esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Gait Cycle Analysis  
The recording of gait cycle was carried out preoperatively as well as three, six and 12 months postoperatively after THA. To trace the joint movement, 16 reflective markers of a Plug-In Gait-Marker set were attached to the subject's skin [21]. These markers were placed bilaterally over specific bony landmarks (spina iliac anterior superior, spina iliac posterior superior, lateral thigh, lateral epicondyle, lateral lower leg, lateral malleolus, base of os metatarsale II, posterior calcaneus). To determine the correct orientation of the markers, as well as to compute the position of the joint centers and the individual segments of the lower limb, a static standing trial was recorded subsequently. Eight infrared MX-cameras sampling at 200 Hz were used to track the markers (Vicon Motion Systems Ltd., Oxford, UK) while the ground reaction forces were measured with two in-ground mounted force plates type BP400600 sampling at 1000 Hz (AMTI, Watertown, MA, USA). The captured kinetic and kinematic data were further processed using Nexus software Version 1.8.5 (Vicon Motion Systems Ltd.), as were spatio-temporal parameters (walking speed, cadence, step length). For the dynamic measurements the subjects walked a distance of 10 m barefooted at a self-selected speed. At about 5 m the two in-ground force plates were located. Ten valid trials were recorded. A trial was counted as valid when the subject struck each plate with one foot without stepping over the plate's borderline.  
The joint movements of the operated and nonoperated limb in each degree of freedom (hip: flexion/extension, abduction/adduction, external/internal rotation) were described in courses of curves. Events of the gait (toe off/heel strike on the in-ground force plates) were detected automatically. Kinematic and kinetic data were time normalized with respect to the gait cycle (101 parts, 0-100% of the gait cycle; Figure 1), whereas kinetic data were additionally normalized to the body weight. The normalization and averaging were performed with Polygon software Version 3.5.2 (Vicon Motion Systems Ltd.). Single values were picked out from continuous joint kinematics regarding heel strike (0% of gait cycle) and toe-off (\(\sim\)60% of gait cycle) for comparisons (Figure 1).
# Improving Performance of a Resonant String-Based Pulsation Attenuator in Hydraulic Systems
## 4 Optimization of RSHPA
### Configuration Optimization  
To investigate the potential configuration for improving the performance of RSHPA, serial and parallel combinations of RSHPA were tested and optimized, and their schematic diagrams are given in Figure 12. For serial configuration, the length and diameter of each chamber was kept the same. For parallel configuration, the length (diameter) of each RSHPA was also assumed to be consistent. To better compare multiple-chamber configuration with the single-chamber, the maximum total length and width of the combination configuration were equal to the upper bound in optimization of the single-chamber case, i.e., the occupied physical space of the multiple-chamber RSHPA could not exceed the single-chamber configuration. It was assumed that the length of the connection pipelines for each chamber was short enough to be ignored.  
Figure 11: Comparison of the theoretical and experimental results.  
The transfer matrix of two multiple-chamber RSHPA are different from the single-chamber form and the serial RSHPA is formulized as:  
\[\mathbf{A}_{serial}=\prod_{i=1}^{N_{RS}}\mathbf{A}_{2}^{i}=\prod_{i=1}^{N_{RS}} \left[\begin{array}{cc}a_{11}^{i}&a_{12}^{i}\\ a_{21}^{i}&a_{22}^{i}\end{array}\right] \tag{29}\]  
where \(N_{RS}\) denotes the number of RSHPA, \(\mathbf{A}_{2}^{i}=\left[a_{11}^{i},\,a_{12}^{i},\,a_{21}^{i},\,a_{22}^{i}\right]\) denotes the transfer matrix of \(i\)-th RSHPA, and \(\mathbf{A}_{serial}\) denotes the total transfer matrix of the serial RSHPA. Because the flow going through each RSHPA is the same, the total transfer matrix of parallel RSHPA can be derived as:  
\[\mathbf{A}_{parallel}=\left[\begin{array}{cc}a_{11}^{i}&a_{12}^{i}/N_{RS}\\ N_{RS}a_{21}^{i}&a_{22}^{i}\end{array}\right]\text{.} \tag{30}\]
## 4 Conclusions  
In the case of LIDAR with pulse coding, the pulse peak power and the maximum measurable distance both increase inversely proportionally to the number of transmitted pulses in order to comply with eye safety standards, and accuracy and precision increase in proportion to the number of pulses. Therefore, dividing the bit input block into several smaller partitions reduces transmission time and the maximum measurement distance, and improves accuracy and precision. Conversely, dividing the bit input block into large partitions increases the transfer time and maximum measurement distance, but reduces precision and accuracy. It is, thus, useful to select a modulation and a spread coding scheme according to the use and conditions of operation of LIDAR. If we need to measure distances even if accuracy and precision are low, we should use a combination of the smallest number of pulses and the smallest number of slots to increase the number of measurement points per second. If accuracy and precision are prioritized, the combination with the largest number of pulses is preferable.  
