esa-sceva/satcom-chunk-collection huggingface dataset:
# Improvements of the xAAL Home Automation System
## 5 The xAAL Communication Protocol
### Alternative Home Automation Protocols over IP
#### 5.7.6 CoAP  
The Constrained Application Protocol (CoAP RFC 7252 [52]) is a popular machine-to-machine protocol. It is based on the REST model [53], adapted for small devices. It is a client-server architecture (i.e., many-to-one), and uses HTTP-like methods such as GET, PUT, POST, and DELETE. It is resource-oriented. For instance, a thermometer has the temperature value as a resource. The server address and the resource name compose the URI managed by CoAP. CoAP is much more lightweight than the HTTP protocol. It is based on UDP. The payload is in XML, JSON, or CBOR format. Packets may be ciphered with DTLS [54]. CoAP support multicast (without ciphering) for one-to-many communications, for instance to send a command towards several devices. CoAP was designed for IoT use cases, to manage devices in large area networks.  
CoAP could also have been a good candidate for home automation and for the xAAL system. Unfortunately, its client-server approach does not fit the many-to-many communication needs. xAAL proposes a full distributed architecture for home automation. Well, CoAP could have been diverted to do the same: if all nodes are both client and server, one gets a multi-server architecture, which is somehow a distributed architecture. With a bus communication, a thermometer posts one message when its temperature changes. With a client-server communication, the thermometer expects request messages then sends reply messages, or posts messages to previously subscribed clients (that have to be kept in memory). For home automation, a bus communication is more efficient than client-server communications even if this could work.  
The main obstacle in using CoAP is for the definition of a security mechanism for many-to-many communications. CoAP uses DTLS (TLS for datagrams), which require a preliminary negotiation stage prior to sending data: to agree on a ciphering algorithm, session keys, nonce, etc. This is feasible for client-server communication, not for bus communications: each one post its messages on the bus without any preliminary knowledge of others. This is why a dedicated communication protocol has been designed within the xAAL system.
## 4 Discussion  
### Contributions and Future Applications of the Suggested Method for Walking Environment  
Considering the human gait's characteristics, the more abnormal gaits in a specific location may indicate the existence of an environmental barrier to walkability. The suggested method can classify pedestrians' normal and abnormal gaits. If pedestrians' gait data on a daily walking environment are collected, then a certain location with a higher ratio of abnormal gaits than other locations can be a candidate that needs to be investigated. This process can help government agencies to determine how an environmental barrier should be handled. In addition, the suggested method can provide the basis for continuous monitoring of the built environment. Therefore, the major contributions of this study can be summarized as follows:  
1. The wearable data show that normal gaits and abnormal gaits in a walking environment can be distinguished through the cascaded LSTM-based DRNN model.
2. In terms of monitoring the walking environment, it is suggested that the built model can distinguish between normal gaits and abnormal gaits even when new data is inputted.
3. In a specific location, the high correlation between the distribution of normal gaits and abnormal gaits and the presence of an environmental barrier is presented.  
Various studies have been conducted to measure HAR with the development of various wearable devices including smartphones and smartwatches. In previous studies, the accuracy of analyzing behavior using the accelerometer and gyroscope of smartphones and smart watches is over 90% [62]. This level of accuracy can be applied to walking, and it can be said that it shows the possibility that the method proposed at the application level can be applied in the future.  
However, the suggested method should handle a problem related to carrying a sensor. In this study, the data are collected from an IMU sensor attached to a pedestrian's ankle. An IMU sensor attached to an ankle should be replaced with a smartphone to convert the suggested method to a real-world application. In a smartphone, there are various sensors, including a 9-degree of IMU and a function to transfer collected data to a server. If pedestrians' data collection through a smartphone is possible, conditions of the walking environment can be collected and analyzed in real-time. Although the strengths of using a smartphone for monitoring the walking environment are evident, privacy issues should be handled in future studies.
