esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Factors for a Successful Telemedicine Solution for Cardiovascular Diseases: A Systematic Review
## 4 Discussion
### Strengths and Limitations
There is a need to consider how telemonitoring could either reduce or exacerbate health disparities, particularly for populations with limited access to technological resources. Understanding these disparities is crucial for evaluating the true public health impact of telemonitoring interventions and ensuring that benefits are equally distributed across different socio-economic groups. Another limitation of this review is the lack of detailed information on the technical reliability and accuracy of the telemonitoring devices used in the studies. Only three of the 22 reviewed studies [44; 46; 50] provided specifics on the performance and consistency of these devices. Without comprehensive data on device reliability, it is challenging to assess the overall trustworthiness of the telemonitoring systems and their impact on the quality of care. Future research should include thorough device accuracy and reliability evaluations to ensure that telemonitoring systems deliver dependable and high-quality data. Moreover, while many studies focused on clinical outcomes, there was a limited exploration of the psychological impacts of telemonitoring. Only two of the 22 studies [35; 40] considered aspects such as anxiety and emotional well-being. Understanding patients' psychological responses, whether telemonitoring provides a sense of control or induces anxiety, is crucial for a holistic evaluation of its effectiveness. The absence of these aspects in most studies highlights a significant gap. Future research should address these psychological factors to provide a more complete picture of how telemonitoring affects overall patient well-being. Publication bias also presents a potential limitation of this review. While we employed a comprehensive literature search strategy across major databases (PubMed, Scopus, and Web of Science) to include a wide range of studies, the possibility remains that studies with negative or null results may be underrepresented. The tendency to publish studies with positive findings could skew the overall assessment of telemonitoring's effectiveness. To mitigate this, future reviews should consider strategies like searching grey literature and unpublished studies to ensure a more balanced representation of evidence. Finally, it's important to consider non-key factors that were anticipated to play a significant role in telemonitoring's success but were found to be less critical based on the literature reviewed. For instance, while the ease of use of mobile applications and the frequency of data transmission were initially expected to have a major impact, they did not consistently show a significant effect on patient outcomes. This suggests that while these factors might be important for patient engagement, they are not as crucial as initially thought for the overall success of telemonitoring interventions. Understanding which factors are truly key versus those that are less influential will help refine future telemonitoring strategies and research.
## 3 Network Model and Protocol Overview  
### Network Model  
We considered a wireless sensor network that consists of randomly and roughly deployed sensor nodes over a finite, two-dimensional planar region. We assume in this work that each node can obtain its own location information by GPS [25] or localization schemes [26; 27]. By periodically exchanging beacon messages [28; 29], every node can determine its one-hop neighbor nodes, and obtain their locations and data relay speeds. When a source detects an event, it generates data with the desired time deadline \(T_{\mathit{setdeadline}}\) of the event. The desired time deadline of an event depends on the event type. In order to deliver data from a source to a static sink within a desired time deadline \(T_{\mathit{setdeadline}}\), the existing protocols [4; 5; 6] exploit a data delivery speed \(S_{\mathit{speed}}\) calculated by the deadline and the distance _d(source, sink)_ between the source and the sink as follows:  
\[S_{\mathit{speed}}=\frac{d(\mathit{source},\mathit{sink})}{T_{\mathit{setdeadline }}}. \tag{1}\]  
Thus, when the protocols disseminate data from the source to the sink, every immediate node elects a next-hop node that is one of the one-hop neighbor nodes closer to the sink than itself with a relay speed larger than the data delivery speed [4; 5]. However, if the sink moves, the distance _d(source, sink)_ between the source and the mobile sink changes (i.e., increases or decreases) due to the movement of the mobile sink, so that the data delivery speed \(S_{\mathit{speed}}\) is continuously altered due to continuous distance changes. Unfortunately, we cannot also determine the static location of a mobile sink due to its continuous movement. Thus, as the first challenging issue, the static location of a mobile sink must be decided in order to obtain the distance _d(source, sink)_ and as a result, to calculate the data delivery speed \(S_{\mathit{speed}}\) by using the distance. To do this, we consider the expected area of a mobile sink with its moving direction and speed information and determine its virtual static location from the expected area. We address our method for solving the first challenging issue in Section 4.1. However, since the virtual static location of the mobile sink is just a location within its expected area, it is not its real location. Thus, as the second challenging issue, it needs to deliver data packets to the real location of the mobile sink in real-time. To do this, we consider flooding-based data forwarding and flooding time within the expected area of the mobile sink in order to support real-time data delivery to the mobile sink. We address our method for solving the second challenging issue in Section 4.2.
