esa-sceva/satcom-chunk-collection huggingface dataset:
The significant physical defects, changes of physicochemical properties of dehydrated foods, and the worsening of their quality such as structure collapse, caking, stickiness, and change of colour can be observed at storage temperatures above \(T_{g}\). To overcome this problem, high molecular weight biopolymers (maltodextrin, gums) with high \(T_{g}\) can be incorporated into fruit materials before drying [24; 25]. As \(T_{g}\) depends on water content, knowledge of the relation between water activity and water content of the product described by the moisture sorption isotherms is necessary to predict the quality and stability of products during storage [26; 27; 28; 22]. Many mathematical models can be used to describe the water sorption isotherms: these include models that refer to multilayer sorption (GAB model, [PERSON] model), kinetics model with monolayer sorption (BET model) and semi-empirical models ([PERSON] model, [PERSON] model) [29]. There are many studies concerning the sorption behaviour and glass transition of dried fruit products [20; 25; 26; 30; 31]. However, the current literature about the effects of fruit material addition on these properties in the case of the dried gel is still limited. Drying of gels enables obtaining novel structures and textures of products [3; 32; 33; 34]. Novel gelled products can be created with different hydrocolloid agents and fruit materials. Among many hydrocolloids used in the production of gels, agar-agar is an effective gelling agent, which forms a thermo-reversible but stable gel over a wide range of temperatures [35]. Agar is a mixture of agarose and agaropectin fractions, but gelation is caused by the presence of agarose, which creates the physical gel. The structure of this gel is only formed by the polymer's molecules, which are linked by hydrogen bonds [36]. The formation of agar gels occurs after the cooling of sol solutions below 40 \({}^{\circ}\)C.  
This study aimed to determine the impact of adding apple puree and maltodextrin to agar sol on the sorption properties and structure of the freeze-dried gel. The scope of research also involved the analysis of the effect of different drying methods on the investigated properties of aerated apple puree gels. The relations between the glass transition temperature, water activity, and water content were also investigated for selected snacks.
# Analytical Design of Fractional-Order PI Controller for Parallel Cascade Control Systems
## 3 Results
### Example 2  
The following primary and secondary process models, Equation (55), were studied by several researchers [2; 3; 8] for the case of an unstable process:  
\[\begin{array}{l}G_{p_{1}}(s)=G_{d_{1}}(s)=\frac{e^{-4s}}{20s-1}\\ G_{p_{2}}(s)=G_{d_{2}}(s)=\frac{2e^{-2s}}{20s+1}\end{array} \tag{55}\]  
The secondary controller is obtained from Equation (24):  
\[\begin{array}{l}G_{cd2}(s)=\frac{20s+1}{2(s+1)}\end{array} \tag{56}\]  
(where \(\lambda_{2}=1\)).  
The primary controller is also derived using the proposed Algorithm 1 for case 2. From Algorithm 1, the value \(M_{s\_desired}\) first needs to be assigned. And in this example, \(M_{s\_desired}=1.1\) to ensure the robustness of the proposed fractional-order controller. The obtained controller parameters are tabulated in Table 3. In this example, the proposed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Nominal**} & \multicolumn{3}{c}{**Perturbed (\(\pm\)20\%)**} \\ \cline{2-7}  & **IAE** & **ISE** & **TV** & **IAE** & **ISE** & **TV** \\ \hline Proposed & 6.1723 & 2.7231 & 3.6211 & 6.6548 & 2.8601 & 4.3013 \\ [PERSON] & 8.8062 & 6.8088 & 4.0936 & 10.8054 & 7.6263 & 4.7519 \\ [PERSON] (case B) & 10.727 & 7.4696 & 7.9901 & 12.3538 & 7.8052 & 12.430 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance index in nominal and perturbed models for example 1.  
Figure 8: The control signals for example 1 in the nominal case.  
Figure 9: The control signals for example 1 in the perturbed case.  
method is compared with other methods reported by [PERSON] [2] and [PERSON] [8], and their results are also listed in Table 3.  
