esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Recommendations Related to Access and Connectivity
### Broadband as an Essential Requirement  
Rural areas remain at a disadvantaged position due to poor connectivity, a problem that needs to be addressed by governments to correct imbalances, encourage proactive attitudes, and limit the brain drain of qualified people moving to urban areas, often outside the country. [PERSON] and [PERSON] (2019) highlight the need to focus on the most disadvantaged areas to avoid further inequalities in the countryside and they propose to act region by region, rather than on the technological infrastructure of the area. Besides, they emphasize that investment in technology should not be seen as the goal, but as the means to achieve many other improvements that can \"rescue\" rural areas.  
Broadband is a key technology that not only ensures connectivity, but also has a decisive impact on companies' ability to innovate and grow ([PERSON], 2019). The absence of broadband leads small agri-food companies to take a passive approach to growth opportunities, particularly posing a serious barrier to diversification and internationalization. In their study on micro and small rural enterprises (Rural Public Access Wi-Fi Service (PAWS) project), [PERSON] and [PERSON] (2019) analyze micro and small enterprises in the agriculture, tourism and art sectors, concluding that, in order to ensure the future of micro and small rural enterprises, the quality of connectivity and digital skills are fundamental, and so the former is necessary to enable the latter. They also argue that small and micro rural enterprises cannot be ignored in technological development programs, and more research should be carried out to study the relationship between connectivity and the development of rural economy.  
In contrast to the findings described, the evaluation work of [PERSON] et al. (2019) of the _Auvergne tres haut debit programme_ in France concludes that the deployment of fiber and high speed has not significantly stimulated the creation of enterprises, having affected positively only to the creation of companies in specific sectors: hostelry; public administration, education, health and social work; and other activities in the services sector. While the deployment of fiber and high speed does not appear to be sufficient on its own to drive rural economic development, it may be a lever for development when certain conditions, related to the territorial economic context and design of digital policy, are met ([PERSON], 2019). From the opposite perspective, the existence of faster internet technologies can even increase the urban-rural digital divide, as they are more expensive and difficult to obtain in rural areas ([PERSON], 2019).
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 2 Related Work  
Nowadays, in general, the works of most of data dissemination protocols focus on two main problems. The first challenge is the broadcast storm problem in well-connected networks; the second challenge is the disconnected network (network link reliability) or network partition problem. The broadcast suppression technique is adopted to mitigate the former issue and the store-carry-forward mechanism is designed for the latter one.
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 3 Results
### Flood Detection Results Using Sentinel-1 Data  
In Figure 8, the flooded areas are differentiated (in red) from the other areas (pre-flood water bodies). The flooded areas on the map were zoomed in to make them more visible.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 6 Literature Mapping
### Optimization Criteria
[32] examine CPU processing power, CPU usage cost, memory usage cost, and bandwidth usage cost as operating cost factors alongside timespan, representing execution time, in their assessment framework. Their approach focuses on minimizing execution time with higher priority unless there is a tradeoff where minimizing total operating costs becomes more crucial than execution time. The specific value of the tradeoff coefficient (\(\lambda\)) is determined based on factors such as budget constraints or the desired response time level. Considering these collective contributions, the papers provide unique perspectives on optimizing task scheduling in Cloud-Fog environments, with a specific focus on processing large-scale Internet of Things (IoT) applications. The introduction of inventive algorithms such as Time-Cost aware Scheduling (TCaS) and the application of a Deep Reinforcement Learning (DRL)-based solution underscores the emphasis on delicately balancing execution time minimization and effective monetary cost management. This collaborative initiative aims to propel efficiency and resource utilization within highly distributed computing platforms, effectively addressing pivotal challenges in the realm of fog computing and IoT applications. Scheduling algorithms that aim to reduce, or in general to optimize, the energy consumed by a fog-based system are studied in [3; 4; 10; 15; 18; 20; 24; 26; 28; 29; 36; 37; 39; 41; 42; 44; 46; 49; 50; 51; 55; 56; 57; 58; 73]. Scheduling based on latency is proposed in [5; 9; 10; 11; 15; 16; 17; 18; 20; 30; 37; 39; 41; 42; 44; 45; 52; 53; 54; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64]. Computation cost is considered in [15; 40; 72; 73]. This cost in fog computing task scheduling refers to the resources used for task execution which is a critical factor impacting system efficiency and performance. The cost of communication as decision criteria is studied by [33; 40; 47; 64; 73], while in [20; 32; 33; 40; 47], the authors studied the influence of resource costs, and in [29; 36; 41; 47; 49; 56], they consider energy costs. The authors focus on optimizing different costs to enhance the overall efficiency and performance of fog computing task scheduling. The importance of execution time metrics while building an optimal task scheduling solution is considered in [21; 31; 32; 33; 34; 35; 36; 38; 72; 73; 68; 72]. Response time is used for scheduling decisions in [3; 6; 11; 19; 20; 25; 26; 27; 29]. A few papers [3; 4; 6; 18; 28; 30; 31] take into account completion time metric. The often-neglected metric is availability, which remains critical in fog computing environments and has been discussed by [43; 74; 75].
