esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 5 Research Status of Experiment
### Experimental Methods  
Experimental methods play an important role in the research of heterogeneous chip task-scheduling methods. It is the core means to evaluate the performance of the method accurately. By carefully designing the experimental environment, it can simulate real or diversified application scenarios and set comprehensive and reasonable parameters. It can effectively measure key performance indicators, such as the execution efficiency of the task-scheduling algorithm under different conditions.  
According to the different experimental subjects, the experimental methods can be further subdivided into model-in-loop (MIL) simulation, software-in-loop (SIL) simulation, and hardware-in-loop (HIL) simulation. Through SIL and MIL, the simulation can partially or completely mathematically model the actual hardware resource management and task-scheduling method, and the effectiveness of the scheduling method can be verifiedwithout deployment on the actual hardware. This not only improves the efficiency and flexibility of the test but also significantly reduces the development cost. By simulating various possible operational scenarios, the simulation enables developers to optimize task allocation and resource utilization strategies at the software level and to identify and solve potential performance bottlenecks and scheduling conflicts in advance. The MIL/SIL method is mainly applied to the detailed design stage of the software to test the function of heterogeneous chips. The HIL is conducted in the system on a chip as a final stage test to verify the actual performance of the task-scheduling method. In the HIL, the hardware test evaluates the heterogeneous chip under real load. With the integration of test objects, the confidence of test results increases accordingly, and the cost also increases. Table 5 compares the characteristics of these different experiment methods.
## 4 Numerical Simulation
### Finite Element Model and Computation Procedure  
The 2D FE model of the saturated calcareous sand with relative density of 50% was established and analyzed. As shown in Figure 9, the dimensions of the FE model are 20 m \(\times\) 40 m (height and width), the height of which is consistent with that of the centrifuge model. The sand material is modeled by QuadUP elements [48] following the \(u\)-\(p\) dynamic consolidation equation, and the thickness of elements is 1 m. According to the laboratory test results, the permeability \(k_{sand}\) of sand materials is set to be 6.4 \(\times\) 10\({}^{-2}\) cm/s. The boundary conditions are set as follows: (1) the displacement of nodes in left and right boundaries sharing the same elevation are forced to be equal in \(x\)-direction and \(y\)-direction; (2) The nodes at the base are fixed, and the input seismic loading is imposed on the base along the \(x\)-direction; (3) The pore pressure on the soil surface is fixed to be zero, and other boundaries are impervious. The earthquake E2 in the centrifuge tests are selected as the input seismic loading, and the peak acceleration is linearly scaled to 0.1 g, 0.2 g, and 0.3 g.  
Uniform linear elastic mechanical properties were applied to all the materials in the model to establish the initial static stress condition as well as to avoid the stress concentration or arching effects. After that, the corresponding input parameters of sand materials were restored, and elastoplastic analysis was performed to obtain the initial stress field and pore pressure field. The ground motion was imposed on the base of the model by means of acceleration time history, namely the vibration method. During the computation,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Medium Dense Sand** \\  & **Dr = 50\%** \\ \hline Bulk modulus/kPa & – \(\star\) \\ Friction angle/(\({}^{\circ}\)) & 37 \\ PT angle/(\({}^{\circ}\)) & 32 \\ Peak shear strain & 0.1 \\ Reference confining pressure/kPa & 101 \\ Pressure dependent coefficient & 0.5 \\ Contraction parameter & 0.006/5.5/0.2 \\ Dilation parameter & 0.45/1.5/0.0 \\ Liquefaction parameter & 1.0/0 \\ \hline \hline \end{tabular} \({}^{\star}\) The bulk modulus is calculated according to the shear modulus and Poisson’s ratio, \(B_{r}=2G_{r}(1+\theta)/(3(1-2\theta))\). The Poisson’s ratio is set to be 0.499 and 0, respectively, for the stress field initialization and dynamic computation.  
\end{table}
Table 5: _Cont._  
Figure 9: Diagram of FE model for level ground of saturated calcareous sand.  
Rayleigh damping of 0.5% was applied to control high-frequency noise and ensure the damping energy dissipation of the soils under small strain condition.
