esa-sceva/satcom-chunk-collection huggingface dataset:
# Telehealth Using PoseNet-Based System for In-Home Rehabilitation
## 6 Conclusions  
This paper presents a telehealth service using the PoseNet-based system for in-home rehabilitation using an ordinary webcam. The proposed system offers benefits for patients in performing daily rehabilitation through remote monitoring. This is especially useful for those elderly patients with limited mobility and chronic condition. The system is also useful to reduce the travelling challenges for those patients from rural areas who need to perform daily rehabilitation exercises. Using an ordinary webcam for rehabilitation is very promising because it enables the patients to adopt in-home rehabilitation without additional cost.  
The proposed system is validated to operate well and is able to detect the variation of the subject's movements in both functional tests. Since PoseNet is completely free and can successfully capture all the skeleton points, PoseNet may be a more economical solution than the Microsoft Kinect for in-home rehabilitation system, helping to reduce the patient's financial burden. In addition, the built-in statistical computation plays a crucial role in the rehabilitation procedure to assist the doctors in performing deeper analysis in determining the patient's recovery rate. The functional tests also demonstrated the usefulness of the statistical results for the subjects to monitor their progress on different days.  
Having completed functional tests, the system is ready for clinical trials that will be carried out in collaboration with the rehabilitation centers to evaluate the proposed telehealth system for actual in-home rehabilitation practices. The clinical trials will further investigate the effectiveness of the PoseNet-based in-home rehabilitation to meet the patients' needs while also maintaining patient safety, study integrity, and data quality. Subsequent future work includes introducing a 3D avatar that can demonstrate the rehabilitation exercises  
more vividly and provide guidance on how to perform them more accurately. On top of this, background music can be added to make the rehabilitation exercise more appealing and relaxing for patients.  
Methodology, J.C.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, L.-Y.O.; visualization, J.C.; supervision, L.-Y.O.; project administration, M.-C.L.; funding acquisition, M.-C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Telekom Malaysia Research & Development, grant number RDTC/191001 (MMUE/190086) and Multimedia University.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Results and Discussions
#### 4.1.4 Experimental Test Bed  
Based on the experimental test bed depicted in Figure 23 we calculate the average packets processing time by only one hardware and one software router using Equation (3).  
The average packets processing time by a hardware and software router during three experiments is defined as follows:  
\[T_{av.hardware)router}=\frac{\left(D_{HR(A-B)_{2}}-D_{HR(A)_{1}}\right)+\left(D_ {HR(A-B-C)_{3}}-D_{HR(A-B)_{2}}\right)}{2} \tag{3}\]  
Figure 21: Comparison of the average packet delays caused by three Cisco 2801 hardware routers and three software routers.  
Figure 22: Comparison of probability density functions of the packet delays caused by three hardware and three software routers.  
\[T_{ax.software\_roture}=\frac{(D_{SR(A-B)_{2}}-D_{SR(A)_{1}})+(D_{SR(A-B-C)_{3}}-D_{SR(A -B)_{2}})}{2} \tag{4}\]  
By substituting the obtained values into Formulas (3) and (4), we obtain:  
\[T_{ax.hard\_roture}=((0.8054-0.69591)+(0.93097-0.8054))/2=0.11753\text{ ms} \tag{5}\]  
and:  
\[T_{ax.software\_roture}=((0.803446-0.69347)+(0.9274-0.803446))/2=0.116965\text{ ms} \tag{6}\]  
Thus, we can see that the performance testing by the delay criterion of the developed software router is similar to the hardware router. Accordingly, the price of the server machine for which the software router is installed is 400 dollars, and the price of a Cisco 2801 is 800 dollars, which is cost-efficient.
