esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitation  
There are a few of limitations in this study. The sample size was small, and thus, this study is considered as a pilot one. The participants covered the age range only between 42 and 78, and the elderly above 80 years old were not included. It is recognized that this study did not measure the muscle strength directly for muscle groups, and various muscles other than the primary agonist(s) may contribute to a specific movement; thus, the force readings include the total effect of involved muscles on the posture. In the future, a new study should consider a unique posture and constraints for a single group of muscles so that FS could measure specific strength for a group of muscles.  
In summary, it was found that the TCC practitioners showed higher force than NTCC in the knee extensor muscles. On the other hand, NTCC practitioners showed higher forces than TCC, with significant differences in the heel force in hip extension, knee flexion and ankle plantar flexion. A major reason that could explain the discrepancy is that the TCC practice emphasizes a crouch posture, which relies on the hip flexion, knee extension and ankle dorsiflexion rather than on the hip extension, knee flexion and ankle plantar flexion. So, there is no significant effect of TCC practice on the hand pushing forces at the shoulder or elbow of 90\({}^{\circ}\). Further studies could be designed to explore muscle electronic activities, i.e., electromyograph during TCC movement.  
This study introduces a novel measurement device and its application in rehabilitation. From this study, a few points were obtained: (1) This device is flexible and portable in measurement setting-up; (2) this device helped to measure the strengths of the limbs and joints and some of the measurements are firstly done and reported in this research field; (3) this applied study helped to understand the TCC exercise in more detail than previous studies from a new angle; and (4) the method provided by this study could be applied in the assessment of other exercises in rehabilitation.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Image Pre-Processing  
Owing to the complex operating environment of the metro railway system, the rail images collected from the field are susceptible to noise and uneven illumination, which would degrade the image quality, affect the ultimate rail corrugation identification precision and impact the period estimation results of the rail surface images. To alleviate this problem and improve the image quality, a two-stage pre-processing method by Bilateral Filter (BF) [27] and Rolling Guidance Filter [28] with Parameter Adaptive Gamma Function [29] (RGF-PAGF) is considered, which are reviewed in the following.
## 3 Use Case Description and Problem Statement
### Opportunistic Sharing of GSO/NGSO Satellite and Terrestrial Systems in Uplink Ka-Band  
This use case deals with GSO and NGSO satellite systems and terrestrial systems operating in the uplink Ka-band (27.5-29.5 GHz), as shown in Figure 1b. Similar to the downlink case, the terrestrial systems represent 5G FWA and MW links, and it is assumed that they use FDMA for their transmission. Furthermore, the assumption of a CC between different entities (satellites, ESIMs, GSs, and terrestrial networks) is applied here. Because of the spectrum sharing and possible spatial alignment of the ESIMs, it becomes possible that GSO, NGSO, and the terrestrial network are impacted by the transmission of the ESIMs. For the uplink case, we consider the following interference components:  
* NGSO-ES towards GSO-SAT;
* GSO-ES towards NGSO-SAT;
* GSO-ES towards terrestrial network;
* NGSO-ES towards terrestrial network.  
The goal here is to protect incumbent terrestrial networks, manage the interference towards GSO and NGSO satellites and the terrestrial network coming from the ESIMs, and improve the performance (spectrum utilization, throughput, PER, latency, etc.) by applying DSS techniques.
# Mobility-Aware Hybrid Flow Rule Cache Scheme in Software-Defined Access Networks
## 4 Proposed Hybrid Flow Rule Cache Scheme
### Problem Formulation  
Where \(r_{i}^{p,j}\) denotes the \(j\)th proactive flow rule among the total \(M_{i}^{t}\) mobile flows into the \(i\)th AD at time \(t\), the ILP problem for the hybrid flow rule cache scheme is formulated as follows:  
\[\text{Minimize }\sum_{i=1}^{N}\sum_{j=1}^{M_{i}^{t}}r_{i}^{p,j} \tag{6}\]  
\[\text{subject to }R_{i}^{t}\leq R_{i}^{max} \tag{7}\]  
\[d_{i}^{t}\leq d_{i}^{th} \tag{8}\]  
\[r_{i}^{p,j}=1\ ifD_{i}^{t}\geq d_{j}^{th} \tag{9}\]  
\[r_{i}^{p,j}=0\ ifD_{i}^{t}<d_{j}^{th} \tag{10}\]  
Since the reactive flow rules should be installed because they are from actual moved flows after handover into each AD, the proposed scheme aims to minimize the proactive flow rules (i.e., predicted flow rules) as shown in Equation (6). Equation (7) denotes that the total number of flow rules at time \(t\) in the \(i\)th AD is always less than or equal to its flow rule capacity constraint. In Equation (7), the total number of flow rules includes static, mobile (reactively and proactively added rules), and expired flow rules as described in Equation (1). Equation (8) represents that the response delay from the flow's perspective should be less than or equal to its response delay constraint, and \(d_{j}^{t}\) can be expressed according to the existence of the proactively cached rule as follows:  
\[d_{j}^{t}=\left\{\begin{array}{l}0\ if\ r_{i}^{p,j}=1\\ D_{i}^{t}\ if\ r_{i}^{p,j}=0\end{array}\right.. \tag{11}\]  
Equations (9) and (10) denote that the controller proactively caches the rule by comparing its delay performance with the flow's delay constraint.  
