esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Energy-Aware Framework  
The Energy-Aware Framework proposed considers the downlink stream with 1 MC and 9 SCs for the sake of conciseness, as shown in Figure 2. MC is equipped with directional antennas, whereas all the SC antennas are assumed to be omnidirectional with constant gains. Same frequency band is utilised by all cells in the framework with frequency re-use factor of 1. Constant bit rate service with full buffer data utility is available in a centralised C-SON architecture with system-wide Proactive-Energy Saving Optimisation-based CO\({}_{2}\) Reduction. Moreover, historical traces of mobility that include time, location, number of passengers, associated cell IDs and received power levels (RSRP) are assumed to be available to the C-SON server. For Proactive-Energy Saving Optimisation-based CO\({}_{2}\) Reduction, consideration has been given to a two-tier HetNet model consisting of a live MC and nine live SCs along with their traffic information in separated control and data planes. Signalling is carried out by the MC with the responsibility of low data rate services while the backhaul MC-connected SCs offer high capacity services. When SCs monitor low traffic activity, they tend to switch-off while offloading their traffic to the MC provided there is enough capacity in the MC to accommodate the offloaded traffic load.
## 2 Methods
### CUDA Implementation of Greedy Agglomerative Procedures
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm or**} & \multicolumn{4}{c}{**Achieved Objective Function Values (1) Summarized after 30 Runs**} \\ \cline{2-6}  & **Min (Record)** & **Max (Worst)** & **Average** & **Median** & **Std. Dev** \\ \hline Lloyd (multistart) & 47.8874 & 54.4850 & 51.6395 & 51.1162 & 1.6453 \\ j-means (SWAP\({}_{1}\) + Lloyd) & 23.6798 & 35.3805 & 31.2554 & 31.8670 & 2.7425 \\ \(\text{AGGL}_{1}\) & 34.3990 & 54.5291 & 42.5890 & 41.8879 & 5.0023 \\ \(\text{AGGL}_{2}\) & 18.6255 & 22.8995 & 20.4858 & 20.3853 & 1.0785 \\ \(\text{AGGL}_{3}\) & 16.7389 & 19.8415 & 18.5376 & 18.6063 & 0.7018 \\ \(\text{AGGL}_{5}\) & 16.5944 & 19.4305 & 17.7512 & 17.5557 & 0.7611 \\ \(\text{AGGL}_{7}\) & 16.1609 & 20.0563 & 17.8918 & 17.8397 & 0.8182 \\ \(\text{AGGL}_{10}\) & 16.4099 & 19.0087 & 17.3922 & 17.2220 & 0.6228 \\ \(\text{AGGL}_{15}\) & 16.0706 & 17.4835 & 16.7584 & 16.7537 & 0.4276 \\ \(\text{AGGL}_{20}\) & 15.7783 & 17.6122 & 16.6852 & 16.7338 & 0.4712 \\ \(\text{AGGL}_{25}\) & 15.6854 & 18.3011 & 16.7847 & 16.6473 & 0.5885 \\ \(\text{AGGL}_{50}\) & 15.7963 & 17.7948 & 16.2860 & 16.2920 & 0.4237 \\ \(\text{AGGL}_{100}\) & 15.1942 & 16.3370 & 15.6951 & 15.6738 & 0.3081 \\ \(\text{AGGL}_{150}\) & 15.2025 & 16.4996 & 15.7898 & 15.7990 & 0.2939 \\ \(\text{AGGL}_{200}\) & 15.1805 & 16.2245 & 15.7252 & 15.7801 & 0.2843 \\ \(\text{AGGL}_{250}\) & 15.0975 & 16.8500 & 15.7103 & 15.6757 & 0.3640 \\ \(\text{AGGL}_{200}\) & 15.2509 & 