esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Malaria Using Object Detection Models
## 2 Method
### Dataset Description  
Two different datasets were considered for each kind of classification: one for parasite classification and one for stage classification. The first dataset used for parasite classification had 172 images and had four classes, each corresponding to the type of parasite. They are as follows: Vivax, Falciparum, Ovale, Malariae. This dataset was collected by using a smartphone camera attached to a microscope eyepiece. The slides were stained with a field stain at \(\times\)1000 magnification, and all the images were of 750 \(\times\) 750 pixels. The dataset consisted of ground truth images indicating the axes where parasites were present. The images were also split into four folders corresponding to the type of parasite present. Additionally, each image name consisted of either an \"R\" (ring), \"S\" (schizont), \"T\" (trophozoite) or \"G\" (gametocyte), indicating the stage of progression. These images were originally in the.png format and were converted to the jpeg format (the significance of which will be explained in the proceeding chapters). The second dataset was taken from the Broad Bioimage Benchmark Collection. It had 1330 images with information corresponding to the stage of progression of the disease: ring, trophozoite, RBC, gametocyte, schizont, leukocyte, and difficult. Out of these classes, leukocyte and RBC corresponded to the uninfected classes, and the rest were infected. An important observation about the dataset was the large number of uninfected red blood cells compared to any other class of object. The significance of this is revisited in the proceeding chapters. The labels were originally stored in the form of a json file that was converted to a csv file containing \(x\)- and \(y\)-axis coordinates corresponding to the presence of the parasite. This csv file was later referenced while creating bounding boxes for the data. Both datasets had to be manually annotated before they could be used for the YOLO algorithms as a major input parameter for the algorithm are the annotations (more specifically, the bounding boxes), that indicate the location of the object of interest in the image. While both datasets did contain supporting label information for the cells, this information had to be integrated into annotations and saved in the yaml format expected by the YOLO object detection algorithms.
# Low-Cost Full Correlated-Power-Noise Generator to Counteract Side-Channel Attacks
## 5 Security Evaluation and Experimental Results
### Test Vector Leakage Assessment, TVLA  
A preliminary security comparison between unprotected and protected CPNG AES-256 is based on the TVLA. Figure 9 depicts the TVLA graph for the cipher without protection (in red) and with protection provided by the CPNG countermeasure (in blue) for the AES-256. The graph highlights 15 operations, each associated with the subkeys that constitute the expanded key, and three consecutive operations executed by the cipher. For the first and second rounds, using the subkeys \(\mathrm{K}_{0}\) and \(\mathrm{K}_{1}\) which form the full 256-bit original key, the protected CPNG AES exhibits smaller leakage peaks, particularly in the second round. Although it is notable that, in some of the intermediate rounds, it attains higher absolute maximum values, the overall mean leakage remains lower. In terms of extracting information from the last round, the original AES reaches a maximum value of 30.51 whereas the protected AES only reaches 20.56, signifying a reduction in information leakage by 32.61%. Therefore, it is possible to assert that the countermeasure enhances the security of the cipher, particularly with regard to the attacks on the first and last rounds, which are typically subject to more extensive analysis. As follows, these attacks are going to experimentally be conducted to validate this claim.  
Figure 8: Experimental setup for SCA analysis in AES.  
Figure 9: TVLA comparison between unprotected AES and protected CPNG AES.
# Variable Scale Pruning for Transformer Model Compression in End-to-End Speech Recognition
## 3 ASR Transformer
### Model Architecture  
Our ASR transformer model adopts the architecture described in [37]. Prior to entering the encoder, the input acoustic features undergo subsampling via two convolution layers. The encoder and decoder modules consist of multi-head attention (MHA) and feedforward (FF) layers. Each layer is accompanied by a residual connection and normalization. Figure 3 provides a simplified illustration of the ASR transformer model's structure.  