Since the above problem needs delay estimation for each flow, the time complexity of the proposed scheme is \(O(N\times w\times M^{2})\), where \(w\) is the number of weights.  
Figure 3: The structure of the LSTM network.
## 3 Performance Evaluation  
The proposed link-cost function is tested in both simulated and experimental environments. In order to gauge the effect of the energy balancing cost function, it is compared to a reference system using a conventional link cost function, where the link cost is the required power to meet a link with the desired \(RSSI_{T}\) (\(M=0\) in Equation (3)). NL is evaluated by measuring the time it takes any node's accumulated normalized energy to cross an arbitrary threshold. The threshold represents the amount of energy stored in a battery. In all simulations and experiments the network was comprised of an AP and _End Devices_ (EDs) acting as nodes. Detailed pseudo-code for the routing algorithm is depicted in Algorithm 1. Similarly, code for calculation of accumulated energy is depicted in Algorithm 2.  
```
1:for i = 1 to # of Nodes do
2:node,visited \(\leftarrow\) 0
3:node,distance \(\leftarrow\) \(\infty\)
4:node,previous \(\leftarrow\) node\({}_{\mathrm{i}}\)
5:end for
6:node\({}_{\mathrm{AP}}\).distance \(\leftarrow\) 0
7:energy\({}_{\mathrm{min}}=\) min(node.energy)
8:while unvisited nodes available do
9:nodes\({}_{\mathrm{source}}\leftarrow\) node with smallest distance
10:for i = 1 to # of Links do
11:if link\({}_{\mathrm{i}}\).source = node\({}_{\mathrm{source}}\) then
12:node\({}_{\mathrm{dest}}\leftarrow\) link\({}_{\mathrm{i}}\).destination
13:if node\({}_{\mathrm{dest}}\).visited = 0 then
14:cost \(\leftarrow\) link\({}_{\mathrm{i}}\).power * (1 + (node\({}_{\mathrm{dest}}\).energy/energy\({}_{\mathrm{min}}\))\({}^{\mathrm{M}}\))/2
15:newdistance \(\leftarrow\) node\({}_{\mathrm{source}}\).distance + cost
16:if distance \(<\) node\({}_{\mathrm{dest}}\).distance then
17:node\({}_{\mathrm{dest}}\).distance \(\leftarrow\) newdistance
18:node\({}_{\mathrm{dest}}\).previous \(\leftarrow\) node\({}_{\mathrm{source}}\)
19:endif
20:endif
21:endif
22:end for
23:nodes\({}_{\mathrm{source}}\).visited \(\leftarrow\) 1
24:end while
```  
**Algorithm 1** Dijkstra's Algorithm with proposed cost function
# Exploration of Multi-Scale Reconstruction Framework in Dam Deformation Prediction
## 2 Materials and Methods
### Research and Design
#### 2.7.2 Determination of K Value Based on VMD  
Since the choice of decomposition modulus K will greatly affect the decomposition effect of VMD, it is necessary to determine the decomposition modulus K before proceeding  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measurement Points** & **Total** & **Training Set** & **Validation Set** & **Prediction Set** \\ \hline EX5(739) & 739 & 539 & 100 & 100 \\ EX2(417) & 417 & 317 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Division of measuring point data set.  
Figure 5: Dam layout plan.  
