esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Working Mechanism
### Traffic Generation Model for HAP Drones  
For the space-air-ground integrated network, the drone-LEO link needs to transmit the traffic generated by the HAP drone itself and the traffic collected from various mobile and IoT users on the ground. This traffic can be divided into traffic types generated with a determined rate, which mainly include HAP drone health status and UE location, and traffic types generated abruptly with random probability, such as malfunction diagnosis and signaling execution. Therefore, the traffic generation state at HAP drones is modeled as a mixture of stochasticity and determinism. Markov chains can be used to describe the traffic generation models of various types uniformly, as shown in Figure 2. Specifically, the generation of each traffic type at HAP drones is modeled as a Markov chain with two states: on and off. In the on state, traffic is generated at a constant rate, whereas traffic generation ceases in the off state. Denote the self-transition probabilities of the \(q\)-th traffic type from on to on as \(p_{1,q}\) and from off to off as \(p_{2,q}\), where \(q\in\{1,2,\cdots,Q\}\) and \(Q\) are the total number of traffic types. For traffic types generated at a constant rate, there are \(p_{1,q}=1\) and \(p_{2,q}=0\). For traffic types generated abruptly with random probability, there are \(0<p_{1,q}<1\) and \(0<p_{2,q}<1\), which means that the state switches randomly between on and off.  
Figure 1: Network architecture of the space–air–ground integrated network.  
In addition, different traffic types have varying levels of importance in practical scenarios. For instance, the traffic carrying the remaining power of HAP drones is more valuable than other traffic types. To account for this, we introduce a value factor \(\mu_{q}\) to represent the value of the \(q\)-th traffic type. The optimization objective is to maximize the average transmitted traffic value of the network in each time slot. Unlike the conventional approach, which treats all traffic types equally, we prioritize the transmission of high-value traffic when system resources are restricted, which aligns better with actual transmission requirements.
### Detection and Prediction of Episodes of Aggression in Patients with Dementia  
In dementia disorders, episodes of agitation and aggression can pose significant challenges for caregivers and mental health facilities when it comes to timely interventions. In a systematic umbrella review, technologies that monitor activities and behavioral symptoms in old-age patients with neurodegenerative diseases were examined. Their application, although promising, faces many challenges because of methodological diversity and the need for standardized metrics in the development of ethical AI in healthcare [14]. The use of wearable sensors is investigated in an attempt to develop a personalized machine-learning model for behavioral and psychological symptoms of dementia (BPSD). That study focused on the development of digital biomarkers, further enhancing the approaches in the digital phenotyping of BPSD [15].  
Another study introduces a public dataset, using non-intrusive sensors, such as wearables or cameras, which monitor agitation-related physical behavior in patients suffering from dementia. The findings indicated a high accuracy rate under controlled conditions, but in leave-one-out scenarios, a decline was observed, showcasing the challenges in the invariance domain [16]. Another study presents an efficient computational technique for real-time aggressive behavior detection with the use of a wrist accelerometer. This wireless actigraphic approach collects behavioral symptom data. Its effectiveness is limited, however, because of low computational power [17]. Subsequently, a machine-learning classification model, based on the MediaPipe holistic model, has been suggested and can detect and classify episodes of aggressive behavior in real-time. That proof-of-concept study presents a new approach with the potential to predict episodes of aggression, in patients with dementia, before their onset [18].
## 4 Imaging Data and Results  
### Simulation Study  
We applied the beamformers to the point-simulation dataset and anechoic-simulation dataset. The two datasets are designed to evaluate lateral resolution and contrast resolution performance, respectively.  
Figure 3a-d shows the simulated point target images created by different methods. Five-point targets are simulated along the depth direction in the image horizontal center, ranging from 5 to 20 mm. There is also one set of nine equally spaced point-targets horizontally located at depths of 12.5 mm.  
In Figure 3a, the original image is shown with the evident grating lobe artifacts. Comparing with DAS, all the beamformers show their ability to reduce the artifacts, but the sSCF and aSCF beamformers still exhibit some artifacts in the near field. The proposed uSCF beamformer outperforms other methods with visually clean background.  
