esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Principle
### Analysis of High Order Harmonic Suppression  
A prominent function of the compensation topology is that it can be utilized to compensate for the large leakage inductances of the LCT. The compensation topology can work as a passive bandpass filter that only allows the specified resonant frequency component to pass because the resonant networks comprise inductors and capacitors. Therefore, the harmonic components can be attenuated [34]. The proposed design method in this study uses partial resonance with leakage inductance, which may lead to concerns that harmonics cannot be completely eliminated. However, as the proposed design method has the structure of an inductive load whose impedance values increase proportionally with frequency, high-order harmonic components can be sufficiently attenuated. The impedance for filtering the harmonic component current can be explained as follows:  
\[Z_{s}=j\omega L_{s}+\frac{1}{j\omega C_{s}}+R_{o} \tag{19}\]  
\[Z_{r}=\frac{(\omega M)^{2}}{Z_{s}} \tag{20}\]  
\[Z_{in}=j\omega L_{p}+\frac{1}{j\omega C_{r}}+\frac{(\omega M)^{2}}{j\omega L_{ s}+\frac{1}{j\omega C_{s}}+R_{o}} \tag{21}\]  
The secondary impedance \(Z_{s}\), reflected impedance \(Z_{r}\), and input equivalent impedance \(Z_{in}\), respectively, are provided in (19)-(21). In addition, to verify the high-order harmonics  
Figure 4: Proposed design methods: (**a**) voltage gain for different load resistances; (**b**) phase angle for different load resistances.  
suppression, Figure 5 shows the result of comparative simulations. By using the fast Fourier transform (FFT) tool to analyze the input current \(I_{p}\), simulation results confirm that there are few high-order harmonic amplitudes of \(I_{p}\). Therefore, the proposed topology can be approximated and computed using the fundamental harmonic approximation (FHA) method.
## 2 Literature Review
The MQSOS is contrasted to several intelligent meta-heuristic approaches, namely, particle swarm optimization (PSO), adaptive PSO (APSO), parallel PSO (PPSO), oppositional SOS in the CEC2013 large-scale optimization test suite (OSOS), and Quasi-Affine Transformation Evolutionary (QUATRE). The findings reveal that the algorithm MQSOS surpassed the other algorithms. An upgraded DV-Hop algorithm [26] was put forward focusing on expanding the communication radius to upgrade the minimum hop-count value of unidentified nodes to a lesser hop-count value to solve the issue of the real distance variation of the same hop-count value to a certain extent. An upgraded parallel whale optimization technique [27] was proposed that outperformed certain existing intelligent computing algorithms in terms of performance. [PERSON] et al. [28] modified the average distance per hop using a differential evolution localization approach based on DV-Hop. For discovering the position of movable target nodes in WSNs, a PSO-based computational intellect approach was introduced. Anchor nodes were placed at the corners of the sensing region, and the algorithm was divided into two segments. The initial phase used distance calculations based on RSSI. Anchor nodes were virtually assumed in six diverse orientations at particular angles (ideally 60\({}^{\circ}\) each) in the subsequent step to locate undiscovered nodes. At this point, centroid computations were used in conjunction with a PSO optimization technique, and the results revealed a quicker convergence time. Other strategies take into account creating sensor networks to directly address the localization issue without relying on Global Navigation Satellite System (GNSS) signals. Since the GNSS has its limitations, the Local Positioning Systems (LPS) is adopted as a viable technology. LPS is made up of ad hoc deployments of sensors that specifically adjust according to the features of the application environment. This increases the GNSS's accuracy and stability, enabling precise localization under challenging circumstances. Power, angle, time, phase, frequency, or even combinations of them are categorized according to the physical attribute measured to determine the target location, lowering the percentage risk of error that each one has individually [29]. A new soft computing technique [30] was developed by combining the Harmony Search Algorithm with a local search procedure that iteratively reduces the previously mentioned non-uniqueness of sparse network deployments. Geometrical restrictions based on connectivity also place boundaries on the possible locations for sensor nodes. The suggested strategy beats previously published soft computing localization algorithms in the majority of the simulated topologies. The majority of meta-heuristic algorithms applied to solve the aforementioned localization complication are competitive, but they have flaws such as delayed convergence and increased localization errors. The comparison of various Meta-heuristic algorithms used for WSN localization is listed in Table 1.
## 2 Materials and Methods
### TMA Simulator Description  
The TMA (Figure 1) consists of two devices both made up of a central body, in non-magnetic metal, characterized by a millimeter scale equipped with a pointer and an upper and lower tray holder, transferable from one device to another.  
