esa-sceva/satcom-chunk-collection huggingface dataset:
# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 3 Method and Datasets
### Description of the Proposed Method
#### 3.2.2 Creating Activity Maps Starting from the Sensor Data  
In one of our previous works ([44]), we defined the concept of virtual pheromones as \"_traces created by the agents not in the environment, but in a representation thereof--A map\"_. Just like natural pheromones, virtual pheromones diffuse in space (see Figure 5a):  
\[p(x)=\left\{\begin{array}{ll}P\big{(}1-\frac{x}{\sigma}\big{)}&for\ x\leq \sigma\\ 0&for\ x>\sigma\end{array}\right. \tag{2}\]  
where \(p(x)\) is the intensity of the pheromones sensed at the distance x from the source, \(P\) is a scalar describing the intensity of the pheromones at the source, and \(\sigma\) is a positive constant that defines the maximum diffusion distance.  
If multiple sources of pheromone are active (see Figure 5b), their aggregated effect due to the superposition is:  
\[P_{R}=\sum_{k=1}^{N}P_{k}\bigg{(}1-\frac{d_{k}}{\sigma}\bigg{)} \tag{3}\]  
where \(P_{k}\) is the intensity of the source \(S_{k}\) and \(d_{k}\) is the distance from the current point to the source \(S_{k}\).  
Finally, the virtual pheromones evaporate, i.e., the intensity of the pheromone traces diminishes with time, so that old traces eventually disappear.  
By placing in each of the BMPs, P1-P4, pheromone sources that release a fixed amount of virtual pheromone whenever a sensor event is reported in the respective place, and integrating the intensities of virtual pheromones over a certain time interval (e.g., one hour), we obtained activity maps for the respective time slices, as shown in Figure 6.  
Figure 5: (**a**) Grayscale image (128 \(\times\) 128 pixels) illustrating the process of spatial diffusion from a single pheromone source placed in P4. (**b**) A similar image illustrating the superposition of the effects of two pheromone sources placed in P1 and P4. The intensity of the pheromone is encoded in shades of gray, where black corresponds to locations with no pheromone at all, and white corresponds to locations with maximum intensity of pheromones.  
This type of activity map has the advantage that it reflects not just the intensity but also the spatial distribution of the activities, and the relative weights of the behaviorally meaningful places in the general structure of the ADL.
## 2 Modern Physical Layer
### Modulation
#### 2.2.1 Non-Coherent Modulation  
At first, works on acoustic communications were mainly focused on non-coherent modulation methods. In particular, on-off keying (OOK) [28, 29] and frequency-shift keying (FSK) [2, 30, 31, 32] modulation schemes based on energy detection were favored, because decoding does not require carrier-phase tracking. The main advantages of these modulations are their reliability and simplicity, so modems do not need high-resource processors with higher power consumption [33]. However, spectral efficiency using these methods is very low, because of inter-symbol and inter-carrier interference generated by Doppler and multipath spread.
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 2 Background
### Ethereum Running Environment  
Blocks, EVM, and smart-contact codes together make up the running environment of Ethereum. The block mining process essentially recognizes legitimate transactions and puts together the corresponding state's transformation into a new block. To achieve this, the miner picks transactions, executes the codes in the smart contract, changes the state, calculates the nonce (proof of work), and attaches the newly mined block onto the previous blockchain. Ethereum accounts are hashed and stored in a data structure called a modified Merkle Patricia tree (Modified MPT) [21] and this Modified MPT's root is hashed and stored in every block. To better understand the smart contract, we explain the Ethereum block structure, as shown in Figure 1.  
_Block_. A _block_ in Ethereum has three parts: header, transaction list, and ommers list. The header contains three Modified MPT' root nodes (stateRoot, transactionRoot, and receipts-Root), and other information about the block. Those three Modified MPTs are world state trie, transaction trie, and receipts trie. Within those tries, every account, its state and transactions are stored. There is another Modified MPT, account storage content trie, and its root node is stored in the account state of world state trie. The two following lists are actual transactions and ommers, which are picked by the miner during the mining process. Ommers are blocks that have a parent equal to the present block's parent's parent [21].  
_World state trie_. Every leaf node of this trie has a mapping between an account state and its address. Therefore, all accounts, including the external account and contract account are tied together in this trie. Every block has only one world state trie.  
