esa-sceva/satcom-chunk-collection huggingface dataset:
# Leveraging the Chain on Goals Model in Football: Applications for Attack and Defensive Play
###### Abstract  
Introduction: Football analysis has experienced significant growth in recent years as an applied research field. This study aims to contribute to this area by applying the chain on goals model to analyze both the attacking and defensive phases of football matches. Additionally, it introduces four practical concepts to better understand player and team performance in Spain's professional football leagues. Method: Data for the 2023/24 season were collected from Football Reference, covering both men's (LaLiga) and women's (LigaF) leagues. Variables analyzed included team performance, attack and defensive performance, goals saved above average (GSAA), goals and possession value (PV), expected goals (xG), and xG on target (xGOT) for attack and defensive phases. Four practical concepts analyzed were off-ball movement (PV-xG), player's offensive quality (xG-xGOT), team's positioning (PVA-xGA), and player's defensive quality (xGA-xGOTA). Descriptive and comparative statistical analyses were performed to compare all variables between the two leagues using an Independent Student's test. Additionally, correlation coefficients were calculated to examine the relationships between the four concepts. Results: Significant differences were observed between leagues in defensive performance (\(p\) = 0.03) and GSAA (\(p\) < 0.001). Practical concepts revealed disparities in off-ball movement and team's positioning (\(p\) < 0.001 in both). No correlations were found between off-ball movement and player's offensive quality or between team's positioning and player's defensive quality. Conclusions: The Spanish women's league exhibited defensive weaknesses, conceding more goals and showing lower goalkeeper performance. PV was the most influential variable in the women's league, while xG was critical in the men's league.  
data analysis; football; expected goals on target; performance indicators +
Footnote †: journal: Applied sciences
# A Microfluidic Approach for Biosensing DNA within Forensics
## 4 Biocompatibility of Materials for Forensic Microfluidic Devices
### Conclusion  
Silicon is a suitable material for well-based PCR systems, due to its good thermal conductivity. Glass or polymers, on the other hand, have a low thermal conductivity, making these chip materials suitable for continuous-flow PCR systems. Polymers, like COC, are also attractive for microfluidic devices for forensic DNA analysis due to their bio(chemical)compatibility, transparency and the the relatively wide variety of available fabrication techniques, which makes it possible to fabricate polymer-based chips at relatively low costs, making them suitable as disposables.
# Fast Pre-Diagnosis of Neoplastic Changes in Cytology Images Using Machine Learning
## 2 Materials and Methods
### The Dataset
#### 2.4.1 The Detection Subsets  
Initial Dataset  
The original images were mapped to the standard resolution of 640 \(\times\) 480 to create the 'initial' dataset. Pascal visual object classes (VOC) [13] format was used to standardize the set and make it digestible for future users. The primary image information is also included: the resolution, filename, and color depth.  
When analyzing samples, the main problem is determining whether a given cell can be qualified as cancerous. During sample preparation, some cells may be damaged or distorted, making them non-diagnostic. Additionally, not all diagnostic cells were tagged in the 'initial' dataset.  
The first models were trained with the 'initial' dataset and the default network parameters. However, the preliminary results were not satisfactory. The inferior performance was related to under-represented cells and the fact that the model detects objects in the context. Consequently, sparse cells and too few images in the selected category posed a challenge for the model. Therefore, so the 'divided' dataset concept was introduced.  
Figure 4: Overview of the datasets relationship.  
Figure 5: Datasets comparison. (**a**) Total number of objects in each class. (**b**) Total number of photos in each class. (**c**) Objects per photo in each class.  
Images with a resolution of \(640\times 480\) were divided into four parts, creating images with a resolution of \(320\times 240\) (Figure 6). This operation increased the number of samples in the dataset and removed areas where no cells or only one is selected. Unfortunately, the number of all objects decreased since the border cells were missed as they were split (see Figure 5).  
After splitting, the lymphoma image collection only doubled since no cells were selected in many areas of this class photos. It was different for the other two classes. They contained more tagged cells, and the cells were dispersed. There was a significant increase in the number of mastocytoma pictures as they form densely packed groups. The split of the images and discarding parts with no labeled data improved the model's performance, so we used this dataset as a basis for further experiments.  
A standard solution to computer vision problems is the augmentation of the dataset that mitigates an unbalanced number of samples of different classes. The tool for image manipulation [14] was used that allowed us to edit not only the images but also the label information file. Using the option to mirror the photo along the X-axis, the two new datasets were prepared. The balanced set consists of a balanced number of photos for each class, and the big set is doubled using reflection along the axis.
