esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Relief Items Distribution Model with Sliding Time Window in the Post-Disaster Environment
## 3 Dynamic RID Model with Sliding Time Window
### Sliding Time Window Optimisation  
In a disaster environment, a multiple time period or periodic distribution model is required to support the disaster victims over a longer period [40; 41]. [PERSON] et al. [50] highlighted the need for a multi-period supply model, based on demand in each period, to make the distribution more effective. In this paper, the sliding time window optimisation has been applied to achieve optimal relief items distribution in each time period over a longer duration. Sliding time window optimisation covers the dynamicity regarding the selection of the optimum distribution schedule in each time period. In this proposedapproach, at the starting time slot (time period), the distribution schedule starts with an optimum distribution schedule based on the available information of the disaster region's status, location, demand and available resources, including the heterogeneous vehicle fleet at supply points. At each time slot, the related information is updated, thus, the distribution schedules are re-optimised to cover the updated information. The basic flow diagram of the sliding window concept is presented in Figure 1. This proposed multi-time slot with a sliding window dynamic RID model will optimise the distribution schedule according to the updated information.  
In the first time slot window, the optimum distribution schedule is generated based on the available information, which includes: vehicles' availability, disaster region priority and relief resources. However, only one time slot distribution strategy does not make the distribution plan effective since the disaster survivors required longer support in disaster environments [51]. Considering this, in this sliding time window approach, optimised distribution schedules for each time slot based on the demand, route condition, priorities and available resources. In this approach, the distribution plan starts with an optimised distribution schedule which is re-optimised on the following time slot, i.e., when the window slides to the next time slot, the distribution schedules are re-optimised and rescheduled. In the re-optimisation process, updated information on resources, routes, priorities, and vehicles' availability along with the pre-planned schedule is used to generate a new optimised distribution schedule at the next time slot.
# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
###### Abstract  
Unlike the high imaging radiation dose of computed tomography (CT), cone-beam CT (CBCT) has smaller radiation dose and presents less harm to patients. Therefore, CBCT is often used for target delineation, dose planning, and postoperative evaluation in the image-guided radiotherapy (IGRT) of various cancers. In the process of IGRT, CBCT images usually need to be collected multiple times in a radiotherapy stage for postoperative evaluation. The effectiveness of radiotherapy is measured by comparing and analyzing the registered CBCT and the source CT image obtained before radiotherapy. Hence, the registration of CBCT and CT is the most important step in IGRT. CBCT images usually have poor visual effects due to the small imaging dose used, which adversely affects the registration performance. In this paper, we propose a novel adaptive visual saliency feature enhancement method for CBCT in IGRT. Firstly, we denoised CBCT images using a structural similarity based low-rank approximation model (SSLRA) and then enhanced the denoised results with a visual saliency feature enhancement (VSFE)-based method. Experimental results show that the enhancement performance of the proposed method is superior to the comparison enhancement algorithms in visual objective comparison. In addition, the extended experiments prove that the proposed enhancement method can improve the registration accuracy of CBCT and CT images, demonstrating their application prospects in IGRT-based cancer treatment.  
c pacs: 12.30.-c, 12.30.-c, 12.30.-c, 12.30.-c, 12.30.-c
# Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
## 4 Results and Verification  
The modulus image is processed according to the threshold value calculated by the OTSU above. Pixel points that are larger than the threshold are retained; pixel points that are smaller than the threshold are deleted by setting their pixel values to zero. The final edge image is shown in Figure 9. As can be seen in the figure, the overall shape of the bridge is basically detected. The steel cables on one side of the bridge are also clear, and even the white lane lines on the bridge deck can be shown regularly. The ground on both sides of the image is clearly divided from the water in the middle, and areas with and without coverage of vegetation are basically distinguished.  
Figure 8: Modulus maximum image.  
Figure 7: Modulus image.  
To further illustrate the advantages of the combined use of the wavelet mode maxima method and the OTSU, two other detection algorithms, the Canny algorithm and the Laplace algorithm, are used to detect the edge of the same image. Detection results are shown in Figure 10. The information entropy and SSIM are also used as indicators to compare these three detection results.  
Based on the test results, the information entropy and SSIM of the three were calculated, respectively, for a more intuitive comparison, as shown in Table 1.  
As can be seen from Table 1, the edge images detected by the wavelet transform modulus maxima method have greater information entropy and SSIM compared to the other two algorithms, indicating that the images are richer in detailed information, provide more information and better detection results. The results demonstrate that the algorithm in this paper has relative advantages.
## 1 Introduction
We conducted extensive evaluations on MedMixtral 8x7B, demonstrating its advantages over strong general large models such as ChatGPT and Llama3 in various aspects of medical Q&As. The efficient offloading architecture saves memory, and research on inference latency suggests key strategies for reducing latency include enhancing disk speed, storing more model weights in the CPU's RAM rather than on disk, and opting for wireless communication. The contributions of this paper are summarized as follows:  
* To obtain efficient medical LLMs for healthcare applications, we fine-tune an LLM based on the MoE architecture, named MedMixtral 8x7B, using medical datasets to meet individuals' personalized healthcare needs. * To deploy MedMixtral 8x7B on loMT devices, we propose a novel offloading strategy, which allows the deployment of MedMixtral 8x7B in the loMT with less resource requirement, thus enhancing the privacy protection for users. * To assess latency's impact on LLM inference speed, we analyze both local and interconnection communication models. We highlight the critical role of latency in inference processes and propose several strategies to reduce it. These include enhancing disk speed, storing more model weights in the CPU's RAM rather than on disk, and opting for wireless communications.