esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Electroluminescence Technique
### IRT Versus EL Inspections  
IRT is the most extended technique in PV image processing to detect failures in panels. It is a non-destructive measurement technique, which provides fast, real-time and two-dimensional distributions of characteristic features from PV modules [17; 18; 19]. In addition, IRT can be performed in illuminated or dark conditions [16; 20; 21; 22].  
In the former instance, the illuminated IRT or IRT under common operating conditions of the PV system are similar to luminescence, where the current flowing through cells increases PV module temperature. In this case, it is used as a contactless method for diagnosing of common thermal and electrical defects in PV modules [23; 24; 25]. Typical anomalies detected by illuminated IRT arise whenever one component becomes warmer than the surrounding ones or the expected rated values, for instance: modules; bypass diodes; single cells without following any pattern, in the lower parts or closer to the frame; a part of a cell respect to the rest; or pointed heating or a bypass diode string part respect to the others when they are equally shaded [26].  
However, results reveal that IRT could not detect all possible failures in PV modules. Recently research about EL shows that it can give complementary information to IRT techniques, adding value to individual findings [27; 28]. Performed IRT measurements proved that not all identified defects lead to an increase in temperature [28]. It has been experimentally observed how using EL is especially interesting to detect cell cracks in PV modules, appearing as dark lines on the solar cell in the EL image, as well as interrupted contacts, or a number of process failures (e.g., shunts or defects inthe anti-reflection layer) [28]. Irregular areas indicate the presence of cracks in the silicon wafers while regular rectangular dark areas can be due to broken front grid fingers. In this case, EL locating can complement outdoor IRT technique by detecting defects that does not produce a temperature increment [29].  
In the latter instance, IRT in dark conditions shares some similarities with EL technique, being the module subjected to a forward voltage. In the EL test, modules operate under forwarding bias like a LED, and therefore have to be power supplied. The excitation current can be less than or equal to \(I_{sc}\)[21]. It has been observed from visual inspection of EL and IR images in the fourth quadrant that most of the defects detected in the dark IRT images under the forwarding bias condition can also be identified in the EL images, whereas not all the defects detected in the EL images can be detected with dark-IRT, as some broken cells or soldering defects over one or more buses [29]. Therefore, the high resolution of the EL images enables resolving most defects more precisely than the dark IRT images.
## 2 Results
### Anttimicrobial Activity Prediction  
Prior to experimentally testing the activity of the peptides, the antimicrobial activity of HRZN peptides was computationally predicted using tools such as support vector machine (SVM), random forest (RF), artificial neural network (ANN), and discriminate analysis (DA). Comparing the output from different tools increases confidence in the results of our antimicrobial activity predictions. [PERSON] et al. developed an SVM classifier (also known as a \"Ferguson classifier\") that predicts the antimicrobial activity of \(\alpha\)-helical peptides and their ability to generate negative Gaussian curvature, which is important in membrane permeability [51]. CAMPR3 predicts antimicrobial activity through SVM, RF, ANN, and DA models that utilize a positive dataset containing experimentally active AMPs and a negative dataset that contains inactive AMPs [52; 53]. The ClassAMP antimicrobial prediction tool utilizes CAMP-positive and -negative datasets [54; 55]. However, the difference lies in the positive dataset that is classified into antibacterial, antigungal, or antiviral, and the prediction results are generated based on these classes. The DBAASP antimicrobial predictor classifies linear sequences to be AMPs and non-AMPs based on a machine learning algorithm that uses physicochemical properties, such as hydrophobicity, and charge density and the propensity of a peptide interact with the membranes [56]. A classifier based on the XGBoost-based regression model described in [PERSON] et al. [57] was also used to evaluate the likelihood that HRZN peptides were AMPs, where each peptide sequence was input and AMP or non-AMP was predicted based on sequence-based features.  