# Difference in Aroma Components of Black Teas Processed on Different Dates in the Spring Season
## 4 Conclusions  
This study investigated the differences in the aroma quality and volatile components of black teas produced at different dates in the spring season. The sensory evaluation showed that the ZYQ-3 black tea (April 15 th) had the best aroma quality. The predominant volatile compounds were geraniol, methyl salicate, linalool, phenylethyl alcohol, and trans-linalool oxide (furanoid), etc. The total content of volatile compounds was obviously different among the black teas and reached the maximum in ZYQ-3 (April 15 th). The flavor index showed the highest value in the ZYQ-2 black tea (April 8 th). The OAV analysis showed that the predominant aroma-active compounds were \(\beta\)-ionone, \(\beta\)-damascenone, linalool, (E)-\(\beta\)-ocimene, and geraniol. The multivariate statistical analysis and OAV analysis implied that linalool, (E)-\(\beta\)-ocimene, geraniol, and (E,E)-2,4-nonadien were the key differential odorants determining the aroma of the black teas. Combined with the sensory evaluation and the differences in aroma components, it is proposed that black teas produced on approximately April 15 th are more likely to have better aroma quality. This study can provide scientific guidance for the production of black tea in the Hunan district, China.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/foods12234368/s1](https://www.mdpi.com/article/10.3390/foods12234368/s1)]([https://www.mdpi.com/article/10.3390/foods12234368/s1](https://www.mdpi.com/article/10.3390/foods12234368/s1)), Figure S1: The average proportion (%) of different kinds of volatile compounds in black tea samples; Table S1: The changes in the flavor index of various kinds of volatile compounds; Table S2: The fold changes of the flavor index of various kinds of volatile compounds.  
Conceptualization, H.Z. and Q.C.; methodology, P.Y. and Y.H.; software, P.Y.; validation, H.Z. and Q.C.; formal analysis, P.Y. and Y.H.; investigation, P.Y., Y.H., Z.L., X.Z., H.H. and N.Z.; resources, H.Z. and Q.C.; writing--original draft preparation, P.Y. and Y.H.; writing--review and editing, H.Z. and Q.C.; supervision, Q.C.; funding acquisition, P.Y., H.Z. and Q.C. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the financial support provided by the China Agriculture Research System of MOF and MARA (CARS-19), the Jiangxi Provincial Natural Science Foundation (20224 BAB205028), the Key Research and Development Project of Hunan Province (2021 NK1020-2), and the Hunan Provincial Natural Science Foundation (2022 JJ40196).  
Data availability Statement: Data is contained within the article and Supplementary Material.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### HS-SPME Conditions and Sample Preparation  
The desorption conditions and the concentrations of salt and fiber were selected according to the literature [13; 18; 31] as follows: the addition of 25% _w/v_ sodium chloride (NaCl) was performed in the samples to control the ionic strength, 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxosen/polydimethylsiloxane) SPME fiber was used, which is suitable for the detection of several volatiles from different compound groups, and a 10 min desorption at 230 \({}^{\circ}\)C was performed. A Box-Behnken Response Surface Methodology (RSM)design was applied for the optimization of the extraction time and temperature, as well as sample volume, which were considered to be the most important factors [38]. Briefly, three different values for each factor were tested on pooled grape marc spirit samples that were representative of all samples. The optimal conditions resulting from the DoE were 45 \({}^{\circ}\)C extraction temperature, 35 min extraction time, and 4 mL sample volume.  
Finally, 4 mL of sample, 1 g of NaCl for the salting-out effect, and 12 \(\mu\)L of a 1000 \(\mu\)g mL\({}^{-1}\) 4-methylpentan-2-ol internal standard solution in methanol, resulting in a final concentration of 3 \(\mu\)g mL\({}^{-1}\), were added in a 10 mL glass vial. The vials were sealed with a PTFE/silicone septum and were equilibrated at 45 \({}^{\circ}\)C for 10 min with agitation. The (DVB/CAR/PDMS) fiber (Sigma-Aldrich (Darmstadt, Germany), 2 cm length, 50/30 thickness) was then introduced to the headspace for 35 min at 45 \({}^{\circ}\)C. According to the manufacturer's recommendations, the fiber was conditioned at 270 \({}^{\circ}\)C.  
A QC sample was prepared as a representative sample by mixing equal volumes of each wine sample and was injected during the study to evaluate the stability of the analytical system. Blank runs were also performed to reveal possible carryover. The samples were analyzed in randomized order. All grape marc spirits were analyzed with the aforementioned method to fingerprint the volatilome of each spirit and determine the differences between them according to their geographical origin. QC samples were analyzed at the beginning, after every 7 samples during sample analysis, and at the end of the batch of analyzed spirits to study the stability and reproducibility of the method. The identified compounds presented a relative standard deviation below thirty percent (RSD < 30%) and multivariate statistical models were applied to investigate the discrimination and correlation among samples.
