esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Since in plant cells, the amount of phaeopigments usually increases with abiotic stress (pH, temperature, etc.) or with increasing age [60; 61; 62], we can deduce that the addition of bicarbonate at the used concentrations did not prejudice the photosynthetic performance of the algal cells. The capability of active fluorescence for rapidly assessing the photosynthetic physiology of phytoplankton live species has been well demonstrated by many studies [63; 64; 65]. Conceptually, the fluorescence measurements of the active chlorophyll \(a\) evaluate the efficiency by which absorbed light is utilized by photosynthesis [66]. In this study, the maximum photosynthetic efficiency of photosystem II (Fv/Fm) for different treatments was measured over time for the entire duration of the experiments. Interestingly, cells supplemented with NaHCO\({}_{3}\) 3 g L\({}^{-1}\) showed a progressively significant increase in Fv/Fm values, up to 95% higher than the control at 72 h, indicating a strong photosynthetic performance. The increase in Fv/Fm could indicate an increase in photosynthetic carbon fixation level and metabolic activity. In PSII, the quanta of excitation light are absorbed and transferred to reaction centers, and the whole efficiency of light trapping and energy transfer is explained by the functional absorption cross-section (\(\sigma\)PSII). In general, thylakoids of photosystems are dynamic membranes that are subjected to reorganization for the number and size of the stacks, volume of the lumen, and place of the complexes [67], based on the growing conditions or metabolic status in which the organism is located. The \(\sigma\)PSII, values observed in this study, showed a sensible decrease in treatments enriched with bicarbonate. \(\sigma\)PSII for NaHCO\({}_{3}\) 3 g L\({}^{-1}\) was, in fact, \(\sim\)20% lower than the control at 72 h, with maximum differences observed between these two cases at 4 h, when \(\sigma\)PSII NaHCO\({}_{3}\) 3 g L\({}^{-1}\) was \(\sim\)40% lower than the control. These results suggest that the increased photosynthetic efficiency in bicarbonate-enriched cells was accompanied by a reduction in \(\sigma\)PSII area. Probably, the reduction in the surface of the photosystem II directly depended on the excellent photosynthetic performance that the cells from bicarbonate enriched cultures showed. The overall data emerging from this study demonstrate that in _Chlorella sorokiniana_, the supplementation of bicarbonate, whose accurate concentration must be considered taking into account the cell density of the culture, led to a significant immediate improvement in the cultures. These benefit and rapid dose-dependent effects on the growth, photosynthetic efficiency, and pigment contents, together with a bactericidal and bacteriostatic effect of sodium bicarbonate addition to the culture medium, indicate this salt as an excellent substitute for gaseous CO\({}_{2}\).
# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 2 Materials and Methods
### Methods
#### 2.1.1 Teacher Model  
Considering that EGG signals contain only information regarding the movement of the vocal cord, it is a double-edged sword for the speech emotion recognition task. On the one hand, we can extract the source feature much more easily and exactly, regardless of any noise. However, on the other hand, lacking the information of the sound track may cause a decrease in the recognition accuracy. Thus, we adopted a strong teacher model to lead the student model and meliorate the representations in the deep emotion feature map.  
The teacher model is responsible for providing supervision to the student speech emotion model at the level of deep emotion features. The structure of the teacher model consisted of three stages:  
1. Selecting and extracting acoustic features from the raw speech signals;  
Figure 2: Illustration of the proposed framework, which consists of two phases: (**a**) training a strong teacher model based on the acoustic features from speech signals and (**b**) training the student model via cross-modal emotion distillation (CMED) with the pre-trained teacher model.  
Going through a deep learning network to obtain deep emotion features;
* Using a simple classifier to convert the deep features to the predicted emotion label.  
As the primary task of the teacher model is to provide the student model with emotion information associated with the sound track, we chose 80-channel log-Mel-spectrograms as the input of the teacher speech emotion recognition model, which is a typical type of feature containing the information of the sound track [39].  
