esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Promising Neuropeptides Inhibitors for the Development of Effective Cancer Therapy
### Neuropeptides Inhibitors: A Promising Approach also for Other Cancers
#### 4.2.3 Glioblastoma  
Primary central nervous system (CNS) tumors account for about 2% of all adult malignancies. Gliomas account for 40% to 67% of initial tumors in adult CNS cancers [151]. Glioblastoma (GBM) is the most aggressive malignant primary brain tumor of the astrocytic lineage and, according to the WHO classification, it is a grade IV glioma [152]. Gastrin-releasing peptide (GRP) released from presynaptic terminals regulates synaptic plasticity, memory, and fear responses by binding to GRPR specifically localized at postsynaptic membranes. GRP is a BB-like peptide found in mammals that regulates a variety of biological responses by activating the GRPR. Many forms of human peripheral tumors have an abnormal expression of both GRP and GRPR. Human glioma has been shown to have extensive expression and a high quantity of GRPR [153; 154; 155]. The development of selective GRPR antagonists as potential targeted anticancer drugs has been motivated by the finding that GRP and GRPRs play a role in cancer progression. For example, the GRPR antagonist RC-3095 induced a reduced proliferation of C6 rat glioma cells in vitro [156]. Furthermore, RC-3095 alone or in combination with TMZ inhibited the growth of C6 gliomas both in vitro and in vivo [157]. These findings lend credence to the idea that GRPR antagonists could be helpful in the treatment of glioma. VIP and pituitary adenylate cyclase-activating polypeptide (PACAP) are structurally related neuropeptides operating through GPCR subtypes named vasoactive intestinal peptide receptors VPAC1, VPAC2, and PAC1. Human GBM tumors and GBM cell lines have these receptors [158; 159]. Thus, targeting of the VIP receptor system and associated signaling pathways could also be an interesting therapeutic option in combination therapy with anti-proliferative drugs.
# An RF-MEMS-Based Digitally Tunable SIW Filter in X-Band for Communication Satellite Applications
###### Abstract  
This paper presents a digitally tunable SIW (substrate integrated waveguide) filter designed for X-band, based on RF-MEMS (radio frequency micro-electrical-mechanical systems) technology. Four commercial off-the-shelf RF-MEMS single-pole single-throw (SPST) switches were directly mounted on the upper surface of the filter, with metallic tuning posts specifically located within each cavity to define the potential achievable frequency range. Fabricated on standard alumina substrate, the design of the filter and the biasing network enabled fine digital frequency control of up to four functional states by the inclusion of wire bondings between each switch and the substrate. A relative tuning range of 2.3% was achieved between the lower and upper discrete states of 2.76% and 2.89% in the 3 dB fractional bandwidths.  
Keywords:bandpass tunable filter; RF-MEMS; substrate integrated waveguide SIW; X-band +
Footnote †: journal: Applied Sciences
## 2 Load Profile Development
### Transformer Thermal/Aging Model Description
In short, the heat generated in the windings by harmonic current increases at a rate that is the square of the harmonic frequency (\(h\)). This is one of the reasons harmonics can rapidly age transformers. Because other stray losses are manifested within the core, clamps, and other structural components, the losses do not increase at a rate equivalent to winding losses. IEEE Std. C57.110-2008 recommends a conservative exponent factor of 0.8 instead of the square of the harmonic frequency (\(h\)). Therefore, to account for harmonics in the IEEE Std. C57.91-2011 transformer model, a harmonic loss factor (\(F_{HL}\)) and a harmonic loss factor for other stray losses (\(F_{HL-STR}\)) were applied to winding losses and other stray losses within the transformer model. Equations (2) and (3) illustrate each of the loss factors, where \(h_{max}\) represents the maximum number of harmonic frequencies present in the spectrum, \(I_{h}\) represents the \(rms\) current at the \(h\) harmonic frequency, and \(I_{1}\) represents the \(rms\) fundamental current. \[F_{HL-STR}=\frac{\sum_{h=1}^{h_{max}}\left[\frac{I_{h}}{I_{1}}\right]^{2}h^{0. 8
## 4 Experimental Results and Analysis
### Simulation Results and Analysis
#### 4.1.3 Robustness Analysis  
To evaluate robustness against system parameter variations, the viscous damping coefficient is increased to 5000 Nm/(rev/min) and the hydraulic cylinder leakage coefficient is set to 5 \(\times\) 10\({}^{-10}\) m\({}^{3}\)/s/Pa. The sinusoidal tracking performance comparison between the PID and ARC controllers under these perturbed conditions is shown in Figure 10.  
