esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study evaluates the feasibility of using banana peels as a substrate for cultivating fodder yeast biomass. Banana peels (BPs), representing approximately 38% of the total fruit weight, are rich in cellulose and hemicellulose, thus presenting a significant opportunity for valorisation. The study investigates the effects of microwave and ultrasound treatments on the hydrolysis efficiency of banana peels and the subsequent cultivation of yeast. Two yeast strains, _Scheffersomyces stipitis_ and _Meyerczyma guilliermondii_, were cultivated in hydrolyses prepared using various methods, including acid-thermal, enzymatic, microwave, and ultrasound treatments. The results demonstrate that enzymatic hydrolysis following microwave or ultrasound pretreatment significantly enhances sugar release, supporting higher biomass yields. The maximum biomass concentration achieved was 7.68 g DM/L, with crude protein content reaching up to 45.46% DM. These results indicate that banana peels can be effectively utilised for single-cell protein production, providing a sustainable alternative for animal feed. The study underscores the potential of integrating microwave and ultrasound technologies in bioprocessing to enhance the efficiency and environmental sustainability of yeast cultivation.  
banana peels; fodder yeast; guilliermondii; stipitis; ultrasound; microwaves +
Footnote †: journal: Applied sciences  
Article  
The Impact of Microwaves and Ultrasound on the Hydrolysis of Banana Peels and the Growth of Fodder Yeasts  
[PERSON], 1, 2025  
10 May 2025
# Multi-Objective, Reliability-Based Design Optimization of a Steering Linkage
## 3 Multi-Objective, Reliability-Based Design Optimization Technique (MORBDO)
Furthermore, the new development technique can reduce the complexity of the double-loop nested problem to a single-loop one by using a multi-objective evolutionary technique that can find a solution set within one optimization run, being called the multi-objective, reliability-based design optimization (MORBDO). From the present aim and the new technique, the traditional steering linkage optimization problem (15), in the previous section, can be changed to be the multi-objective, reliability-based design optimization problem using the worst-case scenario and fuzzy set model with the following explanation. The design constraints are imposed not to violate their predefined values. The uncertainties (\(a\)) are the tolerance of a steering arm length \(L_{a}\) and the tie rod-violated \(L_{t}\), which are assumed to be fuzzy variables. The multi-objective reliability-based design optimization problem can be formulated as  
\[\text{Min }\{f_{1}=\text{max}\big{|}\delta s(\theta_{I})\big{|}\;;\theta_{I} \in[0^{\circ},40^{\circ}],\;f_{2}\;=\;-\text{WCSV}\} \tag{21}\]  
\[\text{Subject to }\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\  
and all constraints can be normalized with their allowable limits \(\overline{g}_{i}\) as follows  
\[g_{i}(\mathbf{x},\mathbf{a})/\overline{g}_{i}-1\ \leq\ 0,i\ =\ 1,2,\ \ldots,\ N \tag{22}\]
## 3 Analytical Modeling of the 2-DOF CPM  
The detailed structure of the 2-DOF CPM has already been determined to guarantee the decoupling performances, and in this section its geometric parameters will be optimized to obtain an ideal mechanism. With the exception of decoupling performance, the concerned performance indicators also include the input and output stiffness, motion stroke, and natural frequency. Their analytical models should be established before optimization.
# A Novel Multi-Task Performance Prediction Model for Spark
## 1 Introduction
[PERSON] et al. [13] used deep neural networks to train a performance prediction model for Spark applications. Therefore, we believe that using deep learning for Spark performance prediction has great potential. However, Spark typically executes multiple different applications sequentially. A trained prediction model can usually only predict the execution time of one application of Spark and cannot predict the execution of multiple different applications. Therefore, we propose a multi-tasking performance prediction model for Spark that combines multi-head attention networks and convolutional neural networks. The model can predict the execution time of multiple Spark applications after one training. The contributions in this paper are as follows:  
1. We developed and implemented an automated Spark history log reader. It efficiently extracts historical data from Spark and stores them in a text file. 2. Among the various configuration parameters of Spark, we carefully selected specific parameters that have a significant impact on execution efficiency. In addition, we employed a dimensionality reduction algorithm to simplify the complexity of the data. 3. We developed and implemented a neural-network-based multi-tasking performance prediction model for Spark. The model can accurately predict the execution time of single or multiple Spark applications. In addition, we conducted a series of comprehensive experiments from different perspectives to demonstrate the accuracy and applicability of our model. Section 2 discusses related work, exploring prior research on the performance prediction of Spark. Section 3 introduces the methodology, offering a comprehensive framework of the proposed performance prediction model. Section 4 presents the experimental section, elucidating the conducted experiments in this study and analyzing the obtained results with precision and rigor. Section 5 summarizes the paper.