_Account storage contents trie_. As we discussed before, the account state has four fields. The account storage content's trie's root node is stored in the field _storage_. Therefore, this trie is not directly stored in the block header, instead of being in the block as part of world state trie. All the contract data is stored in this trie.  
_Transaction trie_. This trie is where the hash of all transactions included in a block is stored. Once the block is mined, this trie and transaction list in the block will never be changed.  
_Recipts trie_. Every receipt contains results of the execution of transactions, accumulated amount of gas used, logs and status code of the transaction. The serialization of this information combined with keys is stored in the receipts trie.
# A Joint Strategy for Fair and Efficient Energy Usage in WLANs in the Presence of Capture Effect
## 5 PHY Capture Induced Unfairness in WLAN and Proposed Solutions Based on Joint Strategy
### Adaptive Transmission Power Control (ATXPR)
#### 5.2.2 Extensive Simulations
We, finally, compared the MAC delay incurred by packets to be successfully transmitted in the group of nodes in Zone 1 and Zone 2 using baseline and AIMD+ as shown in Figure 14. Similar to Figure 10, MAC delay in both zones was determined against the number of nodes in Zone 1 varying from two to 18 while the total number of nodes in network remained constant, i.e., \(N_{total}=20\). At first, we see that when \(N_{1}=2\) and \(N_{2}=18\) delay for Zone 2 was only 13 ms while that for Zone 1 was around 2 ms when using the baseline, however, this delay for both zones increases gradually for both zones when \(N_{1}=18\) and \(N_{2}=2\). From this observation we can say that baseline is extremely unfair as it imposes unnecessary delay on the minimum number of Zone 2 nodes, i.e., when \(N_{2}=2\). Another observation is that when \(N_{1}=10\) and \(N_{2}=10\) the delay incurred by Zone 2 is almost three times that  
Figure 12: Comparison of AIMD+ and baseline with varying inter-zone distance. Figure 13: Throughput comparison of AIMD+ with baseline. of Zone 1 using baseline. In contrast to baseline, AIMD+ is extremely fair. For example, when the number of nodes in Zone 2 is reduced from \(N_{2}=18\) to \(N_{2}=2\) its MAC delay was reduced from 12 ms to 4.6 ms when using AIMD+. It clearly shows that Zone 2 delay was barely influenced by the number of nodes in Zone 1. Another useful observation that we can see from Figure 14 is that when there are equal number of nodes in each zone, \(N_{1}=10\) and \(N_{2}=10\) MAC delay incurred by each group of nodes is almost same showing that AIMD+ is fair in terms of delay among the two group of nodes.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 2 Materials and Method  
### GNSS Buoy Set-Up and Mooring Design  
Our GNSS buoy configuration was the same as that used at Holyhead harbour [8], with the GNSS antenna, receiver and battery attached to the top of the platform and secured between the yellow trawl floats (Figure 1a). The overall cost was around USD 400 (buoy parts USD 140, GPS/logger USD 260; all buoy parts sourced from suppliers in the UK, GPS/logger sourced from Emlid Reach [19]). The GNSS system consisted of a U-blox M8T [20] single frequency receiver (embedded on the Emlid Reach [19]) using a Tallysman patch antenna, with a 10 cm square aluminium ground plane. The ground plane is essential to reduce multi-path reflections and increase the antenna gain [21]; its optimum dimensions (often supplied with antenna manufacturer's specifications) are determined by the antenna size. An EasyAcc 20,000 mAh battery pack was used with the Emlid Reach, which provided enough power to cover the period of the experiment; the expected endurance based on the power requirements is up to 4 days. The Emlid Reach was also Wi-Fi-enabled, and this is the method used for downloading data to a laptop after the deployment (typically \(\sim\)100 Mb of zipped raw U-blox format data per day). In trials with an Alpha network antenna (AC1200 USB Wireless Adapter) connected to a laptop, a distance of up to 500 m was achieved for successful downloads with the mooring still floating in the water (without needing to recover, and open the electronics enclosure).  
GPS and GLONASS raw satellite data were recorded at 5 Hz, including the carrier phase information in the U-blox binary format (ubx). To reduce the chance of water ingress into the electronics box, cable ties were applied around the lid to provide additional support to the clamped lid-closure catch.  