We selected two-point targets for envelope signal presentation in Figure 4 and we could analyze the lateral response of different beamformers graphically. The two points are in the center of the image at depths of 10 and 15 mm. It shows that all the SCF-based beamformers help to increase the lateral resolution compared with DAS, and the uSCF has the narrowest responses both in the near and far field. It improves lateral resolution and decreases the side lobe level over the best of the conventional methods. In order to quantify the lateral resolution, the averaged FWHMs of all the points are summarized in Table 2. Compared with other SCF-based beamformers, the uSCF image has the least artifacts and the best resolution at all depths.  
The anechoic-simulation dataset for evaluating the contrast resolution is shown in Figure 3e-h. The two simulated circular anechoic cysts are at a depth of 25 mm (3-mm-radius) and 35 mm (2-mm-radius) with scatters surrounding them. Figure 3e-h shows the cyst images with different beamformers, respectively.  
As shown in Figure 3e-h, in contrast to DAS, the spatial and angular methods both make the cyst more distinguished, the CRs produced with aSCF and sSCF are numerically approximate, but the sSCF beamformer shows a clearer speckle. Meanwhile, with the uSCF shown in Figure 3h, the noise inside the cyst is reduced more in comparison to the other beamformers. The lesion also shows a higher contrast over the other beamformers. The lesion CRs and gCNRs at different depths are both shown in Table 2. Compared with DAS, uSCF improves CR more than 100% and gCNR more than 10%. It also shows the best contrast resolution among the SCF-based beamformers.
## 2 Background
### Vanet  
Vehicular ad hoc networks (VANETs) are an adaptation of mobile ad hoc networks (MANETs) for transport. In VANETs, each vehicle serves as a network node equipped with an onboard unit. The purpose of the onboard unit is to facilitate communication with other vehicles and infrastructure in the network. VANETs have inherent similarities to MANETs, especially in their capability to operate without a set infrastructure and mobility of nodes. However, the mobility of nodes, that is, individual vehicles, can be significantly higher than in VANETs, which means that the network topology of MANETs is subject to more rapid changes than in VANETs. The consideration of vehicles as network nodes has further implications, for example, in the variability of node density and the stability of the connection [15].  
In contrast to MANETs, where nodes can move freely, the nodes in VANETs are usually constrained by available roads and by the flow of traffic. Therefore, suitable technologies for connection may differ in areas with dense traffic, where vehicles can use dedicated short-range communication (DSRC) for vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication [16] to areas with sparse traffic, where a vehicle can be disconnected from the rest of the network. In such cases, the DSRC is nowadays replaced with 5G, thereby allowing the vehicle to stay connected [17]. However, the poor connection stability of VANETs initially contributed to their low usability, and the lack of computing and storage capacity, mostly restricted to onboard capacities, has made the implementation of intelligent applications arduous [18]. To overcome this setback, a range of work focused on introducing vehicles-as-cloud or the vehicular cloud has emerged [19], thereby enabling vehicles to pool their resources with, however, the disadvantage of inconsistent availability. The intermittent connections by which the VANET is characterized pose further risks and problems in the wide adoption of this technology. The short duration of connection and, therefore, frequent disconnections make it difficult to retain the confidentiality and integrity of messages, especially without a centralized governing authority, which requires the usage of a wide range of authentication and message encryption protocols [20].  
Ad hoc-enabled automobile communities were one of the first uses for VANET systems. With the aid of this application, a group of automobiles might be identified or made to self-identify as a community. This group of automobiles would represent a subset of those on the road who share the same objectives. This objective can, for instance, transport a group of people to the exact location or get a group of emergency personnel to arrive to the same place. Once the vehicles come into contact on the road, they will likely remain concurrent as they travel, since they are all attempting to reach the same destination. This makes it possible to create a more reliable communication scheme, which in normal conditions is quite difficult for VANETs [21].
