esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Related Work
### Active Queue Management
#### 2.1.3 Flow Queue-Controlled Delay (FQ-CoDel)  
FQ-CoDel [5] is an active queue management (AQM) algorithm that combines fair queuing and the CoDel algorithm to mitigate bufferbloat in computer networks. Karlstad University first proposed it in September 2014 [14].  
FQ-CoDel is a hybrid AQM scheme that utilizes multiple queues, each governed by its instance of CoDel for buffer management. As in a typical CoDel instance for a single queue, it drops packets when the packet-sojourn time exceeds a specific interval. To ensure a fair bandwidth among the queues, we employ a flow-queuing algorithm known as deficit found robin (DDR) with an efficiency of O(1) time complexity. This combination of fair queuing and CoDel helps to achieve flow-level fairness and reduce bufferbloat, packet loss, and latency. Another feature of FQ-CoDel is its implementation of two sets of old and new queues. This provides brief periods of priority to lightweight or short burst flows, helping deliver low latency.
# Improving Web Readability Using Video Content: A Relevance-Based Approach
## 5 Results
### Impact on Web Readability with Example  
In addition to our quantitative evaluation, we illustrate the impact of video relevance on web readability through a graphical comparison of two educational websites. One website includes a highly relevant video aligned with the textual content, while the other contains an irrelevant video that detracts from the user's experience.  
* Highly Relevant Video Example: The webpage focuses on biology, with the text discussing \"cell structure\" and \"mitochondria.\" The embedded video clearly supports this content by explaining cell structure in detail, reinforcing key concepts mentioned in the text. The design is clean, with proper contrast, minimal distractions, and strong alignment between the video and the text as shown in Figure 3a.
* Irrelevant Video Example: In contrast, the second website features a page about mathematics but includes an unrelated video, such as an advertisement or a lecture on an entirely different topic. This video creates a disconnect between the user's expectations and the content, leading to distractions and reducing the overall readability of the page. The page layout is cluttered, and the video does not complement the textual information as shown in Figure 3b.  
This visual example complements our findings by showing how relevant videos can enhance readability, while irrelevant content diminishes the user experience. In line with our automated relevance scores and user study, highly relevant videos promote longer page engagement and better user comprehension, as depicted in the clearer, more organized layout of the first example.
## 2 Materials and Methods and Results
### Instrumenting the Scrum Machine  
We purchased a Rhino Raider scrum machine (Legend Fitness, Knoxville, TN, USA). This machine was chosen for its relatively cheap cost, ease of use, and most importantly for instrumentation purposes, its use of a static arm for the pads to mount onto. The static arm ensures that when a player pushes against the pads, the load cells receive all the compression forces; other scrum machines, which have dynamic pad arms that give with variable tension, may better mimic a realistic scrum for training purposes but are not suitable for capturing force data. To instrument the machine, we purchased 8 S-type load cells with 1000 lbf capacity (SM-1000, Interface, Scottsdale, AZ, USA), as well as 8 SGA AC/DC powered signal conditioners (configured for \(-5\) volt at full compressive load; Model SGA, Interface, Scottsdale, AZ, USA) and an analog-to-digital signal converter (hereafter \"ADC\"; 12 bit ADC; Model USB-6008, National Instruments Corporation, Austin, TX, USA). We contracted a machine shop to manufacture 4 4.67 mm (3/16-inch) steel plates (Figure 1) that replicated the connection of each scrum machine pad arm to the pad, with the addition of 2 extra holes drilled in for the bolts to pass through to connect 2 S-type load cells to each of the pads (Figure 2). An additional 14.29 mm (9/16-inch) hole was drilled into the center of 2 of the steel plates so that it could act as a flat platform for the later calibration of each load cell.  
Figure 1: Schematic of steel plate to connect S-type load cells to scrum pads. All measurements in inches. Yellow holes are 14.29 mm (9/16 inch).
# The Performance of LiF:Mg-Ti for Proton Dosimetry within the Framework of the MoVe IT Project
## 2 Materials and Methods
### Dosimeters, Annealing, and Readout Procedures  
The TLDs are widely used in conventional radiation therapy [31; 32; 33], while their use in the field of clinical proton beam dosimetry is not currently a routine practice. Despite care being recommended when using TLDs within the distal-off region of the pristine Bragg peak, due to their energy and LET dependence, they are the choice for dosimetry of small fields in which negligible perturbation of the irradiation beam is required.  