The layers of the encoder refine the representation of the input sequence with a suite of multi-head, self-attention, and linear transformations. The self-attention operation allows frames to gather context from all timesteps and build an informative sequence at a high level [37]. Specifically, the inputs of each layer are projected into queries \(Q\), keys \(K\), and values \(V\) with \(Q\in\mathbb{R}^{t_{q}+d_{q}}\), \(K\in\mathbb{R}^{t_{k}+d_{k}}\), and \(V\in\mathbb{R}^{t_{q}+d_{q}}\). \(t*\) are the elements numbers in different inputs and \(d*\) are the corresponding element dimensions. Usually, these are \(t_{k}=t_{v}\) and \(d_{q}=d_{k}\).  
Scaled Dot-Product Attention [1] is then computed as:  
\[Attention(Q,K,V)=softmax(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{1}\]  
The MHA is applied to take advantage of the different representations that are simultaneously present. The multi-head attention is obtained by performing this calculation \(h\) times. \(h\) is the number of heads.  
\[MultiHead(Q,K,V)=Concat(head_{1},\dots,head_{h})W_{0} \tag{2}\]  
Figure 3: ASR transformer main components.  
where  
\[head_{i}=Attention(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{3}\]  
The projection matrices are \(W_{i}^{Q}\in\mathbb{R}^{d_{model}*d_{q}}\), \(W_{i}^{K}\in\mathbb{R}^{d_{model}*d_{k}}\), \(W_{i}^{V}\in\mathbb{R}^{d_{model}*d_{q}}\), and \(W^{0}\in\mathbb{R}^{h*d_{v}*d_{model}}\). In this work, \(d_{k}=d_{q}=d_{v}=d_{model}/h\).  
The outputs of multi-head attention go through a two-layer position-wise feedforward network (FFN) with hidden size \(d_{ff}\)[1].  
\[FFN(x)=W_{2}ReLU(W_{1}x+b_{1})+b_{2} \tag{4}\]  
\(b_{1}\in\mathbb{R}^{d_{ff}}\) and \(b_{2}\in\mathbb{R}^{d_{model}}\) are the biases. The weight matrices are \(W_{1}\in\mathbb{R}^{d_{model}*d_{ff}}\) and \(W_{2}\in\mathbb{R}^{d_{ff}*d_{model}}\).
# Remote Sensing for International Peace and Security: Its Role and Implications
## 3 Results
### Remote Sensing in International Peace Missions  
Remote sensing technologies can contribute to various components of the fulfillment of the peace process. GIS can help to provide spatial information about homicides, conflict-related deaths, violence, etc., which can help the implementation of SDGs# 16 i.e., to promote peaceful and inclusive societies for sustainable development, and provide access to justice for all and build effective, accountable and inclusive institutions at all levels [24]. Satellite data can provide valuable evidence in remote or conflict zones where field visits are difficult [71]. Remote sensing data can be an effective tool for resolving running disputes at international borders. By addressing the discerning factors causing the conflict in monitoring the border situation remotely can be done through the deployment of remote sensing techniques. It can be used as the identifiable indicators, with little warning signs before a major crisis erupts [71]. Verifying the agreements put forth in conflict zones is an important aspect of handling international disputes of this kind [22]. Satellites have always been identified with national technical means of verification with increased cooperative missions at an international level that are associated with it. For instance, satellite images have been widely used as the source of verification in decommissioning of facilities, disengagements, storage, and destruction that are vital in the peace process--from negotiating to overseeing implementation to post-conflict peacebuilding processes.
# Modeling and Resolving Uncertainty in DIKWP Model
## 2 Problem Description
### Uncertainty in Market Conditions
#### 2.1.3 Counterparty  
Counterparty uncertainty is a key challenge in HFT, as the outcome of the market depends not only on the decisions of individual participants but also on the collective behavior of all market participants [20; 21; 22]. The presence of this uncertainty complicates the formulation and execution of HFT strategies.  
* **Competitors executing similar strategies (\(e_{6}\)):** When multiple participants in the market simultaneously execute similar trading strategies, competition may lead to diminishing profit margins. If multiple high-frequency traders are exploiting the same arbitrage strategy, such as a rapid response strategy based on certain economic indicators, arbitrage opportunities in the market may quickly disappear, as the first participant to execute the trade captures the profit, leaving subsequent participants finding the market adjusted without the expected profit space.