# Two-Stage Classification Method for MSI Status Prediction Based on Deep Learning Approach
## 2 Methods
### Dataset  
The dataset used in this study was provided by the PAIP 2020 challenge, which was aimed at MSI-H classification for CRC [36]. Our dataset included 47 training sets and 10 validation sets. The validation datasets were excluded from the training session and used only in the validation session. Each dataset contains WSIs, XML files annotating a tumor area, and ground truths for MSI classification. The WSIs included in the dataset were the microscopic images of resected tissue, stained with hematoxylin and eosin and scanned at 40\(\times\) magnification using Aperio AT2. In addition to the original image with 40\(\times\) magnification, the WSIs also included images with low magnifications, such as 20\(\times\) and 10\(\times\), to display the surrounding context information. In this study, WSIs with 20\(\times\) and 10\(\times\) magnifications were mainly used, considering computational time and efficiency. All WSIs contain at least one region of the colorectal tumor. The ground truths for MSI classification and tumor area were annotated by pathologists at Seoul National University Hospital (Seoul, Korea), Seoul National University Bundang Hospital (Seongnam, Korea), and Seoul Metropolitan Government Seoul National University Boramae Medical Center (Seoul, Korea) from January 2005 to June 2018.
# A Review on LiFi Network Research: Open Issues, Applications and Future Directions
## 5 Component and Architecture of a LiFi System
### Uplink Receiver ULR  
Using an IR light for UL can be a potential scheme for LiFi systems because IR light is invisible to the human eye. Using RF as a UL increases the complexity of combining space and time to increase the number of users who can access the network with a target value of rate per user. Managing transmissions by multiple users are necessary. To assure connectivity, UL coverage, and sufficient rate, multiple APs must be considered because the sole use of an AP may be inadequate. The opportunity of having the same signal copy at a different AP may induce interference if not adequately regulated through an access strategy. The ULR is called an IR detector, and it is typically placed in the ceiling at the AP beside the DLT, depending on the design that is being used. The CU controls both entities. In [95], the authors proposed a design for a UL with multiple users which consisted of five photodiodes. The UE refers to the user's pieces of equipment, such as laptops, phones, or any other devices. A cell phone is supposed to face upwards for proper ULT because of the Vertical handover (VHO) and a complicated power allocation are required. The ULT greatly depends on the user and AP locations. For instance, when WiFi is used for UL, users can move more freely in the room without considering the device orientation. By contrast, UE should be placed upward to maintain a stable connection for systems that use IR for UL. An IR ULT smart Time Division Multiple Access (TDMA)-based scheme was proposed in [95]. The authors proposed a scheme with hybrid access that aimed to ULT and/or DLT are essential for a complete LiFi system. In Figure 7, all the hardware components used in building, designing, or modelling a LiFi system network are proposed. To build, design, or study the architectural components of a LiFi system, understanding and investigating all the entities that must be included in the development of the LiFi systems are important. Figure 7 illustrates the essential components of infrastructure, AP side and UE side.The AP side includes Internet access [6], lamp driver, and light bulb, which represents the AP [137]; CU to control the flow of the data and support the LB and APA process [94]; and PLC or POE, which make to the use of the power lines as the medium of communication in the infrastructure among Internet servers [16; 18; 143; 174], CU, and APs possible. The UE includes cell phone, PC, or a dongle, including a light detector, such as a PD or camera [118], solar cell [175], or PV [174; 176], as receivers.
# Enhancement of Question Answering System Accuracy via Transfer Learning and BERT
## 2 Background
### Ner  
NER is the identification of named entities with particular meanings in text and classifying them into predefined entity types, such as person name, place name, institution name, time, currency, etc. Named entities usually contain rich semantics and are closely related to the critical information in the data. The NER task can resolve issues on information explosion in text data online, and obtain critical information effectively. Moreover, NER is commonly used in different areas, such as relationship extraction, machine translation, and knowledge graph construction.  
