esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Overall Structure of the Control System
### Control Methods
#### 2.1.1 Water Level Control (\(Wlc\))  
This control system is based on the proportional-integral-derivative (\(PID\)) control of the water level. As indicated by its name, a \(PID\) algorithm consists of three basic control modes: proportional, integral, and derivative. A diagram of a water level control system is shown in Figure 3. The \(PID\) controller has been implemented in a \(PLC\), with the output of the controller being the reference speed for the power converter driving the generator.  
This control system acts on only one parameter of Equation (2), the head (\(h\)). Therefore, this control system does not ensure that the mechanical power of Equation (2) is maximised. The control system will regulate the speed of the electric generator so that the preset head will remain constant. Therefore, once the water level is set, the control system maintains it faithfully. Unfortunately, the choice of this water level does not coincide with the optimum water level. Therefore, the power output is not the maximum.  
This control system receives the set point water level from two ultrasonic devices that measure the water levels upstream and downstream of the \(AST\). Therefore, the correct installation of these devices is also a factor to be taken into account.
## 4 Results  
Experimental results indicated that activities such as using a bottle of milk could be identified and distinguished from using a kettle of hot water (Figure 3) using thermal blobs from T1. While a bottle of milk was seen as monochromatic shades of black due to its low temperature, a kettle of hot water had shades of white representation due to its high temperature, as presented in Figure 3. Moreover, it is important to note that notwithstanding the closeness of the participants to the thermal sensor (Figure 3), their identities were still protected. The RGB equivalents of the activities such as opening the fridge (Figure 4a), heating a hot kettle (Figure 4b), and having tea or coffee at the kitchen table (Figure 4c) are also presented for comparative purposes.  
After preparing a cup of tea, it was easier to know from the thermal blobs whether the user successfully reached the table. In addition, it was necessary to know where the participant placed the hot kettle (after using it), which is a potential hazardous object. As presented in Figure 5, these activities were clearly viewed on the thermal image. Whilst the hot kettle was represented as a large blob adjacent to the participant, the tea/coffee cup was viewed as a small bright spot in what could be viewed as the hand of the user (Figure 5).  
Figure 4: RGB equivalents of activities: (**a**) opening the fridge, (**b**) heating a hot kettle, and (**c**) having tea or coffee at the kitchen table.  
Figure 3: Thermal blobs of a bottle of cold milk (shades of black) distinguishable from a hot kettle (shades of white).  
In some instances, the heat spot of a cup or kettle may be occluded by a participant when it is viewed from the lateral thermal sensor (see, Figure 6). When this happens, abnormal behaviours or activities may go unnoticed. To address these concerns, the ceiling sensor (T2) can be used to collect an aerial view as presented in Figure 7. Hence, the essence and usefulness of dual sensing in this study.  
Figure 5: Distinguisable thermal blobs. On thermal_407, the blue arrow points to the hot kettle, the black arrow points to the participant, and the red arrow to the tea/coffee cup after the initial act of tea/coffee making.  
Figure 6: Thermal images from the lateral sensor indicating instances of occluded tea/coffee cups that are visible on the ceiling sensor. Refer to the thermal blobs with the same name as thermal_345, thermal_357, thermal_587, and thermal_598 in Figure 7.  
Figure 7: Heat pots from tea/coffee cups occluded from the lateral thermal sensor (T1) but indicated by the ceiling thermal sensor (T2). The black arrow on thermal_242 points to the location of T1, the white arrow points to the heat spot, and the red arrow points to the hand of the participant (occluding the heat spot).
## 3 Experimental Results Analysis
### Peak Strength Analysis  
This section discusses the influence of preexisting fissure length, angle, and distance on the UCS values of rock-like materials.  