## 4 Discussion
### Effectiveness of the FAW Module  
After incorporating the FAW module, the model exhibited a considerable decrease in FNs by 12.6% and IDSWs by 22.7%, respectively, resulting in a 3.7% improvement in MTs, a 1.8% improvement in the MOTA, and a 0.3% improvement in the IDF1. This demonstrates that the FAW module reduces missed detection for tracking, thus leading to an improvement in the MOTA, which primarily measures the detection performance. Additionally, both a decrease in IDSWs and an increase in MTs indicate that the model's ability to track complete trajectories is improved. This further confirms that the FAW module utilizes features from the previous two frames to enhance the current frame's feature representation, thereby improving the tracking robustness and consistency, ultimately reflected in the improvement of the IDF1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Baseline** & **FMME** & **FAW** & **IoU CM** & **MOTA**\({}^{\dagger}\) & **IDF1**\({}^{\dagger}\) & **FN**\(\downarrow\) & **MT**\({}^{\dagger}\) & **IDS**\(\downarrow\) \\ \hline \(\blacktriangledown\) & \(\blacktriangledown\) & & & 26.6 & 42.9 & 502,392 & 30.1 & 8882 \\ \(\blacktriangledown\) & \(\blacktriangledown\) & & & 28.4 & 43.2 & 438,951 & 33.8 & 6866 \\ \(\blacktriangledown\) & \(\blacktriangledown\) & \(\blacktriangledown\) & & 28.4 & 43.3 & 438,956 & 34.3 & 6809 \\ \hline \multicolumn{8}{c}{CenterTrack} & 26.3 & 42.7 & 511,462 & 31.0 & 11,741 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study for feature-mapping motion estimation (FMME), feature aggregation wrapper (FAW), and IoU-prior cascade matching (IoU CM). \(\uparrow\) indicates that higher is better and \(\downarrow\) indicates that lower is better.
## 2 Negative-Stiffness Device and Its Mechanical Model
### Mechanical Model
Since the spring providing negative stiffness in the device does not have an energy-dissipation capacity, additional viscous dampers are considered. Although additional damping can improve the energy dissipation capacity of the isolation layer, it will also increase the seismic response of the superstructure [6; 12]. Therefore, it is necessary to comprehensively consider the seismic response of the superstructure and the displacement of the isolation layer to ensure that the set damping parameters are reasonable and effective. In this paper, the form of the orbital surface function is the cosine function:  
\[f(x)=b\cdot\cos(\beta x) \tag{11}\]  
As \(\beta=10\), \(b=0.05\), \(\Delta l=0.5\), \(u=0.2\), and \(k=50\), the negative-stiffness mechanical model of the device can be drawn from Equation (6), as shown in Figure 4.  
The relationship between negative stiffness, \(K_{NSD}\), and displacement, \(x\), can be drawn from Equation (7), as shown in Figure 5.  
Figure 4: Mechanical model of negative\(-\)stiffness device. It can be seen from Figures 6 and 7 that the shape of the mechanical model of damping is basically unchanged, while the period and peak value of the model function change.
# IoT Group Membership Management Using Decentralized Identifiers and Verifiable Credentials
## 2 Background and Related Work  
### CoAP Group Communication  
CoAP [4] is a lightweight protocol, which is designed to be the \"HTTP of the IoT\". CoAP resources are identified by a URI scheme, similar to HTTP URIs, and the CoAP interaction model is similar to the client-server model of HTTP. Therefore, IoT endpoints act as CoAP \"servers\", exposing one or more CoAP URIs that can be accessed by CoAP \"clients\" using a suitable CoAP \"method\".  
CoAP group communication is a CoAP extension that allows CoAP clients to retrieve (or set) resources from a group of CoAP servers, e.g., retrieve the temperature measurements from all sensors of a building, turn on and off all the lights of a smart city and so forth. With CoAP group communication, a request to a CoAP URI is received by all group members. An approach for realizing CoAP group communication is by using IP multicast (Section 2 of [1]). With this approach, CoAP servers belonging to the same group join an IP multicast address, and CoAP clients learn the IP multicast address of a group using DNS resolution. Then, CoAP clients can send CoAP requests to an IP multicast address and receive the corresponding response(s) using unicast.  
The RFC-recommended approach for realizing CoAP group communication is the following: CoAP group URIs are associated with an IP multicast group, all CoAP servers join the appropriate IP multicast groups, and DNS servers map group URIs to the corresponding IP multicast address.