## 3 Results
### Implication of the Expanded S-Curve Model  
According to the correlation analysis of the increase in DWPC and GDP per capita in Sections 2.2 and 3.1, we can conclude that the expanded S-curve can be calibrated with three key points, which are the takeoff point, the turning point, and the zero-growth point. Meanwhile, the long-term DWPC trends with GDP per capital were also divided into four stages according to the growth rate transition, including slow growth, accelerated growth, decelerated growth, and zero/negative growth. Figure 5 shows the key points and stages of the S-curve, and the points for each country are summarized in Table 2.  
The takeoff point is the starting point for the accelerated growth in DWPC in the range of USD $1500-5000, implying an adjustment of agriculture society to industrial society with the economic boom. Before this takeoff point, the DWPC was in the slow-growth section. The takeoff points for developed economics, such as the UK, the USA, France, and South Korea, occurred after USD 3000, whereas for developing economies, such as India and China, it occurred between USD 1500 and USD 2500. The turning point made an adjustment period of an industrial structure in the process of industrialization, with a GDP of USD 10,000-USD 13,000 for the researched countries without diversity. After the turning point, the growth rate in the DWPC transited from accelerated growth to decelerated growth until the zero-growth point. The zero-growth points were concentrated around USD 17,000-USD 22,000, which indicates that the DWPC entered a zero-growth or slow-decline stage. This is also consistent with the post-industrial stage [20], when the living standards were improved substantially to promote the technical progress of water-saving facilities.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Country** & \begin{tabular}{c} **Takeoff Points** \\ **GDP per Capita** \\ \end{tabular} & \
\begin{tabular}{c} **Turning Points** \\ **GDP per Capita** \\ \end{tabular} &
\begin{tabular}{c} **Zero-Growth Points** \\ **GDP per Capita** \\ \end{tabular} \\ \hline US & 4000–4200 & 10,100–11,500 & 20,000–22,000 \\ UK & 4500–4800 & 12,000–13,000 & 17,000–18,000 \\ France & 3500–3800 & 12,000–13,000 & 19,000–20,500 \\ Japan & 3000–3500 & 10,000–11,000 & 22,000–23,000 \\ China & 2000–2500 & 11,000–12,000 & – \\ Germany & 3200–3800 & 11,000–12,000 & 18,000–19,000 \\ India & 1500–1800 & – & – \\ Indonesia & 2300–2500 & – & – \\ Canada & – & 19,000–21,000 \\ South Korea & 3000–4000 & 10,000–12,000 & 18,000–20,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key points of expanded S-curve for each country (1990 GK in USD).
# Mathematical Modelling of the Effects of Plasma Treatment on the Diffusivity of Biofilm
## 2 Materials and Methods
### Verification of the Mathematical Model  
In order to verify the mathematical model, the value of \(log_{10}\frac{\Phi}{\Phi_{0}}\) for each plasma treatment time was calculated (Table 1) and these values were plotted against the CHX treatment time (Figure 5), and the diffusion lag time was then found by a linear regression according to Equation (26). The lag time was then obtained from the regression line according to the calculation of \(D_{e}\) from Equation (25).  
Figure 4: Typical emission spectrum of He plasma jet using 1 kHz pulse frequency and 4 μs pulse width.  
From the graph in Figure 5, the x-intercept, which is \(t_{lag}\), can be calculated as,  
\[t_{lag}=\frac{0.3541}{0.1045}=3.3\;\mathrm{s}\]  
From Equation (25), the effective diffusivity of CHX is given by the relationship \(t_{lag}=\frac{32}{\pi^{4}D_{e}}\), where \(\delta\) is the thickness of biofilm or 50 \(\mathrm{\SIUnitSymbolMicro m}\) as measured by the confocal microscope (TCS SP5, Leica Microsystems). Therefore, the effective diffusivity of CHX after the plasma treatment is,  
\[D_{e}=2.49\times 10^{-6}\;\mathrm{cm}^{2}/\mathrm{s}\]  
As we can see, this value of \(D_{e}\) is significantly less than the previously calculated diffusivity of CHX at 20 \({}^{\circ}\)C (\(D_{AB}=3.77\times 10^{-6}\mathrm{cm}^{2}/\mathrm{s}\)), and we can conclude that the diffusion path through the biofilm is not completely open. Assuming that the tortuosity is \(\sim\frac{1}{\epsilon}\), then from Equation (5), we have,  
\[\begin{array}{ll}\epsilon&\cong\sqrt{\frac{D_{e}}{D_{AB}}}\times 100\%\\ &&=81.26\%\end{array} \tag{27}\]  
These results support our hypothesis that the plasma decontaminates the biofilm to some extent and disrupts it, which opens up the structure (\(\sim\)81.26% porous) to allow the CHX to get in and completely sterilize the biofilm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plasma Treatment Time (s)** & **CHX Treatment Time (s)** & \(\mathbf{log_{10}}\frac{\Phi}{\Phi_{\circ}}\) \\ \hline
15 & 5 & \(-0.10867783\) \\  & 15 & \(-1.29736253\) \\
30 & 5 & \(-0.37619369\) \\  & 15 & \(-1.12858295\) \\
60 & 5 & \(-0.01996643\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated values of \(log_{10}\frac{\Phi}{\Phi_{\circ}}\) for different chlorhexidine (CHX) treatment times after plasma treatment.  
