lower, with most winds occurring southwards, northeastwards, and northwards. During the sampling period, the wind was dominated by a northerly breeze with an average speed of 1.8 m/s to 2 m/s. The meteorological conditions in the Karakoram were calm to calm, and the wind velocity was frequently greater than 2 m/s. The majority of the winds were from the south and southwest. Furthermore, the wind was characterized by south-west to southwards directions with an average speed of more than 2 m/s between sampling durations. Meteorological conditions in Himalaya were also calm, with wind speeds of more than 2 m/s at times. Most of the winds were from the southwest and north-east, with the average speeds being over 2 m/s.  
Figure 6: Trend resulting from regression model of BC and WSA (**a**) Hindukush, (**b**) Karakoram, (**c**) Himalaya, (**d**) cumulative graph of three mountain ranges.
# Motion Planning in UAV-Aided Data Collection with Dynamic Jamming
## 3 Global Replanning Based on CFC
### Minimum Time Path Initialization Based on CFC
\right]\)
6:\(a^{T}\Lambda p=a^{T}\Lambda\mathbf{p}_{initCFC}(i)\)
7:Loop for\(m=1,2,\cdots\)
8:if\(\frac{|a^{T}\Lambda p_{m}-a^{T}\Lambda\mathbf{p}_{initCFC}(i)|}{|a|}\leqslant r _{safe}\) and \(\left\|\mathbf{p}_{initCFC}(i)\text{ - }p_{m}\right\|\leqslant r_{m}\)
9: Calculate the vertical line
10:\(a^{T}p=a^{T}\mathbf{p}_{initCFC}(i)\)
11: Take a point on the vertical line \(p_{v}\)
12: Let \(a_{v}=\left[p_{v}-\mathbf{p}_{initCFC}(i-1)\right]\)
13:while\(\frac{|a_{v}^{T}\Lambda p_{m}-a_{v}^{T}\Lambda p_{v}|}{\|a_{v}\|}<r_{safe}\)
14:\(p_{v}=p_{v}-\lambda\cdot\frac{p_{v}-\mathbf{p}_{initCFC}(i)}{\|p_{v}-\mathbf{ p}_{initCFC}(i)\|}\)
15: and \(a_{v}=\left[p_{v}-\mathbf{p}_{initCFC}(i-1)\right]\)
16:endwhile
17:\(\mathbf{p}_{initCFC}(i)=p_{v}\)
18:endif
19:End Loop
20:End Loop
21:\(\mathbf{p}_{initOA}=\mathbf{p}_{initCFC}\)
22:return \(\mathbf{p}_{initOA}\)
23:end
```  
**Algorithm 3**For a given initial CFC path \(\mathbf{p}_{initCFC}\), the position of obstacles \(p_{m}\), the safe distance \(r_{safe}\), and step factor \(\lambda\) to find the initial global obstacle avoidance discrete path.
# Design of a Wearable Healthcare Emergency Detection Device for Elder Persons
## 3 Fall Detection System
### Model Training  
In this work, we used Google TensorFlow for neural network configuration and learning, since it makes neural network implementation convenient, because it provides functions used for machine learning, including activation functions and an initialisation function.  
To exploit the advantages of neural networks while keeping a simple model, able to be deployed on the Arduino board, we designed a sequential neural network model for ADL recognition consisting of:  
* A dense layer with 50 neurons and a _sigmoid_ activation function;
* A dense level with 25 neurons and a _sigmoid_ activation function;
* A final level with four neurons and an activation function _softmax_.  
