esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Stroke is a leading cause of disability of motor function. Recovery of arm function is essential to regain activities of daily living after stroke. Several robot-assisted exercises for the hemiplegic upper limb of stroke patients have been proposed in recent years [1]. The InMotion ARMRobot (MIT-MANUS/InMotion2, Interactive Motion Technologies, Inc., Cambridge, USA) has been demonstrated to be effective in neurorehabilitation therapy for the motion of shoulder and elbow joints [2; 3; 4; 5]. In the exercise using InMotion2, a grip position of the robot arm is indicated on a display in front of the patient, who guides the grip to a specified target using the horizontal reach motion of the hemiplegic upper limb. If the patient cannot operate the robot arm sufficiently, the reach motion is performed through assistance from the robot. The reach motion is repeated \(>\)1000 times per day for stroke rehabilitation.  
Neurorehabilitation training techniques are based on motor learning principles [6; 7; 8; 9]. Motor functional recovery after stroke by the motor learning is influenced by difficulties of the training, motivation factors of the patients, transferability of a learned skill, and density and frequency of the training sessions. Especially visual and proprioceptive feedbacks, which compliment motion errors, are important for motor learning. Previous studies of reaching movements by healthy subjects revealed that sensory feedback has played an important role in motor adaptation [10; 11]. In a patient study, [PERSON] and [PERSON] reported that visual feedback complements motion when proprioception is impaired [12]. [PERSON] and [PERSON] (2009) also augured in a clinical study that the central proprioceptive capability is important for learning patterns of movement [13]. Sensory disorder is considered a factor preventing recovery from motor paralysis. Proprioception is correlated with the perceived level of physical activity and, in combination with superficial sensation impairment, prevents the performance of activities of daily living (ADL) and upper limb motor recovery [14]. [PERSON] et al. (1992) reported that patients with severe sensory disorder experience less functional improvement after stroke than patients with mild sensory disorder [15].  
[PERSON] et al. (2009) examined the effectiveness of robot training in four chronic stroke patients and reported that, in one case of a patient with sensory disorder, the improvement in motor paralysis improvement was small [16]. Research on InMotion2 has been in upper limb paralysis motor patients [17; 18], but few studies have focused on the improvement of paralysis in stroke patients with sensory disorder.  
In the present study, we provided training using InMotion2 for upper limb function of stroke patients, and examined the impact of complete sensory loss on the improvement of motor paralysis in various degrees of paralysis.
## 2 Methodology
### Building Modeling and Climatic Data  
The heating and cooling demands have been calculated by specific software (Design Builder (v.3.2)). Three different energy efficiency standards were studied to compute the heating and cooling demands of a two-story rural dwelling with a total useful surface of 150 m\({}^{2}\). However, only the worst standard corresponding to a building constructed before 1979 was used in this study. The U-values for the rural dwelling considered were based on the 1979 Spanish building standard (External walls 1.2, Roof 0.7, Ground floor 0.86, and Internal partitions 1.2 W/m\({}^{2}\)-K). The infiltration rate is assumed to be 5.64 air changes per hour (ac/h) at 50 Pa, with a ventilation rate of 0.4 ac/h. The air permeability of windows and doors is set at 100 m\({}^{3}\)/h\(\cdot\)m\({}^{2}\)[33]. The window-to-wall ratio is 15% for all the walls, and aluminum window frames (without a thermal break) with a U-value of 5.8 W/m\({}^{2}\)-K are used. The usage profile considered is included in the Spanish regulation [34] and consists of the following aspects:  
* Heating is available from January to May and from October to December, with a set point of 20 \({}^{\circ}\)C from 8:00 a.m. to 11:59 p.m. and 17 \({}^{\circ}\)C from 0:00 a.m. to 7:59 a.m.
* Cooling is available from June to September, with a set point of 25 \({}^{\circ}\)C from 4:00 p.m. to 11:59 p.m. and 27 \({}^{\circ}\)C from 0:00 a.m. to 7:59 a.m. From 8:00 a.m. to 3:59 p.m., cooling is not available.
* A metabolic rate of 117.21 W/person and an occupancy density of 0.03 people/m\({}^{2}\) resulted in a thermal load per person of 3.51 W/m\({}^{2}\).
* A 100% occupancy is considered 24 h a day on Saturdays and holidays. A total of 61% of the occupancy load is sensitive, while 39% corresponds to latent load.