A greater preexisting fissure length substantially impacts the UCS of rock-like materials. Figure 4 shows that the UCS value decreases with increasing preexisting fissure length, especially when the fissure angles were 60\({}^{\lx@math@degree}\) and 70\({}^{\lx@math@degree}\). Through an analysis of the experimental results, it could be concluded that, compared with short preexisting fissures, long fissures have larger influence areas on rock-like materials. During the loading process, the newly generated cracks appeared more easily at or near the tip of the long preexisting fissures. These newly generated cracks typically caused the failure of the specimens. The UCS values of rock materials decreased with increasing the fissure length. However, the UCS value did not increase with an increase in fissure angle (fissure angle 0\({}^{\lx@math@degree}\) and fissure distance of 20 mm); this was mainly caused by differences in the specimens, but the results were acceptable. Hence, in engineering practice, more attention should be given to long preexisting fissures.  
Figure 3: RYL-600 rock mechanics shear creep testing system.  
Figure 4: _Cont._  
However, the influence of the preexisting fissure angle (Figure 5) on the UCS value of rock-like materials was not obvious. The UCS value of the rock-like materials with large preexisting fissure angles was larger than that with small fissure angles. For example, for rock-like materials with a fissure lengths of 50 mm and a distance of 40 mm, the UCS value of specimens with a fissure angle of 90\({}^{\circ}\) was 33.71 MPa, while that of specimens with a fissure angle of 15\({}^{\circ}\) was 25.77 MPa. Due to the interactions of long and short preexisting fissures, and of preexisting fissures and newly generated cracks, the variation in the UCS values of rock-like materials containing two unequal parallel preexisting fissures were not obvious; however, it should be noted that specimens with small preexisting fissures have low strength; as such, these results are similar to those for specimens with multiple equal parallel fissures [10].  
The influence of fissure distance (Figure 6) on the UCS value of rock-like materials is the same as that of the preexisting fissure angle. It can be difficult to find a clear trend with an increase in the distance between two preexisting fissures. Nonetheless, the impact of long preexisting fissure is so great that the impact of the two previous fissures can be ignored.
## 3 Results
### FT-IR Analysis  
The molecular characteristics of _U. barbata_ fractions are presented in Figure 6. Noticeable differences in absorbance values between fractions were observed for almost all peaks,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Element Content**} & \multicolumn{3}{c}{**Sample**} & \multicolumn{1}{c}{**F-Value**} \\ \cline{2-5}
**(ug/g)** & _d_UB** & _mc_UB** & _cc_UB** & \\ \hline Al & 87.879 \(\pm\) 1.152 b & 285.828 \(\pm\) 2.748 a & 21.111 \(\pm\) 0.608 c & 18,444.13 *** \\ As & ND & 0.219 \(\pm\) 0.005 & ND & - \\ Ba & 3.782 \(\pm\) 0.052 b & 12.113 \(\pm\) 0.604 a & 0.596 \(\pm\) 0.004 c & 866.03 *** \\ Ca & 979.766 \(\pm\) 12.285 b & 1549.600 \(\pm\) 18.406 a & 434.269 \(\pm\) 22.222 c & 2846.40 *** \\ Cd & ND & 0.164 \(\pm\) 0.001 & ND & - \\ Co & ND & 0.225 \(\pm\) 0.001 & ND & - \\ Cr & 1.002 \(\pm\) 0.008 b & 2.494 \(\pm\) 0.010 a & 0.864 \(\pm\) 0.007 c & 34,521.18 *** \\ Cu & 1.523 \(\pm\) 0.013 b & 2.246 \(\pm\) 0.005 a & 1.286 \(\pm\) 0.014 c & 5771.15 *** \\ Fe & 52.561 \(\pm\) 2.582 b & 282.468 \(\pm\) 1.149 a & 33.107 \(\pm\) 0.322 c & 21,398.25 *** \\ Li & ND & 0.185 \(\pm\) 0.007 & ND & - \\ Mg & 172.721 \(\pm\) 0.647 b & 524.239 \(\pm\) 2.419 a & 53.482 \(\pm\) 0.597 c & 81,353.06 *** \\ Mn & 101.425 \(\pm\) 1.423 b & 354.041 \(\pm\) 1.083 a & 29.098 \(\pm\) 0.347 c & 78,943.06 *** \\ Ni & 0.449 \(\pm\) 0.011 b & 1.033 \(\pm\) 0.005 a & 0.355 \(\pm\) 0.007 c & 5658.89 *** \\ Pb & 1.296 \(\pm\) 0.007 b & 2.177 \(\pm\) 0.014 a & 0.584 \(\pm\) 0.004 c & 21,958.36 *** \\ V & 0.241 \(\pm\) 0.004 b & 1.237 \(\pm\) 0.005 a & ND & 92,152.36 *** \\ Zn & 20.536 \(\pm\) 0.125 b & 33.223 \(\pm\) 0.164 a & 13.588 \(\pm\) 0.097 c & 17,179.89 *** \\ Hg & 0.671 \(\pm\) 0.020 b & 0.708 \(\pm\) 0.005 a & 0.539 \(\pm\) 0.017 c & 99.48 *** \\ Sb & ND & 0.152 \(\pm\) 0.003 & ND & - \\ \hline \hline \end{tabular}  
* The analysis was performed in triplicate. The results are shown as the mean \(\pm\) standard deviation (SD). ND—not-detected, Al—aluminum, As—arsenic, Ba—barium, Ca—calcium, Cd—cadmium, Co—cobalt, Cr—chromium, Cu—copper, Fe—iron, Li—lithium, Mg—magnesium, Mn—magnanese, Ni—nickel, Pb—lead, V—vanadium, Zn—zinc, Hg—mercury, Sb—antimony. Mean values followed by different letters are significantly different, *** significant at \(p<0.001\).  
\end{table}
Table 2: Mineral composition of _Usnea barbata_ (L.) [PERSON] ex F.H. [PERSON] dried lichen (_d_UB) and both fractions: medulla–cortex (_mc_UB) and central cord (_cc_UB).  
with the highest intensities obtained for _mc_UB, followed by _d_UB and _cc_UB. Furthermore, in the regions 1380-1700 cm\({}^{-1}\) and 2310-2380 cm\({}^{-1}\), different peaks appeared in _cc_UB and _d_UB samples compared to _mc_UB.
## 3 Simulation Results
### Case 3: SST with a Renewable Interface  
To cope with the increasing trend of using RESs, SST provides a very handy infrastructure. RES integrated with SST, as shown in Figure 1, can be used solely to provide power to the consumer, making SST working as stand-alone system. The proposed topology also makes bidirectional power flow possible just by changing the gate pulses of the SST-VSC to make it perform as SST-VSI.  
A high frequency transformer, which in previous cases was used to reduce the voltage level, now in this scenario, was working as a step up HFT. DAB, in this case, would maintain the DC -link for the SST-VSC. The SST-VSC, now working as SST-VSI, used SVPWM pulses to generate a three phase voltage. In this scenario SST was working as a grid-tied inverter, feeding power to the utility.  
The RES block, represented in Figure 1, would be any single RES or the hybrid-RES incorporating batteries as well. In this research work, only the design of controlled multi-input multi-output circuitry of SST is under investigation, so, no specific constraints regarding the RESs were taken into consideration.  
When a constant DC source was used as RES, simulations show that SST provided power not only to consumer but also to the utility as well, as shown in Figure 22, so, this architecture provided by SST would be a solution to all problems that will be faced in the future when smart and super grids will be kicked in the power system.  
Figure 21: SST simulation results for 60 Hz output. (**a**) Phase A voltage and current in phase. (**b**) SST 60 Hz filtered output voltage.  
Figure 22: SST simulation results when integrated with renewable energy resource. (**a**) SST line to neutral voltage at the consumer end. (**b**) SST line to line voltage at the grid end.
# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 3 Materials and Methods
### Selected Dataset  
The dataset employed in this research corresponds to real data from a Spanish utility over a period of three years. The load distribution presents similarities to that of a microgrid [16], with a value range between 7370 and 39,550 kW. The values have annual periodicity, also with strongly defined daily and weekly periodicity (Figure 1).  