We can also estimate the biofilm porosity using [PERSON]'s equation Fournier (4 th edition), which treats the biofilm as impermeable spheres.  
\[\frac{D_{e}}{D_{AB}}=\frac{2\varepsilon}{3-\varepsilon} \tag{28}\]  
Using the values of \(D_{e}=2.49\times 10^{-6}\) cm\({}^{2}\)/s and \(D_{AB}=3.77\times 10^{-6}\)cm\({}^{2}\)/s, we get \(\varepsilon=74.4\%\), which is not significantly different than the result obtained in Equation (27).
# Wide Area Key Distribution Network Based on a Quantum Key Distribution System
## 3 Public-XOR-Key Scheme for QKD Network
### The Public-XOR-Key Scheme with Chain Structure  
First, we introduce the chain structure of two endpoints. In the scheme, each relay node has only two connections with the upstream node and the downstream node. Assume the whole chain network contains \(n+1\) nodes. Each node executes QKD protocol with the neighbor nodes. The \(n+1\) nodes make up \(n\) point-to-point QKD systems. There are two endpoints, \(n-1\) relay nodes and \(n\) pairs of QKD keys \(Qk_{i}\) for \(i=1,2,...,n\).  
Protocol 2: public-XOR-key scheme for the relay QKD system:  
1. _Each node executes QKD protocol with the neighbor nodes, which generates \(n\) pairs of QKD keys \(Qk_{i}\) for \(i=1,2,...,n\) as shown in Figure 2._
2. _Each relay node does the XOR operation with the QKD keys, which are shared with the upstream relay node and the downstream relay node, to obtain the XOR keys. For relay node \(i\), its XOR key is \(a_{i}=Qk_{i}\oplus Qk_{i+1}\), where \(Qk_{i}\) is the QKD key shared by relay node \(i-1\) and relay node \(i\), \(Qk_{i=1}\) is the QKD key shared by relay node \(i\) and relay node \(i+1\). Then each relay node immediately dismisses the QKD keys and publishes the XOR keys. We assume that the process of dismissing QKD keys is secure._
3. _[PERSON] calculates the final key with all of the public-XOR-key and his QKD key \(Qk_{n}\). That is, \(\bigoplus_{i=1}^{n-1}a_{i}\oplus Qk_{n}=Qk_{1}\). Then [PERSON] and [PERSON] share the common key \(Qk_{1}\). [PERSON] can transmit the message secretly to [PERSON] with the key \(Qk_{1}\) latter._  
**Remark 1**: _There is a security assumption in the protocol that the process of dismissing QKD keys is secure. For the practical realization of the assumption, we give a kind of method as follows. Use the physical isolation to protect XOR operation and the process of dismissing the original QKD keys from the outside world to access its physical devices, so that even if the relay node is compromised, the adversary can not get the original key._  
Correctness: [PERSON] calculates the final key with \(a_{i}\) and \(Qk_{n}\), where \(a_{i}=Qk_{i}\oplus Qk_{i+1}\). Then he gets  
\[\bigoplus_{i=1}^{n-1}a_{i}\oplus Qk_{n}=(Qk_{1}\oplus Qk_{2})\oplus(Qk_{2} \oplus Qk_{3})\oplus...\oplus(Qk_{n-1}\oplus Qk_{n})\oplus Qk_{n}=Qk_{1}, \tag{9}\]  
which ensures the correctness of the key distribution scheme.