## 5 Analysis
### Artificial Project Dataset  
Network topology refers to the logical layout of a network (a project schedule). It defines the way different activities (often referred to as nodes) are placed and interconnected with each other. To test the quality of our \(\mu_{p}\) and \(\sigma_{p}\) estimates, an artificial network dataset was used consisting of 4100 different networks (project schedules) with varied topologies. This dataset was developed by the Operations Research and Scheduling Research Group at Ghent University (Belgium). It can be downloaded from [[http://www.projectmanagement.ugent.be/research/data](http://www.projectmanagement.ugent.be/research/data)]([http://www.projectmanagement.ugent.be/research/data](http://www.projectmanagement.ugent.be/research/data)) (MT dataset in the \"artificial project data\" section (MT = measuring time)).  
This set contains 4100 activity-on-node networks with 32 activities each. However, two activities out of the 32 are included simply to signpost the project start and end and are considered dummy activities (zero duration). The network dataset was generated with the RanGen2 algorithm. RanGen2 is a robust random network generator capable of generating a wide range of network topologies, which was validated in several studies [32,33]. Also, this very same network dataset was representatively used in other research studies on earned value management (for example, References [31,34,35,36]) and schedule risk analysis (for example, References [11,12,37]).  
In particular, the network dataset was generated under pre-set values of four topological indicators: the serial-parallel (\(SP\)), the activity distribution (\(AD\)), the length of arcs (\(LA\)), and the topological float (\(TF\)). These four topological indicators were initially proposed by [PERSON] et al. [38] and later refined by [PERSON] et al. [33] and [PERSON] [11]. More precisely, the \(SP\) indicator, introduced earlier, describes how close a network is to a serial or parallel network. \(AD\) describes the distribution of activities in the different schedule paths. \(LA\) measures the distance between two activities in the project network. \(TF\) measures how dense a network is (the amount of topological slacks) at the topological level. The values of these indicators all range between 0% and 100%, and they are considered representative and accurate descriptors of a network topology. For the interested reader, the values of all these indicators for the 4100 networks can be found in Appendix B of the Supplementary Materials (\(\mu_{p}\) and \(\sigma_{p}\) regression results file).
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation  
In this section, we perform a series of experiments to assess the effect of our proposed approach on the performance experience of mobile users. We use four performance indicators, including average packet delay, weighted average packet loss rate, average handover delay and network overhead. We developed the proposed mobility support method and the proposed dynamic buffer resource allocation strategy on Mininet [40], and we also implemented ProPull [20] and LPBMMS [21]. ProPull [20] and LPBMMS [21] both adopt the Complete Sharing (CS) strategy [37], in which ProPull sets the mobile buffer at the old access router before handover, and LPBMMS sets the mobile buffer on the source base station before handover and the predicted target base station, respectively. The default LPBMMS position prediction success probability is 50%.
# Numerical Study of Internal Flow Field in a Disc Stack Centrifuge Based on Mixture-PBM Model
## 2 Physical Models and Numerical Simulation
### Model and Meshing
Due to the complex structure of the disc stack centrifuge bowl and the translational periodicity of the separation channel in the Z-axis direction, a geometric chunking, translational replication, and O/H meshing approach is used to structurally mesh the computational domain. The separation channel mesh is configured according to Scheme 5 in Table 2 and the mesh near the wall is encrypted, i.e., the height of the first layer of the mesh at the wall is less than 0.05 mm and the mesh growth rate is 1.1. There are about 18 million meshes in the computational domain. The Y PLUS \(<20\) in the region close to the wall meets the requirements of the RNG k-epsilon turbulence model. The RNG k-epsilon model in the model library has a wider range of applicability as well as higher reliability and accuracy compared to other models based on the consideration of fluid physics phenomena, simplification of special problems, and the requirement of simulation accuracy [2]. Therefore, in this paper, the RNG (Re-Normalization Group) k-epsilon turbulence model with wall function treatment has been chosen for the numerical simulation. The transport equations for the RNG k-epsilon turbulence model are [25]:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}\left(\alpha_{k}\mu_{eff}\frac{\partial k}{ \partial x_{j}}\right)+G_{k}+G_{b}-\rho\varepsilon-Y_{M}+S_{k} \tag{2}\]  