[PERSON] conducted experiments and wrote the manuscript under the supervision of [PERSON].  
This research was funded by the Information Technology Research Center (ITRC) support program (IITP-2018-2016-0-00313) and the Basic Science Research Program (2017R1E1A1A01074345).
## 3 Results and Discussion
### In-Situ Electro-Chlorination Experiment with CFD Simulation
(**a**) Chlorine concentration; (**b**) expanded view of electrode side in image (a); (**c**) velocity magnitude; (**d**) pressure. The average error between the simulated and experimental results varied from 5% to 15%. Considering that the simulation results are expressed as time-averaged values, inevitably there would be some error between the real and simulated values. Furthermore, the Reynolds number obtained from our experiment ranged from 12,700 to 13,000 depending on the flow rate, indicating strong turbulence. However, the simulation results fit the experiment data well even under intensively turbulent conditions. Estimated Chlorine Profile of the Full-Scale Cooling System Installed with In-Situ Electro-Chlorine Generator  
To evaluate the chlorine dynamics along a full-scale cooling system with in-situ electro-chlorine generator, the simulation was performed based on the cooling system of a full-scale power plant in Korea (a pressurized-water reactor 1400 MW nuclear power plant). The simulated result for the full-scale cooling system is shown in Figure 8.  
Two different cathode positions were simulated. In both cases, the generated chlorine did not mix well. Even though Reynolds number was over one million, chlorine flowed along the inner wall of the pipe. This is because concentrations were calculated as time-averaged values. In addition, the simulation domain of Figure 8 was not long enough to display the ultimate state of the chlorine diffusion. Figure 9 presents a comparison of the estimated chlorine demands of in-situ electro-chlorination and direct chlorine injection in a full-scale application. For this estimation, the following conditions were considered:  
1. A straight cooling-water pipeline with a 2 m diameter and 1 km length (no elevation). 2. The residual concentration of chlorine was set to 0.5 mg L\({}^{-1}\) in the pipeline. 3. The chlorine decay kinetics was applied in both cases. For the case of direct chlorine injection, it was assumed the chlorine is uniformly distributed when injected into the pipeline due to a limitation on defining chlorine injection method. For in-situ electro-chlorination, a series of electrodes were assumed. The chlorine concentration was estimated based on the simulated results of the first 10 m because of the cost limitations associated with a full-scale
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
###### Abstract  
As people age, more careful health monitoring becomes increasingly important. The article presents the development and implementation of an integrated system for monitoring the health of elderly individuals using Internet of Things (IoT) technology and a wearable bracelet to continuously collect vital data. The device integrates MAX30100 sensors for heart rate monitoring and MPU-6050 for step counting and sleep quality analysis (deep and superficial sleep). The collected data for average heart rate (AR), minimum (mR), maximum (MR), number of steps (S), deep sleep time (DST), and superficial sleep time (SST) is processed in real-time through a health anomaly detection algorithm (HADA), based on the dimensionality reduction method using PCA. The system is connected to the Azure cloud infrastructure, ensuring secure data transmission, preprocessing, and the automatic generation of alerts for prompt medical interventions. Studies conducted over two years demonstrated a sensitivity of 100% and an accuracy of 98.5%, with a tendency to generate additional alerts to avoid overlooking critical events. The results outline the importance of personalizing the analysis, adapting algorithms to individual characteristics, and the system's potential to prevent medical complications and improve the quality of life for elderly individuals.  
