esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Computational Results  
In this section, a real-life instance from a machinery manufacturing company in China is provided to further illustrate the MILP model and the proposed algorithm. Then, two sets of contrasting experiments with randomly generated instances are conducted, aiming to show the good performance of the algorithm. The algorithm is coded in MATLAB R2015b and the experiments are run on an Intel(R) Core(TM) i7-4790 3.60 GHz processor with 16 GB of memory under the Windows 7 operating system. For benchmarking, the greedy insertion heuristic algorithm proposed by [PERSON] et al. [9] is adopted for our contrast tests.  
The TOU electricity tariffs used for all instances are those implemented in Shanxi Province, China, as given in Table 1. It can be seen from Table 1 that the off-peak period is between an on-peak period and a mid-peak period, which means that the actual electricity prices meets the first type of TOU electricity tariffs. Assume that the time horizon starts at 8:00 a.m. of the first day.  
\(C_{max}\) and \(C_{B}\) denote the given makespan and the total processing times of all the jobs, respectively. The parameter \(e\) = \(C_{max}/C_{B}\) (\(e\) \(\geq\) 1) is used to measure the degree of time tightness. In these instances, \(C_{max}\) can be obtained by the formula \(C_{max}\) = \(e\)\(\times\)\(C_{B}\) as long as the parameter \(e\) is set. Obviously, as \(e\) increases, \(C_{max}\) increases. Note that \(b_{m+1}\) is calculated by \(b_{m+1}\) = \(\left[C_{max}/24\right)\)\(\rceil\)\(\times\) 24. Let \(TEC_{F}\) and \(TEC_{H}\) be the total electricity cost calculated by our algorithm (GIH-F)and [PERSON] et al.'s algorithm (GIH), respectively. The runtimes of the two algorithms are represented by \(CT_{F}\) and \(CT_{H}\), respectively. The gaps between \(TEC_{F}\) and \(TEC_{H}\) are represented by \(G\), \(G\) = \((TEC_{F}-TEC_{H})/TEC_{H}\)\(\times\) 100%. The ratio of \(CT_{H}/CT_{F}\) is represented by \(R\).
## 3 Performance Analysis of iPyroxy Block
### S21 Transmission Coefficient With and Without Block  
The structural characteristics of these materials are analyzed using transmission electron microscopy (TEM) to elucidate their shape and interface quality. The mutual coupling coefficient of the system, which signifies effective isolation between connected components, consistently registers values significantly below \(-15\) dB across the entire operating spectrum. This is crucial for ensuring minimal interference among components. An examination of the S21 transmission coefficient without a blocking device shows that the system efficiently transmits signals with low coupling, as depicted in Figure 10. Incorporating an iPyrovy block results in minimal changes to the S21 transmission coefficient, indicating that the block has a negligible effect on the mutual coupling characteristics of the system. The blocking component maintains strong transmission capacity while simultaneously facilitating impedance matching and signal absorption, evidenced by a transmission rate of 90% when the iPyrovy block is included. These attributes make the system ideal for applications requiring reliable and high-quality communication across a wide operating range, such as phased-array antennas and wireless communication systems, where precise signal transmission and minimal coupling are critical. The S21 transmission coefficient for the system with and without the iPyrovy block is over a frequency range of 1 GHz to 15 GHz, highlighting the block's impact on signal transmission between the input and output ports. Without the iPyrovy block, the S21 values exhibit relatively higher transmission levels, with minimal attenuation across the frequency spectrum. This suggests that the absence of the block allows for more excellent signal passage through the system, resulting in lower coupling losses.  
Conversely, when the iPyrovy block is present, a notable drop in the S21 transmission coefficient occurs, especially at higher frequencies. This reduction implies that the iPyrovy block introduces additional impedance matching and signal absorption, decreasing transmission while enhancing selectivity. The interaction of the block with electromagnetic waves likely contributes to this variation by reducing mutual coupling and reflecting or absorbing a portion of the transmitted energy, which is advantageous for applications requiring signal isolation.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 3 Datasets Used
### ICD-10  
ICD is a medical coding system, also known as medical ontology, that is popular and used worldwide [24]. It follows a tree hierarchical structure, where the chapters are represented at the top, followed by categories, sub-categories and codes. The 10 th revision of ICD was used in this work, but the latest version is the 11 th revision. As an example, ref. [19] used diagnosis records of the Taiwan University Hospital, applying NLP techniques on the deep neural network architecture, using the attention mechanism to extract keywords from diagnoses, for ICD-10 encoding.
