esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Morphological Analyses of the Plasma-Treated SiO\({}_{2}\) Nanofilers
These actions resulted in a strengthening of the bonding and adhesion at the interfaces of the nanoparticles and polymer matrices. The lower-agglomeration nanoparticles offered larger surface areas, which had a significant impact when the nanoparticles came into contact with the polymer matrices. The nanoparticles were then characterized using EDX to observe the changes in their chemical composition after the treatment. Table 2 shows quantitative atomic percentage concentrations of the nanoparticles after treatment. We can see that after both the silane and plasma treatments, their oxygen (O) content increased, while their silicon (Si) content decreased. The O content in the sample increased from 44.80% for the untreated samples to 48.08% after silane treatment and 47.60% after plasma treatment. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multicolumn{3}{c}{**Atomic Content (\%)**} \\ \cline{2-4}  & **C** & **O** & **Si** \\ \hline Untreated & 7.50 & 44.80 & 47.70 \\ Silane-treated & 25.00 & 48.08 & 26.93 \\ Plasma-treated & 9.40 & 47.60 & 43.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Atomic concentrations of nanoparticles after treatment. Figure 5: Morphology images of silicon dioxide nanoparticles: (**a**) untreated; (**b**) silane-treated; and (**c**) plasma-treated. The plasma treatment for surface modification of the nanoparticles consisted of the interaction process of plasma discharge and the surface of the nanoparticles. The plasma discharge involved many active species, such as ions, electrons, and photons, the bombardment of which led to the formation of radicals. The recombination of radicals resulted in a surface morphology change in the nanoparticles. During the discharge, however, some of the active species reacted with oxygen-containing radicals. As a result, the process increased the uptake of oxygen. This increase in O content after the plasma treatment indicates that surface oxidation due to the discharge had occurred. This result is in agreement with research by [PERSON] et al. [16], who showed that plasma treatment introduced more oxygen-containing groups than other treatments. The higher O content led to a hydrophilic surface area that enhanced the bonding of the nanofiller and polymer matrices. In this current study, surface compatibility between the silicone rubber and the hydrophilic nanofiller (silica) was achieved, thereby altering the interaction zones (interfacial area) between the silicone-rubber matrices and the nanofiller and, thus, increasing the strength of the filler-matrix interaction. Besides, C content showed a significant increase while Si content decreased after silane treatment. This is due to the silane coupling agent which is 3-(trimethoxysilyl)propyl methacrylate (H\({}_{2}\)C=C(CH\({}_{3}\))CO\({}_{2}\)(CH\({}_{2}\))\({}_{3}\)Si(OCH\({}_{3}\))\({}_{3}\)) with alkyl chains attached to the nanoparticle surfaces. The coupling agent altered the chemical content of the SiO\({}_{2}\) nanoparticles.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 1 Introduction
#### Notations  
Random variables are denoted by capital italic font, e.g., \(X\) with realizations \(x\in\mathcal{X}\). \(I(X;Y)\), \(p(y|x)\) and \(p(x,y)\) represent the mutual information, conditional probability, and joint probability distribution of the two random variables \(X\) and \(Y\). Multivariate random variables are represented with capital bold italic font, e.g., \(\mathbf{Y}=[Y_{0},Y_{1}]^{T}\). Matrices are represented with capital bold font, e.g., \(\mathbf{Y}\), and vectors are represented using a lower case bold font, e.g., \(\mathbf{y}\). All vectors are column vectors.
# Phytotoxic Substances Involved in Teak Allelopathy and Agroforestry
## 6 Agroforestry System with Teak  
Agroforestry systems and the allelopathy of teak are discussed in this section. Agroforestry systems are characterized by a combination of tree and crop cultivations and have been recognized as a sustainable land use system over conventional agriculture [74]. Trees can provide microclimate conditions suitable for crop production, and contribute a large amount of litter on the soil surface. The litter fall increases organic matter in the soil and decreases soil erosion [75; 76; 77; 78]. However, it is essential to design agroforestry systems with an appropriate selection of tree and crop combinations, and the distribution of resources, such as light, water, nutrients, and space requirements, for those combinations [74; 79].  