and  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}} (\rho\varepsilon u_{i})=\frac{\partial}{\partial x_{j}}\left(\alpha_{k}\mu_{ eff}\frac{\partial\varepsilon}{\partial x_{j}}\right)+G_{1\varepsilon}\frac{ \varepsilon}{k}(G_{k}+C_{3\varepsilon}G_{b})-C_{2\varepsilon}\rho\frac{ \varepsilon^{2}}{k}-R_{\varepsilon}+S_{\varepsilon} \tag{3}\]  
In these equations, \(k\) is the turbulence kinetic energy; \(\varepsilon\) is the turbulence dissipation rate; \(G_{k}\) represents the generation of turbulence kinetic energy due to the mean velocity gradients; \(G_{b}\) is the generation of turbulence kinetic energy due to buoyancy; \(Y_{M}\) represents the contribution of the fluctuating dilatation in compressible turbulence to the overall dissipation rate; and \(C_{1}\varepsilon\), \(C_{2}\varepsilon\), and \(C_{3}\varepsilon\) are constants. The quantities \(\alpha_{k}\) and \(\ae\) are the  
Figure 3: Grid independence verification. inverse effective Prandtl numbers for \(k\) and \(\varepsilon\), respectively, and \(S_{k}\) and \(S\varepsilon\) are user-defined source terms. Figure 4 shows the overall mesh layout and local area mesh details of the computational domain. Using this meshing method, the computational requirements are met while capturing the flow details in the separation channel.
# Supplement Use Among Athletes: Insights from Gyms in Sarajevo
## 3 Results
Significant gender differences (\(p<0.001\)) were also observed in the use of amino acid pills (86.2%), creatine (89.4%), glutamine (92.9%), branched-chain amino acids (BCAA) (81.1%), protein powders (whey and casein) (80.7%; 100%), multivitamins (83.3%), B vitamin (79.7%), fish oil pills (83.3%), caffeine (87.1%) and herbal supplements (79.7%), all of which were more prevalent among males. While younger respondents used more supplements than older ones, no significant differences were found between age groups. However, specific product consumption showed statistically significant differences (\(p<0.034\)), with higher usage of creatine (48.9%), whey protein (44.6%), B vitamins (47.8%), and caffeine (61.3%) among younger participants (18-25 years old). In contrast, multivitamins (42.9%) and fish oil pills (37.0) were used significantly more by the middle-aged respondents (aged 26-35) while glutamine (57.1%) and casein protein (57.1%) were used more by older respondents (aged 36-45) compared to other age groups (\(p<0.0023\)). Figure 1: Types of FS used by the respondents. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Age**} & \multicolumn{4}{c}{**Cender**} \\ \hline
**Fitness Motivation** & \begin{tabular}{c} **Supplements** \\ **Consumers** \\ **(n = 208)** \\ \end{tabular}  & \begin{tabular}{c} **18-25** \\ **Year Old** \\ **(n = 72)** \\ \end{tabular}  & \begin{tabular}{c} **26-35** \\ **Year Old** \\ **(n = 80)** \\ \end{tabular}  & \ \begin{tabular}{c} **36-45** \\ **Year Old** \\ **(n = 70)** \\ \end{tabular}  &
\begin{tabular}{c} **Year Old** \\ **(n = 80  
The most reported reasons for consuming FS were muscle repair or recovery (29.8%), increase muscle mass or weight (25.8%), treatment of medical problems (15.3%), and increase performance (13.7%) (Table 2). Among males, the top reasons were muscle repair or recovery (33.0%), increase muscle mass/weight (29.5%), and increase performance (15.9%). Women, on the other hand, primarily cited treatment of medical problems (staying healthy) (38.9%), muscle repair or recovery (22.2%), and increase muscle mass/weight (16.7%) as their main motivations. When comparing age groups, younger participants (aged 18-25) were more likely to mention increase muscle strength (71.4%), increase performance (52.9%) and increase muscle mass/weight (50.0%) as their reasons for starting FS use, while older participants (aged 36-45) emphasized staying healthy (52.6%) and muscle repair or recovery (43.2%). Notably, only 4 (1.6%) respondents selected \"Weight loss\" as a motivation for using FS. The most frequently reported FS recommendation sources were self-prescribed (40.3%), gym trainer (21.8%), internet (15.3%), and doctors (11.3%). Women showed a stronger preference for obtaining FS recommendations from medical experts (30.6% vs. 3.4%), while men leaned towards self-prescription (45.5% vs. 27.8%) (Table 4).
