esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Synthesis of the Homopolymer pEFS8-x  
The procedure for each homopolymerization was identical except for the monomer/initiator ratio and the type of atmosphere (nitrogen or vacuum). The polymerization of pEFS8-46 under vacuum was described as typical. The CuBr\({}_{2}\)/PMDTA complex (0.00066 mmol) was first added as a 0.0134 M solution in methanol (3 g/L of CuBr\({}_{2}\)) in a 10 mL Carius tube, which was then dried under vacuum for 1 h. EF88 (0.7013 g, 1.34 mmol), 1-BEB (3.0 \(\mu\)L, 0.022 mmol), and anisole (1.5 mL) were then added to the tube and the mixture was degassed with three freeze-pump-thaw cycles. Then SnEH\({}_{2}\) (1.4 mg, 0.0022 mmol) and PMDTA (0.7 \(\mu\)L, 0.0022 mmol) dissolved in 0.1 mL of anisole were added and three more freeze-pump-thaw cycles were performed before the polymerization was started at 110 \({}^{\circ}\)C under vacuum and with magnetic stirring. After 17.5 h, the reaction was stopped by exposure to air and quenching to room temperature. The crude product was precipitated three times from chloroform into \(n\)-hexane, then dissolved in chloroform, passed through a neutral alumina column, and evaporated to dryness under vacuum. The final polymer (yield 79%) containing 46 repeating units of EFS on average was named pEFS8-46 and was characterized by \({}^{1}\)H and \({}^{19}\)F NMR and GPC analyses.  
\({}^{1}\)H NMR (acetone-d6): \(\delta\) (ppm) = 6.9-7.2 (5 H, aromatic), 5.0-5.3 (1 H, ArCHBr), 4.2-4.4 (92 H, ArOCH\({}_{2}\)), 3.4-3.9 (1472 H OCH2), 3.39 (138 H, OCH3), 1.8-3.1 (138 H CHCH\({}_{2}\)).  
\({}^{19}\)F NMR (chloroform-d, hexafluorobenzene): \(\delta\) (ppm) = \(-\)142.2/\(-\)146.1 (2F, _meta_ F), \(-\)155.9/\(-\)158.0 (2F, _ortho_ F).  
GPC in chloroform: \(M_{\rm n}\) = 19,800 g/mol, \(D\) = 1.25.
# Solution of Bin Packing Instances in Falkenauer T Class: Not So Hard
## 1 Introduction
### Bin Packing Benchmarks  
A wide collection of benchmark problems are listed on the homepage of Operations Research Group Bologna [28]. These instances can be used to compare algorithms to each other. We only list the benchmark classes; the interested reader can find them on the homepage. We introduce in detail only those classes that are most important to us in our present research.  
* Falkenauer [18]--two classes, with 80 instances in each. The first class, Falkenauer U, is divided into four subclasses (20 instances in each), where the number of items is \(n=120\), \(n=250\), \(n=500\), and \(n=1000\), respectively. In each subclass, the item sizes are uniformly distributed between 20 and 100, and the bin capacity is \(S=150\). The authors proposed a simple greedy-type algorithm to solve these instances in [27]. The instances of the second class, Falkenauer T, include items that have to be assigned to the bins in triplets (three items per bin) in any optimal packing, where \(n\) is 60 or 120 or 249 or 501, and the bin capacity is \(S=1000\). Here, again, there are 80 instances. We will focus on these instances in the present paper.
* Scholl [29]--1210 instances in total.
* Wascher [30]--17 instances in total only, but they are considered some of the most difficult ones.
* Schwerin [31]--divided into two sets, Schwerin 1 and Schwerin 2, of \(100+100\) instances. In [27], all these are solved optimally with a greedy algorithm.
* Schoenfield_Hard28 [32]--as its name indicates, these are \"hard\" instances, difficult to solve optimally, with 28 instances in total.
* More benchmarks: There are three further classes on the Unibo homepage, _Randomly Generated Instances_, in total 3840 instances, the _Augmented Non-IRUP and Augmented IRUP Instances_, in total 500 instances, and the _GI Instances_, in total 240 instances.  
The total number of instances listed on the homepage [28] is 6195. The items in each instance are listed in decreasing order of their sizes, and we shall also assume the same in all our further discussion.  
