esa-sceva/satcom-chunk-collection huggingface dataset:
# A Systematic Review of the Usability of Telemedicine Interface Design for Older Adults
## 4 Discussion
### Limitations  
The first limitation of this study is the age restriction applied for the elderly population. Given the earlier onset of geriatric diseases, we set the minimum age at 50 years old and included as many studies as possible. However, user characteristics across different age ranges may lead to varying needs and results. Future research may stratify older adults into narrower age groups to allow for more targeted interface design strategies in telemedicine. Secondly, in terms of the literature review, we excluded conference papers, the gray literature, and unpublished articles, limiting our sources to journal articles written in English. Due to the substantial number of articles retrieved, only the literature from the past five years was considered, since design trends may evolve over time. We believe that the number of articles reviewed is sufficient for analysis. The use of only four databases may limit the screening, but it is believed that Web of Science, Scopus, PubMed, and CINAHL cover most papers on telemedicine for the elderly. Additionally, preliminary keywords were tested in multiple databases to avoid missing the relevant literature due to differing search strategies.
## 3 Experiments and Results
### Comparisons with Other Methods  
To verify the superiority of the proposed algorithm, Table 5 shows the recognition accuracy of different rolling bearing fault diagnosis methods. As can be seen from Table 5, the proposed method achieves the highest average accuracy of 99.86% under the same working conditions. The methods in Table 5 fail to extract the complete time-frequency information, and some even directly take the original signal as the input, which leads to information overload and increases the training time. Moreover, the model also learns irrelevant information, affecting the recognition accuracy. The proposed method introduces an attention mechanism from three perspectives of the channel, time and frequency combined with residual connection, which can obtain useful time-frequency information more effectively and facilitate subsequent model diagnosis.  
Figure 8: RTFANet model confusion matrix on test set.  
Figure 7: Training of different combination models: (**a**) curve of training set loss; (**b**) accuracy curve of the test set.
## 1 Introduction
In contrast to work presented in [2], where BER characteristics were given only for developed prototypes, this paper allows comparison of theoretically expected results and results of simulation. Also, the system developed in this paper allows easy changes in the system structure and further analysis of the other possible options in the system design. The paper contains six Sections starting with this Introduction. In the second Section a theoretical structure of the system is presented and procedure of signal processing in each block is demonstrated when AWG noise is present in the channel. The complete analysis is done for a single-correlator and \(N\)-correlator receiver. Section 3 presents the behavior of the system in presence of fading. The interleaver and deinterleaver structure can mitigate the fading in the system. The theoretical model of the system and derivation for the bit error rate expressions for this case are presented in Section 4. The system is simulated for all cases analyzed in previous theoretical sections and simulation results are presented in Section 5. Conclusions of the paper are contained in Section 6.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 3 System Model
### Machine Learning for Wireless Indoor Localization
#### 3.1.3 K-Nearest Neighbor Algorithm  
K-Nearest Neighbor (kNN) is considered the simplest classifier used for indoor positioning systems. The kNN algorithm uses a feature-similarity approach for prediction. The kNN algorithm can be used for both classification- and regression-type problems. In this study, the nearest neighbors were those data points with a minimal distance in the feature space from the new data point. Additionally, the value \(k\) was the number of such data points we considered in implementing the algorithm. For example, a number \(k\) was selected by KNN algorithms as the nearest neighbor to the data point that needed to be categorized. For instance, if \(k\) is 3, it will search for the three closest neighbors to that data point, where \(k\) is the hyperparameter of the kNN algorithm. The value of \(k\) has an impact on the model accuracy. A prepared table that lists the relative strengths of two or more separate received signals from neighborhood routers is necessary for the kNN algorithm. The offline phase is when the predetermined table is built.
## 4 Results and Discussion
### Test Setup  
We first describe the environment of our work for carrying out simulations of the developed path planning algorithm. In this work, a square area of size 1.5 km\({}^{2}\) was chosen for our tests. The selected area is a section from Beiberstedt Butte summit, located in Oregon, USA. The reason behind our selection is that this summit is composed of a variety of soil types, some of which are nontraversable by the UGV type selected in this work. Secondly, this summit is rough and experiences elevation differences, which is a good example over UGV deployment and navigation in real-life under harsh conditions. The corresponding DEM map to the area under study, referred to in Figure 4, contains 2500 randomly-generated nodes, where each node has eight adjacent neighbors connected via links with a minimum of three meters spacing. We can also see a satellite image of the same area in Figure 5. Moreover, this section of the summit has a peak height of 1412 m, while the lowest point on it is 1039 m above sea level.  