IoT system; cloud computing; real-time sensors; azure infrastructure; wearable device; heart rate; number of steps; sleep time; algorithm; bracelet Article
## 1 Introduction
The aim of the current study is to include the Coriolis force in simulations using a FBL technique. This is performed by including the mean wind veer in the FBL and combining it with a Coriolis correction for the deviations from the mean flow. The FBL technique used in this article gives you more control over the setup of the boundary layer, compared to a precursor simulation, since any mean wind profile can be added in the domain using body forces. This makes it possible to mimic for exempel a measured wind shear or a wind shear obtained from simulations using a Meso scale model. The wind shear can also include the mean wind veer due to the Coriolis force. A mean wind profile will however not take local deviations, e.g. from wakes and atmospheric turbulence, from the mean profile into account and therefore neglects a part of the Coriolis force. A correction for this neglection is proposed in this article which is also added as a source term, defined as the Coriolis correction. The study will also examine the impact of the wind veer and the change in Coriolis force in the wake region on the development of the wake. The setup regarding wind veer and latitude is similar to [PERSON] [15] to be able to compare his results using RANS by these using LES. Using the FBL technique and the Coriolis correction also allows for the study of the impact of wind veer and local impact of the Coriolis force in the wake region independently. The results can be used to conclude if the Coriolis force needs to be considered for studies of long distance wakes and what needs to be included, i.e., the mean wind veer, the Coriolis correction or both. This is of importance to asses the results from studies using the FBL-technique without including the effects of the Coriolis force (for example [13; 14]) or only including the wind veer (for example [8]). Further this study will investigate how the Coriolis force could impact the power output in a wind farm cluster.
## 4 Application and Results
### Phase 1-Selection of the Baseline Articles  
For this phase of ERSA process, the specificities that made the interaction for users in low-income countries different from high-income countries are used as a starting point to identify baseline articles. According to the literature, four criteria are specific to low-income countries: (1) low literacy rate [30], (2) low level of computer literacy [31], (3) high rate of multilingualism in those regions with a high number of local languages, often oral [32], (4) and the impossibility of accessing IT tools, either for financial reasons, such as telecom charges and the cost of acquiring and maintaining the device, or structural reasons. These structural reasons can be because of white or poorly covered areas or the distance of the user from the electricity network. Then there is a more general criterion: (5) the integration of user culture into the design of user interfaces. This criterion comes into play when the target users have a different culture than that of the design team [33]. At least one baseline study was collected on each of the five criteria previously described, as described in Table 1.
# Anomaly Monitoring of Process Based on Recurrent Timeliness Rules (AMP-RTR)
## 3 Design of AMP-RTR Model
### Implementation of AMP-RTR Model  
It must be ensured that the timeliness score meets the normalization (R1) and interval scaling (R2) in each recurrent update of the data, and make the monitoring model interpretable (R3) and effective (R4). According to the timeseries generated in the production process, we propose the Anomaly Monitoring of Process based on Recurrent Timeliness Rules (AMP-RTR). Its core idea is to calculate the timeliness evaluation value of data in each recurrent update of data, and update the timeliness score value iteratively with the recurrently updated data.