Figures 10 and 11 show the nominal closed-loop responses and the corresponding control signals in example 2. In this example, a negative unit step disturbance is also inserted at \(t=125\) (s) to evaluate the capability of the proposed control in the regulator problem. In general, the proposed tunning settings have a better performance in set-point change with a faster response and a shorter settling time. However, in the regulator problem, the proposed method yields a slight oscillatory compared to the method proposed by [PERSON] [8]. The performance indexes are summarized in Table 4.  
Moreover, the robustness of the proposed controller is testified by perturbing the gains and time delays of \(G_{p_{1}},G_{p_{2}},G_{d_{1}}\) and \(G_{d_{2}}+20\%\), whereas the time constants of those transfer functions \(-20\%\). Figures 12 and 13 illustrate the process responses and control signals respectively in the perturbed case. The performance indexes, in this case, are also listed in Table 4. It is obvious that the proposed method is comparable to the method proposed by [PERSON] and has a much better performance compared to the settings proposed by [PERSON] in both servomechanism and regulator problems.
# Trajectory Planning for a Mobile Robot in a Dynamic Environment Using an LSTM Neural Network
###### Abstract
The authors concluded that the LSTM layer is a fundamental part of the algorithm since the speed command given to the robot should have time characteristics, which means that the current speed command and the previous speed command must have a certain correlation to ensure continuity and smoothness. As for future work, they assume that their method can be applied for more complicated tasks, such as in the case of moving obstacles. This paper proposes an LSTM neural network to achieve safe trajectory planning and navigation for a mobile robot from an initial point to an end point while avoiding a dynamic obstacle. The training examples were obtained by operating the mobile robot in a known dynamic environment and recording and labeling its sensor readings, target desired location, mobile robot location with respect to the odometry reference frame, and the velocity readings. The main contributions of this work can be summarized as follows: an LSTM network with an Adam optimization method is proposed to learn the trajectory planning strategy for the mobile robot in a dynamic environment; the algorithm is verified in a computationally simulated environment and then uploaded to GitHub, which is an open-source repository for code and software applications. The results show an accuracy of training of above 99%. With shorter trajectories (\(<2\) m) the model can generalize other trajectories for which it was not specifically trained; however, it must be within a 15 cm radius of a trained destination position. In the case of longer and more complex trajectories, the mobile robot can successfully arrive at the destination point for which the model was trained. The LSTM network with the Adam optimization method lets a robot find a trajectory from the specified start location to the desired location while avoiding dynamic and static obstacles. The rest of the paper is organized as follows: Section 2 theoretically describes the basis of artificial neural networks, recurrent neural networks, LSTM neural networks, [PERSON]'s optimization model, the robotic operating system framework, and the Gazebo simulator; Section 3 refers to the design and methodology of the experiments; Section 4 presents the results; and Section 5 exposes the conclusions and future work.