The following Listing 3 shows the implemented model:  
```
1#buildthemodelandtrainit
2 model=tf.keras.Sequential()
3 model.add(tf.keras.layers.Dense(50,activation='ReLU'))
4 model.add(tf.keras.layers.Dense(25,activation='ReLU'))
5#softmaxisuned,becauseweonlyexpectonegesturetocourperinput
6 model.add(tf.keras.layers.Dense(NUM_GSTUREes,activation='softmax'))
7 model.compile(optimizer='adam',loss='mine',metric=['accuracy'])
8 history=model.fit(inputs.train,outputs_train,epochs=80,batch_size=1,validation_data=(inputs_validate,outputs_validate))
```  
**Listing 3**. Neural network model definition and training.  
Since the sample size of the experiment was 50, and we had three components for acceleration and angular velocity per sample (\(x\), \(y\) and \(z\); \(gx\), \(gy\) and \(gz\)), the number of variables in the input layer was set to \(50\times 6\). In the hidden layers, a ReLU function was used as the activation function, for performance reasons.  
The output layer consists of four neurons--[0, 1]--for the \"all\", \"fall\", \"walk\" and \"run\" activities. In the output stage, the activation function is _softmax_, so the sum of the output probabilities has to be 1. In our case, having four different classes, we obtained a probability for each of them. The predicted movement is the one with the highest probability.
## 2 Mathematical Models
### GFIF Combination for Carrier Phase Bias Analysis  
For a receiver-satellite pair, the ionosphere-free (IF) linear combination of dual-frequency code and carrier phase observations can be written as [15; 21]:  
\[\mathrm{IF}(\varphi_{i},\varphi_{j})=\frac{f_{i}^{2}}{f_{i}^{2}-f_{j}^{2}} \varphi_{i}-\frac{f_{j}^{2}}{f_{i}^{2}-f_{j}^{2}}\varphi_{j}=\rho+\lambda_{c}N _{c}+b_{\varphi}-B_{\varphi} \tag{3}\]  
where \(\rho\) is the sum of all time-dependent and frequency-independent (or non-dispersive) items, mainly including the receiver-satellite range, receiver clock error, satellite clock error, and tropospheric error; \(N_{c}\) and \(\lambda_{c}\) are the ambiguity and corresponding wavelength of IF combination; \(b_{\varphi}\) and \(B_{\varphi}\) are the receiver and satellite hardware delays in carrier phase measurements, respectively.  
The BDS satellites broadcast signals at B1, B2, and B3 three frequencies of 1561.098 MHz, 1207.140 MHz, and 1268.520 MHz, respectively. Given the triple-frequency observables in BDS, a geometry-free and ionosphere-free (GFIF) combination can be formed [13]:  
\[\mathrm{GFIF}\left(\varphi_{1},\varphi_{2},\varphi_{3}\right)=\mathrm{IF}( \varphi_{1},\varphi_{2})-\mathrm{IF}(\varphi_{1},\varphi_{3})=-0.457\cdot \varphi_{1}-1.487\cdot\varphi_{2}+1.944\cdot\varphi_{3} \tag{4}\]  
The coefficients \(-0.457\), \(-1.487\), and \(1.944\) correspond to the BDS frequencies B1, B2, and B3, respectively. The GFIF combination mainly includes a weighted sum of receiver noise, multipath, and hardware delay in each frequency and a combination of carrier phase ambiguities. The high-order ionosphere, phase wind-up, and phase center variations in the GFIF combination is sufficiently small and they can be neglected [13; 21]. Equation (4) is formed only with carrier phase observables, thus both noise level and multipath effect are much lower. It is, thus, suitable for investigating the characteristics of hardware delays in BDS carrier phase observables.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 2 Related Work
### Attention Mechanism  
The attention technique was initially employed in the machine translation problem and is now extensively used in several deep learning disciplines [4; 17]. Humans selectively focus on a portion of all information while disregarding others due to the information processing bottleneck. Similar to how a human brain analyzes information, a neural network employs its attention mechanism to quickly focus on a small subset of important data.  