# Unlocking Everyday Wisdom: Enhancing Machine Comprehension with Script Knowledge Integration
## 1 Introduction  
In recent years, the rapid development of natural language processing (NLP) technologies has taken place. They have been widely applied into numerous everyday products such as search engines, smart assistants, mobile devices, and more. Very recently, large language models such as ChatGPT and Bard have ushered in a new era of NLP research and application, thereby showcasing their unprecedented performance results across many tasks and their gaining extensive adoption by users worldwide.  
Despite the significant achievements, the journey towards developing an NLP system with human-level cognitive abilities is far from over. One of the pressing challenges is the integration of commonsense knowledge into machine comprehension systems--an area which has seen growing attention [1]. Commonsense knowledge refers to an extensive array of \"everyday wisdom\", which is considered to be universally known to humans. For instance, in the sentence, \"He hit the window with a bat,\" it is obvious to a human reader that the \"bat\" refers to a sporting equipment rather than the animal. However, for machines, leveraging such knowledge poses considerable challenges, primarily because it is rarely explicitly stated in human communication, which comes in addition to its vast scope and diversity. By combining NLP systems with this \"everyday wisdom\", we can significantly enhance their capabilities, thus leading to smoother and more human-like interactions.  
In this paper, we aim to enhance the integration of a particularly nuanced form of commonsense knowledge: script knowledge. The term \"script\" refers to typical sequences of activities that describe well-known situations. To illustrate, consider a \"someone opening the door\" scenario: an individual reaches for his key, and, if it is not found, he rings thedoorbell with the hope that someone inside will open the door. This narrative seems intuitive to a human; so, when given the sentence \"He must ring the doorbell\", it is a logical inference that \"he forgot his key\". However, a machine lacking this knowledge would struggle to form this connection. Improving script knowledge integration would enable NLP systems to produce better responses to everyday human activities, thus enhancing their practical applicability.  
We investigated techniques to augment the script knowledge understanding of an established baseline system known as the TriAN [2]. Our experiments leveraged the MCScript [3] dataset, which is a machine question-answering (QA) dataset that is specifically designed to assess a model's script knowledge comprehension. We delved deeper into the dataset and the baseline method in subsequent sections. Our main contributions are the following:  
* We trained a generative LSTM language model on \"scripts\" of everyday human activity and used it as a feature encoder.
* We used multiple layers of attention interactions to enable multi-hop reasoning.
* We added positional embeddings to the intermediate features to enhance the temporal sequential reasoning.
## Materials and Methods
### Procedure for Determination of Concentrations with SPRi Biosensors
#### 2.4.2 SPRi Measurements  
SPRi measurements were performed using a specially made apparatus and biosensors which have been successfully used and described previously [35]. The samples of serum and urine were placed directly on the prepared biosensor for 10 min to allow interaction with the receptor. The volume of the sample applied to each measuring field was 3 \(\mu\)L. After this time, the biosensor was washed with water and HBS-ES buffer solution at pH = 7.4 (0.01 M 4-(2-hydroxyethyl)) piperazine-1-ethanesulfonic acid, 0.15 M sodium chloride, 0.005% Tween 20, 3 mM EDTA) to remove unbound molecules from the surface.  
The SPRi signal was measured at a constant angle of incident light. The image was recorded twice: after immobilisation of the receptor and then after interaction with the sample containing the analyte. The SPRi signal, which is proportional to the quantity of coupled biomolecules, was obtained for each spot separately, as there was a difference between the signals before and after interaction with the biomolecule.  
Non-specific binding was eliminated by a background correction. This is the signal difference between the site of the receptor-analyte complex and a receptor-free site treated with a natural sample.