# (-)-Fenchone Ameliorates TNBS-Induced Colitis
## 2 Results  
### Effect of (-)-Fenchone in Intestinal Acute Inflammation in a TNBS-Induced Colitis Model  
The parameters: ulcerative lesion area (ULA), lesion score, weight/length ratio, and diarrhea were evaluated 48 h after TNBS administration, as shown in Table 1. TNBS administration to coliti groups led to the development of large lesions and intense signs of inflammation in gross ulcerative areas. These effects were followed by an increased weight/length ratio of the colonic segment and the diarrhea score, which was determined by the evacuation index (EI) when compared to the non-coliti group (\(p<0.001\)). The treatments with (-)-fenchone (37.5, 75, 150, or 300 mg/kg) (\(p<0.05\), \(p<0.01\); \(p<0.001\)) or prednisolone (\(p<0.001\)) reduced those parameters in all evaluated doses, showing evident signs of disease recovery (representative images in Figure 1). The most effective dose of (-)-fenchone (150 mg/kg) was selected for the following experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Dose** & **ULA (mm2)** & **\% Lesion** & **Lesion** & **Weight/Length** & **Evacuation** \\  & **(mg/kg)** & **ULA (mm2)** & **Inhibition** & **Score** & **(mg/cm)** & **Index (EI)** \\ \hline Non-colitic & - & ND & ND & ND & 74.9 \(\pm\) 5.9 & 12.0 \(\pm\) 3.0 \\ Colitic & - & 286.6 \(\pm\) 5.7 *** & - & 6.0 \(\pm\) 0.9 *** & 162.7 \(\pm\) 22.4 *** & 20.0 \(\pm\) 2.4 * \\ Prednisolone & 2 & 36.7 \(\pm\) 5.7 *** & 87\% & 3.0 \(\pm\) 0.5 *** & 107.5 \(\pm\) 15.0 *** & 7.0 \(\pm\) 3.0 *** \\ (-)-Fenchone & 37.5 & 240.9 \(\pm\) 24.9 * & 16\% & 4.5 \(\pm\) 1.2 * & 126.7 \(\pm\) 10.9 * & 9.0 \(\pm\) 1.1 * \\  & 75 & 181.4 \(\pm\) 22.7 *** & 37\% & 4.0 \(\pm\) 1.5 * & 118.5 \(\pm\) 32.9 ** & 7.0 \(\pm\) 0.7 *** \\  & 150 & 72.2 \(\pm\) 10.0 *** & 75\% & 3.0 \(\pm\) 0.7 *** & 113.1 \(\pm\) 18.7 ** & 6.0 \(\pm\) 1.1 *** \\  & 300 & 73.8 \(\pm\) 10.8 *** & 74\% & 3.0 \(\pm\) 0.8 *** & 112.8 \(\pm\) 9.7 ** & 4.0 \(\pm\) 1.3 *** \\ \hline \hline \end{tabular} Data are expressed as mean \(\pm\) 50 or median (minimum/maximum values). For parametric data, we used one-way ANOVA followed by [PERSON]’s and [PERSON]’s post-tests. For non-parametric data, we used the Kruskal-Wallis test and Dunny's test. * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) compared to coliti groups; * \(p<0.05\), *** \(p<0.001\) compared to non-colitic group. (\(n=7\)–10). ND = not detectable.  
\end{table}
Table 1: Effect of (-)-fenchone in intestinal acute inflammation in a TNBS-induced colitis model.  
Figure 1: Representative images of rat colons from non-colitic group (**A**); colitic groups (5% tween 80) (**B**); prednisolone, 2 mg/kg (**C**); and (-)-fenchone, 37.5 mg/kg (**D**), 75 mg/kg (**E**), 150 mg/kg (**F**), and 300 mg/kg (**G**) subjected to acute TNBS-induced ulcerative colitis model.
# From Analogue to Digital Photogrammetry: Documentation of Padise Abbey in Two Different Time Stages
## 3 Re-Processing of Original Images
### Data  
All 12 images of photogrammetric survey carried out in 1991 were available for digitizing and re-processing. Most of the glass plates evinced some kind of mechanical decay. Most common decays were scratches and cracks. Overall, the negatives were assessed as they were in relatively good condition without any major imperfections.  
For creating a complete picture of the original survey, it was necessary to acquire every available documentation. Despite the glass plate negatives, we had the sketch of the scene with six marked points on the south wall and measured distances between the marked points. We also had a report with measured distances between each image of each stereo-pair. For re-processing and following an accuracy check, we wanted to use all information about the original survey.
## 3 Results and Discussion  
### Transmission Electron Microscopy (TEM) Results  
The samples were named as S1, S2, and S3 according to their shell thickness \(\sim\)14 \(\pm\) 1 nm, 18 \(\pm\) 1 nm and 22 \(\pm\) 1 nm, respectively. The SiO\({}_{2}\) core size of all of the samples was \(\sim\)120 \(\pm\) 2 nm. TEM images of all of the core-shell samples (S1, S2, and S3) are shown at different magnification in Figure 1, along with those of the bare SiO\({}_{2}\) nanoparticles (sample S). The images display uniform size distribution sets of spherical nanoparticles. It was observed that the shells with smaller thicknesses exhibited better surface smoothness as compared to the larger ones.  
Figure 1: TEM images of the sample sets S, S1, S2, and S3. Sample S represents the bare SiO\({}_{2}\) nanoparticles. Higher magnification TEM images are given for sets S1, S2 and S3, which have shell thickness of approximately 14, 18, and 22 nm, respectively. The inset in the upper right corner of S3 shows a lower magnification TEM image of the same sample.
