esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### E-Service Quality  
E-SQ is central to e-commerce and online services as it profoundly shapes CS and the overall experience within the digital marketplace. Defined as customers' evaluations and opinions related to service quality in the virtual marketplace, e-SQ encompasses various facets, such as convenience, accessibility, and user experience tailored to online environments, as well as traditional factors such as speed, accuracy, and reliability [14]. As customers have high expectations of their online service encounters, they anticipate a quality level commensurate with these expectations.  
The success of online commerce has been intricately linked to e-SQ enhancement. Components such as swift and reliable payment systems, accurate product information dissemination, and responsive and friendly customer support play crucial roles in augmenting CS. Failure to meet quality standards or align with customer expectations can result in customer dissatisfaction and erode their trust in the company. This effect underscores the importance of enhancing e-SQ to boost CS and loyalty [15].  
E-SQ also significantly enhances companies' competitiveness in the online landscape. Businesses offering exemplary service quality are more likely to overtake their competitors by securing customer preference and trust, which potentially leads to increased sales and market share. Hence, companies are increasingly focusing on improving e-SQ to remain competitive and drive growth in the e-commerce and online service industries [4].  
The strategic importance of e-SQ extends beyond immediate customer interactions to broader business outcomes. Optimizing e-SQ not only drives customer retention and satisfaction, but also catalyzes long-term business success [16; 17]. By consistently delivering superior digital service experiences, companies can establish themselves as leaders in their respective markets and attract and retain a loyal customer base [18]. This strategic focus on e-SQ is crucial as digital interactions continue to dominate consumer behavior and expectations. It also underscores the role of e-SQ as a cornerstone of competitive advantage in the evolving landscape of digital commerce.
# Dual-Band Multiple-Element MIMO Antenna System for Next-Generation Smartphones
## 2 MIMO Antenna Design  
Figure 1a shows the configuration of the proposed MIMO antenna system. One can observe from the figure that eight antenna elements are arranged on both side-edges of the smartphone printed circuit board (PCB). This kind of configuration provides more space for other RF components to be integrated on the smartphone PCB. The dimensions of the main PCB are noted to be 150 \(\times\) 75 mm\({}^{2}\), while the dimensions of the side-edge are 150 \(\times\) 8 mm\({}^{2}\). The distance among radiating elements is kept at 32.375 mm, as shown in Figure 1b. The single element of the MIMO system consists of a P-shaped radiating element embedded inside a semi-rectangular resonating structure, as shown in Figure 1c. The total size of the radiating element is noted to be 8 \(\times\) 4.5 mm\({}^{2}\). The rest of the design parameters of the radiating element are also provided in Figure 1c. In addition, a low-cost FR-4 substrate is used with a relative permittivity of 4.4 and a thickness of 0.8 mm.  
Figure 2 shows the evolution process of the proposed single antenna element. The proposed antenna design was implemented in four stages. In the first stage, a P-shaped radiating element is implemented with a small extended strip (see Figure 2). This resonator  
Figure 1: Proposed MIMO Antenna System. (**a**) Perspective view; (**b**) sub-array designed on side-edge frame; (**c**) single antenna element.  
provides resonance at a frequency of 5.6 GHz with an impedance bandwidth of 800 MHz, as shown in Figure 2. In the second stage, an extension is made in the strip by adding a horizontal strip, which leads to an L-shaped resonator, as shown in Figure 2. At this stage, a resonance frequency of 5.2 GHz was achieved with an impedance bandwidth of 400 MHz (see Figure 2). In the third step, the length of the L-shaped horizontal strip was extended. With the extension of the horizontal strip, a dual-band resonance was achieved at 4.6 GHz and 6.4 GHz, as shown in Figure 2. In the last step, a vertical strip was added to the inverted L-shaped resonator, which increased the current patch and shifted the resonances towards the desired frequency bands (3.5 GHz and 5.4 GHz), as shown in Figure 2. The impedance bandwidths for both of the frequency bands were noted to be 200 MHz and 700 MHz, respectively.  