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 7 Conclusions  
In the present work, the thermal conductivity of 638 unfrozen specimens and 860 frozen specimens sourced from the Qinghai-Tibet expressway geological exploration project has been measured by the TPS method. Then, based on the cluster analysis of typical soils, the probability distribution of dry density, water content and thermal conductivity, and the influence of the dry density and water content on the thermal conductivity of three soil clusters, have been investigated. Finally, using water content, dry density and unfrozen soil thermal conductivity as fitting parameters, binary fitting, RBF neural network and ternary fitting calculation models have been developed and compared for the three soil clusters' thermal conductivity prediction. The results show that:  
1. The particle size and intrinsic heat-conducting capacity of the soil skeleton have a significant influence on the soil thermal conductivity, and the typical specimens in the QTEC can be classified as three clusters according to their thermal conductivity probability distribution and water-holding capacity.
2. Dry density as well as water content sometimes have statistically low correlation with thermal conductivity of natural soil samples, especially for multiple soil types and complex compositions.
3. Both the RBF neural network method and the ternary fitting method have favorable prediction accuracy and wide application range, which can be used to predict the soil thermal conductivity in the QTEC. As the ternary fitting method can only be used for frozen soils, the RBF neural network method is considered as the optimal prediction approach.
4. The prediction effect and error analysis of the prediction results indicate that the accuracy of the binary fitting method is relatively poor compared with the RBF neural network and the ternary fitting method, but the main body interval of the thermal conductivity distribution can still meet general engineering requirements.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/7/2476/s1](http://www.mdpi.com/2076-3417/10/7/2476/s1)]([http://www.mdpi.com/2076-3417/10/7/2476/s1](http://www.mdpi.com/2076-3417/10/7/2476/s1)), Table S1: Experimental database of 12 soil types; Table S2: Experimental database of 3 soil clusters.  
**Author Contributions:** Conceptualization, Z.-Y.L. and F.-Q.C.; Writing--original Draft: Z.-Y.L. and F.-Q.C.; Writing--Review and Editing: Z.-Y.L. and [PERSON]-[PERSON]; Investigation: F.-Q.C., J.-B.C. and Y.-H.D.; Resources: Z.-Y.L. and L.J.; Data Curation: F.-Q.C., H.P.; Formal analysis: Y.-H.D. and L.J.; Validation: Y.-H.D.; All authors have read and agree to the published version of the manuscript.  
**Funding:** This research was supported by the National Science Foundation of China (Grant No. 41502292, 51574037), the Applied Fundamental Research Project of China Communications Construction Co., Ltd. (No. 2018-ZJKJ-PTJS03), and the Project funded by China Postdoctoral Science Foundation (NO. 2014M560739).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Method
### Analysis  
#### 2.6.1 Data pre-Processing  
Raw EMG signals from the orbicularis oculi were filtered, rectified and computed into amplitudes by the Root-Mean-Square-method by the software package BioTrace\({}^{+}\) provided by Mind Media BV. Through visual inspection only single amplitudes clearly occurring between about 100 ms and 200 ms post-startle probe were selected for further analysis. If several peaks occurred, if the EMG signal was distorted by activity with an onset prior to the startle probe or if a response was non-existent altogether, no startle amplitude was calculated. This procedure resulted in a total of 0.007% of missing values. The amount of startle reflex modulation was calculated by dividing the mean of the five startle amplitudes elicited during lead stimulus presentation (virtual walks) by the baseline amplitude of the corresponding condition.  
SC and HR were also analyzed by using the BioTrace\({}^{+}\) software. For both HR and SC the first 15 seconds of each trial (_i.e._, before presentation of the baseline startle probe) were analyzed and mean HR or SC was extracted for this period and subsequently treated as the baseline for the respective condition. Then mean HR and SC was calculated for the period 30 seconds-5 minutes, representing the average HR and SC during lead stimulation. Analogue to the startle data, the SC and HR values during lead stimulation were then divided by the baseline, resulting in a percentage of change in autonomic arousal induced by each condition. It is important to mention that 5 startle probes were presented during these periods. Of course, these startle probes had an effect on both HR and SC. However, this effect is considered consistent across our conditions because in all of them 5 startle probes were presented.