## 4 Inventory and Analysis
### Analysis of Rooftop Agricultural Production Potential in Shibei District
#### 4.2.1 Determine the Annual Yield per Unit Area of Vegetables under Different Cultivation Methods
(2) Public and industrial buildings usually have their own construction scope and a certain degree of openness and openness, making them more suitable for agricultural planting activities. In the future, there should be follow-up on corresponding building regulations for rooftop agricultural facilities, and regulations and specifications should be made from the height, form, and other aspects of the facilities. Selecting agricultural planting methods for roofs under different building types and areas in Table 3, combined with the corresponding annual yield data per unit area of facility cultivation and soil cultivation obtained in Table 4, the corresponding annual yield of roof agricultural planting was obtained after calculation, as shown in the rightmost column of Table 5. It can be seen from the data in the table that the roof agriculture planting in Shibei District can produce 25,800 tons of vegetables in total, and the annual yield per unit area is 7.95 kg/m\({}^{2}\), which is basically in line with the annual harvest of roof-grown vegetables of 8-21 kg/m\({}^{2}\), as mentioned by [PERSON]. 2.3. Determine the Number of Rooftop Vegetables Supplied to the Population Based on the per Capita Annual Consumption of Vegetables  
The annual per capita vegetable consumption in China's Food and Nutrition Development Outline (2014-2020) is 140 kg. Based on this standard, combined with the annual vegetable production of 25,800 tons of rooftop agriculture planting in the Shibei District of Qingdao, it can be calculated that this production can supply the urban population of 184,300, accounting for 16.9% of the total population of 1.09 million in the Shibei District of Qingdao.
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 2 Methodology
### Evaluation Criteria  
The following evaluation criteria were used to evaluate the model's accuracy.  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{N}\left(y_{obs}-y_{pred}\right)^{2}}{\sum_{i=1 }^{N}\left(y_{obs}-y_{mean}\right)^{2}} \tag{4}\]  
\[\text{Mean Square Error}\ \left(\text{MSE}\ \right)=\frac{1}{N}{\sum}_{i=1}^{N} \left(y_{obs}-y_{pred}\right)^{2} \tag{5}\]  
\[\text{Root Mean Square Error}\ \left(\text{RMSE}\right)=\sqrt{\frac{1}{N}{ \sum}_{i=1}^{N}\left(y_{obs}-y_{pred}\right)^{2}} \tag{6}\]  
\[\text{Mean Average Error}\ \left(\text{MAE}\right)=\sqrt{\frac{1}{N}{ \sum}_{i=1}^{N}\left|y_{obs}-y_{pred}\right|} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multirow{2}{*}{**Tuning Range**} & \multicolumn{3}{c}{**Tuned Value**} \\ \cline{3-4}  & & **BR** & **DGAN** \\ \hline learning\_rate & [0.01, 0.1, 0.2] & 0.1 & 0.1 \\ n\_estimators & [50, 100, 200] & 100 & 200 \\ max\_depth & [3, 5, 7] & 7 & 7 \\ Subsample & [0.6, 0.8, 1.0] & 1 & 1 \\ colsample\_bytree & [0.6, 0.8, 1.0] & 0.6 & 0.6 \\ reg\_alpha & [0, 0.1, 0.5, 1] & 1 & 1 \\ reg\_lambda & [0.1, 1, 10] & 1 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters for XGBoost models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multirow{2}{*}{**Tuning Range**} & \multicolumn{3}{c}{**Tuned Value**} \\ \cline{3-4}  & & **BR** & **DGAN** \\ \hline estimator\_C & [0.1, 1, 10] & 0.1 & 0.1 \\ estimator\_gamma & [0.1, 1, 10] & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameters for SVM models.  
where \(N\) is the number of observations, \(y_{obs}\) is the actual observed value, while \(y_{pred}\) is the predicted value, and \(y_{mean}\) is the mean of actual observed values.
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Fine-Tuned Datasets  
**PASTIS-R:** The PASTIS-R [43] agricultural dataset contains 2433 optical and radar SITS from the ESA's Sentinel 1 and 2 satellites. Each SITS contains between 38 and 61 images taken between September 2018 and November 2019. The dataset provides an annotated semantic segmentation map for each of 2433 spatial locations, where every pixel is given a semantic label from one of 20 different crop type classes. Many optical images are partially occluded by clouds. Note that we only consider the semantic segmentation labels from this dataset and DO NOT perform parcel classification experiments (as carried out in the original paper), as semantic segmentation is a strictly more challenging task. Table 1 shows the semantic segmentation classes for the labels of this dataset.  