The performance of the proposed single antenna element can further be verified with the help of surface current distribution (see Figure 3). From the figure, it can be seen that for the first resonance (3.5 GHz), the current is induced strongly at the top right strip of the antenna (see Figure 3a), while at 5.4 GHz, the strong current is distributed on the lower middle side of the radiating element, as shown in Figure 3b.
# Towards Understanding the Function of Aegerolysins
## 2 Aegerolysins
### Fungal Aegerolysins from Basidiomycota--Agaricomycotina
#### 2.1.3. Aegerolysins from _Agrocyhe aegerita_  
The poplar mushroom _Cyclocyhe aegerita_ Vizzini 2014 (_Agrocyhe aegerita_, _A. cylindracea_, or _Pholiota aegerit_) (_Agaricales_), also known as tea tree mushroom, Cha shu gu, Yanagi-matsutake, velvet pioppini, or sword-belt mushroom, is a high-value commercially grown edible mushroom. It is a saprotrophic, wood-inhibiting fungus that decomposes mainly dead wood of deciduous trees, especially poplar and willow, and causes weak white rot on deciduous trees in temperate forests; it may become pathogenic on some other tree species [17].  
The aegerolysin protein family is named after aegerolysin from the mushroom _A. aegerita_[63]. Northern hybridizations with total RNA extracted from four developmental stages of _A. aegerita_ SM51 (_A1B1/A2B2_) showed that the **Aa-pri1** gene is specifically expressed during primordia or the immature fruiting body stage and that it is not expressed (at list not to a detectable level) during vegetative growth or late stages of fruiting body maturation [100].  
**Aa-pri1** is one of six genes for putative aegerolysins encoded in the _A. aegerita_ AAE3 genome. Only one out of these genes has a neighboring gene for component B, which is homologous to the MACPF domain-containing protein **PlyB**.
## 2 Background  
### Working Modes of DCIM  
As shown in Figure 1, the DCIM macro is primarily constituted by a memory array, a parallel adder tree, and a partial sum accumulator [11]. The memory array is chiefly composed of memory cells and logic operation gates. The memory cells are used to store reusable weights, which perform parallel multi-bit multiplication with input data in the logical operation units. The parallel adder tree is a full adder array that achieves carry addition by expanding bit width. The partial sum accumulator is made up of shifters and registers. Based on the DCIM macro, matrix vector multiplication (MVM) can be implemented. First, the weight parameters are loaded into the memory cells from the input buffer and are then fed into the memory array in sequence. Subsequently, the memory array performs parallel multiplication of 1-bit inputs and multi-bit weights in one computational cycle and transfers the product to a multilevel full adder to obtain a partial sum. Finally, the shift accumulator accomplishes the addition of products of the low-bit input and high-bit input.  
The basic memory cell in a DCIM memory array is principally implemented with SRAM technology, whose elemental structure is depicted in Figure 2a. The 6T-SRAM cell consists of two cross-coupled inverters formed by transistors M1-M4, which store a single bit of data, and two additional access transistors, M5 and M6, controlling the access to the cell during read and write operations. This fundamental structure induces low storage density, incurring substantial area overhead in large-scale integrated arrays. Concurrently, the volatility of SRAM also leads to high static power dissipation. On the contrary, a SOT-MRAM cell merely necessitates two transistors, significantly diminishing the integrated area cost, as shown in Figure 2b. The SOT-MRAM cell contains an MTJ for data storage, consisting of two ferromagnetic layers separated by a thin insulator. The value of '0/1' is represented by the low/high resistance state of the MTJ. Transistors T1 and T2 are utilized to regulate the reading and writing of the stored data, respectively. Moreover, SOT-MRAM also exhibits excellent characteristics including nonvolatility, high endurance, and rapid switching, rendering it an ideal candidate for DCIM [12; 13; 14; 15; 16; 17].  