Chinese-oriented NER started later. One has immediately noticed that Chinese is quite different from West Germanic languages such as English due to its language characteristics. Hence, NER in the Chinese field mainly has the following three particularities. (1) Chinese NER should solve the difficulty in boundary ambiguities. It is because Chinese unit vocabulary boundaries lack clear separators such as spaces in English text and have no apparent morphological transformation features. (2) Chinese NER must be combined with CWS and grammatical analysis, only which can correctly classify named entities and improve the performance of NER. (3) Context may have significant influence on NER tasks. In Chinese text, there may exist complex sentences, flexible expressions, and many omissions. The exact words in different fields have different meanings, and there may be multiple expressions for the same semantics, which can only be clarified by the context. In addition, the internet has developed so rapidly, and the greater personalization and randomization of text descriptions in online texts, makes identifying entities more difficult.  
Nowadays, mainstream NER methods have three categories, including rule- and dictionary-based methods, statistical machine learning-based methods, and deep learning-based methods. This paper mainly studies the methods based on deep learning. The typical representative of Transformer-based methods is the pre-trained model BERT model. [PERSON] et al. [31] put forward a BERT-CRF(Conditional Random Field) model for the NER task, combining BERT's transfer capability with the structured CRF prediction. [PERSON] et al. [32] addressed the lack of large-scale labelled clinical data by pre-training the BERT model on unlabeled Chinese clinical electronic medical record text, thereby leveraging unlabeled domain-specific knowledge. [PERSON] et al. [33], who follows the work by [PERSON] et al., proposed a model based on Roberta and character root features, using Roberta to learn medical features, and using Bi-LSTM to extract character features. [PERSON] et al. [34] designed a model using Albert-AttBiLSTM-CRF and migration learning for fine-grained entity recognition text. In this model, A more lightweight pre-training model ALBERT was adopted to embed words in the original data Bi-LSTM was employed to extract the features of words with embedding and obtain contextual information, and a decoding layer using CRF was utilized for label decoding.
# The Performance Study of a Novel Vertical Variable Stiffness Disc Spring Seismic Isolation Bearing
## 2 Working Mechanism of VVSDSI Bearing
### Theoretical Study of VVSDSI Bearings  
#### 2.4.1 Mechanical Properties of Disc Springs  
The Chinese disc spring specification (GB/T 1972-2005) [30] states that the formulas for load \(F_{\mathrm{s}}\) and deformation \(\delta\) of an individual disc spring are as follows:  
\[F_{S}=\frac{4E}{1-\mu^{2}}\frac{t^{4}}{M_{1}D^{2}}\frac{\delta}{t}\left[ \left(\frac{h_{0}}{t}-\frac{\delta}{t}\right)\left(\frac{h_{0}}{t}-0.5\frac{ \delta}{t}\right)+1\right] \tag{1}\]  
Figure 4: Diagram of VVSDSI bearing operational status. (**a**) Operational state of the seismic isolation stage: (**b**) operational state of the protection stage.  
Figure 3: Schematic diagram of the friction force of the disc spring assembly.  
where \(E\) and \(\mu\) represent the elastic modulus and Poisson's ratio of the disc spring material, respectively: \(D\) and \(t\) denote the outer diameter and thickness, respectively: \(h_{0}\) denotes the compressible stroke of the disc spring, as illustrated in Figure 5. \(M_{1}\) is given by the following:  
\[M_{1}=\frac{1}{\pi}\left(\frac{Dd^{-1}-1}{Dd^{-1}}\right)^{2}\left(\frac{Dd^{-1 }+1}{Dd^{-1}-1}-\frac{2}{\ln(Dd^{-1})}\right)^{-1} \tag{2}\]  
where \(d\) is the inner diameter of the disc spring.  
The friction force of the disc spring group mainly originates from three aspects: (1) friction between adjacent disc springs: (2) friction between the end of the disc spring and the loading plate: (3) friction between the disc spring and the guide rod. In actual design, the inner diameter of the disc spring is usually larger than the outer diameter of the catheter, which can effectively avoid friction with the catheter. Therefore, the bearing capacity of the disc spring group is expressed as follows:  
\[F_{G}=F_{S}\frac{n}{1\pm f_{M}(n-1)\pm af_{R}} \tag{3}\]  
where \(f_{M}\) represents the friction coefficient between the conical surfaces of the disc springs (see Table 1): \(f_{R}\) represents the friction coefficient at the load-bearing edges of the disc springs (see Table 1): \(n\) represents the number of disc spring stacks: \(\alpha\) denotes the number of contact surfaces between the outer edges of the disc springs and the upper and lower surfaces; and the symbol '\(-\)' denotes loading, whereas '\(+\)' signifies unloading.  