# Polarization-Discriminated RSOA-EAM for Colorless Transmitter in WDM-PON
## 3 Simulation Results and Discussion
### Downstream Signal Suppression
Therefore, the increased carrier density in turn provides increased gain to the downstream signal, as shown in Figure 3b, through which the saturation is enhanced, hence the cleaner erasing of the downstream signal is obtained. The ER of the suppressed downstream signal is plotted in Figure 3c as a function of the Faraday rotation angle. Compared to the situation without any rotation (\(0^{\circ}\)), the suppression of the downstream signal is further improved by more than 2 dB in addition to the normal 3 dB suppression, when the Faraday rotation angle is \(90^{\circ}\). Figure 3: (**a**) Averaged carrier density inside the SOA active region, (**b**) the downstream signal gain, and (**c**) extinction ratio (ER) of the suppressed downstream signal as functions of the Faraday rotation angle. (ER\({}_{\text{DS}}\): ER of the downstream signal at input). Figure 2: (**a**) The gain saturation curves of the downstream signal at different Faraday rotation angles. (**b**) The downstream signal output power versus the input power at different Faraday rotation angles. The inlets show the erased eye-diagrams when the input power is \(-15\) dBm. Note that the output powers and eye-diagrams are extracted from the suppressed downstream signal before entering the EAM. The device's colorless working ability is also characterized and shown in Figure S3 in the Supplementary Material. The ER of the incident downstream signal is 6 dB and the rotation angle is 90\({}^{\circ}\). It is seen that the colorless working range is significantly broadened as the downstream signal input power increases from \(-\)25 dBm to \(-\)5 dBm. Nonetheless, the suppressed downstream signal ER can be kept below 2 dB for a broad wavelength span from 1505 nm to around 1560 nm, when the input power is not less than \(-\)25 dBm.
## 4 Results
### Sulfur Isotopic Compositions of Pyrite  
The LA-MCICP-MS sulfur isotopic data for Jiaojia pyrite are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Sample** & **Ti** & **Cr** & **Co** & **Ni** & **Co/Ni** & **Cu** & **Zn** & **As** & **Ag** & **Au** & **Cu/Au** & **Bi** \\ \hline J[20-4](1)-10 & 12.50 & \(\leq\)b.d.1 & 862.00 & 126.00 & 6.84 & 0.05 & 1.51 & 558.00 & 0.01 & \(\leq\)b.d.1 & \(\leq\)b.d.1 & \(\leq\)b.d.1 \\ J[20-4](1)-11 & 12.80 & \(\leq\)b.d.1 & 555.00 & 72.20 & 7.69 & 0.04 & 0.00 & 768.00 & 0.00 & \(\leq\)b.d.1 & \(\leq\)b.d.1 & 0.12 \\ J[20-4](1)-12 & 173.00 & 9.45 & 6.36 & 48.80 & 0.13 & 1.10 & 0.52 & 0.69 & 0.10 & \(\leq\)b.d.1 & \(\leq\)b.d.1 & 6.41 \\ J[20-4](13) & 9.83 & \(\leq\)b.d.1 & 12.90 & 21.30 & 0.61 & 0.93 & 0.38 & 7.74 & 9.50 & 0.21 & 4.43 & 1.80 \\ Max & 504.00 & 9.45 & 925.00 & 199.00 & 295.65 & 5.06 & 2.19 & 4057.00 & 334.00 & 5.84 & 22.93 & 7.67 \\ Min & 2.07 & 2.96 & 1.04 & 0.04 & 0.13 & 0.00 & 0.00 & 0.69 & 0.00 & 0.08 & 0.17 & 0.11 \\ Median & 12.10 & 5.00 & 52.30 & 11.40 & 6.84 & 0.28 & 0.31 & 277.50 & 0.04 & 0.20 & 4.43 & 1.34 \\ Average & 43.37 & 5.43 & 216.59 & 33.67 & 23.90 & 0.75 & 0.58 & 802.38 & 1.91 & 0.61 & 6.44 & 2.29 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Diagram of primitive-mantle-normalized trace element contents of pyrite in the Jiaojia gold deposit. Primitive mantle values were obtained from [PERSON] et al. [37].  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Sample** & \(\delta^{34}\)S & **Sample** & \(\delta^{34}\)S \\ \hline Py II & JJ20-2-1 & 5.7 & & \\  & JJ20-2-2 & 6.6 & & \\  & JJ20-2-3 & 7.8 & & \\  & JJ20-2-4 & 8.0 & & \\  & JJ20-1(1)-1 & 7.3 & & \\  & JJ20-1(1)-2 & 7.2 & & \\  & JJ20-6-1 & 6.4 & & \\  & JJ20-6-2 & 7.3 & & \\  & JJ20-6-3 & 6.2 & & \\  & JJ20-6-4 & 6.8 & & \\  & JJ20-6-5 & 7.3 & & \\  & JJ20-6-6 & 5.9 & & \\  & JJ20-6-7 & 6.3 & & \\  & Max & 8.0 & & \\  & Min & 5.7 & & \\  & Median & 6.8 & & \\  & Average & 6.8 & & \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._  
Figure 7: Plot of the sulfur isotopic composition of the pyrite of different generations in Jiaoja gold deposit. Data sources: orogenic gold deposit [39], metasomatic mantle wedge [40; 41; 42], fertile mantle [43], depleted mantle of blue line [43], Ailaoshan gold deposit [44], Linglong granite and Guojialing [6], and intermediate- mafic dikes [17].  