* **Opposing strategy opponents (_e7_):** Other traders may be executing strategies that are entirely opposed to yours, which may directly impact your trading results negatively. For instance, if one HFT firm is executing a buy strategy based on pattern recognition, while another firm may be executing a sell strategy based on the same data or predictive model. If the latter's trading volume is larger or executed faster, it may lead to market price trends contrary to the expectations of the former, resulting in losses for the former.
* **Unpredictable market participant behavior (_e8_):** Market participant behavior may be driven by various factors, including irrational behavior, making it extremely difficult to predict the behavior of other participants. For example, the 2021 GameStop (GME) trading event [23] demonstrated the extreme unpredictability of collective market participant behavior when driven by non-traditional factors such as collective action on social media. This behavioral pattern is far from predictable based on traditional financial theories and is challenging for HFT algorithms to accurately forecast.
* **Oponents using covert strategies (_e9_):** New participants may continuously join the market, employing covert strategies or using technologies not widely known, adding additional uncertainty to market behavior. For example, an emerging HFT firm may develop an advanced artificial intelligence algorithm capable of identifying and exploiting minor fluctuations in the market more rapidly. The deployment of such a new algorithm may suddenly alter market dynamics, causing unexpected impacts on existing participants.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Aerial Sensing for ITS
#### 3.4.3 UAV for Infrastructure Sensing  
On the infrastructure side, UAV has been utilized for some ITS sensing services, such as road detection, lane detection, and infrastructure inspection. UAV is extremely helpful at locations where it is hard for humans to reach. Road detection is used to localize the regions where traffic appears from UAV sensing data. It is crucial to support applications such as navigation and task scheduling (2020; 2021; 2022). For instance, [PERSON] et al. (2021; 2022) designed two of the popular methods for road detection in UAV imagery. While there were some studies before these two, the paper (2021) was the first targeting speeding up the road localization part with a proposed tracking method. [PERSON] (2022) presented a fully automatic approach that detects roads from a single UAV image with two major components: road/non-road seeds generation and seeded road segmentation. Their methods were tested on challenging scenes. UAV has been intensely used for infrastructure inspection, particularly bridge inspection (2021; 2022; 2023; 2024) and road surface inspection (2021; 2022; 2023). Manual inspection for bridges and road surfaces is costly in terms of both time and labor. [PERSON] et al. (2023) proposed a high-level framework for bridge inspection using LiDAR-equipped UAVs. It contained planning a collision-free optimized path and a data analysis framework for point cloud processing. [PERSON] and [PERSON] (2019) developed an approach with vertically features, DBSCAN clustering, and robust plane fitting to process point cloud for automated extraction of road surface distress.
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
## 2 Materials and Method  
### Study Area  
Southeast Tibet generally includes two regions, Nyingchi and Qamdo. The main mountain system in the region is the Nyaingentanglha Mountains. This mountain system has a wide range. Moreover, it has the widest distribution of glacial lakes on the Qinghai-Tibet Plateau. On the whole, the terrain of the region is high and steep, with an average elevation of more than 4000 m and a maximum elevation of more than 6000 m (Figure 1).  
Under the influence of the Indian monsoon, numerous terminal moraine lakes have developed in this area. Glaciers and ice lakes are mainly distributed in the Nyaingentanglha Mountains. Although this region has more than 8500 glaciers, they are rapidly receding due to climate change. Consequently, glacial activity is intense and a large amount of meltwater is produced, leading to a large number of glacial lakes. Most of the glacial lakes in this area are moraine-blocked lakes, including terminal moraine-blocked lakes and lateral moraine-blocked lakes.
## 3 Results and Discussion  
Our workflow provides, in automatic fashion, radiometrically and atmospherically corrected, stitched, orthorectified, and coregistered images with associated cloud and tree masks (Figure 4). This is the beginning of further parallel processing routines that yield numerous qualitative and quantitative results and offers use as value-added product in applications such as precision agriculture (e.g., [4]). Here, we present three post-processing branches for utilizing the application-ready dataset of the basic workflow. First, we describe an application of the image workflow that extracts spectral and textural statistics from individual fields and aggregations of fields. The second application demonstrates the information available from the workflow and data feeding a crop spectrotemporal library. Last, the near-real time phenological development of a crop is presented through the use of a time-series.  