Teak was introduced into the agroforestry systems in many countries in tropical and subtropical regions because of its significant economic potential [3; 4; 7]. The effects of teak trees on crop productions have been evaluated in several agroforestry systems. Coffee cultivation in agroforestry systems with teak increased organic matter and phosphorus contents in the soil, as well as coffee yield [80]. Crop productions with cultural rotations of upland rice, cotton (_Gossypium arboretum_ L.), cassava (_Manilot esculent_ Crantz), chilli, and ginger succeeded in teak agroforestry systems [81]. These systems also resulted in good production of peanut (_Arachis hypogaea_ L.), soybean (_Glycine max_ (L.) [PERSON].) [82], and rice [83]. In addition, teak leaf marching provided a significantly high rhizome yield of ginger [84]. The reasons for the increases in crop productions were not mentioned clearly; it may be because of the inhibition of the emergence and growth of the competitive weed species [80; 81; 82]. The selection of crop plant species and development of their production systems with teak are necessary to avoid the phytotoxic effects of teak and enhance crop productivity. Agroforestry systems with teak may be able to integrate the balance of crop production, ecological aspects and sustainability [85]. Teak agroforestry systems are reported to provide 40% of household income through the production of the crops and woods [86], and to increase energy efficiency for crop production [87].
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 3 Link-Level Model  
### Assumptions and Performance Metrics  
In this study, we consider class \(A\) EDs for the LoRaWAN, where \(N\) number of EDs are uniformly distributed around a single GW. An ED always initiates a transmission in the UL by randomly choosing a channel frequency from \(\hat{C}\), where \(\hat{C}\in\{868.1,\ 868.3,\ 868.5\}\) MHz. However, in the DL, the same SF used in the UL is assigned for RW1 and SF 12, and the 869.525 MHz channel is used for RW2 [37].  
We consider the packet success ratio, average end-to-end, and average acknowledgment delays as LoRaWAN performance metrics in this study.  
The packet success ratio (\(SR\)) is the ratio of the number of packets successfully received to the number of packets transmitted by the EDs. It is defined as:  
\[SR=\frac{N_{r}}{N_{s}}, \tag{1}\]  
where \(N_{r}\) is the number of packets successfully received at the GW, and \(N_{s}\) is the number of total sent MAC packets.  
When an ED transmits a confirmed UL packet toward the NS, it expects to receive a DL ACK in one of the RWs. In the absence of the DL ACK, an ED re-transmits the same confirmed UL packet at least _ACK_TIMEOUT_ seconds after the RW2, as presented in [1]. After retransmission, if ED receives the DL ACK from NS during its RW1, as soon as ED demodulates the DL ACK, it is free to transmit a new confirmed UL packet on a random channel. In a confirmed mode, if an ED receives a DL ACK from the GW, a packet is considered delivered. The average end-to-end delay \((\bar{\mu}D)\) and average ACK delay \((\bar{\mu}D_{ack})\) of the confirmed UL packet can be computed using Equations (2) and (3):  
\[\bar{\mu}D=\frac{\sum(T_{r}-T_{s})}{N_{r}}, \tag{2}\]where \(T_{r}\) is the time a packet has completely arrived and successfully received at the GW, and \(T_{s}\) is the time a packet leaves the ED queue for transmission:  
\[\hat{\mu}D_{ack}=\frac{\sum(T_{ack}-T_{tx})}{N_{r}}\, \tag{3}\]  
where \(T_{ack}\) is the ACK reception time measured at the ED and \(T_{tx}\) is the time a confirmed UL packet was transmitted from the ED for the first time. The \(\hat{\mu}D_{ack}\) is the delay time between the first transmission of a packet at the MAC layer and the moment the ED receives the corresponding ACK. It is averaged over the total number of MAC packets received during the duration of the experiment.  