In Table 2, we list the predicted antimicrobial activity for all our designed peptides. HRZN-13 and -14 have a high prediction score of 0.99 or 1 probability in the Ferguson classifier. CAMPR3 [53] and CLASSAMP [54] were predicted to be AMP in CAMPR3 (ANN), DBAASP, and PepVAE3 classifiers followed by HRZN-15 and -16, which scored at least 0.96 in all algorithms and were predicted to be AMP. HRZN-17 had the lowest prediction score in CAMPR3 RF (probability 0.6) and achieved \(\geq\) 0.93 across the other predictors with the potential to be antimicrobial. DBAASP, CAMPR3, and PepVAE3 predicted LL-37 to be an AMP, and notably, the probability scores of CAMPR3 for LL-37 range from 0.75 to 0.77. LL-37 scored \(\geq\) 0.95 in the Ferguson classifier and CLASSAMP. IDR-1018 was predicted to be non-AMP by DBAASP and scored very poorly in the Ferguson classifier (0.15). However, the CAMPR3 and CLASSAMP prediction results gave a probability of 0.97-0.99 for IDR-1018. These results suggest that our HRZN peptides will likely be antimicrobial, with the possible exception of HRZN-17. They also reveal some of the issues with these predictors, such as the disagreements between them for the same peptide, and disagreement with actual experimental results, such as for IDR-1018.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Genetic Algorithm
#### 3.2.4. Gabeec  
GABEEC is a GA based for energy efficient clusters in WSN proposed in [46] in order to improve the network lifetime. Contrary to LEACH-GA, GABEEC has only two states: set-up and steady-state. In the first phase, all clusters are created statically one time, but CHs within a cluster are changing dynamically based on the residual energy. GABEEC uses a binary representation of the network where each sensor node is a bit in which \"1\" correspond to a CH and \"0\" to a simple node. An instance of the network represents a specific chromosome, the GA evaluates each chromosome and selects the best profile so that the energy is efficiently used then increases the network lifetime. GABEEC is compared at first to LEACH (100 homogenous nodes), afterwards to HCR and the algorithm proposed in [44] (200 nodes used). The results show that GABEEC has a better percentage of alive nodes in both simulations.
# Performance Optimization of the Low-Capacity Adsorption Oxygen Generator
## 2 Adsorption Based Oxygen Separation Method
The cycle of pressure changes is shown in Figure 2.  
The cycle of pressure and adsorbency changes of the bed concerning a single adsorption vessel is shown in Figure 3. Figure 3 shows that an appropriate process recipe, taking into account the process conditions (pressure and temperature) and the time of individual stages, has a key impact on both the purity of the separated oxygen and the energy consumption. Step 2 (see Figure 3) is the stage in which the oxygen is transferred from the adsorber to the oxygen tank. The time in which stage 2 is driven is called the transfer time. Too long transfer time causes too much nitrogen to be supplied with the air stream, which exceeds the maximum degree of adsorption (line \(a_{max}^{T}\) in Figure 3). That causes the oxygen purity to drop. If the transfer time is too short, the adsorbent capacity is not used to the maximum. It lowers the stream of separated oxygen, and thus increases the energy consumption of the process per unit mass of oxygen. That makes the optimization of step 2 critical for minimizing energy consumption while maintaining a high purity of the separated oxygen. This is especially true as the optimal transfer time changes with the pressure according to the shape of the adsorption isotherm. For the low adsorption pressure, the slope of the adsorption isotherm is high. The result is the increase  
Figure 1: Scheme of an oxygen generator using the PSA method. Figure 2: The pressure cycle in adsorptive oxygen generator. 1, 4—air compression/bed regeneration; 2, 5—oxygen transfer to the oxygen tank; 3, 6—pressure equalization. in the optimal transfer time with adsorption pressure. The bed capacity increase slows down for higher pressures, and the optimal transfer time remains constant [22]. Additionally, while analyzing the generator diagram (Figure 1), the authors noticed that the pressure difference between the pressure in the oxygen tank (\(P_{oxyTank}\)) and the adsorption pressure (\(P_{ads}\)) may influence the purity of the separated oxygen. The difference of these pressures is denoted as \(\Delta P_{oxy}=P_{oxyTank}-P_{ads}\). In the particular case where \(\Delta P_{oxy}\) is high enough, the authors suspect that the air may flow too rapidly through the adsorber, resulting in short residence time in the adsorber. Additionally, by the law of volume stream continuity, the increased gas flow velocity through the porous bed may cause a local pressure reduction (injector effect), which may reduce the local adsorbent adsorption capacity.