16.2803 & 15.7108 & 15.7224 & 0.2694 \\ \(\text{GH-VNS1}\) & 21.5583 & 31.4467 & 27.7853 & 27.9011 & 2.5876 \\ \(\text{GH-VNS2}\) & 15.5928 & 17.6197 & 16.6424 & 16.6345 & 0.5166 \\ \(\text{GH-VNS3}\) & 15.3488 & 16.8864 & 15.9644 & 15.8850 & 0.4229 \\ \(\text{SWAP}_{5}\) (the best of SWAP & 22.3193 & 30.7398 & 27.5174 & 27.9711 & 1.9760 \\ \(\text{SWAP}_{7}\) (the best of SWAP & & & & & \\ by median) & 24.0243 & 30.9356 & 27.6329 & 27.8934 & 1.9714 \\ \(\text{GA-1 POINT}\) & 34.4429 & 45.0868 & 40.1539 & 39.6896 & 2.3970 \\ \(\text{GA-UNIFORM}\) & 37.4806 & 53.3750 & 43.8100 & 43.7275 & 3.7585 \\ \(\text{Aggl-EA}\) & 14.8354 & 19.4531 & 15.4576 & 15.3310 & 0.8131 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparative results for Mopsi-Joensuu dataset. 6014 data vectors in \(\mathbb{R}^{2}\), \(p=300\) centers, time limitation 5 s.
# A Deep-Learning-Based Quality Control Evaluation Method for CT Phantom Images
## 2 Materials and Methods
### AI Network
#### 2.3.1 Base Network: YOLOv8  
The architecture of the YOLOv8 network is depicted in Figure 6[18]. Representing the latest advancement in the YOLO network series, YOLOv8 introduces modifications to its detail layer, transitioning from C3 to C2f. This alteration adjusts the convolution size and reduces the number of steps, thereby enhancing speed without compromising accuracy, as validated through experimental verification. Furthermore, YOLOv8 implements anchor-free detection, enabling rapid execution of non-maximum suppression by minimizing the number of bounding box trainings and retaining only the most probable locations. Additionally, it incorporates the closing technique to the mosaic augmentation method of YOLOv5, mitigating performance degradation resulting from extensive augmentation. Recognized for its high accuracy in the state-of-the-art (SOTA) domain of machine learning, YOLOv8 is user-friendly, even for non-experts, owing to its integrated libraries for implementation. This aspect is taken into consideration by medical imaging experts when configuring a network for additional experiments. Also, there have been studies incorporating attention blocks into YOLOv8 to enhance accuracy without significantly increasing the model size. Additionally, studies such as Pediatric Wrist Fracture Detection have demonstrated performance improvements by adding attention blocks to YOLOv8-AM [19]. Based on these findings, we hypothesized that integrating attention blocks into YOLOv8 could enhance performance, thus selecting YOLOv8 as the base network for our study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resolution** & **Pass or Non-Pass** & **Raw Data** & **Augmented Data** \\ \hline \multirow{3}{*}{Spatial} & Pass & 49 & 51 \\  & Non-pass & 20 & 80 \\  & Total & 69 & 131 \\ \hline \multirow{3}{*}{Contrast} & Pass & 87 & 13 \\  & Non-pass & 9 & 91 \\ \cline{1-1}  & Total & 96 & 104 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset.
