esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sensor Node Implementation Result  
The sensor readings are displayed on the server, which can be accessed anywhere. The created database shows the result of the DHT11 sensors in two real-time charts: one shows the temperature readings, and the other shows the humidity readings. Figure 13 displays the outcome of a web client monitoring the temperature and humidity in a web server.  
With this setup, the user has complete control over the server and can easily switch hosts, if necessary. There are numerous free and paid cloud options available for publishing the readings, but each has its drawbacks, such as limitations on the number of readings posted, the number of linked devices, and who may see the data. Furthermore, the cloud service can be changed or discounted at any moment. The output is as simple as possible to understand, and it can be reprogrammed to modify the web page's layout, post different sensor values, publish from numerous ESP boards, and much more.
## 2 Materials and Methods
### Algorithm for Detecting Weak Signals (\(\phi\)-Method)
For any practical situation, it can be simply adjusted for the best accuracy, but as a general rule, the shift value \(s\) must be not less than the size of the smoothing window \(M\). As the SNR decreases, an increasingly large width of the smoothing window \(M\) is beneficial for detection. At the same time, the picked onset of the event shifts more and more to the right along the time scale with respect to the actual known position of the signal. Accordingly, to compensate for this increasing time lag, the increasing values of temporal shift \(s\) are chosen manually. Thus, in summary, compared to the previously reported procedure focused solely on the \(\phi\)-parameter definition [59], the proposed method includes the following new steps pivotal for signal detection:  
* The calculation of the decision function \(g(m)\) from the \(\phi\)-parameter (involving zeroing the negative part of \(\phi\), recursive smoothing and normalizing steps);
* The calculation of the start and end times (as well as the amplitudes) of events from the decisive function;
* The use of an amplitude filter to cut off false alarms;
* The \"left shift\" of the obtained times to compensate for the delay caused by the smoothing window used for calculating \(\phi\);
* The use of an amplitude filter to cut off false events. Results of the application of the proposed algorithm and its testing against several other popular signal detection techniques are discussed in the next section.
## 2 Background and Literature Review  
Herein, we present a comprehensive literature assessment in the following subsections, including research about NDN, the methodology intended for its interest as well as a mechanism for forwarding, the SDN and its architecture, along with a review of mobility in NDN.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Results on Kvasir-SEG Segmentation  
For the evaluation of the efficacy of DEFI-Net, we carried out a series of experiments using the Kvasir-SEG dataset, with performance benchmarks compared against multiple existing approaches. DEFI-Net achieved the highest scores in both DSC and IoU, with results of 87.45% and 80.51%, respectively (Table 3). In comparison to convolutional-network-based methods, including Att-UNet [3] and U-Net [1], DEFI-Net exhibited significant enhancements, improving DSC by 4.46% over U-Net and 4.60% over Att-UNet. Additionally, DEFI-Net surpassed hybrid CNN-Transformer methods, including TransUNet [20] and HiFormer [24]. Specifically, it improved DSC and IoU by 1.84% and 2.36% over TransUNet and by 1.49% and 2.25% over HiFormer, respectively. These findings underscore DEFI-Net's superior segmentation capabilities on the Kvasir-SEG dataset.  
Furthermore, the segmentation results of DEFI-Net and other comparative methods on the Kvasir-SEG dataset were visualized. As depicted in Figure 5, DEFI-Net demonstrated better performance in segmenting colon polyp images, outperforming other methods. Notably, while most competing methods encountered issues with incomplete segmentation, DEFI-Net effectively mitigated this challenge, thereby reinforcing its effectiveness in colon polyp image segmentation.  
Figure 4: Qualitative results of multiple models on the ACDC dataset, with the right ventricle highlighted in red, the left ventricle in blue, and the myocardium in green.  
Figure 5: Qualitative results of different models on the Kvasir-SEG dataset.