# A Local Feature Engineering Strategy to Improve Network Anomaly Detection +
## 7 NSL-KDD Results
### Heterogeneous Network Scenarios  
The previous validation process of the proposed LFE strategy, performed in relation to each category of events in the NSL-KDD dataset, is here extended with further experiments, which were carried out on other two datasets that involve new classes of devices and events. Differently to what we have done for the NSL-KDD dataset, where the analysis was focused on the goal to perform an in-depth evaluation of the LFE strategy behavior with regard to specific network events, here we consider these datasets in their entirety, with the aim to simulate a real-world exploitation of the LFE strategy, in order to offer an overview in terms of average performance, with respect to heterogeneous network scenarios.  
The additional datasets that we used to this purpose are publicly available, and are the following:  
* **CICIDS2017**[114]: This dataset (hereafter, CIC) involves network activity based on the HTTP, HTTPS, FTP, SSH, and email protocols, related to normal events and several intrusion attacks such as Brute Force SSH, Brute Force FTP, Denial of Service, Infiltration, Heartbleed, Web Attack, Botnet, and Distributed Denial of Service. It is freely downloadable from the Internet ([[https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html)]([https://www.unb.ca/cic/datasets/ids-2017.html](https://www.unb.ca/cic/datasets/ids-2017.html))).
* **UNSW-NB15**[115]: This dataset (hereafter, UNS) was created in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) ([[https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/](https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/)]([https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/](https://www.unsw.adfa.edu.au/unsw-canberra-cyber/cybersecurity/ADFA-NB15-Datasets/))). It contains nine different types of attacks: Fuzzers, Analysis, Backdoors, Denial of Service, Exploits, Reconnaissance, Shellcode, Worms, and Generic.  
The optimal values of \(\delta\), shown in Table 14, have been detected by following the same criteria reported in Section 6.4, as well as the adopted metrics and algorithms, which are the same as those previously described, respectively, in Sections 6.2 and 6.3.  
The experimental results related to these datasets are shown in Table 13.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Dataset** & **Algorithm** & \(\delta\)**Value** & **Dataset** & **Algorithm** & \(\delta\)**Value** \\ \hline \multirow{4}{*}{CIC} & AB & 256 & AB & 259 \\  & DT & 100 & & DT & 239 \\ \cline{1-1}  & GB & 150 & UNS & GB & 246 \\ \cline{1-1}  & MP & 217 & & MP & 12 \\ \cline{1-1}  & RF & 273 & & RF & 239 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Optimal \(\delta\) value detection—additional datasets.  
Figure 11: NSL-KDD dataset—overall mean performance in relation to all events.
## 3 Results and Discussion
### Imaging Results  
The developed AFM is able to operate in constant-force mode. Unlike constant-height mode imaging in which the deflection signal is used directly to generate the topographic data; in constant-force mode, the image is generated from the motion of the \(Z\)-piezo with the cantilever deflection held constant. However, our AFM setup is limited to contact mode imaging currently and not able to perform imaging in more advanced modes such as non-contact imaging and tapping mode imaging. Figure 11 illustrates AFM images of the author's hair acquired in constant-force mode. All images presented here are raw data. Further data processing procedures such as plane leveling, flattening, and filtering were not applied to the obtained images. Each image was recorded at 150 \(\times\) 150 pixels with a scanning time of approximately 18 min. Trace and retrace images were taken simultaneously. Three sets of images with scan size of 20 \(\upmu\)m \(\times\) 20 \(\upmu\)m (Figure 11a,b), 15 \(\upmu\)m \(\times\) 15 \(\upmu\)m (Figure 11c,d) and 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m (Figure 11e,f) were obtained consecutively with the same AFM cantilever. The cantilever used has a force constant of 0.2 N/m with a resonant frequency of 13 kHz  
Figure 10: Soft front panel oscilloscope of NI ELVISmx.  
(BudgetSensors: Contact-G). Figure 11g depicts the 3D surface plot of Figure 11a. As can be seen in these images, the hair cuticle which is the outermost part of thehair shaft and the overlapping layers are clearly visible. This demonstrates that our developed AFM is capable of measuring features on the submicron scale.  