* Internal heat gains from equipment and lighting are 4.40 W/m\({}^{2}\). On the other hand, the lighting load is 50% transmitted by convection, 30% by long-wave radiation, and 20% by short-wave radiation.  
The annual demands of this building for heating and cooling are 22,645 kWh and 1044 kWh, with peak demands of 12.35 kW and 5 kW, respectively. The indoor temperature was set to 20 \({}^{\circ}\)C from 8 to 22 h and 17 \({}^{\circ}\)C at night, which means that the daily heating peak is found at 8 h when there are lower external temperatures and the indoor set-point temperature is raised 3 \({}^{\circ}\)C. Demands have been calculated for the Spanish climate of Teruel city (36,000 inhabitants). The Koppen climate classification for the Teruel capital is Cfb (oceanic climate) with continental influences, thereby considering milder summers with winters in acclimatization. Hourly demands during the year were then linked with TRNSYS, as well as the local temperature and humidity ratio in the zone to control the part load operation and defrosting activations of the HP. A schematic of the modeled building is given in Figure 2.
## 3 Results and Discussions
### Analysis of Finite Element Calculation Results
#### 3.2.3 Prediction of Remaining Compressive Strength of Concrete  
In this section, using the compressive strength obtained from the uniaxial compression numerical model in the reference literature [21] and the corrosion data obtained from finite element calculations, after normalization processing, linear fitting was performed. The fitting image is shown in Figure 14. From the figure, it can be seen that the strength of concrete and the corrosion depth of chloride ions show a negative correlation in a linear function relationship.  
By substituting \(S/S_{0}\) and \(d_{t}/l_{0}\) into the fitted linear function, we can obtain Formula (4) for the current compressive strength s of reinforced concrete, which can be used to predict the strength of corroded concrete:  
\[S_{t}=(1-k\frac{d_{t}}{l_{0}})S_{0} \tag{4}\]  
where \(S_{t}\) is the remaining compressive strength of reinforced concrete (MPa), \(S_{0}\) is the initial compressive strength of reinforced concrete (MPa), \(l_{0}\) is the edge length of the reinforced concrete specimen along the corrosion direction (m), and \(k\) is the strength loss coefficient.  
Formula (4) essentially reflects the strength reduction in reinforced concrete after corrosion. The strength loss coefficient \(k\) is related to the concrete mix ratio, steel bar material properties, etc. According to Figure 13, the strength loss coefficient \(k\) is 0.75. By directly substituting the prediction formula (4) for chloride ion corrosion depth, we obtain Formula (5), which is the prediction formula for the remaining compressive strength of  
Figure 14: Fitting curve of the relationship between reinforcement concrete corrosion depth and compressive strength.  
reinforced concrete after corrosion for a certain time under different voltages and chloride ion concentrations:  
\[S_{t}=S_{0}-\frac{0.75\,S_{0}}{l_{0}}\cdot 10^{-5}\cdot\left(\begin{array}{ccc}-10^{6}& 255450&-15453&397\\ -10^{6}&-318456&17157&1831\\ 3053.2&-610&75.9&6.1\\ -408215&94375&-4482&-570\\ -2.2&0.4&-0.04&-0.003\\ -1453.2&325&-23.3&1.3\end{array}\right)\left(\begin{array}{c}\psi^{3}\\ \psi^{2}\\ \psi\\ 1\end{array}\right)^{T}\left(\begin{array}{c}1\\ C_{C^{I-}}\\ I\\ C_{C^{I-}}^{2}\\ C_{C^{I-}}^{2}\\ C_{C^{I-}}^{2}\\ C_{C^{I-}}I\end{array}\right) \tag{5}\]
## 2 Materials and Methods
### Feature Extraction
#### 2.6.5 Dominant Frequency  
The frequency with maximum energy amongst all the frequencies present in the power spectrum is called the dominant frequency of a signal. We can easily find the dominant frequency by taking the Fourier transform of the time domain signal and then comparing the values of the energies of different frequencies.