Besides, in the unconfirmed mode, if a GW correctly receives a packet, then it is determined to be delivered. On the other hand, in the confirmed mode, a packet is considered delivered if the corresponding ED receives a DL ACK confirmation from the GW. Both \(\hat{\mu}D\) and \(\hat{\mu}D_{ack}\) are limited to the confirmed mode only. Moreover, the symbols used in the link-level model with descriptions are highlighted in Table 1.
# An Energy-Efficient Convolutional Neural Network Processor Architecture Based on a Systolic Array
## 2 System Architecture
### Analysis of the Configurability of the Systolic Array  
Configurability is important to CNN processors because CNNs with various parameters can be processed by reusing the same hardware. We analyze the configurability of the systolic array in the following aspects.  
1. [label=(0)]
2. Configurable image size: We can simply realize the configuration of the image size in the systolic array by broadcasting the image rows with different lengths to the PEs without changing the hardware structure.
3. Configurable filter size: We note that the number of PEs in the above systolic array is required to be equal to the size of the filters. For example, a systolic array with three PEs cannot complete the convolution with the filter size of five in one computing process due to an insufficient number of PEs. We can solve this problem by performing two computing processes, as shown in Figure 7. The processing sequence is horizontally split into upper and lower parts. The upper part completes the convolution of the first three weight rows, and the output intermediate values are stored in FIFO2. The lower part completes the convolution of the last two weight rows, the intermediate values stored in FIFO2 are regarded as the bias values in this process.  
Figure 6: (**a**) Reconfigured systolic array; (**b**) processing sequence of convolution in the second channel.  
3. [label=(3)]
4. Configurable 3D convolution: The convolution with multiple input channels discussed in Section 2.2 is limited to the computing of one output feature map. One systolic array can complete the convolution with multiple filters serially if the time overhead is not considered. A more common method is to expand the number of systolic arrays to support parallel computing between filters, i.e., between the output feature map channels, and the operation throughput increases accordingly with the number of systolic arrays.
5. Fully connected computation: The fully connected computation can be regarded as a special kind of convolution due to the input image size being equal to the filter size, which can be completed by configuring the systolic array to reuse some of the PEs. For an output neuron result, only the first PE in the systolic array is needed to perform the multiplication of the image values and the weight values and accumulate the product. The operation can be expressed as follows: \[\text{OUTn}=\text{bias}_{\text{n}}+\sum_{\text{j=1}}^{\text{H}\times\text{W} \times\text{C}}\text{i}_{\text{jw}}[\text{n}]_{\text{j}}\] (3) where \(\text{OUT}_{\text{n}}\) denotes the n-th output neuron; H, W and C denote the height, width and number of channels of the input image, respectively; and \(\text{i}_{\text{j}}\) and \(\text{w}[\text{n}]_{\text{j}}\) denotes the j-th data of the input image and the j-th weight value of the n-th filter, respectively. Similar to convolution, fully connected computation can also use multiple systolic arrays to compute output neurons in parallel.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 5 Discussion  
### Connection Between Atmospheric Vertical Movement and El Nino  
The VV anomalies show a significant positive correlation with the Nino 3.4 index (CC = 0.46, \(p\) < 0.05). The impact of an El Nino event on atmospheric vertical movement in the ARB is mainly achieved by altering the Walker circulation pattern in the tropical Pacific [70]. During an El Nino event, the abnormal warming of sea surface temperatures in the central and eastern equatorial Pacific causes the weakening of the Walker circulation. The ascending airflow originally in the western Pacific shifts eastward to the central Pacific, while the western Pacific (including the ARB) turns into abnormally descending airflow [71]. This circulation adjustment directly inhibits convective activities in the ARB, leading to a significant reduction in PRE. From June to October 2023, the average VV anomaly in the ARB was 0.08 Pa/s (descending), which was a 60% increase in descending intensity compared to the climatological mean (0.05 Pa/s) [8]. There was a significant positive correlation between VV anomalies and the reduction in PRE (CC = 0.50, \(p\) < 0.05). In 2023, the PRE in the ARB was about 60% less than the normal level. The adiabatic warming caused by the enhanced descending airflow exacerbated the high-temperature drought. In September 2023, the average temperature in the ARB was 1.5-2 \({}^{\circ}\)C higher than normal [72].  