**Africa Crop Type Mapping:** The Africa Crop Type Mapping dataset [14] contains multi-modal SITS over various regions in Africa. Ground truth labels in this dataset were collected for 4 classes in 2017. Table 2 shows the semantic meanings of the labels for the Africa Crop Type Mapping dataset. For our experiments, we used 837 fields in the South Sudan partition.
# Accelerating FVM-Based Parallel Fluid Simulations with Better Grid Renumbering Methods
## 2 Numerical Fluid Simulations Based on Finite Volume Method
### An Improved Matrix Structure Indicator Based on the Mean Distance of Median Points  
The above two traditional sparse matrix quality metrics are very widely used in most renumbering algorithms because of their simple implementation and intuitive clarity. In a computer with hierarchical storage structure, the sparsity of the sparse matrix will greatly affect the cache hit rate during the solution process and thus the overall iteration efficiency of the case, since the sparse matrix is ultimately involved in the CFD solution calculation. However, the above metrics only express the concentration of the outermost nonzero elements of the sparse matrix and do not strictly take into account the dispersion of the inner nonzero elements. For numerical calculations during CFD solving or MPI message passing between different processes, the cache line is so small that the cache data are constantly updated. Therefore, hitting more data in the same cache is the focus of the study. In other words, the presence of more non-zero elements within a smaller distance from the diagonal of the sparse matrix is the desired effect of the renumbering algorithm.  
A median point represents the number in the middle of a set of data arranged in order, and equally divides the set of values into two parts. As a statistical variable, the median point values are not easily affected by individual maxima or minima, so they have better stability.  
Based on the median points of non-zero elements in a sparse matrix, we define the set of all points whose distance to the diagonal is \(D\). The elements in \(D\) are sorted in ascending order and defined as \(\mathbf{d_{k}}\)  
\[\mathbf{d_{k}}=\left|i-j\right|,\ \ (a_{ij}\
eq 0\ \left|\ \right.i\leq j\ \left|\ \right.k=0,\ldots,n\ \left|\ \right.d_{0}<\cdots<dk<\cdots<d_{n}\ \left|\ \right.d_{k}\in D) \tag{11}\]  
When \(k=\frac{n}{2}\), \(d_{k}\) is the distance of the median point and the mean distance of median point, named MDMP, could be expressed as  
\[\tilde{d}=\sum_{k=1}^{\frac{n}{2}}\frac{d_{k}}{k} \tag{12}\]  
\(\tilde{d}\) can well characterize the degree of dispersion of this sparse matrix. It was is verified that the degree of dispersion of non-zero elements within the median is an important indicator of the quality of the sparse matrix. The advantages of MDMP over traditional indicators will be analyzed in detail in the Results presented in Section 5.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 2 Materials and Methods
### Determination of Optimal Parameters for Enzyme Production
#### 2.4.1 Effect of Initial pH on Cellulase and Xylanase Production  
In optimizing for pH, the fermentation medium was adjusted to initial pH range of 3-11 with 1N NaOH or 1N HCl as the case may be, before autoclaving. The sterilized fermentation medium was inoculated with 1 mL of 48 h cell suspension and incubated for 72 h.
## 4 Discussion
In our study, compared to single-thread designs (S-Co, S-FCo), dual-thread designs (D-Co and D-PCo) were proven to increase insertion torque more than pullout force, which corresponded to other studies [22; 23]. The screws in the D-PCo groups afforded 62% more mean insertion torque than the S-PCo groups; however, only 27% more mean axial pullout force. It appears that the proximity of the fine threads in the dual-threaded design might have interfered with more bone purchase and then created a more compressive force than coarse threads in the single-threaded screw, thus obviously increasing the insertion torque to 62%. Instead of the tapper compression mechanism in insertion torque, the performance of axial pullout force was achieved through bone-thread embedded volume and resistance, which are far less affected by thread design [22; 23; 34]. The present study had some limitations. First, standard L4 Sawbones(r) were used as substitutes for cadaveric vertebrae to eliminate the effects of the variability of bone density and anatomical morphometry but may not represent the real clinical situation. Second, the effect of proximal-conical dual-thread screws should be evaluated with various screw sizes and densities of bone surrogates. Third, there was no significance between cylindrical and conical screws, whether in dual-thread or single-thread profiles, which is inconsistent with other studies [24; 33]. This phenomenon might be due to the fixation stability of pedicle screws being influenced by multiple factors, including thread shape, core shape, outer diameter shape, pitch depth, insertional depth/trajectory, and pilot-hole size [35; 36; 37; 38]. In our study, to highlight the effect of the proximal-conical shape, only the outer screw shape and thread type were controlled. Various inner/outer screw shapes, thread shapes and implantation techniques should be tested in future studies.