Figure 1: The typical architecture of DCIM macro including memory arrays, adder trees, and accumulators.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Cache Management
#### 3.4.3 LSTM Based Prefetcher  
Prefetch's role is to determine what data needs to be cached to ease the memory wall [30]. For prefetch performance, many existing tasks rely on tables [2; 4; 5; 29]. However, as the workload increases, the table also must expand, which puts pressure on the hardware. Considering that the previous long history will affect the current prefetching, this paper proposes a prefetcher using a sequence-based neural network. In this paper [14], two designs are introduced.  
The first is Embedding LSTM, the second is Clustering + LSTM. In the Embedding LSTM (see Figure 11), the PC and the delta are individually embedded, and the two features are connected to become a two-layer LSTM input. Then, the model outputs the top-10 deltas for each time stamp and selects the highest probability delta among the last timestamp K.  
Clustering + LSTM (see Figure 12) is built on the assumption that the interaction between addresses occurs locally. Therefore, the address space is clustered using k-means. The PC and Address are divided into each cluster, and the delta between adjacent addresses within each cluster is calculated. Assuming that there are two clusters in Figure 12, \(PC_{1\sim N}\) and \(Addr_{1\sim N}\) are clustered, and the PC and Delta existing in the first cluster are called \(PC_{1,n}\) and \(Delta_{1,m}\). This separate set of data is embedded with appropriate values, and the two embedding values are concatenated to become the input of the LSTM. At this time, to reduce the size of the model, multi-task LSTM is used to model each cluster. LSTMs are an independent model, but the weights of model tie and cluster ID provide to give a bias. Because this structure can calculate delta within the address set, the vocabulary size can be significantly reduced compared to Embedding LSTM. However, because Clustering + LSTM trains the model in a separate address region, it has a disadvantage that access to other regions cannot be handled.
# PCCNoC: Packet Connected Circuit as Network on Chip for High Throughput and Low Latency SoCs
## 3 Proposed Architecture
### Routing Algorithm  
Many researchers have explored routing algorithms to improve NOC performance [2; 16; 39]. The PCCNoC does not need to establish and revoke circuits frequently; thus, the routing algorithm is not the focus of this paper. The routing algorithm adopted by PCCNoC needs to ensure that no deadlock or livelock [16] occurs and to improve the packet exchange efficiency as much as possible. The adaptive XY routing we use is based on the traditional XY [2] routing method and has been improved.  
Each network node has a unique address. The address is encoded in two segments in the east-west direction and north-south direction. The routing is guided by the the control packet. The routing algorithm adopted consists of the following steps.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Port Name** & **Bit Width** & **Direction** & **Function** \\ \hline Data & N bits & DS to DR & Payload or packet. \\ Enable (EN) & 1 bit & DS to DR & To indicates whether the transmission is valid \\ Unlink (UL) & 1 bit & DS to DR & To unlock the constructed path \\ Overtime (OT) & 1 bit & DR to DS & To notice the transaction has time violation \\ CRCe (CRC) & 1 bit & DR to DS & To states the data transmission fails \\ Trans\_grt (TG) & 1 bit & DR to DS & To authorize data transaction of data sender \\ Trans\_ref (TF) & 1 bit & DR to DS & to refuse data transmission \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information for each part for router interface.  
Figure 7: The structure of the routing packets. (**a**) route packet structure, (**b**) short packet structure, (**c**) multicast packet structure and (**d**) broadcast packet structure.  
1. The address codes of the two sections are X for east-west (the east is bigger) and Y for south-north (the north is bigger).
2. When routing, the address of target node is compared with the local node address. When the X address of target node is bigger, they move eastward, and vice versa; when the Y address of target node is bigger, they move northward, and vice versa;
3. The routing decision of each routing node needs to take the congestion of the surrounding routing nodes into account. The X direction has higher priority.  
Figure 8 shows the strategy when the router faces congestion.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 2 Related Literature
### Covering-Array-Related Literature  
The generation of optimal values is the most challenging component of many problems for covering arrays [5]. Several approaches have been presented to generate covering arrays, each with its own set of pros and cons, but they all share similarities with various systems, including the automatic efficient test generator (AETG) system [5], the deterministic density algorithm (DDA) [6; 7], the in-parameter-order system [8], and the advanced combinatorial testing system (ACTS) [5; 6; 7; 8; 9].  