Consequently, the vertical stiffness of the disc spring assembly under friction is  
\[K=\frac{F_{G}}{m\delta}=\frac{nF_{S}}{m\delta(1\pm f_{M}(n-1)\pm af_{R})} \tag{4}\]  
where \(\delta\) is the deformation of a single disc spring and \(m\) is the number of layers of a disc spring group. For disc springs of type A and B, their stiffness can be approximated as the linear stiffness, i.e., the stiffness value of the disc spring when \(\delta=0.75h_{0}\).
## 5 Example Simulation and Analysis  
### Different Scheduling Scenarios  
According to the scheduling method proposed in this paper, the following five kinds of simulation scheduling scenarios are designed to analyze the impact of the energy storage system, demand response, and carbon cost on absorbing renewable energy power.  
Case1: This scene contains wind, solar, and thermal generator units, regardless of battery storage power station, user-side demand response, and cost of carbon treatment.  
Case2: Battery energy storage scene, in which a battery storage power station is introduced in Case1, with a capacity of 50 MW and the initial energy storage is 0. The maximum charge and discharge power are 20 MW and the charge and discharge loss is 0.04.  
Case3: Generation-load interactive scene, in which the day-ahead price demand response virtual unit and the intraday IDR virtual unit are introduced in Case1. When the user does not participate in the price-type demand response, the electricity price is 71 $/MW-h, and the load self-elastic coefficient is \(-0.3\)[30].  
Case4: Low-carbon scene, in which the carbon cost is introduced based on Case1. For traditional coal-fired units, the CO\({}_{2}\) emission cost of unit electricity quantity is 0.88-1.21-t/(MW-h), and the value  
Figure 4: Flow chart based on the two-point estimation method.  
in this region is set as 0.798-t/(MW-h). The carbon treatment price is 17$/t, and other relevant parameters can be found in [31].  
Case5: Comprehensive scene, in which battery energy storage power station, demand response, and carbon cost are introduced in Case1. Detailed parameters are the same as above.
## 2 Materials and Methods
### Influence of Temperature on Parameters
#### 3.1.3 Determination of Carotenoids  
[PERSON] et al. employed high-performance liquid chromatography with a diode array detector (HPLC-DAD) to analyze tomato powder [19]. Their objective was to separate, detect, and quantify carotenoids, specifically all-trans lutein, \(\beta\)-carotene, and all-trans lycopene. Using their methodology, they noted that all-trans lutein exhibited a retention time of 8.058 min on the chromatogram, \(\beta\)-carotene a retention time of 14.468 min, and all-trans lycopene a retention time of 22.573 min (Figure 1).  
The identification of carotenoids (lyco-red) extracted from tomato peels was conducted using HPLC, as described in the literature [23]. These carotenoids were separated into thirteen fractions based on their functional groups, and nine compounds were identified through HPLC analysis. The carotenoids identified from tomato peels, along with their corresponding retention times, are as follows: (1) lutein--5.86 min, (2) lycopene--22.21 min, (3) cis-lycopene--25.15 min, (4) \(\gamma\)-carotene--31.64 min, (5) cis-\(\zeta\)-carotene--35.80 min, (6) \(\zeta\)-carotene--39.11 min, (7) \(\beta\)-carotene--41.90 min, (8) phytofluene--43.86 min, and (9) phytoene--54.85 min [10].  
We also used mixtures of solvents for the extraction because, after our research, a much better extraction with mixtures of solvents than with pure solvents and some higher results were observed. In Table 7, the composition of carotenoids in seven samples is presented. The first sample represents pomace extracted with ethyl acetate/ethanol, the second with ethyl acetate/acetone, the third with ethanol/acetone, the fourth with ethyl acetate/ethanol/acetone, the fifth only with acetone, the sixth only with ethanol, and the seventh only with ethyl acetate. Among the seven samples, the highest amount of carotenes was found in samples 3 and 7.
