esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Macroscopic Properties  
Representative images of the obtained films are shown in Figure 1. Although all the films appear practically identical, which is relevant from the point of view of product commercialization, only one image has been included, as it is representative of all blends due to their similarity, as can be seen in Supplementary Materials Figure S1. The macroscopic appearance is quite homogeneous, with no visible heterogeneities. They have a good appearance, with no phase differentiation noticeable to the naked eye. They are practically opaque white, which must be due to the thickness (120 \(\pm\) 10 microns, practically uniform). All of them have appropriate resistance.  
Figure 1: Representative image of a TPS-PBAT film. A specific picture for each composition can be found in Supplementary Materials Figure S1.  
Table 2 shows the different melt densities of all formulations, revealing remarkably similar values among the various modifications. Regardless of the addition of Vinnex, Joncryl, or the blending with PBSA, the melt density remained consistently close to the baseline formulation. These consistent values suggested that the incorporation of compatibilizers effectively homogenizes the macromolecular distribution within the blends, reducing interfacial differences and promoting phase integration. This parameter is critical for extrusion and film-blowing processes, as uniform melt density minimizes defects such as uneven flow or phase separation during processing, ensuring consistent material properties in the final product. Although melt density values do not show significant differences between compatibilized and non-compatibilized blends, this parameter does not directly reflect the interfacial interactions promoted by compatibilizers. Instead, these interactions are better evidenced by the enhanced mechanical properties and phase integration observed in compatibilized formulations. This behavior is consistent with previous studies, such as those by [PERSON] et al. (2023) and [PERSON] et al. (2019), which highlight that melt density is less sensitive to interfacial modifications while remaining critical for processing performance.
# An Analysis of the Use of Feed-Forward Sub-Modules for Transformer-Based Image Captioning Tasks
## 4 Experiments and Results
### Materials: Dataset and Evaluation Metrics  
The MSCOCO dataset [42], using the pre-defined Karpathy splits was used for training, validation and testing. Using the pre-defined Karpathy splits makes it easier to make comparisons with other models. The dataset consists of 113 k, 5 k and 5 k images for the training, validation and test sets, respectively. The effectiveness of the proposed methods was quantitatively evaluated using the standard evaluation metrics. The BLEU [43] evaluation metric is an n-gram precision based metric where the \"n\" represents number of n-grams from 1-4, i.e., BLEU-1 to BLEU-4. The CIDEr [44] evaluation metric uses the Term Frequency-Inverse Document Frequency (TF-IDF) statistical measure to give more weightage to important n-grams. ROUGE [45] is a metric more commonly used in automatic summarization, but can also be used to compare generated captions and the ground truth captions. METEOR [46] performs unigram matching. The SPICE [47] metric calculates an F1-score over caption scene-graph tuples, i.e., computing the balance between the precision and the recall.  
Figure 3: Graphs showing a comparison between the memory usage of the self-attention based variant (FC-SA) and our best model (FC4) at: (**a**) different batch sizes. (**b**) different sequence lengths. The memory usage reported is for usage during the cross-entropy optimization stage.
## 1 Introduction
The evaluation of the switching angles can be adjusted for any frequency of the triangular signal allowed by the device on which it will be implemented. The higher the switching frequency of the carrier signal, the greater the displacement of the generated harmonics and the lower the THD that can be achieved. The algorithm was implemented in MATLAB and verified by evaluating the output signal's total harmonic distortion (THD). The THD obtained in the results validated the  
Figure 1: Analog implementation of the unipolar SPWM with an op-amp. First signal: sinusoidal signal, second: triangular signal, third: output of the Op-amp, fourth: SPWM signal in the output of the inverter. proposed algorithm; the algorithm and the obtained angles can be digitally implemented in any simple processor like a microcontroller or a Raspberry Pi. The rest of this article is organized as follows: Section 2 shows the assessment of the SPWM angles, and in Section 3, the results for two cases are presented, one simple case with a unitary output frequency of \(f_{o}=1\) Hz and a carrier frequency of \(f_{c}=12\) to obtain \(p=5\) pulses in a half cycle, and a second more practical case proposed to avoid the presence of harmonics until the harmonic 49 to show the possibility of complying with this standard. In the second case, a unitary frequency \(f_{o}=1\) Hz and a carrier frequency of \(f_{c}=48\) to obtain \(p=23\) pulses in a half cycle were proposed. Section 4 briefly discusses the results, and the conclusions are outlined in Section 5.