# Possibility of Using Fermented Curly Kale Juice to Manufacture Feta-Type Cheese
## 3 Results and Discussion
### Amino Acid Composition after Ripening
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Amino Acid**} & \multicolumn{2}{c}{**Analyzed Manufacture Stage**} \\ \cline{2-3}  & **Cheese before Ripening** & **Cheese after Ripening** \\ \hline Alanine & \(3.09\pm 0.08\)\({}^{\text{a}}\) & \(15.03\pm 0.81\)\({}^{\text{b}}\) \\ Arginine & \(2.38\pm 0.25\)\({}^{\text{a}}\) & \(5.84\pm 0.15\)\({}^{\text{b}}\) \\ Asparagine & \(0.00\pm 0.00\)\({}^{\text{a}}\) & \(19.79\pm 0.76\)\({}^{\text{b}}\) \\ Aspartic acid & \(0.00\pm 0.00\)\({}^{\text{a}}\) & \(7.19\pm 0.09\)\({}^{\text{b}}\) \\ GABA & \(1.54\pm 0.06\)\({}^{\text{a}}\) & \(22.04\pm 0.69\)\({}^{\text{b}}\) \\ Glutamine & \(0.00\pm 0.00\)\({}^{\text{a}}\) & \(11.49\pm 0.85\)\({}^{\text{b}}\) \\ Glutamic acid & \(3.64\pm 0.20\)\({}^{\text{a}}\) & \(24.08\pm 1.29\)\({}^{\text{b}}\) \\ Glycine & \(0.68\pm 0.05\)\({}^{\text{a}}\) & \(5.75\pm 0.11\)\({}^{\text{b}}\) \\ Histidine & \(0.79\pm 0.08\)\({}^{\text{a}}\) & \(5.36\pm 0.15\)\({}^{\text{b}}\) \\ Isoleucine & \(0.40\pm 0.02\)\({}^{\text{a}}\) & \(14.74\pm 0.31\)\({}^{\text{b}}\) \\ Leucine & \(3.16\pm 0.15\)\({}^{\text{a}}\) & \(41.35\pm 0.26\)\({}^{\text{b}}\) \\ Lysine & \(4.32\pm 0.38\)\({}^{\text{a}}\) & \(29.05\pm 0.84\)\({}^{\text{b}}\) \\ Ornithine & \(2.34\pm 0.07\)\({}^{\text{a}}\) & \(27.28\pm 0.67\)\({}^{\text{b}}\) \\ Methionine & \(0.00\pm 0.00\)\({}^{\text{a}}\) & \(3.82\pm 0.11\)\({}^{\text{b}}\) \\ Phenylalanine & \(2.87\pm 0.07\)\({}^{\text{a}}\) & \(36.29\pm 0.86\)\({}^{\text{b}}\) \\ Proline & \(1.34\pm 0.05\)\({}^{\text{a}}\) & \(8.74\pm 0.27\)\({}^{\text{b}}\) \\ Serine & \(1.51\pm 0.09\)\({}^{\text{a}}\) & \(13.42\pm 0.37\)\({}^{\text{b}}\) \\ Threonine & \(0.99\pm 0.02\)\({}^{\text{a}}\) & \(12.12\pm 0.52\)\({}^{\text{b}}\) \\ Tyrosine & \(0.89\pm 0.03\)\({}^{\text{a}}\) & \(5.84\pm 0.17\)\({}^{\text{b}}\) \\ Valine & \(2.83\pm 0.16\)\({}^{\text{a}}\) & \(33.67\pm 0.52\)\({}^{\text{b}}\) \\ \hline Total & \(32.77\pm 1.05\)\({}^{\text{a}}\) & \(342.90\pm 5.30\)\({}^{\text{b}}\) \\ \hline \end{tabular} Mean values \(\pm\) SD. Results with a different letter in the same row are significantly different (\(p<0.05\)).  
\end{table} 
Table 4: Amino acid composition (mg/100 g dw) between two stages of Feta-type cheese ripening. presumed that it was a result of the activity of the starter culture used in the cheese manufacture. GABA has recently gained attention in the food industry due to its different beneficial effects on human health, including its activity as an inhibitory neurotransmitter, hormonal regulator, blood pressure regulator and immunity enhancer. It has also been found to exert a positive effect on cancer, brain and psychiatric diseases and cardiovascular and respiratory system conditions [10; 58].