There are several lower bounds for Bin Packing. The simplest one is to sum up all item sizes, divide by the bin capacity, and round it up. Another simple lower bound is the number of items bigger than the bin capacity. There are also further ones; see [33; 34].  
Lower bounds are useful in the sense that if the result of the algorithm matches a lower bound, then it is sure that the given solution is already optimal.
# Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication
## 2 Millimeter-Wave Antenna Array for 5G Communication
### Millimeter-Wave Phased Arrays for 5G Access Terminals
#### 2.2.2 Millimeter-Wave RFIC Phased Arrays
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Ref \#** & \begin{tabular}{c} **Array/IC** \\ **Elements** \\ \end{tabular} & \begin{tabular}{c} **Elements in** \\ **Package** \\ \end{tabular} & \begin{tabular}{c} **T/N/K Polarizations** \\ **Per IC** \\ \end{tabular} & \begin{tabular}{c} **\(f_{\text{g}}\) **(GHz)** \\ \end{tabular} & \begin{tabular}{c} **Beam-Scanning** \\ **(deg)** \\ \end{tabular} & \begin{tabular}{c} **Gain (T/N/Ro)** \\ **(dB)** \\ \end{tabular} & \
\begin{tabular}{c} **T/N/RX** \\ **Interface** \\ \end{tabular} &
\begin{tabular}{c} **Package Specification / Integration** \\ **Level** \\ \end{tabular} \\ \hline
[7] & \(32\times 32\) & 1024 & - & 28 & - & - & Single-layer PCB & - & **(HUST)** Array Antenna architecture \\ \hline
[13] & \(6\times 8\) & 48 & - & 28 & \(\pm 110^{\circ}\) & 21 & Single-layer PCB & - & **(Samsung MIMO** \\ Antenna transceiver & \(\begin{array}{c}[9]\\ \end{array}\) & \(1\times 7\) & 7 & - & 28 & \(\pm 27^{\circ}\) & 24.2 & Multilayer PCB & - & Antenna only \\ \hline
[15] & \(16\times 4\) & 64 & - & 28 & \(\pm 67^{\circ}\) & 29/27 & Single-layer PCB & - &
\begin{tabular}{c} **(Bell Labs)** MIMO transceiver with \\ **(BIF architecture** \\ \end{tabular} \\ \hline
[55] & \(4\times 8\) & 32 & - & 27.925 & \(\pm 30^{\circ}\) & 18 & Single-layer PCB & - & **(Samsung) Array** \\ Antenna architecture & & & & & & & & Antenna architecture \\ \hline
[53,66] & \(16\times 2\) & 128 & 2/2 & 28 & \(\pm 50^{\circ}\) & 32/34 &
\begin{tabular}{c} 0.13 \(\mu\)m \\ SGe BCMOS \\ \end{tabular} & 3 GHz IF & **(IBM)** Antenna in package \\ \hline
[54,60] & \(2\times 2\) & 4 & 1/1 & 29 & \(\pm 50^{\circ}\), \(\pm 25^{\circ}\) & 18/12 &
\begin{tabular}{c} 0.18 \(\mu\)m \\ SGe BCMOS \\ \end{tabular} & RF & **(UCSD)** Antenna on PCB \\ \hline
[61] & \(2\times 4\) & 8 & 1/1 & 28 & \(\pm 20^{\circ}\) & 14 & 28 \(\mu\)m CMOS & Analog IQ BB & **(LC)** Antenna in package \\ \hline
[63] & \(4\times 6\) & 24 & 2/2 & 28 & \(\pm 45^{\circ}\) & 44/34 & 28 \(\mu\)m CMOS & 6.5 GHz IF & **(Qualcomm)** Antenna in package \\ \hline
[66] & \(2\times 2\) & 4 & - & 28 & \(\pm 45^{\circ}\) & - & Multilayer PCB and & - & **(Qualcomm)** Antenna on PCB with \\  & & & & & & & & & & \\  & & & & & & & & & & \\  & & & & & & & & & & \\ \hline
[67] & \(4\times 4\) & 64 & 2/2 & 28 & \(\pm 40^{\circ}\) & 35 & Multilayer PCB + & 3 GHz IF & **(IBM)** Antenna in package \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance variation of various phased arrays for 5G access terminals.