# A Machine-Learning-Based Cyberattack Detector for a Cloud-Based SDN Controller
### Contributions  
* This work proposes a novel and scalable architecture of distributed cybersecurity components integrated within TeraFlowSDN, the open-source ETSI reference implementation for SDN controllers.
* The proposed design is integrated as a series of ML-based security components into a microservice-based cloud-native SDN architecture. By using open and standardized interfaces (e.g., Protocol Buffers and gRPC), this solution allows an easy interconnection, exchange and substitution of security components in a seamless and modular way that does not impact the remainder of the components in the controller. Furthermore, the standardized design of the proposed components is general enough to be used by other ML-based components that the TeraFlowSDN controller could require in the future.
* Contrary to other solutions that only offer either network attack detection or mitigation solutions, our components offer a fully integrated pipeline, including both of these actions.
* Existing energy optimization techniques were examined and compiled into a novel set of 11 optimization strategies to reduce the energy consumption of deep neural networks. These novel strategies were applied to the TeraFlowSDN ML-based attack detectors without a noticeable negative impact on the models' performance. This allowed for a reduction in the average energy consumption by up to 83.30% with minimum performance degradation.
* In order to protect TeraFlowSDN ML-based components against adversarial attacks, this study proposes a new mechanism to generate high-quality adversarial examples that can be used to retrain and fortify ML models. To this end, we present, as a novelty, an extension of the MalGAN generative network equipped with a Smirnov Transform in the generator network to produce adversarial examples that can fool ML models but are still very close to real attack examples.
* Previous works have typically relied on outdated network traffic datasets, such as the NSL-KDD dataset for training IDS. Our work makes use of 5G network traffic generated in a new environment based on a fully virtualized 5G network that generates realistic traffic reflecting current standards.
## 3 Materials and Methods
### SPE Sorbent Evaluations  
Several sorbents for SPE were evaluated, including C18, Oasis HLB, Oasis MAX. After filtration, the pH was measured and adjusted to pH of \(7.0\pm 0.1\) using either acetic acid or ammonium hydroxide when C18 was the SPE sorbent. When the SPE sorbent was Oasis HLB or Oasis MAX the pH of the filtered water sample was adjusted to 3.00, according to prior developed methods [42; 43]. To improve the flow through SPE sorbents 10 mL of methanol was also added per 1 L water. C18 SPE (or other SPE sorbents) were conditioned with 5 mL of ethyl acetate, 10 mL of methanol, and 10 mL of H\({}_{2}\)O at pH = 7 for C18, and pH = 3.0 for Oasis HLB or Oasis MAX. For testing of recoveries on these three sorbents, selected target analytes (BPA, 4 OP, 4 NP, E2, EE) were dissolved into 100 mL H\({}_{2}\)O and loaded onto the sorbents. Oasis HLB and C18 sorbents were washed with water and target analytes eluted with acetonitrile, while for Oasis MAX a wash solvent of 5 v% NH\({}_{4}\)OH (aq) and elution solvent of 2 v% formic acid in acetonitrile was selected. The volume of the wash solvent and elution solvent were optimized.
## 7 Open Issues and Research Challenges
### Blockchain-Based Spectrum Sharing  
6G can operate in a 3.5 GHz unlicensed spectrum band, allowing different users to share the band for application requirements. However, band access can be monopolized by centralized auction-based strategies that favor a particular group of users for larger quantum-of-band access for higher bandwidth. This drastically affects the bandwidth requirement of other honest users. Thus, 6G leverages blockchain-based spectrum sharing for smart access to band services with equal quantum to all users to maintain trust in operations. Addressing blockchain integration with 6G is an active area of research.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Wireless Sensor Networks (WSN) Technology  
WSN is a set of sensing nodes that are arranged in a manner that allows them to function together to monitor or read some physical quantity (such as temperature, humidity, pressure, etc.) and communicate to a central point or device wirelessly. The full structure of WSN comprises the sensors (nodes), the communication technique, and the nature of their inter-connectivity. This implies that WSN has the capabilities of sensing, processing, storing, and communicating with its data destination through the wireless medium.