esa-sceva/satcom-chunk-collection huggingface dataset:
# A Filtering Switch Made by an Improved Coupled Microstrip Line
## 3 Results  
### Design and Measurement  
Figure 5 shows the physical layout of our proposed prototype filter switch based on the performance analysis of the selected microstrip line (Figure 2) and the construction of the schematic diagram (Figure 3). Based on the physical layout of Figure 5, the filter switch's physical prototype is fabricated (Figure 6).  
Figure 4: Equivalent circuit of Skyworks SMP1345-079 LF PIN diode.  
Figure 3: Equivalent circuit schematic.  
Figure 5: Filtering switch’s physical layout.  
Figure 6: Photograph of the fabricated filtering switch.  
To effectively control the implementation of the on- and off-state function of this filtering switch in practical engineering, PIN diodes are connected to the parallel-coupled feed line. Figure 5 depicts the typical design of the filtering switch, with the complete circuit used being fabricated on a Rogers_RT_Duroid5880 (\(\epsilon_{re}=2.2\), \(h=0.8\) mm, \(\tan\delta=0.0009\)) substrate. Murata capacitors C\({}_{b}\) = 470 pF are used for DC blocking off and RF sign pass devices. In this paper, a PIN diode (SMP1345-079 LF) from Skyworks is used, and the equal circuit [10] is given in Figure 4, i.e., \(L_{s}\) = 0.7 nH, \(R_{s}\) = 2 \(\Omega\), \(C_{s}\) = 0.15 pF. An external DC voltage equal to 16 V is applied to the diode through a 1 k\(\Omega\) resistor, which adjusts the on-state or off-state of the diode, thus achieving the filtering switch's on-and-off effect.  
Under the simulation and debugging of the ADS 2019 software, the impedances of the coupled microstrip lines we chose to analyze in even and odd modes are \(Z_{oc1}\) = 205 \(\Omega\), \(Z_{oo1}\) = 83.7 \(\Omega\), \(Z_{oc2}\) = 186.1 \(\Omega\), and \(Z_{oo2}\) = 76 \(\Omega\). According to the actual situation, the external equivalent switching device will produce an excitation effect on the filter circuit using the ADS software to optimize the overall circuit, and the physical dimensions obtained after the system processing are as follows: W\({}_{1}\) = 2.38, L\({}_{1}\) = 17.8, \(W_{2}\) = 0.25, L\({}_{2}\) = 73.1, g\({}_{1}\) = 0.193, W\({}_{3}\) = 0.30, L\({}_{3}\) = 68.828, g\({}_{2}\) = 0.17, W\({}_{4}\) = 0.33, L\({}_{4}\) = 71.45, g\({}_{3}\) = 0.18, W\({}_{5}\) = 0.4, L\({}_{5}\) = 70.98, and g\({}_{4}\) = 0.14 (unit: mm).
# Delay-Sensitive NOMA-HARQ for Short Packet Communications
## 3 Delay-Sensitive NOMA-HARQ
### Generalized \(N_{u}\) User Setup
However, the model in this paper can be extended for maximum \(M\) retransmission case with \((M+2)^{N_{u}}\) MM states, where in general, the success probability of the \(i\)-th user at the \(m\)-th retransmission can be written as \(\epsilon(\left[\gamma_{1}^{(i)},\ldots,\gamma_{m-1}^{(i)}\right])-\epsilon( \left[\gamma_{1}^{(i)},\ldots,\gamma_{m}^{(i)}\right])\). Then, various combinations of the product of \(m\) terms according to individual user's states defines the state transition matrix. We skip the details to keep the presentation simple._
## 2 Related Work
### Image Feature Data Extraction
Some studies suggested that extracting image style features can better describe image semantic information. [PERSON] et al. believed that style features could better represent the overall image features and proposed a global style and local matching contrast learning network (GLCNet) for remote sensing image (RSI) semantic segmentation [28]. [PERSON] et al. found that the representation of content and style in convolutional neural networks is separable, and they proposed that style representation is a multi-scale representation including multi-layer neural networks [29]. [PERSON] et al. designed a multi-task learning method based on an improved convolutional neural network [30]. The network used two channels to train the network simultaneously, and each channel was responsible for the learning tasks of different attributes in the image. The two learning tasks helped each other learn by sharing underlying parameters, which improved the convergence speed of the network and the generalization ability of the model. Existing research has made a lot of contributions in terms of the high complexity of image feature extraction and the need for domain experts and manual intervention. However, the traditional convolutional neural networks only train and classify the dimensions of category and color of item images, ignoring important information, such as fabric, style, and brand of items. They cannot meet the actual needs of image feature extraction on e-commerce platforms. Designing a neural network that can extract multi-dimensionalfeatures of images and introducing style features is conducive to more fully extracting image information for recommendation tasks, and mining user preferences with style features to make recommendations from more angles.
