esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The application-specific instruction set processor (ASIP) concept offers a trade-off in terms of the efficiency of the application-specific integrated circuit (ASIC) and the flexibility of the general purpose (GP) processor by customizing the datapath structure and functionality by using a custom instruction set. In cases where the tailored hardware is dedicated for a particular fixed application, the process of instructions' specification and describing forms an overhead. Instead of dynamic scheduling, no instruction set computer (NISC) concept adopts static scheduling of operations to simplify the ASIP approach. Figure 2 shows the transition from the ASIP design approach to the NISC design approach. Figure 1: Block diagram of the MIMO-OFDM turbo receiver. By eliminating the task of finding and designing a custom instruction set, the design productivity is increased. Furthermore, the design quality is better achieved by shrinking the design complexity to match the exact requirements of the desired application. The typical controller functionalities, such as instruction decoding, dependency analysis and instruction scheduling, are carried out by the compiler in NISC. The compiler is responsible for scheduling operations and decoding them into control words (CWs), where each represents the group of control signals that must be loaded to the datapath components in every clock cycle. At run time, the CWs that are stored in the control memory are loaded by means of a simple controller, which applies the control signals to their corresponding components in the datapath. Moreover, an NISC-based architecture may be reused for different applications or various system configurations of the same application. Flexibility is attained by re-exploiting the hardware architecture design without any modifications on the structures of the datapath or controller. Different groups of control words are only re-generated statically and re-loaded to the control memory of the design. In this paper, we aim to present in detail the entire design and prototype flow, starting from architecture specification till FPGA implementation, in addition to hardware validation and performance evaluation. The rest of this paper is organized as follows. The following section illustrates the proposed NISC architectures. The adopted prototyping flow is presented in details in Section 3. Section 4 presents the on-chip validation and summarizes the obtained results. Finally, Section 5 concludes the paper.
## 3 Results and Discussion
### Floating Gate and Photogating of Photoresponse  
We further conducted I\({}_{\rm{DS}}\) - V\({}_{\rm{G}}\) transfer characteristics of forward sweep and backward sweep at 1 V/s of the device to investigate floating gate and photogating of the photoresponse. Figure 7a shows the I\({}_{\rm{DS}}\) - V\({}_{\rm{G}}\) transfer characteristics in dark conditions. Figure 7b shows the I\({}_{\rm{DS}}\) - V\({}_{\rm{G}}\) transfer characteristics under illumination intensity from 0.025 to 4 nW (power density of less than 1 nW was calculated from the transmittance of the filter). Figure 7c shows the I\({}_{\rm{DS}}\) - V\({}_{\rm{G}}\) transfer characteristics under illumination intensity from 2.14 to 4.48 uW. A clockwise hysteresis loop behavior was observed in both dark and illuminated conditions, indicating the charge trapping and de-trapping process at the interface between p-type doped WSe\({}_{2}\) and MoS\({}_{2}\). The trap charge density (\(N_{t}=3.79\times 10^{12}\) cm\({}^{-2}\)) can be calculated using following formula:  
\[N_{t}=\frac{\Delta V\cdot C_{\mathrm{g}}}{q} \tag{5}\]  
where \(q\) is the element charge, \(\Delta V=16.1\) V in Figure 7a is the maximum voltage shift between the transfer curves in the forward and backward sweeps and \(C_{\mathrm{g}}=3.77\times 10^{-8}\) F-cm\({}^{-2}\) is the total capacitance of the 90 nm thick SiO\({}_{2}\) layer and 5 nm WSe\({}_{2}\) layer. The \(N_{t}\) is comparable with carrier density in MoS\({}_{2}\)[36; 37; 38; 39; 40; 41], which indicates that the trap states can substantially enhance the current under different illumination conditions [38; 41]. Threshold voltage left-shift is observed in \(\mathrm{I_{DS}-V_{DS}}\) transfer characteristics of forward sweep and backward sweep under increasing optical powers, which indicates that the trapping at the interface of p-doped WSe\({}_{2}\) increases as illumination power, resulting in a photogating effect. Therefore, our device has high photoresponsivity and detectivity. In addition, our device can be used as optoelectronic memory for recording the perceived light information, benefiting from the large memory window.  
Figure 5: Dependence of photoresponse on gate voltage and illumination intensity of the transistor. (**a**) Responsivity (\(R\)) and (**b**) detectivity (\(D^{\mathrm{r}}\)) of the device for \(V_{\mathrm{DS}}=5\) V, \(V_{\mathrm{G}}=-20\) to 20 V, and 633 nm illumination intensity from 1 nW to 3.28 \(\upmu\)W. (**c**) Photocurrent (\(I_{PH}\)) and (**d**) photoresponsivity of the device as a function of the incident power for \(V_{\mathrm{DS}}=5\) V, \(V_{\mathrm{G}}=-20\) to 20 V, and 633 nm illumination intensity from 1 nW to 3.28 \(\upmu\)W.
