esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Study
#### 4.5.2 Analysis of Different Neural Networks  
In the SLRBC proposed in this paper, BiLSTM is selected as the encoding layer. In order to explore the influence of different neural networks on the overall model and subsequently optimize the neural network layer, we replace the BiLSTM with CNN and Transformer in ablation experiments. The experimental results are shown in Table 8, which indicates that the model performs best when using BiLSTM in the encoding layer. When employing CNN and Transformer, there is a discernible disparity in F1 on both datasets compared to BiLSTM. It can be inferred that the constrained receptive field of CNN hinders its ability to capture long-distance dependencies, and the ability of Transformer to capture  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**CCKS2018**} & \multicolumn{2}{c}{**CCKS2019**} \\ \cline{2-5}  & **Training Speed** & **Testing Speed** & **Training Speed** & **Testing Speed** \\ \hline CNN-CRF & 18.39 st/s & 46.04 st/s & 21.97 st/s & 54.29 st/s \\ BiLSTM-CRF & 18.80 st/s & 48.21 st/s & 21.65 st/s & 54.31 st/s \\ BERT-BiLSTM-CRF & 15.53 st/s & 20.86 st/s & 17.73 st/s & 22.22 st/s \\ SoftLexicon-BiLSTM-CRF & 18.56 st/s & 46.45 st/s & 21.51 st/s & 54.36 st/s \\ SoftLexicon-BERT & 15.21 st/s & 20.37 st/s & 17.14 st/s & 21.96 st/s \\ SLRBC & 11.85 st/s & 13.56 st/s & 13.57 st/s & 14.13 st/s \\ \hline \hline \end{tabular}  
*st/s* represents the number of sentences processed by the model per second.  
\end{table}
Table 6: Comparison of training speed and testing speed of each model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**Four Modules in Representation Layer**} & \multicolumn{2}{c}{**F1 on Two Datasets**} \\ \hline
**Char** & **Bichar** & **SoftLexicon** & **RoBERTa** & **CCKS2018** & **CCKS2019** \\ \hline \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & 94.35\% & 84.12\% \\ \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & 93.88\% & 84.75\% \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & 93.35\% & 83.04\% \\ \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 93.73\% & 84.70\% \\ \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 93.12\% & 83.38\% \\ \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & 92.38\% & 82.83\% \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 94.97\% & 85.40\% \\ \hline \hline \end{tabular}  
*means to add the module, and “\(\times\)” means to remove the module.  
\end{table}
Table 7: Effects of different modules in representation layer on model performance.
# _Ku_-Band CMOS Power Amplifier with Three-Stack Power Stage to Enhance Output Power and Efficiency
###### Abstract  
In this study, we proposed a power amplifier structure with improved efficiency while securing high output power. First, the characteristics of the common-source and stack structures were investigated. In particular, the output power and output impedance characteristics of the stack structure were analyzed compared with the common-source structure. A common-source structure was applied to the driver stage to minimize dc power consumption, and a stack structure was applied to the power stage to ensure high output power. In order to verify the proposed structure, a _Ku_-band power amplifier was designed using the 65-nm RF CMOS process that provides nine metal layers. At the operating frequency of 15 GHz, saturation output power and maximum power-added efficiency were confirmed to be 22.1 dBm and 17.2%, respectively.  
CMOS; common source; efficiency; power amplifiers +
Footnote †: journal: Applied
## 2 Materials and Methods
### Service Value Network  
With the rapid growth and application of information and communication technology, smart business networks emerged, in which emerging technologies promote cooperation and value co-creation [30]. With the emergence and development of servitization, the service value network (SVN) has become a new research focus. [PERSON] [31] argued that service value networks are smart business networks, which provide business value through the agile and market-based composition of complex services from a steady, but open pool of complementary, as well as substitutive standardized service modules, by use of ubiquitously accessible information technology. Therefore, the new way of value generation proposed by service value networks has greatly expanded the understanding of value and value generation scenarios. SVNs encompass multi-way collaborations between different service actors, comprising service providers, enablers, consumers, and other stakeholders [16]. As a node in the service value networks, firms must recognize and act on value creation in the context of networks and integrate and transform micro-specialized competences into complex value propositions with market potential [32]. In SVNs, everything is viewed as a service, and all the actors are connected to the SVNs by providing standardized service modules. Therefore, in essence, the focus of SVNs is still the composition of service. [PERSON] [33] presented a progressive service value network design scheme for bi-lateral service scenarios, in which a broker is introduced to composite services or resources into a whole and provide it for customers. [PERSON] [34] argued that a service intermediary created a service value network by offering complex services to customers and consuming services from suppliers and considered an end-to-end service level management for service value networks. [PERSON] [35] argued that the service modularization of network members can not only comply with the collaborative requirements of the service platform, but also protect the competitiveness of the member enterprises. At the same time, it is difficult to generate value by relying on a single module. Therefore, it is necessary to fully integrate the service modules among partners to complete value creation.  