The TLD-100 chips (Harshaw Chemical Company, Cleveland, OH, USA) utilized in our investigation are the foremost common and cheapest thermoluminescent materials, containing a natural lithium composition (LiF:Mg, Ti). They have size of 3.2 \(\times\) 3.2 \(\times\) 0.89 mm\({}^{3}\), spatial resolution of 2 mm, and density of 2.64 g/cm\({}^{3}\). This type of TLD has an effective atomic number equal to 8.2 [34], comparable to water (Zeff = 7.42 [35]), as well as a high sensitivity and linearity range that extends from 10 uGy up to 10 Gy, where the sub-linearity starts. These characteristics represent the main advantages for dosimetry applications, especially in clinical measurements [31; 36; 37].  
Annealing of the TLD material before the irradiation was performed according to the standard procedure: 1 h heating in an oven at 400 \({}^{\circ}\)C; 2 h heating at 100 \({}^{\circ}\)C; cool-down to ambient temperature. Dosimeters were analyzed with a hot gas manual TLD reader, Harishaw model 3500, installed at the Department of Physics of the University of Naples Federico II. A dedicated software (Thermo Scientific _WinREMS_, Waltham, MA, USA) was used to set the acquisition setup parameters and to monitor the TLD readings. The time-temperature profile for each chip started when the gas was at 100 \({}^{\circ}\)C, at which point the gas temperature increased by 5 \({}^{\circ}\)C/s to a maximum of 300 \({}^{\circ}\)C, where it was held constant for 40 s, after which the gas was cooled to 100 \({}^{\circ}\)C before the subsequent chip was read. The photomultiplier charge was integrated over the temperature ramp-up and plateau regions of the heating profile. During the measurement process, a continuous nitrogen flow was used to reduce chemiluminescence and spurious signals not associated with the irradiation [38]. A total of 80 dosimeters from two batches of TLDs were used during MoVe IT activity (2017-2021).
## 5 Experimental Evaluations and Results
### Data Preprocessing  
The NIH malaria dataset is balanced with 13,779 parasitized and 13,779 uninfected cell images. To evaluate the performance of the deep learning models, the dataset is split into training, testing, and validation subsets. To this end, 60% data are used for training and 10% data for validation and the remaining 30% unseen dataset is used for testing the performance of the trained model. Partitioning details of the dataset are presented in Table 2.  
Data augmentation techniques are usually used to improve the accuracy of deep learning models by providing variety in the dataset [73]. Neural networks require large datasets for better generalization and to avoid over-fitting. The image data generator builds powerful deep learning models by using a small dataset [74]. After resizing images of the dataset, ImageDataGenerator module of Keras library [[https://keras.io/](https://keras.io/)]([https://keras.io/](https://keras.io/)) (accessed date: 18 February 2021) is used for augmenting malaria cell images of training data by applying image transformation operations: 0.1 zoom value, 25 degree rotation, 0.05 sheer range with horizontal flip, (0.1, 0.1) translation for shifting both width and height. Image augmentation is not applied to testing and validation data because model performance is being evaluated for them. After data augmentation, the size of the training dataset is 173,700.
## 2 Materials and Methods
### Model Fitting
#### 2.4.5 Accuracy Validation of Yearly and Seasonal Time Series Patterns  
The continuous reconstruction results of the four models are compared in the two patterns: (1) yearly time series reconstruction; and (2) seasonal segment time series reconstruction. Both continuous-time series reconstructions use the above-mentioned methods including the time series outlier gap-filling algorithm and the Enhanced SG filter noise smoothing process, and then the accuracies of the four models for the continuous-time series reconstruction are evaluated by \(R^{2}\) and RMSE respectively.  
For the yearly time series pattern, the following steps compare the reconstruction results of the four models in the yearly time series reconstruction pattern. First, the yearly cropland, forest, and grassland NDVI time-series datasets are selected to evaluate the trend-fitting ability for its application in phenology extraction. In total, 494 cropland sampling points, 456 forest sampling points, and 2261 grass sampling points are chosen to evaluate the fitting abilities among different models. All the NDVI time series points are preprocessed, including outlier recognition, Gap filling, and the Enhanced SG filter. The yearly time series that remove some detailed variations and only retains the yearly trend is not too challenging for the four models. Therefore, for the yearly time series, the SG filter iteration time is set to be less than 100, and the yearly characteristics will not be lost but detailed variations are nearly lost during the process. Six or 8 parameters are not too challenging for smoothed time series. Second, the fitting accuracies are evaluated using the indices \(R^{2}\) and the RMSE calculated from the preprocessed time series and the fitted time series.  