The experimental results reveal that under parameter variations (where the viscous damping coefficient and hydraulic cylinder leakage coefficient are significantly altered), the ARC controller maintains its tracking performance with an error increment not exceeding 0.15\({}^{\circ}\), whereas the PID controller's tracking error increases by more than 1.5\({}^{\circ}\). These results validate the enhanced parametric robustness of the ARC controller.  
Figure 10: Robustness comparison under parameter variations: (**a**) ARC tracking performance; (**b**) ARC tracking error; (**c**) PID tracking performance; and (**d**) PID tracking error.
# Multi-Level Elasticity for Wide-Area Data Streaming Systems: A Reinforcement Learning Approach
## 2 Related Work
### System Architectures
It provides a layer, organized as a MAPE control loop, that is responsible for application performance monitoring, resource allocation and scaling at the infrastructure level. The proposed solution is based on two greedy algorithms that, exploiting a performance model based on queuing theory, are triggered when the application performance degrades. The first allows determining the minimum number of resources that satisfy the constraint on the mean application response time, while the latter assigns the resources to the DSP operators in such a way to minimize the mean application response time. However, DRS does not support elasticity at the application level. SpanEdge [26] uses a cloud- and fog-based architecture to unify stream processing and adopts a master-worker architecture, also implemented in Apache Storm. Foglets [27] is a fog-specific programming model supporting the migration of application components but it does not focus on elasticity. GigaSight [28] is a hybrid cloud- and edge-based architecture for a specific use case of computer-vision analytics: a federated system of proximal edge nodes filters and processes mobile video streams in near real-time, and sends only the processing results to remote cloud servers. However, this research effort focuses more on on privacy and access controls rather than on system elasticity. A unified cloud-edge data analytics platform based on a serverless stream application model has been presented by [PERSON] et al. [29]. Finally, Firework [30] is a another recently proposed framework for hybrid cloud-edge analytics, but it supports elasticity only at the infrastructure level. By explicitly considering wide-area distributed environments, we designed E2 DF with a hierarchical distributed architecture that can scale with the number of DSP applications and computing resources that should be efficiently managed. Being adaptation a key feature of modern DSP systems, we focus our efforts on devising efficient elasticity policies that can meet application performance requirements and reduce resource wastage, while avoiding too frequent reconfigurations.
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
## 2 Materials and Methods
### Statistical Analyses  
Statistical analyses were conducted in R v 3.5.2 [32] through RStudio v 2021.09.0. _Tidyverse_ v 1.2.1 [33] and the _vegan_ v 2.5-6 [34] R packages were used for data manipulation and plotting, and ecological analyses, respectively. Initially, the OTU table was rarefied (\(\times\)10 times resampling with the median value taken per OTU) to 7,978 reads per sample using the function _rrarefy_. Three final datasets were established: (i) all fungi, (ii) mold samples, and (iii) dust samples.  
Differences in alpha diversity, fungal richness (number of OTUs), and the Shannon index were evaluated by analysis of variance (ANOVA) tests. Beta diversity patterns were assessed through non-metric multidimensional scaling (NMDS) ordination of both samples and OTUs, applying _metaMDS_ and Bray-Curtis dissimilarity index on the Hellinger-transformed rarefied OTU tables. To evaluate the correlation between relevant factors (building, apartment, room, material, building structure) and the observed variance in fungal community composition, permutational multivariate analysis of variance (PERMANOVA; 999 permutations) was performed individually on each factor using _adonis2_. To assess the overlap between mold and dust mycobiota, we compared the OTUs detected in the different types of samples and rooms (damaged and central rooms) using two different estimates: numbers/percentages of OTUs across overall data and mean percentages of OTUs per apartment.  
To evaluate the two fungal identification approaches (microscopy and DNA metabarcoding), the taxa reported by microcopy were compared to the most abundant genera (>1% of the total number of rarefied reads) provided by the DNA metabarcoding. Considering the well-known limitations of species identification based on short DNA fragments, the results from the taxonomic assignment of OTUs are mainly shown and discussed at the genus level. It is well-known that species in some abundant mold genera, such as _Penicillium_, _Aspergillus_, and _Cladosporium_, can hardly be separated by the ITS marker. To monitor the spread of the major genera identified in the mold samples, their presence/absence and relative abundances (RA, as total percentages of sequences) were tracked in the dust samples collected from the same apartment.