It was possible for us to generate this DEM map with its corresponding soil data by using the publicly available geological data from the _U.S. Geographical Survey_ (_USSS_) agency. These data were utilized in _ArcGis Pro_ and _MATLAB R2018a_ programs for simulations. The simulations were carried on a desktop computer with the following specifications: six core 1.7 GHz Intel Xeon Bronze 3104 CPU with 16 GB RAM memory. In a similar fashion to [22], we set the UGV's mass to 300 kg without any payload and its constant velocity to 0.5 m/s. The static and dynamic friction coefficients are selected to be 1 and 0.1, respectively. The UGV's maximum power output is assumed to be \(P_{max}=1280\) W. To appropriately select the UGV's VCI value that reflects real-life scenario deployment, we assume the UGV has the structure of an M1 tank (which has been tested and proven in [26] to be good at off-road navigation) with \(VCI=26.34\) for single crossings over terrain. A single UGV crossing over a certain soil spot is a safe assumption to make because our simulation results show that there are barely any nodes being revisited throughout all the simulation runs. However, it should be said that the higher the number of UGV crossings on the same terrain, the higher the UGV VCI will be. Additionally, the chosen \(VCI\) value, according to (1), justifies why \(Type\,B\) soil area in Figure 4 is nontraversable, as in this scenario, \(VCI\,>\,RCI\).
## 4 Results  
### Experimental Desorption Rate  
Figure 4 shows that as the solution temperature increased, the desorption rate increased. This behavior was non-linear because the vapor pressure exponentially increased as the solution temperature increased, meaning an exponential increase in the driving force. As the cooling water temperature decreased, the desorption rate increased. This effect occurred because a decrease in the cooling water temperature led to an increase in the vapor pressure difference across the membrane; thus, the desorption rate was enhanced [32]. The highest desorption rate was 5.69 kg/m\({}^{2}\) h at 95.2 \({}^{\circ}\)C LiBr solution and 14.6 \({}^{\circ}\)C cooling water; the lowest desorption rate value was 1.53 kg/m\({}^{2}\) h with a 75.6 \({}^{\circ}\)C LiBr solution and 25.1 \({}^{\circ}\)C cooling water. As is shown in Figure 4, the water desorption from aqueous LiBr solution using the AGMD process could occur within wide thermal operating conditions at atmospheric pressure. This means that renewable thermal energy sources, like solar energy, could be used to power the desorption process. In addition, according with this figure, the effect of the H\({}_{2}\)O/LiBr solution temperature was higher than the condensation temperature, i.e., when the solution temperature was 75.3 \({}^{\circ}\)C (average), the desorption rate increased from 1.53 kg/m\({}^{2}\) h to 2.26 kg/m\({}^{2}\) h, with the cooling water temperature of 25.1 \({}^{\circ}\)C and 14.4 \({}^{\circ}\)C, respectively, which means an increment of 0.48 times. On the other hand, when the cooling water temperature was 25.2 \({}^{\circ}\)C (average), the desorption rate increased from 1.53 kg/m\({}^{2}\) h to 5.03 kg/m\({}^{2}\) h, with the solution temperature of 75.3 \({}^{\circ}\)C and 95.2 \({}^{\circ}\)C, respectively, which represents an increase of 2.3 times. This behavior has been reported in the literature; according with [PERSON] et al. [33], the effect of the coolant temperature on the vapor desorption is not as significant as the hot saline solution temperature and air gap width. For conventional boiling desorbers, the influence of the condenser temperature is similar [34; 35].  
Figure 4: Desorption rate as a function of LiBr solution and cooling water temperatures.
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
## 3 Results and Discussion
### Impact of Temperature Variation on Friction and Wear Properties  
This section discusses the effect of temperature on the friction and wear properties of Ti-6 Al-4V alloy and alloyed coating. The test parameters are as follows: using the Si\({}_{3}\)N\({}_{4}\) ceramic ball with a diameter of 6.3 mm as the grinding material, apply 6 N load, sliding speed of 16 m/min, and wear for 30 min.