## 3 The Proposed CA-CWA Algorithm
### The Contention Window Adaption Scheme
On the other hand, if the channel congestion reduces, a station will also reduce the value of \(\theta\) and ensure a lower CW to minimize the access delay. In addition, to integrate CA-CWA into the IEEE 802.11 ah protocol, CA-CWA defines a multiplier factor \(\lambda\) for the first backoff state (free contention period) in IEEE 802.11 ah. For each station, the minimal CW is initialized to the product of the default \(CW_{min}\) and \(\lambda\) because the channel is more likely to be congested. The CA-CWA algorithm is summarized in Algorithm 1, and a flowchart is illustrated in Figure 4 to make the algorithm process more clear. The two functions defined in Algorithm 1 are the two most upper arrows that appear in Figure 4. Besides, the specific values of the parameters in the algorithm will be introduced in the simulation part. Figure 4: CA-CWA algorithm. ```
1:function estimation
2:for each fixed interval \(T_{\rho}\)do
3: calculate \(\rho^{\prime}\) by Equation (6)
4:\(\rho_{avg}{}^{\prime}\leftarrow\) calculate the smoothed \(\rho^{\prime}\) by Equation (9)
5: update \(\rho_{avg}{}^{\prime}\)
6:endfor
7:endfunction
8:
9:function CA-CWA
10: /*initialization process*/
11:if second backoff state then
12:\(CW_{min}\gets CW_{min}\)
13:else
14:\(CW_{min}\gets\lambda CW_{min}\)
15:endif
16: initialize other parameters
17:
18:while (1) do
19:...
20: /*after a transmission triggered*/
21:if successful then
22:for\(n\in[1,4]\)do
23: obtain \(\rho_{avg}{}^{\prime}\)
24: calculate \(\theta_{n}\) by Equation (10)
25: update \(CW_{n}\) by Equation (11)
26: transmission end (successful)
27:endfor
28:else
29:if reach retry limit then
30: transmission end (failed)
31:else
32:\(CW_{n}\leftarrow\omega CW_{n}\)
33:endif
34:endif
35:endwhile
36:endfunction
```  
**Algorithm 1** Channel-aware CW adaption algorithm.
## 4 Experimental Evaluation and Results
### Comparison to Other Approaches  
The best performing methods are summarized in Table 7, comparing methods with 90 % or higher precision on the MPII dataset [54]. Our approach managed to achieve an mAP of 0.950 for the task of predicting the joint position of a single subject using the MPII dataset as a benchmark. Therefore, the proposed method achieved state-of-the-art accuracy when comparing the mAP with other existing state-of-the-art approaches. Additionally, the proposed solution allows for a seamless evaluation of posture in a 3D environment, allowing for the subjects performed to be evaluated from multiple angles from a single filmed camera feed. Further incorporation of depth sensors or multiple camera perspectives could even further improve the results when dealing with depth precision.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dimension** & **Mean** & **Variance** & **Rating** \\ \hline Attractiveness & 1.3887 & 1.4490 & Above average \\ Perspicuity & 1.4581 & 1.4890 & Above average \\ Efficiency & 1.3602 & 1.2962 & Above average \\ Dependability & 1.6233 & 1.4434 & Good \\ Stimulation & 1.4736 & 1.2523 & Above average \\ Novelty & 1.5207 & 1.4131 & Good \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of UEQ dimensions.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Model** & **Feature Introduced** & **Accuracy (\%)** \\ \hline _Regression-based approaches_ & & \\ \hline [PERSON] et al. [103] & distribution-aware coordinate representation & 90.60 \\ [PERSON] et al. [50] & using soft-argmax operation & 91.20 \\ \hline _Detection-based approaches_ & & \\ \hline [PERSON] et al. [104] & reducing variations of human poses statistically & 91.00 \\ [PERSON] et al. [105] & using pyramid residuals to enhance the invariance in scales & 92.00 \\ [PERSON] et al. [106] & structure-aware convolutional network & 92.10 \\ [PERSON] et al. [107] & Deep conv-deconv hourglass model & 92.10 \\ [PERSON] et [PERSON] [108] & LSTM architecture for pose estimation & 92.70 \\ [PERSON] et al. [109] & parsing induced learner to exploit parsing information & 92.40 \\ [PERSON] et al. [110] & gated skip connection combined with HourGlass and U-Net & 94.10 \\
**Our approach** & **activity-independent architecture for anthropometric regularity** & **95.0** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model performance using MPII dataset.
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
###### Abstract  
In this paper, a universal H-bridge circuit is used as a loading emulator to investigate the loss and thermal models of the power semiconductor. Based on its operation principle and modulation method, the dominating factors' (e.g., power factor, loading current, fundamental frequency, and switching frequency) impact on the thermal stress of power semiconductors is considerably evaluated. The junction temperature in terms of the mean value and its swing is verified by using Piecewise Linear Electrical Circuit Simulation (PLECS) simulation and experimental setup. It helps to allocate the loading condition in order to obtain the desired thermal stress.  