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Comparison of IoT Communication Approaches
#### 3.8.1 Transport  
UDP traffic is supported by all of the approaches, the exception being MQTT, as this protocol needs to have an active connection to allow consumers to be notified of changes. There is, however, an extension of _MQTT for sensor networks (MQTT-SN)_[25] which can support UDP. The trade-off of relying on this extension is a lower control in terms of QoS support.
# A Comprehensive Analysis of Smartphone GNSS Range Errors in Realistic Environments
## 2 Methodology
Take one specific epoch for an example, with the coordinates of receiver \(A(X_{A},Y_{A},Z_{A})\), satellite \(m(x^{m},x^{m},z^{m})\) and the lever arm \((X_{0},Y_{0},Z_{0})\), [PERSON]'s formula can be applied to \(\rho_{B}^{m}\) at the approximate position of \(A\) as Equation (5):  
\[\rho_{B}^{m}|_{(x,y,z)=(X_{A},Y_{A},Z_{A})}=\rho_{A}^{m}+f^{\prime}(x,y,z)\cdot (X_{0},Y_{0},Z_{0})+f^{\prime\prime}(x,y,z)\cdot\frac{(X_{0},Y_{0},Z_{0})}{2 }+\cdots \tag{5}\]  
The second order of Equation (5) can then be derived as \(\frac{1}{2}(\frac{X_{0}}{\rho_{A}^{n}}+\frac{Y_{0}}{\rho_{A}^{n}}+\frac{Z_{0} }{\rho_{A}^{n}})\) and can be ignored because of the meter-level lever-arm. In this way, only first order is taken into consideration, and hence Equation (3) can be further reparameterized as Equation (6):  
\[ \begin{split} rangeerror=\
abla\triangle P-((\frac{X_{A}-x^{m}}{ \rho_{A}^{m}}X_{0}+\frac{Y_{A}-y^{m}}{\rho_{A}^{m}}Y_{0}+\frac{Z_{A}-z^{m}}{ \rho_{A}^{m}}Z_{0})\\ -(\frac{X_{A}-x^{m}}{\rho_{A}^{n}}X_{0}+\frac{Y_{A}-y^{m}}{\rho_{ A}^{n}}Y_{0}+\frac{Z_{A}-z^{m}}{\rho_{A}^{n}}Z_{0}))\end{split}  \tag{6}\]It can be noted from Equation (6) that the coefficients of the lever-arm values are the line-of-sight values from receiver \(A\) to satellites, and corresponding coefficients can be determined reliably even with a ten-meter-accuracy approximate position. Therefore, the measurement-differenced range errors can be derived with only the pseudorange measurements and the fixed lever arm. Aside from the measurement-differenced method based on Equation (1), there is another straightforward approach to estimate range errors through pseudorange residual differencing between satellites. This state-differenced method can better show the estimated state characteristics than the measurement-differenced method. Correspondingly, all receiver-dependent states are eliminated with the relative range errors remaining, and the state-differenced or PPP-based range errors can be expressed as Equation (7):  
\[\triangle P=P_{A,j}^{m}-P_{A,j}^{n}=(\rho_{A}^{m}-\rho_{A}^{n})+(I_{A,j}^{m}- I_{A,j}^{n})+(T_{A}^{m}-T_{A}^{n})+(\epsilon_{A}^{m}-\epsilon_{A}^{n}) \tag{7}\]  
Compared to the aforementioned measurement-differenced range errors, the calculated range errors noise is only amplified once from the differencing. However, it requires accurate satellite-dependent states such as geometric ranges, ionospheric delays, as well as tropospheric delays during computation. Furthermore, other terms such as relativity corrections, Sagnac corrections, solid tide corrections, and satellite antenna corrections [45] need to be taken into consideration. A geodetic receiver, NovAtel OEM7, is utilized in the following processing, and satellite-related states are estimated through PPP processing.
## 4 International Runway Skid Resistance Standards and Regulations  
The International Civil Aviation Organisation (ICAO) publishes a number of international standards regulating aerodrome and runway design and operation [64]. The main standard for aerodromes and runways is Annex 14 to the Convention on International Civil Aviation, commonly known as Annex 14. In terms of skid resistance and runway friction, there are five manuals to Annex 14, and the Procedures for Air Navigation Services (PANS)-Aerodromes (Doc 9981), and the associated Circular 355--Assessment, Measurement, and Reporting of Runway Surface Conditions, contain guidance on runway surface characteristics. All of these standards provide specifications, recommendations, and additional information about the requirement to provide and maintain surfaces with an acceptable level of skid resistance (Figure 13).  