# An Obstacle-Tolerant Path Planning Algorithm for Mobile-Anchor-Node-Assisted Localization
## 5 Performance Evaluation
### Simulation Results
#### 5.3.2 Impact of Resolution  
Figure 14a,b show the impact of resolution (\(CR/d\)) on localization errors and coverage. The field size is 128 m \(\times\) 128 m and the communication range increases from \(d\) to \(2.25d\). According to Lemma 1, the minimum communication range, \(CR\) must be equal to or larger than \(\sqrt{(5/2)}d\ \cong 1.6d\) to ensure that all unknown nodes in a basic square can receive at least three position packets from the MAN in an obstacle-free environment. In Figure 14a, with an increase in resolution, the localization errors of the OTPP decreases together with that of the Z-curve, with both stabilizing when the resolution is larger than \(1.6d\). OTPP performs better than Z-curve with respect to localization errors even though the resolutions are not large enough to fully cover a basic square. In Figure 14b, with an increase in resolution, the coverage of the OTPP increases together with that of the Z-curve. The coverage of the OTPP stabilizes when resolution is sufficient (\(\cong 1.6d\)) but the coverage of the Z-curve does not stabilize until the resolution is \(\geq 2d\).  
Figure 14: Impact of resolution on (**a**) localization error and (**b**) coverage ratio.  
Figure 13: Impact of blocking rate on (**a**) localization error and (**b**) coverage ratio.
## 2 Data Acquisition and Preprocessing
### Data Preprocessing  
#### 2.2.1 Data Standardization and Normalization  
The experiment measured a total of six sets of water quality data, including pH, water temperature, dissolved oxygen, conductivity, salinity, and total dissolved solids. As the six water quality parameters are inconsistent in terms of unit and dimension, and the value distribution is wide (3.4-512), if the dimensionality is not unified problems such as poor effect of the subsequent training model and slow convergence speed will be caused. Normalization of the data set to mean 0, variance 1, and standardized data within the interval [0, 1] can solve the problem of feature skewness with larger dimensions of the model. Hence, the results predicted by the model are biased to features with larger dimensions, while the impact of features with smaller value changes on the predicted results is ignored. At the same time, the standardized data can effectively maintain the capture of useful information and reduce the impact of error values on the model.  
Figure 1: Experimental base and water quality data collection diagram. (**a**) Location map of the testing site, (**b**) Experimental data collection flowchart.  
Dimensionless processing of datasets is a data preprocessing method often used before model training, of which standardization and normalization of data are the most commonly used. When the data \(x\) is centered on mean \(\mu\) and then scaled by the standard deviation \(\sigma\), the data will conform to a normal distribution with mean 0 and variance 1 (i.e., the standard normal distribution), and this process is known as data standardization (Standardization, also known as Z-score normalization), with the following formula:  
\[f(x)=\frac{x-\mu}{\sigma} \tag{1}\]  
where \(\mu\) is the mean and \(\sigma\) is the standard deviation. When the data \(x\) is centered according to the minimum value, and then scaled according to the extreme deviation, it will converge to between [0, 1] and this process is called data normalization (Normalization, also known as Min-Max Scaling), and the formula is as follows:  
\[f(x)=\frac{x-min(x)}{max(x)-min(x)} \tag{2}\]  
where \(x\) is the current sample value to be tested, \(min(x)\) is the minimum value among all samples, and \(max(x)\) is the maximum value among all samples [31].
# Earth Observations for Sustainable Development Goals
## 5 Applications in Climate  
While climate is covered by the SDG 13, the indicator framework acknowledges that the United Nations Framework Convention on Climate Change is the primary international, intergovernmental forum for negotiating the global response to climate change and SDG 13 proposes only a limited number of indicators related to deaths due to climatic disasters and the development of strategies and finance to climate change, both not measurable by Earth Observation techniques. However, two papers in this Special Issue are related to climate and its impact.  
One study synthesizes the key contributions of satellite observations into characterizing effects of the climate change in the Arctic and their amplification [8]. The study reveals that the satellites captured a number of important environmental transitions in the Arctic region. Additional efforts are needed to improve cross-sensor calibrations and retrieval algorithms, as well as to reduce uncertainties.  