# Coordination of Multiple Autonomous Agents Using Naturally Generated Languages in Task Planning
## 5 Experiments and Results
### Different Scenarios  
We also implement our algorithm to the path-planning problem in 30 grid-world scenarios. For each scenario, we separately run 500 RC tasks in the setting of two, three, and four agents. Figures 8 and 9 demonstrate the number of agents' joint states and our language words used in these tasks, respectively. The number of words is far smaller than the states, which means that agents require less effort to understand the messages by our approach. To assess the benefits of the languages in terms of communication cost and behavioral flexibility of agents, we compute their coordination sentences and specified plans for all task plans. Bars in Figure 10 represent the average decrement of the length of sentences compared with plan sequences. The communication costs decline by 60% on average using the languages. Bars in Figure 11 indicate the average amount of plans specified by the sentences. From these results, we can learn that the constructed languages are quite useful for multi-agent coordination.  
Figure 8: Number of states involved in each scenario.  
Figure 6: Number of plans specified by sentences.  
Figure 7: Computation cost of sentences generation.  
It should be noted that the classical planning problem is known to be PSPACE-complete [40]. The LGP is considered to be more difficult, and finding a minimal language is PSPACE-hard [34]. It is challenging to solve the LGP involving more agents. In this work, we only apply our method to the path-planning problem with a small number of agents, and a more efficient method remains to be further exploited when the problem increases. Fortunately, the LGP can be addressed offline in a centralized manner. Generating a communication sentence for agents' coordination is rather simpler than language generation, which is not harder than finding an optimal plan.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 4 Results
### Long-Term Interannual Variations of LST
(**a**) Annual LST in 2005, (**b**) annual LST in 2018, (**c**,**d**) significant increase and decrease along with Sen’s slope from 2005 to 2018.  
with small deviations throughout the years. The country's maximum areas of LST were approximately 28 to 50 \({}^{\circ}\)C.
# Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator
## 3 Programmable Deterministic Multi-Queue Accelerator Model
### Serverless-Friendly Programmable Multi-Queue Scheduling  
This module consists of a flow classifier and multiple hardware queues, and each queue consists of RX and Tx structures. The RSS is a core component which realizes the rule-based flow classification and forwarding function. It is associated with the network interface of the accelerator, and the Agent is used for remote management. The cloud server programs the forwarding action by issuing a flow table to the Agent. The Forwarder transponder is responsible for forwarding incoming packets according to the flow table matching signals.  
RSS has multi-queue-oriented packet distribution capabilities compared to traditional NICs and extends multi-layer serverless packet forwarding features. Usually, RSS uses the standard Toeplitz hashing algorithm [27] to hash the packet's 5(4) tuples, redirect the packets to specified hardware queues according to the results, and achieve flow classification at layer three or layer four. When transmitting data of emerging transport protocols such as QUIC, it is also possible to identify the data with the same stream identifier in the QUIC payload and then forward them to the same hardware queue to achieve forwarding in layer 4.5 based on the rule of QUIC that a single transport object uses the same stream number.  
Figure 2: Hardware descriptor usage.  
The forwarder is the main functional component of the programmable multi-queue module, and Figure 3 shows its workflow. When a packet arrives, the interface hands the received data to the RSS flow classifier bound to the interface, processing by the packet receiving function defined in the RSS. First, the classifier obtains the packet header information, identifies the packet type, and sends it to the flow table for matching. After matching, forwarded control of the packet is given to the Repeater along with a customizeable hash key. The flags match in the flow table. After finding the corresponding flow table entry, a signal is generated and passed to the Repeater, which combines the different signal values, obtains the individual fields of the packet for hash calculation, and forwards it to the corresponding queue according to the hash value.  
The Agent is controller of the RSS classification component, which is connected to the remote server through a network interface and is responsible for actively reporting node changes or requesting service data for the cloud server. The cloud server can also update the flow table content through the Agent to achieve programmability of forwarding behavior.