## 4 Experiments
### Performance Comparison for CDS Prediction  
In order to verify the effectiveness of TCN for CDS prediction, we compare kmer+TCN with existing state-of-the-art methods, including kmer+SVM [23], C2+DanQ [39], kmer+ skipBRNN [24], and C2+gkm+CNN+skipBRNN [40]. For fairness, all methods were trained and tested on the same datasets. All the comparable methods were implemented according to the parameter settings in their original studies. As for DanQ network, C2 encoding was used instead of C4 encoding because C2 encoding is more efficient. DanQ sets the input sequence length to 1000, while 90 is used here like other methods. Moreover, in order to prevent underfitting or overfitting, the scale of the neural networks is set large enough, and two regularization techniques, Dropout [41] and early stopping [42], are adopted. Moreover, to avoid the side effect of data imbalance on predictive model, we use random undersampling for the negative samples and make the ratio of positive samples to negative samples approximately 1:1.  
Table 3 shows the performance comparison of kmer+TCN with existing state-of-the-art methods on human and mouse datasets. It is observed that kmer+TCN achieves the best prediction performance and consistently outperforms the other methods on all the datasets, with an average sensitivity of 99.7%, specificity of 99.2%, and auROC of 0.9992, improvements of 0.61%, 1.15%, and 0.0007 over the second best method, C2+gkm+CNN+skipBRNN. Moreover, kmer+TCN only uses 64-dimensional codons and 1-dimensional position encoding, and the scale of TCN is very small, so it is easy to train and requires very little time to converge, while C2+gkm+CNN+skipBRNN requires several hours to train due to large dimensions of C2 encoding and gkm features. Moreover, Figure 5 plots the ROC curves of different methods on human and mouse transcripts, from which we can observe that kmer+TCN not only achieves competitive performance in human and mouse transcript sequences but also consistently outperforms the other existing state-of-the-art methods, which verifies the significance of biological features (e.g., codon usage) and primary structural information (e.g., codon label consistency). All the experimental results demonstrate that kmer+TCN is a highly accurate method for predicting CDSs.
## Materials and Methods  
The low-toxicity gel casting system used was composed of N, N-dimethylformamide (DMF) as solvent, hydroxyethyl methacrylate (HEMA) as monomer, 1,6-hexanediol diacrylate (HDDA) as cross-linker, tert-butyl peroxybenzoate (TBPB) as initiator, and polyvinyl pyrrolone (PVP) as dispersant. All the reagents were chemically pure and supplied by Shanghai National Pharmaceutical Reagents Group Co. Ltd., China. Commercially available 99.7% pure TiH\({}_{2}\) powder (supplied by Shanghai Bike New Materials Co. Ltd., Shanghai, China) and 99.85% pure Al powder (supplied by Shanghai Bike New Materials Co. Ltd., Shanghai, China) were used as raw materials.  
Premixed solutions containing 15 vol % to 40 vol % monomer relative to the volume of the solutions were prepared by dissolving HEMA, HDDA, and PVP in DMF at room temperature by means of ultrasonic dispersion. A TiH\({}_{2}\) and Al powder mixture with a molar ratio of 1:1 was added to the solutions and mixed by a stirrer for about 30 min to obtain 40 vol % to 48 vol % TiH\({}_{2}\)/Al slurries. In all experiments, the ratio of HEMA to HDDA was fixed at 15:1. Later, a little dose of TBPB was dropped into the slurries and fully stirred. After that, the slurries were poured into individual silicone molds and held at 80 \({}^{\circ}\)C in a vacuum drying chamber for 2 h to complete the gelation process. Subsequently, the wet green bodies were carefully removed from the molds and soaked in ethanol for 8 h in order to remove residual solvent. To avoid cracking during drying, the green bodies were dried in a vacuum chamber at 25, 80, and 120 \({}^{\circ}\)C for 8 h in sequence. Finally, the TiH\({}_{2}\)/Al green bodies were sintered at 1400 \({}^{\circ}\)C under vacuum conditions for 2 h.  