A value network can also be thought of as a service eco-system, which may better capture the adaptive and evolutionary characteristics of a value network [32]. From this perspective, it is very similar to the service system mentioned in Section 2.1. In a word, the research literature on service value networks is relatively sparse, and the level of attention is generally abstract. The focus is mainly on value collaborative creation, based on service composition, and less attention is paid to the specific value creation process.
## 1 Introduction
### Laser Profilometry (LPM)
The development and use of spatial assessments of the surface structure are inextricably linked to the growing demands for accuracy, quality, and complexity in production technologies. As new materials and technologies such as nanotechnology emerge, there is anticipated to be a surge in demand for methods such as laser profilometry. With their inherent advantages, spatial evaluation of surface structures is viewed as a promising metrological method for the future [29]. Laser profilometry has its advantages and disadvantages, which must be considered when considering the use of this method. Laser profilometry offers several advantages, including non-contact measurement, the ability to detect cracks and micro-irregularities, the repeatability of the measurements, its suitability for curved surfaces, comprehensive surface evaluation, and the capacity to measure the entire sample's surface. However, there are also notable drawbacks to consider. These include challenges with measuring shiny surfaces, difficulties with multi-layered surfaces, higher equipment costs, and the need for an experienced operator to properly configure and interpret the results [30]. The results presented in the article apply to the creation of 3D models that predict the relationship between surface quality and technological parameters. The study of parameters in this combination and range is considered unique, where quality control and the evaluation of surfaces are important. The main novelty in this article is the unique combination of the effects of the color of the light (wavelength) of the laser in the laser profilometer on the reflection and the correct evaluation of the measurement  
Figure 1: Illustration of the principle of active triangulation. of the roughness of the surface parameters, especially for materials with a shiny surface. Our work focused on supplementing the results on the influence of the colors of the laser light on measuring the roughness of shiny surfaces, whether the color of the laser light reduces the reflection of light from the surface and whether it can affect the accuracy and reliability of surface roughness measurements, especially for specific shiny surfaces. We hope that these data will be beneficial from a research and practical point of view for choosing the right colored laser lights for specific materials and surfaces, which can lead to improved measurement processes, a better understanding of the effect of the light's colors on reflection, and a more accurate assessment of the roughness of surface parameters.
# _electronics_
## 5 Results  
### Simulation Parameters  
This article uses the Iridium constellation [26] as the forwarding layer, which contains 66 LEO satellites evenly distributed on 6 orbital planes and uses 3 GEO satellites as controllers to control the entire network in real time. According to [27], this paper sets the content number to 500; the content size in the network is a random number in the range of 1-50 MB; the requests arriving at the node obey the Poisson distribution with \(\lambda=100\) req/s; the parameter \(\omega\) of Zipf [28] ranges from 0.2 to 1.6; finally, the default value is 0.7.  
The value of the parameters in the ant colony algorithm has a great influence on the performance of the algorithm. The authors in [29; 30] conducted a large number of experimental analyses on important parameters in the ant colony algorithm and determined the parameters that make the algorithm's convergence speed and solution performance optimal. This article refers to the above references \(N_{\max}=200\), \(Z~{}=~{}100\), \(\alpha~{}=~{}1\), \(\beta~{}=~{}5\), \(\rho~{}=~{}0.7\), \(Q~{}=~{}100\), where \(N_{\max}\) represents the maximum number of iterations, \(Z\) represents the number of ants in the colony, \(\alpha\) represents the pheromone, \(\beta\) represents the importance of heuristic function factors, \(\rho\) represents the pheromone volatile factor, \(Q\) represents the total amount of pheromone for an ant to find its way at one time.  