## 3 Materials and Methods
### Datasets  
Three datasets from India, the United States, and China are used in this study, namely Asia Pacific Tele-Ophthalmology Society (APTOS) [55], Eye Picture Archive Communication System (EyePACS) [56], and Ocular Disease Intelligent Recognition (ODIR) [57] datasets, respectively. For the APTOS and EyePACS datasets, only the normal and PrDr retinal databases are employed in this study. The PrDR is labeled class 1, and the normal retina class 0.  
The APTOS DR severity scale is a reliable and valid tool for grading the severity of DR. It has been extensively applied in Asia-Pacific clinical practice and research. It has also been utilized in creating and assessing computer-aided diagnostic tools for DR that employ retinal pictures to identify and categorize the condition's severity automatically. The dataset contains 5990 retinal images, of which 3662 are in the training set and 1928 are in the testing stage. The test phase data has no specific labels provided; hence, the 3662-training data are downloaded for this study. There are 1805 class 0 images and 295 class 1 images within it. The statistics indicate that it is approximately six times as many images in class 0 as in class 1.  
A Chinese dataset called ODIR was compiled by Shanggong Medical Technology Co., Ltd., Shanghai, China, from data on 5000 patients at various hospitals. Eight labels comprise this dataset, one normal and the other seven representing various retinal illnesses, including pathological myopia, hypertension, glaucoma, cataract, age-related macular degeneration, DR, and other abnormalities. There are 14,400 images in this dataset. DR is only considered for this study, which consists of 2873 normal retinal images and 1608 with DR.  
A free retinopathy screening platform with headquarters in the United States offers the EyePACS dataset. EyePACS has been extensively utilized in numerous DR screening programs, making it possible to test sizable populations efficiently and affordably. There are 35,126 images total for both eyes, divided into five classes, where 73.4% of the images  
Figure 1: The methodology for DR classification.  
in this dataset are healthy and free of DR symptoms, which is a significant imbalance issue [58]. The work is based on a randomly selected portion of the images. The class distribution of this dataset is 2010 images for class 0 and 708 images for class 1.
# Progress in Ski Jumping Technology Based on Biomechanical Sport Research Methods
###### Abstract  
(1) Background: Previous studies have compared research into ski jumping in different motor processes, but there is a lack of comparative analysis of the biomechanical research methods used to investigate different ski jumping sports. (2) Content: Our study compared the advantages and disadvantages of six research methods and proposes future research directions. Motion video collection and analysis show that controlling angular momentum and achieving stable flight attitude in the take-off process are the most critical factors in ski jumping performance. Most research on force platforms focuses on dynamic performance at the time of take-off, but there are few training sites with an embedded force platform, and so, more empirical research is required. Wearable inertial measurement units, including gyroscopes and accelerometers, can be used to determine a series of forces, calculate the joint angle, and speculate the position of the centroid during motion. Surface EMG studies are primarily used to compare the activity characteristics of the lower limb muscles in the actual field of the jump, the exercise simulation, and the lack of complete training process data. Wind tunnel measurement can satisfy fluid mechanics simulation experiments and provide theoretical support for optimizing special ski jumping technology. Based on the theory of computational fluid dynamics, the optimal drag reduction posture data of ski jumpers can be derived using computer simulations. (3) Conclusions: Due to the wide range of ski jumping sports, the present research focused on the kinematics and dynamics of different movement stages, lacking the study of the complete exercise training process. The range of wearable inertial measurement and sensor equipment can cover the whole process of ski jumping, including kinematics and dynamics data, and is a feasible and reliable test method for monitoring ski jump training in natural environments. The simultaneous testing of surface electromyography, kinematics, and dynamics requires further exploration. (4) Future direction of development: Under computational fluid dynamics, wearable inertial measurement units and global navigation satellite systems (GNSSs), intelligent wind tunnel experimental training areas will become essential tools for ski jumping research.  
ski jumping; attitude; aerodynamics; take-off forces; wind tunnel +
Footnote †: journal: Applied
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
## 3 Trust and Service Delegation Model
### Service Delegation Mechanism  
After calculating the trust vector of the SP \(v_{j}\) based on the method proposed at last subsection, we further study the issue of service delegation. In SIoT, the SR \(u_{i}\) will not only consider the trust of the SP \(v_{j}\), but also concern the utility. Therefore, we present the trust-based service delegation method to solve the _Problem 3_. We define the decision function of service delegation as follows:  
\[\begin{array}{l}U_{v_{j}\gets u_{i}}(\varphi)=f_{\text{tu}}(\overrightarrow {T}_{v_{j}\gets u_{i}}(\varphi),\psi_{v_{j}}(\varphi))\\ =\widehat{T}_{v_{j}\gets u_{i}}(\varphi)(\zeta_{u_{i}}(\varphi)-\psi_{v_{j} }(\varphi))+(d_{v_{j}\gets u_{i}}(\varphi)+(1-a_{v_{j}\gets u_{i}}( \varphi))u_{v_{j}\gets u_{i}})(-\overline{\zeta}_{u_{i}}(\varphi)),\end{array} \tag{27}\]  
Figure 4: The design of discounting operator.  