Figure 16: VIWVF and water vapor flux divergence in the ARB and its surroundings during June and September 2023. The length of the arrows indicates the magnitude of the VIWVF, and the direction of the arrows indicates its transport direction. The color scale indicates water vapor flux divergence.
## 4 Performance Evaluation of Improved SOSK-Means
### Experiment 4  
In this experiment, the ISOSK-means results were compared with other existing non-hybrid algorithms (DE, PSO, FA, IWO) from the literature [60] for the same problem, and the results are presented in Table 10. The ISOSK-means recorded the best mean scores in four datasets: A1, A2, Birch2 and Housec5. The FA recorded the best mean performance, followed by the proposed ISOSK-means algorithm. FA and ISOSK-means recorded better average performance scores than DE, PSO and IWO. This showed that the improved hybrid algorithm demonstrated a high competing capability with other metaheuristic algorithms in automatic clustering of high dimensional datasets.
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## 4 Summary and Conclusions
Less common techniques, such as the adaptation of the original MLAs or the combination of several classifiers into one single predictor, can help to achieve a better performance in Li exploration. The integration of remote sensing data with geological information (mineral prospectivity) will increase the ability to target new deposits. The development and improvement of the geobotanical approach may enable Li exploration using remote sensing in highly vegetated areas, which for now is unthinkable. The generalization of the mentioned approaches to other Li deposit types (e.g., brines) will also contribute to establishing remote sensing as a fundamental tool in Li exploration. All the authors designed and schematized the manuscript; [PERSON] wrote the original draft; [PERSON], [PERSON], [PERSON], and [PERSON] revised and edited the paper. All authors have read and agreed to the published version of the manuscript. The authors would like to thank the financial support provided by FCT--Fundacao para a Ciencia e a Tecnologia with the ERA-MIN/0001/2017--LIGHTS project, FEDER through operation POCI-01-0145-FEDER-007690 funded by the Programa Operacional Competitividade e Internacionalizacao--COMPETE2020 and by National Funds through FCT within the ICT, R and D Unit (reference UIDB/04683/2020). The University of the Basque Country (UPV/EHU) contributed economically with grant GIU18/084. [PERSON] is financially supported within the compass of a Ph.D. Thesis, ref. SFRH/BD/136108/2018, by national funds from MCTES through FCT, and co-financed by the European Social Fund (ESF) through POCI--Programa Operacional Capital Humano. The authors declare no conflict of interest.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 4 Results of Global Two-Class Models
### Internal Retest  
Retesting the models that have been trained on the merged version of CIC-IDS2017 with their own data shows that these models are consistent with the results described in [11]. This is as expected and it is a necessary requirement to start evaluating the models with samples from CSE-CIC-IDS2018. During the evaluation, five classification metrics were taken into consideration: balanced accuracy, precision, recall, F1-score and standard accuracy.  
Every tree-based classifier has classification metrics that converge above 99% with as little as 10% of the data used for training. The neighbor-based methods also stay consistent with previous findings, with knn converging on classification metrics above 98% with 10% of data used fir training. The nearest-centroid classifier fares much worse with a metric profile that is invariant to the amount of data used for training, reaching F1-scores of only 45% (hampered by low precision, and recall is relatively high at 70%). Similarly flat profiles have been observed for the linear support vector machine and the logistic regression. With these models, the F1-score does reach 82%. The RBF-kernel SVM does improve when given access to more training data, reaching an F1-score above 90%.  
