esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 FPGA Implementation of PID Controller and BB-BC  
The FPGA technology provides an effective solution in several industrial control applications due to its low development cost, high flexibility, and limited power consumption, while it has been proved an ideal processing platform for industrial applications with real-time constraints due to the acceleration achieved by the hardware parallelism. In this paper, we propose the use of FPGA technology to implement the BB-BC algorithm that calculates and optimizes the PID control parameters, (\(k_{p}\), \(k_{i}\), \(k_{d}\)) in order to accelerate the control system process, which can be very effective for latency-sensitive applications.  
Figure 4: Convergence and time response of the widely used objective function in controller tuning via optimization (_ITAE_, _ITSE_, _IAE_, _ISE_) for different number of iterations (**a**) 100, (**b**) 200, (**c**) 500 and (**d**) 1000 iterations.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 2 Background and Related Work
### UAV Wireless Relay  
As a communication relay, a UAV can transmit communication signals from the sender to the receiver to expand the communication range and improve communication quality [13]. In disaster response missions, UAVs can relay communication signals to first responders and rescue organizations. This can speed up response times for rescue operations while providing real-time data and information to support decision making. In the military domain, UAVs can be used as relay stations for battlefield communications. UAVs can transmit data, video, and images to support military decision-making and combat operations. In urban traffic monitoring, UAVs can be used as relay stations to transmit videos and images to the monitoring center. This can strengthen the ability of urban traffic monitoring and improve urban safety. With the development of UAV technology and the continuous iteration of wireless devices, UAV relay is an effective means to expand the communication coverage, improve the Quality of Service (QoS) of communication, and meet the communication needs of users.
## 9 Assumptions, Approximations, and Extensions of the Excitation Model  
In order to keep the code as general as possible, and therefore, more applicable to the largest number of users/targets, we acknowledge that a number of simplifications were made. Firstly, the population modelling only considers the relative populations of three energy levels (ground-, single-, and double-excited states). In reality, the number of electronic states may be large and the dynamics of a system more complex, e.g. involving evolutions that can occur within the excitation pulse length. Stimulated emission pumping may be added if the carrier frequency, dynamic Stokes shift, and pulse duration support the process. Quantification of the stimulated-emission cross section can be conducted on the basis of modelling power density dependence of femtosecond transmission under such conditions [25]. Such modifications can be added in the provided toolbox by specifying the additional rate equations. Secondly, we make no assumptions about how the relative populations will evolve in the time between the excitation (pump) pulse and a potential measurement (probe), be that spectroscopic or diffraction-based. To tailor it to a specific scheme, the code could be simply modified to include more complex rate equations and further temporal evolution. The presented applications considered electronic excitation only. However, for low bandgap or spin transition systems, thermal excitation and lattice  
Figure 6: Modelled population distributions for the same biaxial crystals described in Figure 5. In each case, the crystal was excited by a 130 fs, 1 uJ pulse in a 100 μm (FWHM) spot. In each case, the cross sections were \(\sigma_{S_{0}\to S_{1}}=7.3\times 10^{-17}\) cm\({}^{-1}\), \(\sigma_{S_{1}\to S_{2}}=3.32\times 10^{-18}\) cm\({}^{-1}\), and \(\sigma_{S_{\omega}}=4.6\times 10^{-17}\) cm\({}^{-1}\). It can be seen that the distributions of \(E*\left\langle\left(\hat{\mu}\cdot\hat{e}\right)^{2}\right\rangle\) (Figure 5) were encoded into the distributions of \(S_{0}\), \(S_{1}\), and \(S_{2}\). In certain regions, strong pumping results in significant depletion of \(S_{0}\), which occurred until a state was saturated.  
heating should be considered as well [26]. Finally, we do not consider the macroscopic crystal shape, such as the refraction that will occur at the crystal edges or any focusing that could occur with a curved-facet crystal (such as needles), both phenomena that will vary depending on exact crystal size and each's orientation. For many applications, however, the refraction could be minimal if index matching of the solution or solvent is achieved.