Figure 6: Deformation monitoring data graph(**(a)** EX5, **(b)** EX2).  
to decompose the data. In this section, EX5 is taken as an example, the data are decomposed nine times using the VMD model, and the instantaneous frequency mean value change line chart of the component under different K values is calculated, as shown in Figure 7. It can be seen from the figure that when K = 6, the instantaneous frequency mean change of the two components is significantly reduced. When K > 6, this trend is more obvious, indicating that there is modal confusion in the decomposition results at this time, which will make the sub-sequences obtained by decomposition not able to express the dam deformation characteristics well. Therefore, K = 5 is selected as the best decomposition modulus, and the measurement point of EX2 is selected as K = 6 according to the above method; the specific decomposition results of EX5 and EX2 are shown in Figure 8.  
It can be seen from the decomposition results that the frequency characteristics of all components are obvious, and there are no undesirable phenomena such as modal confusion. In the decomposition results of the corresponding deformation data of EX5, the frequency of IMF1-IMF3 is lower, IMF1 is basically consistent with the dam deformation trend, and the IMF2-IMF3 component process line is relatively smooth; IMF4-IMF5 has obvious periodicity, and ER has high volatility. Therefore, IMF1-IMF3 will be used as the input of RF; IMF4-IMF5 and ER will be the input of LSTM. Similarly, in the decomposition result of the corresponding deformation data of EX2, IMF1-IMF4 are used as the input of RF, and IMF5-IMF6 and ER are used as the input of LSTM.
# A Telepresence System for Therapist-in-the-Loop Training for Elbow Joint Rehabilitation
## 4 Experiments and Results
### Performance Evaluation for the Therapist-Haptic Device Interaction  
Real-time feedback with high transparency can be provided to make the therapist aware of the forces being applied to the patient's forearm through the haptic device. The performance when the therapist receives the force feedback should also be evaluated and stable feedback is a precondition for the high transparency. The sEMG that reflects the electrical activity produced by skeletal muscles is a convincing and direct method to evaluate the feedback.  
The experimental process is illustrated in Figure 8, where the therapist was requested to do the elbow flexion and extension movement following the order. Also, a similar visual model without unnecessary parts, that is, just one beam which corresponds to the elbow motion was developed for reminding the volunteer of their position. Meanwhile, the volunteer should resist the force exerted by the haptic device and keep a steady speed. Only signals from biceps were recorded, and the method is similar to that introduced in Section 4.1. Here, the resulting force that is exerted by the haptic device is  
Figure 6: sEMG signal and interaction force during forearm flexion for case one (the motor did not provide the power to the robot frame).  
Figure 7: sEMG signal and interaction force during forearm flexion for case two (the motor provided the power to the robot frame while an impedance controller was applied).  
calculated with Equations (12) and (13), and only the angular position error was used with different \(k_{\theta}\) as given in Table 1.  
The experimental result for three trials with different \(k_{\theta}\) is shown in Figure 9. It can be seen that the amplitude of the value was increased although a relatively small force was exerted by the haptic device.
## 3 Methods
### Empirical Mode Decomposition  
The Empirical Mode Decomposition (EMD), introduced by [PERSON] et al. [45], is a method designed to decompose a time series \(x(t)\) into a set of components known as Intrinsic Mode Functions (IMFs). These IMFs must fulfill two criteria: (1) the count of local extrema and zero-crossings can differ by no more than one; and (2) at every point in time, the mean of the upper and lower envelopes--constructed from the local maxima and minima, respectively--is zero. While Empirical Mode Decomposition (EMD) was adopted in this study due to its adaptive and data-driven nature, it is worth noting that other signal decomposition techniques, such as the Wavelet Transform (WT) [46] and Singular Spectrum Analysis (SSA) [47], are also commonly employed. EMD offers the advantage of not requiring a priori selection of basis functions or decomposition levels (as in WT), or the number of components (as in SSA), making it particularly suitable for non-linear and non-stationary time series such as those analyzed in this work.  
To decompose a series into the IMFs, the following sifting algorithm is employed [48]:  
1. Detect all minima and maxima points of the original signal \(x(t)\).
2. Form the lower envelope \(E_{\text{lower}}(t)\) and upper envelope \(E_{\text{upper}}(t)\) by using the cubic spline interpolation technique for minima and maxima data points.