Class activation mapping (CAM) [18] has recently gotten more and more attention. CAM works as follows: first, delete the convolutional neural network's (CNN) last fully connected layers; Secondly, substituting a global average pooling (GAP) layer for the maxpooling layer; computing the characteristics' weighted average comes last. However, it must change CNN's structure, and accuracy must be gradually increased by training, which slows the model's convergence rate. Then, a variety of enhanced CAMs have been put out to expand CAM to more intricate CNN structures: Grad-CAM [19] relies on gradients to weight features learned in the final convolutional layer and generalizes CAM without changing the model. Grad-CAM++ [20] improves Grad-CAM visualization by weighting the gradients pixel by pixel. CBAM [21] is a lightweight general-purpose module that can be smoothly integrated into any convolutional neural network architecture [22] to participate in end-to-end training. It infers the attention map along two distinct dimensions (channel and spatial).  
Since the attention mechanism needs to be optimized over several iterations, it is time-consuming and not easy to locate and cover the entire object. The accuracy of the activation mechanism generally remains low because it often only focuses on a part of the object and may capture a lot of pointless information. We use instance segmentation methods in our object localization module to achieve accurate localization in order to prevent information redundancy and misinformation. The instance segmentation method approach accurately and completely obtains objects by masking off non-object regions to eliminate the interference of background and pseudo-objects. It makes feature extraction more effective.
## 2 Navigation Sensors and Techniques for Drones
### Network Positioning  
One of the most promising forms of absolute positioning, other than GNSS and LocataTech, is network positioning. The most common method of network positioning is known as fingerprinting, which utilizes a database of all recorded network access point locations, including Wi-Fi routers and cell towers. These access points are used in conjunction with the list of network access points available at that time with their respective ranges to determine location estimates. [PERSON] (2012) mentions that Wi-Fi positioning can achieve 43 to 92 m accuracy, while [PERSON] et al. (2010) reports that cell tower localization is able to provide median errors down to 55 m [22; 23]. Although the accuracy of network positioning is lower than that of GNSS, it serves as a great source of alternative position, navigation, and time (APNT) for drones. It will complement the GNSS receivers, especially when GNSS availability is limited, or when GNSS integrity is compromised due to spoofing. For example, when the GNSS signal is attenuated due to jamming or obstructions, the signal carrier to noise ratio density (C/No) and the quality of the code and carrier measurements decline, leading to a degradation in the PVT accuracy. Beyond a certain attenuation point, such as inside a building basement, there will no longer be a GNSS solution available. However, in the case of network positioning, there may be signal sources inside buildings, which mitigates this issue. Furthermore, the frequency spectrum of the Wi-Fi and cell signals do not overlap with the GNSS signals, and so are unaffected by GNSS RFI. In the presence of GNSS spoofing, it is proven that although the GNSS receiver is successfully spoofed, the network location engine is unaffected [17]. In addition, the widespread adoption of 5G technology would significantly improve the accuracy of network positioning in the years to come. In addition, the network chipset inside smartphones is made to have low power consumption and a small physical size, allowing the sensor to be added to the drones while minimally impacting the operation duration and payload.
## 4 Discussion
Using sensor technology during an SLVHT, as used in this study, the jump height can be accurately measured in a clinical setting or at a routine follow-up visit without referring the patient to a specialised training facility that costs time and resources. The test can be performed with minimal set-up time, even in a space-constrained environment. As the digital sensor is connected to a tablet app, test values can be easily transferred to a digital database via Bluetooth and displayed immediately. This makes it possible to demonstrate and discuss the results immediately with the patient and provide other professions that are involved in the rehabilitation process, such as trainers and physiotherapists, with the data. Because of the central digital database, larger groups of patients could easily be enrolled in a multicentre setting. With such larger datasets, it would be possible not only to document and manage rehabilitation progress at an individual level, but also to compare the results of different surgical techniques, such as the choice of graft for ACL-R. Trainer-administered tests can also ensure standardisation of the test and high-quality data collection.
# Improved FraSegNet-Based Rock Nodule Identification Method and Application
## 4 Reliability Analysis  
### Dataset Creation  
The palm face images were collected from the Huayingshan Tunnel Project in the Kangyu section of the Xiyu High-Speed Railway, captured using both smartphones and professional cameras before the first support phase. A total of 160 images were collected, representing a variety of perspectives and scales, which accurately depict the tunnel excavation scene. This diversity in image collection underscores the practicality of the nodal identification algorithm. Representative examples are illustrated in Figure 5.  