Load values were aggregated in time-slots of one hour. The total number of time-slots corresponded to 26,302 h. Additional exogenous features were also considered: (a) Date/time features: month, time, day of the week, and weekend indicator. (b) Weather features: mean and standard deviations for the atmospheric pressure, wind speed, wind direction (degrees), humidity, and solar radiation. Continuous features were scaled in the range [0,1]. Categorical features were one-hot-encoded. We considered four different sets of features out of the many possible feature combinations: (a) 1 feature: electricity load, (b) 45 features: date/time (day of week, weekend, hour, month) and load, (c) 57 features: date/time, weather and load, and (d) 76 features: date/time, load, plus day of the month (one-hot-encoded). The number of features for the different feature sets is denoted by \(f\) (Figure 2). The weather and day of the month features did not provide a clear improvement in the forecast metrics.  
Figure 1: Chart showing the load values for a period of 30 days and time-slots of 1 h. The vertical red lines indicate a 24 h period (1 day). We can observe the strong daily and weekly periodicity.  
Figure 2: Division of the original dataset into training, validation, and test sets with a data proportion of 64, 16, and 20%, respectively. A rolling window process was used to extract the predictors sequence (\(p\) time-slots used as predictors) and the real time-ahead load values (\(k\) time-ahead forecast length). Each time-slot is represented by a vector of predictors with \(f\) components (load value, date/time features, etc.).  
Figure 2 presents how the original dataset was divided into training, validation, and test sets with a data proportion of 64, 16, and 20%, respectively. Figure 2 also presents the rolling window process used to extract the predictors sequence (\(p\) time-slots used as predictors) and the real time-ahead values (\(k\) time-ahead forecast length). The validation data were used to assess model performance during training. The test set was used to provide all final results in Section 4. As a summary, we consider the following ranges of values for the parameters \(p\), k, and \(f\): (a) \(p\): 24 (using the previous day of data--24 h), 168 (previous week of data--168 h), or 720 (previous month of data--720 h); (b) k: 24 (1-day forecast horizon), 168 (1-week horizon), or 720 (1-month horizon); and (c) \(f\): 1, 45, or 57. The forecast results for different combination for these values (\(f,\ p,\ k\)) are reported separately in Section 4 and the Appendix A.
# A Review on Internet of Things for Defense and Public Safety
## 5 Building IoT for Tactical and Emergency Environments
### Enabling Protocols  
This subsection briefly addresses two main concerns: network routing and identification, and RFID identification protocols. Regarding routing protocols, Routing Protocol for Low Power and Lossy Networks (RPL) is an IETF routing protocol based on IPv6 created to support minimal routing requirements through a robust topology (Point-to-Point (PtP), PMP).  
On the other hand, the unique addresses follow two standards today, Ubiquitous ID and EPC Global. The EPC (Electronic Product Code) is a unique identification number stored on an RFID tag that is used basically in the supply chain management to identify items. In order to decrease the number of collisions in the EPC Gen-2 protocol, and to improve tag identification procedure, researchers have proposed to use Code Division Multiple Access (CDMA) instead of the dynamic framed slotted ALOHA. A performance analysis of the RFID protocols in terms of the average number of queries and the total number of transmitted bits required to identify all tags in the system can be seen in [131]. The expected number of queries for tag identification using the CDMA technique is lower that of the EPC Gen-2 protocol, because CDMA decreases the number of collisions. However, when comparing the number of transmitted bits and the time to identify all tags in the system, EPC Gen-2 protocol performs better. The EPC Global architectural framework is based on the EPC Information Service, which is provided by the manufacturer, and the ONS (Object Naming Service) that offers features similar to DNS (Domain Name Service). Being a central lookup service, the root of the ONS can be controlled or blocked by a company/country, unlike the DNS system.  