For the seasonal time series pattern, the time series of the sampling points in Section 2.3.1 are preprocessed by outlier recognition and gap-filling methods in Sections 2.3.1 and 2.3.2. The preprocessed time series was then smoothed by the Enhance SG filter. Finally, with the seasonal division strategy, the time series are separately reconstructed by the four function fitting models. The fitting \(R^{2}\) and RMSE are separately calculated from the preprocessed time series and the final time series to evaluate the fitting accuracy of the four function fitting models.
# Assessing the Security and Privacy of Baby Monitor Apps
## 6 Discussion & Conclusions
### Recommendations for Developers  
In Section 4, we identified shortcomings in the privacy policies of the apps considered in this study, e.g., the lack of statements on the legal basis of collection and processing of personal data. Recent studies have indicated that app developers are concerned about the strictness of existing privacy regulations that lead to fewer app revenues [44, 45], as well as having to compromise when it comes to the use of tracking and advertising services [46]. Furthermore, studies reported that app developers seek help in developer forums on privacy topics primarily when required by legal obligations to understand privacy regulations and communicate their concerns and questions about the collection of personal data or privacy-sensitive permissions [45, 47]. While the existing skill set of app developers could lead to the implementation of privacy-friendly practices in apps, disclosing the apps' compliance with existing privacy regulations by writing a privacy policy is not an easy task and requires knowledge about existing often complex and vast privacy regulations. To the best of our knowledge, there exists no study on developers being asked to write a privacy policy for their app, followed by an assessment of its correctness and compliance. A recentstudy proposed to facilitate the creation of compliant privacy policies for developers by introducing a privacy policy generator [48] called PrivacyFlash Pro, which automatically generates privacy policies based on static code analysis and questionnaires. Since a recent study indicates that common privacy policy generators for mobile apps have shortcomings in, e.g., detecting requested permissions [49], we suggest that app developers use reviewed and tested privacy policy generators, such as PrivacyFlash Pro, but also verify the completeness of the automatically generated privacy policy themselves.  
Our security analysis has shown that there are mainly security issues with essential security features, such as building secure channels, proper peer authentication, and authorization. We suggest that developers follow a guide for secure product development offered by the Open Web Application Security Project (OWASP) [50]. This would increase the chances that the developed application would conform to generally accepted security design best practices. Almost all security issues found could be solved by using secure communication in combination with a secure default configuration and adherence to the zero trust principle. Zero trust includes authentication and authorization mechanisms that ensure that only authorized users can access sensitive data and resources.
## 4 Technology Evaluation Criteria
### Coverage and Robustness  
Given definition:  
Coverage refers to the spatial extent to which the system's performance must be guaranteed, while robustness is defined as the resistance to disturbances, including temporary or permanent network failures.  
Additional commentary:  
Coverage and robustness of signal propagation and possible non-LOS signal propagation and even massive rock penetration must be considered at different levels. This is connected to the density of needed installations and robustness of coverage of new or distant areas. Extendable coverage with additional hardware infrastructure must be given (under consideration of data transmission capabilities).  
Suggested scale:  
We suggest differentiating signal transmission capability of signals under various conditions regarding LOS, harsh environment and penetration of rock or rock masses.  
1. Sensitive to (direct) line-of-sight (LOS)
2. Near LOS (signal transmission tens to hundreds of metres), insensitive to harsh conditions (e.g., dust and humidity)
3. NLOS, propagation of signal (signal transmission 100 th to 1000 th of meters) or even penetration of rock or even rock masses (signal penetration 10 th to 100 th of meters)
# Nitrogen Budget of Short Rotation Forests Amended with Digestate in Highly Permeable Soils
## 3 Results and Discussion
### Components of the Soil N Budget
#### 3.1.3 N Emissions  
The total N emissions (output) (Figure 3) were lower from SRF (3.46 and 4.60 kg-N ha\({}^{-1}\) y\({}^{-1}\), respectively, for A1 and A2) than from permanent meadow (5.35 and 8.11 kg-N ha\({}^{-1}\) y\({}^{-1}\), respectively, for P1 and P2), and the most were in the form of NH\({}_{3}\). N emissions from all the plots corresponded to 2.2-3.2% of the applied N (Table 1).  