To allow for more free movement of the buoy, the mooring used two additional surface floats (Figure 1a), with the GNSS buoy attached directly to a small Gael Force trawl float and then to a larger yellow polyform A3 buoy. This mooring set-up allows the GNSS buoy to float on the horizonal water level and reduces any angled inclination towards the anchor point. Figure 1a shows the top part of the mooring with the GNSS buoy, trawl float and polyform buoy (\(\sim\)5 m of rope between the GNSS buoy and the polyform buoy). Small 200 g weights were attached to the middle sections of each rope segment to keep the connections under water and to reduce the chance of tangling with the GNSS buoy float arms. Figure 1b shows the 15 m of rope connected to the ground anchor (1 m in length) which was screwed into the sand (the swivel helps to prevent the main mooring line from twisting). Figure 1c shows the two different parts of the mooring connected together on the beach at low water.
## 3 Numerical Model
### Bridge and Vehicle Models  
The bridge model used in this paper is a 20 m span simply supported Euler-Bernoulli beam (a commonly used bridge model) that is 4 m wide (representing one lane of a highway bridge). By treating the lane as independent, there is an inherent assumption of negligible load sharing with any adjacent lanes. The deck consists of three 'Y3' beams and a 200 mm deep (in-situ) concrete slab. The FE model of the structure contains 20 discretized beam elements. Each element has four degrees of freedom, one rotational and one vertical translational degree of freedom at each end. Bridge accelerations are calculated at the ends of each element. The damping is taken as 3%, and the viscous damping matrix is generated on the assumption of Rayleigh damping [36]. Table 1 provides the properties of the bridge model.  
A half-car vehicle model is used to represent a two-axle vehicle [37]. The coupled vehicle-bridge system, shown in Figure 1, has been used in many other studies [32; 33; 34; 35; 36; 37; 38]. The  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Span** & **20 m** \\ \hline Number of finite elements & 20 \\ Total degrees of freedom & 42 \\ Young’s Modulus, E & \(3.5\times 10^{10}\) N/m\({}^{2}\) \\ Cross sectional area & 2.046 m\({}^{2}\) \\ Second moment of area, J & 0.2991 m\({}^{4}\) \\ Damping, \(\xi\), & 3\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of bridge model.  
half-car model has a total of four degrees of freedom, namely: bounce displacement (\(y_{s}\)) of sprung mass (\(m_{s}\)), pitch rotation (\(\theta_{s}\)) of sprung mass, and two-axle hop displacements (\(y_{u,1}\) and \(y_{u,2}\)). The total weight of the vehicle consists of the main body sprung mass (\(m_{s}\)) and two unsprung axle masses (\(m_{u,1}\) and \(m_{u,2}\)). As shown in Figure 1, the body mass is connected to the axle masses through springs with stiffnesses, \(K_{s,i}\) and \(K_{i,i}\) and viscous dampers, with coefficients, \(C_{s,i}\). The sprung mass moment of inertia is denoted as \(I_{s}\). The distance between axles and the centre of gravity are \(D_{1}\) and \(D_{2}\), respectively. In order to consider the effect of road roughness, a profile is generated according to the ISO standard [39].  
A 'carpet profile' is generated with 11 correlated profiles extending in the longitudinal direction, spaced at 0.2 m intervals transversely. For any given vehicle pass, one of the 11 profiles is selected according to a truncated Normal distribution, using Monte Carlo simulation [40]. Some of the properties for the population of vehicles used in the analysis are chosen from the Long-Term Pavement Performance Weigh-in-Motion (WIM) database from Wisconsin, the USA, collected in 2012 [41; 42]. Other properties are generated randomly by Monte Carlo simulation using population means and standard deviations from the literature [43; 44]. Table 2 provides a summary of all vehicle population properties. The sprung mass is calculated as the GVW minus the sum of upsprung masses. The position of the center of gravity is calculated using the individual axle weights from the WIM database.