## 2 Materials and Methods
### FCG Assessment of the Energy Release Rate with VCCT
#### 2.1.3 Mesh Sensitivity Analysis  
Fatigue crack growth simulations were performed with varying levels of mesh refinement in an attempt to verify the FEM crack propagation analysis as the accuracy strongly relies on the minimum element size around the crack tip; therefore, the computational cost compared to the error level based on mesh size is somewhat a trade-off [30; 31]. In the analysis, for comparison purposes, the same tests are also carried out for the 3D solid FEM model by creating a batch file (.bat) coding in MSC Marc/Mentat. The script enables multiple meshes to run with less computer memory consumption. A direct solver is used for the solving process to run the solid mechanic computation with a multi-frontal parallel sparse algorithm.  
The software-running environment is 12-GB RAM, CPU 3.10 GHz, and Intel-i5 processors. Firstly, multiple files with different mesh size/number of elements are created  
Figure 5: (**a**) Clipped model of crack faceted surface path and orientation with tetra mesh; (**b**) crack front propagation with patran-tetra remeshing.  
to verify the mesh size dependency on the crack propagation rate and the corresponding number of load cycles. The analysis in Figure 6 determines the choice of 2 mm mesh size. Secondly, the crack front radius is determined from a distance between nodes along the front [20]. Because of the complicated stress field singularity along the crack front, a compact mesh must be produced and regenerated as the crack grows [32]. The adaptive remeshing (in this case, the faceted surface in simulation software) with initial quarter-point element behaviour is computed with 2 mm. The broken line indicates the adopted mesh size within the numerical FE analysis.
# Comparative Study of Energy Efficient Routing Techniques in Wireless Sensor Networks
## 3 The Proposed Taxonomy
### Protocol Operation
#### 3.7.1 Multipath-Based
It uses multiple interference-minimized node disjoint paths between communicating nodes to minimize interference over and route coupling effect to improve the network lifespan, PDR and latency, but the effects of service rate of the active nodes and its buffer capacity are not considered in the estimation and adjustment of the traffic rate of the active paths [101]. Data Transmission Reliability  
In order to the increase network reliability and to improve the probability of successful packet delivery, some multipath-based routing protocols send multiple copies of data simultaneously through many available paths. Among these protocols, we find:  
* **Multi-Constrained QoS Multipath Routing (MCMP)** MCMP is a multipath routing protocol. It uses multiple braided paths to route data to the sink node taking into consideration the shortest route to augment the network performance according to certain QoS requirements with moderate energy cost. The significant interference caused by high data rate transmission of redundant data isthe major disadvantage of this protocol [102]. * **Energy Constrained Multipath Routing (ECMP)** ECMP presents an enhancement of MCMP. Indeed, it improves the QoS and the energy conservation in the network and formulates the routing problem as an energy optimization issue strained by the reliability and geospatial path selection. In fact, compared to MCMP, in which next-hop neighboring nodes are selected randomly without energy consideration, ECMP attenuates the selection of next-hop nodes based on the energy efficiency of paths, but the two protocols are equivalent in terms of delivery ratio and transmission delay [103]. Table 8 establishes a comparison between multipath-based routing protocols [104].
# A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model
## 2 Datasets and Methods
### Methodology of Research
#### 2.2.1 Data Preprocessing  
Dataset division is a vital step in deep learning and machine learning that helps in training and optimizing the model, evaluating the performance of the model, and understanding the generalization ability and robustness of the model. For the crop growth stage, disease recognition, and poultry recognition tasks, the data division of the training and test sets is shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Cabbage** & **Strawberry** & **Chili Pepper** & **Tomato** & **Poultry** \\ \hline Training set & 320 & 557 & 2229 & 5000 & 1003 \\ Test set & 80 & 230 & 246 & 500 & 211 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The division of different datasets.  
Figure 1: The framework of AgriVLM.  
Suitable prompts can provide with context, constraints, and guidance to enable the larger model to have a better understanding of the question and to generate more accurate responses that match human expectations. Here, AgriVLM achieves accurate recognition of each task by manually designing the following prompt. For different recognition tasks, the prompt is designed as \"Please tell me which type of {dataset task category} [categories] this image belongs to\", and the answer of the model is the corresponding name of the category. In particular, for the cabbage growth stage recognition task, the prompt is designed as \"Please tell me which of the cabbage growth stages germination, seedling, rosette, and dormancy this image belongs to?\". AgriVLM will answer with the name of one of the four stages, e.g., \"peramination\". Other tasks can be designed as the same way. In this way, the type of task that the model is required to implement can be clearly indicated and the model can be effectively guided to generate output that conforms to a specific format. This not only ensures clarity of the task, but also improves the standardization of the output.  