Secondly, another paper studies the additional injection of black carbon (BC) aerosol in the Amazon atmosphere that was produced in September 2019 due to the uncommonly extensive wildfires used to clear the land [9]. This injection was visible in MODIS imagery and forced a significantly change in the radiative balance and reduced the radiation reaching the top-of-atmosphere (TOA) in a 30% across the whole of South America continent compared to 2018. Most likely, this reduced the rainfall due to the cooling surface and enhanced thermodynamic stability of atmosphere due to the atmospheric heating effect.
# Si Photonics for Practical LiDAR Solutions
## 3 Si Photonics for LiDAR
### LiDAR System Design Considerations
#### 3.2.4 Ambient Light Suppression  
Many LiDAR systems are used in outdoor environment and the effect of sunlight on SNR must be carefully dealt with. The choice of 940 nm in consumer 3D sensors results from its superior outdoor performance. The solar irradiance at 940 nm is about a third of that at 850 nm or 905 nm and also lower than that at 1550 nm as shown in Figure 2. An extremely low solar irradiance window from 1350 to 1400 nm exists, though this wavelength band is rarely explored due to the poor availability of high-power laser sources. To suppress ambient light at the receiver, sensor read-out frontend circuit must be carefully designed to minimize effect of ambient light photocurrent. Using an ambient photocurrent cancellation circuit at the expense of sensitivity loss, some indirect ToF frontend circuits can enable a receiver operating under direct sunlight at around 100 klx illuminance. Direct ToF or FMCW is generally more immune to ambient light thanks to very short pulse duration or coherent detection, respectively.
## 2 Preliminaries  
### Petri Nets  
A PN is a mathematical and graphical modeling tool for many systems, especially for DES. From an informal point of view, a PN is a directed graph with two types of nodes (place and transition) and has a dynamic behavior. The places and transitions are connected by oriented arcs. An arc connects either a place to a transition or a transition to a place but never connects a place to a place or a transition to a transition. Moreover, from a formal point of view, a PN is a bipartite graph: \(PN\ =\ \langle P,T,Pre,Post\rangle;P\) is a set of places. \(T\) is a set of controllable and uncontrollable transitions \(T\ =\ T_{c}\cup T_{u}\)[1] ([PERSON], 1989); the transitions are partitioned into two disjoint subsets: the set of controllable transitions \(T_{c}\), which can be disabled by the controller, while the set of uncontrollable transitions \(T_{u}\) cannot. \(Pre:P\times T\rightarrow\mathbb{N}\) represent a pre-incidence function that specifies weighted arcs from \(P\) to \(T\). \(Post:P\times T\rightarrow\mathbb{N}\) is the post-incidence function that specially weights arcs from \(T\) to \(P\). (\(\mathbb{N}\) is a set of nonnegative integers.) Let the set \(p^{(t)}\) (respectively \({}^{(t)}p\)) be the output transitions (respectively input transitions) of a place \(p\). Indeed, a PN can be expressed by an indexed matrix \(C\) such that \(C\big{(}p_{i},t_{j}\big{)}\ =\ w\big{(}t_{j},p_{i}\big{)}\) if \(t_{j}\in{}^{(t)}p_{i}\) and \(C\big{(}p_{i},t_{j}\big{)}\ =\ -w\big{(}p_{i},t_{j}\big{)}\) if \(t_{j}\in p_{i}^{(t)}\), or else \(0\); where \(w:F\rightarrow\mathbb{N}\) is a valuation function of arcs (the finite set of arcs \(F\subseteq\ (P\times T)\cup(T\times P)\). Moreover, let \(t^{(p)}\)(respectively \({}^{(p)}t\)) be the set of output places (respectively input places) of a transition \(t\). The reachability graph designed from the initial marking \(M_{0}\) is denoted by \(G(N,M_{0})\). The generated markings in the RG are denoted by \(\mathrm{M}\). A transition \(t\) is enabled from a state \(M\in\mathrm{M}\) (denoted by \(M[t>)\) if and only if \(M\geq Pre(.,t)\). An enabled transition \(t\) may fire-yield a new state \(M^{\prime}\) such that \(M^{\prime}\ =\ M+C(.,t)\) this statement can be defined by \(M[t>M^{\prime}\). A new state \(M^{\prime}\) is reachable from a marking \(M\) if a firing sequence \(\sigma\ =\ t_{1}t_{2}\ldots t_{n}\) exists by firing \(\sigma\), and any state \(M^{\prime}\) reachable from the initial state \(M_{0}\) satisfies the following Petri net state equation: \(M^{\prime}=M_{0}+C.