## 2 Materials and Methods
### Determination of Flavonoid Bioaccessibility  
An in vitro digestion model [18], based on [PERSON] et al. [19], was used to study the bioaccessibility of flavonoids from infusions prepared from green and yellow leaves. The process included an initial phase, a salivary phase with amylase addition, followed by simulated gastric digestion using porcine pepsin and HCl. After adjusting the pH, an intestinal phase was mimicked by adding pancreatic juices. The final digestion step involved incubation and pH adjustment to 7, followed by centrifugation. Supernatants were analyzed by previously reported HPLC-DAD method [20] with slight modifications. In summary, an Agilent 1260 Infinity II high-performance liquid chromatography (HPLC) system (Agilent, Santa Clara, CA, USA) equipped with a diode array detector (DAD) was utilized. The separation of components was performed using a Zorbax 300 Extend-C18 column (Agilent, Santa Clara, CA, USA) at 40 \({}^{\circ}\)C. The mobile phase consisted of a combination of phase A--0.1% formic acid solution--and phase B--acetonitrile, with a flow rate of 1 mL/min over a 45 min period. The mobile phase gradient was as follows: 0 min, 98% A; 10 min, 79% A; 15 min, 77% A; 20 min, 75% A; 25 min, 64% A; 30 min, 62% A; 35 min, 51% A; 40 min, 25% A; 43 min, 8% A; and 45 min, 98% A. Ginkgo infusions were filtered through 45 \(\mu\)m PTFE syringe filters. Standards of biflavonoids were used to prepare calibration curves, and chromatograms were recorded at 330 nm. A representative chromatogram is available in the Supplementary Materials. To determine flavonoid bioaccessibility, we recorded the area under all peaks at 330 nm. The accessibility during each specific digestion phase was calculated as follows [19]:  
Bioaccessibility in salivary phase = (BAsalivary phase/BAinitial phase) \(\times\) 100  
Bioaccessibility in gastric phase = (BAagastric phase/BAinitial phase) \(\times\) 100  
Bioaccessibility in intestinal phase = (BAintestial phase/BAinitial phase) \(\times\) 100  
where BAsalivary phase, BAagastric phase, and BAintestinal phase corresponded to the area under the peaks at 330 nm in salivary, gastric, and intestinal phase, and BAinitial to the area under the peak at 330 nm in the initial phase.
## 1 Introduction
The frequency of changes in the load on the vertical axis of the rotor results from its rotation frequency. In the field of external forces resulting from the influence of wind and gravity, gyroscopic effects are created and, as a result, the rotor rotation axis cannot reach the position of dynamic equilibrium [7]. Vertical axis wind turbines are not as expensive to build and operate as horizontal axis wind turbines. Despite this, they have not become that common, mostly because of the limited speed and their low efficiency [8; 9]. The three basic designs of VAWT are: Darrieus, Savonius, and H-type rotors [10; 11; 12]. The Darrieus rotor patented in 1931 has two thin arc-shaped blades that rotate around the vertical axis. The rotor design is simple, but with a  
Figure 1: Basic types of wind turbines: (**a**) the HAWT turbine; (**b**) the VAWT turbine. low starting torque. It was modified several times in the following years, including the use of an additional rotor as a starting motor [13]. The authors of Ref. [14] proposed a Darrieus multi-turbine system in which the coupled rotors meshed with each other. The authors found that such an arrangement improved the efficiency of a single rotor. An overview of the Darrieus rotors development and their applications was described in Ref. [15]. In turn, the authors of Refs. [16; 17] focused on practical applications of the Darrieus rotor, which was the application of such a rotor in wind or hydro microgenerator. The starting torque of the Darrieus rotor is practically zero, therefore an additional drive is required for starting [18; 19]. As an auxiliary drive for starting the Darrieus rotor, an electric motor or a secondary rotor of a different design that provides high starting torque, such as the Savonius rotor, can be used. This rotor consists of two curved blades arranged in a S-shape. There is usually a gap between the blades of 10% to 15% of the rotor blade diameter [20]. Refs. [21; 22; 23] present research on how to obtain a more uniform static torque coefficient or the possibility of improving the power factor with respect to the basic Savonius rotor or its improved forms. Combining the structural and aerodynamic advantages of several different turbines, the other modified designs of wind turbines can be obtained. For example, the TURBY's turbine [24], which is a modification of the Darrieus turbine and is designed for operation on the roof of a building. Blades of the rotor, which are mounted obliquely, make it possible to use the energy of wind acting both horizontally and at different angles. Other examples are the H-Darrieus turbine [25] or the spiral wind turbine [26]. These designs provide uniform operation for any wind direction and are quiet. One of the main advantages of VAWTs is that there is no need to use a wind direction adjustment system, and the design of the rotors and the mechanical and electrical equipment used in these turbines is less complex than for HAWTs.