## 2 Notation and Preliminaries  
The notation of this work is as follows. \(s\in\mathbb{R}\), \(\mathbf{v}\in\mathbb{R}^{n_{s}}\) and \(\mathbf{M}\in\mathbb{R}^{n_{t}\times n_{c}}\) represent the arbitrary scalar, vector, and matrix. \(\mathcal{F}_{I}\) and \(\mathcal{F}_{B}\) represent the inertial and body frames, respectively, where the expression of a vector for these frames is written as \({}_{i}\mathbf{v},\ i=I,B\). Unit vectors in orthonormal frames are denoted as \(\mathbf{u}_{1}=\left[\begin{array}{cccc}1&0&0&1\end{array}\right]^{T}\), \(\mathbf{u}_{2}=\left[\begin{array}{cccc}0&1&0\end{array}\right]^{T}\) and \(\mathbf{u}_{3}=\left[\begin{array}{cccc}0&0&1\end{array}\right]^{T}\). The composite rotation matrix \({}_{I}\mathbf{C}_{B}\in SO(3)\) is written from \(\mathcal{F}_{B}\) to \(\mathcal{F}_{I}\), the rotation sequence is depicted as \(z-y-x\), and associated Euler angles are denoted as \(\psi\), \(\theta\) and \(\phi\). \(g=9.81\) m/s\({}^{2}\) is the gravitational acceleration. \(\mathit{col}\{\cdot\}_{m=1}^{M}\), \(\mathit{row}\{\cdot\}_{l=1}^{M}\), and \(\mathit{diag}\{\cdot\}_{m=1}^{M}\) represent column, row and diagonal concatenation, respectively, of the entity within the parenthesis.
# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 4 Using ECADLL Information for Spoofing Detection  
The ECADLL architecture was originally proposed as a multipath mitigation technique. It is able to eliminate the tracking errors under multipath scenarios for signals having a delay larger than \(\approx\)50 ns relative to the satellite signal [12]. The goal of this work is to study and propose a reliable detection technique, able to recognize the spoofer's presence. In Figure 4, a basic block diagram is presented, where \(D_{i}(t_{k})\) is the decision variable for the \(i\)-th channel at time \(t_{k}\). Its values are defined as:  
\[D_{i}(t_{k})=\left\{\begin{array}{ll}2&\text{Spoofer}\\ 1&\text{Impairment}\\ 0&\text{No impairment}\end{array}\right. \tag{8}\]  
It is important to notice that the information provided by the ECADLL architecture is soft information on the estimation of the delay, amplitude and phase of the different signals (\(\hat{a}_{0}...\hat{a}_{n},\hat{\tau}_{0}...\hat{\tau}_{n},\hat{\theta}_{0}... \hat{\theta}_{n}\)). This information is combined to be used as an effective spoofing detection method and has the potential to help with estimating the real user position under spoofing attacks.  
Figure 4: Decision making block diagram.  
Figure 3: Monitoring block for ECADLL. It receives as inputs the estimations of delay, amplitude and phase, and it controls the powering and insertion of units inside the loop.
## 3 Method
### Example  
To further illustrate the evaluation methodology, we present an additional example about responses for the Grammar & Syntax task (the original questions were in Greek, but for clarity, we present equivalent text in English). In this task, the evaluators receive a sentence with grammar or syntax errors and need to evaluate LLM responses that try to fix the errors. For the generation of the responses, the third prompt in Table 1 was used. As such, given the incorrect input sentence: _\"Migrants are goes Greece and not finding any helps for paper and doctors is missing\"_, the following six responses are generated (as mentioned, the evaluators are not aware of the sources of the responses):  
* **Response 1:** Migrants going to Greece cannot find help with their documents or access medical care.
* **Response 2:** Migrants who go to Greece are not able to find help about papers and doctors.
* **Response 3:** Migrants who go to Greece cannot find help with their documents or medical care.  
* **Response 4:** Migrants are going to Greece but cannot find any help with their papers, and medical care is unavailable.
* **Response 5:** Migrants going to Greece are not able to find help with their papers or medical attention.
* **Response 6:** Migrants goes to Greece but don't finding help with their documents and no doctor can be found.  
The correct LLM response, based on both human evaluation and the LLM's self-evaluation, is the first one, generated by Claude 3.7 Sonnet, which properly adjusts verb tense, prepositions, and sentence structure. The other responses still contain errors, such as incorrect verb forms (\"are goes\" instead of \"are going\"), incorrect pluralization (\"helps\" instead of \"help\"), and unnatural phrasing (\"doctors is missing\" instead of \"doctors are unavailable\").  