# Computing the Gromov-Wasserstein Distance between Two Surface Meshes Using Optimal Transport
## 5 Computational Experiments
### Shape Similarity: Morphodynamics of Protein Structure Surfaces
The same observation can be made when comparing the 51 conformations with the holo structure based on cRMS (dashed red line) and based on the GW distance between surfaces (blue x’s). The cartoon representations of the high resolution structures and surface representations of the same structure are shown for a few conformations along the trajectory below the horizontal axis. used the coordinate Root Mean Square (cRMS) distance computed over the \(C_{\alpha}\) atoms of the high-resolution structures (see Refs. [47; 48] for details on how to compute the cRMS). Second, we compared the same structures using their skin surfaces [49]. To derive those skin surfaces, we started with the common convention in chemistry to represent a structure as a union of balls, with each ball corresponding to an atom. The coordinates of an atom define the center of a ball that is associated with it. The atom is also characterized with a van der Waals radius based on its chemical nature. The radius of the ball is then set to this vdW radius, plus a probe radius of \(R=1.4\) A, designed to mimic a water molecule in its proximity. The skin surface is then defined as the boundary of this union of balls. We generated a triangular mesh on the skin surface using the program smesh [50; 51]. We found that those meshes have similar sizes for all 51 conformations we considered, with on average approximately 40,000 vertices and 70,000 triangles. 1000 points were selected from each mesh using the Euclidean farthest point sampling procedure described above for the TOSCA dataset. We compared these sampled meshes using FreeGW. We compared all 51 conformations of calmodulin with both the apo and holo forms, using the cRMS and the GW distances. Results of these calculations are shown in Figure 5. We do observe that the GW distances measured based on the low resolution skin surfaces correlate well with the cRMS distances computed from the high resolution, atomistic representations of the proteins. The correlation have coefficients above 0.96.
# Correlation of Pavement Distress and Roughness Measurement
## 2 Literature Review  
### Background  
Any pavement management system (PMS) is required to contain data of the pavement condition. The main goal of collecting and evaluating the condition of the pavement is to evaluate the current state of pavement condition at the time of inspection in order to determine urgent pavement maintenance requirements and plan for future objectives [2].  
Pavement management systems (PMS) are increasingly being employed at all levels of government for effective highway management. Most PMS techniques may provide support at two levels: the network-level and the project-level. The data collected at the network level gives management comprehensive information on the whole system. The network-level level frequently provides information for planning reasons and financial analyses. Project-level information, on the other hand, contains construction, engineering design, and cost accounting details. The amount of data necessary for each level varies greatly. The general goals of network-level pavement condition evaluation are to analyze current pavement conditions, determine immediate pavement repair needs, and make a strategy for future requirements. Distress survey, roughness, structural capacity, and skid resistance are four common approaches for evaluating pavement conditions.
# Image Error Concealment Based on Deep Neural Network
## 5 Conclusions  
In this paper, we developed a novel image EC method based on the AE-P neural network. Both the local correlation and non-local self-similarity of natural images were taken into account in reconstructing the missing pixels. We used the local correlation to predict the missing pixels and the non-local information to correct the predictions. The designed neural network could be divided into two parts: the prediction part and the auto-encoder (AE) part. The prediction part utilized the local correlation among pixels to predict the missing ones. The AE part extracted image features, which were used to collect similar samples from the whole image. The predictions of the missing pixels were corrected through the collected similar samples. In addition, we proposed a novel adaptive scan order based on the joint credibility of the support area and reconstruction to alleviate the error propagation problem. The experimental results showed that the proposed algorithm could reconstruct corrupted images effectively and outperform the compared state-of-the-art methods in terms of objective and perceptual metrics.  
**Author Contributions:** [PERSON] designed and performed the experiments, analyzed the data, and wrote the paper with contributions from all authors; [PERSON], [PERSON], and [PERSON] supervised the study and verified the findings of the study. All the authors read and approved the submitted manuscript, agreed to be listed, and accepted this version for publication.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grants Nos. 11572084, 11472061), the Fundamental Research Funds for the Central Universities (Nos. 16D110412, 17D110408) and DHU Distinguished Young Professor Program (No. 18D210402).  
**Conflicts of Interest:** The authors declare that there is no conflict of interests regarding the publication of this article.
## 3 Crowding, Phase Separation, and the Cell Cycle  
Biomolecular condensates are a class of hyperstructures that play major roles in prokaryotic and eukaryotic physiology (for a review see [18]). They form because of phase separation, which is due to transient, low-affinity, cohesive interactions between their constituent polymers, whilst stereo-specific, macromolecular interactions only play a secondary role [19]. These interactions are determined by molecular and macromolecular crowding [20]. Different crowding agents can have different effects, as illustrated by the compaction and DNA binding of HU [21]. This paves the way for hyperstructures to respond in different ways in transducing information from inside and outside the cell. In the case of the initiation of chromosome replication, a low membrane occupancy is needed for the activation or rejuvenation of DnaA [22; 23], whilst macromolecular crowding is required for the replication of _oriC_ in vitro [24]; in the case of chromosome segregation, phase separation is proposed to underpin the segregation of newly replicated chromosomes [25]; in the case of cell division, macromolecular crowding is required for the formation of FtsZ droplets in vitro [26]. Fundamental questions here include: Are crowding and phase separation the primary determinants of hyperstructure dynamics? Do they determine the assembly and disassembly of hyperstructures? Do they control the fusion and fission of hyperstructures? In short, are they the essence of the clock that controls the cell cycle?