where \(\psi_{v_{j}}(\varphi)\) denotes the benefit value when the task is successful and \(\overline{\zeta}_{u_{i}}(\varphi)\) denotes the lost value when the task is failed. Therefore, the decision of the service delegation (e.g., Equation (4)) can be rewritten as follows:  
\[\begin{split}& DSP=\operatorname*{arg\,max}_{v_{j}}\;\;U_{v_{j} \gets u_{i}}(\varphi)\\ &\text{\emph{s.t.}}\quad\hat{T}_{v_{j}\gets u_{i}}(\varphi) \geq th_{u}(\varphi)\end{split} \tag{28}\]  
Through the proposed decision-making method for service delegation, the SR can make a plan to maximize its own utility under the consideration of trust of SPs. For the entire SIoT system, on the one hand, our proposed method can guarantee a high task success rate based on trust analysis. On the other hand, we can improve the overall social welfare and boost the cooperation.
# A Comprehensive Analysis of Real-Time Car Safety Belt Detection Using the YOLOv7 Algorithm
## 5 Discussion
Safety belt detection using computer vision has witnessed significant advancements in recent years; however, several gaps and potential avenues for future research remain to be addressed. One of the primary challenges pertains to the robustness of detection algorithms across diverse environmental conditions. While existing models excel in controlled settings, the ability to accurately identify seat belts in varying lighting conditions, camera angles, seat designs, and occupant body sizes warrants further investigation. Real-time performance is another critical consideration, particularly in dynamic scenarios. The imperative to balance accuracy and real-time processing necessitates the development of algorithms optimized for swift and accurate detection. The quality and diversity of training data emerge as a central concern; comprehensive datasets that encompass a wide array of real-world scenarios are indispensable for enhancing model generalization. The intricate task of detecting safety belts amidst multiple occupants or occlusions demands innovative solutions. Future research directions encompass strategies to mitigate privacy concerns inherent in capturing passenger images while ensuring effective seat belt detection. Furthermore, the adaptation of detection algorithms to unconventional seating arrangements, as observedin certain vehicle types, requires specialized attention. Integrating safety belt detection seamlessly into user interfaces, such as infotainment systems or driver assistance displays, emerges as a viable strategy to enhance passenger awareness and compliance. Deep learning, although efficacious, presents challenges in terms of interpretability, particularly in safety-critical applications. Exploring techniques to render deep learning models more transparent and interpretable is thus a pertinent research trajectory. Moreover, optimizing detection algorithms for resource-constrained hardware and edge computing installations is crucial for widespread deployment within vehicles. The longevity of system performance and accuracy necessitates continuous monitoring, calibration, and maintenance strategies. Lastly, the alignment of safety belt detection technology with industry standards and regulatory frameworks underscores the importance of collaborative efforts between researchers, industry stakeholders, and regulatory bodies to establish benchmarks and guidelines for effective implementation. As the field of computer vision evolves, ongoing research endeavours are poised to bridge these gaps and refine the efficacy of safety belt detection systems.
## 2 Materials and Methods
### Data
#### 2.2.3 MODIS GPP Product  
This study also used the MOD17A2H GPP product as representative of light-use efficiency (LUE)-based GPP and compared it to the VI-based GPP estimation results. The MOD17A2H GPP product provided spatio-temporally continuous GPP data with 500-m spatial resolution for eight-day averages [2]. We chose the \(3\times 3\) pixels' region around the flux tower from the MOD17A2H product, and the mean value of these pixels was set as the MODIS-based GPP result.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Performance Analysis
#### 6.2.4 Throughput  
The efficiency of the blockchain network is crucial for the overall system performance. Throughput is an important indicator of the performance of a blockchain network. Therefore, this study conducted throughput tests on the blockchain network to evaluate the system's performance. The Caliper tool was utilized to conduct throughput tests for adding and querying relevant records in the blockchain. Figure 6 illustrates the impact of transaction request volume on the throughput of the blockchain network when adding _TreatmentData_ and _DataUsageRecord_ to the blockchain. It also depicts the effect of different data sizes on the throughput of the blockchain network when medical data are directly stored (DS) on the blockchain. The throughput represents the number of successfully submitted transactions per second (TPS). As the transaction request volume gradually increases, the throughput of our scheme also increases. When the transaction request volume reaches around 550, the throughput of our scheme tends to stabilize at around 50 TPS.  
