esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study: Analysis of a Device
### Comparative Analysis and Discussions
In this case, conventional forensic solutions can only extract data if the device is unlocked. The application of the proposed methodology, which combines the use of ISP and Combination Firmware, made it possible to bypass the lock screen and the subsequent acquisition and analysis of the data. Also, it is emphasized that the data integrity was preserved, which is fundamental in forensic analysis. Taking into account that Samsung is the company with the largest market share of mobile devices [3], we believe that its application is a worthy contribution to forensic analysts of law enforcement divisions, due to the abundance of devices and the number of security barriers that can be overcome.
# On Self-Interference Cancellation and Non-Idealities Suppression in Full-Duplex Radio Transceivers
###### Abstract
Regardless of the immense benefits and increased throughput provided by FD technology, a few practical obstacles prevent its widespread deployment, and the industry has shown a very limited and slow adoption, i.e., in terms of commodity in cellular and Wi-Fi devices. An accurate and desired level SI cancellation and suppression of non-idealities of the transceiver's components requires complicated and complex circuity which, in return, impart a huge challenge to fit compactly in small/cellular/smart devices. Currently, FD operation has a major barrier of space constraints and bulky size of circuitry that prevents its usage on end user devices. The FD communication system is extremely energy/power hungry. These are all factors that mean it can consume approximately 40% additional power in a mobile phone in active RF mode [11]. Moreover, the traffic patterns in higher layers of are highly asymmetric i.e., downlink traffic demand is higher than uplink demand and another major drawback of the FD communication system is that it shows maximum gain and throughput when there is symmetric traffic pattern in higher layers i.e., equal traffic flow in both directions [12]. FD can play a vital role in domain where the traffic is symmetric in nature, and it requires high throughput, as with collaborative sensing for autonomous vehicles. FD devices has been applied in numerous wireless communication systems such as cognitive radio, cooperative networks, device to device, multi-input multi-output (MIMO) communication and relay networks [13]. As per the report of North Atlantic Treaty Organization's (NATO) Science and Technology Organization, FD technology has the ability to alleviate spectral congestion issues in tactical communications. Similarly, FD technology can be used for the security of civilian wireless communications, i.e., through the use of jamming and receiving wireless signals simultaneously, which can in turn prevent eavesdropping. Moreover, it can also help in the prevention of unauthorized usage of the radio spectrum [14]. The FD communication system can also be used for military and defense purposes such as Radio Detection and Ranging (RADAR) sensing and RF backscatter.
## 3 The ILDACP Algorithm  
Through the study of the power consumption characteristics of some typical electrical appliance, we found that even everyday appliances with similar power profiles show the time characteristics, we hope to apply these characteristics to disaggregate the consumption of multiple electrical appliances. We extract the measured power consumption of fridge in AMPds database [38], as shown in Figure 1. The fridge exhibits a cyclic pattern of power consumption due to duty cycling of its compressor. Since there may be multiple electrical appliances with a similar power level, a power values does not represent a particular appliance. Thus, our work disaggregates the consumption of multiple appliances based on the consumption pattern characteristics.  
Our approach, ILDACP, is based on the following assumptions: First, each appliance, contributing to the total power consumption, has its own consumption pattern. Second, we can identify the appliance and estimate its power consumption through its power consumption pattern. The workflow of the approach includes four steps: appliance signatures extraction, consumption clustering, appliances identification and power consumption estimate, and result correction. The last three stages of them are carried out iteratively, until every appliance is disaggregated or only normally-open appliances left. The flowchart of ILDACP is shown in Figure 2. In this section, we provide details on each step of ILDACP in the next paragraphs.