Figure 4: Raw data (**left**) and a stitched, radiometrically, and atmospherically corrected, orthorectified, co-registered, and tree/cloud masked image (**right**).
# The RUNE Experiment--A Database of Remote-Sensing Observations of Near-Shore Winds
## 3 Setup of the Lidars
### VAD Scans  
The vertical profiling lidars performed VAD scans. Alize (pos. 2) was configured to measure at 20 heights between 100 and 1500 m above lidar level (ALL). WLS66 (pos. 2) started to measure at 40 m ALL, and it was configured such that some of the range gates coincided with the dual-setup heights. With a terrain height of 26 m, the range gates at 74 and 124 m ALL are close to the heights 100 and 150 m ASL. The same idea was used for the range gates at 57 and 107 m ALL for 3E (pos. 4), and 75 and 125 m ALL for Bura (pos. 5). Bura measured at more range gates than usual in a WindCube WLS7v1 (Leosphere, Orsay, France), because it was using modified software (WindSoft LR). All range gates of the VAD lidars are given in Table 3.  
The sampling time per LOS was \(\approx\)3.5, 1, 1, 5, and 1 s for Bura, 3E, WLS66, Alize and the lidar buoy, respectively. All lidars were sampling in 4 positions around the zenith, with a 90\({}^{\circ}\) azimuthal separation between each other. The zenith angle was 30\({}^{\circ}\) for the short-range lidars and 15\({}^{\circ}\) for the long-range lidar (Alize). The CNR threshold for the VAD lidars was set to \(-\)22 dB, except for Alize which used a threshold of \(-\)27 dB. All samples within a 10 min period at all heights up to 130 m had to fulfill this threshold.  
Figure 4: Overview of the scanning strategies during phases 2 and 3. Collocated points from the dual setup are shown in black circles and the two black lines show two laser beams focusing in position 7. The points from the PPI scans are denoted with light blue points and dark blue points denote the first 60\({}^{\circ}\) scan that is collocated with the first dual setup point at 50 m ASL. The vertical coloured lines denote the positions of the profiling lidars and the grey lines denote the terrain height at the northing locations of positions 1–3.  
The lidar buoy was installed on November 4. The floating lidar was installed as far offshore as possible, but still within the maximum range of the sector-scan setup (pos. 6). This location was chosen because the RUNE project aimed to evaluate mesoscale models and their grid spacing is usually in the order of kilometers. On 15 November, after a storm, it was found that the blades of the wind-power buoy generators were damaged and the buoy's motion sensor was not working properly. During another storm, the blades were again damaged and the lidar did not have electricity after 7 December. Due to the harsh weather conditions, it was only possible to relocate the buoy to a location (pos. 7) with less-frequent breaking waves by 11 February. There, the lidar buoy measured successfully until 30 March. Detailed documentation about the lidar buoy can be found in [24].
# Review of Convective Heat Transfer Modelling in CFD Simulations of Fire-Driven Flows
## 4 Specific Treatment of Burning Surfaces
### Convective Mass Transfer: Analogy with Convective Heat Transfer  
The dimensionless number involved in mass transfer problems, equivalent to the Nusselt number for heat transfer, is the Sherwood number defined as:  
\[Sh=\frac{h_{m}L_{c}}{D}, \tag{27}\]expressing the ratio of the convective mass transfer to the rate of diffusive mass transport across a fluid layer. In the equation above, \(h_{m}\) is the convective mass transfer coefficient, \(L_{c}\) is the characteristic length of the problem and \(D\) is the mass diffusion coefficient.  
Additionally, the Schmidt number (i.e., equivalent to Prandtl number in heat transfer) is defined as:  
\[Sc=\frac{\
u}{D}=\frac{\mu}{\rho D}, \tag{28}\]  
expressing as the ratio of momentum diffusivity and mass diffusivity. The \(Sc\) number is used to characterise fluid flows in which simultaneous momentum and mass diffusion processes occur.  