The compressive strength of the rocks, as well as the values for RQD (Rock Quality Designation) and GSI (Geological Strength Index), are provided as follows: Compressive Strength: The compressive strength of the rock samples ranged from 160 MPa to 300 MPa, depending on the type of rock. This variation reflects differences in rock composition, fracture density, and other geological factors. RQD (Rock Quality Designation): The RQD values for the rock samples ranged from 52% to 73%, indicating the degree of fracturing in the rock mass. Higher RQD values suggest better-quality rock with fewer fractures. GSI(Geological Strength Index): The GSI values ranged from 42 to 35, providing a quantitative measure of the rock mass's strength and quality, factoring in both the geological conditions and the characteristics of the rock fabric. These parameters are critical for understanding the mechanical properties of the rock mass and have been used to assess the tunnel's excavation stability and the effectiveness of the nodal identification algorithm.  
The Labelme software 4.5.7 was used to manually mark the joints in the tunnel palm face images. The labeled images were saved in two subfolders, matching the original image names, and stored in the same main directory. Since the original dataset was limited and varied, it was processed for consistency and accuracy. This included adjusting format and resolution, converting to grayscale, and augmenting the data. Common deep learning techniques like rotation, flipping (up, down, left, and right), and gamma correction were used to expand the dataset, as shown in Figure 6.  
Figure 5: Typical palm face image.  
Figure 6: Schematic diagram of image data augmentation.  
The augmented dataset includes 1000 images and 1000 labeled graphs (annotated with the Labelme software). It is split into three subsets: a training set, a testing set, and a validation set, following a 7:2:1 ratio. The training set, labeled \"tra_\", has 700 images with labels; the testing set, labeled \"tes_\", has 200 images with labels; and the validation set, labeled \"val_\", contains 100 images with labels. Table 8 shows the number distribution of datasets.
## 4 Conclusions  
The isotherms of the agglomerates presented shapes and courses similar to those of the isotherms of the mixtures from which they were obtained, so it can be concluded that the agglomeration process did not have a significant impact on the course of the isotherms. The agglomeration process influenced the protection of the stability of fatty acids during the storage process in the analysed powdered infant formulas. Based on the obtained results, it can be stated that the agglomeration process of the powdered infant formula limited the adverse impacts of the external environment on the essential unsaturated fatty acids. This is most likely due to the protective effect of the amorphous form of the lactose, which is able to produce protective structures on the surface of milk powder particles. Lactose combined with whey proteins seals the structures of milk powder particles, limiting the effects of, for example, oxygen and water. The composition of the fatty acids changed during the storage process. The shapes and courses of the DSC curves registered for the mixtures and the agglomerates obtained from them did not show any significant differences, so it can be summarised that agglomeration did not significantly affect the thermal properties of the powdered infant formulas. Differences were not observed for either the heating or cooling DSC curves. It is worth emphasising that using the DSC method, it is possible to determine whether the fat was a natural component of the powder or it was added in the form of fatty acid preparations.  