# Management Information Systems for Tree Fruit--1: A Review
## 3 Literature Evaluation
### Adoption Barriers and Drivers  
Despite their promise to increase the efficiency of farm operations, farm MIS adoption has been less than expected, with MIS technology described as 'under-utilized' in agricultural production [1; 33]. The major barriers to farm MIS adoption identified in previous studies [1; 19; 33; 34; 35; 36; 37] include:  
1. 'Internal' operational issues, including system 'bugs', feature incompleteness, poor user experience through poor wording of logic flow, the effort required for data entry, and poor system value as manifested in a low integration of system output into decision-making;
2. 'External' operational issues, including poor system reactivity caused by limited internet connection, poor input data quality and poor lack of system integration and interoperability through failure to use standardised data formats;
3. System maintenance issues, including low adaptation rates, high costs and poor user support resources;
4. Lack of trust in system reliability and data security;
5. Affordability. Similar operational challenges were reported in the reviewed orchard MIS papers, including: 1. 'Internal' operational issues, including e-device or platform compatibility, scalability and performance/efficiency [38; 39; 40];
2. 'External' operational issues, including connectivity and adequacy of bandwidths [40] and poor integration of third-party services and new technologies [41];
3. System maintenance issues, such as software features and poor support services and training [42];
4. Data privacy and trust [38; 39; 40];
5. Affordability and user willingness to pay for the services [43; 44; 45; 46]. The issues noted above are often inherent in system design, resulting from a mismatch of designer and developer focus and the requirements of the ultimate users of the system. As with any technology adoption, there exists a scale-up problem between the proof-of-principle stage and the offer of a user-friendly, cost-effective product to a large user base. MIS adoption is also a function of the client base. For example, a relationship was noted between the 'technical efficiency' of Brazilian citrus farms and their adoption of a MIS, with MIS users producing greater output using the same level of inputs [47]. The perceived benefits of orchard MIS use are related to increased efficiency in planning and the coordination and monitoring of production, particularly in larger-scale enterprises.
# Establishing Two-Dimensional Dependencies for Multi-Label Image Classification
## 1 Introduction
### Establishing effective long-range spatial feature dependencies.  
The Spatial Feature Dependency Module (SFDM) is used to establish effective long-range spatial feature dependencies. SFDM utilizes the ResNet network and extract feature maps from different layers, which preserves the information of targets at different spatial positions. These feature maps are then fed into our designed Feature Fusion Module (FFM) to fuse, establishing the spatial dependency of target features. For the highly abstract feature maps output by the last layer of ResNet, we designed a Feature Enhancement Module (FEM) to enhance and enrich the high-dimensional information. Through cross-layer fusion and feature enhancement, the spatial dependencies between targets are modeled, long-range dependencies are established, and the limitations of fixed convolutional kernel receptive fields are alleviated. By fusing features across different layers, we alleviated the limitation of local feature extraction and preserved detailed information as much as possible. In doing so, we effectively addressed the issues associated with using multiple layers of CNN for establishing spatial dependencies. In addition, our method adds only a few convolutional and fully connected layers in addition to the backbone network, which does not over-increase the network complexity and does not lead to gradient vanishing or explosion problems. This is also a strength compared to stacking multiple layers of CNNs.
# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 3 Proposed System
### Data Pre-Processing  
The relationship between cognitive tests, medication, and mental health is depicted graphically in Figure 3. We have investigated the utility of multimodal time-series data in making prognoses about the course of AD.  
Each patient is represented by four rows in each of the four time-series representations, with each row containing information from a single visit. The ADNI compiles attendance statistics once every six months. Researchers on the ADNI project gathered massive amounts of information over the course of more than a decade.  
We have used T1-weighted MRI images acquired from a cohort of 100 patients diagnosed with AD and 100 age-matched healthy controls. The MRI images having a resolution of 1 mm \(\times\) 1 mm \(\times\) 1 mm and dimensions of 256 \(\times\) 256 \(\times\) 160. The intensity values of each pixel in the MRI images ranged from 0 to 255.  
Before training the Mayfly optimization algorithm, the MRI images were pre-processed by applying skull stripping, intensity normalization, and spatial normalization using the SPM12 toolbox. The images segmented into grey matter, white matter, and cerebrospinal fluid using the FSL software.  