# Effect of Processing Methods on Amino Acid and Fatty Acid Composition of _Parkia biglobosa_ Seeds
## 3 Results  
### Amino Acid Compositions of Fermented, Defatted, and Protein Isolates of Parkia biglobosa Seeds  
The amino acid composition of fermented (FPB), defatted (DPB), and protein isolate (PI) of _Parkia biglobosa_ is presented in Table 1. The total non-essential amino acid was observed to be highest in the protein isolate sample (35.37 mg/100 g) compared to the defatted (34.49 mg/100 g) and fermented (23.62 mg/100 g) samples. In addition, PI showed the highest significant glutamic acid value (17.3 mg/100 g), whereas FPB showed the lowest serine values (2.8 mg/100 g) in the category of the tested non-essential amino acid. The PI (19.59 mg/100 g) also showed the highest total conditionally essential amino acid composition compared to DPB (19.41 mg/100 g) and FPB (14.74 mg/100 g) samples. However, tyrosine (6.02 mg/100 g) in DPB was the highest in total conditionally essential amino acids of the samples, whereas cystine (0.19 mg/100 g) in FPB showed the lowest. The PI (40.89 mg/100 g) showed the highest significant total essential amino acid composition compared to DPB (34.93 mg/100 g) and FPB (28.36 mg/100 g) samples. By comparing lysine and methionine, lysine (8.44 mg/100 g) in PI dominated the essential amino acid composition, whereas methionine (0.50 mg/100 g) in FPB showed the lowest value.
# Multi-Hazard WebGIS Platform for Coastal Regions
## 3 Showcasing the Results: Application to the Central West Portuguese Coast
### Cova-Gala Observatory  
#### 3.2.1 Overview  
Although the MOSAIC platform covers the whole mainland Portuguese coast, a particular coastal observatory out of the three available in MOSAIC was selected here for a detailed assessment of hazard conditions, exposure and vulnerability: the Cova-Gala observatory. The area is a sandy-dune system located south of the Mondego river mouth, which stretches for near 50 km in the Central west Portuguese coast featuring significant geomorphological and human occupancy properties. The Cova-Gala observatory is located in the municipality of Figueira da Foz, civil parish of S. Pedro and covers an area of 72 ha.  
Regarding the exposed elements, the observatory covers a heterogeneous typology ranging from residential areas, a camping site, a hospital, coffee shops, restaurants, surf schools, parking lots and fishery infrastructures. The three statistical blocks that cover the area of the Cova-Gala observatory has a total of 1363 inhabitants, although the number of residents in the observatory area is expected to be lower. The present population fluctuates considerably between the summer months (June to September) and the rest of the year. The vulnerability assessment considered 145 buildings.
# Data Chunks Placement Optimization for Hybrid Storage Systems
###### Abstract  
\"Hybrid Cloud Storage\" (HCS) is a widely adopted framework that combines the functionality of public and private cloud storage models to provide storage services. This kind of storage is especially ideal for organizations that seek to reduce the cost of their storage infrastructure with the use of \"Public Cloud Storage\" as a backend to on-premises primary storage. Despite the higher performance, the hybrid cloud has latency issues, related to the distance and bandwidth of the public storage, which may cause a significant drop in the performance of the storage systems during data transfer. This issue can become a major problem when one or more private storage nodes fail. In this paper, we propose a new framework for optimizing the data uploading process that is currently used with hybrid cloud storage systems. The optimization is concerned with spreading the data over the multiple storages in the HCS system according to some predefined objective functions. Furthermore, we also used Network Coding technics for minimizing data transfer latency between the receiver (private storages) and transmitter nodes.  
cloud computing; cloud storages; optimization algorithms; network coding +
Footnote †: journal:  
In this paper, we address the issue of cost-effective redundancy in hybrid storage systems. We build a new framework using \"Network Coding\" technology on top of the peer-to-peer connection, between HCS nodes and categorized storage systems, in two subgroups:  
* Transfer Nodes--Data storages, which keep a combination of the chunks from different objects; and
* Receivers Nodes--Private storages, to keep the object for user access. If a single receiver node fails and/or is replaced with new storage, we transfer all necessary fragments from transfer nodes to rebuild the object in the storage. Meanwhile, when maintaining redundancy in the system, we seek storages, which we can use for minimizing the amount of the bandwidth for object recovery in the new receiver storage. For this, we introduced a new heuristic for finding optimal data chunks placement. The paper treats this material in the following structure:  
* in Section 2, we provide a brief literature survey,
* in Section 3, we formulate the problem of optimal file upload,
* in Section 4, we present the optimal solution to the problem, and
* in Section 5, we describe the stimulation environments and results.