3. Calculate the mean envelope \(E_{\text{mean}}(t)=\frac{E_{\text{lower}}(t)+E_{\text{upper}}(t)}{2}\).
4. Compute the signal \(s(t)=x(t)-E_{\text{mean}}(t)\).
5. Verify the two conditions for \(s(t)\) to determine if it qualifies to be an IMF.
6. If \(s(t)\) qualifies to be an IMF, then set IMF\({}_{n}(t)=s(t)\) and repeat the procedure from step (1) for the residual signal \(\text{res}(t)=x(t)-s(t)\); otherwise, replace \(x(t)\) with \(s(t)\) and repeat the procedure.
7. Repeat steps (1) to (6) until a monotonic residual signal \(\text{res}(t)\) is obtained.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 2 Disclose the Valuable Information
### Transformations
#### 2.1.1 Wavelet Transform (WT)  
A comprehensive note on wavelet transform (WT) is provided in [13]. WT is a widely used feature extraction approach in different fault diagnosis systems. Practically, to obtain characteristics of voltage and current signals in multiple frequency bands, discrete wavelet transform (DWT) is used than continuous WT (CWT). Thus during DWT implementations, it is important to select which mother wavelet (MW) and decomposition level to be used before actually creating the features. [PERSON] et. al. provided a detailed comparison of various mother wavelets for fault detection and classification [14] and recommended Bior3.9, Db10, Meyer, Sym8, and Coif5 mother wavelets for fault detection. It is to be noted that different sampling rates were adopted but frequency bounds are important than the decomposition level itself. Coefficients are selected in detail levels as a feature in [15; 16; 17]. [PERSON] et al. adopted a Mayer wavelet with frequency bands of 1-2 kHz [15] while the Db2 wavelet is selected in [16; 17] with frequency bands of 4-8 kHz. Summations of absolute coefficients of detail levels are used to extract features in [18; 19; 20; 21]. The coefficients in 97-195 Hz or 99-199 Hz frequency bands are assumed in [18; 19; 20] and three Daubchies wavelets, namely Db1, Db4, and Db8 are selected.  
Coefficients can be used in another way by calculating the energies of the detail levels. The energy of the 3540-7680 Hz frequency band is chosen in [22] while the frequency band of 1.5625-3.125 kHz is used in [23]. Wavelet energy entropy (WEE) is introduced in [24] based on the wavelet energies and used in [25]. Moreover, wavelet singular entropy (WSE) is introduced in [26], which is a combination of singular value decomposition and Shanon entropy, to produce the features. Discussed literature proved the success of the DWT methods for fault detection and classification with a variety of mother wavelets and coefficients adopted in both low and high-frequency detail levels.
For this comparison, each result is statistically evaluated according to several accuracy metrics and ground truth points (GTPs). To achieve the second objective, the study classifies the most common tree species in a selected region within the metropolitan city of Naples, Italy. Due to its high settlement density and relatively low amount of urban green spaces, Naples depends on the many services provided by urban trees, and has been carrying out several initiatives over the past few years to improve and manage their urban tree cover. Tree inventories are an important tool in this regard, but take up a significant amount of the public authority's resources, which could be used more efficiently with improved knowledge of trees' spatial distribution. In order to carry out its objectives, the study is laid out as follows: Section 1 contains the preceding introduction describing relevant background information and identifying research objectives and contributions; Section 2 describes the materials and methods employed, beginning with a brief presentation of the study area, followed by a detailed explanation of the data preparation, the pansharpening method, and the classification procedure, including the related selection of ground truths (GTs) and classes; Section 3 contains the results, presenting the accuracy values obtained with the original image (GSD 30 m) and the enhanced image (GDS 5 m); Section 4 discusses key findings in the context of the literature and concludes with the investigation's main contributions, their implications for research and for practice, their limitations, and some suggestions for future directions. Finally, this study makes the following contributions:* It introduces a novel application using pansharpening techniques to improve classification performance by enhancing the spatial resolution of PRISMA hyperspectral data and demonstrates its efficiency with a case study example;
* It demonstrates a practical real-world use of pansharpened image classification for urban tree monitoring, which could help municipalities that are currently struggling to meet international and national policy demands.
## 3 Mobile Communications Background
### The 5G Development Timeline  
The first comprehensive release of the 3 GPP's 5G specifications was Release 15 (Rel. 15), which was frozen in two stages: the Non-Standalone variant's specifications were released in December 2018, and the Standalone variant's specifications were released in June 2019 [51]. These standards serve as the foundation for all network deployments and devices that are now being introduced. They have all the essential features required to operate an eMBB service using the newest 5G NR radio technology.  