## 7 Results
### Optimization Process with In-Laboratory Measurements
#### 7.1.4 Sample Placement  
Three measurement sessions were conducted on sample fastening using concrete, and two sessions were conducted to monitor plaster drying. The concrete fastening measurement sessions were as follows:  
* Session 1: 19 May-30 May 2022, the stiffness measurement was performed on days 1, 5, 6, 7, 9, and 12 from the day of fastening;
* Session 2: 31 May-9 June 2022, the stiffness measurement was performed in 1, 2, 4, 5, and 6 hours from fastening and then on the following days: 1, 3, 6, 7, 8, 9, 10;
* Session 3: 28 June-4 July 2022 stiffness measurement was performed on days 1, 2, 3, 4, and 7 from the day of sample fastening.  
Plaster fastening measurement sessions:  
* Session 1: 14-16 June 2022. The sample was set on 13 June and monitored on the following drying days: 1, 2, 3. At the end of the measurement on the third day, the sample lost adhesion and was found to be disconnected from the gypsum layer, so monitoring was stopped.
* Session 2: 12-14 July 2022. The sample was set on 11 July and monitored on the following drying days: 1 and 3. Thereafter, the sample lost adherence.  
Figure 13 shows the results of the drying processes. Regarding concrete fastening, as expected, in the first hours after laying (Cement Session 2, in red), the stiffness values are lower because the cement is not yet very solid. Hardening is achieved as early as the first day of drying, and valid measurements should be those taken from day 1 through day 7. For concrete Sessions 1 and 2 (red, blue) from day 7 onward, there is a gradual decrease in stiffness, hypothetically due to the fact that the specimen is beginning to detach from the cement layer. For Session 3, the decrease takes place as early as day 4. In the case of plaster fastening, however, monitoring was performed from the first to the third day of drying in two measurement sessions (in green and black). The plaster's monitoring sessions show stiffness values comparable to each other and to those obtained in the cement drying sessions. In addition, those results appear to provide greater measurement repeatability than those obtained using concrete.  
Figure 13: Trends in dynamic stiffness as a function of drying days for all five sessions. The x-axis is on a logarithmic scale because in Concrete Session 2 (in red), monitoring was also performed for the first few hours after fixation, unlike the others where daily monitoring was performed.
# Simplified Energy Model and Multi-Objective Energy Consumption Optimization of a Residential House
## 2 Materials and Methods  
### Mathematical Model  
A grey-box energy model of a residential house was created. The model contains several energy sources and appliances--see Figure 1. Firstly, the model contains appliances that have a fixed power consumption when turned on. Although these appliances might not necessarily have a fixed consumption on start-up, such dynamics were not included in the simulation, as the timescale of the simulations was several days. These appliances were built into the model by specifying a timeframe for when they should operate on any given day of the week. Examples of such appliances are kitchen appliances, a washing machine, a TV, a PC, and lighting fixtures.  
Secondly, the model contains electric heating and cooling systems that most affect the overall power consumption. Predominantly, the outside temperature and the heating effect of solar irradiation forecasts have the biggest effect on indoor temperature. The heating system respects the demands of the user by keeping the desired temperature.  
Thirdly, the model contains a photovoltaic system. Its expected energy generation forecast profile is taken from the average values of solar irradiation in the last several years [36]. The weather forecast also contains information about expected cloudiness. This expected cloudiness was then used to bring variability to the forecasted solar irradiation. Lastly, a model of a battery of an electric vehicle was built into the model but it was only used as an appliance in the current version of the model.  
The model was primarily designed for a mid-latitude residential house in Europe, where the most important appliance is heating in winter and cooling in summer. However, it can easily be parameterized for other regions.