## 5 Results and Discussion
### Effects of Using Different Numbers of MGAMs  
For the purpose of evaluating the effect of using different numbers of Mix Global Attention Modules (MGAMs), we conducted an experiment.  
As shown in Table 4, adjusting the numbers of MGAMs in our proposed PCHNet would affect the final segmentation accuracy. The accuracy of the model gradually in  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Activation Functions** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline Sigmoid [20] & 0.887 & 0.768 & 0.959 & 0.731 \\ Tanh [21] & 0.891 & 0.772 & 0.965 & 0.734 \\ ReLU [22] & 0.912 & 0.803 & 0.981 & 0.787 \\ PReLU [20] & 0.915 & 0.807 & 0.982 & 0.792 \\ Swish [24] & 0.929 & 0.823 & 0.989 & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effects of using different activation functions.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Loss Functions** & **Cracktree200** & **CRACK500** & **CFD** & **OAD\_CRACK** \\ \hline Mean Square Error Loss Function [39] & 0.903 & 0.785 & 0.962 & 0.736 \\ Cross Entropy Loss Function [34] & 0.917 & 0.795 & 0.973 & 0.772 \\ Weights Cross Entropy Loss Function [40] & 0.922 & 0.811 & 0.983 & 0.792 \\ Focal Loss Function [41] & 0.929 & 0.823 & 0.989 & 0.801 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effects of using different loss functions.  
creases and then slowly decreases, indicating that the MGAMs could extract more global features and these global features have a positive impact on the final segmentation accuracy. However, using too many MGAMs would bring redundant global features, and these features are interfering features. Thus, in the experiment, we used only four MGAMs in our PCHNet.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 5 Discussion
### Future Work  
Many applications require downlink communication, i.e., communication from the reader to the tag. Several research groups have investigated various forms of downlink modulation, e.g., amplitude shift keying of the carrier ([PERSON] et al., 2018), on-off keying ([PERSON] et al., 2018) and binary frequency-shift keying ([PERSON] et al., 2018). Modulation on the carrier poses two challenges: first, the wireless energy transfer may be affected during modulation. Second, the downlink signal must be decoded very simply, as the tags are highly resource-constrained devices, eliminatinghigh sampling rates and complex filters on the tag. In future work, we will investigate different downlink modulation schemes and demodulation and decoding schemes.  
An additional challenge arises when multiple tags are charged and read out with a single reader. We will investigate different typical methods for medium access control and multiple access for suitability for acoustic backscatter communication, e.g., TDMA, FDMA or orthogonal codes. Negotiation of suitable carrier frequencies for multiple tags at different locations may also be a challenge in multi-tag environments. Here, investigation of dynamic beamforming may be beneficial to not only increase transmission efficiency but also power and read out specific sensor nodes individually.  
Moreover, our experiments, as well as prior work, have been performed in controlled environments. In many possible applications, additional noise sources exist. For example, traffic causes strong vibrations in bridges, or motors introduce strong vibrations over a large spectrum. More work is required to identify the real-world conditions in such scenarios and explore potential mitigation strategies to deal with additional noise.
# How Trend of Increasing Data Volume Affects the Energy Efficiency of 5G Networks
## 1 Introduction
Analyzed device density classes represent expected device densities which average uplink (UL) and downlink (DL) throughputs prescribed by ETSI standards and contribute to the total expected DV of one of four specific device density classes. These device density classes represent typical operating environments served by corresponding 5G HetNet deployments. Performed analyses in this study highlight the impact of such an increase in DVs on future trends of 5G network EE metrics, with respect to different deployment and operating HetNet scenarios and device density classes. The rest of the paper is organized as follows: In Section 2, an overview of energy requirements of 5G networks is been presented. In Section 3, standardized metrics used for evaluating 5G network EE are elaborated. The types of different device density classes used in the analyses are explained in Section 4. Modeling of DVs' increase used for analyses of the impact of increasing DVs on EE of 5G networks is described in Section 5. The 5G radio access network (RAN) deployment and operation approaches and their performance in terms of EE are presented in Section 6. Section 7 analyses the results, which indicate how continuous increase in DVs impacts the values of EE metrics, instant power consumption and yearly energy consumption of the 5G network. In Section 8, a discussion on performed analyses and obtained results is performed. Finally, conclusions related to the analyses presented in the paper are given in Section 9.