Two separate circumstances of aircraft skid resistance need to be considered: a wet runway, and a snow or ice-contaminated runway surface [65]. Wet runways are focused on in this review. To avoid potential problems caused by inadequate aircraft skid resistance provided to an aircraft, there are two possible approaches: the provision of reliable data related to skid resistance, in real time, to allow pilots to predict the actual braking performance of the aircraft, and the provision of adequate skid resistance at all times, under all reasonable and foreseeable environmental and operational conditions. In general, the second approach is implemented in most ICAO procedures and specifications, because the  
Figure 13: ICAO standards and regulations in regard to skid resistance of runways.  
first approach is too difficult to manage and has the potential to create more risk. However, real-time contamination reporting is also implemented in the Runway Condition Report (RCR) system, which includes the runway condition code (RWYCC), based on the weather conditions, operator experience, and friction measurements [66].  
Skid resistance is a complex phenomenon that is hard to measure directly. Basically, it is the maximum braking force that can be generated between a given tire, and a given surface, in given conditions. This significantly limits the possibility of precise friction measurements and the development of a unified minimum friction value. Therefore, the measurement of friction in controlled conditions is preferred, which can be compared to a reference number for an assessment of surface conditions. Besides that, indirect measurements, such as micro/macrotexture measurement, can also be used.
# An Inspection of IFC Models from Practice
## 4 Methodology
### Models Inspection
#### 4.3.2 Inspection and Assessment of Object Modelling  
The organization of the spatial structures (_IfcSite_, _IfcBuilding_, _IfcBuildingStorey_ and _IfcSpace_) in the models was the first checked aspect.  
The grouping of _IfcSite_ and _IfcBuildings_ was generally very simple and no scoring system was necessary. Instead, scores are assigned to the grouping of _IfcStorey_s, which could be very helpful to simplify a number of processing, on the condition that they would be grouped consistently. The chosen scores were:  
**1**: in case the storey contained only the elements belonging to the represented floor;  
**0.8**: when the grouping was consistent although some elements ran for more than one storey due to their modelling nature. For example, some structural elements or installations or other building elements could occupy more than one storey, although being correctly modelled. Although not being an error, any processing relying on a consistent grouping of storeys was prevented.  
**0.5**: with some inaccuracies (which could be fixed through quite simple processing);  
**0.3**: very inaccurate;  
**0**: not used at all.  
_IfcSpace_ was the last assessed element about modelling. It was only assessed on the 30 architectural models, not including the facade model. Although a good modelling of spaces is a useful support to many use cases, different practices could be noted. We assessed the models on this aspect as:  
**Well used;**  
**Redundant**: overlapping spaces described different volumes, even hierarchical (e.g., room, apartment, floor, gross surface, net surface, gross volume, balcony and so on);  
**Approximated**: they did not follow the ceiling pitch but were an extrusion of the room floor to an approximated height;  
**Partially used**;  
**Displaced**: spaces appeared displaced with respect to their correct location;  
**Not used**.  
It is still valuable to add that in some cases spaces were correctly stored as sub-group of _IfcBuildingStorey_, as implied by the IFC schema, while in others they were not. However, this may depend on the used modelling software, whose settings have an influence on how the models are exported to IFC. For example, in the models exported by _Autodesk Revit_ this generally did not occur. Moreover, in 13 out of 30 models, _IfcSpaceBoundaries_ were represented in addition, describing the surfaces enclosing the space volume.  
Other considerations regarding the geometries modelling: the geometry was inspected by means of the IFC viewers, to check the most apparent features. However, it is hard to give exhaustive comments on it, since a tool systematically validating IFC geometries is at present missing.  