###### Abstract  
Traditional sensor nodes are usually battery powered, and the limited battery power constrains the overall lifespan of the sensors. Recently, wireless power transmission technology has been applied in wireless sensor networks (WSNs) to transmit wireless power from the chargers to the sensor nodes and solve the limited battery power problem. The combination of wireless sensors and wireless chargers forms a new type of network called wireless rechargeable sensor networks (WRSNs). In this research, we focus on how to effectively deploy chargers to maximize the lifespan of a network. In WSNs, the sensor nodes near the sink consume more power than nodes far away from the sink because of frequent data forwarding. This important power unbalanced factor has not been considered, however, in previous charger deployment research. In this research, a power balance aware deployment (PBAD) method is proposed to address the power unbalance in WRSNs and to design the charger deployment with maximum charging efficiency. The proposed deployment method is effectively aware of the existence of the sink node that would cause unbalanced power consumption in WRSNs. The simulation results show that the proposed PBAD algorithm performs better than other deployment methods, and fewer chargers are deployed as a result.  
wireless rechargeable sensor networks; minimum dominating set; charging power Article
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 3 Fractional Frequency Reuse
### ICIC Techniques
#### 3.1.1 Strict Frequency Reuse  
The Strict Frequency Reuse (Strict FR) splits the bandwidth into two sub-bands, as illustrated in Figure 2. The cell-center User Equipments (UEs) are allocated to a common sub-band shared by all cells (reuse factor of 1) and the cell-edge UEs are allocated to a private sub-band (reuse factor of 3). Hence, cell-center users share bandwidth with neighboring cells, thus increasing spectral efficiency, but cell-edge users do not, thus reducing interference. Additionally, a higher power level is used for the private sub-band.  
In order to determine whether a UE is allocated to the common or private sub-band, the ICIC algorithm uses a metric defined in 3 GPP, the Reference Signal Received Quality (RSRQ). It indicates the quality of the signal and it takes into account various metrics, such as noise, power from interfering signals, and the number of allocated Resource Blocks (RBs). If the RSRQ reported by a user is higher than a threshold, the UE is allocated to the common sub-band (cell-center). Otherwise, it is allocated to the private sub-band (cell-edge). The RSRQ threshold is a user-defined parameter.  
Figure 1: Scenario with reuse factor of 3. In this topology, no adjacent cells share bandwidth, i.e., their frequency bands are disjoint.
# Evaluating Commercial Electrical Neuromodulation Devices with Low-Cost Neural Phantoms
###### Abstract  
Non-invasive transcranial electrical stimulation is a category of neuromodulation techniques used for various disorders. Although medically approved devices exist, the variety of consumer electrical stimulation devices is increasing. Because clinical trials and animal tests are costly and risky, using a brain phantom can provide preliminary experimental validation. However, existing brain phantoms are often costly or require excessive preparation time, precluding their use for rapid, real-time optimization of stimulation settings. A limitation of direct electric fields in a phantom is the lack of 3D spatial resolution. Using well-researched modalities such as transcranial direct current stimulation (tDCS) and newer modalities such as amplitude-modulated transcranial pulsed-current stimulation (am-tPCS), a range of materials was tested for use as electrical phantoms. Based on cost, preparation time, and efficiency, ground beef and agar gel with a 10% salt mix were selected. The measured values for the total dosages were 0.55 W-s for am-tPCS and 0.91 W-s for tDCS. Due to a low gain on the recording electrodes, the signal efficiency measured against the power delivered was 4.2% for tDCS and 3.1% for am-tPCS. Issues included electrodes shifting in the soft material and the low sensitivity of the recording electrodes. Despite these issues, the effective combination of the phantom and recording methodologies can enable low costs and the rapid testing, experimentation, and verification of consumer neuromodulation devices in three dimensions. Additionally, the efficiency factors (EFs) between the observed dosage and the delivered dosage could streamline the comparison of experimental configurations. As demonstrated by comparing two types of electrical neuromodulation devices across the 3D space of a phantom, EFs can be used in conjunction with a cost-effective, time-expedient phantom to rapidly iterate and optimize stimulation parameters.  