# A Fast Sparse Coding Method for Image Classification
## 2 Methods
### ScSPM Feature Building and Classification  
The ScSPM model [13] is employ for generalizing vector quantization to sparse coding followed by multi-scale spatial max pooling and employed a linear SPM kernel based on SIFT sparse codes. For any image represented by a set of descriptors, we can compute a single feature vector based on the descriptors' codes generated by sparse coding algorithm. Let U be the code obtained via sparse coding algorithm; ScSPM feature is computed by the histogram pooling method:  
\[z=\frac{1}{M}\sum_{m=1}^{M}u_{m} \tag{1}\]  
In ScSPM, an approach of using linear SVMs based SC of SIFT is advocated. Let U be the result of applying the sparse coding Equation (1) to a descriptor set; assuming the dictionary to be pre-learned and fixed, image feature is computed by a pre-chosen pooling function:  
\[z=F(\text{U}) \tag{2}\]  
where the pooling function \(F\) is defined on each column of U, and each column of U corresponds to the responses of all the local descriptors to one specific item in dictionary. The pooling function \(F\) is a max pooling function on the absolute sparse codes:  
\[z_{j}=\max\left\{|u_{1j}|,|u_{2j}|,\cdots,|u_{Mj}|\right\} \tag{3}\]  
where \(z_{j}\) is the \(j\)th element of \(z\), \(u_{ij}\) is the matrix element at \(i\)th row and \(j\)th column of U, and M is the number of local descriptors in the region.  
Let image \(l_{i}\) be represented by \(z_{i}\), a simple linear SPM kernel, as shown in Equation (4), are employed in SVM, so that image can be labeled by SVM training and classification algorithm.  
\[k(z_{i},z_{j})=z_{i}^{\text{T}}z_{j}=\sum_{l=0}^{2}\sum_{s=1}^{2^{l}}\sum_{t= 1}^{2^{l}}\left\langle z_{i}^{l}(s,t),z_{j}^{l}(s,t)\right\rangle \tag{4}\]  
where \(\left\langle z_{i},z_{j}\right\rangle=z_{i}^{\text{T}}z_{j}\), and \(z_{i}^{l}(s,t)\) is the max pooling statistics of the descriptor sparse codes in the \((s,t)\)th segment of image \(l_{i}\) in the scale level \(l\).
## 3 Conceptual Model: Procurement Order Quantities Regulation for Shortage Scenarios
### Simulation Scenarios  
Based on the developed simulation model, multiple scenarios can be designed, executed, and analyzed. However, to check the hypothesis of a model that is able to respond to supply disturbances by applying a feedback control loop based on the definition of different POQ policies, the research proposes the following scenarios as shown in Figure 8:  
Figure 5: Production flow of the simulation (own elaboration).  
Figure 6: Input–output diagram of the simulation model (own elaboration).  
Figure 7: Sub-models areas of the simulation model (own elaboration).  
* Scenario 1, a reliable supply behavior: The supply execution of the suppliers is 100% aligned in time and quantity with the planning;
* Scenario 2, non-reliable supply behavior--week disruptions: The supply execution of the suppliers is not aligned with the planning. Disruptions of one week without supply in each four weeks in the planning horizon exist. As a result, the supply for this week is delayed by one week;
* Scenario 3, non-reliable supply behavior--2 weeks disruptions: the supply execution of the suppliers is not aligned with the planning. Disruptions of two weeks without supply in each four weeks in the planning horizon exist. As a result, the supply for these two weeks is delayed by one week.
## 4 Stability Analysis of Crowd Flow Considering Internal Disturbances  
### Lyapunov-Based Crowd Stability Analysis  
The definition of crowd stability based on Lyapunov is as follows. Assume that the equilibrium flow of the macro crowd flow model is \(q_{e}(x,y,t)=\left[\rho_{e}(x,y,t),v_{he}(x,t),v_{le}(y,t)\right]^{T}\). The disturbance flow is \(q_{\xi}(x,y,t)\). If the spatial gradient of the disturbance flow is bounded, then the propagation of the crowd flows is stable under the disturbance of \(q_{\xi}(x,y,t)\), i.e., \(\left\|\frac{q_{\xi}}{q_{x}}(x,y,t)\right\|<\infty\) and \(\left\|\frac{q_{\xi}}{q_{y}}(x,y,t)\right\|<\infty\) at \(t>0\), \((x,y)\in M\). \(M\) is the whole region of the crowd movement scenario. In addition, when \(lim_{t\to\infty}\left\|\frac{q_{\xi}}{q_{x}}(x,y,t)\right\|=0\) and \(lim_{t\to\infty}\left\|\frac{q_{\xi}}{q_{y}}(x,y,t)\right\|=0\), the propagation of the equilibrium flow \(q_{e}(x,y,t)\) is asymptotically stable.  