## 1 Introduction
The simulation results demonstrate that in terms of accuracy, by including a higher number of modulation types, the proposed model achieves better performance to extract features compared to contemporary modulation classification techniques. The remainder of the paper is organized as follows: The literature review is presented in Section 2, the proposed design is presented in Section 3, and Section 4 comprises the simulation results. Finally, Section 5 concludes the paper.
# Multi-Objective Optimization of Production Objectives Based on Surrogate Model
## 2 Background of Multi-Objective Optimization Based on a Surrogate Model
### Response Surface Models  
Design of experiments within its techniques offers the possibility to generate and optimize regression models through many experimental designs. Particularly, one very known and widely used RSM methodology concerns mostly fitting and optimizing quadratic models. They are constructed as the approximation given by Equation (2) for the second-order model, referring e.g., to [PERSON] and [PERSON] [34], where \(\beta_{0}\), \(\beta_{i}\) (\(i=1,2,\ldots,\,k\)) and \(\beta_{ii}\), \(\beta_{ij}\) (\(i=1,2,\ldots,k;j=1,2,\ldots,k\)) are unknown regression coefficients, \(x_{i}\), \(x_{j}\) are input variables that influence the response \(y\), and \(\varepsilon\) denotes a random error (or a noise) observed in \(y\).  
\[y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}x_{i}+\sum_{i=1}^{k}\beta_{ii}x_{i}^{2}+ \sum_{i=1}^{k-1}\sum_{j=2}^{k}\beta_{ij}x_{i}x_{j}+\varepsilon,\;i<j \tag{2}\]  
Box-Behnken Design (BBD) is used to build a quadratic model based on such an experimental structure in which we observe the responses at the midpoints of the edges of the experimental space [35]. It does not ensure runs at the extreme combinations of all the factors. This disadvantage is compensated by having better prediction precision in the center of the experimental space. It requires at least three factors, and the effect of each of them is tested on three levels.  
Face-Centered Design (FCD) represents a special case of a variety of Central Composite Designs (CCDs). CCDs are based on a two-level screening factorial design, which is augmented with center and axial points to fit quadratic models [35]. Regular CCDs have five levels for each factor. The adaptation by choosing an axial distance \(\alpha=\pm 1\) produces a Face-Centered, Central Composite Design with three levels per factor. The added axial points lay at the center of each face of the factorial space. FCD design is cuboidal rather than rotatable, which is the supposition for better prediction ability in the corners of the selected experimental space. We applied BBD and FCD designs for the construction of metamodel of a vector function of production goals within this study.  
The Proposed Multi-Optimization Procedure Based on a Surrogate Model Implemented in Production Control  
The designed procedure of surrogate-based MOOP solving is introduced in this section. We illustrate its application in a production environment. Firstly, the method of derivation of the surrogate models for the selected production goals of the batch manufacturing system via the integration of the simulation and DoE method is described. Secondly, we show how the best derived analytical models are applied in objective functions of MOOP solving to find such effective input system parameters that ensure the desired values of production objectives.
# A Comprehensive Review on Network Protocol Design for Autonomic Internet of Things
## 2 Autonomic IoT Network
### IoT Network Characteristics  
An IoT network usually consists of a large number of nodes as the endpoints, either sensors or actuators, a gateway or base station, and a server or cloud platform in the internet. In a large-scale network, this is usually divided into several clusters where each cluster can have a node that serves as the cluster head. Cluster head has an important role as the intra-cluster coordinator and as a relay point for the data before it is sent to the gateway. This gateway is the point of sink that collects data before it is sent over the internet to the server or the cloud platform of IoT. An example of IoT network architecture is shown in Figure 2.  