## 2 Related Work & Background
### System Architectures for Industry 4.0
#### 2.1.3 Communication Protocols and Interoperability  
Message Queuing Telemetry Transport (MQTT), seen in Figure 3, is a lightweight protocol for IoT communication, facilitating real-time data transmission between industrial devices. It is used for robot control, remote monitoring, and energy-efficient systems [34]. Blockchain and smart contract integrations enhance MQTT security [35].  
OPC UA (Open Platform Communications Unified Architecture) is a standardized communication protocol for industrial automation, ensuring interoperability between machines, controllers, and IT systems [37]. It supports real-time communication (OPC UA TSN), security, and integration across diverse production environments [38].
## 1 Hysteresis Introduction  
The phenomenon of hysteresis in engineering systems has been with us for ages and has been attracting the attention of many investigators for a long time. The reason is that hysteresis is ubiquitous. It is encountered in many different areas of science. Examples include magnetic hysteresis, ferroelectric hysteresis, mechanical hysteresis, superconducting hysteresis, adsorption hysteresis, optical hysteresis, electron beam hysteresis, etc. However, the very meaning of hysteresis varies from one engineering area to another, from paper to paper, and from author to author. As a result, a stringent mathematical definition of hysteresis is needed in order to avoid confusion and ambiguity. Such a definition will serve a twofold purpose: first, it will be a substitute for vague notions, and, second, it will pave the road for more or less rigorous treatment of hysteresis. The aim of this Special Issue is to gain new, unique knowledge about the hysteresis in engineering systems. Another goal is gathering the main contributions of academics and practitioners in mechanical, aerospace, and civil engineering to provide a common ground for improvements approaches of Hysteresis in Engineering Systems in mechanical, structural, electrical, materials, and other engineering fields. Studies concerning sensor technologies, vibration-based techniques, artificial-intelligence-based methods, and related fields are all welcome, both numerical and experimental.  
Understanding the phenomenon of hysteresis and the mechanism of hysteretic behavior is important in the design and analysis of numerous types of engineering systems. Hysteretic behavior is observed in the study of magnetic fields, the dynamic response of many structures under high-intensity cyclic or random loadings, in the force-displacement relationships of vibration-control systems, and in the dynamic response behavior of various connections and fasteners, to cite a few examples. Over the past few decades, a large body of work has been published on the development of mathematical models that can reproduce this behavior for various engineering design and analysis applications, as well as those predicting, for instance, the energy absorption or dissipation characteristics of various hysteretic materials. In recent years, an increasing body of work has also been published on the use of various data analysis methods for modeling and analyzing this highly nonlinear behavior. Hysteresis loops describe the system response of a sample exposed to one of different load types such as stresses in mechanical systems, magnetic field in magnetic system, and current in electrical system, etc. The shape of a hysteresis loop can be diagnostic of the systems. Magnetic systems tend to produce narrow loops, whereas mechanical systems cause wider hysteresis loops. A typical hysteresis loop of different systems modeling is shown in Figure 1.