## 3 Results and Discussion
### Batch Mode Operation Using Leachate as Substrate  
The MFC operated in batch mode using the ceramic electrodes for the three different preparation techniques (WC, BC and SC). Polarization experiments determined the maximum power output values for the three different cases. Figure 6 presents the dependence of the MFC voltage, V, and the produced power, P, on the current passing through the MFC, when the MFC was set up with the different electrodes.  
As can be seen from Figure 6, the maximum power output (2.67 mW) was obtained with the ceramic electrode produced with the SC technique. Additionally, the maximum power output for the MFC operation with the WC and BC electrodes was in the same range (WC: 1.12 and BC: 0.96 mW). Moreover, the slope of the V-I curves (Figure 6, continuous  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Electrode** & \begin{tabular}{c} **Coat Thickness** \\ **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Catalyst Mass** \\ **(g)** \\ \end{tabular} &
\begin{tabular}{c} **Conditions** \\ \end{tabular} \\ \hline WC & 1.5 & 1.5 & 4 wash coats \\ BC & 1.1 & 0.9 & 3 brush coats \\ SC & 0.8 & 0.7 & 10 min under ultrasound \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical properties of the three different produced cathode electrodes.  
Figure 5: (**a**) Horizontal images and (**b**) vertical images of four mullite tubes with stainless steel mesh (**left** to **right**: blank, WC, BC, and SC).  
Figure 6: Polarization curves (V–I continuous lines) and power curves (P–I dashed lines) of the leachate-fed MFC for the three cathode electrode cases (WC, BC  and SC ).  
lines) indicated that ohmic losses dominated in the MFC, across the three electrode cases. Table 3 presents the open circuit values (OCV) and the maximum power output per catalysts mass and per volume of leachate, for the cycles' operation with the different electrodes.  
Figure 7 depicts the variations in the conductivity and the COD removal efficiency values at the end of each operation cycle in comparison with the initial values of the leachate: As can be seen from Figure 7, high COD removal efficiencies were achieved in all cases (COD removal > 70%), while the leachate conductivity decreased after its treatment. It is worth mentioning that the conductivity decreased by almost 50% when the wastewater was treated using the electrodes prepared with the SC technique. The conductivity decrease may be attributed to the decomposition of the substances present in the landfill leachate. The electrochemically active biofilm oxidizes the available substrate (leachate), decomposing the substances that contribute to the high conductivity, thus resulting in the decrease in the conductivity. A similar decrease in the anolyte's conductivity has been observed elsewhere [36], although comparison between the two cases is difficult, due to the different acclimation, MFC set-up and feedstock conditions.
# Effects of Manure Waste Biochars in Mining Soils
## 2 Materials and Methods
### Selected Mining Soils  
Ten mining soils were selected from three different mining areas of Spain (Figure 1): Zarandas-Andalusia, Mijarojos-Cantabria, and Portman-Murcia. Three soils were collected in the nearby area to the copper mine of Zarandas-Andalusia: a _Cambisol_ Z1 (latitude: 37 deg40'26.6412\" north (N), longitude: 6 deg34'25.3205\" west (W)) and two _Technosols_ Z2 (latitude: 37 deg40'26.2776\" N, longitude: 6 deg34'05.6150\" W) and Z3 (latitude: 37 deg40'36.8868\" N, longitude: 6 deg34'04.0657\" W). This area is located in the Iberian Pyrite Belt, one of the largest metallic sulfide deposits in the world with presence of Fe, Zn, Cu, Pb, Cd, As, Ag, Sn, or Au. The other three soils in the Mijarojos-Cantabria zone, a lead-zinc mine, were as follows: two _Technosols_ M1 (latitude: 43 deg519'57.9573\" N, longitude: 4 deg5'2.8573\" W) and M3 (latitude: 43 deg20'15.9000\" N, longitude: 4 deg4'28.2415\" W), and a _Cambisol_ M2 (latitude: 43 deg20'03.9876\" N, longitude: 4 deg4'49.5997\" W). The main minerals of this area are sphalerite, galena, pyrite, and secondary minerals of Zn and Pb. Finally, four soil were collected from another nearby area to a lead-zinc mine in Portman-Murcia: two _Cambisols_ P1 (latitude: 37 deg35'24.1593\" N, longitude: 0 deg51'39.6248\" W) and P2 (latitude: 37 deg35'22.0308\" N, longitude: 0 deg51'54.4914\" W), and two _Technosols_ P3 (latitude: 37 deg35'19.2552\" N, longitude: 0 deg51'54.6936\" W) and P4 (latitude: 37 deg35'23.0712\" N, longitude: 0 deg51'38.8381\" W). The main minerals of this area are sulfides (sphalerite, galena, pyrite), phyllosilicates, and carbonates. The sampled _Technosols_ are mine tailings. All soil samples were air-dried, crushed, and sieved below 2 mm.