## 6 Limitation and Future Work  
The present study had some limitations. This 3D model simulation did not include the fluid-structure interaction (FSI) between the fluid and the solid, where the pressure flow behavior inside the SPA would be analyzed. Future studies should include the simulation of the pressure from the SPA inlet using an FSI analysis to determine the appropriate pressure to be applied on the skin surface. Moreover, the experimental testing was performed using a single mannequin leg. It is suggested that future studies should consider studying the pressure transmission on the human leg profile with different types of skin stiffness and fat layers. One limitation of conducting experiments on the stress-pressure relationships of soft pneumatic actuators is the difficulty in accurately measuring the internal pressure within the actuator. Initially, the experimental pressure was indirectly obtained through the FSR sensor. Direct uniaxial tension should be quantified using a tensometer to attain the stress-strain curve of the SPA. Lastly, the pain and comfort assessment as well as experimental testing of the established SPA model on human subjects can be conducted to further test its deformation capabilities.  
Conceptualization, H.B., K.H., N.A.A.R., R.M.R., A.A.K., M.'A.A., A.T. and D.R.; Methodology, H.B., K.H., A.A.K. and M.'A.A.; Software, H.B., A.A.K. and D.R.; Validation, H.B., K.H., N.A.A.R., R.M.R. and M.'A.A.; Formal analysis, H.B. and A.A.K.; Investigation, H.B., A.A.K. and M.'A.A.; Resources, H.B., K.H., N.A.A.R., R.M.R., A.T. and D.R.; Data curation, H.B.; Writing--original draft, H.B. and K.H.; Writing--review & editing, H.B., K.H., N.A.A.R. and R.M.R.; Visualization, H.B. and K.H.; Supervision, H.B., K.H., N.A.A.R., R.M.R. and D.R.; Project administration, K.H., N.A.A.R., R.M.R. and A.T.; Funding acquisition, A.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Medical Revolution Sdn Bhd, Kuala Lumpur, Malaysia.  
Not applicable.  
**Data Availability Statement:**[[https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) HKmxOBrZCqjLmJaGD8 sZHEO9P1y5_Nv?usp=share_link]([https://drive.google.com/drive/folders/1](https://drive.google.com/drive/folders/1) HKmxOBrZCqjLmJaGD8 sZHEO9P1y5_Nv?usp=share_link), accessed on 20 February 2023.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Results
### Metrics  
To quantitatively evaluate the effectiveness of the proposed network, overall accuracy (OA), recall, precision, F1 score, IoU and mIoU were utilized to evaluate its performance. They are defined as follows:  
\[\text{OA}= \frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{TN}} \tag{12}\] \[\text{recall}= \frac{\text{TP}}{\text{TP}+\text{FN}}\] (13) \[\text{precision}= \frac{\text{TP}}{\text{TP}+\text{FP}}\] (14) \[\text{F1}= \frac{2\times\text{precision}\times\text{recall}}{\text{precision} +\text{recall}} \tag{15}\]  
Figure 8: The IoU obtained with different pairs of thresholds for AEUNet++ in the WHU data set.  
\[\text{IoU}= \frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{16}\] \[\text{mIoU}= \frac{1}{N}\sum_{k=1}^{N}\frac{\text{TP}_{k}}{\text{TP}_{k}+\text{ FP}_{k}+\text{FN}_{k}} \tag{17}\]  
where TP denotes true positives (correctly extracted building pixels), FP denotes false positives (pixels mislabeled as buildings in results), TN denotes true negatives (correctly identified nonbuilding pixels), and FN denotes false negatives (pixels incorrectly labeled as non-buildings or that could be interpreted as missed building pixels). TP\({}_{k}\), FP\({}_{k}\) and FN\({}_{k}\) denote the TP, FP and FN of class \(k\), respectively, and \(N\) is the number of classes.