The base station of an IoT network is usually a device that has large resource. The device can be a computer with a high processing power and high memory capacity with a sustainable supply of energy, because it is connected directly to a power source. A base station also serves the function of a gateway, which provides the required communication interface for delivery to the internet, either via Wi-Fi, ethernet, or cellular communication.  
On the other hand, the endpoint node of an IoT network is usually a device that has limited energy resource, processing power, and memory. The device can be a microcon  
Figure 1: Autonomic computing elements [6].  
Figure 2: Example of IoT network architecture.  
troller equipped with sensors or actuators and a wireless communication module, with the energy supply from a battery. It may also be equipped with an electronic module that can harvest energy from the environment. The device may also have mobility. It can change its position from one location to another. The constructed network topology can be a tree, star, or mesh.  
One major concern in IoT is the energy constraint of the nodes. The node's operation should have low energy consumption, including the operation for communication. A data transmission from distant locations can be done through a multi-hop communication. However, there is a possibility of missing data on communication at any time because the intermediate node may die or change its position or due to inter-node interference. This type of network is often referred as a Low Power and Lossy Network (LLN) [7].
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 4 Experimental Results
### FPGA Resource Utilization  
The FPGA resource utilization for the kernel implemented on the Alveo U200 demonstrates efficient resource allocation with minimal hardware overhead (Table 3). The design utilizes 13,515 lookup tables (LUTs) (1.23%) and 12,000 registers (REGs) (0.66%), indicating that small data structures such as intermediate computations and partitioned arrays are stored in registers rather than on-chip memory. The DSP utilization is 71 (0.98%), suggesting arithmetic operations involving multiplications and accumulations. Notably, no block RAM (BRAM) or ultra RAM (URAM) resources are utilized, which indicates that all large data structures, including predictions, weights, gradients, and loss values, are stored in external DDR memory and accessed through AXI interfaces. This design choice minimizes on-chip memory consumption but may introduce latency due to frequent DDR accesses. The low overall resource consumption suggests an optimized implementation, leaving significant room for additional computational tasks or concurrent kernel instantiations. Future enhancements could focus on optimizing memory access patterns, leveraging on-chip BRAM for reduced latency, and increasing DSP utilization to improve computational throughput.
## 4 Potential Research Directions of RSMA-Assisted ISAC Systems
### Vehicle-to-Everything  
V2X is an emerging technology for 6G wireless networks, supporting diverse traffic scenarios [76]. As the number of connected users is growing explosively, there would be strong interference in V2X networks by sharing the network infrastructure with celluar networks [77]. Prior research on RSMA-assisted V2X networks has demonstrated that RSMA can effectively mitigate interference and enhance data transmission rates. In [58], V2X systems utilizing RSMA demonstrated superior performance at higher SNR levels, achieving an average of 25% higher spectral efficiency compared to NOMA technology. Since RSMA showed its ability to maintain high spectral efficiency even in high-mobility scenarios, this advantage is especially relevant for satellite-to-vehicle networks, as it heavily depends on the accuracy of CSI estimation. In [78], a non-cooperative game based on an incentive mechanism was proposed, showing that RSMA-based satellite-to-vehicle networks outperform other MA technologies in terms of WSR.  
Recently, there has been growing interest in V2X research incorporating ISAC technology. There has been research on integrating sensors such as on-board units within vehicles and roadside units into ISAC-supported V2X networks to provide high-quality communication services [59]. The combination of RSMA's ability to flexibly mitigate interference along with ISAC's dual-functional capabilities makes RSMA-assisted V2X-ISAC a very interesting research area. In [79], a joint computation offloading and resource allocation strategy was investigated to make a reliable and efficient V2X network with mobile-edge computing (MEC) and ISAC technologies.
## 3 Results and Discussion  
### LMO Monitoring Project from 2009 to 2013  
Since 2009, the MOE has been conducted, monitoring projects for the unintentional release of LMOs into natural environments. This study analyzed project results from 2009 to 2013 to efficiently survey and systematically manage LMO collection sites.  