It is worth mentioning that the human evaluators assessed not only the quality of responses but also their correctness across the four key criteria. For Reading Comprehension and Text Summarization, they evaluated the accuracy by comparing the LLM-generated responses against the original texts, which were extracted from the official website of the Ministry of Asylum in Greece. Specifically, for Reading Comprehension, evaluators read the original text, reviewed the given question, and assessed whether the LLM's response reflected the content. Similarly, for Text Summarization, they compared the generated summary against the full text to verify its accuracy and completeness. For Grammar and Syntax, correctness was assessed based on the clarity and meaning of the sentences, ensuring grammatical and syntactical accuracy. Lastly, for Sentiment Analysis, evaluators examined the sentiment identified by the LLM in a given text to determine whether it correctly captured the intended emotions.
# Multiple Genetic Rare Variants in Autism Spectrum Disorders: A Single-Center Targeted NGS Study
## 3 Results
### De Novo and Inherited Variants  
It was possible to perform family segregation analysis for 28 out of 40 patients. We found 4 de novo (Table 5) and 101 inherited variants.  
As for the de novo variants, two of them were found in the same patient (A14/55), for whom no other candidate variants were identified. The first variant is located in the ANKRD11 gene, frequent, and predicted to be damaging by Polyphen. The second variant is located in the NF1 gene, rare, and predicted to be tolerated by SIFT and benign by Polyphen. In patient L14/95, we found a frequent, tolerated, and benign variant in the SLIT3 gene. This variant has previously been associated with ASD, as described by [PERSON] et al. (2014) [23]. In patient LDM167, we detected a small duplication of two nucleotides (c.1131_1132 dupTA) in the PTEN gene, which caused the formation of a premature stop codon. This variant is not present in the GnomAD database and is predicted to be disease-causing by \"Mutation taster\" (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Patient** & **Gene** & \begin{tabular}{c} **Gene SFARI** \\ **Score** \\ \end{tabular} & **Variant** & **Inheritance** & **Predictions** & **Reference** & \begin{tabular}{c} **Allele Frequency** \\ **(GnomAD)** \\ \end{tabular} \\ \hline A14/55 & ANKRD11 & 2S & \begin{tabular}{c} c.890C\textgreater{}T, \\ p.Thr297 Met \\ \end{tabular} & De novo & \begin{tabular}{c} S damaging, P \\ probably \\ damaging \\ \end{tabular} & / & 0.0012 (1/800) \\ \hline  & NF1 & S & \begin{tabular}{c} c.1985A\textgreater{}G, \\ p.Lys662 Ag \\ \end{tabular} & De novo & \begin{tabular}{c} S tolerated, P \\ benign \\ \end{tabular} & / & 0.0002 (1/50,000) \\ \hline L14/95 & SLIT3 & no rating & \begin{tabular}{c} c.1886G\textgreater{}A, \\ p.Ser629 Asn \\ \end{tabular} & De novo & \begin{tabular}{c} S tolerated, P \\ benign \\ \end{tabular} & \
\begin{tabular}{c} [PERSON] et al., \\ 2014 [23] \\ \end{tabular} & 0.007 (1/140) \\ \hline LDM167 & PTEN & 1S &
\begin{tabular}{c} c.1131\_1132 dupTA, \\ p.Arg378 Ilefsfer39 \\ \end{tabular} & De novo & S/, P/ & / & not present \\ \hline \hline \end{tabular}
\end{table}
Table 5: De novo variants.  
As for the inherited variants, we observed that all the variants identified in our cohort came equally from the paternal and maternal genetic makeup. This equal contribution is also observed if we consider only extremely rare variants (***, **) (23 paternal and 26 maternal) or variants with a deleterious pathogenicity prediction in silico (23 are paternal and 28 are maternal) (Figure 1).
# Photogrammetric Measurement and Analysis of the Shape Profile of Pneumatic Artificial Muscles
## 3 Procedure
### Image Acquisition
The aperture's f-stop is set to a minimum to achieve a narrow depth-of-field which enables the background to be out of focus [11]. The motion of the PAM will be very slow (less than 0.1 in/s), so the choice of shutter speeds is minimally constrained. Therefore, exposures can be iteratively tested until the desired result is achieved through adjustments of the shutter speed and ISO sensitivity. For the scene setup in this research, desired exposure is achieved with the f-stop set at f/5.6, the shutter speed of 1/160 s, and the ISO sensitivity at ISO 3200. To provide an accurate color representation in the artificially lit indoor laboratory, white balance is set to the incandescent setting which equates to a color temperature of 3000 K.  