The \(\delta^{34}\)S values of pyrite in the Jiaojia gold deposit range from 4.5 to 8.0%.. Py I has \(\delta^{34}\)S values ranging from 4.5 to 7.4%, with an average of 6.4%. Py II has \(\delta^{34}\)S values ranging from 5.9 to 8.0%., with an average of 6.8%. Py III has \(\delta^{34}\)S values ranging from 6.4 to 7.9%, with an average of 7.4%.
## 2 Methodology
### Spatiotemporal Box Filter  
A box filter is a fast-filtering algorithm performed recursively in a 2D space. The basic idea is to make full use of the previous computation result during the loop computation, which ensures that the next computation result can be acquired with only a small computational effort. The typical implementation is a sliding window. The summing process is accelerated by adding move-in pixels to the sliding window and subtracting the move-out pixels. The computation is independent of window size. Therefore, a box filter that eliminates redundant information is more prominent than an integral image method [35]. The box filter can reduce the complexity of the summation operation from \(O(w_{x}w_{y})\) to \(O(1)\). The traditional box filter is used only in the spatial domain. This method is now extended to the temporal domain. This STBF is schematically shown in Figure 5.  
\[S(x+1,y)=S(x,y)+\sum_{t=1}^{N}\sum_{h=-w_{y}/2}^{w_{y}/2}\Big{[}I_{(x,y)}(w_{ x}/2+1,h,t)-I_{(x,y)}(-w_{x}/2,h,t)\Big{]}, \tag{6}\]\[S(x,y+1)=S(x,y)+\sum_{l=1}^{N}\sum_{l=-w_{x}/2}^{w_{x}/2}\Big{[}I_{(x,y)}(l,w_{y} /2+1,t)-I_{(x,y)}(l,-w_{y}/2,t)\Big{]}. \tag{7}\]  
The acceleration computation of STBF in the row and column directions is given in Equations (6) and (7), where \(S(x,y)\) is the sum of grey values in the spatiotemporal rectangular window centred on \((x,y)\). To perform fast-matching cost computations in the entire disparity cube, the sum and the sum of squares of grey values in the matching window in Equation (1) must be calculated in advance by STBF.
## 7 Practical Application
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Phase A**} & \multicolumn{2}{c}{**Phase B**} & \multicolumn{2}{c}{**Phase C**} \\ \hline  & \(P_{a+}^{a}\) & \(Q_{a+}^{a}\) & \(P_{b+}^{b}\) & \(Q_{b+}^{b}\) & \(P_{c+}^{c}\) & \(Q_{c+}^{c}\) & \(P_{+}\) & \(Q_{c+}\) \\ \hline Load 1 & 11,077.32 & 7306.69 & 7948.36 & 2551.11 & 13,596.30 & 2279.77 & 32,551.98 & 12,137.58 \\ Load 2 & 1560.41 & 543.46 & 3151.92 & 656.18 & 2450.88 & 447.45 & 7163.20 & 1647.09 \\ Load 3 & 2259.38 & 473.88 & 3050.17 & 1069.62 & 2138.86 & 1456.60 & 7448.41 & 3000.10 \\ Line 1-2 & 3574.94 & 2589.27 & 3551.66 & 1586.16 & 4545.42 & 1660.58 & 11,672.02 & 5836.01 \\ Line 2-3 & 244.53 & 222.28 & 394.20 & 366.40 & 267.63 & 317.69 & 906.36 & 906.36 \\ Bus 1 & 18,646.58 & 11,135.59 & 18,096.31 & 6229.46 & 22,999.08 & 6162.09 & 59,741.97 & 23,527.13 \\ Bus 2 & 15,071.64 & 8546.31 & 14,544.65 & 4643.30 & 18,453.66 & 4501.51 & 48,069.95 & 17,691.13 \\ Bus 3 & 3819.79 & 1017.34 & 6202.09 & 1725.79 & 4589.73 & 1904.05 & 14,611.61 & 4647.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Components of the apparent power vector and unbalanced power due to positive-sequence voltage. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**\(I_{a}\) (A)**} & \multicolumn{2}{c}{**\(I_{b}\) (A)**} & \multicolumn{2}{c}{**\(I_{c}\) (A)**} \\ \hline  & **Modulus** & **Modulus** & **Modulus** & **Modulus** & **Modulus** & **Angle** \\ \hline Load 1 & 73.314 & \(-\)28.268 & 48.219 & \(-\)132.487 & 79.632 & 115.789 \\ Load 2 & 10.259 & \(-\)15.942 & 19.990 & \(-\)128.500 & 15.496 & 112.913 \\ Load 3 & 14.334 & \(-\)8.586 & 20.070 & \(-\)136.064 & 16.068 & 89.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Line currents of the loads. Table 9 shows the values of the components that form the vectors of apparent power and unbalanced power, considering the zero-sequence voltage. To calculate \(P_{z0}^{z}\) and \(Q_{z0}^{z}\), expressions (26) and (27) have been used. Finally, Tables 10 and 11 show the moduli of the apparent power vectors and unbalanced power vectors, respectively.