Still regarding the modelling, the use of _IfcOpening_ was checked to model voids, which can be relevant to some geometric processing, besides representing the optimal way of modelling geometries. In this case, for _IfcOpenings_ the given scores were:  
**1**: well used;  
**0.8**: well used, although irregular elements;  
**0.5**: partially used;  
**0.3**: not used;  
**0**: wrongly used.
## 4 NR-Based Solution Strategy for EFC  
The essence of the steady-state EFC problem is to solve a high-dimensional nonlinear system of equations. Therefore, the NR-based method becomes one of the most widely used EFC solution strategies. Herein, all subnetworks in the MES are considered as a whole, and the unified EFC solution strategy is adopted. The basic iteration format of the NR method for the whole MES is written as  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}-(\mathbf{J}_{k})^{-1}\mathbf{F}_{k} \tag{76}\]  
where \(\mathbf{x}=[\mathbf{x}_{\rm{e}};\mathbf{x}_{\rm{h}};\mathbf{x}_{\rm{g}}]\) is the unknown variable vector of MES; \(k\) is the iteration time; \(\mathbf{F}=[\mathbf{F}_{\rm{e}};\mathbf{F}_{\rm{h}};\mathbf{F}_{\rm{g}}]\) is the mismatch vector of MES; and \(\mathbf{J}\) is the Jacobian matrix of MES, which is formulated as  
\[\mathbf{J}=\begin{bmatrix}\mathbf{J}_{\rm{ee}}&\mathbf{J}_{\rm{eh}}&\mathbf{J}_{\rm{eg}}\\ \mathbf{J}_{\rm{he}}&\mathbf{J}_{\rm{hh}}&\mathbf{J}_{\rm{hg}}\\ \mathbf{J}_{\rm{ge}}&\mathbf{J}_{\rm{gh}}&\mathbf{J}_{\rm{gg}}\end{bmatrix}=\begin{bmatrix} \frac{\partial\mathbf{F}_{\rm{e}}}{\partial\mathbf{x}_{\rm{e}}}&\frac{\partial\mathbf{F}_ {\rm{e}}}{\partial\mathbf{x}_{\rm{h}}}&\frac{\partial\mathbf{F}_{\rm{e}}}{\partial\mathbf{x }_{\rm{e}}}\\ \frac{\partial\mathbf{F}_{\rm{h}}}{\partial\mathbf{x}_{\rm{e}}}&\frac{\partial\mathbf{F}_{ \rm{h}}}{\partial\mathbf{x}_{\rm{h}}}&\frac{\partial\mathbf{F}_{\rm{hThe flow chart of the NR-based unified EFC solution strategy is shown in Figure 6, and the main steps are as follows:  
* Step 1: Input the basic parameters, including the MES data, convergence tolerance \(\zeta\), and maximum iteration number \(K\).
* Step 2: Initialize the unknown variables, i.e., \(\theta\), \(\mid\)\(V\)\(\mid\), \(\mathbf{m}\), \(\mathbf{T}_{\mathrm{n}}^{\mathrm{g}^{\prime}}\), \(\mathbf{T}_{\mathrm{n}}^{\mathrm{R}^{\prime}}\), \(\mathbf{\Pi}\), \(\mathbf{f}_{\mathrm{E}}\), \(\mathbf{\rho}\), and \(\mathbf{q}\). Specifically, flat initialization is applied to all unknown variables except for \(\mathbf{\Pi}\); \(\theta\) and \(\mid\)\(V\)\(\mid\) are initialized to 1 p.u. and 0\({}^{\circ}\), respectively; \(\mathbf{T}_{\mathrm{n}}^{\mathrm{g}^{\prime}}\) and \(\mathbf{T}_{\mathrm{n}}^{\mathrm{R}^{\prime}}\) are set to \(\mathbf{T}_{\mathrm{sp}}^{\mathrm{G}}\) and \(\mathbf{T}_{\mathrm{sp}}^{\mathrm{R}}\), respectively; \(\mathbf{m}\) and \(\mathbf{f}_{\mathrm{E}}\) are valued as the design flow of typical pipelines; \(\mathbf{\rho}\) and \(\mathbf{q}\) are set to the specific gravity and GCV of natural gas.
* Step 3: Calculate the mismatch vector \(\mathbf{F}\) according to the formulations in Table 2; calculate the Jacobian matrix \(\mathbf{J}\) and update the unknown variables \(\mathbf{x}\) according to Equation (77) and Equation (76), respectively; update the intermediate variables according to the proposed MES model in Table 2.
* Step 4: If the maximum absolute value of \(\mathbf{F}\) is less than \(\zeta\), or if the iteration number exceeds \(K\), proceed to Step 5; otherwise, go to Step 3.
* Step 5: Output the steady-state energy flow results of the MES.