esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE
## 2 Literature Review
### Forecasting of COVID-19 Cases Using Deep Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Purpose** & **Dataset** & **Methods** & **Best Results** \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & Predict number of newly infected cases & Centre of Disease & & The MAPE was 0.52\% \\  & using additional event & Control and Prevention (CDC) & LSTM + NLP & for the predictions in Wuhan and 0.38\% in the predictions for Beijing. \\  & & & 11 months of data from the Worldometer & LSTM & Their model achieved an RMSE of 2 for the New Zealand data. \\ \multirow{4}{*}{[PERSON] et al. [22]} & Predict containment of the virus & Johns Hopkins & & RMSE error of 34.83 \\  & & University and the Canadian Health & LSTM & with and accuracy of 93.4\%. \\  & & & & 93.4\%. \\ \multirow{4}{*}{[PERSON] et al. [23]} & Predict number of newly infected cases & Turkish Ministry of Health & LSTM & MAPE of 0.70. \\  & & & & DeepFM & Accuracy of 63.7\%. \\ \multirow{4}{*}{[PERSON] et al. [24]} & Predict number of newly infected cases & & Random Forest (RF), & \\  & & & Multilayer Perception (MLP), LSTM, LSTM & M-LSTM performed best with an MAPE of 0.509\%, RMSE of 458.12, & best with an MAPE of 0.0509\%, RMSE of 458.12, & best with an MAPE of 0.001624, \\ \multirow{4}{*}{[PERSON] et al. [26]} & Predict number of newly infected cases & Johns Hopkins, applied the best model to Iran & LSTM with extended features and Multivariate LSTM (M-LSTM) & 0.509\%, RMSE of 0.001624, \\  & & & Multivariate LSTM & and \(R^{2}\) score of 0.99997. \\ \multirow{4}{*}{[PERSON] et al. [27]} & Predict number of newly infected cases & WHO COVID-19 & & MAPE of 01.9, RMSE of 13,275.00, and RRMSE of 5.30. \\  & & & & MAE for confirmed cases of 58.50 and \\ \multirow{4}{*}{[PERSON] et al. [28]} & Predict number of newly infected cases & UAE data from Johns Hopkins dataset & LSTM & MAPE for death cases of 0.63. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Literature Review for Deep Learning approaches. which is a public dataset that has accumulated all the data from over the world and uses three common features, which are Confirmed Cases, Recovered Cases, and Deaths, and future forecasts for the same. We also see a lack of studies carried out on the consideration of the effect of the number of vaccinated people on the trends in the spread of COVID-19. Furthermore, only [PERSON] et al. [21] have used events as an extra feature in training the model for forecasting.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Data Collection  
The acquisition of data for this investigation, which is depicted as \"dataset\" in Figure 3, was accomplished through the deployment of unmanned aerial vehicles (UAVs), which procured high-resolution imagery pertaining to pollution in the Shatt al-Arab locale. Two models of commercially available UAVs were employed--the DJI Mavic Air 2, equipped with a 1/2\" CMOS 48 MP camera yielding 4 K/60 fps video, and the DJI Mini 3, furnished with a 1/1.3\" 12 MP camera enabling 4 K video recording. Flight times near 30 min facilitated broad coverage. Survey locations included Shatt al-Arab stretches frequently impacted by oil spills, sewage drainage points, and litter-accumulated shorelines near populated areas. Scheduling flights across various times (early morning, afternoon) and seasons (summer, winter) captured diversity, with early morning flights detecting overnight discharges. Imported JPEG and MP4 files were decomposed into frames.  
In the interval between November 2022 and April 2023, examinations were conducted over critical regions of the Shatt al-Arab River as well as its adjacent tributaries and waterways. During these six months, over 300 km of UAV flight trajectories were executed to image the survey zones. One such trajectory is illustrated in Figure 4. The high-resolution visual data encompassed diverse forms of pollution such as river debris, oil spills, refuse, and wastewater. Cumulatively, more than 18 h of high-definition aerial footage was obtained, encompassing over 1000 images and videos encapsulating the primary classifications of contamination in situ. This extensive dataset presents varied real-world manifestations and serves as a crucial basis for devising and verifying machine learning algorithms aimed at detecting and categorizing pollution incidents.