The particle size distributions of the TiH\({}_{2}\) and Al powders were measured by a laser analyzer (S3500, Microtrac, USA). The micromorphology of the powders and the fracture surfaces of the green and sintered bodies were observed by a scanning electron microscope (Nova Nano SEM 230, FEI, USA). The rheological behavior of the TiH\({}_{2}\)/Al slurries was measured at room temperature by a rotary rheometer (Gemini 200 HR, Bolin, UK) with the shearing rate ranging from 0 to 1000 s\({}^{-1}\). The flexural strength of the green bodies was measured by a universal testing machine (Z020, Zwick/Roell, German) with a crosshead speed of 1 mm/min. Samples with a size of 40 mm \(\times\) 5 mm \(\times\) 5 mm for the test were held in a vacuum chamber at 120 \({}^{\circ}\)C for 2 h to avoid the influence of moisture before testing. The degreasing process of the dried green body was studied by using a TG analyzer (TGA8000 TM, PerkinElmer, USA), and the samples were heated at a rate of 10 \({}^{\circ}\)C/min from room temperature to 700 \({}^{\circ}\)C under a pure argon flow. An X-ray diffraction analyzer (D/max 2500, Rigaku, Japan) was used to identify the phases of the sintered samples. The porosity and pore size distribution of the sintered component were detected by a mercury intrusion method.
## 4 Experiment Results  
In this section, three experiments are conducted to illustrate the performance and effectiveness of the designed MIMO radar system for 2D imaging. All parameters used in the experiments are the same as given in Table 1. The frequency range \(B\) of the radar system is 0.4-2.6 GHz, which results in the range resolution of \(\delta_{r}=c/(2B)=6.8\) cm. The number of frequencies is 256, which causes the unambiguous range to be 17.4 m. The S12 measurement mode for the Agilent N9925a VNA is selected. The imaging method is mainly based on the BP algorithm. In order to suppress the artifacts, the CC-TDBP method is also applied for comparison with the traditional TDBP algorithm. To make the target imaging clearer, background subtraction and direct coupling subtraction are also conducted for the real experimental data before applying the imaging algorithm. The direct coupling wave signal is obtained by collecting data while facing the MIMO array towards the open sky in an open area. Therefore, the received signal only contains the direct coupling component between the antenna elements. In general, the direct coupling data of one measurement result can be directly subtracted and applied to most 2D imaging of experimental scenes. The method of obtaining the background component is to measure the data without the real targets (human or corner reflector) in the experimental scene. Then, we can simply subtract the saved signal containing the background when we do the real measurement. This background removal method can eliminate the reflections of the wall, other stationary clutter, and the direct coupling between the antenna elements. It is relatively complicated and may be limited by the actual imaging situation. Because the background data are different for each specific imaging scene, the background data of each new experimental scene needs to be measured.
## 1 Introduction  
The presence of mobile sink nodes, mobile sensors or both in a wireless sensor network (WSN) characterizes a mobile wireless sensor network (MWSN) [1; 2]. An MWSN has a dynamic topology, and for this reason it poses a series of restrictions to the system design, for instance as concerns routing and medium access control (MAC) protocols. Additionally, the communication links may become unreliable as the components of the network move, bringing restrictions to the design of strategies for quality of service (QoS) control. Moreover, determining the optimum location of the sink nodes for maximum energy efficiency becomes a quite involved problem in mobile networks. Nonetheless, mobility brings not only problems and restrictions to the MWSNs. It can be explored to improve coverage, to increase the network lifetime and to handle energy control [2]. Energy management is another important issue to be considered, since most of the sensor networks have their lifetime (or lifespan) increased if the limited energy residual to the sensor nodes can be saved somehow.  