Conceptualisation, E.O.-L.; methodology, E.O.-L., I.P. and R.B.; investigation, M.W.-W., A.G. and I.P.; formal analysis, E.O.-L., A.G., M.W.-W., I.P. and R.B.; writing--original draft preparation, E.O.-L. and A.G.; writing--review and editing, E.O.-L., A.G. and M.W.-W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was financially supported by sources of the Ministry of Education and Science within funds of the Institute of Food Sciences of the Warsaw University of Life Sciences (WULS) for scientific research.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data generated or analysed during this study are available from the corresponding author on reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Seismic Response Analysis of Multi-Story Steel Frames Using BRB and SCB Hybrid Bracing System
## 2 Cyclic Behaviors of BRB and SCB  
Figure 1 shows that the cyclic behaviors of BRB and SCB are usually simplified as a bilinear elasto-plastic and FS constitute model, respectively. The simplified models have been either experimentally or numerically validated by many prior studies [6; 13; 15; 20]. As suggested in prior studies [6; 11; 15], the post-yield stiffness ratio of BRBs is within the range of 1% to 5%. In this analysis, this value for BRB is defined to 1%. An idealized FS model is used to describe the cyclic behavior of SCB in this study. Besides with the \"yielding\" strength and elastic stiffness, the cyclic behavior of the FS model is determined by introducing another two critical parameters, e.g., the post-yield stiffness ratio \(\alpha\) and hysteresis width \(\beta\). In fact, the values of these two parameters may vary with the practical configuration of the bracing components and configurations. Through properly tuning the contribution of each individual subassembly member, the SCBs can achieve a number of combinations of \(\alpha\) and \(\beta\). To be consistent with the post-yield strength of BRB and to maximize the energy dissipation capacity, the values of \(\alpha\) and \(\beta\) currently considered for SCBs are set to be 1% and 1.0, respectively. It is noted as well, to simplify the problem, the degradation mechanism associated with stiffness and strength is excluded.  
Figure 1: Cyclic behaviors of the considered braces: (**a**) buckling-restrained brace (BRB); (**b**) self-centering brace (SCB).  
As aforementioned, SCBs are assumed to perform identically as BRBs in the initial elastic stage, which is the primary premise made in the comparative analysis. In such a way, the comparison is focused on highlighting the seismic response characteristics of CBFs when different types of braces are installed. It should be noted that the \"yield\" behavior of SCB is analogous to that of a normal steel brace but that the structural member does not really yield. The \"yield\" strength of a SCB actually refers to the stiffness degradation behavior of the component. In detail, for SCBs using post-tension technique, the \"yield\" behavior is caused by the decompression or activation force [30] while for those based on SMAs, it stands for the strength triggering the austenite to martensite phase transformation upon loadings [29]. In this analysis, the fracture problem of braces is not considered, which follows the assumption made by a variety of earlier studies [6; 13; 14; 15].
# Blockchain-Based Authentication in Internet of Vehicles: A Survey
## 1 Introduction
However, despite discussing the authentication protocols from different points of view and introducing diverse categorization criteria, all the above surveys have the common factor of reviewing the cryptographic-based authentication schemes in IoV or VANETs and none of them has reviewed the authentication schemes that are based on blockchains. On the other hand, blockchain-based applications in IoV have been addressed by many surveys recently. For instance, the authors in [53] have surveyed a number of blockchain-based applications that aim to improve the security, privacy, trust and cooperation in IoV networks. Seven different aspects where blockchain technology can be incorporated with IoV have been discussed in [54] such as IoV security, trust management, and data management. Moreover, different blockchain-based IoV security methods have been categorized and reviewed in [55]. Although these surveys might have mentioned a few blockchain-based authentication schemes in IoV, they have briefly mentioned them on the run as a small part of the broad field of IoV security and none of them has provided a detailed survey that is only dedicated to the blockchain-based authentication schemes in IoV. Thus, the main contributions of this survey are:  
* Highlighting the significance of the blockchain technology in IoV and VANETs by presenting a wide range of the blockchain-based authentication schemes that are proposed in the recent literature. * Providing the first detailed survey focusing on the application of blockchain technology to a specific aspect of IoV security; that is, the authentication. Considering both IoV and VANETs technologies when surveying the different blockchain-based authentication schemes instead of restricting them to only one vehicular technology, which can provide a comprehensive source for researchers interested in the field of blockchain-based authentication. The organization of the paper is as follows: Section 2 presents some preliminary information related to IoV and blockchain. Then, the security requirements and challenges along with some common security attacks and threats that can be encountered in IoV and VANETs paradigms are discussed in Section 3. After that, the main part of the paper is represented by Sections 4 and 5 where a wide range of blockchain-based authentication schemes in IoV and VANETs are reviewed and compared. Section 6 then suggests some future research challenges after which the paper is finally concluded in Section 7.