Based on the above definition of crowd stability, this study uses the wavefront theory in fluid dynamics to express the crowd disturbance propagation, as shown in Figure 2[33]. The equilibrium state of the crowd flow \(q_{e}(x,y,t)=\left[\rho_{e}(x,y,t),v_{he}(x,t),v_{le}(y,t)\right]^{T}\) is disturbed suddenly at position \(x_{0}\). The wavefront propagation curve of crowd flow is shown in Figure 2.  
Figure 2: Wavefront of crowd disturbance propagation.  
If the crowd flow system is stable, then the initial disturbance will not increase in the process of its propagation, and will gradually disappear; that is, the wavefront curve of the disturbance flow is bounded. However, if the crowd flow system is unstable, the disturbed flow wave may increase in the propagation process and eventually form a shock wave or bottleneck in the crowd flow scenario.
# Accurate Modeling, Operation Laws and Commutation Timing Matching for Concrete Pumping Systems
## 3 Results and Discussion
### Commutation timing of the CPS  
The pumping system achieves continuous pumping through the reciprocating and coordinated action of the pumping and swinging cylinders. It is therefore important to identify the commutation timing of the pumping system in order to achieve high precision and efficiency in pumping.  
Figure 14 shows the reversing sequence of the main cylinder, signal valve, swing valve and main valve during the pumping process. The commutation timing of the CPS is characterized as follows:  
(1) Sequential, cyclic: The signal valve, swing valve, main valve, swing cylinder and main cylinder are commutated in sequence to form a pumping cycle.  
(2) Hysteresis: The main cylinder reversal always lags behind the swing cylinder, which is the inherent characteristic of the hydraulic-controlled pumping system. The timing cannot be changed; we can only change the delay time.  
(3) Coupling: The main cylinder reversing and the swing cylinder reversing affect each other; the reversing signal is feedback of each other.
# Recent Developments in Low-Power AI Accelerators: A Survey
## 1 Introduction  
Deep learning is a popular machine learning approach, with many application areas [1, 2]. However, advances in technology and the ending of Moore's Law [3, 4] have resulted in a rise in hardware devices specifically designed to accelerate Artificial Intelligence (AI) and Deep Neural Networks (DNN), with a strong focus on Convolutional Neural Networks (CNN) [5]. Both DNNs and CNNs are prevalent in acceleration due to their numerous uses in a variety of fields (e.g., robotics, object detection, video processing, branch prediction [1]) and their use of matrix operations, which is easy to parallelize.  
As accelerator architectures continue to evolve and develop, and even commercialize, the demand for efficient accelerators continues to grow. A focus on accelerating machine learning directly at the edge has emerged, e.g., in low-power devices such as cellphones or cameras, adding concerns for privacy and an ever-increasing demand for higher performance [2]. With stricter power requirements, ranging from 1 mW [6] to \(\leq\)10 W, new strategies to accommodate the limitation of both power and area are necessary.  
This paper presents recent advances in low-power AI accelerators over the last four years. Unlike previous surveys on AI accelerators [2, 5, 7, 8, 9], this survey focuses on a range of different types of accelerators (e.g., CNN, DNN, LSTM, Transformer) and with an upper bound on power of \(\leq\)10 W. The paper presents and discusses the findings using five different aspects. AI accelerators are investigated in terms of (i) power, performance, and power-efficiency, (ii) the types of models accelerated, i.e., acceleration targets, (iii) precision and number formats supported, (iv) neuromorphic accelerators, and finally (v) accelerators developed by companies.  
The paper is organized as follow. Section 2 presents the background and related work. This is followed by Section 3, which contains the aims and scope, and the methodology used. Section 4 presents the findings. This section is divided into several subsections, detailing the different aspects, e.g., power efficiency or accelerators by companies. Finally, the findings are discussed in Section 5, and our conclusions are presented in Section 6.