The applications for MWSNs include those of traditional WSNs, viz. environmental monitoring, military surveillance, smart homes, health monitoring, manufacturing control, vehicle tracking and other telematics applications. Specific MWSN applications are, for instance [2]: mobile phones acting as sink nodes to gather information from sensors placed anywhere, in applications related to smart transportation systems, security, social interaction, health, wildlife monitoring, and so on. A potentially interesting envisaged application is related to the recent concept of cognitive radio (CR) [3]: a dedicated or multipurpose MWSN can be used for spectrum sensing, and mobile secondary terminals can act as sink nodes to gather information on spectrum availability for subsequent dynamic spectrum access [4]. Additionally, health applications of WSNs are gaining special attention in recent research, for example the emerging wireless body area networks (WBANs) with implanted sensors [5]. In this application, the battery lifetime and, thus, the network lifetime are crucial problems since batteries cannot be replaced without a complex invasive medical procedure. Aiming at maximizing the battery lifetime, non-active sensors can be kept in a sleep state. During this state, a wake-up and charge process can take place when a charging node is close enough to use some form of wireless power transmission technique [6; 7; 8]. As far as the wireless power transmission is concerned, [6] discusses theoretical and practical aspects of the far-field powering for low-power wireless sensors. Results for a prototype and design steps are also put forward in [6]. Special attention is devised for the project of the rectenna (rectifying antenna), which is the main element responsible for converting the electromagnetic incident wave energy into the energy used to charge the sensor nodes' batteries.
# Advantages of Corrosion-Resistant Overlay Welding on Steel S355J2N
## 4 Results
### Vickers Hardness Test  
When measuring the hardness of welded joints, the hardness (HV10) of the base metal (BM), the heat-affected zone (HAZ), and the weld metal (WM) must be measured in all cases. In the case of overlay welding, according to the ISO 15614-7 standard [21], an indentation series must be made at a 15\({}^{\circ}\) angle, as shown in Figure 11.  
Figure 10: Microscopic photos: (**a**) BM–S355J2N base metal microstructure: ferrite + perlite; (**b**) WM1, 1 st layer microstructure: delta ferrite + austenite; (**c**) WM1-WM2, the edge of the 1 st layer and 2 nd layer; (**d**) WM2-WM3, the edge of the 2 nd layer and 3 rd layer; (**e**) WM3, 3 rd layer microstructure: delta ferrite + austenite; (**f**) WM3 microstructure in higher magnification.  
As shown in Figure 12, due to the low carbon content, no significant hardening occurred in the heat-affected zone, and the hardness of the weld does not differ practically from the base metal. Hence, it can be safely concluded that no significant precipitations were formed.  
Insignificant but noticeable differences can be observed in the hardness distribution. The minimum hardness differences are basically determined by the differences between the mechanical properties of the flux-cored wires and the base metal, based on the differences in chemical compositions. We measured an average tensile strength of 530 MPa on the base metal, according to the EN 10025-2 standard [20]. Its converted hardness: 165 HV. According to the technical documents of the flux-cored wires, the tensile strength of the filler material of the first layer (electrode: E309 LMo) is 700 MPa (218 HV), and the tensile strength of the same filler material of the 2 nd and 3 rd layers (E316L) is 560 MPa (175 HV). The data in the technical certificates are certified data; therefore, their determinations by new measurement are not necessary. The difference between the tensile strength of the base metal and the E316L electrode is only 30 MPa, which when converted to Vickers hardness corresponds to a value of 10 HV. Figure 12 shows almost identical values at the end points (point 1: 183 HV, point 18: 180 HV). The explanation for this is that, at point 1, the micro-structure of the material also contains tempered martensite, due to the intensive heat removal from the back side, while at point 18, as we will see later, Cr enrichment occurred. The highest hardness values are found at measurement points 7-8 (211-210 HV). These values are almost identical to the values characteristic of the E309 LMo electrode, and their minimal decreases can be explained by the diffusion of Cr into the surface layers.  
Figure 11: Line of hardness (HV10) measuring in the cross-section of the sample.  
Figure 12: Hardness measurement points and the hardness measurement diagram.  
During examinations, no appreciable carbide precipitates were found. Consequently, the material structure can be considered homogeneous.  
Furthermore, it can be concluded that the hardness values measured in the base material do not exceed the values specified in the standard (320 HV10).