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 4 Experimental Results  
The proposed MBGNN model is evaluated using 39 cases selected from the Helsinki database. The EEG epoch is labelled as a seizure epoch if all the three experts agreed; otherwise, it is labelled as a non-seizure epoch. A 10-fold cross validation is performed on each case independently. The dataset of each case was randomly divided into ten equal-sized folds. Each fold is used once for testing and the other nine for training, resulting in 10 different performance metrics that are gathered for a comprehensive assessment. The Adam algorithm [33] is used as the optimizer for model training, with the number of training epochs set to 200 and the learning rate set to \(1\times 10^{-5}\).  
Considering that the neonatal seizure detection is an imbalanced classification problem, the following metrics are generated to demonstrate the overall performance of the MBGNN model:  
* Area under the receiver operating characteristic curve (AUC): The receiver operating characteristic (ROC) curve is a plot of sensitivity versus (1-specificity) and the AUC illustrates the performance of a binary classifier with varied decision threshold. The AUC values range from \"0\" to \"1\", and an ideal classifier will have an AUC equal to \"1\";
* Positive predictive value (PPV): The PPV is the probability that epochs with positive labels are truly seizure epochs. In this paper, the PPV illustrates the ability of a classifier to capture the real seizure epochs;
* Negative predictive value (NPV): The NPV is the probability that epochs with negative labels are truly non-seizure epochs. In this paper, the NPV illustrates the ability of a classifier to avoid false alarms.  
As a note, the decision threshold for PPV and NPV is set according to the optimal Youden index [34]. All the metrics are given in the form of mean \(\pm\) standard deviation. Table 3 presents the patient-specific classification performance of the proposed MBGNN model. In most cases, the MBGNN shows promising performance, with the PPV and NPVboth exceeding 90% and close in value, which means the proposed method could identify seizure epochs accurately with few false alarms. In cases 11, 13, 20, and 23, the PPV and NPV values are relatively low or show significant imbalance, which hinders the overall performance of the proposed method to some extent. The mean AUC, PPV, and NPV in 39 neonates are 99.11%, 95.34%, and 96.66%, respectively.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 4 Experiment
### Data set Configuration and Experimental Conditions
#### 4.1.3 Inference Parameters at Training  
For training, the time-frequency image is first scaled into a 640 \(\times\) 640 image input to the network, the maximum number of training cycles (epochs) is 100, the batch size of each update is 16, and the SGD (Stochastic Gradient Descent) optimization algorithm [32] with default parameters is used for training. In the experiments, the algorithm is trained and tested using NVIDIA Titan V 12G, and the network model is implemented on PyTorch1.11.0  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Setting** \\ \hline Broadband data sampling rate & 240 kSps \\ Time-frequency images horizontal coordinate & 0–120 kHz \\ range (bandwidth range) & \\ Time-frequency images vertical coordinate & 0–1 s \\ range (time duration) & 875 \(\times\) 656 \(\times\) 3 \\ Time-frequency images size & 0.2–1 s \\ Target signal duration & 2–8 \\ Number of target signals per image & 2–8 \\ Target signal modulation mode & AM FM MFSK MPSK \\ Signal-to-noise ratio range & 0–16 dB \\ channel simulation conditions & Rayleigh fading channel + Non-smooth \\  & undulating noise \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training dataset parameter setting.  
Figure 9: Comparison of the differences in the time-frequency diagrams generated at each SNR.  
framework using Python language. Other important environment configurations include opencv4, CUDA 11.6, and Python 3.9.12.
# IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data
## 4 Results  
### Selected Results of IAQ Monitoring in Apartments  
Figure 4 shows the data collected during the IAQ monitoring study. It includes the results of three days of measurements performed in two apartments (apartments A3 and A4). The indoor air parameters, i.e., T, RH, CO\({}_{2}\) concentration, SGP30 sensor response (indicative for TVOC content), and SGPC3 sensor response (also indicative for TVOC content) are displayed as the time series collected at the measurement point located in the kitchen and the living room.  