Calibration of the scale of the PAM is acquired by taking a picture of a ruler in the image frame prior to testing. The 305 mm (12 in) ruler is positioned in the image alongside the PAM as seen in Figure 8. Placement of the ruler in close proximity to the PAM helps ensure accurate calibration of scale, and enables the ability to correct for any optical distortion as a function of its position along the ruler. The ruler attaches to the top MTS fixture by a rigid mounting plate that ensures a vertically fixed orientation of the ruler. Since the ruler would obstruct the radial expansion of the PAM during testing, it must be removed prior to testing after the calibration picture is taken. It is very important that the calibration ruler is placed at the mid-plane of the PAM to ensure that the scale is calibrated at the same depth as the shape profile outline to be measured. Prior testing for this test setup revealed that scale calibration would change by about 2% of the mid-plane calibration value per inch of depth change from the PAM's mid-plane. This indicates that performing the scale calibration with the ruler at the mid-plane of the PAM is critical to acquiring accurate shape profile measurements of the PAM.
## 2 Basic Principles of Particle Transport in Plasmas
### RF Plasma  
RF plasma is one of the types of application technologies commonly used in semiconductor manufacturing. RF plasma is generated by applying a high frequency (MHz) sinusoidal voltage, which creates an electric field in which charge carriers are accelerated and participate in the ionization of the gas. Because this study was performed using capacitively coupled plasma (CCP), the plasma is created by applying an electric field between two electrodes. In an RF CCP discharge, while the plasma is nearly equipotential, it forms a sheath with the transition region between adjacent dielectrics (including vacuum chamber walls, electrodes, substrates, etc.). This sheath is characterized by large potential changes and low electron density. There is a strong electrostatic field in the sheath; the direction is from the plasma area to the electrode surface. The electric field of the sheath can confine electrons to a certain extent, accelerating positive ions to attack the electrode plates. Another critical part of an RF plasma source is the matching network. The matching network allows an impedance of 50 \(\Omega\) between the generator and the load impedance (plasma) and creates resonance in the power transfer from the generator to the plasma to reduce the occurrence of power losses.
# Distributed Reputation for Accurate Vehicle Misbehavior Reporting (DRAMBR)
## 4 System Model
### Workflow and DRAMBR Phases
#### 4.4.3. DRAMBR Privacy  
Integrating our reputation system with the SCMS ensures privacy by employing PCs that hide actual identities. However, the question of when and how a PC change occurs remains unresolved. Numerous techniques have been proposed by scientific investigations to ascertain the location and pace of change in PCs [45]. Mechanisms that focus on node-centric misbehavior detection (MBD) require a consistent identity to effectively monitor and evaluate the behavior of the \(\mathit{Tar_{\mathrm{Veh}}}\). However, privacy-preserving strategies based on PCs introduce identity changes, complicating accurate tracking. Therefore, considering an appropriate PC change strategy, as outlined below, becomes essential to balancing privacy and accountability within our system.  
* Random: The PC has a predefined possibility of changing with each outgoing message.
* Disposable: The PC is used for a predetermined number of messages, such as beacons and warnings.
* Periodical: After a predetermined amount of time, the vehicle changes its PCs.
* Distance: After a predetermined number of kilometers, the vehicle changes its PCs.
# Promising Cr-Doped ZnO Nanorods for Photocatalytic Degradation Facing Pollution
## 3 Results  
### XRD Measurements  
XRD measurements of the synthesized ZnO-Cr samples are represented in Figure 2. The ZnO diffractograms confirmed the hexagonal phase crystallinity of the entire samples [37]. No changes in the structure phase were noticed after adding Cr ions. The absence of impurity phases shows the proper localization of the Cr ions on the sites within the ZnO lattice. However, the minor shift position changes of the diffraction peaks to a lower angle by Cr doping reflects the substitution of Zn ions with those of Cr. Nevertheless, the full width at half-maximum \"FWHM\" of the main diffraction peaks also increases by increasing Cr doping, highlighting their good incorporation. This demonstrates the effective doping process given here. Thus, the XRD measurements demonstrated the high crystallinity of the various studied samples.  