To train the algorithm, leave-one-out cross-validation approach has been used where one patient was left out for testing, and the remaining 199 patients for training. The input to the algorithm consisted of the segmented grey matter images, which were resized to 128 \(\times\) 128 \(\times\) 80 to reduce the computational burden.  
By providing this level of detail about the input data, the authors can help readers better understand the characteristics of the MRI images used in the study and how they may have influenced the results. This information can also be useful for other researchers who want to reproduce the study or compare it with other studies that use similar input data.  
Data Labelling  
Our sample size was also set after we finished pre-processing and labelled our data for binary classification. Since we are engaging in binary classification, each record in the dataset has been either given a zero or a one for the Clinical Dementia Ratio (CDR). Keep in mind that a CDR of 0 denotes perfect health (i.e., no signs of dementia), and a CDR of 1 indicates severe Alzheimer's (i.e., demented). To date, 28 patients have been diagnosed with CDR 1. We used a total of 28 patients with AD and 28 controls to make our classifications. We have two pictures of each patient. We then randomly split the dataset  
Figure 3: Before and After Denoising.  
into halves, creating an 8:2 split. It means that eighty per cent of the data is used for training and twenty per cent for testing. The proposed DBN-MOA method diagram is depicted in Figure 1.
# Negative Poisson's Ratio Lattice Structure with Chiral and Re-Entrant Properties
## 2 Structural Design and Methods  
### Unit Cell Design  
Based on the geometric characteristics of traditional Chinese window grills, a lattice metamaterial was developed that demonstrated re-entrant and chiral properties. The structure depicted in Figure 1b was derived from the design of the traditional Chinese window grill illustrated in Figure 1a. The unit cell depicted in Figure 1c was derived through the process of duplicating, rotating, and reflecting the quarter unit cell. The lattice materials depicted in Figure 1d were acquired through the arrangement of the unit cell.  
Figure 1: Structure of window grills (**a**), design of quarter unit cell (**b**), design of unit cell (**c**), the lattice metamaterial (**d**).  
The geometrical parameters of the unit cell are shown in Figures 1 and 2. In this study, \(a\) is the length of the chiral cross, \(b\) is the diameter of the re-entrant circle, \(C\) is the unit cell length, \(d\) is the side length of a quarter unit cell, \(t\) is the thickness of the chiral cross or re-entrant circle, \(\theta\) is the chiral cross deflection angle, and \(r\) is the arc radius connecting the cross structure and the concave structure. The \(C\) and \(t\) are constant values, which were 52 mm and 3 mm, respectively. The relationship of some parameters can be expressed as follows:  
\[C=2\text{d} \tag{1}\]  
\[\text{r}=\frac{a}{2}-t \tag{2}\]  
Because of the fixed total length of the quarter unit cell d, an increase in a leads to a decrease in b. Therefore, we defined the length ratio (a/b) and the deflection angle (\(\theta\)) as the independent variables of structural geometric parameters. \(a\) is the length of the cross and b is the diameter of the concave arc. The two are connected by an arc with a radius of \(r\) and \(a\) center angle of \(\theta\).  
Thus, 4 independent parameters are defined in this study. They are length ratio (\(a\)/b), deflection angle (\(\theta\)), unit cell length (\(C\)), and the thickness of the chiral cross or re-entrant circle (\(t\)). After defining the geometrical parameters and their relationships, we could explore the influences of changes in structural geometrical parameters on NPR.
# Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion
## 3 Experiment and Results
### Results  
An example of gestures and their representation by Leap motion is shown in Figure 6.  
The experiments were implemented using stratified 10-fold cross validation and assessed using the macro-accuracy (averaged over classes and folds) performance measure [47]. The results of the gesture recognition are presented in Figure 7. The averaged recognition accuracy (mean \(\pm\) SD) achieved is 86.1 \(\pm\) 8.2%.  
The confusion plot of classification is presented in Figure 8. We have obtained a true positive rate (TPR) of 0.854, an F-measure of 0.854 and a Cohen's kappa [48] value of 0.987.  