# An Automated Behaviour-Based Clustering of IoT Botnets
## 4 The Proposed Approach
### Overview  
The proposed approach consists of four steps, as shown in Figure 1 below:  
1. **Obtaining IoT Botnet Samples**. The IoT botnet samples are obtained from honeypots deployed on the Internet and from URLhaus, a service for tracking malware URLs.
2. **Sandbox Execution and Analysis**. The obtained IoT botnet samples are executed inside a sandbox while the botnets are actively propagating on the Internet. Their behaviour is recorded by tracing the system calls, and by capturing the network traffic in a network traffic capture file (pcap). Because the system call traces can vary significantly, the captured behaviour is abstracted at higher level, as a set of actions and objects the actions were performed on. The set of actions and corresponding objects is identified by analysing the recorded system calls and the network traffic capture file, respectively.
3. **Creating Behaviour Profiles**. A behaviour profile containing the actions identified by the sandbox analysis is created for each sample executed in the sandbox. The behaviour profiles are text files describing the actions performed by the sample and the objects on which the actions were performed. They serve as input to the clustering algorithm.
4. **Clustering**. The clustering of behaviour profiles involves three steps. The behaviour profiles are first transformed into feature vectors using TF-IDF. The pairwise similarities between the behaviour profiles in the dataset are then computed as the cosine similarity between their respective TF-IDF vectors. The computed pairwise similarities are stored in a similarity matrix. Finally, the DBSCAN clustering algorithm is run using the pairwise similarity matrix as input.
## 3 Main Framework, Dataset, and Feature Extraction Overview
### Feature Extraction
* MFDM: average degree (\(\alpha_{MFDM}\)); the maximum degree (\(M_{MFDM}\)); average clustering coefficient (\(C_{MFDM}\)); density: (\(\rho_{MFDM}\)); global efficiency: (\(G_{MFDM}\)); network diameter (\(D_{MFDM}\)); average path length: (\(L_{MFDM}\)). * QG: and average jump length (\(\Delta\)). **Remark 1**. : _For the QG algorithm, the most representative feature, namely the average jump length, is selected. As suggested by the literature [4], when \(Q=30\) and \(k=4\), significant differentiation is observed within the dataset. Hence, we chose \(Q=30\) and \(k=4\). The corresponding box plot is presented in Figure 9._The feature vector \(V_{\text{EEG signals}}\) is constructed using multiple-measures composited strategies and is typically described as follows:  
\[V_{\text{EEG signals}}=[\alpha_{\text{VG}},\dots,L_{\text{VG}},\alpha_{\text{ HVG}},\dots,L_{\text{LPVG}},\alpha_{\text{MFDM}},\dots,L_{\text{MFDM}},\Delta_{\text{QG}}]\]  
Therefore, the feature vector \(V_{\text{EEG signals}}\) is composed of twenty-seven network measures used for subsequent classification tasks.
## 5 Vitamin D Health-Promoting Activities
### Anti-inflammatory Activity  
Vitamin D's anti-inflammatory qualities are essential for healthy skin maintenance, as it keeps inflammation under control by regulating both innate and adaptive immune responses and subsequently ensures the skin's safety against illness, while efficiently fostering healing and barrier function. Because of this, vitamin D plays a crucial role in the treatment and prevention of inflammation-related skin disorders [27; 172]. Vitamin D exhibits anti-inflammatory properties through its active metabolite 1\(\alpha\),25(OH)\({}_{2}\)D\({}_{3}\), which binds to the _VDR_ present in various skin cells such as keratinocytes, fibroblasts, and immune cells like macrophages and dendritic cells [30]. This interaction not only regulates calcium homeostasis, but also modulates immune responses, by primarily reducing inflammation and promoting skin barrier integrity [27; 172].  