# A Review of the Use of Native and Engineered Probiotics for Colorectal Cancer Therapy
## 3 Genetically Modified-Bacteria for Colorectal Cancer (CRC) Treatment  
Although naive bacteria have insufficiently potent anti-tumor properties, the excellent heterologous protein production capacity of these bacteria has made it possible to improve  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Probiotic Strain/Symbiotic Intervention** & **Types of Patients/Sample Size** & **Duration** & **Key Outcomes** & **Refs.** \\ \hline Wheat bran/_Lactobacillus casei_/wheat bran + _Lactobacillus casei_/no treatment & Patients who have had at least 2 colorectal tumors removed (\(n=380\)) & & Wheat bran alone: multivariate-adjusted OR for the occurrence of tumors was 1.31; _L. casei_ group: multivariate-adjusted OR for the occurrence of tumors was 0.76; Wheat bran + _Lactobacillus casei_ group: no notable synergistic effects observed \\ \hline Symbiotic (_Lactobacillus rhamns Bifidobacterium lactis Bb12_ + oigidrottocse-enriched inulin) & 43 polyectomized patients and 37 CRC patients (\(n=80\)) & & Polytectomies patients: increased _Bifidobacterium_ and _Lactobacillus_ and decreased _Clostritium perfringens_ in fecal flora; reduced exposure to genotoxins; suppressed IL-2 increase CRC patients: increased the number of _Bifidobacterium_; promoted IFN-\(\gamma\) production \\ \hline Probiotics containing six viable microorganisms of _Lactobacillus_ and _Bifidobacteria_ strains & Patients with CRC at 4 weeks after surgery (\(n=52\)) & 6 months & Probiotics group: reduced the levels of serum pro-inflammatory cytokines TNF-\(\alpha\), IL-10, IL-12, IL-17A, IL-17C, and IL-2 below their respective baseline levels; the level of IL-6 decreased to 1.44 \(\pm\) 1.39 pg/mL. \\  & & & Placebo group: increased the levels of TNF-\(\alpha\), IL-12, IL-17C, IL-22, IL-10, and IL-17A; reduced the level of IL-6 to 0.91 \(\pm\) 0.49 pg/mL \\ \hline Dietary symbiotic & Patients with CRC who were preparing for colorectal resection surgery (\(n=73\)) & 7 days & Synbiotic group: Postoperative infectious complications (2.8\%); mean antibiotic usage time (1.42 days); length of hospital stays (3.0 days); no morbidity Placebo group: Postoperative infectious complications (18.9\%); mean antibiotic usage time (3.74 days); length of hospital stays (4.0 days); three deaths \\ \hline \hline \end{tabular} Note: IL-2: Interleukin-2; IFN-\(\gamma\): Interferon-gamma; TNF-\(\alpha\): tumor necrosis factor-alpha; IL-6: Interleukin-6; IL-10: Interleukin-10; IL-12: Interleukin-12; IL-17A: Interleukin-17A; IL-17C: Interleukin-17C; IL-22: Interleukin-22.  
\end{table}
Table 1: Clinical trials using probiotics for the prevention and treatment of CRC.  
their effectiveness as anti-CRC agents. Here, we describe in detail the use of different strategies to modify wild-type bacteria to deliver anti-cancer molecules.
## 5 Conclusions  
In this paper, we proposed the underlay CRN using TAS/SC to enhance the OP performance for the primary network, and using DNC TWR, ICT, and PRS to enhance the performance of the secondary network, in terms of OP and data rate. Additionally, we derived the closed-form expressions of the transmit power of the secondary transmitters and exact closed-form expressions of OP at the secondary sources over the Rayleigh fading channel. To check the correction of the derived formulas, Monte Carlo simulations were realized. Because all the derived expressions are in closed-form, they can be easily used to design and optimize the considered network. Then, as proven in Section 4, enhancing the primary OP performance with TAS/SC also increased the spectrum access possibility and transmit power of the secondary transmitters as well as increased the second OP performance. However, under the impact of the co-channel interference from the primary network, the secondary network only obtains the coding gain (i.e., the diversity gain equal to zero). The results show that the OP performance of the secondary network could be improved by increasing the number of secondary relays and placing the relays near one of the secondary sources. Finally, it is worth noting that increasing the number of secondary relays also provides performance fairness for the secondary sources.  