Identification methods, such as ubiquitous codes (uCode) and Electronic Product Codes (EPC), are not globally unique, although they provide a clear identity for each object within the network. Addressing methods of IoT objects, that include IPv4/IPv6, assist to uniquely identify objects.
## 3 Modeling Method for \(\mathbf{S_{h}}|\mathbf{X}^{F},\mathbf{X}^{S}\)
### Introduction of Gustafson Correction to Address NDB Covariate: RQ2
\begin{cases}\pi_{j}\sim\mathbf{Beta}(g_{0}+\Sigma z_{j},\ h_{0}+n_{j}-\Sigma z_{j})\\ \widehat{\kappa}_{j}\sim\mathbf{MVN}\big{(}\big{[}(\widehat{\Sigma}_{k}^{-1}+ \mathbb{K}_{1}^{\mathbb{T}}\mathbb{K}_{1})^{-1}(\widehat{\Sigma}_{k}^{-1} \mathbb{K}+\mathbb{K}_{2})\big{]},\ \widehat{\lambda}_{j}^{2}\big{[}\widehat{\Sigma}_{k}^{-1}+ \mathbb{K}_{1}^{\mathbb{T}}\mathbb{K}_{1}\big{]}^{-1}\big{)}\\ \widehat{\lambda}_{j}^{2}\sim\mathbf{InvGa}\big{(}\frac{c_{0}+n_{j}}{2},\ \tfrac{1}{2}(d_{0}+\Sigma(x_{j}-\widehat{\kappa}_{0j}+\widehat{\kappa}_{1}z_{j })^{2})\big{)}\\ \boldsymbol{\mathfrak{T}}_{j}^{2}=(1-\zeta)\widehat{\lambda}_{j}^{2}\end{cases} \] (35)The derivations of the posterior densities for the covariate model parameters--\(\pi_{j}\), \(\widehat{\kappa}_{j},\widehat{\lambda}_{j}^{2}\)--are thoroughly outlined in Part 1 of the Supplementary Materials. The scaling factor \(\zeta\) is set by researchers based on the findings from the sensitivity analysis, ensuring it aligns with the anticipated error levels in the NDB covariate across various scenarios. A more in-depth exploration of this experiment will be presented in the following section. 2. In line 18, we utilize the system of equations from Equation (33) to refine the estimated outcome parameter values \(\theta_{j}^{(s)}:\{\beta_{j}^{(s)},\sigma_{j}^{(s)}\}\) by using the parameter samples from the incomplete joint model: \(\widehat{\beta}_{j0},\widehat{\beta}_{j1},\widehat{\beta}_{j2},\widehat{\sigma }_{j},\widehat{\lambda}_{j},\widehat{\kappa}_{j0},\widehat{\kappa}_{j1}\), along with the variance \(\mathfrak{T}_{j}^{2}=(1-\zeta)\widehat{\lambda}_{j}^{2}\) (where \(0<\zeta<1\)) of the linking component in Equation (27). It is important to note that the parameter samples obtained from the incomplete joint model during the Gibbs sampler must satisfy the specific criteria outlined in Equation (33). For instance, as indicated in Equation (33a), \(\widehat{\lambda}_{j}^{2}\) values must always exceed \(\lambda_{j}^{2}\) values. Additionally, according to Equation (33g), \(\widehat{\sigma}_{j}^{2}\) must be always greater than the value given by \(\frac{\beta_{1}^{2}\mathfrak{T}_{j}^{2}\lambda_{j}^{2}}{\widehat{\lambda}_{j} ^{2}}\). These constraints ensure that the sampled parameters maintain valid relationships with the true parameters, allowing the Gibbs sampler to filter out any samples that do not conform to these requirements.