In contrast, the throughput of the DS is significantly lower than our scheme. Additionally, as the size of the stored data increases, the throughput of the DS obviously decreases. As shown in Figure 5, the size of the data stored by our scheme on the blockchain remains relatively constant at about 6 KB. Therefore, our scheme has a clear advantage in this aspect.  
Figure 5: Storage overhead comparison between blockchain and IPFS.  
Figure 6: Throughput when adding _TreatmentData_ and _DataUsageRecord_ to the blockchain.
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 5 Results and Discussion
### Comparison Between All of the Systems  
A comparison between the SDS, the ORB, the SURF, and the SIFT systems for automatic blood type determination is provided in Table 14. The SDS has the shortest processing time due to the simplicity of the implementation. However, it is an inappropriate choice for blood determination due to its low accuracy. The number of matching points detected by the ORB algorithm is the fewest among the matching algorithms. This is due to the fact that the ORB algorithm features are mostly concentrated in objects at the center of the image, whereas the keypoint detectors in the SURF and SIFT algorithms are distributed over the entire image. Hence, the processing time of the ORB algorithm is shorter than the processing times of both the SIFT and SURF algorithms. Shorter processing time is an advantage for the algorithm to perform the blood typing quickly, especially in emergency situations. The most important issue in the evaluation results is the accuracy of blood typing. It is clear that the ORB system has the highest accuracy among other systems with a reasonable processing time. Therefore, it is the best system that can be used for blood typing.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Actual Class/Predicted Class** & **Agglutination = Yes** & **Agglutination = No** & **Total** \\ \hline agglutination = yes & 731 & 68 & 799 \\ \hline agglutination = no & 2 & 699 & 701 \\ \hline Total & 733 & 767 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Confusion matrix for the ORB algorithm over the 1500 patches.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Actual Class/Predicted Class** & **A+** & **A\(-\)** & **B+** & **B\(-\)** & **AB+** & **AB\(-\)** & **O+** & **O\(-\)** & **Total** \\ \hline A\(+\) & 157 & 3 & 0 & 0 & 0 & 0 & 1 & 10 & 171 \\ \hline A\(-\) & 1 & 28 & 0 & 0 & 0 & 0 & 0 & 0 & 29 \\ \hline B\(+\) & 0 & 0 & 63 & 1 & 0 & 0 & 0 & 5 & 69 \\ \hline B\(-\) & 0 & 0 & 0 & 7 & 0 & 0 & 0 & 0 & 7 \\ \hline AB\(+\) & 0  
The confusion matrix of the optimized ORB system is shown in Table 15; the extracted measures are listed in Table 16. The effect of adding another reference image is obvious in the improved accuracy from 95.13% for the ORB algorithm to 99.8% for the optimized version. The improvement was specific in detecting the existence of agglutination (i.e., positive patches). The sensitivity was increased from 91.5% to 99.9%. The optimized ORB results revealed significant improvement in the right prediction of the blood types, as illustrated in Table 17. As a result, the optimized ORB system succeeded in determining the blood type of 498 blood samples out of the 500 sample with an accuracy of 99.6%. The average processing time slightly increased to 0.25 s per sample, but it was still considered reasonable. Figure 22 summarizes the results of the evaluated algorithms
## 2 Related Works
### MQTT-SN (Message Queue Telemetry Transport for Sensor Networks)  
MQTT-SN [33] is a variation of the MQTT protocol aimed at embedded devices on non-TCP/IP networks. The specifications provide three elements: connection semantics, routing, and endpoint. It is adapted to the peculiarities of a wireless communication environment such as low bandwidth, high link failures, short message length, etc. It is also optimized for the implementation on low-cost, battery-operated devices with limited processing and storage resources. MQTT-SN is characterized by the following differences: (a) the connection and messaging processes detail from the very outset of communication the topic that will be available for the Publish/Subscribe manager, (b) the topic name in the PUBLISH messages is replaced by a short, two-byte long \"topic id\", (c) \"Pre-defined\" topic identifiers and \"short\" topic names are introduced, for which no registration is required, (d) a discovery procedure helps clients without a pre-configured server or gateway's address to discover the actual network address of an operating server or gateway, (e) the semantic meaning of a \"clean session\" is extended to the Will feature (as stated in the connection process detailed above), i.e., not only client's subscriptions are persistent, but also Will topic and Will message and (f) a new offline keep-alive procedure is defined for the support of sleeping clients.  
All of these are useful characteristics in streamlining the process of Publish/Subscribe messaging, which is a must in bandwidth constrained environments, so messaging for a Publish/Subscribe paradigm has been taken into account while designing and implementing the main features of the proposed protocol.