The gauge features a sliding rail system that allows the recording of the individual patient's mandibular excursion. Once the impressions have been taken (only for the anterior sextant) in the pre-operative phase, the two trays are placed on the gauge using a precision joint (Figure 2). The device is positioned between the patient's arches and the free mandibular excursion is recorded (from the position of maximum retrusion to the position of maximum advancement). From this measurement, it is possible to obtain the free excursion range for each patient, i.e., the millimetric value within which to make progressive advancement adjustments for observations in DISE.  
Figure 1: TMA simulator: AB-Gauge (**a**); AB-Slide (**b**).  
The slide, instead, allows the possibility of advancing or retruding the mandibular position by using a screw system, having full control of the titration thanks to the indicator on the millimeter scale of the device itself.  
Once the impression trays already used in the gauge have been transferred to the TMA slide system, the device is inserted into the patient's mouth in the first stages of sedation and adjusted to the position of maximum retrusion previously recorded with the gauge. We then proceed with the DISE, initially visualizing the effect on the airways and respiratory parameters only with the occlusal rise determined by the device, without any mandibular advancement. After a stable observation phase of at least five minutes, a progressive mandibular advancement begins. This procedure is repeated with subsequent protrusion increments in order to identify the minimum advancement needed to avoid upper airway obstruction.  
Thus, the detected value can be used, in case of indication for MAD therapy, for the construction of the device. In fact, this can be considered a pre-titration of the MAD. The identification of a minimum effective advancement value is a useful indication for the clinician, but also the patient.
# A GeoNode-Based Platform for an Effective Exploitation of Advanced DInSAR Measurements
## 3 GeoNode Platform
Moreover, once the data have been uploaded, GeoNode lets the user search by geographic coordinates or via keywords, in order to create maps with GeoExplorer. Note that all the layers are automatically re-projected to web Mercator for maps display, making possible the use of different popular base layers like Open Street Map [92] or Google Satellite [93]. Accordingly, the GeoNode capabilities make this platform a perfect candidate to build up a SDI for managing, visualizing, and analysing DInSAR deformation time-series and the related products, and for their integration with other data sources. In this context we remark that one of the key problems related to an effective exploitation of the advanced DInSAR products is the efficient handling of the spatio/temporal characteristics of these data, which may reach significantly large volumes, thus requiring the devolpment of innovative tools with respect to the already existing ones. In the next Section we describe the necessary changes in the GeoNode platform functionalities in order to efficiently exploit the results achieved through advanced DInSAR processing techniques. However, before going into details of these modifications, let us provide some words on the reason way we focused on GeoNode. Indeed, various open source software tools are available for building SDIs but GeoNode appeared to us as the most appropriate choice because:  
* it permits development of geospatial services within a fully free and open source framework;
* it is an environment originally developed to manage geographic content that easily allows the extension of its functionalities. To clarify the impact of these issues let us refer to another platform, named Easy SDI [94], which has various features similar to GeoNode. In this case our preference goes to GeoNode because of the following considerations:  
* GeoNode uses as geodatabase PostgreSQL/PostGIS, developed into the free and open framework of the Open Source Geospatial Foundation (OSGeo) [95], while Easy SDI uses mySQL [96] which is owned by the Oracle Corporation [97];
* GeoNode is a dedicated environment to the SDIs development which is build in Django, an environment that allows the easy extension of the GeoNode functionalities by using Python codes. Instead, Easy SDI is developed as a plug-in of the Joomla [98] Content Management System (CMS), a system not originally developed to manage geographic contents. Figure 4: Flow chart of the GeoNode core components interactions.