The IPO method expands the covering array column by column, adding rows as necessary to achieve full t-way coverage. Different studies have been conducted to better understand how the in-parameter-order technique can be used to generate covering arrays. The in-parameter-order-general (IPOG) technique was developed to make it possible to use the strategy to generate covering arrays of variable strength. A change to IPOG in [34] allowed for shorter generating times and smaller covering arrays in some situations. The number of possible permutations was reduced thanks to the recursive construction method presented in [35]. The size of covering arrays can be decreased by employing graph-colouring approaches, as suggested in [36].  
It should be noted that while prior literature has explored the use of various methods to optimise hyperparameters for deep learning models in the context of static malware detection, the present study is the first to utilise cAgen, a covering-array tool, for this purpose. Therefore, our work provides novel insights into the potential benefits of using cAgen in improving the performance of deep learning models for static malware detection. cAgen was chosen for its ability to efficiently explore the hyperparameter search space and identify the best hyperparameter configurations.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 6 Conclusions  
Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution, but field edge results are usually rough, resulting in lowered overall accuracy. In this paper, we proposed a new approach for extracting fine crop spatial distribution information. As can be seen from the comparison experiment, our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. The structure of the proposed model can adapt to the characteristics of the crop area in the GF-2 images, which is the key to the success of our model.  
The main contributions of this study are as follows: (1) the convolutional network structure designed in this study fully considers the proportional relationship between the area occupied by the target object and that of a pixel in the image. Experiments proved that the proportional relationship and the network structure affect the accuracy of the extraction results, and provide a reference for designing other land use type extraction models. (2) The model uses both \(1\times 1\) type and \(3\times 3\) type convolution kernels to extract pixel features. This reveals the semantic features of the pixels and extracts the spectral features of the pixels. Thus, the advantage of the numerous channels in a remote sensing image fully emerges and compensates for the limitation of fewer details in a remote sensing image.  
The main disadvantage of our approach is that it requires more pre-pixel label files. Future research should test the use of semi-supervised classification to reduce the dependence on pre-pixel label files.  
Conceptualization: C.Z.; methodology: C.Z. and Y.C.; software: Y.C. and S.W.; validation: [PERSON], and F.L.; formal analysis: [PERSON] and F.L.; investigation: [PERSON]; resources: [PERSON]; data curation: [PERSON]; writing--original draft preparation: [PERSON], Y.C. and S.W.; writing--review and editing: [PERSON], [PERSON] and F.L.; visualization: [PERSON]; supervision: [PERSON]; project administration: [PERSON]; funding acquisition: [PERSON]. This study was funded by the Science Foundation of Shandong (Grant # ZR2017 MD018); the Key Research and Development Program of Ningxia (Grant # 2019 BEH03008); the Open Research Project of the Key Laboratory for Meteorological Disaster Monitoring, Early Warning and Risk Management of Characteristic Agriculture in Arid Regions (Grant # CAMF-201701 and CAMF-201803); the arid meteorological science research fund project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA (Grant # IAM201801). The authors declare no conflict of interest.  
Figure 10: Segmentation errors comparison: (**a**) original images, (**b**) ground truth, (**c**) errors of RefineNet results, and (**d**) errors of CEM results.