\overset{\rightarrow}{\sigma}\), where \(\overset{\rightarrow}{\sigma}:T\rightarrow\mathbb{N}\) is a vector of nonnegative integers called the occurrence of \(t_{i}\) in \(\sigma\). A Petri net is said to be \(k\)-bounded if in each place \(p_{i}\in P\), the number of tokens does not exceed \(k\). A Petri net is reversible, if from any reachable marking \(M^{\prime}\), an enabled sequence \(\sigma\) exists, transforming \(M^{\prime}\) into \(M_{0}\). Finally, a Petri net is said to be live if every transition is live.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 5 Development of 1-D CNN Models
### 1-D CNN Model Using SVD Responses
#### 5.2.2 Training Procedures of SVD Responses  
Figure 19 shows loss values of the CNN model trained by SVD responses. Both the training and validation losses consistently decreased over 40 iterations and converged to nearly zero by the end of training. The CNN model designed with a compact architecture demonstrated efficient convergence after only a few epochs.
## 2 DTPPF Fault Identification Method
### Method for Identifying Overcurrent Faults of Filter Capacitors  
The overcurrent fault in the capacitors can be detected based on the magnitude of the per-unit value of the DTPPF phase current. Different combinations of filter capacitors in the DTPPF result in different allowable currents. From Equations (1) and (4), it can be observed that when a capacitor in the capacitor bank shorts, the reactance of the DTPPF decreases and the phase current increases. Therefore, by measuring the phase current of the DTPPF it is possible to identify whether the filter capacitor has experienced a short-circuit fault.  
The phase voltage \(u_{1}\) and phase current \(i_{1}\) at the DTPPF power supply side (after FFT transformation) can yield the fundamental voltage and current as well as the harmonic voltage and current. Specifically,  
\[\left\{\begin{aligned} u_{1}&=\sqrt{2}\sum\limits_{h=1}^{M }U_{1(h)}\sin(\omega_{(h)}t+\theta_{u(h)})\\ i_{1}&=\sqrt{2}\sum\limits_{h=1}^{M}I_{1(h)}\sin( \omega_{(h)}t+\theta_{i(h)})\end{aligned}\right. \tag{8}\]  
where \(M\) is the highest harmonic order and \(U_{1(h)}\), \(I_{1(h)}\), \(\theta_{u(h)}\), and \(\theta_{i(h)}\) are the \(h\)-th harmonic voltage RMS and current RMS, initial voltage phase angle, and initial current phase angle at the power supply side, respectively.  
The current RMS in the filter capacitor is provided by  
\[I_{\text{IRMS}(x)}=\sqrt{\sum\limits_{h=1}^{M}I_{1(h)}^{2}}. \tag{9}\]  
When the combination of filter capacitors (\(C_{1}\)-\(C_{x}\)) is connected, the per-unit value of the phase current at the power supply side can be obtained:  
\[K_{i(x)}=\frac{I_{\text{IRMS}(x)}}{\sum\limits_{j=1}^{n}I_{CN(x)}(j)} \tag{10}\]  
where \(I_{CN(x)}\) is the rated current of the \(x\)-th filter capacitor and \(n\) is the number of filter capacitors.  
The steps for detecting overcurrent faults of the filter capacitors are as follows:  
Step 1. Identify the overcurrent fault based on the per unit value \(K_{i(x)}\); if the \(K_{i(x)}\) is greater than 1, this indicates an overcurrent fault in the filter capacitors. In this case, proceed to Step 2 to identify the faulty capacitor. Otherwise, there is no overcurrent fault and the identification process ends.  
Step 2. Use the elimination method to determine which capacitor has experienced an overcurrent fault. Check all the active filter capacitors one by one by disconnecting them one at a time one. For each disconnected capacitor, recalculate the per unit value \(K_{i(x)}\) based on Equation (10). If the value is less than or equal to 1, this confirms that the disconnected capacitor has experienced an overcurrent fault.