Figure 11: AFM images of author’s hair acquired in constant-force mode: (**a**) Trace image with scan size of 20 \(\upmu\)m \(\times\) 20 \(\upmu\)m; (**b**) Retrace image of (**a**); (**c**) Trace image with scan size of 15 \(\upmu\)m \(\times\) 15 \(\upmu\)m; (**d**) Retrace image of (**c**); (**e**) Trace image with scan size of 10 \(\upmu\)m \(\times\) 10 \(\upmu\)m; (**f**) Retrace image of (**e**); (**g**) 3D surface plot of (**a**).  
During scanning, the feedback controller constantly adjuststhe \(Z\)-piezo in order to maintain a fixed set point. However, the tip of the cantilever will not track the surface properly if the feedback mechanism reacts slowly. Two main causes are high scanning speed and low feedback gains. The real-time line scans for trace and retrace can be compared during image scanning. If the tracking is good, the shapes of the trace and retrace are almost identical. Most of the time the quality of the image can be improved by reducing the scanning speed or increasing the feedback gains. Figure 12 shows several line scans for trace and retrace which are extracted from AFM images in Figure 11. Note that the data points have been shifted horizontally to enablea better comparison in each pair of trace and retrace lines. As can be seen in these plots, the trace and retrace lines coincide closely. This shows that the developed feedback mechanism is responsive enough during imaging at a reasonable scanning speed.
# An Improved Method for Ship Target Detection Based on YOLOv4
## 3 Methods
### Improved Network Model  
When CNN performs ship detection, the region containing ships in the remote sensing image is used as the effective region that plays a dominant role. However, due to the large variety of ship sizes in the image, small ships are easily confused with noise, resulting in their features not being effectively extracted. In contrast to SPP's max-pooling, RFB introduces dilated convolution and residual connection, which can enhance the receptive field of the effective region and retain small ships in the feature map.  
The YOLOv4 network begins forward propagation after inputting the image: the backbone performs an extensive number of convolution operations on the input image to extract multi-scale feature maps, and then the neck fuses the extracted feature maps. This means that the fused feature combines the features extracted from each channel and location equally. However, the feature maps for each channel and location can actually be seen as a response to specific semantic information. The detection of the target is facilitated if the features representing the target are provided with appropriate weights. The attention mechanism captures the global and local relationships in the input image and thus focuses on finding the key information about the target. Therefore, we introduce CBAM in the YOLOv4 network to improve the original structure. The channel attention sub-module of CBAM establishes interdependencies between channels and enhances the representation of specific semantic information. The spatial attention sub-module acquires the weights of local features to improve the localization of candidate regions, which can enhance the useful features representing the target and resist background interference at the same time.  
In order to address the problems of complex background interference and poor detection of small targets in remote sensing images with high resolution, an improved YOLOv4 ship detection model based on the RFB_s module and CBAM module is proposed. The RFB_s module is introduced to enhance the receptive domain and improve the detection effect of small ships; the CBAM module is introduced to adaptively adjust the weights of different feature layers to effectively capture the information of small ships while improving the ability of the model to resist complex background interference. Figure 6 illustrates the structure of our improved model. RFB_s is used instead of the SPP structure; two CBAM modules are added between the backbone part and the neck, and the feature map extracted from the backbone network undergoes a convolution operation to reduce the number of channels with the purpose of reducing the number of introduced parameters. Then, the feature map is enhanced by the CBAM module for feature fusion. Similarly, a CBAM module is added between the RFB_s module and the neck, and the feature maps are enhanced and passed into the neck PANet for feature fusion.  
Figure 5: CBAM structure diagram [14].
## 3 Protocols Verifying the Authenticity of Digital Files in Distributed Networks
### Example of Use Case in Distributed Networks
#### 3.2.1 Initial Enrollment and Distribution of the Files  
The client uses a CRP mechanism during an initial set up phase, as described in Section 3.1, then distributes the information to the network. In most implementations, a compact seed replaces the challenges, as described in Section 2. The client is either equipped with the software needed to operate a CRP mechanism or employs a contract agent to perform the confidential task. From \(F_{i}\) and nonce \(\omega_{i}\), the CRP mechanism generates the ciphertext \(C_{i}\) and the file \(M^{\circ}_{i}\): \(\{S_{i}\), \(M^{\circ}_{i}\}\). The stream \(M^{\circ}_{i}\) is the ciphertext encrypted with ephemeral key \(K\) of the message of authentication \(M_{i}\), which can be the public key \(M_{i}=PK_{i}\) needed for the DSA of \(F_{i}\). An active participant such as an agent can take care of the distribution of \(C_{i}\) and \(M^{\circ}_{i}\). In Figure 4, the distribution is the following:  
* The smart contract has the technical capability to perform CRP mechanisms.
* The storage node keeps \(C_{i}\) and \(M^{\circ}_{i}\).
* The client keeps \(F_{i}\) and \(\omega_{i}\).