For better modeling of deep emotional feature spaces, we chose the classic ResNet18 (deep residual network [40]) as our encoder, which has been proven to be efficient in many classification tasks. The output of ResNet18 (\(dim=512\)), regarded as the deep emotion feature, was then fed in a fully connected layer to generate the predicted emotion label. The output of ResNet18 also acted as a supervision to guide the student model to capture and imitate its embedded emotion representations.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 2 Preliminaries and Related Work
### Traffic State Estimation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Max. Density**} & \multirow{2}{*}{**\(\mathbf{\rho_{\text{critical}}}\)**} & \multirow{2}{*}{**\(\mathbf{\mu_{\text{max}}}\)**} \\ \cline{4-6}  & & & & **Max. Density** \\ \hline \multirow{6}{*}{**Model**} & Calibrate & Each parameter & & & \\  & each & carries a certain & & divided by avg. & \\  & parameter & physical meaning & & vehicle length & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & Calibrate & Augment states & & & \\  & parameter & with parameters & & & \\  & and predict & estimated using & & & \\  & state jointly & DA [15; 16; 17; 18] & & & \\ \cline{2-6}  & & & Fit parameters & & & \\  & Calibrate FD & associated with a & & & \\  & pre-selected & & \(u=0\) & & \\  & FD [19; 20; 21; 22; 23; 24; 25] & & & & \\ \hline \multirow{6}{*}{**Model**} & Calibrate FD & Fit parameters & & & \\  & & associated with a & & & \\  & & pre-selected FD & & & \\  & along with & & \(u=0\) & & \\  & & parameters of & & & \\  & DNNs [26; 27; 28] & & & & \\ \cline{2-6}  & & & Reduce variable & & \\  & & and parameter & & & \\  & ML surrogate & sizes while & & & \\  & maintaining the & & & & \\  & minimum physical & & & \\  & relevance [29; 30; 31] & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: State-of-the-art approaches for parameter calibration. The emerging traffic sensors mounted on connected and automated vehicles (CAVs) are expected to generate terabytes of streaming data [34], which can serve as \"probe vehicles\" or \"floating cars\" for traffic measurements. Future cities will be radically transformed by the internet of things (IoT), which will provide ubiquitous connectivity between physical infrastructure, mobile assets, humans, and control systems [35] via communication networks (e.g., 5G [36], DSRC [37; 38], x-haul fiber networks [39], edge-cloud [40], and cloud servers). Figure 3 illustrates the observational data types for TSE. Building on individual vehicle trajectories, we can aggregate the velocity and density for each discretized cell and time interval. With the availability of high-resolution multi-modality data, we should also consider developing disaggregation methods for TSE that can directly use individual trajectories or images as inputs. In the TSE problem, the physics-based approach refers to the scientific hypotheses about the evolution of traffic flow on micro-, meso-, and macro-scales; while the ML approach refers to data-driven models that mimic human intelligence using deep neural networks, reinforcement learning, imitation learning, and other advanced data science methods [6].
# The Use of Radio and Telemedicine by TMAS Centers in
## 3 Results
### Study Characteristics
#### 3.4.1 Medical Assistance via Radio  
Radio was the first telecommunication system used to provide medical assistance at sea. It became an essential tool for ship captains in the early 20 th century, allowing them to inform onshore centers and authorities about medical issues aboard their ships. During the literature search, two published works were found on radio medical assistance for seafarers. One study from the Denmark TMAS center (i.e., RMD) adopted radio-telephone communication to analyze the injury patterns of workers and the factors that contribute to injuries [22]. The system allowed ship captains to communicate with a medical doctor onshore and receive immediate medical advice over the radio. This allowed physicians to guide ship captains, who were responsible for providing emergency medical care to injured crews and passengers.  