## 4 Conclusions  
A novel application of SERS using a home-made silver substrate and a cooler for improved measurement of samples was presented. The determination of lipid oxidation in packaged ground pork was performed. The proposed method was fast, with minimal handling of the fat sample. Better discrimination than the TBARS method to follow lipid degradation in short time was successfully achieved. The detection of differences between conventional and active packaging was confirmed. A cooling setup was shown to improve the sensitivity of SERS and allowed the study of a complex matrix. Furthermore, an outstanding SERS enhancement factor of more than \(10^{7}\) was obtained compared to conventional Raman.  
Conceptualization, M.W., J.S. and C.J.; methodology, M.W., J.L. and J.S.; software, M.W., J.L. and J.S.; validation, M.W. and J.S.; formal analysis, M.W., J.L. and J.S.; investigation, M.W., J.L. and J.S.; resources, J.S. and C.N.; data curation, J.S. and C.N.; writing--original draft preparation, M.W., J.L., J.S., C.J. and C.N.; writing--review and editing, M.W. and J.S.; visualization, M.W. and J.S.; supervision, M.W. and J.S.; project administration, J.S. and C.N.; funding acquisition, J.S., C.J. and C.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Figure 7: Results of TBARS of meat samples from LDPE and active packaging expressed as average concentration of MDA \(\pm\) standard deviation, \(n=3\).  
**Acknowledgments:** Warm and sincere thanks are given to Thermo Fisher Scientific for the fruitful collaboration and effective exchange of experience and knowledge. [PERSON] acknowledges the FPU grant (reference number AP2012-2716) received from the MEC, Ministerio de Educacion, Cultura y Deporte, Spain. [PERSON] acknowledges the IUT Lyon 1 through the scholarship EXPLORA sup of the Region Rhone-Alpes. We also thank Gobierno de Aragon and Fondo Social Europe for the financial help to GUIA T53_20R. The authors would like to acknowledge the use of Servicio General de Apoyo a la Investigacion-SAI, Universidad de Zaragoza.
# Effects of Differential Jump Training on Balance Performance in Female Volleyball Players
###### Abstract  
The purpose of this study was to determine whether coordinative jump training that induces neuromuscular stimuli can affect balance performance, associated with injury risk, in elite-level female volleyball players. During the competitive season, the balance performance of 12 elite female players (highest Austrian division) was obtained via a wobble board (WB; 200 Hz) placed on an AMTI force plate (1000 Hz). Three identically repeated measurements defined two intervals (control and intervention phases), both comparable in duration and regular training. The intervention included 6 weeks of differential training (8 sessions of 15-20 min) that delivered variations in dynamics around the ankle joints. Multilevel mixed models were used to assess the effect on postural control. WB performance decreased from \(27.0\pm 13.2\%\) to \(19.6\pm 11.3\%\) during the control phase and increased to \(54.5\pm 16.2\%\) during the intervention (\(\beta=49.1\pm 3.5\); \(p<0.001\)). Decreased sway area [cm\({}^{2}\)] (\(\beta=-7.5\pm 1.6\); \(p<0.001\)), anterior-posterior (\(\beta=-4.1\pm 0.4\); \(p<0.001\)) and mediolateral sway [mm] (\(\beta=-2.7\pm 0.6\); \(p=0.12\)), and mean velocity [mm\(\cdot\)s\({}^{-1}\)] (\(\beta=-9.0\pm 3.6\); \(p<0.05\)) were observed during the intervention compared with the control phase. Inter-limb asymmetry was reduced (\(\beta=-41.8\pm 14.4\); \(p<0.05\)). The applied training concept enhanced balance performance and postural control in elite female volleyball players. Due to the low additional physiological loads of the program and increased injury risk during the competitive season, we recommend this intervention for supporting injury prevention during this period.  
postural control; center of pressure; in-season intervention; injury risk; ankle strain prevention Article
## 2 Materials and Methods  
### Experimental Test  
An experimental test was carried out on a steel tank obtained by cutting a 4 m long pipeline section and by using two semi-elliptical end caps welded to the pipe ending sections (Figure 1). The material of the pipe was the API 5L X60 steel. Mean diameter and average thickness were equal to 510 mm and 8.8 mm respectively. The damage was simulated in the middle of the pipe by machining a localized area so as to achieve a considerable local wall loss (nearly 80%), in such a way as to simulate the effects of corrosion. The so-obtained defect presented sizes of 254 mm \(\times\) 128 mm \(\times\) 7.1 mm, see Figure 1. The machining process was carried out in a controlled manner using a milling machine in order to obtain a defect uniform in size. This is a common practice in simulating external corrosion effects on a pipeline [16].  