###### Abstract  
Extension of usable frequency spectrum from microwave to millimeter-wave (mmWave) is one of the key research directions in addressing the capacity demands of emerging 5 th-generation communication networks. This paper presents a thorough analysis on the azimuthal multipath shape factors and second-order fading statistics (SOFS) of outdoor and indoor mmWave radio propagation channels. The well-established analytical relationship of plain angular statistics of a radio propagation channel with the channel's fading statistics is used to study the channel's fading characteristics. The plain angle-of-arrival measurement results available in the open literature for four different outdoor radio propagation scenarios at 38 GHz, as well as nine different indoor radio propagation scenarios at 28 GHz and 38 GHz bands, are extracted by using different graphical data interpretation techniques. The considered quantifiers for energy dispersion in angular domain and SOFS are true standard-deviation, angular spread, angular constriction, and direction of maximum fading; and spatial coherence distance, spatial auto-covariance, average fade duration, and level-crossing-rate; respectively. This study focuses on the angular spread analysis only in the azimuth plane. The conducted analysis on angular spread and SOFS is of high significance in designing modulation schemes, equalization schemes, antenna-beams, channel estimation, error-correction techniques, and interleaving algorithms; for mmWave outdoor and indoor radio propagation environments.  
angular spread; shape factors; coherence distance; average fade duration; level-crossing-rate; mmWave +
Footnote †: 2019, 7, 9; doi:10.3390/technologies/7010009
## 3 Results
### Changes in the Aroma Profile Induced by DBD Plasma
The fruity aroma remained as the main aroma descriptor of pineapple juice despite the reduction in short-chain esters concentration observed in the 500 Hz plasma-treated juice. Figure 3: Aroma descriptors radar for pineapple juice subjected to dielectric barrier discharge plasma application: (**a,b**) OAV of the untreated, 500 Hz/10 min, and 500 Hz/20 min samples; (**c,d**) OAV of the secondary descriptive notes of the fruity aroma for the untreated, 500 Hz/10 min, and 500 Hz/20 min samples. The changes observed in the short-chain esters profile resulted in the reduction of esters with high odor thresholds and the increase of esters with low odor thresholds. Therefore, the sum of the OAVs of the fruity-flavored compounds did not change significantly. At high frequency (1000 Hz), the impact of the fruity descriptor in the aroma also increased, but much less (14%) than at mid-range frequency (69%). The secondary descriptive notes of the fruity aroma followed the same trend observed at mid-range frequency, presenting an increase in the fresh note and a decrease in the ethereal note. The increase in the fresh note was related to the rise in the concentration of the 1,3,5-undecatriene (Figure 4). The plasma treatment at 1000 Hz also increased the OAVs of the floral, waxy, fatty, and sulfurous aromas of pineapple juice. Among these increased OAVs, the increase in the sulfurous descriptor was the most relevant. At this operating condition, the fruity to sulfurous OAV ratio decreased from 190.1 (natural) to 85.4 (plasma-treated), indicating the higher contribution of the sulfurous aroma that can compromise the odor balance in pineapple juice. For this reason, the operation at 1000 Hz would not be recommended for pineapple juice processing. The OAV of the fatty and waxy aroma descriptors increased following the same trend observed for the plasma-treated juice at mid-range frequency. A positive effect of the plasma treatment at 500 and 1000 Hz was the reduction of the methyl hexanoate concentration, a compound that gives a pungent sweet aroma to pineapple and is considered an off-flavor for some customers [23]. Evaluating the aroma descriptors attained at the three frequencies tested, the treatment carried out at 500 Hz enhances the fresh and fruity aroma characteristics of pineapple juice. The operation at 50 Hz (the lowest frequency tested) is not recommended for this juice because it significantly reduces its fresh and fruity aroma and increases the fruity-to-sulfurous ratio. However, the operation at 50 Hz, if carried out for less than 10 min, may  
Figure 4: Aroma descriptors radar for pineapple juice subjected to dielectric barrier discharge plasma application: (**a,b**) OAV of the untreated, 1000 Hz/10 min, and 1000 Hz/20 min samples; (**c,d**) OAV of the secondary descriptive notes of the fruity aroma for the untreated, 1000 Hz/10 min, and 1000 Hz/20 min samples. decrease the pungent note of methyl hexanoate without compromising the remaining influence of the descriptors.