As confirmed by literature, NH\({}_{3}\) emissions from slurry applied in the field are quite variable, ranging from 0.0 to 60% of the applied NH\({}_{3}\)[25]. N\({}_{2}\)O emissions ranged from 0.194 to 0.760 kg-N ha\({}^{-1}\) y\({}^{-1}\), which corresponded to about 0.2% of the total N distributed, a value slightly lower than that reported in literature [22]. Moreover, weather conditions have a crucial importance in determining N emission [9]. The hot weather conditions that characterized 29 June 2010 led to higher NH\({}_{3}\) emission for A1 and A2 plots. This did not occur in P1 and P2 plots; in fact, the bare soil in May led to a higher emission with respect to late June, where weeds had a shadowing effect that may have reduced emissions. In October 2010, the low emissions were probably caused by a combination of spontaneous grass covering between rows, cool temperature and litter cover. The high variability in the presented data can also be related with the different chemical characteristics of the digestate (Figure 3), which have a great influence on NH\({}_{3}\) emissions [26].  
Figure 3: N emissions from A1, A2, P1 and P2 plots; in each column, dark gray is NH\({}_{3}\), light gray is N\({}_{2}\)O (bars represent standard errors for each column). All the mean values are significantly different from each other (Tukey’s HSD, * \(p\) < 0.05).
# Decisive Shots: Unveiling Disparities between Winning and Losing Pairs in High-Level Men's Padel
## 4 Discussion  
The present research aimed to describe and distinguish shot characteristics between set-winning and losing pairs in high-level male padel players from Finland. The novelty of \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Shot Family**} & \multicolumn{6}{c}{**Pressure Training Matches**} & \multicolumn{6}{c}{**Competition Matches**} \\  & **Shot Type** & **Set Winning Players** & **Set Losing Players** & **Set Winning Players** & **Set Losing Players** & **Set Losing Players** \\  & & **\%** & **CSR** & **\%** & **CSR** & **\%** & **CSR** & **\%** & **CSR** \\ \hline \multirow{4}{*}{Without bounce} & Smash & \multirow{4}{*}{0.0 a} & \(-\)1.2 & 0.7 a & 1.2 & 0.0 a & \(-\)0.9 & 0.3 a & 0.9 \\  & Porahand volley & 7.1 a & \(-\)0.9 & 9.3 a & 0.9 & 11.7 a & 0.8 & 9.4 a & \(-\)0.8 \\  & Backhand volley & 17.1 a & 0.0 & 17.0 a & 0.0 & 17.9 a & 0.5 & 16.2 a & \(-\)0.5 \\ \hline With bounce and & \multirow{4}{*}{0.0 a} & Forehand & 11.4 a & 0.3 & 10.7 a & \(-\)0.3 & 5.4 a & \(-\)2.3 & 11.1 b & **2.3** \\  & Backhand & 10.4 a & \(-\)0.8 & 12.7 a & 0.8 & 12.1 a & \(-\)2.2 & 19.2 b & **2.2** \\ \hline \multirow{4}{*}{With bounce and  with wall} & Forehand biajada & \multirow{4}{*}{7.1 a} & \multirow{4}{*}{0.2} & \multirow{4}{*}{6.7 a} & \multirow{4}{*}{0.2} & \multirow{4}{*}{4.9 a} & \multirow{4}{*}{0.1} & \multirow{4}{*}{5.1 a} & \(-\)1.2 \\  & Back hand biajada & & & & & & & & \\ \cline{1-1}  & Back wall forehand & 7.1 a & \(-\)0.5 & 8.3 a & 0.5 & 6.7 a & \(-\)0.2 & 7.1 a & 0.2 \\  & Back wall backhand & 7.1 a & 0.2 & 6.7 a & \(-\)0.2 & 4.9 a & \(-\)0.1 & 5.1 a & 0.1 \\ \cline{1-1}  & Out of the court & 1.9 a & 0.9 & 1.0 a & \(-\)0.9 & 1.8 a & 0.8 & 1.0 a & \(-\)0.8 \\ \cline{1-1}  & Side wall forehand & 3.3 a & \(-\)0.8 & 4.7 a & 0.8 & 3.1 a & 0.5 & 2.4 a & \(-\)0.5 \\ \cline{1-1}  & Side wall backhand & 2.8 a & \(-\)0.5 & 3.7 a & 0.5 & 4.0 a & \(-\)2.0 & 8.4 b & **2.0** \\ \cline{1-1}  & Double wall forehand & 6.6 a & 0.0 & 6.7 a & 0.0 & 8.5 a & 1.2 & 5.7 a & \(-\)1.2 \\ \cline{1-1}  & Double wall backhand & 8.1 a & 1.2 & 5.3 a & \(-\)1.2 & 9.4 a & 1.9 & 5.1 a & \(-\)1.9 \\ \hline \multirow{4}{*}{Other} & Recovery smash & 0.5 a & \(-\)0.7 & 1.0 a & 0.7 & \multirow{4}{*}{**2.4**} & \multirow{4}{*}{5.7 b} & \multirow{4}{*}{\(-\)2.4} \\  & Contrapared & 7.6 a & \(-\)0.2 & 8.0 a & 0.2 & 11.7 a & **2.4** & 5.7 b & \(-\)2.4 \\ \cline{1-1}  & Serve & \multirow{4}{*}{0.0 a} & \multirow{4}{*}{**2.1**} & \multirow{4}{*}{4.3 b} & \multirow{4}{*}{\(-\)2.1} & \multirow{4}{*}{2.2 a} & \multirow{4}{*}{\(-\)0.8} & \multirow{4}{*}{3.4 a} & \multirow{4}{*}{0.8} \\ \cline{1-1}  & Return & & & & & & & & \\ \hline \hline \end{tabular} Note. % = percentage; CSR = corrected standard residuals; CSR > 1.96: Bold; a, b = indicate significant differences in the Z tests for comparison of column proportions from \(p<0.05\) adjusted according to Bonferroni.  
\end{table} 
Table 4: Differences in forced errors between set winning and set losing players according to the shot type.
# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
## 1 Introduction
Generally, the state of the art has mostly considered the segmentation of CME fluid accumulations and, in a lesser extent, SRD ones (as DRT accumulations do not present a defined border to segment). Most of the works mainly extract specific segmentations of the fluid regions using techniques such as thresholding and candidate filtering based on size, intensity or shape constraints ([PERSON] et al. [14], [PERSON] et al. [15]), clustering algorithms ([PERSON] et al. [16]) and even transformations to alternate domains/graph search-cut strategies ([PERSON] et al. [17; 18]). To the best of our knowledge, only two works tried to generate a segmentation on the three clinical types we present, the work of [PERSON] et al. [19] which used texture, intensity and clinical-based features to localize CME, DRT and SRD fluid accumulations in OCT images and the work of [PERSON] et al. [20], which uses these detections to further generate a segmentation of the different fluid accumulations. Regarding deep learning techniques, recently, works like the ones proposed by [PERSON] et al. [21], [PERSON] et al. [22], [PERSON] et al. [23] or [PERSON] et al. [24] also aimed for a precise segmentation, most of these deep approaches deriving from the U-Net architecture of [PERSON] et al. [25] which has been used too to characterize some of the segmented fluid accumulations ([PERSON] et al. [26]). Despite the variety of proposals, these approaches imply, as main limitation, the necessity of visible defined contours for the analyzed fluid regions. To improve the detections in these diffuse regions, [PERSON] et al. [27; 28] proposed a complementary strategy based on a regional identification. Afterwards, in [PERSON] et al. [29] this strategy was used to analyze the OCT images and generate an intuitive visualization of the detected region. In this work, and considering satisfactory preliminary results [30], we propose to expand this strategy to be able to simultaneously detect and characterize the aforementioned types of DME. Using this identifications and characterizations, we also designed a graphical intuitive representation of the defined types of DME to facilitate the inspection and diagnosis by the expert clinicians. Our methodology was trained and tested using a dataset composed by images obtained in clinical practice where experts selected the images with the intraretinal fluid categories of interest, and made sure to include the maximum heterogeneity in both severity and variety within each type of fluid accumulation. The work hereby presented is divided in three main sections. Section 2: Materials and methods describes the tools, libraries and dataset used; as well as the methodology followed. Section 3: Results presents the performed experiments and a brief comment on them. Afterwards, these results are analyzed in Section 4: Discussion. Finally, Section 5: Conclusions presents a summary of what was achieved in this work as well as possible future lines of work.