## 3 Results  
### Fixed Sensor Behavior  
Following the design phase, the passive RFID sensor was manufactured on a 25 \(\upmu\)m thick Kapton Dupont Pyralux AP 8515R substrate and a phthalocyanine active layer for sensing acetone vapors/gas was applied. To assess the performance of the proposed sensor in a more realistic scenario, experimental tests were performed in which tetrasulfonated copper phthalocyanine [14; 38]--copper (II) phthalocyanine-34\({}^{\prime}\)4\({}^{\prime\prime}\)4\({}^{\prime\prime\prime}\)-tetrasulfonic acid tetrasodium salt, abbreviated as CuPcTS--was used as an active layer. This type of metallophthalocyanine was modified to improve its solubility and sensing response compared to the basic one, which suffered from a very low solubility in polar and non-polar solvents. The sensing response of basic metallophthalocyanines is based on the change in impedance in the presence of the detected gaseous analyte. A solution of the respective phthalocyanine in distilled water was prepared with a concentration of 1.45 mmol/l. Two thickness values of the sensitive layer, 0.6 \(\upmu\)m and 1.2 \(\upmu\)m, were used. The sensitive layer was applied on the surface of the IC-UD by pipetting and subsequent drying. The sensor tag sensitivity was verified by measurement inside a waveguide line with the Agilent E8364A vector network analyzer. The tag was inserted into an R32 waveguide chamber. This \"test gas part\" was separated from the adjacent parts of the measurement line by Tedlar membranes to hermetically isolate the solvent vapors, as illustrated in Figure 6. The acetone vapor with the specified concentration of 10,000 ppm in the synthetic air was introduced into the \"test gas part\" through a suction pump that drew it from the Tedlar bag.  
Table 2 gives an overview of the measured scattering response of the IC-UD coupled to the loop and treated with the CuPcTS sensitive layer as measured by the reflection coefficient of the waveguide line both with and without the 10,000 ppm acetone vapor. In the presence of the acetone vapor, there were noticeable frequency shifts of 9 and 12 MHz caused by the sensitive phthalocyanine layer of 0.6 and 1.2 \(\upmu\)m in thickness, respectively. The purpose of this comparison was only to verify the function and concept of resonant detuning using a varactor diode. Further optimization of the sensor and full calibration under different conditions may be the subject of future work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phthalocyanine Layer Thickness (\(\upmu\)m)** & **Frequency (GHz)** & **Af** & **Af** \\  & & **(GHz)** & **(s)** \\ \hline
0.6 \(\upmu\)m & 3.276 & 0.009 & 0.3 \\  & 0.6 \(\upmu\)m & 3.285 & \\  & 12 \(\upmu\)m & 3.264 & 0.012 & 0.4 \\  & 1.2 \(\upmu\)m & 3.276 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measured frequency shifts depending on the sensitive phthalocyanine layer’s parameters.  
Figure 5: Layout of the proposed RFID chipless sensor tag: (**a**) top layer with U-folded dipole and metallic loop; (**b**) bottom layer with rectifying circuit. Blue—dielectric kapton substrate; grey—copper layer.
## 3 Results
### Studies Characteristics  
All nine articles included in the present systematic review used a RCT design. All articles were recently published, in a period ranging from 2010 [15] to 2024 [29]. Four studies were conducted in Europe: two in England (one in a specialist clinic for Tourette Syndrome in a London hospital [30] and one across two Child and Adolescent Mental Health Services [22]), another one in Sweden, in the Child and Adolescent Psychiatry Research Center in Stockholm [21], and one in the Netherlands, by the Dutch Tourette Association [29]. Two studies were carried out in Israel, one in the Pediatric Movement Disorders Clinic at the Dana-Dwek Children's Hospital (DDCH) and the other by the Tourette Syndrome Association (TSAI) [20,31]. Another study was conducted across three American universities: Johns Hopkins University in Baltimore (Maryland), the University of California at Los Angeles, and the University of Wisconsin-Milwaukee [15]. The remaining two studies did not specify the research location [18,19]. In all the studies, participants were randomly assigned to the experimental group (the treatment was delivered either via videoconference or face-to-face) or to the control group. While active control groups received a parallel intervention [18, 19, 20, 21, 22, 30, 31], participants of passive control groups did not receive any treatment [15] or received the same intervention after some time [29].  
A total of 844 children and adolescents were studied, 428 of which were included in the experimental group, while 416 in the control group. The sample size ranged from 18 [18] to 224 subjects [22], and the age of participants from 7 to 18 years. Moreover, studies were consistent in terms of the measurement tools used (see the Section 3.2.2), although they differed in the intervention methods, number of subjects, and follow-up periods. All study characteristics are summarized in Table 10.