Figure 1: Schematic representation of the photocatalysis process every 10 min.
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### Qualitative and Semi-Quantification Analysis of Volatile Compounds in the Fish Sauces
#### 3.3.6 Others  
Pyrazines, commonly found in fermented foods, contribute to the grill aroma of foods [28]. Pyrazine, a key flavor compound, is frequently used as an indicator of food sensory quality. 2,6-Diethylpyrazine and 2,3,5-methylpyrazine occur in both unheated and certain cooked fish sauce samples. These compounds are formed via slow Maillard reactions during prolonged fermentation [3; 23]. 2-Ethyl-5-methylpyrazine and 2,5-dimethylpyrazine were detected exclusively in cooked fish sauce, likely generated via Maillard reactions triggered by cooking. 5-Hydroymethylfurfural is an intermediate product of the Maillard reaction [29]. Boiling accelerates the formation of 5-hydroxymethylfurfural, while stir-frying facilitates its involvement in subsequent reactions, leading to significant consumption.. Inde was identified only in B30, B60, and F10, contributing an off-flavor (animal and fecal odor) to fish sauce [11]. A previous study suggests that indole may form in food under specific cooking conditions [11].
# UAV Dynamic Object Tracking with Lightweight Deep Vision Reinforcement Learning
## 1 Introduction
We decide to eliminate the need of sequence models and use a stack of consecutive frames to achieve the same goal but with much fewer computations (approx. a 50% reduction in the inference time, as shown in Figure 1). In this paper, we propose a LDVRL framework to minimize the computational resources for UAVs to perform dynamic object tracking, which consists of:  
Figure 1: Comparison of the inference time between our proposed frameworks and other conventional methods (in seconds). * A segmentation map method to extract the object boundaries. This method can run in \(O(1)\) time complexity in the simulation tool. For UAVs to operate in the real world in future work, we implement the YOLOv4-tiny model to detect objects. * We eliminate the computational cost of sequence models such as RNN, LSTM, and GRU by using stacks of consecutive frames. * We construct our state space to use depth images instead of RGB images, not only to reduce computational resources but also to improve the obstacle avoidance ability. The two DRL algorithms implemented in our experiment are DQN and DDPG. They are implemented under the same LDVRL system that we propose in Section 4. This paper introduces not only the LDVRL approach but also significant insights and comparisons between DQN and DDPG performance in the dynamic object-tracking task. Our finding shows that the LDVRL is capable of adapting to different DRL algorithms and a more complex algorithm is not necessary to be more efficient in every task. The comparison between our methods and the ordinary methods is given in Section 5.
## 2 Materials and Methods
### Ecosystem Greenness Assessment
#### 2.4.2 Leaf Area Index (LAI)  
The Leaf Area Index (LAI) is defined as the surface area of leaves per unit of ground area [40; 41]. This metric integrates both horizontal coverage and vertical structure, effectively reflecting vegetation cover, structure, and distribution. The LAI can be conveniently obtained from satellite remote sensing data.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 3 Background
### k-Shell Algorithm  
The k-shell decomposition algorithm (ks) [30] is a classical and efficient algorithm for measuring the node influence in networks based on node position information. Moving from the outermost layer to the innermost layer, the relative importance of nodes increases. One advantage of this method is its low computational complexity; however, the k-core decomposition process does not consider the topological position of nodes, leading to poor node identification [31]. The steps are as follows:  
* First, remove all isolated nodes from the network. Then, all the nodes in the network whose degree value is 1 are noted as \(k_{s}=1\), and then remove these nodes from the network with their connected edges to obtain a new network. Consecutively perform the same steps until there are no nodes whose degree value is 1 in the network;
* After, mark all the nodes in the network whose degree value is 2 as \(k_{s}=2\), and then remove these nodes with connected edges from the network similarly, repeating the steps until all nodes with a degree value of 2 are removed;
* Continue repeating the above steps. After each operation, the degree value is increased by 1, and the \(k_{s}\) value is also increased by 1, until all the nodes in the network are deleted. At this point, all nodes' \(k_{s}\) values are obtained, which better describes the hierarchical structure of the nodes in the network.  
The decomposition process is shown in Figure 1. Nodes in higher layers are more important; however, the importance among multiple nodes in the same layer cannot be judged.