Low-quality conversation data may increase the training time and computational requirements of the model and reduce the accuracy. High-quality conversation data plays an important role in improving model performance, enhancing interaction experience, and enhancing model generalization ability. Therefore, in order to improve the quality of agricultural conversation data, we clean the meaningless and repeated conversation data among them, and finally obtained 5593 available data. The definition of meaningless data is shown in Table 2.
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.4.6 Performance on Other Organoid Datasets  
To rigorously assess cross-domain generalization, this study employs a dual validation strategy using two external datasets: a human duodenal biopsy organoid dataset (1750 images, 14,242 instance annotations) from [12] and a murine intestinal organoid dataset (840 images, 23,066 four-class annotations) from [22]. Representative images are presented in Figure 9, highlighting the substantial visual disparities among the three datasets.  
Table 12 lists the comparison results of the detection performance of Orga-Dete with mainstream detection models on these two representative datasets.  
As shown in Table 12, Orga-Dete achieves the highest detection accuracy on the human duodenal organoid dataset (Dataset (b), [EMAIL_ADDRESS]: 92.5%), validating its cross-domain generalization. On the murine intestinal organoid dataset (Dataset (c)), Orga-Dete achieves an [EMAIL_ADDRESS] of 84.4%, slightly below YOLOv8n's 84.6%. However, Orga-Dete significantly reduces model complexity, with only 2.25 M parameters and 6.3 GFLOPs--25.0% and 22.2% less than YOLOv8n, respectively. These results demonstrate that Orga-Dete maintains competitive performance with a lightweight architecture well-suited for edge deployment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**A** & **B** & **C** & **D** & **Lumen** & **No Lumen** & **[EMAIL_ADDRESS]** & **APSmall** & **APmedium** & **APall** \\ \hline ✓ & & & & 70.8 \(\pm\) 0.2\% & 84.9 \(\pm\) 0.2\% & 77.9 \(\pm\) 0.2\% & 45.1 \(\pm\) 0.2\% & 58.8 \(\pm\) 0.1\% & 58.1 \(\pm\) 0.1\% \\ ✓ & ✓ & & & 73.8 \(\pm\) 0.1\% & 86.0 \(\pm\) 0.2\% & 79.9 \(\pm\) 0.1\% & 48.8 \(\pm\) 0.3\% & 61.4 \(\pm\) 0.2\% & 60.7 \(\pm\) 0.2\% \\ ✓ & & ✓ & & 72.3 \(\pm\) 0.2\% & 86.1 \(\pm\) 0.1\% & 79.2 \(\pm\) 0.1\% & 49.1 \(\pm\) 0.1\% & 59.9 \(\pm\) 0.1\% & 59.1 \(\pm\) 0.2\% \\ ✓ & & & ✓ & 74.9 \(\pm\) 0.2\% & 83.7 \(\pm\) 0.1\% & 79.3 \(\pm\) 0.1\% & 45.4 \(\pm\) 0.2\% & 59.0 \(\pm\) 0.2\% & 58.5 \(\pm\) 0.1\% \\ ✓ & ✓ & ✓ & & 74.3 \(\pm\) 0.1\% & 87.0 \(\pm\) 0.2\% & 80.7 \(\pm\) 0.1\% & 50.7 \(\pm\) 0.2\% & 62.7 \(\pm\) 0.2\% & 62.1 \(\pm\) 0.2\% \\ ✓ & ✓ & ✓ & 76.2 \(\pm\) 0.1\% & 86.5 \(\pm\) 0.2\% & 81.4 \(\pm\) 0.2\% & 51.6 \(\pm\) 0.1\% & 63.5 \(\pm\) 0.2\% & 62.8 \(\pm\) 0.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison results of ablation experiments of different models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**A** & **B** & **C** & **D** & **Params (M)** & **GFLOPs** & **FPS** & **Model Size (MB)** \\ \hline ✓ & & & & 2.58 & 6.3 & 280 \(\pm\) 6 & 5.2 \\ ✓ & ✓ & & & 1.92 & 6.3 & 269 \(\pm\) 9 & 4 \\ ✓ & & ✓ & & 2.89 & 6.4 & 300 \(\pm\) 13 & 5.3 \\ ✓ & & & ✓ & 2.58 & 6.3 & 276 \(\pm\) 8 & 5.2 \\ ✓ & ✓ & ✓ & & 2.25 & 6.3 & 243 \(\pm\) 4 & 4.6 \\ ✓ & ✓ & ✓ & ✓ & 2.25 & 6.3 & 246 \(\pm\) 5 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Computational cost of ablation experiments using different models.