# Infrared and Visible Image Fusion via Feature-Oriented Dual-Module Complementary
## 4 Experimental Setting and Results Analysis
### Objective Evaluation  
To evaluate the proposed method more comprehensively, we use the six indicators to test the fusion performances on the TNO and RoadScene datasets containing 30 sets of images, and these results are shown in Figures 6 and 7. From Figure 6, we clearly observe that our method achieves the best average values for all six indicators. Figure 7 demonstrates that our method performs well as it achieves the four highest averages and  
Figure 4: Fused images using ten different methods when the source image is “FLIR_video_00018”.  
two top-three highest averages among the six metrics. Judging from the overall trends of the ten methods on the two datasets, our method has a high probability of exceeding the performances of other state-of-the-art methods.
Nevertheless, in practical applications, the communication links of MASs are often affected by external environments and system failures, leading to communication interruptions in harsh environments or when agents fail. Additionally, the introduction of new agents may alter the existing connection structure, further increasing the time-varying nature of the communication network. These factors significantly complicate the control of MASs. Therefore, when designing collaborative control strategies for MASs, the key challenge is to rely on local information to design effective control mechanisms that maintain system synchronization despite issues such as communication interruptions, reconnections, and delays. Reference [32] uses a dynamic event-triggered mechanism to address communication resource overload and solves the formation control problem with communication delays caused by factors such as signals and bandwidth. Reference [33] proposes a finite-time output control algorithm combined with heterogeneous nonlinear systems to achieve formation control strategies. Reference [34] uses fuzzy control methods to address the impact of uncertain functions in uncertain systems and employs distributed optimization algorithms to achieve formation control objectives with time-varying delays. Inspired by the above literature, the innovations of this paper are as follows:  
First, a heterogeneous MAS is constructed by combining leaders and followers with different nonlinear term structures. Unlike time-invariant formations, a desired TVF vector is designed to satisfy TVF conditions, ensuring that followers track the state trajectories of leaders while achieving the desired TVF. Second, for the communication topology of heterogeneous nonlinear agents, unlike fixed topologies, the communication topology switches periodically. This ensures that the designed controller achieves the expected TVF and leader trajectory tracking under a jointly connected topological graph. Third, the controller in this paper is fully distributed. By designing adaptive weight adjustment using only the relative errors between adjacent agents, it avoids the non-zero minimum eigenvalue constraint problem in global communication topologies to achieve TVF control.
## 5 Discussion
### Limitations and Future Studies  
While this study has made significant progress in sinkhole susceptibility mapping using explainable machine learning models, certain limitations must be acknowledged. In particular, geological factors such as the contact surface between soluble and insoluble rocks, the dip angle of rock layers, and the thickness of rock layers were not included in the model due to data unavailability. These factors are important parameters that could influence sinkhole formation but could not be evaluated within the scope of the current dataset. In this study, groundwater levels and hydrogeological data were measured during both dry and wet seasons, and the differences between these periods were analyzed. This approach allowed the model to partially capture the dynamic processes influencing sinkhole formation. However, the lack of long-term and continuous monitoring data may limit the model's ability to fully capture temporal variations. Future research should focus on collecting missing geological and hydrological data to improve model performance. Additionally, incorporating long-term monitoring of climate change scenarios and groundwater fluctuations could provide more accurate predictions of future sinkhole occurrences. Investigating the effects of human activities, such as urbanization and agricultural practices, using time-series data would also enhance our understanding of sinkhole formation mechanisms. Finally, the development of real-time monitoring systems and early warning mechanisms could be a significant step in reducing sinkhole risks. Such systems could track critical factors like groundwater levels and land subsidence, providing timely alerts to authorities before sinkhole formation occurs. These future research directions will further enhance the accuracy and practical applicability of sinkhole susceptibility models, contributing to more effective risk management and sustainable land-use planning in karst regions.