The overall procedure to obtain the repaired tank is shown in Figure 2. The repairing procedure with the composite sleeve consisted of the following steps:  
* measuring the defect;
* improving the pipe surface finish (SA 2 \(\frac{1}{2}\));
* cleaning the spool with acetone;
* applying dry composite on the pipe and marking the edge of the sleeve;
* removing the composite sleeve and attaching the starter pad;
* increasing the pipe surface temperature to achieve the number of activators;
* mixing filler and the related activator;
* mixing adhesive and the related activator;
* applying the filler on the defect and the starter pad edge;
* applying adhesive to the entire pipe between the marked lines;
* removing the backing of the starter pad and applying the composite sleeve from the starter pad;
* applying the adhesive on each layer of composite and wrapping the eight layers of the composite sleeve;
* securing the cinch bar strap to the dual lock and applying a steady pressure (of 110-140 Pa);
* wrapping filament tape around the composite sleeve.  
A burst-test was performed for the so obtained composite-repaired pipeline section. Water was considered an internal fluid, continuously monitored with a manometer during  
Figure 1: (a,b) CAD model showing size and shape of the machined defect; (c) CAD model of the whole pipe showing the defect positioning.  
the whole test. Readings of the manometer were recorded in a video format to enable the analysis of pressure over time. A monotonically increasing internal fluid pressure was applied up to the burst of the tank.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 5 Results
### Experiment Design and Test Method  
The experimental phase primarily intends to validate the developed MR-based support on assembly tasks by testing the users' performance and experience.  
The experiment took place at The Royal Institute of Technology (KTH) in the Lean assembly laboratory facility and involved ten participants aged between 23 and 26. The participants were recruited from different engineering master programs at KTH. All selected participants were inexperienced operators and did have previous experience using a MR application with HoloLens.  
The experiment was divided into two parts: (a) a subject trial performed to collect quantitative measures, and (b) a post-experiment questionnaire to gather qualitative measures on the user experience.  
In the first phase, the equipment was prepared, and each subject was asked to test it to become familiar with its features. The experimental procedure consisted of the participants completing the product assembly one time using the MR system. While completing the assembly, time measurements for each step, the number of mistakes made by the participants (i.e., incorrect placements and selections of components) and the success rate of the component selection validation algorithm were calculated by dedicated scripts and gathered in a dedicated database for analysis. In this phase, it was planned to collect the same data also from inexperienced subjects performing the operations with paper instructions guidance, but due to a lack of time and resources, the plan was re-scheduled for future experiments, as is better explained in Section 7.1.  
The collected inexperienced subjects' performance was then compared with predefined benchmarks. The definition of the benchmark was performed in a separate session in which an expert operator executed the assembly procedure 10 times. The time to complete each assembly was recorded with and without the guidance of the MR application. The number of errors the expert made, and validation results for the assessment of components placed correctly, incorrectly, or not placed at all were recorded only by using the MR system.  
In the second phase, the participants were asked to fill out the post-experiment questionnaire about their subjective perception of the MR system experience. The collected inexperienced subject perceptions were then compared with predefined benchmarks, and any feedback and suggestions were used to improve the application and validate the MR system's user-experience.