Keywords:power semiconductor; loss dissipation; thermal stress +
Footnote †: journal: Applied Physics Letters B
# Reinforcement Learning Based Query Routing Approach for P2P Systems
###### Abstract  
Peer-to-peer (P2P) systems have offered users an efficient way to share various resources and access diverse services over the Internet. In unstructured P2P systems, resource storage and indexation are fully distributed among participating peers. Therefore, locating peers sharing pertinent resources for a specific user query is a challenging issue. In fact, effective query routing requires smart decisions to select a certain number of peers with respect to their relevance for the query instead of choosing them at random. In this respect, we introduce here a new query-oriented approach, called the reinforcement learning-based query routing approach (RLQR). The main goal of RLQR is to reach high retrieval effectiveness as well as a lower search cost by reducing the number of exchanged messages and contacted peers. To achieve this, the RLQR relies on information gathered from previously sent queries to identify relevant peers for forthcoming queries. Indeed, we formulate the query routing issue as the reinforcement learning problem and introduce a fully distributed approach for addressing it. In addition, RLQR addresses the well-known cold-start issue during the training stage, which allows it to improve its retrieval effectiveness and search cost continuously, and, therefore, goes quickly through the cold-start phase. Performed simulations demonstrate that RLQR outperforms pioneering query routing approaches in terms of retrieval effectiveness and communications cost.  
query routing; P2P; reinforcement learning; cold-start +
Footnote †: journal:
# Matched-Filter Thermography
## 4 Matched Filtering in Thermography  
The first report of matched filtering in thermal-wave-based systems dates back to 1983 by [PERSON] et al. [(29)] in which cross-correlation using a pulsed matched filter was used in a photothermal radiometry setup to recover photothermal responses of individual layers in ultrathin multilayer samples. This work was complemented by a series of papers by [PERSON] et al. in 1986, who reported on thermal-wave matched filtering using a continuous-wave matched filter incorporating a frequency-modulation pulse compression technique [(30; 31; 32)]. More recently, [PERSON] and [PERSON] [(21; 23)] and [PERSON] and [PERSON] [(24; 25)] incorporated a linear frequency modulation matched filtering in thermography platforms and demonstrated the ability of thermographic matched-filter systems in maintaining range resolution while inspecting deep into samples, laying the foundation of the \"Thermal-Wave Radar (TWR)\" technique, which is used today by researchers for non-destructive evaluation of materials [(33; 34; 35; 36; 37; 38)]. The concept of cross-correlation phase was introduced by [PERSON] and [PERSON] [(23)] in 2011 as an emissivity-normalized contrast channel in thermal-wave matched filtering, similar to that of lock-in thermography [(39)]. A significant breakthrough was achieved in the field by [PERSON] and [PERSON] introduction of thermal coherence tomography (TCT) [(22; 26)], which used the binary phase coding pulse compression technique (i.e., a narrow-band phase encoding technique) to optimize the thermal-waves' matched-filter responses in order to resolve overlaying absorbers in a diffusive field. Application of matched-filtering in a thermography system using pulsed excitation was first demonstrated by [PERSON] and [PERSON] [(40)] in 2011 and was later optimized to yield a highly depth-resolved thermal-wave tomographic imaging modality, termed truncated-correlation photothermal coherence tomography (TC-PCT) [(41; 42; 43; 44)]. Some other fundamental advancements in the field of TWR include: incorporation of [PERSON] [(45)] and [PERSON] [(46; 47)] code pulse compression techniques and performance optimization using spectral shaping [(48)]. Sections below review the fundamentals of TWR (i.e., frequency-modulated pulse compression) and TCT (i.e., phase-modulated pulse compression) based on the works of [PERSON] and [PERSON] [(21; 22; 23; 26)]. Review of TC-PCT will be discussed in another paper of this special issue on \"Novel Ideas for Infrared Thermography Also Applied in Integrated Approaches\".