**Author Contributions:** The main contributions of [PERSON], [PERSON], and [PERSON] were to review related works and conceive the main ideas; the main contributions of [PERSON]-[PERSON] and [PERSON] were to execute the performance evaluation and Monte Carlo simulations; the main contributions of [PERSON] and [PERSON] were to revise and finalize the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 6: OP of the secondary sources as a function of \(x_{\text{R}}\) when \(P_{\text{PT}}=10\) (dB).
## 4 Data Collection and Preparation
### Determination of Significant Variables  
The relative importance (significance) of explanatory variables was determined using an RF model, using MDA (Mean Decrease Accuracy) as the criterion of relative variable importance. The ranking of the relative variable importance is shown graphically in Figure 3 for both the crash likelihood model and the crash severity model. The vertical red (cordon) lines denote the separation between the significant variables to be considered (to the right of the cordon line) and variables that should be excluded as insignificant (to the left of the cordon line). The cordons were determined where the gap between variables was relatively large in terms of MDA.  
It should be noted that in an attempt to include more parameters as models' input, it was decided to use the second largest gap between the variables, in terms of MDA, to include (or eliminate) the variables in consideration. The literature suggests that models such as RF and GBM, are not affected by the inclusion of insignificant variables [20]. In RF, in general, nodes with the greatest decrease in impurity are found at the start of the trees, while nodes with the least decrease in impurity occur at the end of trees. Thus, the less important nodes have little effects on the models' performance.  
In both models the variables SHOULDER, Sunglare, Weekend, and MEDIAN_TY were not significant. Furthermore, the plots in Figure 3 suggested that HourlyPrecipitation and LANES should be omitted from the crash likelihood and crash severity models, respectively, due to their low relative importance.  
Figure 3: RF variable importance plot for: (**a**) the crash likelihood model, (**b**) the crash severity model.
## 3 Methodology
### Dimensionality Reduction with Autoencoders  
Autoencoders are neural network models designed to reconstruct their input by compressing it into a lower-dimensional representation, then decoding it back [30]. They consist of two main components: the encoder, which maps the input \(x\) to a latent code \(h=f(x)\), and the decoder, which reconstructs the input as \(r=g(h)\). The training objective is to minimize the reconstruction error between \(x\) and \(r\), typically using mean squared error (MSE).  
\[h=f(x),\quad r=g(h) \tag{1}\]  
In our study, we utilize stacked autoencoders to compress high-dimensional BERT embeddings from \(n\times 1024\) to lower-dimensional spaces (e.g., 512, 256, 128). The encoder layers gradually reduce the feature dimensionality, while the decoder layers attempt to reconstruct the original embedding. This compression retains essential features while reducing computational cost and memory usage during model training.  
The use of stacked autoencoders for dimensionality reduction is well-founded in their ability to learn non-linear manifolds within high-dimensional spaces through unsupervised representation learning [31; 32]. In the context of protein sequence modeling, stacked autoencoders have been widely adopted for feature compression tasks due to their capacity to preserve biologically meaningful information while minimizing reconstruction loss [33; 34]. In this study, the dimensionality of ProtBERT-derived embeddings was reduced from 1024 to as low as 32 through empirical tuning, guided by a trade-off between reconstruction fidelity and downstream classification performance (F1 score).  
The reconstruction loss in autoencoders is quantified using the Mean Squared Error (MSE), which measures the average squared difference between input vectors and their reconstructions:  
\[\mathcal{L}_{\text{MSE}}=\frac{1}{n}\sum_{i=1}^{n}\lVert\mathbf{x}_{i}- \hat{\mathbf{x}}_{i}\rVert^{2} \tag{2}\]  
Here, \(\mathbf{x}_{i}\in\mathbb{R}^{d}\) is the input embedding for residue \(i\), and \(\hat{\mathbf{x}}_{i}\) is its reconstruction, with \(d\) being the original feature dimension and \(n\) representing the number of input residues per batch. The use of MSE is theoretically grounded in its role as a convex loss function that promotes global minimization of Euclidean distance in the feature space. In the context of autoencoders, minimizing MSE encourages the network to learn a compressed latent representation that retains essential information from the input while discarding noise or redundancy. This choice is consistent with prior studies that utilized MSE to encode high-dimensional biological representations [34; 35].