## 2 Immunomodulatory Function of Extracellular Hsp60, Hsp70, and Hsp90
### Multiple Sclerosis  
MS is a disease of the CNS with autoimmune components that provoke the damage of myelin around nerves and axons, impairing the transmission of information between brain and the rest of the body [100]. There is little information on the role that chaperonesmight play in MS. For example, it is not yet established if Hsp60 plays a role in the immunopathogenesis of MS. Serum and cerebrospinal fluid (CSF) samples with untreated, relapsing-remitting MS showed antibody signatures targeting epitopes of various proteins, including Hsp60 [101]. Although extracellular Hsp70 is associated with neuroprotective functions in AD and PD as it helps in lowering the levels of misfolded proteins, in MS, it may intensify the immune response. Hsp70 has been found in MS lesions, often in association with the two major myelin proteins of the myelin sheath, namely, myelin basic protein (MBP) and proteolipid protein (PLP) [102]. In the experimental autoimmune encephalomyelitis (EAE) model, Hsp70 promotes an immunological response mediated by its myelin peptide adjuvant capacity [103]. Moreover, Hsp70 overexpression in vitro leads to enhanced presentation of MBP in an MHC class-II-dependent manner [104]. It may be hypothesized that the association of Hsp70 with myelin proteins would be required also for demyelination during the repair process, and that its deficiency could compromise this process. This view is supported by findings with autopsy tissue of MS lesions which show a quantitative reduction of Hsp70 compared with normal brain tissues, a reduction that parallels the impairment of the demyelination process [105]. Oligodendrocyte precursor cells (OPCs) are the targets of autoimmune attack in MS, which prevents remyelination. CSF from MS patients contains antibodies that can specifically recognize Hsp90 molecules located on OPCs with consequent activation of the complement and significant reduction of the OPCs [106]. These features indicate that the use of Hsp90 inhibitors could be beneficial in EAE and probably also in MS.
# Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework
###### Abstract  
Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites' optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.  
coronal mass ejections; solar-terrestrial relations; deep learning; attention mechanism; satellite observation image +
Footnote †: journal: remote sensing
## 3 Attitude Determination
### The Real-Valued Float Solution  
The float solution is defined as the solution of Equation (11) without the constraints. When we ignore the integer constraint on \(Z^{k}\) and the orthonormality constraint on \(R^{k}\), the float solutions \(\hat{Z}^{k}\) and \(\hat{R}^{k}\) and their variance-covariance matrices are obtained from solving the system of normal equations:  
\[\left[\begin{array}{cc}Q_{\hat{x}^{k}\hat{x}^{k}}&Q_{\hat{x}^{k} \hat{x}^{k}}\\ Q_{\hat{x}^{k}\hat{x}^{k}}&Q_{\hat{x}^{k}\hat{x}^{k}}\end{array}\right]^{-1} \left[\begin{array}{c}\mathrm{vec}(\hat{Z}^{k})\\ \mathrm{vec}(\hat{R}^{k})\end{array}\right] = \mathcal{A}_{k}^{ T}Q_{\chi^{k}\chi^{k}}^{-1}\mathrm{vec}(Y^{k}) \tag{12}\]  
with:  
\[\left[\begin{array}{cc}Q_{\hat{x}^{k}\hat{x}^{k}}&Q_{\hat{x}^{k} \hat{x}^{k}}\\ Q_{\hat{x}^{k}\hat{x}^{k}}&Q_{\hat{x}^{k}\hat{x}^{k}}\end{array}\right] = \left(\mathcal{A}_{k}^{ T}Q_{\chi^{k}\chi^{k}}^{-1}\mathcal{A}_{k}\right)^{-1},\;\mathcal{A}_{k}= \left[\begin{array}{c}I_{n_{k}}\otimes A^{ T}\\ B_{0}^{k}\otimes G^{ T}\end{array}\right]^{ T}\]  
The \(Z^{k}\)-constrained solution of \(R^{k}\) and its variance-covariance matrix can be obtained from the float solution as follows:  