Figure 4: The structure of the original interest package and coded interest package.
to as robust design optimization) and tolerance optimization. Thus, there are different views on the robust design problem in early and late development stages.  
Despite the obvious interrelation of the single activities [8; 11], these divergent views imply that the specific effect of early robust design on subsequent steps is neither transparent nor quantifiable so far. In order to overcome this shortcoming, this contribution analyzes and compares the different views and studies the effect of early robust design principles, represented by [PERSON]'s axioms, on parameter and tolerance optimization. Thus, the interrelations between early robust design activities and subsequent robust design steps are explicitly analyzed for the first time, revealing the specific benefits of an early robustness consideration.  
The paper is structured as follows. First, Section 2 summarizes related work considering robust design in product development. Then, Section 3 analyzes the similarities and differences between early and late robust design activities and compares the terminology used. Furthermore, the effects of early robust design on the subsequent optimization process are studied. These theoretical relationships are demonstrated with the aid of the case study in Section 4, leading to a discussion of the observed effects in Section 5. Finally, Section 6 gives a conclusion and an outlook.
## 2 Principles and Methods
### The Label Switching-Based ISTNs
#### 2.2.1 Principles of Label Switching
```
1:Initialize CR-LDP configure;
2:if message received then
3:switch type of received message do
4:case LSP Build Request message:
5: Reserve the bandwidth required by the forward label switching path, generate a label distribution request message and send it to the downstream node;
6:case LDP Label Request message:
7:switch View the displayed route type length value (TLV) and judge the node location do
8:case Intermediate node:
9: Reserve bandwidth for the bi-directional label switching path, then update the label distribution request message and send it to the downstream node;
10:case Tail node:
11:Allocate bandwidth directly for the backward label switching path, deliver the forwarding information base table to FPGA board in the data plane, generate a label distribution mapping message, and allocate label for the upstream node;
12:case LDP Label Mapping message:
13:switch View the displayed route TLV and judge the node location do
14:case Intermediate node:
15:Allocate bandwidth for the bi-directional label switching path, deliver label forwarding information base tables to FPGA board in the data plane, generate a label distribution mapping message, and allocate label for the upstream node;
16:case Tail node:
17:Allocate bandwidth for the forward label switching path, deliver forwarding information base table to FPGA board in the data plane;
18:endif
```  
**Algorithm 2** CR-LDP, Constraint Label Distribution under the QoS Requirements.
## 4 Discussion
### Changed Genes Associated with the Cell Wall in DaMDHAR-Expressing Yeast Cells to FT2 Stress  
_DaMDHAR_-expressing DM cells were induced to respond to FT2 stress to upregulate genes encoding the cell wall-related proteins Mnn4p, Cwp1p, Pir3p, and Fit1p compared to WT cells, but they were decreased in the same stress response to downregulate genes encoding Ynl190 wp and Ncw1p (Table 2 and Table S2). Upresulated _MNN4_ and _CWP1_ may be directly related to the mannosyl phosphate transfer reaction conferring a net negative charge in the cell wall mannoprotein in yeast, thereby regulating the leakage of proteins from the periplasmic space and import of macromolecules [121; 122]. Additionally, high _FIT1_ expression encodes a mannoprotein that is incorporated into the cell wall, and its deletion results in the abolished uptake of iron bound to siderophores [123]. As expected, Fit1p may play a role in regulating the concentration of iron associated with the cell wall and PM.  