Five case studies presented by the French TMAS center (CCMM) illustrated the use of radiotelegraphy to diagnose and follow up on different medical conditions among seafarers living on ships [23]. To advance these practices, there is a need to develop video-conferencing technology. It is mentioned that radio calls enabled ships in the middle of the ocean to communicate effectively with medical personnel ashore, reducing the number of medical emergencies and deaths at sea.
## 5 Evaluation: A Healthcare Scenario
### Implementation of the Attack Path Generation
#### 5.2.1. Identify Entry, Target Point Assets, and Possible Dependencies  
This section combines the first three steps of the attack paths, providing a list of assets identified based on the healthcare use case scenario, specifically, the healthcare service delivery and related healthcare information infrastructure. Each device needs an interface such as a wireless or network interface to connect with the other devices. For instance, an insulin pump management system that is physically located at home has one wireless interface to interact with another device interface. Note that we have only considered the devices for the demonstration of the attack path. Entry and Target Point Assets: Medical and IT device:  
* **Infusion Pump (A1):** Braun's Infusion System 871305U aims to deliver fluid such as nutrients and medications into the patient's body. Trained healthcare practitioners should program the rate and duration for the medication. The pump stores patient drug information;
* **Insulin pump (A2):** Medtronic MiniMed 508 is one of the most widely used pumps for delivering a specific amount of insulin to the diabetic patient's body. The device is programmed to inject a specific amount of insulin set by the doctor into the patient body. The pump stores patient sensitive insulin information;
* **Insulin management system (A3):** Omnipod DASH Insulin Management System 19191 is a tubeless and wireless system that allows continuous insulin delivery for 3 days. It consists of a pod that is worn directly on the patient's body and a personal diabetic manager which programs and controls the delivery;
* **IoT devices (A4):** There are several IoT devices that are relevant to the scenario. A heartrate monitor (Maxim's 700-MAXREFDES117) can be used to monitor the heart rate (wearable device). Additionally, a smart light system (Philips) is also considered for the healthcare service delivery;
* **Information and communication network (A5):** This includes multiple devices, such as routers, WiFi, switches, wireless interface cards, and others that are responsible for the connection from the device to the network;
* **Computer system (A6):** Windows-based workstation and servers connected to the medical devices, patient interfaces and servers;
* **Rugged tablets (A7):** These tablets are commonly used for patient care applications such as medication alerts and tracking, Electronic Health Records (EHR) support, blood pressure monitoring, and connecting to barcode readers and can directly interface to the other medical equipment. Healthcare practitioners can directly use these tablets for patient treatment.
## 3 Anomaly Detection and Quantification of Seismicity Parameters
### Kacst Seismographic Network (SANDSN)  
In 1993, the King Abdulaziz City for Science and Technology (KACST) established a seismological network and observatory. This network comprises several seismic stations that gather data and transmit it to the central facility located within KACST. Subsequently, this facility was upgraded to become the Saudi Arabian National Digital Seismographic Network (SANDSN), also managed by KACST. This development represents a significant enhancement in the region's seismic monitoring capabilities. Set to become operational in 1999, this network incorporates modern technology with broadband sensors and real-time data transmission. It includes thirty-two seismic stations, providing comprehensive coverage for monitoring seismic activity. The network is capable of handling large volumes of data, ensuring efficient processing and analysis [39; 40]. Figure 2c compiles the seismic activity data reported by SANDSN, encompassing the period from 1993 to 2005.  