The visual inspection of Figure 3 allows for several important observations. 1. Different air parameters monitored in one apartment, even at the same measurement point, displayed distinct temporal variation patterns. This fact justifies predicting each of these parameters individually. 2. The time series of a particular air parameter measured in the kitchen and in the living room in the same apartment behaved similarly in time. It supports the idea of using IAQ monitoring in one of these locations, e.g., in the kitchen, to determine the IAQ in another location, e.g., in the living room. 3. It should be emphasized that the similarity between the temporal variation of a particular air parameter measured in the kitchen and the living room in the same apartment was observed in all the flats. This fact encourages the examination of the relationship between IAQ in the two locations for various flats. If confirmed, one MLT model could be developed to predict IAQ in multiple flats. 4. The temporal variation patterns of one air parameter in two apartments are distinct. This could raise some concerns about the possibility of MLT prediction model transfer between apartments.  
It shall be noted that the difference between the values of individual air parameters in the kitchen and the living room is partially due to the offset of sensor devices. In real conditions, frequent calibration is difficult to realize. In this work, we addressed the problem in two ways. First, a check was made to confirm that the responses of all devices to the individual air parameters were highly correlated and to identify potential shifts between them. For that purpose, before deployment in the flats, all the devices were left in one room for 3 \(\times\) 24 h to perform comparative measurements. This approach surely has its limitations, but it may be easily and widely applied in practice. Second, in this study,the offset did not have any impact on the results of the MLT performance assessment. This problem was overcome by using the same exemplar of a sensor device for measurements in all kitchens and another exemplar for measurements in all living rooms.
## 2 Materials and Methods
### Data Processing
#### 2.5.2 NEE Partitioning and GPP Estimation  
In this study, photosynthesis is quantified as GPP, which represents the total amount of CO2 fixed by vegetation at the ecosystem scale. The estimation process was performed in two steps. First, ecosystem respiration (Reco) was estimated by partitioning the NEE based on environmental drivers. Second, GPP was derived by subtracting NEE from Reco.  
The NEE data were partitioned based on the light response curve of NEE to \(R_{\text{g}}\), the effect of \(T_{\text{air}}\) on \(Reco\), and the influence of \(R_{\text{g}}\) and vapor pressure deficit (\(VPD\)) on GPP. The temperature sensitivity parameter \(E_{0}\) was fitted using nighttime NEE and \(T_{\text{air}}\) data within a 15-day window, with \(T_{0}\) fixed at \(-46.02\)\({}^{\circ}\)C and \(T_{\text{Ref}}\) at 15\({}^{\circ}\)C. The optimal \(E_{0}\) was determined as the value corresponding to the smallest standard deviation and used as the annual \(E_{0}\). Based on the fitted \(E_{0}\), the reference respiration rate \(R_{\text{Ref}}\) was calculated using a 7-day moving window with a 4-day step size (3-day overlap). Curve fitting and linear interpolation were used to derive a unique \(R_{\text{Ref}}\) for each sample [31].  
\[NEE=\frac{\alpha\beta R_{g}}{\alpha R_{g}+\beta}+R_{Ref}exp\left[E_{0}\left( \frac{1}{T_{Ref}-T_{0}}-\frac{1}{T-T_{0}}\right)\right] \tag{1}\]  
\[\beta=\begin{cases}\beta_{0}\,exp(-k(VPD-VPD_{0}))&VPD>VPD_{0}\\ \beta=\beta_{0}&VPD<VPD_{0}\end{cases} \tag{2}\]  
In these equations, \(R_{\text{Ref}}\) is the baseline ecosystem respiration rate at the reference temperature, \(E_{0}\) is the temperature sensitivity parameter, \(T_{\text{Ref}}\) is the reference temperature, and \(T_{0}\) is a constant. \(\alpha\) is the canopy light use efficiency (\(\mu\)molC\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)), \(\beta\) is the maximum CO2 uptake rate (\(\mu\)molC\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)), and the parameter k is fitted using a 4-day window, assuming \(VPD_{0}\) = 0.  
Following the estimation of \(Reco\), GPP was calculated based on the following equation [30]:  
\[GPP=Reco-NEE \tag{3}\]  
where GPP is gross primary production (gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)), Reco is ecosystem respiration (gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)), and NEE is net ecosystem carbon exchange (gC\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)).  
All flux data processing was performed using the REddyProc package in the R environment (v4.5.0) [31].