Upon stress exposure, the CWI pathway induces the expression of specific cell wall-remodeling genes to build the adaptive strength of the cell wall, including _PIR3_ encoding O-glycosylated cell wall protein, as shown in the results (Table S2). The adaptive change in the cell wall after CWI-responsive gene expression is critical for protecting yeast cells against FT2-mediated cell wall stress [36]. By contrast, downregulated _YNL190W_ is a hydrophilic essential in the dehydration-rehydration process for CWI [124]. At this point, _DaMDHAR_-expressing yeast cells appear to prefer _MNN4_ and _CWP1_ expression instead of _YNL190W_ during the dehydration-rehydration process after repeated FT conditions. Taken together, among the components of the CWI process, cell wall mannoproteins (_MNN4_, _CWP1_, and _FIT1_) and cell wall remodeling factor (_PIR3_) appeared to be particularly involved in the cell wall stress response to FT2 challenge, supporting that _DaMDHAR_-mediated stress tolerance to FT2 conditions is achieved by enhancing cell wall stabilization via the effective rapid expression program of the known and unknown genes. A previous study reported that budding yeast FT tolerance is influenced by growth phase, mitochondrial function, PM and CWI stability, metabolites (trehalose and \(\alpha\)-ketoglutarate), protein (aquaporin and N-acetyltransferase), multi-stress tolerance genes, the target of rapamycin, Ras/cyclic adenosine monophosphate, CWI signaling pathways, and the proteasomal catabolic mechanism [43]. However, our results significantly differed from previously published data, with the exception of the organelle stabilization effects. Hence, our data provide new insight into the molecular mechanism of FT stress resistance in yeast (Figure 9).
# Using the Weibull Accelerated Failure Time Regression Model to Predict Time to Health Events
## 3 Log-Weibull Distribution  
The log-Weibull distribution is also known as the Gumbel distribution, or type I extreme value distribution [7].  
Let us consider a random variable \(T\), which follows a Weibull distribution \(W(\rho,\gamma)\), and we have a one-to-one transformation \(Y=\log(T)\) that maps support \(T=\{t|t>0\}\) to \(Y=\{y|-\infty<y<\infty\}\). The inverse of \(Y\) is given by:  
\[T=g^{-1}(Y)=e^{Y}\]  
The Jacobian is calculated as:  
\[|J|=|\frac{dg^{-1}(Y)}{dY}|=e^{Y}\]  
Using Equation (2), we can derive the PDF of \(Y\):  
\[f_{Y}(y)=f_{T}(g^{-1}(y))|J|=\frac{\gamma}{\rho}(\frac{e^{y}}{\rho})^{\gamma-1 }\exp\biggl{[}-(\frac{e^{y}}{\rho})^{\gamma}\biggr{]}e^{y}\]Simplifying further:  
\[f_{Y}(y)=\gamma\frac{e^{\gamma y}}{\rho^{\gamma}}\exp\biggl{[}-\frac{e^{\gamma y}} {\rho^{\gamma}}\biggr{]}=\gamma e^{\gamma(y-\log\rho)}\exp\biggl{[}-e^{\gamma(y- \log\rho)}\biggr{]}\]  
Here, we let \(\gamma=\frac{1}{b}\) and \(\log\rho=a\):  
\[f_{Y}(y)=\frac{1}{b}\exp\bigl{(}\frac{y-a}{b}\bigr{)}\exp\biggl{[}-\exp(\frac{ y-a}{b})\biggr{]}\text{ where }-\infty<y<\infty \tag{5}\]  
This demonstrates that the log-Weibull distribution corresponds to a Gumbel distribution \(G(a,b)\), where \(a=\log\rho\) and \(b=\frac{1}{\gamma}\).  
The CDF \(F_{Y}(y)\) of the log-Weibull distribution can be derived as:  
\[F_{Y}(y)=P(Y\leq y)=P(log(T)\leq y)=P(T\leq e^{y})=F_{T}(e^{y})\]  
By Equation (1), we obtain  
\[F_{Y}(y) =F_{T}(e^{y})=1-\exp\biggl{[}-(\frac{e^{y}}{a})^{\gamma}\biggr{]} =1-\exp\biggl{[}-\frac{e^{\gamma y}}{a^{\gamma}}\biggr{]}\] \[=1-\exp\biggl{[}-\frac{e^{\gamma y}}{e^{\gamma\log\rho}}\biggr{]} =1-\exp\Bigl{[}-e^{\gamma(y-\log\rho)}\biggr{]}\] \[=1-\exp\biggl{[}-\exp(\frac{y-a}{b})\biggr{]} \tag{6}\]  
where \(\gamma=\frac{1}{b}\) and \(\log\rho=a\)  
The survival function of \(S_{Y}(y)\) is given by  
\[S_{Y}(y)=1-F_{Y}(y)=\exp\biggl{[}-\exp(\frac{y-a}{b})\biggr{]} \tag{7}\]  
The hazard function \(h_{Y}(y)\) is given by  
\[h_{Y}(y)=\frac{f_{Y}(y)}{S_{Y}(y)}=\frac{1}{b}\exp(\frac{y-a}{b})\]  
These equations provide a comprehensive understanding of the log-Weibull distribution and its relationship to the Gumbel distribution, including its PDF, CDF, survival function, and hazard function.