The MOE surveyed spills of LMOs in all natural ecosystems of six administrative districts from 2009 to 2013 (Figure 1). The MOE's survey manual provided to all investigators played an essential role in keeping the unity of the MOE's surveys. Suspicious samples and environmental information were collected in annual survey sites each season. The sample collection site was recorded with a handheld GPS device. For five years, spilled LMO crops were collected at 65 sites: 8 in 2009, 10 in 2010, 10 in 2011, 19 in 2012, and 18 in 2013 (Figure 1A). The number of LMO collection sites doubled between 2009 and 2013, indicating increased concerns about the unintentional release of LMOs into natural ecosystems nationwide. Notably, 38.4% of LMO collection sites remained unchanged during this period (Figure 1B). This information highlights the importance of the intensive management of LMO re-collection sites to minimize their potential risks in natural environments.
# Dynamics Modelling and Simulation for Deployment Characteristics of Mesh Reflector Antennas
## 2 Dynamic Modelling of the Truss Antenna
### Dynamic Equations of System  
The total kinetic energy and strain energy of the deployable antenna could be obtained as:  
\[T=T_{t}+T_{c},\quad U=U_{c} \tag{16}\]  
Thus, dynamic equations of the deployable antenna can be obtained in a compact form using the Lagrangian multiplier method  
\[\left\{\begin{array}{l}\mathbf{M}\tilde{\mathbf{q}}+\mathbf{C}_{\mathbf{q}}^ {\mathrm{T}}\mathbf{\lambda}+\mathbf{Q}_{k}=\mathbf{Q}_{e}\\ \mathbf{C}(\mathbf{q})=0\end{array}\right. \tag{17}\]  
with \(\mathbf{q}=[\theta\quad\mathbf{e}]^{\mathrm{T}}\) being the vector of generalized coordinates, in which \(\theta\) is the vector of the deploy angle of each cell. \(\mathbf{M}\) is the mass matrix of the antenna system. \(\mathbf{Q}_{k}\) and \(\mathbf{Q}_{e}\) are the vector of elastic forces and vector of external forces, respectively. \(\mathbf{C}\) is the vector of the constraint equations. \(\mathbf{C}_{\mathbf{q}}\) is the Jacobian matrix obtained by differentiating the constraint equations with respect to the generalized coordinates. \(\mathbf{\lambda}\) is the vector of Lagrange multiplier.
## 2 Background and Related Work
In the context of VANETs, a protocol known as a GeoBroadcast was proposed by the authors of [28] for implementation within the SDN architecture. Within this protocol, the transmitting node is responsible for dispatching a recurring warning message to apprise the receiving node. To facilitate the effective transmission and delivery of warning messages to the intended recipients, the Floodlight SDN controller was implemented with RSU location management and GeoBroadcast components. In their study, the authors of [29] put forth a routing protocol specifically designed for cognitive radio software-defined vehicular networks. The protocol consists of two phases, namely, the registration phase and the route prediction phase. The RSU served as the local controller for the registration phase, while the main controller was responsible for the route prediction phase. In their study, the authors of [30] put forward a cross-layer routing approach. In addition to channel allocation and link duration, the routing protocol incorporates the metric of vehicles' location and velocity. The vehicle initiates the registration process by transmitting a hello message to the local controller. In [31], the authors proposed a routing protocol for network state management that operates based on the concept of lifetime. The utilization of 4G cellular technology was employed for communication purposes. The protocol initially verifies the existence of a V2V routing path from the source to the destination. Security is one of the major concerns in VANET routing. The secure routing was proposed in [31; 32; 33; 34; 35; 36; 37; 38], which provided security in VANET routing. Other methodologies like artificial intelligence and machine learning [39; 40; 41] for different cases [42; 43; 44] are also used in the literature. While the routing protocols discussed above have their unique strengths in certain aspects of VANET communication, their limitations in sparse networks, dependence on accurate location data, and the lack of QoS support highlight the need for more adaptable and flexible network architectures. Moreover, the application of SDN in VANETs presents its own set of challenges, necessitating innovative solutions to fully harness its potential in these highly dynamic environments.