Figure 6: Example of signs recognized.  
Figure 7: Accuracy of ASL Gesture Recognition.  
In the typing experiment, during the training pre-stage the subjects learned how to use the research system consisting of a software application and the Leap Motion Controller. Then their task was to type three times each of the pangrams. The pangrams were presented in a random order. In case of error, the subjects were instructed to ignore errors and keep typing the phrases.  
We used the words per minute (WPM) as a performance measure and the minimum string distance (MSD) as an error rate as suggested in Reference [49]. The obtained results are presented in Figures 9 and 10 and are summarized as follows (mean \(\pm\) SD): 3.09 \(\pm\) 0.53 WPM and 16.58 \(\pm\) 5.52 MSD.  
Figure 8: Confusion matrix of gesture classification (1-vs-all).  
Figure 9: Gesture typing performance of all subjects.  
We performed the linear regression analysis of relationship between gesture typing speed and error rate and found the following linear relationship, which is within 95% of confidence:  
\[msd=-5.2*wpm+32.7 \tag{18}\]  
here \(msd\) is the minimum string distance and \(wpm\) is the words per minute.  
The results, demonstrated in Figure 11, show that more proficient users demonstrate both higher performance and lower error rate and vice versa.
## 2 Case Studies
### Isolation Models  
The isolation system is located between the base of the column and the surface of the foundation (at z = 0.00 m) and it consists of 2 isolators for every column. The isolators (behaving in the longitudinal direction only) have been modelled very stiff in the other directions (vertical and transversal). They were designed following the methodology described in Section 10 of [34]:  
1. Determination of the equivalent elastic stiffness of the device by assuming a target value for the period of the base isolated structure greater than the fundamental period of the fixed-base structure: \[K=\frac{4\pi^{2}M}{T_{BI}^{2}}\] (1) where \(M\) is the mass of the system and \(T_{BI}\) is the fundamental period of the isolated system.
2. The vertical loads under static and seismic conditions are calculated to choose the horizontal stiffness as well as its geometrical characteristics (diameter of the elastomer, total height of the isolator and the thickness of the elastomer); and the mechanical properties, such as the viscous damping of the rubber, the elasticity modulus, the maximum vertical load at the Ultimate Limit State, and the vertical load under seismic conditions.
3. The maximum displacements resulted from the analyses are finally compared with the design displacement, in order to verify the performance of the isolation.  
The number of designed devices (2 at the base of each column) is determined to minimize the eccentricity between the center of the stiffness of the isolation system and the center of the mass of the superstructure. Two types of models were implemented for the isolators:  
Model B2: implements a linear model (represented by equivalent linear springs), and Model B3: applies the simplified two-spring model (described in [2; 40]) implemented inside Opensees by [41] and already applied in [42]. This model was assumed to consider the non-linear effects and the stability under large deformations.  
Both the models were used in the longitudinal direction only, while in the other directions (transversal and vertical) high stiffness have been considered (3.5 \(\times\) 10\({}^{7}\) N/m\({}^{2}\)).  
It is worth noting that the properties of the two models (bearing stiffness) have been set up in order to have 2.5 s as the isolation period of the isolated system. The damping has been considered as 10% for both the models (see Table 3).
# An Energy Aware Adaptive Sampling Algorithm for Energy Harvesting WSN with Energy Hungry Sensors
## 2 Related Work
They propose two adaptive transmission schemes, in which they adjust the transmission delay based only on the battery percentage and output voltage of the solar panel. However, the authors do not present any data or model of the harvested energy and the consumed energy by the platform used. In addition, the proposed approach is only evaluated on one platform. In many WSN examples in the literature, adaptive sampling algorithms are used only as a tool to minimize the communication between the sensor node and the base station [20]. However, dynamically adapting the sampling frequency of a power hungry sensor according to the maximum frequency of the signal (which depends on the phenomenon observed), one can effectively reduce the power consumption of the sensor and at the same time reduce the radio activity. In this paper, we extend this vision by enhancing the algorithm with a power aware functionality, and verify the concept in simulation with two different deployment scenarios and different types of energy harvester. To the best knowledge of the authors, this is the first paper that presents this approach supported by simulation with in-field long term experimental evaluation. The algorithm proposed Energy Aware ASA (EASA) can be useful especially for wireless sensor nodes equipped with energy harvesting capabilities, where the battery is periodically recharged from an environmental sources.