## 3 Methods
### Benchmarking Methods  
#### 3.4.1 Standardized Precipitation Evapotranspiration  
The SPEI was developed by [PERSON] et al. [20] to assess drought by considering the climatic balance between precipitation and potential evapotranspiration (PET). Unlike the SPI, the SPEI considers temperature in addition to precipitation. Similar to SPI, SPEIcan be calculated on different time scales, with negative values indicating drought periods. Due to the sensitivity of vegetation to evapotranspiration, the SPEI is considered to have a relative advantage in being closely associated with drought events. After calculating the climatic water balance over different accumulation periods, the data are fitted to a parametric distribution and the probabilities are converted to a percentile of the standard normal distribution to obtain the SPEI values [95]. Typically, the three-parameter log-logistic distribution is used as the reference distribution. The parameters are estimated using L-moments, following the approach proposed by [PERSON] et al. [96]. The SPEI used in this study was obtained from the global dataset produced by Begueria [97], which has a spatial resolution of 0.5 degrees and a monthly temporal resolution. This dataset uses precipitation and temperature data produced by the Climate Research Unit to perform SPEI calculations. To assess the effectiveness of CDI in this study, 1-, 3-, 6-, and 9-month SPEI values were obtained, represented by the abbreviations SPEI_1, SPEI_3, SPEI_6, and SPEI_9.
# A System for Controlling and Monitoring IoT Applications
## 3 Block 1
### Connected Devices to the RPi
#### 3.4.3 SquidBee Module  
SquidBee is a design development kit created by the company Libelium. It allows the change and optimisation of each one of its components due to being completely based on open technologies. SquidBee uses an open code software and hardware design as a platform for remote control and detection. Through its humidity, temperature and light sensors it allows the system to capture environmental data and to send it through the SquidBee net employing the ZigBee wireless communication protocol. The SquidBee activity can be summarised by: acquiring the environmental parameter values (temperature, humidity and light), transmitting those values by ZigBee (low power consumption) or through a serial USB connection and changing to an idle state to save energy until the process is activated again.
###### Abstract
Based on the previous literature, existing studies have used local earthquakes, limited databases, or a specific response spectrum (i.e., acceleration, velocity, or displacement) to compute DRFs. For this reason, there is still a need to develop DRF models for the vertical component of earthquakes. The purpose of this study is to develop new DRF models for acceleration (DRFa), displacement (DRFd), and velocity (DRFv) response spectra considering 775 vertical ground motion records selected from active tectonic regions throughout the world. The response spectra of selected earthquakes corresponding to various damping ratios were computed to calculate the DRF values derived from the acceleration, displacement, and velocity response spectra. This database is a subset of the NGA-West2 database, which is part of a research program organized by the Pacific Earthquake Engineering Research Center (PEER). Three main equations are developed to predict the DRF values (DRFa, DRFd, and DRFv) and subsequently compared with those proposed in the literature and seismic codes in the latter part of this paper.
Sensors_**2012**, _12_, 10906-10919; doi:10.3390/s120810906  
_Sensors_  
ISSN **1424-8220**  
www.mdpi.com/journal/sensors  
Novel Fiber Optic Sensor Probe with a Pair of Highly Reflected Connectors and a Vessel of Water Absorption Material for Water Leak Detection  
**[PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1-1 and [PERSON] 2**  
1 Center for Safety Measurement, Korea Research Institute of Standards and Science,  
1 Doryong-dong Yuseong-gu, Daejeon 305-340, Korea; E-Mails: [EMAIL_ADDRESS] (T.-S.C.);  
[EMAIL_ADDRESS] (K.-S.C.); [EMAIL_ADDRESS] (D.-C.S.)  
2 Department of Aerospace Engineering, Jeonbuk National University, Jeonju 561-756, Korea;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-42-868-5326; Fax: +82-42-868-5757.  