Since \(Pr\approx Sc\approx 0.7\) for combustion gases, then \(Le=Pr/Sc\approx 1\) and it can be shown that \(h_{m}=h/c_{p}\). This way, the mass transfer coefficient can be computed from heat transfer results (i.e., it stems from the Reynolds analogy which relates heat and mass transfer when \(Le\approx 1\)). The mass transfer coefficients, \(h_{m}\), for forced convection problems can be obtained from Table 2 with the Prandtl number, \(Pr\), replaced by the Schmidt number, \(Sc\), and the Nusselt number, \(Nu\), replaced by the Sherwood number, \(Sh\) (i.e., \(Sh=f(Re,Sc)\) ). An overview of these correlations is presented in Table 5.  
Alternatively, use can also be made of experimental correlations for the Sherwood number [9] derived from burning surfaces. A brief overview of some, typically used, correlations in literature is presented below.  
* Natural convective burning over vertical plates [57]: \[Sh=\frac{\dot{m}^{{}^{\prime\prime}}c_{p}L_{c}}{k}=\frac{0.66 Ra_{*}^{1/4}}{ \Sigma Pr^{3/4}},\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \  
\[r_{o}=\frac{Y_{O_{2},\infty}}{rY_{F,\rho}} \tag{36}\] \[B=\frac{Y_{O_{2},\infty}\Delta H_{O_{2}}-c_{p}(T_{ign}-T_{\infty})}{L_{\
u}}, \tag{37}\]  
where \(Ra_{*}=Gr_{*}\cdot Pr\) is a modified Rayleigh number, \(L_{\
u}\) is the latent heat of vaporization of the fuel, \(\
u\) is the kinematic viscosity, \(Y_{F,\rho}\) is the mass fraction of the fuel, \(O_{2,\infty}\) is the oxygen mass fraction in ambient air and \(r\) is the stoichiometric mass oxygen to fuel ratio.
* Forced convective burning of a flat plate [58]: \[Sh=\frac{\dot{m}^{\prime\prime}c_{p}L_{c}}{k}=0.385\frac{uL_{c}}{\
u}^{1/2} Pr\frac{ln(1+B)}{B^{0.15}}\] (38) where \(u\) is the flow velocity, \(\
u\) is the kinematic viscosity of the fluid and \(B\) is the B-number of the fuel.
# On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware
## 1 Introduction
In this paper, we present a UAV-LS system including a dynamic collision-free formation transformation algorithm, a software package that consists of an animation design interface (ADI) and a real-time monitoring application, and a complete hardware design and realization. The proposed formation transformation algorithm dynamically solves the TAP in a conditional-triggering way and constructs repulsion field around obstacles to avoid inter-collisions of UAVs. Compared with the traditional static task assignment methods, the TAP is not only solved offline to obtain an initial task assignment with the minimum total flight distance of all UAVs, but is also dynamically solved online to consider the impact of real-time trajectory deviation due to collision avoidance. The collision avoidance between UAVs is set as a trigger condition to reduce the frequency of dynamic task assignment, and the TAP is only solved within a group of local UAVs to avoid a high computational cost of solving a global TAP. In addition, formation drawing and 3D animation simulations are provided in the ADI to facilitate the animation design process. During the light show, the monitoring and control of UAVs is conducted through the real-time monitoring application. The UAV-LS hardware consists of subsystems of decision-making, real-time kinematic (RTK) global positioning system (GPS), wireless communication, and UAV platforms. The UAV platform is made up of a number of homogeneous quadrotors without onboard collision avoidance sensors for economic reasons. Finally, outdoor experiments using six quadrotors are performed. Details of implementations of centimeter-level positioning, communication and computation are given. Results show that the developed UAV-LS system can successfully complete a light show, and the proposed algorithm is better than the traditional static task assignment one in terms of scalability, efficiency, and performance. The remainder of this paper is organized as follows. In Section 2, the formation transformation algorithm is introduced. Section 3 introduces the software package developed for the UAVs-LS. Section 4 provides details of the self-developed quadrotor prototype and the UAVs-LS system. The simulations of proposed formation transformation algorithm and experiments of UAV-LS are presented in Section 5. Finally, Section 6 gives concluding remarks.