# Shear Strength and Durability of Expansive Soil Treated with Recycled Gypsum and Rice Husk Ash
## 2 Materials and Methods
### Experimental Setup  
This investigation is interested in determining the impact of gypsum and rice husk ash on expansive soil's shear strength and durability properties. This study used varying gypsum and RHA percentages for the direct shear tests. It also adopted the most effective mixture in the study of [PERSON] and [PERSON] [37] (15% gypsum + 10% RHA) to treat cylindrical expansive soil samples for the durability tests. Five different curing periods were applied to all specimens under air-dried conditions before the unconfined compression strength test was conducted for samples not subjected to moisture fluctuations. Durability and water immersion tests were performed for cured samples treated with gypsum and RHA to assess the impact of moisture fluctuation and inundation on soil strength. The effect on durability was quantified using the collapse index and the percentage of strength loss. The effectiveness of the treatment on the expansion potential of the cured samples was also verified through the expansion index test using an oedometer.  
The following standardized laboratory procedures were performed with a methodological sequence shown in Figure 3.  
Figure 3: Methodological sequence of the study.  
Figure 2: EDX spectrum of test rice husk additive [34].  
* Shear Strength and Angle of Internal Friction
* ASTM D3080-11 (Direct Shear Test of Soils Under Consolidated Drained Conditions) [42].
* ASTM D2166-16 (Standard Test Method for Unconfined Compressive Strength of Cohesive Soil) [43].
* Cyclic Wetting-Drying--ASTM D559-03 (Guidelines for Wetting and Drying Compacted Soil-Cement Mixtures) [44].
* Modified Durability Test (procedure adopted from [PERSON] [45] and [PERSON] et al. [46]).
* Strength Loss (calculation adopted from [PERSON] [45]).
* Modified Water Immersion Test (procedure adopted from [PERSON] et al. [47] and [PERSON] et al. [48]).
* Expansion Index--ASTM D4829-11 (Standard Test Method for Expansion Index of Soils) [35].
* Collapsibility Index (calculation adopted from [PERSON] [49]).
## 3 Simulation Results and Discussion
### Algorithm Verification
This situation is known as overhearing and will cause more consumption of energy [21]. Overhearing exists in the practical environment, so we have to consider the overhearing issue in our simulation to approach the practical underwater transmission environment. We use network topology to imitate overhearing [22]. We import the network topology into a neighbor table, and a node send packets to all neighbors when the packet needs to be sent to the next hop. So, the neighbor will experience overhearing. The simulation has the following steps:  
1. Input node number, condition, and node's IP in the PC. 2. Through the distance between every two nodes calculate each node's neighbor. 3. Send start order, and nodes begin communication. 4. The simulation program calculates send delay from the packets received from nodes by socket, puts packets into a buffer and send them in its own time slot. 5. The node adds bit error and noise according to the bit error rate to check whether conflict will occur. If yes, it records conflict messages and processes the packet with some operation. 6. Send packet to all neighbors. We perform the laboratory simulation with nine nodes. We divided nine time pieces for nodes according to their location that was preset before simulation. The results showed the consumption of the nodes and the total data is in equilibrium, the average delay and bit error rate are low.
# Adaptive Fuzzy Command Filtered Tracking Control for Flexible Robotic Arm with Input Dead-Zone
## 6 Conclusions  
In this paper, an adaptive fuzzy output feedback control method was proposed to address the issues of dead-zones and unknown model parameters in joint flexible robotic arm systems. The method integrates command filtering and fuzzy observer techniques to achieve the tracking control of desired trajectories for the flexible robotic arm system. The approach firstly involves employing a fuzzy logic system to approximate the nonlinear functions within the flexible robotic arm system. It subsequently involves designing a state observer to estimate the unobservable states of the system. The application of command filtering helps to address computational complexity concerns during controller design. Moreover, the Nussbaum function is introduced to tackle unknown control gain problems. The stability of the system can be verified by designing a Lyapunov function. Our simulation results confirm the effectiveness of the closed-loop system in addressing the challenges posed by dead-zones and unknown parameters in flexible robotic arm systems, thereby achieving effective control of the flexible robotic arm. The future extension of this study will focus on collaboration issues among multiple robotic arms in unstructured and collaborative environments. It will explore control strategies capable of achieving collaborative operations, task allocation, and coordination to accomplish complex objectives.  