# Customer Churn Prediction in B2B Non-Contractual Business Settings Using Invoice Data
## 6 Conclusions
### Managerial Implications  
From a business perspective, our results indicate that top-performing models devised for any of the explored churn definitions have a significant potential to bring measurable gains to the company. To illustrate, reported TDL values show that by using this approach in cases of two and three-month churn definitions, a company would be able to identify almost five times more churners than by using random sampling within the top decile. This could directly lead to significant savings in retention activities, especially if they are in the form of giving a discount. As this is a rather customary approach to customers which the company expects will not make a purchase in the following period, assuming equal likelihood among both churners and non-churners to accept the incentive, the savings would directly depend on the number of actual churners targeted with the offer (otherwise, non-churners would be given a discount even though they would make a purchase anyway).  
In addition to this, experimental results also indicate that the company in question could use not just one, but several churn definitions to reliably identify customers not likely to make a purchase in the following period. This directly opens up possibilities for tailoring custom retention strategies for groups of customers predicted to churn in distinct future time periods, thus maximizing the likelihood of keeping them active. Predictions of models devised using different churn definitions could also serve as a valuable input to client segmentation activities, especially if coupled with expected customer lifetime value calculations or other data sources potentially available at the company. For example, customers could be grouped according to their average spending and the most likely period in which they are expected not to make any purchases, and then offered customized incentives (e.g., different discount rates or discounts on particular product groups valid only for the number of months they are predicted to stay dormant in).  
On that note, the simplicity of requirements in terms of data pertinent to the proposed approach is one of its greatest strengths. Even though it has been demonstrated that additional sources of data might be invaluable for creating even more accurate predictive models compared to using something as common as transactional data alone (i.e., without any additional customer or business-specific information), simple solutions often mean lower barriers for implementation in real-world settings and can thus potentially yield measurable business results faster. This especially holds for the B2B domain, which has been somewhat slower (compared to B2C) in adopting data gathering and analytics solutions, so this approach might be a good way to fairly quickly obtain some measurable results and hopefully convince management to consider exploring other approaches and devising more advanced strategies.
# Utilization of By-Products from the Fruit and Vegetable Processing Industry in Pasta Production
## 4 Challenges in the Industrial Application of By-Product Enriched Pasta  
The first issue or challenge associated with these applications is the need to obtain a regular and homogeneous product, in sufficient quantities and throughout the year, for an industry to consider producing this type of product. Regarding consistency, although few studies have analyzed it, there are usually significant differences between fruit varieties or the prior processing of certain by-products. For example, [PERSON] et al. [48] showed that the composition of hazelnut skins from different varieties differed, and these variations affected the quality of the pasta in which they were incorporated. Similarly, [PERSON] et al. [65] observed discrepancies in the polyphenol content of different grape skins depending on the grape variety. It is also possible to treat these by-products to obtain distinct fractions with variations in macronutrient content, which will differently affect the quality of the resulting pasta [98]. Furthermore, no studies have evaluated the effect of prior processing of by-products on the quality of the pasta produced, but factors such as particle size or drying conditions may have an impact. It is well known that the particle size of semolina is a key factor in the quality of the resulting pasta [34], so it is reasonable to assume that this would also apply to ingredients derived from by-products. This has already been demonstrated in other applications, such as cookies or cakes [7]. However, in the case of pasta, it has been scarcely studied, although the limited studies available indicate a significant influence of this factor.  
The cost of these by-products is also an aspect to consider, although it is rarely addressed in scientific publications. Even if by-products are available at minimal cost, it must be considered that those with high moisture content must be dried quickly to extend their shelf life and ground to the appropriate particle size. Both processes involve costs, which, along with logistical costs, must be factored in. Additionally, these processes have environmental impacts, such as carbon footprint, which should be assessed through life cycle analysis or other techniques to verify their environmental viability [122].  
The need to have these by-products available regularly throughout the year will depend on each case. Some by-products, such as brewer's spent grain, are produced year-round and would pose no supply issues. Other by-products generated during specific months of the year can be safely stored once dried and ground. However, many of them lose organoleptic characteristics with prolonged storage. In such cases, seasonal production can be implemented as a sales strategy.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 5 Strengths and Limitations of the STM Method
The STM method has two major limitations. (I) Historical images are required to initialize the model before the STM method is used for forest fire detection. If there are too many noisy pixels in the images, the modeling results could be unsatisfactory; (II) The remote sensing images used by the STM method should be acquired at approximately the same time of day. The comparison of multi-temporal images that are acquired at different times of day, especially for day and night, is meaningless.