Release 15 of the 5G system contains the foundation for URLLC, particularly in terms of support for low latency [52]. In addition, the machine-type communications technologies, LTE-M, and Narrow Band IoT (NB-IoT), which 3 GPP established in Release 13, are added to NR for the mMTC component. These technologies provide unrivaled low-power wide-area performance in a wide range of data rates and deployment scenarios. Release 16, which 3 GPP finalized in 2018, added the network slicing and Dynamic Spectrum Sharing (DSS) features made specifically for private 5G networks in addition to continuing prior work on Release 15 features, including mobile broadband. It improved primarily the eMBB service of Release 15.  
Release 17 possesses enhanced features from Releases 15 and 16, such as DSS and private 5G network capabilities, and added new features, such as enhanced battery health and satellite access [51]. More enhancements are still expected to come as 5G standard organizations continue to evaluate other prospects of the 5G technology. Details of the 5G standardization timeline are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**3 GPP Release** & **Frozen Date** & **Main Feature** & **Data Rate** & **Spectral Efficiency** \\ \hline Release 15 & 2018 & \begin{tabular}{c} 5G support, LTE EPC support for E-UTRAN \\ Ultra-Reliable \\ \end{tabular} & 10–20 Gbps & High \\ \hline Release 16 & 2020 & \begin{tabular}{c} Supports Waveforms above 52 GHz, Massive MTC \\ support, Shared and unlicensed spectrum, Interworking \\ with trusted non-3 GPP access, Fixed Mobile \\ Convergence, Network Slicing in RAN, V2X \\ communication with 5G, Broadcast support in 5G \\ \end{tabular} & 10–20 Gbps & High \\ \hline Release 17 & 2022 & \
\begin{tabular}{c} Reduced Capability (RedCap) Devices, New Radio (NR), \\ Sidelink Enhancements, UAV (Unmanned Aerial Vehicle) \\ Enhancements, Improvements to 5G RAN, Enhancement \\ in 5G IIoT \\ \end{tabular} & 10–20 Gbps & High \\ \hline Release 18 & 2022 &
\begin{tabular}{c} Extended reality enhancement, UAV enhancement, \\ Sidelink evolution, Multi-SIM enhancement, \\ Mobile-terminated small data transmission-enhanced \\ mobility support, Network energy saving, \\ Non-terrestrial network, Multicast and Broadcast, \\ Coverage enhancement, MIMO evolution, Improved \\ positioning, AI and ML for NG-RAN \\ \end{tabular} & 10–20 Gbps & High \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._
## 5 Conclusions  
A new technique is proposed to increase the transmission capacity of Li-Fi based optical wireless transmission links. The MAE-based sparse coded mask modeling technique presented in this paper was implemented through the sequential execution of the following steps. First, the entire length of the DMT signal waveform, encoded using QPSK or 16-QAM symbols, is divided equally into a predefined number of DMT patches to obtain segmented patches. Subsequently, these segmented DMT patches are compressed based on a predefined masking ratio using the MAE technique. The compressed patches are then transmitted via an optical wireless channel based on Li-Fi. Following optical wireless transmission, the received DMT patches were decoded to reconstruct the DMT signal and recover the original QPSK and 16-QAM symbols.  
In summary, the experimental results demonstrate that both QPSK and 16-QAM symbols can be successfully transmitted over a 1-m optical wireless link with a masking ratio of up to 85%, meeting the first-generation GFEC EVM threshold. This result indicates that the transmission capacity can be enhanced by up to 1.85 times compared to the physical bandwidth of 10 MHz. Additionally, the EVM of the QPSK and 16-QAM symbols, which were obtained by demodulating the DMT signal reconstructed from the DMT patch compressed with a 75% masking ratio, was repeatedly measured under varying received light intensity. This result tells us that the proposed technique is viable for use in Li-Fi networks within illuminance environments of 240 lux or higher.  
Conceptualization, Y.-Y.W. and S.M.Y.; methodology, H.H.; software, D.C.; validation, Y.-Y.W. and S.M.Y.; formal analysis, H.H.; investigation, H.H. and D.C.; resources, Y.-Y.W.; data curation, H.H. and D.C.; writing--original draft preparation, Y.-Y.W.; writing--review and editing, Y.-Y.W. and S.M.Y.; visualization, H.H.; supervision, Y.-Y.W.; project administration, Y.-Y.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.