Reducing pro-inflammatory cytokines is one of vitamin D's most significant impacts observed in several inflammation-related disorders [30; 173]. Regarding skin inflammation, 1\(\alpha\),25(OH)\({}_{2}\)D\({}_{3}\) suppresses cytokines implicated in initiating inflammatory reactions, such as interleukins 2 and 17 (IL-2 and IL-17), as well as TNF-\(\alpha\). Reduced inflammation is linked to this suppression, which alters the immune response from a pro-inflammatory Th1/Th17 state to a more tolerogenic Th2 state [172]. This alteration is notably helpful in the treatment of inflammatory skin conditions including acne, eczema, and psoriasis, because these cytokines are major pathogens in these conditions [128; 129; 130]. Additionally, vitamin D stimulates the production of regulatory T cells (T-regs), essential for promoting the immunological tolerance maintenance and avoidance of exaggerated inflammatory responses [172]. T-regs also aid in reducing skin inflammation by producing anti-inflammatory cytokines like IL-10. Vitamin D contributes to maintain a more regulated immunological milieu by increasing T-reg activity, which actively induces in turn the prevention of chronic inflammation that could possibly cause skin damage [172].  
To further reduce inflammation, vitamin D also aids in the regulation of innate immune responses in the skin [172]. Vitamin D, in fact, reinforces the skin's defense mechanisms against infections, by directly stimulating antimicrobial peptides such as cathelicidin and \(\beta\)-defensins, while also lowering inflammatory reactions [172; 174]. Such dual function emphasizes how crucial vitamin D is for preserving skin homeostasis [27; 172]. Beyond its direct immune-modulatory effects, vitamin D also plays a role in inhibiting oxidative stress, which is often a trigger for inflammation. By reducing the levels of ROS in the skin, vitamin D minimizes oxidative damage that can lead to chronic inflammatory states. This antioxidant action works in synergy with immune-modulatory functions, to protect the skin from both internal and external inflammatory stimuli [27].
# Marine UAV-USV Marsupial Platform: System and Recovery Technic Verification
## 2 Fully Autonomous Recovery System  
This section begins with an analysis of the challenges involved in the development and application of fully autonomous recovery systems built on USVs. This is followed by an explanation of the characteristics and advantages of the system designed in this work, in order to solve the aforementioned challenges. Based on this, the key technical issues of the system are briefly explained.  
First, in order to ensure a smooth implementation of the verification test and the stability of the system, it is necessary to study methods such as USV motion simulation and compensation stabilization in the case of wave fluctuations. Since the two types of platforms, UAVs and USVs, are heterogeneous platforms, they have many differences in structure, kinematics and dynamic models, and control methods [22; 23; 24; 25; 26]. Therefore, techniques such as active modeling of relative motion of heterogeneous platforms, coordinated autonomous control of heterogeneous platforms, and relative position and attitude measurement during high-speed motion of heterogeneous platforms need to be studied. At last, in order to make the recovery system have a certain compliance, and to ensure a certain compliance and buffer time when the UAV is in contact with the recovery system or the environment or there is a collision or impact [27; 28], the compliance control of recovery systems needs to be studied.  
Figure 3: Main process of fully autonomous recovery of small ship-based fixed-wing UAV. (**a**) UAV and arresting cable coordinate attitude adjustment; (**b**) UAV tail-hook hooks the arresting cable; (**c**) UAV speed drops rapidly, and it lands on the recovery net; (**d**) the manipulator removes the drone from the arresting cable and recovery net and places it in a storage box.
###### Abstract  
The aim of this study was to examine the influence of biological maturation on young football players adaptations and monitor and analyse variations in external (EL) and internal load (IL) during a running-based HIIT programme, according to the players' maturity status. A total of 41 players (13.9 \(\pm\) 0.7 years) participated, divided into an experimental group (EG) (\(n\) = 19) and a control group (CG) (\(n\) = 22). Acceleration (5 m and 30 m) and intermittent endurance (vIFT via the 30-15 IFT test) were assessed before and after eight weeks of intervention. EL and IL load variables and the rating of perceived exertion (RPE) were recorded. The results showed better initial performance in advanced maturity status players (post-PHV). Players from the EG had significant improvements in the 5 m and 30 m sprints and in the vIFT, whereas the CG only showed significant improvements in the 30 m sprint. Post-PHV players perceived less intensity (RPE) and thus, less IL than players with a delayed maturity status (pre-PHV). The pre-PHV group also exhibited significant improvements in the 30 m sprint and vIFT. However, no significant interaction effect (time \(\times\) maturational group) was detected for any variable. These findings underscore the importance of considering maturity status in performance evaluation, training load prescription, and adaptations.  
**Keywords:** maturity status; peak height velocity; bio-banding; intermittent endurance; 30-15 IFT