All models were found to be resistant to feature removal. All tree-based and neighbor methods never lost more than a flat 5% on any metric, even on the most aggressive feature reduction setting, with the removal of the 20 most discriminative features (on a total of 79 available features). The logistic regression and linear SVM did lose up to 10% in flat metrics (i.e., X-10% as opposed to X-(X*10%). The RBF-kernel SVM never lost more than 5%. Different methods of feature scaling typically had a limited effect on these results. Overall, a case could be made for the normalization of the data over min-max or no scaling, because normalization worked best and most stably for all methods, regardless of algorithmic class.
# Representing and Validating Cultural Heritage Knowledge Graphs in CIDOC-CRM Ontology
## 3 Aligning COURAGE Entities to the CIDOC CRM Ontology
### Mapping Tables  
Mapping tables establish the basis for the automated migration process. These tables were developed in accordance with the previously mentioned principles.  
**People.** The following steps were applied in order to translate a COURAGE person entity to the E21 Person entity in the CIDOC-CRM ontology. First, a list of essential  
Figure 8: Stakeholder role representation in CIDOC-CRM.  
Figure 7: Founder role representation in CIDOC-CRM.  
Figure 9: An example of an organizational type representation in CIDOC-CRM.  
COURAGE properties with their equivalent classes and properties had to be determined (see Table 1)  
Structural diagrams that clarify the mapping process were also created. For instance, Figure 10 demonstrates how the birthdate and birthplace properties in COURAGE were represented by the E67 Birth entity in CIDOC-CRM. In contrast to [12], we did not create an instance of E41_Appellation as there was no other property we needed to assign to it. Figure 10 exemplifies the representation mechanism utilized for all entity properties.  
**Organizations.** The organization entity and the person entity in COURAGE share common properties. Organization entities were translated into the E74 Group entity type in CIDOC-CRM. Table 2 displays all the organization's particular properties.  
**Collections.** A collection entity in COURAGE was mapped to the E78 collection type in CIDOC-CRM. The mapping rules for each collection's properties are as in Table 3.  
**Featured Items.** A featured item entity (earlier named masterpiece) in COURAGE was represented by E22 man-made object, the most relevant class in CIDOC-CRM. The mapping rules for the featured item's properties are as in Table 4.  
A creator role representation is shown as an example in Figure 11. The basis is E87 curation activity in CIDOC-CRM, here its subclass E87a content creation activity can be seen.
## 2 Structure and Operation Principle of the Proposed Converter  
An ultra-high-gain DC-DC converter was used to verify high efficiency and load power applications for RESs, facilitating the elevation of low input voltages ranging from 25 V to 40 V to variable output voltages of 325 V to 500 V, while accommodating a 325 W load. In Figure 1a, the integrated setup of the proposed converter with PV Panels and applications, inverter, and load is depicted. The schematic diagram of the proposed converter is illustrated in Figure 1b.  
Compprising four primary inductors, five capacitors, seven diodes, and two MOSFETs, as shown in Figure 2a, the proposed converter boasts a simplified structure with numerous advantages. Sw\({}_{1}\) is the main first switch and Sw\({}_{2}\) is the auxiliary MOSFET. One notable advantage is the elimination of the need for coupled inductors and transformers traditionally required for voltage step-up. The reduction in the values of capacitors and inductors, after the proposed converter operates at a high switching frequency, is significantly high, thereby enhancing overall efficiency. The converter's structure is simple to implement. The addition of a single input capacitor eliminates pulsations in the input current, even at very low duty cycles, enhancing its reliability--especially for photovoltaic applications. Moreover, the diodes in the MHSLCS, namely D1 and D2, experience remarkably low voltage stress. Furthermore, the proposed converter ensures low voltage stress on both the main and auxiliary switches. This feature enhances the reliability and longevity of the switches, minimizing the risk of failure and improving overall performance. By effectively managing voltage stress, the converter provides a more robust and durable solution for high voltage gain applications. The proposed converter achieves a higher voltage gain compared to previous DC-DC converters while employing fewer inductors and capacitors. The PWM generator controlling the MOSFET switches is simple, with both devices turning on and off simultaneously, simplifying the control mechanism.