# Energy Harvesting over Rician Fading Channel:
###### Abstract  
In this paper, a rigorous analysis of the performance of time-switching energy harvesting strategy that is applied for a half-duplex bidirectional wireless sensor network with intermediate relay over a Rician fading channel is presented to provide the exact-form expressions of the outage probability, achievable throughput and the symbol-error-rate (SER) of the system under the hardware impairment condition. Using the proposed probabilistic models for wireless channels between mobile nodes as well as for the hardware noises, we derive the outage probability of the system, and then the throughput and SER can be obtained as a result. Both exact analysis and asymptotic analysis at high signal-power-to-noise-ratio regime are provided. Monte Carlo simulation is also conducted to verify the analysis. This work confirms the effectiveness of energy harvesting applied in wireless sensor networks over a Rician fading channel, and can provide an insightful understanding about the effect of various parameters on the system performance.  
half-duplex; relay networks; wireless energy harvesting; time-switching; Rician fading channel; hardware impairment +
Footnote †: journal: Journal of the Acoustical Society of America
###### Abstract  
This paper studies the trajectory tracking control problem of an Air Cushion Vehicle (ACV) with yaw rate error constraint, input effective parameters, model uncertainties and external wind disturbance. Firstly, based on the four-degree of freedom (DOF) vector mathematical mode of ACV, the radial basis function neural network (RBFNN) is adopted to provide the estimation of model uncertainties and external wind disturbance. Then, an adaptive Nussbaum gain-based approach is incorporated with the backstepping control scheme to handle the unknown input efficient parameters. To avoid the complicated derivative of the virtual control laws, the command filter and auxiliary systems are introduced in backstepping. Furthermore, combing a barrier Lyapunov function (BLF) with backstepping technique, a novel trajectory tracking safety controller is designed to ensure all signals of the closed-loop system are uniformly ultimately bounded, while the yaw rate error is within the pre-set safe range. Finally, the simulation results show the effectiveness of the controller scheme.  
air cushion vehicle; input effective parameters; Nussbaum gain; barrier Lyapunov function; trajectory tracking +
Footnote †: journal: Applied Sciences
## 7 Experimental Verification
### Interleaving Improvement Verification  
In this part, we further verify the interleaving improvement as presented by the simulation results earlier.  
Figure 24a shows the interleaving improvement on mutual coherence with different packet lengths. We can see that using only RCS as the projection matrix, when \(M/N\) is the same, a larger packet length yields larger mutual coherence, and as the \(M/N\) increases, the coherence difference caused by packet length decreases. While using RCS with the interleaving technique, their mutual coherence is almost the same with identical \(M/N\). When considering that coherence increases with a larger packet length using RCS projection, we attribute the cause to the larger packet length, making data loss bursty, which is bad for signal recovery; while data interleaving is actually rearranging the data sampling sequence and, thus, can make data loss more random. Therefore, after interleaving, the packet length effect on mutual coherence is almost eliminated, and with the same \(M/N\), their mutual coherence is almost the same.  
Figure 24: Interleaving improvement with different packet lengths. (**a**) Interleaving improvement on mutual coherence with different packet lengths; (**b**) interleaving improvement on recovery error with different packet lengths.  
Figure 24b further shows interleaving improvement on the recovery error with different packet lengths. When \(M/N\) is the same, a shorter packet length generally can achieve a lower recovery error, and once its measurement number exceeds the threshold required for accurate recovery (Equation (3)), its recovery error diminishes drastically. While interleaving eliminates the packet length effect, the trend of recovery error with varying \(M/N\) is almost the same.