amplitude modulated transcranial direct current stimulation; brain phantom; electrical neuromodulation; neural phantom +
Footnote †: journal: Applied Sciences
## 5 Conclusions
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Advantages**} \\ \hline \multirow{4}{*}{**ANN**} & (1) Extracts the essential attributes from inputs that contain irrelevant data. (2) Deals with vague cases. (3) Has been successfully applied in many fields such as disease prediction. (1) Works well with unstructured and semi-structured datasets such as images and text. (2) Can attain accurate and robust results. (3) Is successfully used in medical applications. (1) Works well with the data that has missing values or unbalancing issues. (2) Works with categorical and continuous values. (3) Requires a small amount of data to its parameters. (1) Easy to understand and implement especially when using a graphical representation. (2) Works well with numerical and categorical data. (3) Does not require much data pre-processing work. (1) Performs automatic feature extraction. (2) Can show the correlation between the elements for the provided inputs. (3) Requires loss time in classification and result in a high accuracy especially in image processing. (1) Outside for classification, regression, and prediction problems. (2) Can be employed to different types of data: image, text, etc. (1) More accurate in predicting what will be next thanks to remembering capability. (2) Powerful in complex problems. (3) Deals with sequential data inputs instead of fixed-sized vectors. (1) Encompasses of multiple memory blocks that can overcome the vanishing gradient problem. (2) Used in Natural Language Processing applications. \\ \hline \hline \multicolumn{1}{c}{**Disadvantages**} \\ \hline \multirow{4}{*}{**SVM**} & (1) Too many hidden layers will result in an overfitting problem. (2) The random initialization of the weight does not always lead to the best solution. (3) The structure can be determined only through trial and experience. (4) Works only with numerical data. (1) Requires long training time when it is used with large datasets. (2) Is hard sometimes to select the right kernel function. (3) The weights of the variables are difficult to interpret in the final model. (1) All the features must be independent to each other. (2) The conditional independence might cause a drop in the accuracy. (1) Small change in the data can have a significant effect on the final predictions. (2) Does not perform well with large datasets due to the increase complexity of the tree. (1) The computational cost is high. (2) Can’t perform well without a large amount of training data. (1) Inadequate for modern advanced computer vision field. (2) The total number of parameters can be very large. (1) Vanishing gradients problem. (2) Difficult to calculate the weights and biases which drop the accuracy. (1) High computational time required to calculate the memory bandwidth for the processing units which makes it harder to train the data. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the advantages and disadvantages of ML and DL algorithms.
# Exploring CP Array Antenna Characteristics and Its Applications to a Deployable Structure
###### Abstract  
In this article, we investigated the performance of circularly polarized (CP) array antennas for wireless power transmission (WPT) application. During the development process, three different mounting configurations for the transmitting CP antenna are considered, assuming that the proposed CP antenna could be a part of the WPT system. The wireless power transmission system employing the proposed antenna design as the transmitting antenna is intended for power transfer either between satellites in space or between a satellite and a ground segment. To implement the various mounting configurations, multiple transmitting array antennas, designed to operate at a center frequency of 5.8 GHz, were fabricated and tested. The performance of the proposed CP antenna was evaluated for each configuration in terms of return loss, gain, axial ratio, and radiation pattern. The experimental results of all three configurations support the design theory very well.  
array antenna; circular polarization; wireless power transmission; C-band; CubeSat; deployable antenna +
Footnote †: journal: Physics Letters A  
0000-0002-4000-0000][PERSON]  
0000-0002-3391-5580][PERSON]  
0000-0002-3391-5580][PERSON]