# Methods of Atmospheric Coherence Length Measurement
## 2 Retrieval of \(r_{0}\) Based on \(C_{n}^{2}\)
### Temperature Structure Measurement Method  
The principle of the temperature structure measurement method for measuring the atmospheric coherence length is that the transformation of temperature is measured using a temperature pulsation meter, and then the relationship between the temperature structure function and the atmospheric refractive index structure function is applied to find \(C_{n}^{2}\)[12]. According to the [PERSON] theory of atmospheric turbulence, the structure function of the temperature fluctuations is subject to the two-thirds universal law in the inertial range under the assumption of statistical homogeneity and isotropy  
\[\begin{split} D_{T}(|r_{1}-r_{2}|)&=\langle\ [T(r_{1})-T(r_{2})]^{2}\ \rangle,\\ D_{T}(|r_{1}-r_{2}|)&=C_{T}^{2}|r_{1}-r_{2}|^{2/3 },l_{0}<<R<<L_{0},\end{split} \tag{1}\]where \(T(r_{1})\) and \(T(r_{2})\) denote the temperatures of two spatial points with \(r_{1}\) and \(r_{2}\) away from the reference point, respectively; \(C_{T}^{2}\) is the temperature structure parameter; and \(\langle\cdot\rangle\) indicates the ensemble average. Accordingly, \(C_{T}^{2}\) can be expressed by  
\[C_{T}^{2}=\frac{\left\langle\left|T(r_{1})-T(r_{2})\right|^{2}\right\rangle}{ \left|r_{1}-r_{2}\right|^{2/3}}. \tag{2}\]  
Further, the atmospheric refractive index structure parameter \(C_{n}^{2}\) is related to \(C_{T}^{2}\) as  
\[C_{n}^{2}=(79\times 10^{-6}\frac{P}{T^{2}})^{2}C_{T}^{2}, \tag{3}\]  
where \(P\) is the atmospheric pressure in millibar; and \(T\) is the atmospheric ambient temperature in K. Therefore, \(C_{n}^{2}\) can be calculated by substituting Equation (2) into Equation (3). A schematic sketch of the traditional temperature structure measurement method is shown in Figure 1. The experimental setup contains four resistors, two standard resistors \(R\) and two platinum wire resistors, \(R\)s\({}_{1}\) and \(R\)s\({}_{2}\), which together form the Wheatstone bridge. The two standard resistors are allocated under the two platinum wire resistors \(R\)s\({}_{1}\) and \(R\)s\({}_{2}\), and \(R\)s\({}_{1}\) and \(R\)s\({}_{2}\) are located on the probes of a platinum wire thermometer separated by the length of \(r\). The platinum wire thermometer measures the values of \(R\)s\({}_{1}\) and \(R\)s\({}_{2}\), which are eventually converted into \(T(r_{1})\) and \(T(r_{2})\). \(C_{T}^{2}\) can be calculated by merely employing Equation (2). The temperature structure measurement method is able to provide the turbulence profile along the vertical direction, so it can also be adopted to reveal the relevant physical processes involved in the evolution of atmospheric turbulence. In practice, the sensor in Figure 1 is oriented following the streamwise.
## 4 Simplify Problem by Using Linear Equivalence
For instance, Figure 5 illustrated the specific case of 2 redundant actuators  
Figure 4: Specific case of a certain 1-redundant actuator-planar CDPM when general coordinates of moving platform are presented in the wrench-closure workspace. planar CDPM when a general coordinate is presented in wrench-closure workspace and linear equivalence was applied to the wrench matrix generated by the considered general coordinate. In the special case, when \(n\,-\,m\,=\,1\), the sufficient condition for the general coordinate belonging to WCW is simplified as (15). \[\mathbf{w}_{n}\in\mathbb{R}_{-}^{m} \tag{15}\]  
In the general case, when \(n\,-\,m\,>\,1\), we cannot determine whether \(x\) does or does not belong to WCW only based on the signs of elements in \(\boldsymbol{\omega}_{B}\). However, the linear equivalence nonnegative span set of \(\boldsymbol{\omega}\) simplifies the condition (10). Since \(\boldsymbol{\omega}_{A}\) already non-negatively spans \(\boldsymbol{R}^{m\,+}\), in Equation (14), it is equivalent to \(\boldsymbol{H}_{A}>0\), \(\forall\,\,\boldsymbol{t}_{A}>0\), the sufficient condition for the general coordinate \(x\) belongs to WCW is defined by (16). \[\exists\mathbf{t}_{B}>0:\mathbf{M}^{-1}\mathbf{H}\mathbf{t}_{B}<0 \tag{16}\]  
For a more compact system of inequalities, we can rewrite (16) as (17). \[\exists\mathbf{t}_{B}:\left[ \begin{array}{c}-\mathbf{M}^{-1}\mathbf{H}\\ \mathbf{I}_{n\,-m}\end{array} \right]\mathbf{t}_{B}>0 \tag{17}\]  
**Theorem 3**. : _A general coordinate \(x\) belongs to the wrench-closure workspace of a certain CDPM if (8) and (17) are satisfied._  
In the next section, we will introduce the procedure in order to determine whether (17) does or does not have solution.
# DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments
## 4 Experiments  
### Experiment Based on the Improved Yolov5s  
To evaluate the performance of the improved Yolov5s object detection network, we used the VOC2007 + VOC2012 dataset, which includes 20 object categories split into training and validation sets in a 1:1 ratio. Data augmentation techniques such as random horizontal flipping, color jittering, and random cropping were applied to enhance the robustness.  
Figure 7: Results of dynamic feature exclusion across four TUM RGB-D sequences: fr3_sitting_static, fr3_walking_halfsphere, fr3_walking_xyz, and fr3_walking_static. For each sequence, subfigures (**a**–**e**) represent five moments in time. The top row shows ORB features used by ORB-SLAM3, often affected by dynamic objects. The bottom row shows results from our method, where dynamic features are effectively removed, preserving only static keypoints for improved localization accuracy and robustness.  
In terms of network architecture, we reduced the model parameters by incorporating parameter sharing in the CBAM module and optimizing the C3 module, which also improved feature extraction. The improved Yolov5s reduced the parameters by 12.16% and increased the inference speed. During training, we used the Adam optimizer with an initial learning rate of 0.001, along with a cosine annealing learning rate scheduler, training for 150 epochs with early stopping to prevent overfitting.  
The experiments were conducted on an NVIDIA RTX 3070 Ti GPU. As shown in Table 1, the results showed a 13.01% increase in inference speed and a 13.35% improvement in detection accuracy for the improved Yolov5s, demonstrating an effective balance between efficiency and accuracy.
## 3 The Concept of Modulated Acquisition of Projected Patterns  
The performance of the proposed system mainly depends on the ability to capture a sharp and accurate image of the projected pattern. The better the contrast between the projected pattern and background; less time will be required to detect the projected pattern in the snapshot. Missing parts of the pattern or excess lines not belonging to the projected pattern can lead to erroneous results in the range map.  
The problem of distinguishing the projected pattern from the background and interfering projections at the sensor's view area appears to be very similar to the _general receiving problem_[27] in multiuser communication systems, where multiple transmitter-receiver pairs communicate simultaneously over the same channel. Each receiver in such system should be able to filter out the data addressed to it individually from the observed signal containing data sent to other users.  
In the _direct sequence spread spectrum_ (DSSS) scheme, the problem of user separation at the receiver side is solved by multiplying the data stream transmitted to a certain user by the pseudo-random periodic sequence of {-1;1} known as _signature_ or _spreading code_. The frequency of this periodic signal is much higher than the frequency of the original data stream. Thus, the energy of the original signal becomes spread across the whole band of transmitted noise-like signal; hence the name of this technique is _spectrum spreading._ To reconstruct the original data, the receiver multiplies the observed noise-like signal by the personal signature, which yields in raising the level of original data constituent above the noise and the interfering signals. This effect of _signal-to-noise ratio_ (SNR) improvement is known as _processing gain._ Signals addressed to other users are modulated with different signatures; they produce no processing gain at the given receiver and can be easily suppressed along with the channel noise. This is the essence of _code division multiple access_ (CDMA) communications [28]. As shown in recent research results [29, 30, 31], the performance (_user capacity, bit error rate, SNR_) of CDMA systems is mainly determined by the correlation properties of spreading codes.  
In the following sections we show that a similar effect of \"selective perception\" is also achievable with imaging. By applying the technique of _direct sequence modulation_ to sequential image acquisition while illuminating the scene with the source of light modulated by the same sequence, we can capture _only_ the reflection of the modulated illumination pattern without the influence of other sources of illumination. The captured reflection carries the information about the spatial distortion of the projected pattern needed for range measurement. The interfering projections as well as general (background) illumination are eliminated from the acquired image due to the nature of CDMA system.