_Received: 23 July 2012; in revised form: 3 August 2012 / Accepted: 3 August 2012 / Published: 7 August 2012_  
**Abstract:** The use of a fiber optic quasi-distributed sensing technique for detecting the location and severity of water leakage is suggested. A novel fiber optic sensor probe is devised with a vessel of water absorption material called as water combination soil (WCS) located between two highly reflected connectors: one is a reference connector and the other is a sensing connector. In this study, the sensing output is calculated from the reflected light signals of the two connectors. The first reflected light signal is a reference and the second is a sensing signal which is attenuated by the optical fiber bending loss due to the WCS expansion absorbing water. Also, the bending loss of each sensor probe is determined by referring to the total number of sensor probes and the total power budget of an entire system. We have investigated several probe characteristics to show the design feasibility of the novel fiber sensor probe. The effects of vessel sizes of the probes on the water detection sensitivity are studied. The largest vessel probe provides the highest sensitivity of 0.267 dB/mL, while the smallest shows relatively low sensitivity of 0.067 dB/mL, and unstable response. The sensor probe with a high output value provides a high sensitivity with various detection levels while the number of total installable sensor probes decreases.  
**Keywords:** water leak detection; highly reflected connector; reference connector; sensing connector; sensor probe; water combination soil; vessel
## 2 Materials and Methods  
### Data Collection and Feature Extraction  
Twenty-two elite soccer players (age = 21.96 \(\pm\) 4.53 years; height = 180.68 \(\pm\) 5.23 cm; weight = 72.36 \(\pm\) 4.19 kg) competing in an Italian championship during the 2016/2017 season were recruited in this study. All movement players were included in this study: central backs (\(n\) = 3), fullbacks (\(n\) = 5), midfielders (\(n\) = 5), wingers (\(n\) = 5), and forwards (\(n\) = 4). Goalkeepers were excluded from this study. Participants gave their written informed consent to participate in the study.  
Players' physical activity was recorded during 160 training sessions and 35 matches by using a portable non-differential 10 Hz global position system (GPS) (Playertek, Dundalk, Ireland) that is also characterized by a 400 Hz Tri-Axial Accelerometer and 10 Hz Tri-Axial magnetometers [19]. The GPS devices were placed between the players' scapulae through a tight vest and were activated 15 min before the data collection, in accordance with the instructions of the manufacturer to optimize the acquisition of satellite signals. A total of 1674 individual sessions corresponding to 195 collective sessions performed during the 2016/2017 season was recorded. Eighty-eight (88) workload indicators--21 kinematic (e.g., distance in m or time covered during the training session at specific velocity), 37 metabolic (e.g., distance in m or time covered with a metabolic power above specific thresholds expresses as watt per kg) and 30 mechanicals (e.g., number or m of accelerations/decelerations above specific thresholds)--were extracted from the GPS data. Moreover, the players' rate of perceived exertion (RPE) was collected in isolation about 30 min after the training sessions and matched using the CR-10 Borg scale [20]. This scale is a simple numerical list where 0 refers to \"No exertion at all\", while 10 refers to \"Maximal exertion\". The players were asked to tell their rate of exertion (i.e., overall physical stress and fatigue) during the activity. Moreover, the RPE is multiplied to the duration of the training session to obtain the training load (S-RPE) of each training session per player. In this study, this value is divided in a set of ranked data of ten equally large subsections.  
In accordance with the Helsinki Declaration as revised in 2013, the study was approved by the Ethical Committee of the University of Milan that do not release any document because it didn't consider the formal approval necessary for this study.
# Deepwater 3D Measurements with a Novel Sensor System
## 2 Materials and Methods
### Data Recording
#### 2.2.2 Geometric Modeling, Calibration, and 3D Data Calculation
It enables an immediate response to incomplete measurements, for example, the ability to repeat the measurements. On the other hand, a high-resolution and accurate 3D model of the measurement scene is created offline using motion compensation (see the following section). In order to assess the dimensional accuracy of a reconstructed object, it is necessary to understand the accuracy of the measurement system. The indications for this are systematic and random measurement errors. Typically, these errors are determined by the precision measurements of special reference bodies and are used as estimates when measuring unknown objects.
# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 4 Approach, LPWAN Communication Technology, and Energy Consumption Analysis
### LPWAN Communication Technology  
In this section, we discuss several characteristics and overview of the communication technology and perform the power consumption analysis.