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 2 Proposed Facial Expression Recognition System Using the Mixture of CNN and SVM Classifier  
### System Overview  
The proposed system processes video sequences and recognizes facial expressions. The system consists of three modules: data collection of motion flow and facial landmark flow, CNN-based facial motion flow classification and SVM-based facial geometry flow classification, and expression sequence classification by mixing the CNN and SVM-classifier as shown in Figure 1. To process the localized facial motion from a video sequence, global head translation, in-plane head rotation, and scaling effects are eliminated based on face normalization from two eye locations, as explained in Section 2.2.1. The optical flow from the first frame of each sequence is estimated (Section 2.2.2) and a collection of these optical flows is used for CNN-based facial expression classification (Section 2.3). Landmark flow from model-based landmark tracking is used for the SVM-based facial expression classification (Section 2.4). The weighted summation of the probabilistic estimation of the two classifiers is used for the final facial expression classification for each frame of the test sequence (Section 2.5).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Publications** & **Method** & **Database** & **Accuracy** & **Weakness** \\ \hline [PERSON] et al. [47] & Using DNN & CK+, MMI and SFEW & 91.74 & Small number of classes \\ \hline [PERSON] et al. [50] & Using WLS-RF & CK+, BU4D and SFEW & 92.97 & Requires handcrafted features \\ \hline [PERSON] et al. [52] & Using Salient facial patches & CK+ and JAFEE & 94.09 & \begin{tabular}{c} Misclassification between \\ anger and sadness \\ \end{tabular} \\ \hline [PERSON] et al. [38] & Using DTAN + DITGN & CK+ and Oulu-CASIA & 96.64 & Network complexities \\ \hline [PERSON] et al. [48] & Using CNN + SIFT & FER-2013 and CK+ & 99.10 & Small number of classes \\ \hline [PERSON] et al. [44] & Using BoMW & BU4D & 63.8 & Lack of diverse databases \\ \hline [PERSON] et al. [51] & Using 2D-HMM & \begin{tabular}{c} Newly created dynamic \\ 3D facial expression database \\ \end{tabular} & 68.3 & \begin{tabular}{c} Semi-automatic process to \\ select features at initial stage \\ \end{tabular} \\ \hline [PERSON] et al. [53] & Using LBP-TOP & BU4D & 71.6 & \begin{tabular}{c} Produces large number of interest points, \\ which become difficult to handle for large \\ databases \\ \end{tabular} \\ \hline [PERSON] et al. [49] & Using FFD & BU4D & 73.4 & Small number of classes \\ \hline [PERSON] et al. [45] & Using DGIN & BU4D & 92.22 & Lack of diverse databases \\ \hline [PERSON] et al. [54] & Using HOG3D (all frames) & \
\begin{tabular}{c} 82.80 \\ + \\ 96.64 \\ \end{tabular} &
\begin{tabular}{c} Uses two-phase feature selection, \\ which makes the system complex \\ \end{tabular} \\ \hline [PERSON] et al. [46] & Using DSF (whole video) & BU4D & 94.18 & Lack of diverse databases \\ \hline \end{tabular}
\end{table}
Table 1: Comparison and weaknesses of related work.
# Additively Manufactured Polymers for Electronic Components
## 3 Polymers Used for AM in Electronic Applications
### Intrinsically Conductive Polymers (ICPs)
Inks with low PEDOT:PSS concentrations (1-3 wt%) were unable to maintain the pattern shape, as they exhibited lateral spreading after printing. By contrast, inks with high PEDOT:PSS concentrations (7-9 wt%) frequently led to nozzle clogging due to PEDOT:PSS aggregates in the ink, resulting in broken patterns. The ink with an optimal concentration of 5 wt% PEDOT:PSS allowed for stable printing, maintaining a well-defined pattern without spreading after printing [47]. Recent efforts have extended additive manufacturing (AM) technologies, especially DIW, to other ICPs. For example, fully additive fabrication of polyaniline (PANI)-based electrodes has been demonstrated via DIW, enabling 3D printing of electrical circuits [55]. In another study, PANI nanofibres were dispersed in photocurable acrylic resins to enable DLP printing of conductive microstructures with enhanced print quality and surface  
Figure 5: Rheological properties and printability of PEDOT:PSS inks: (**a**) photographs of PEDOT:PSS inks at varying concentrations; (**b**) relationship between apparent viscosity and shear rate of the PEDOT:PSS inks; and (**c**) apparent viscosities of the PEDOT:PSS inks at a shear rate of 1 s. Adapted and reprinted with permission from ref. [47]. conductivity, overcoming the limitations of pure PANI systems [56]. Moreover, pressure sensors have been fabricated via FDM-DIW using PANI and CNTs within acrylic matrices, achieving a conductivity of around \(15\times 10^{-3}\) S cm\({}^{-1}\) with a filler concentration of 2 wt% [57]. Furthermore, polypyrrole (PPy) has also been integrated into direct-write processes. DIW printing has been used to fabricate PPy-containing 3D electrodes and conductive hydrogel scaffolds, such as PPy nanotube inks printed for flexible supercapacitors [58] or composite hydrogels (PPy/alginate-gelatin), delivering conductivity values of up to 1.5-2 S m\({}^{-1}\)[59].
## 3 Methodology
### Prediction Model
#### 3.2.2 Auto Encoder (AEC)  
An auto encoder (AEC) is a form of ANN used to learn effective encodings of unlabeled input (unsupervised learning). Working towards the aim of recreating the initial input is an excellent way for evaluating and improving encoding. Auto encoders are neural networks that may be trained to learn a representation (encoding) of a collection of inputs by filtering out irrelevant information (noise). The categorization of one-class scenarios, and the administration of unlabeled data provided by wearable sensing devices, are currently beyond the capability of AECs. With a mean absolute error (MAE) of 37.3 and an accuracy of 52.8%, the AEC method allows us to predict when someone will fall. Figure 2a,b both illustrate the same thing for us to see.  