## 3 Background and Related Works
### Authentication with HIP DEX Protocol  
HIP BEX is designed to be used in authentication between a client and a base station and is not suitable for IoT and constrained environments. HIP DEX preserves general HIP architecture and protocol semantics. Precisely, it redefines the cryptographic namespace based on the network device's public key fixed as a Host Identity (HI). As a consequence, a new layer, placed between the network and the transport layer, is implemented based on the defined namespace. Another fundamental parameter is the Host Identity Tag (HIT) calculated using HI to represent its stable format. It serves as a fixed device identifier at this layer on the network stack. It represents a marked characteristic of the HIP protocol by allowing the mobility of devices without being dependent on IP addresses' double role of identification and geographical location. HIP DEX protocol aims to permit secure communication of two entities based on cryptographic operations to guarantee a secure communication link between hosts in a constrained environment. The first entity is known as the initiator and the second one the responder. Then, HIP DEX is deprived of thecalculation of transitory Diffie-Hellman keys and digital signatures. As a substitute, it defines a light handshake phase based on static DH for mutual key agreement. HIP DEX defines four types of messages (I1, R1, I2, R2). II is the first message that contains the sender host identity tag (HIT_I) and a receiver host identity tag (HIT_R) considered as an optional field [5]. The second message, R1, contains a puzzle (a cryptographic challenge) that must be resolved by the Initiator like in HIP BEX, and the public key \(PK_{resp}\). The initiator and responder produce DH Key by performing Elliptic Curve Diffie-Hellman (ECDH) for the session key generation using public key (PK) values. The solution to the puzzle must be sent in the third message I2 that contains the \(PK_{init}\) and the key parameters F(\(DH_{k}\), x). The random values x and y are, respectively, the initiator's and responder's private keys used to the final session secret key computation. The reinforcement of the security in HIP DEX against the falsification of messages is guaranteed in I2 after the definition of the MAC (Message Authentication Code) value. The fourth message R2 also contains the responder's key parameters F(\(DH_{k}\), y). The ECDH generated key is used to encrypt the secrets (x, y), used after that to calculate the session key. Figure 1 presents the standard exchanged messages during the handshake in HIP DEX.
###### Abstract  
Information-centric networking (ICN) is a promising approach to address the limitations of current host-centric IP-based networking. ICN models feature ubiquitous in-network caching to provide faster and more reliable content delivery, name-based routing to provide better scalability, and self-certifying contents to ensure better security. Due to the differences in the core architecture of ICN compared to existing IP-based networks, it requires special considerations to provide quality-of-service (QoS) or quality-of-experience (QoE) support for applications based on ICNs. This paper discusses the latest advances in QoS and QoE research for ICNs. First, an overview of ICN architectures is given, followed by a summary of different factors that influence QoS and QoE. Approaches for improving QoS and QoE in ICNs are then discussed in five main categories: in-network caching, name resolution and routing, transmission and flow control, software-defined networking, and media-streaming-based strategies. Finally, open research questions for providing QoS and QoE support in ICNs are outlined for future research.  
ICN; QoS; QoE; CCN; NDN; Future Internet +
Footnote †: journal: Network
## 3 Methods
### Winding Angle Detection Method  
#### 3.2.1. Grayscale by Slice Method  
There are many methods to convert from color images to grayscale images, such as the maximum value method, the average method [29], the weighted average method [30], etc. Figure 8 shows the grayscale effect obtained by the maximum value method and weighted average method.  
Observing the above images, the grayscale images obtained by the maximum method and the weighted average method have one common feature, the difference in brightness between the two rectangular boxes at the top and bottom of each image is too large, and the follow-up is not conducive to determining the segmentation threshold for binarization. To optimize the grayscale display effect, based on the principle of adaptive histogram equalization, a slicing method is proposed for grayscale equalization of such images with a grayscale difference in this paper, and the method steps mainly contain:  
1. First, the height of the ROI image is \(h\), the width is \(w\), and the number of horizontal slices is \(n\); the pixel number \(area_{i}\) of each sub-image after segmentation is: \[area_{i}=(\frac{h}{n})\times w\] (1)
2. Next, calculate the gray level distribution \(L_{i,j}\) (\(i\in 1-n,j\in 0\)-255) of each sub-image, the ratio \(P_{i,j}\) of the total pixels of the sub-image occupied by each gray level, and the cumulative gray level The proportion of \(S_{i,k}\), (\(k\in 0\)-255): \[P_{i,j}=\frac{L_{i,j}}{area_{i}}\] (2) \[S_{i,k}=\sum_{j=0}^{k}P_{i,j}\] (3)
3. In the final normalization process, multiply \(S_{i,k}\) by the maximum gray level minus the difference between the minimum gray level, and round up, and after calculation, \(N_{i,k}\) is: \[N_{i,k}=round[S_{i,k}\times(L_{\text{max}}-L_{\text{min}}+1)]\] (4) At this time, \(N_{i,k}\) is the corresponding \(L_{i,j}\) after adaptation.  