Figure 11: The trajectories of \(e\).  
Figure 12: The trajectories of \(v\).  
**Author Contributions:** Conceptualization, C.W.; Writing--original draft, H.C.; Writing--review & editing, Z.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was supported by the Natural Science Program of the Henan Provincial Department of Education (22A440009); High-level Talents Research Start-up Fund Project of Henan University of Technology (2020 BS011); Open Project of Key Laboratory of Grain Information Processing and Control (KFJ)-2021-111); Natural Science Project of Zhengzhou Science and Technology Bureau (22 ZZRDZX07); Open Project of Henan Engineering Laboratory for Optoelectronic Sensing and Intelligent Measurement and Control (HELPSIMC-2020-005); Henan Provincial Science and Technology Research and Development Plan Joint Fund (222103810084); Science and Technology Research Plan Project of Jiyuan City in 2019 (19022011); Henan Province science and technology research project (222102220100).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data and materials are available from the authors upon request.  
**Acknowledgments:** The authors would like to thank everyone who helped with the creation of this study for their insightful remarks.  
**Conflicts of Interest:** The authors declare they have no conflict of interest.
# An Attention-Based Method for the Minimum Vertex Cover Problem on Complex Networks
## 3 Complement Information Based Attention Network
### LSTM Decoder  
The embedding vector produced by the encoder side of the architecture in Figure 2 is then used to produce the final probability distribution \(p(C_{i}|C_{1},\dots,C_{i-1},\mathcal{P})\) as requested by Problem 1. This is carried out by passing as input to the LSTM decoder the sequence of embedding produced \(\mathcal{P}=P_{1},\dots,P_{n}\). As described in Section 3.3, The LSTM decoder can model the conditional probability distribution \(p(P_{i}|P_{1:t-1})\) as function of the parameters learned during training. During inference, given a sequence \(\mathcal{P}\) the learned parameters \(\theta^{*}\) are used to select the sequence \(\mathcal{C^{P}}^{*}\) with the highest probability, i.e., \(\mathcal{C^{P}}^{*}=\operatorname*{argmax}_{\theta}p(\mathcal{C^{P}}|\mathcal{ P};\theta)\).  
To produce the final ordered scores, a SoftMax activation function is applied to the output of the LSTM layer. The final SoftMax activation normalizes the output vector to be an output distribution over the dictionary of inputs.  
The entire network is trained to minimize the negative log-likelihood loss function over the log-SoftMax activation function,  
Figure 3: Schema of the implemented attention mechanism. \(A\) is the adjacency matrix of a graph and \(\overline{A}\) its complement. The dense layer represents a non-linear transformation performed with the ReLU activation function, the linear layer represents a linear transformation, while the positional embedding is a non-linear transformation of the position of the node in the graph.  
\[l(\theta)=-\sum_{i=1}^{n}(y_{i}\log\hat{y}_{\theta,i}+(1-y_{i})\log(1-\hat{y}_{ \theta,i})) \tag{11}\]  
where \(y_{i}\) is the target value of the \(i\)-th node (i.e., 1 if node \(i\) belongs to the covering set, 0 otherwise), and \(\hat{y}_{\theta,i}\) is the computed probability that the \(i\)-th node belongs to the covering set.  
This loss function is used to ensure that estimated probabilities (assuming that the network is properly trained) actually reflect real ones. Indeed, for each node of the sample graph in input, there is a target value that is either 1 if the corresponding node is a covering node or 0 otherwise. Thus, each predicted class probability is compared to the desired output of 0 or 1. The calculated loss penalizes the probability based on how far it is from the expected value. The penalty is logarithmic, yielding a large score for significant differences close to 1 and a small score for minor differences close to 0.