As shown in Figure 2a, the ten-fold accuracy ranges between 49% and 51.6%, with a floor of 49%. The root means square error (RMSE) was 86.488, and the mean square error (MSE) was 7.4803 \(\times\) 10\({}^{3}\). A single-stage classifier with 80% training and 20% testing data yielded the findings. AEC classifies unlabeled data using unsupervised deep learning. This procedure yielded results.
## 4 Results and Discussion
### Performance Analysis
#### 4.1.1 Detection Rate  
The detection rate metric plays an important role in IDS since it directly affects network performance. Figure 5a illustrates the performance of attack detection rate versus attack percentage. The detection rate represents the percent of correct detections in packet sorting. The proposed method obtains a detection rate of 96.41%. Prevailing approaches lack optimal classifiers for packet classification, which involve complex mathematical calculations and time-consuming processes. The analysis concludes that the BSHNN method outperforms the previous ML-IDP method in terms of detection rate.
## 3 LCC Implementation in GPP in Greece--Methodology  
### Data Collection  
Stemming from the European Commission's encouragement to facilitate the widespread application of LCC among public authorities in the European Union, in order to take more cost-effective decisions, this study used the LCC tools developed by the European Commission to assess in Greece the economic impact of green procurement beyond the criterion of product's purchasing cost. The inherent assumption was that the purchase price alone does not reflect the financial and non-financial gains that are offered by environmentally and socially preferable assets as they accumulate during their use, and thus an LCC tool can evaluate the costs of an asset throughout its life cycle [23].  
Based on this, the life cycle costs of green products vs. non-green products used in Greek GPP projects were compared. The data regarding public procurement projects that applied green criteria carried out in the last 15 years was obtained from [PERSON] et al. [32], who provide an exhaustive record. These projects were thoroughly reviewed in order to collect the purchased products' necessary technical specifications that would be used in the LCC tool, such as operating costs and externalities costs. Furthermore, financial bids and product specifications were collected through official procurement processes from contracting authorities and suppliers.  
Since LCC was not used during the bidding process, in order to be able to make the comparison between green and non-green products, an extensive market research was conducted for the corresponding products used prior to the green policy. These products and their respective costs were identified, along with the data necessary to feed the LCC tool. Then the life cycle analysis of the green and corresponding non-green products was carried out. Therefore, in this case LCC is used after the bidding process (ex-post), yielding life cycle cost estimates for alternative bids--including the awarded one- in order to evaluate the results and use them in future tenders.  
As European Commission LCC tools are used, data are applied only for their respective product categories. These are Indoor and Outdoor Lighting and Computers and Monitors. For the first category, the development of energy-efficient lighting technology, the shorter payback time brought on by lower electrical costs and the encouragement of public authorities for its use are the factors contributing to a widespread GPP adoption [33]. On the other hand, due to increased functionality, popularity and availability, computers and monitors are widely used in the public sector. During their life-cycle, the energy consumption seems to be associated with environmental impacts and is highly considered.
###### Abstract  
The Nintendo Wii Balance Board (WBB) has been suggested as an inexpensive, portable and accessible alternative to costly laboratory-grade force plates for measuring the vertical ground reaction force (vGRF) and center of pressure (COP). Kinetic gait analysis provides important information for the rehabilitation of patients with gait disorders; however, the validity of the WBB for measuring kinetic gait parameters has not been evaluated. Therefore, the purpose of this study is to determine the accuracy of walking force measurements--which change dynamically in a short period of stance time--collected with the WBB. Three healthy adults were asked to walk 10 steps along both straight and curved paths in clockwise (CW) and counterclockwise (CCW) directions while measurements were taken using the WBB and the force plate. The accuracy of the vGRF, COP trajectory, and stance duration were evaluated using the root-mean-square error (RMSE), Pearson's correlation coefficient and Bland-Altman plots (BAPs) to compare the WBB and the force plate. The results of the vGRF showed high accuracy (\(r>0.96\) and \(\%\)RMSE \(<6.1\%\) in the mean values), and the stance duration as defined by the vGRF and COP trajectory was equivalent to that of commercial instrumented insoles, which are used as an alternative to the force plates. From these results, we determined that the WBB may be used for kinetic gait analysis in clinical settings where lower accuracy is acceptable.  
Wii Balance Board; force plate; gait; ground reaction force; center of pressure; curved walking; iterative closest point algorithm
# A Survey of LoRaWAN for IoT: From Technology to Application
## 5 Physical Layer Performance Evaluation
### Interference Impact on LoRa
#### 5.2.1 Interference from Other Technologies
For other environments (hospital complex, industrial area and residential area), interference chances are always lower than 5% for both sub-bands. Thus, in such an environment, LoRa can be a feasible technology to be used for different applications, due to low level of signal activity in LoRa bands [65].