# Two-Stage Stochastic Programming for Precast Module Water Transportation: A Case Study in Hong Kong
## 3 Problem Statement
### Mathematical Model  
After an in-depth problem description, we then formulate it into a two-stage stochastic programming model [M1] for the water transportation of the whole MiC transportation process. The notation used in this work is listed as follows:  
**Indices**  
\(t\)--index of week  
\(k\)--index of ship type  
**Parameters**  
\(T\)--the number of weeks in the planning horizon  
\(K\)--the number of barge types  
\(N_{k}\)--the maximum number of modules that can be carried by a barge of type, \(k\) in one trip  
\(c_{k}\)--the cost of a round trip for **booked** barges of type \(k\)  
\(p_{k}\)--the cost of a round trip for **temporarily rented** barges of type \(k\)  
\(\widetilde{d}_{t}\)--random demand for modules in week \(t\)  
\(d_{t}\)--a realization of \(\widetilde{d}_{t}\)  
**Decision variables**  
\(a_{k}\)--integer variable of stage 1, indicating the number of trips to be finished by **booked** barges of type \(k\) per week  
\(\beta_{t,k}\)--integer variable of stage 2, indicating the number of trips to be finished by **temporarily rented** barges of type \(k\) in week \(t\)  
**Mathematical model**  
\[[\mathbf{Stage1}]\text{min}\left\{T\sum_{k=1}^{K}c_{k}a_{k}+ \mathbb{E}\left[F\left(\mathbf{\alpha},\widetilde{\mathbf{d}}\right)\right]\right\}\] (1) s.t. \[\alpha_{k}\in Z_{+},k=1,\ldots,K \tag{2}\] \[[\mathbf{Stage2}]F(\mathbf{\alpha},\mathbf{d})=\text{min}\left\{\sum_{t =1}^{T}\sum_{k=1}^{K}p_{k}\beta_{t,k}\right\}\] (3) s.t. \[\sum_{k=1}^{K}N_{k}(\alpha_{k}+\beta_{t,k})\geq d_{t},t=1,\ldots,T\] (4) \[\beta_{t,k}\in Z_{+},\ t=1,\ldots,T,\ k=1,\ldots,K \tag{5}\]  
Objective function (1) aims to minimize the total cost of transporting modules, where \(T\sum_{k=1}^{K}c_{k}a_{k}\) indicates the costs of booking barges in advance to transport modules for the whole planning horizon while \(\mathbb{E}[F(\mathbf{\alpha},\widetilde{\mathbf{d}})]\) is the expected costs of temporarily renting barges to transport modules. Constraints (2) define the domains of the first-stage variable. Equation (3) calculates the cost of renting barges temporarily. Constraints (4) guarantee the trips transported by the booked and temporarily rented barges have enough capacity to transport the actual demand of modules in week \(t\). Constraints (5) define the domains of the second-stage variable.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 2 Related Works
### LBP-Based Reversible Image Steganography Method  
In 2022, [PERSON] et al. [44] proposed the LBP-based reversible data-hiding technique. They partition the image into \(3\times 3\) non-overlapping blocks. They collect LBP codes from each block. They take eighteen embeddable bits and divide them into three segments where two segments contain 8 bits and the remaining is 2 bits. Then they XORed those three segments with the LBP code of one block and concatenate them. They again divide those XORed values into nine segments and each contains two consecutive bits. Next, they implant the first eight segments of bits by the LSB method in neighbor pixels of a block, and bits of segment nine are implanted into the center pixel of this block. In this way, all data are implanted. During data implantation, two stego images are generated. At the extraction phase, secret message bits and cover images are restored using the reverse process.  
Figure 1: Generating LBP code: (**a**) the way of representing contents in a pattern; (**b**) a sample pattern; (**c**) the pseudo-code for LBP; (**d**) LBP code.
## 5 Methodology  
The methodology that is used in this research is as follows. A text message is transmitted over the direct path and relay path by using different digital M-PSK modulation schemes. The performance is analyzed by varying the transmit antenna gain and the received SNR is measured at the destination. The capacity of channel is measured by using Shannon capacity theorem. In CC, the AF relaying mode is used with amplifying gain of 10 dB. Two time slot transmission protocols are used. In first time slot, text message is transmitted to relay and destination for 50 s. In the second time slot after 20 s  
Figure 5: Experimental Setup of cooperative communication (CC) without obstacle.  
Figure 6: Experimental Setup of CC with obstacle.  
delay, relay again retransmits text to the receiver for 50 s. The receive SNR form direct path and relay path is combined at the receiver using the MRC technique. At first capacity analysis for experimental, setup 1 is performed and then the same analysis is performed for setup 2. Optimal position of the relay is analyzed by varying the position of the relay. First relay is placed near the source then at center, and finally near the destination. Extensive experiments are performed within the lab environment. The following setup and system parameters are analyzed for the performance investigation of the system shown in Table 2.