###### Abstract  
Engineering structures, including rock slopes and embankments, are vulnerable to wetting-drying cycles caused by tidal shifts and rainfall, which exacerbate mechanical degradation in hole-fissured sandstone. This study investigated the effects of 0, 10, and 20 wetting-drying cycles on sandstone samples using uniaxial compression tests combined with digital image correlation (DIC), computed tomography (CT), and scanning electron microscopy (SEM). The results revealed that wetting-drying cycles progressively reduced peak strength and the elastic modulus while increasing macroscopic crack quantity and width. Internal crack networks simplified, transitioning from tensile-dominated to combined tensile-shear and shear failure modes. An energy analysis showed diminished energy storage capacity--both the total energy density at peak stress and elastic strain energy density declined with increasing cycle numbers, whereas dissipated energy density decreased initially before rising. SEM observations indicated that wetting-drying cycles enhanced the surface roughness of the sandstone, characterized by a scaly texture, thereby compromising its structural integrity. This study provides a theoretical basis for stability and safety assessments of protective engineering systems.  
hole-fissured sandstone; wet-dry cycles; digital image correlation; CT scanning; energy evolution
# Indoor Signal Strength Evaluation of the Orbcomm Low Earth Orbit Satellite Constellation +
## 4 Results
### Outdoor  
In order to obtain a point of reference, multiple outdoor measurements were performed. The outdoor measurements were performed in a garden located in a suburban area. Some fading was induced by multiple trees, a shed, neighboring houses, and others. Figure 3 shows a clear relation between the elevation angle and the \(C/N_{0}\). A higher elevation angle implies a stronger received signal. This must be attributed mainly to the shorter range that the signal has to travel and to the fact that the antenna has a higher gain for higher elevation angles. For elevation angles above 40\({}^{\circ}\), the outdoor \(C/N_{0}\) peaked at almost 70 dB-Hz. Due to the geographical location where the measurements took place, no satellite passes were recorded with a maximum elevation angle above 55\({}^{\circ}\). Furthermore, satellite passes with lower elevation angles were still well received, such as the pass with a max elevation angle of 5.5\({}^{\circ}\) as seen in Figure 3.  
Figure 3: Comparison of signal strength of multiple outdoor satellite passes in a suburban garden with different maximum elevation angles.  
Figure 2: Flowchart of employed processing algorithm to transform incoming IQ samples to \(C/N_{0}\) of a selected Orbcomm downlink channel.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
###### Abstract  
Transformer models have achieved great results in the field of computer vision over the past 2 years, drawing attention from within the field of remote sensing. However, there are still relatively few studies on this model in the field of remote sensing. Which method is more suitable for remote-sensing segmentation? In particular, how do different transformer models perform in the face of high-spatial resolution and the multispectral resolution of remote-sensing images? To explore these questions, this paper presents a comprehensive comparative analysis of three mainstream transformer models, including the segmentation transformer (SETRnet), SwinUnet, and TransUnet, by evaluating three aspects: a visual analysis of feature-segmentation results, accuracy, and training time. The experimental results show that the transformer structure has obvious advantages for the feature-extraction ability of large-scale remote-sensing data sets and ground objects, but the segmentation performance of different transfer structures in different scales of remote-sensing data sets is also very different. SwinUnet exhibits better global semantic interaction and pixel-level segmentation prediction on the large-scale Potsdam data set, and the SwinUnet model has the highest accuracy metrics for KAPPA, MIoU, and OA in the Potsdam data set, at 76.47%, 63.62%, and 85.01%, respectively. TransUnet has better segmentation results in the small-scale Vaihingen data set, and the three accuracy metrics of KAPPA, MIoU, and OA are the highest, at 80.54%, 56.25%, and 85.55%, respectively. TransUnet is better able to handle the edges and details of feature segmentation thanks to the network structure together built by its transformer and convolutional neural networks (CNNs). Therefore, TransUnet segmentation accuracy is higher when using a small-scale Vaihingen data set. Compared with SwinUnet and TransUnet, the segmentation performance of SETRnet in different scales of remote-sensing data sets is not ideal, so SETRnet is not suitable for the research task of remote-sensing image segmentation. In addition, this paper discusses the reasons for the performance differences between transformer models and discusses the differences between transformer models and CNN. This study further promotes the application of transformer models in remote-sensing image segmentation, improves the understanding of transformer models, and helps relevant researchers to select a more appropriate transformer model or model improvement method for remote-sensing image segmentation.  
transformer; multihead attention; remote-sensing image segmentation; deep learning; SwinUnet; TransUnet; SETRnet; visual classification