## 4 Angiogenic Factors
### Endothelins  
The endothelin family consists of a group of 21 amino acid peptides produced primarily in the endothelium, epithelium, and smooth muscle cells [101]. Among the three isoforms of endothelins, endothelin-1 (ET-1) is the most abundant and widely expressed. Several studies have described their pleiotropic biological activity. These peptides are involved in the regulation of different processes, such as cell proliferation, angiogenesis and apoptosis. Furthermore, changes in transcriptional activity of endothelin and its receptors may be involved in the process of carcinogenesis and in the pathogenesis of numerous diseases. Their role has been evaluated in the development of breast, prostatic, colorectal, ovarian, lung, kidney, and endometrial cancer. ET-1 works through plasma membrane-localized endothelin receptor A (ETAR) and endothelin receptor B (ETBR), which are two classical G-protein-coupled receptors (GPCR). ETAR has been reported to be an important vasoconstrtor and growth-promoting receptor, it also sustains tumor progression, angiogenesis and metastatic diffusion [102]. In fact, ETAR-specific antagonists, such as BQ123, are utilized as potential cancer therapeutic drugs, while ETBR can block cell growth and vascular constriction [103]. Several studies showed that the overexpression of ET-1 and its receptors in many cancers (prostate, ovary, bladder, bowel, and kidney) are closely related to MVD and to VEGF levels in tumor cells, proving that ET-receptor antagonists could potentiate the therapeutic efficacy of conventional anticancer drugs [104; 105; 106; 107; 108]. In disagreement with previous studies in which the authors detected an increase of ET-1 and endothelin receptors (ET-Rs) expression, other investigators found that in CCA the binding of ET-1 to its receptors blocks in vitro the cellular growth [75]. In addition, ET-1 exerts an inhibitory effect on tumor proliferation accompanied to a decrease of VEGF and VEGF-R expression on CCA cells. In line with this observation, the inhibition induced by ET-1 is reversed by two antagonists of ET receptors, confirming their ability to mediate this uncommon effect in CCA [75]. In the end, endothelin receptor antagonists could open the possibility of a therapeutic use as adjuvant treatment of human cancer, but we still need to fully understand the usefulness of endothelin receptor antagonism in the clinical setting and to completely determine the frequency and the role of receptor subtypes in cell proliferation.
## 2 Data
Though GLADS has datasets from multiple land models, we used the 1\({}^{\circ}\) dataset from offline Community Land Model (CLM2) which has the largest number of soil layers [10] for a total soil depth of 3.4 m. The downward solar radiation at the surface is obtained from MERRA (Modern-Era Retrospective Analysis for Research and Applications) data ([[http://gmao.gsfc.nasa.gov/merra](http://gmao.gsfc.nasa.gov/merra)]([http://gmao.gsfc.nasa.gov/merra](http://gmao.gsfc.nasa.gov/merra))) Version 5.2.0 of the GEOS-5 DAS (Data Assimilation System) at 1/2 \(\times\) 2/3 degrees resolution. Sea surface temperature (SST) anomalies for the Nino3 region (Nino3-SST) are obtained from the CPC (Climate Prediction Centre), NOAA ([[http://www.cpc.noaa.gov/data/indices/sstoi.indices](http://www.cpc.noaa.gov/data/indices/sstoi.indices)]([http://www.cpc.noaa.gov/data/indices/sstoi.indices](http://www.cpc.noaa.gov/data/indices/sstoi.indices))). The Multivariate Enso Index (MEI) for characterizing ENSO is obtained from [[http://www.esr/noaa.gov/psd/enso/mei.ext](http://www.esr/noaa.gov/psd/enso/mei.ext)]([http://www.esr/noaa.gov/psd/enso/mei.ext](http://www.esr/noaa.gov/psd/enso/mei.ext)). Net irrigated area and fertilizer consumption data is obtained from the Department of Agriculture and co-operation, Government of India ([[http://eands.dacnet.nic.in/latest_2006.htm](http://eands.dacnet.nic.in/latest_2006.htm)]([http://eands.dacnet.nic.in/latest_2006.htm](http://eands.dacnet.nic.in/latest_2006.htm))). Forest plantation and afforestation statistics are obtained from the Ministry of Environment and Forests, Government of India ([[http://envfor.nic.in/nfap/table-geographic-area.html#plantation](http://envfor.nic.in/nfap/table-geographic-area.html#plantation)]([http://envfor.nic.in/nfap/table-geographic-area.html#plantation](http://envfor.nic.in/nfap/table-geographic-area.html#plantation))) [28].
# A Case Study on the Design and Implementation of a Platform for Hand Rehabilitation
## 5 Results
### RehabHand Engineer Viewpoint
#### 5.2.3 Presentation of Generated Code  
Amended generated code of the exercise in Figure 4a is presented in Figure 8. Because of the actual Arduino code's length, some parts were omitted and replaced with descriptive comments.  
Arduino code is a C++ code with some additions. Generated code is always a sum of static and dynamic code. No matter what exercise we define, static code always appears in the generated code. In the case of RehabHand, the static part of the generated code was the inclusion of library (i.e., Finger.h), global variables for each finger (thumbersv), and other useful variables that control the sequence of exercise execution (number of repetitions, motion delay, etc.). Variables representing fingers are instantiated with several parameters (Arduino port number, option reverse, and motion range--min and max position of a servo). Each Arduino program constitutes of function setup() and loop(). The setup() function is called when the Arduino program starts, and is responsible for initializing different parts of device functions. It only runs once, after each start or reset of the Arduino board. Our function setup() is responsible for initializing servos connected with fingers (thumbersv.init()), and so forth.  