# A Modular Mobile Robotic Platform to Assist People with Different Degrees of Disability
## 2 Modular Assistive Robotic Platform
### Environment Perception and Control System
#### 2.2.1 Computer Vision System  
The activities of daily living (ADLs) require the capability to perform reaching tasks within a complex and unstructured environment. This problem should be solved in real time to be able to deal with the possible disturbances that the element may suffer during the interaction. Moreover, the objects are commonly textureless.  
Currently, several methods have been proposed. However, despite the great advances in the field (especially using deep learning techniques), it has not been solved effectively yet, especially with nontextured objects. Some authors have used commercial tracking systems such as Optitrack or ART Track [18; 19; 20]. The main limitation of these devices is the necessity to modify the objects to track through the inclusion of optical markers, to reconstruct their position and orientation. The main lines of investigation in the field of 3D textureless object pose estimation are methods based on geometric 3D descriptors, template matching, deep learning techniques, and random forests.  
Our system incorporates a computer vision system based on the use of three devices (Figure 1). The first one is Tobii Pro Glasses 2. This eye-tracking system allows the user to select the desired object. The second one is the Orbbec Astra S RGB-D camera used for the 3D pose estimation of textureless objects with which the system can interact. This camera is attached directly to the back of the wheelchair by means of a structure that places it on top of the user's head, focusing on the scene. Finally, a full HD 1080p camera able to work at 30 fps is placed in front of the user, under the screen. This camera is used to estimate the 3D pose of the mouth of the user. This information helps the system know which position the exoskeleton must be in for tasks such as eating or drinking.  
This computer vision system was tested in real conditions with patients and was also thoroughly evaluated both qualitatively and quantitatively. The results and a more detailed explanation of the algorithms developed can be seen in [17].
# Composing as a Service: A Real-World IoT Implementation
## 2 Related Work
### Composing in the IoT Era  
The smart-city paradigm has inspired several research initiatives to address the notion of centralized management of cities' resources and waste. Composing and the ability to perform it efficiently, unsupervised and uninterrupted, has been a rather popular issue to tackle. [PERSON] et al. in Reference [15] presented a low-cost station for measuring air temperature, where the composting procedure took place to provide an overview of the overall procedure's progress to experts.  
Figure 1: Temperature evolution during the composting process.  
In Reference [16], [PERSON] et al. presented a more elaborate infrastructure that integrated a Wireless Sensor Network (WSN) to provide experts with continuous remote monitoring of the composting procedure. Similarly, [PERSON] et al. in Reference [17] introduced an evaluation system that assesses the compost's maturity, and therefore its progress, by implementing O\({}_{2}\) and CO sensors (electronic nose), and feeding the sensor data to the assessment system that was trained with preconstructed compost profiles. Furthermore, [PERSON] et al. in Reference [18] presented a system that measures the gas emissions of the compost material and determines whether to insert or remove oxygen from the frame, to guarantee an uninterrupted and efficient composting procedure. A more sophisticated approach was presented in Reference [19], where [PERSON] et al. introduced a composting infrastructure that periodically performs specific tasks (stirring, ventilating, etc.), but also performs dynamically invoked actions, according to the compost material's temperature.  
To the best of our knowledge, this work is the first time that a cloud-based architecture following the edge-computing paradigm is introduced for controlling composting frames. In contrast to other typical client-server bibliography architectures, such as the one reviewed above, our approach can support scalability, since the cloud infrastructure we introduce here can potentially monitor and control several composting frames in parallel, distributed across remote locations. In addition, this takes place without any lack of efficiency, since the scheduling of tasks is decided on the Cloud, but execution is assigned to the edge modules that run very close to the composting frames.