## 3 Results and Discussion
### Characterization of Molecularly Imprinted Polymers (MIPs) and Coatings
#### 3.1.3 Brunauer-Emmett-Teller Analysis  
Figure 4 shows the Brunauer-Emmett-Teller (BET) analysis of the coating of the bisphenol A molecular imprinting stirring rod. According to the analysis results of specific surface area and pore volume, the BET specific surface area of the prepared material is 4.0 m\({}^{2}\)/g, and the total pore volume is 0.005 cm\({}^{3}\)/g.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Computational Architecture
#### 3.2.3 Definition of the Finite Element Model  
The definition of the finite element model of the bender element test involves three steps: algorithmic and material parameters; finite element mesh; and boundary conditions.
# A Deep Learning Model for Data-Driven Discovery of Functional Connectivity
## 1 Introduction  
Functional connectivity, which is often computed using cross-correlation among brain regions of interest (ROIs), is a powerful approach that has been shown to be informative for classifying brain disorders and revealing putative bio-markers that are relevant to the underlying disorder [1, 2, 3, 4]. Inferring and using functional connectivity through spatio-temporal data, e.g., functional magnetic resonance imaging (fMRI), has been an especially important area of research in recent times. Functional connectivity can improve our understanding of brain dynamics and improve classification accuracy for brain disorders, such as schizophrenia. Recent work [5] uses functional network connectivity (FNC) as features to predict schizophrenia related changes. Whereas, [PERSON] et al. [6] uses functional connectivity obtained by a fixed formula with phenotypic and imaging data as inputs and to extract graphic features for the classification of AD and Autism. [PERSON] et al. [7] also uses the connection strength between brain regions as edges, being typically defined as the number of white-matter tracts connecting the regions. [PERSON] et al. [8] employs spectral graph theory to learn similarity metrics among the functional connectivity networks.  
These papers, as well as many others, have shown the efficacy of functional connectivity and feature extraction based on neural network models. However, existing studies often heavily depend on the underlying method of functional connectivity estimation, in terms of classification accuracy, feature extraction, or learning brain dynamics. Studies, like [9, 10, 11], depend on hand-crafted features based on methods, like ICA (Independent Component Analysis). These studies work very well on classification, but they do not learn a sparse graph and are not helpful in identifying bio-markers in the brain.  
Many functional connectivity studies [12] on brain disorders utilize ROIs predefined based on anatomical or functional atlases, which are either fixed for all subjects or based on group differences.  
These approaches ignore the possibility of inter-subject variations of ROIs, especially the variations due to the underlying disease conditions. They also rely on the complete set of these ROIs discounting the possibility that only a small subset may be important at a time. A disorder can have varying symptoms for different people, hence making it crucial to determine disorder and subject specific ROIs.  
In this work, we address the problems of using a fixed method of learning functional connectivity and using it as a fixed graph to represent brain structure (the standard practices) by utilizing a novel attention based Graph Neural Network (GNN) [13], which we call BrainGNN. We apply it to fMRI data and (1) achieve comparable classification accuracy to existing algorithms, (2) learn dynamic graph functional connectivity, and (3) increase the model interpretability by learning which regions from the set of ROIs are relevant for the classification, enabling additional insights into the health and disordered brain.
## 3 Traffic Model
### Temporal Traffic Variation  
We start out with the assumption that traffic is periodic (day-night pattern) with the period length equal to \(|T|\cdot\Delta t\), where \(|T|\) denotes cardinality of the set \(T\). We distinguish the subsets of time periods \(T_{low}\subseteq T\) and \(T_{high}\subseteq T\) corresponding to a set of low-demand time periods and a set of high-demand time periods, respectively.  
The traffic demand curve \(\tilde{d}_{a,b}(t)\) is different for each node pair \((a,b)\in V^{2}\) due to the values \(A^{a,b}_{max}\) and \(A^{a,b}_{min}\) (see the secondary \(y\)-axis of Figure 1a). However, we assume in this step that the relative demand \(\tilde{d}_{a,b}(t)/A^{a,b}_{max}\) (taking values in the range \([0,1]\)) is the same for all node pairs \((a,b)\in V^{2}\) (shown in the primary \(y\)-axis of Figure 1a). Temporal traffic variation does not change spatial traffic distribution over time if the ratio \(A^{a,b}_{min}/A^{a,b}_{max}\) is equal for all traffic demands \(\tilde{d}_{a,b}(t)\), and if the sets \(T_{low}\), \(T_{inc}\), \(T_{high}\), and \(T_{dec}\) are identical for all traffic demands \(\tilde{d}_{a,b}(t)\). This is usually not the case in realistic networks, therefore we consider also spatial traffic variation over time in the next step.  