The dynamic part starts with setting fingers at the close hand position (thumbersv.move(100)). This generated code corresponds to the first line of the program in Figure 4a (close hand). The rest of the dynamic code is contained in the loop() function, which repeats infinitely--when loop() function execution is completed, it starts again from the beginning. Observe that code generation may not always be one to one mapping between exercise definition to the generated Arduino code. Line speed slow from Figure 4a is a typical example. It corresponds to both delay statements in the loop() function, as well as global variable definition (milliSec).  
The library from the beginning of the generated code in Figure 8 simplifies finger manipulation of a hand. Our library is adapted for the servo motors that we use. This  
Figure 8: Generated code for Arduino platform.  
library needs to be updated for different actuators (stepper motors) and the rehabilitation hands we use.  
Note that the generated source code is usually not formatted. In Figure 8 we inserted indentation in the code for presentation purpose. The generated code is not intended to be read by programmers. It is uploaded, build, and run automatically on an Arduino device.
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 3 Experimental Setup and Conditions
### Experiments  
Figure 8 shows the sensors that were installed in the mixing heads and molds and embedded systems for monitoring the HP-RTM processes. The size of the specimen mold was \(550\times 450\) mm and the carbon fiber fabric used was HPT(r) 300 C45 (with fiber orientation \(\pm 45^{\circ}\)) and HPT(r) 300 C090 (with fiber orientation \(0^{\circ}\)/\(90^{\circ}\)) from the SGL Carbon group. To enhance the tensile and flexural strength in the \(X\) and \(Y\) directions, carbon fiber sheets were cross-stacked to \(0^{\circ}\)/\(90^{\circ}\) and \(\pm 45^{\circ}\) and carbon fiber sheets were combined with binders to form. Then, CFRP preform was placed in the lower mold.  
An epoxy resin (KER-9610) and hardener (KCA9610) made by Kumho petrochemical were used for the HP-RTM process. In the HP-RTM equipment, the epoxy resin was heated to \(80\ ^{\circ}\)C and the hardener was processed at \(45\ ^{\circ}\)C in the high-pressure supply unit. The mixing head pressure was set to approximately 125 bar so that the resin and hardener were mixed homogeneously. The corresponding fiber volume fraction was 40% (seven layers) and 43% (eight layers) in the present study. After the CFRP preform was placed in the mold and the vacuum step was set to 60 s with 0.5 mm end-gap, the  
Figure 6: HMI of the HP-RTM monitoring.  
Figure 7: Embedded monitoring system.  
resin was injected at 64.7 g/s for 9.5 s. The end-gap was closed after the injection was completed and the pressing force of 3000 kN was applied for 500 and 600 s. Table 2 shows the experimental conditions.
## 5 BER and Data Rate Calculation of the Proposed Network
### Applying the Graph Signal Processing Concepts  
The different research works in the GSP field aimed to create advanced tools for data processing that are defined on different irregular graphs. Ref. [40] introduces the main ideas in the GSP field and their relation to traditional DSP analysis [40]. This includes the latest developments of the different GSP tools, i.e., the different methods of sampling, filtering, and graph learning.  
Figure 7: Simple \(M\times N\) MIMO system diagram.  
The proposed network coverage area is assumed to be a cell within a total of 100 cells that cover Alexandria city. The SU-TX tower interconnection with the main control of Alexandria city is calculated by applying the modern concepts of GSP, which are simply explained in [40].  
Therefore, this section aims to describe the relationship between the GSP tools and the proposed network that includes the PU system and the SU system so that the GSP delivers the best channel path that interconnects the transmission between the main control station of the city and the suggested SU system within the proposed cell, i.e., the proposed network.  
The novelty of using the GSP tools is that they use singular value decomposition (SVD) mathematical analysis, which applies to the adjacency matrix which includes the relation between the 100 cells within the wide area network. The SVD elects the eigenvalues at the end, as per the details given in [40], to the best channel selection of the transmission within the Alexandria city area network.