After the above process, Figure 9 is the grayscale image obtained when \(n\) is taken as 20/40/80, respectively. It is observed that when the value of \(n\) is larger, the stripes in the image are less obvious and the equalization effect is better. However, if the value of \(n\) is too large, a larger workload will be generated, and the performance requirements of the processor will become higher. Considering that the size of the intercepted ROI image is 640 \(\times\) 480 pixels, the CPU of the test platform used in this paper is an i7-6700 HQ @2.60 GHz  
Figure 8: Image graying: (**a**) maximum value method; (**b**) weighted average method.  
(Intel, Santa Clara, CA, USA), with \(n=40\) as the slice value, and the processing time is 0.28 s, which can retain the details of the image without generating excessive workload.
# Effect of the Metro Train on the Smoke Back-Layering Length under Different Tunnel Cross-Sections
## 3 Dimensionless Analysis  
### Without Metro Train Blockage in Tunnel  
Previous research [4; 5; 8] showed that the governing parameters for smoke back-layering length are the heat release rate, longitudinal ventilation velocity, tunnel geometry, air density, ambient temperature, the thermal capacity of the air, and gravitational acceleration. [PERSON] and [PERSON] [3] demonstrated with experiments that for tunnels with the same height, the critical ventilation velocity varies depending on the tunnel width, and the tunnel height cannot fully reflect the influence of tunnel geometry on the smoke flow. Given the tunnel cross-section geometry, the hydraulic diameter of the tunnel was used as the characteristic length. For the sake of simplicity, a horizontal tunnel was assumed. Therefore, the smoke back-layering length can be expressed as [4]:  
\[L=f\big{(}\ Q,V,\rho_{a},C_{p},T_{a},g,\overline{H}\big{)} \tag{7}\]  
Based on dimensional analysis, this function can be expressed as:  
\[L^{*}=\frac{L}{\overline{H}}=f\Bigg{(}\ \frac{Q}{\rho_{a}\overline{H}^{2}V^{3}}, \frac{C_{p}T_{a}}{V^{2}},\frac{g\overline{H}}{V^{2}}\Bigg{)} \tag{8}\]  
The first and second terms on the right side of Equation (8) can also be incorporated. A dimensionless longitudinal ventilation velocity was defined as:  
\[V^{*}=\frac{V}{\sqrt{g\overline{H}}} \tag{9}\]  
Then, the above equation can be transformed into:  
\[L^{*}=f\Bigg{(}\ \frac{Q}{\rho_{a}C_{p}T_{a}g^{1/2}\overline{H}^{5/2}}, \frac{V^{2}}{g\overline{H}}\Bigg{)}=f(Q^{*},V^{*}) \tag{10}\]  
where \(Q^{*}=\frac{Q}{\rho_{a}C_{p}T_{a}g^{1/2}\overline{H}^{5/2}}\).  
According to previous studies [4; 5; 8], when there is no blockage in the tunnel, the dimensionless smoke back-layering length can be expressed as:  
\[L^{*}\propto\ln\left(\frac{Q^{*1/3}}{V^{*}}\right) \tag{11}\]
# Image Human Thorax Using Ultrasound Traveltime Tomography with Supervised Descent Method
###### Abstract  
The change of acoustic velocity in the human thorax reflects the functional status of the respiratory system. Imaging the thorax's acoustic velocity distribution can be used to monitor the respiratory system. In this paper, the feasibility of imaging the human thorax using ultrasound traveltime tomography with a supervised descent method (SDM) is studied. The forward modeling is computed using the shortest path ray tracing (SPR) method. The training model is composed of homogeneous acoustic velocity background and a high-velocity rectangular block moving in the domain of interest (DoI). The average descent direction is learned from the training set. Numerical experiments are conducted to verify the method's feasibility. Normal thorax model experiment proves that SDM traveltime tomography can efficiently reconstruct thorax acoustic velocity distribution. Numerical experiments based on synthetic thorax model of pleural effusion and pneumothorax show that SDM traveltime tomography has good generalization ability and can detect the change of acoustic velocity in human thorax. This method might be helpful for the diagnosis and evaluation of respiratory diseases.  
Keywords: thorax imaging; ultrasound traveltime tomography; supervised descent method +
Footnote †: journal: Medical Imaging