The length of each time period \(t\in T\) is set to \(\Delta t=1\) h in Figure 1a in order to make it easy to analyze (particularly in the comparison between the discrete data and continuous data). We change the period length to \(\Delta t=5\) min in Figure 1b, and show (using discrete data) a comparison with the sinusoidal model from [5]. The maximum and minimum demands (\(A^{a,b}_{max}\) and \(A^{a,b}_{min}\)) for both models are assigned with the same values. The increasing and decreasing trends are the same for both models (particularly consider the curve by [PERSON] and [PERSON] shifted by a quarter of a day, i.e., \(|T|\Delta t/4\)). Durations of \(T_{low}\), \(T_{high}\), \(T_{inc}\), and \(T_{dec}\) can be adjusted to make the match between the two models even closer than in Figure 1b.
# Degradation Characteristics and Reliability Assessment of 1310 nm VCSEL for Microwave Photonic Link
## 3 Discussions
### Leakage Current Characterization  
By monitoring reverse leakage, an early indication of device reliability can be attained. The measured leakage current characteristics of the 1310 nm VCSELs before and after aging are graphed in Figure 3. We observe that the leakage current increases and reverse bias breakdown voltage decreases after the accelerated current aging test. The increase in leakage current can be explained by the tunneling mechanism. The long-term current stress increases the temperature of the active region, and the carriers originating from thermal excitation at high temperatures cause an increase in the tunneling dislocations. It is easier for the carriers to escape the restriction of the quantum barrier, resulting in an increase in the leakage current. The reverse voltage reflects the number of defects in the active region of VCSEL. Non-radiative recombination will be introduced because of lattice defects, and the energy will be transmitted to other charged particles and phonons, which results in Auger recombination and lattice vibration. The vibration will promote the slip of defects, thus leading to defect enhancement and reverse voltage reduction.  
Figure 3: Reverse biased leakage current curves of the devices before and after aging at different bias currents, (**a**) 6 mA, (**b**) 8 mA and (**c**) 10 mA.  
Figure 2: Optical output power versus drive current from a device before and after aging at (**a**) 6 mA, (**b**) 8 mA and (**c**) 10 mA, respectively.
## 2 Materials and Methods
### Conversion of a bioreactor into an IoT-Enabled System
#### 2.4.3 IoT Architecture  
The IoT system developed for bioreactor monitoring implements a four-tier architecture comprising perception, processing, transmission, and application layers [27].  
The perception layer consists primarily of the following sensing elements: a digital temperature sensor, pH electrode, REDOX electrode, and ammonium ion sensor. While the temperature sensor interfaces directly with the ESP32 through digital communication, the pH, REDOX, and ammonium ion sensors transmit analog signals. The system also incorporates gas measurement devices utilizing the water displacement method, which are triggered by capacitive sensors (Figure 3).  
At the processing layer, the ESP32 microcontroller serves as the central processing unit. It performs several key functions such as converting raw sensor data (DC voltage or current) into meaningful parameter values through calibration equations, calculating running averages, and preparing data packets for transmission.  
The transmission layer utilizes Wi-Fi communication over the 2.4 GHz band to establish connectivity between the ESP32 and the cloud infrastructure. Data transmission is implemented through HTTP POST requests, enabling secure and reliable data transfer to a MySQL database.  
Figure 3: Instrumentation block diagram and communication protocols of the IoT bioreactor.  
Figure 2: Measurement cell for external electrode sensors made by acrylic. 1. Cable management of electrodes. 2. Perforated holder for electrodes. 3. Measurement cell apertures for electrode mounting. 4. Primary measurement cell body for receiving liquid samples from the bioreactor. 5. bioreactor sample inlet. 6. Content outlet. 7. Maintenance solution inlet for electrode preservation. 8. Base.  
The application layer consists of a web-based interface developed using PHP and HTML. This interface provides remote monitoring capabilities and allows users to access historical data. Furthermore, it incorporates data export functionality, enabling users to perform analysis and processing of the collected parameters.