\[\mathrm{vec}\left(\hat{R}^{k}(Z^{k})\right) = \mathrm{vec}(\hat{R}^{k})-Q_{\hat{x}^{k}\hat{x}^{k}}Q_{\hat{x}^{k }\hat{x}^{k}}^{-1}\mathrm{vec}\left(\hat{Z}^{k}-Z^{k}\right) \tag{13}\] \[Q_{\hat{R}^{k}(Z^{k})\hat{R}^{k}(Z^{k})} = Q_{\hat{R}^{k}\hat{R}^{k}}-Q_{\hat{R}^{k}\hat{x}^{k}}Q_{\hat{x}^ {k}\hat{x}^{k}}^{-1}\] (14) \[= \left(B_{0}^{k}{P_{n_{k}}}^{-1}{B_{0}^{k}}^{T}\right)^{-1} \otimes\left(G^{ T}Q_{yy}^{-1}G\right)^{-1}\]  
Using the above estimators, the original problem in Equation (11) can be decomposed as:  
\[\min_{Z^{k}\in\mathbb{Z}^{f\times m_{k}},R^{k}\in\mathbb{O}^{3 \times q_{k}}}\left\|\mathrm{vec}\left(Y^{k}-AZ^{k}-GR^{k}B_{0}^{k}\right) \right\|_{Q_{\chi^{k}\chi^{k}}}^{2} \tag{15}\] \[= \left\|\mathrm{vec}\left(\hat{E}^{k}\right)\right\|_{Q_{\chi^{k} \chi^{k}}}^{2}+\min_{Z^{k}\in\mathbb{Z}^{f\times m_{k}}}\left(\left\|\mathrm{ vec}\left(\hat{Z}^{k}-Z^{k}\right)\right\|_{Q_{\hat{Z}^{k}\hat{Z}^{k}}}^{2}\right.\] \[+\min_{R^{k}\in\mathbb{O}^{3\times q_{k}}}\left\|\mathrm{vec} \left(\hat{R}^{k}(Z^{k})-R^{k}\right)\right\|_{Q_{\hat{R}^{k}(Z^{k})\hat{R}^{ k}(Z^{k})}}^{2}\right)\]  
with \(\hat{E}^{k}=Y^{k}-A\hat{Z}^{k}-G\hat{R}^{k}B_{0}^{k}\) being the matrix of least-squares residuals. Note that the first term on the right-hand side is constant, as it does not depend on the unknown matrices \(Z^{k}\) and \(R^{k}\).
# Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images
## 4 Experiment and Analysis
### Experiments on Multiple Types of Oceanic Phenomena in SAR Images  
In order to prove the detection ability of MFNN for images of multiple coexisting phenomena, experiments were performed on images with multiple phenomena in the same scene. Due to the limited number of samples, no images of multiple different coexisting oceanic phenomena were added  
Figure 8: The comparison of two network test results, where **ED** is oceanic eddy, **RC** is rain cell, **OF** is oceanic front, **SW** is ship wake, **OS** is oil spill, and **UN** is the unknown category.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Accuracy (\%)** \\ \hline DeepLabV3+ & 91.1 & 88 & 89.5 & 88 \\ MFNN & 93.8 & 91 & 92.4 & 91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances of different methods.  
Figure 9: The confusion matrix of the oceanic phenomena detection results, where **ED** is oceanic eddy, **RC** is rain cell, **OF** is oceanic front, **SW** is ship wake, **OS** is oil spill, and **UN** is the unknown category. (a) The confusion matrix of the MFNN. (b) The confusion matrix of DeepLabV3+.  
to the training dataset. But as shown in Figure 10, the results show that when multiple phenomena coexist in the image, MFNN can still perform better than DeepLabV3+.  
It can be seen that in Figure 10a, compared with DeepLabV3+, MFNN detected a complete rain cell. In Figure 10b, MFNN detected two small-scale oil spills. The performance of the two methods in Figure 10c is similar. The experimental results show that MFNN achieves better performance when multiple phenomena coexist.  
As shown in Figure 11, although we did not include the atmospheric front in the training dataset, our method still detected the atmospheric front as a front. This is because atmospheric fronts and oceanic fronts are both fronts and have similar features. This also illustrates the superior ability of our method for feature extraction. In further research, we will add atmospheric fronts to the training set to further distinguish between atmospheric fronts and oceanic fronts, or improve the accuracy of detecting them as one category.  
Figure 10: The detection results when multiple types of oceanic phenomena coexist in the image. (**a**) Oceanic front and rain cell. (**b**) Oil spill and rain cell. (**c**) Ship wake and oil spill.  
The above results show that MFNN has good performance in SAR images with multiple types of oceanic phenomena. Due to the limitation of samples, the precision, recall, F1 and accuracy of this section are not considered. In future research, we will expand the dataset of SAR images with multiple types of oceanic phenomena to further verify and improve detection performance.