The detailed statistical analysis of seismic activity in the Red Sea area from 1993 to 2005, as recorded by the SANDSN is given in Table 2. The dataset encompasses 34,812 seismic events, providing an extensive overview across a 13-year span. An analysis of the temporal distribution, as reflected in the annual data, reveals a notable increase in seismic activity in the latter part of the period under study. This clustering, emphasized by the negative skewness, could be reflective of a period of increased tectonic activity or improved seismic detection capabilities. Monthly and daily distributions exhibit a near-uniform spread over the course of a year and a month, with minimal skewness and kurtosis. This indicates a consistent, non-seasonal pattern of seismic occurrences. The spatial distribution of events, as depicted in Figure 2c, demonstrates concentrated seismic activity within specific geographic regions, as the patterns previously illustrated by KSUSN data. The depth data exhibits a wide range of seismic event depths, as indicated by the large standard deviation and high kurtosis. Magnitude data indicates that the majority of seismic events were of low to moderate intensity, with a mean magnitude of 1.697. The data reported by both KSUSN and SANDSN exhibit analogous temporal and spatial characteristics and behaviors, indicating consistency in seismic activity patterns as captured by these networks.  
\begin{table}
\begin{tabular}{l r r r r r r r r r r r r} \hline  & **Count** & **Mean** & **Std** & **Min** & **2\%** & **50\%** & **75\%** & **Max** & **IQR** & **Skewness** & **Kurtosis** & **Quantile** & **Deviation** \\ \hline
**Year** & 22,304 & 2000 & 2,836 & 1985 & 2000 & 2002 & 2002 & 2003 & 2,000 & \(-\)1.849 & 3.160 & 1.000 \\
**Month** & 22,304 & 6,778 & 3.501 & 1.000 & 4.000 & 7.000 & 10.000 & 12.000
# A Survey of Graphical Page Object Detection with Deep Neural Networks
## 5 Evaluation
### Intersection Over Union  
Intersection Over Union (IOU) is a well-known evaluation metric commonly used in evaluating the capabilities of object detection algorithms. Since object detection techniques have been widely exploited to solve graphical page object detection, we have decided to discuss this metric in our paper. IOU calculates how much the area of the predicted bounding box intersects with the area of the actual ground-truth. For the sake of convenience, an example of computing IOU is illustrated in Figure 13. Mathematically, it is explained as follows:  
\[IOU=\frac{\text{Area of Overlap region}}{\text{Area of Union region}} \tag{4}\]  
The problem of graphical page object detection is to localize the boundaries of formulas, figures, and tables. For the sake of visual convenience, we have divided the performance evaluation between the explained methodologies into three separate tables. The quantitative analysis for detection of tables, figures and formulas are summarized in Tables 3-5 respectively. Various approaches have evaluated their methods on distinctive IOU thresholds.  
The double horizontal line divides the employed datasets.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Literature** & **Year** & **Dataset** & **IOU** & **Precision** & **Recall** & **F-Measure** & **Method** \\ \hline [PERSON] et al. [32] & 2019 & ICDAR-17 POD & 0.6 & - & - & 0.971 & Mask R-CNN \\ \hline [PERSON] et al. [27] & 2018 & ICDAR-17 POD & 0.6 & 0.965 & 0.971 & 0.968 & Deformable Faster R-CNN \\ \hline [PERSON] et al. [34] & 2020 & ICDAR-13 & 0.5 & 1 & 1 & 1 & CDEC-Net \\ \hline [PERSON] et al. [32] & 2019 & ICDAR-13 & 0.5 & 0.982 & 1 & 0.991 & Mask R-CNN \\ \hline [PERSON] et al. [35] & 2018 & ICDAR-13 & 0.5 & 0.981 & 0.981 & 0.981 & Fully Convolutional Network \\ \hline [PERSON] et al. [27] & 2018 & ICDAR-13 & 0.5 & 0.996 & 0.996 & 0.996 & Deformable FPN \\ \hline [PERSON] et al. [29] & 2017 & ICDAR-13 & 0.5 & 0.974 & 0.962 & 0.968 & Faster R-CNN \\ \hline [PERSON] et al. [34] & 2020 & UNLV & 0.5 & 0.960 & 0.770 & 0.865 & CDe-Net \\ \hline [PERSON] et al. [32] & 2019 & UNLV & 0.5 & 0.946 & 0.910 & 0.928 & Mask R-CNN \\ \hline [PERSON] et al. [27] & 2018 & UNLV & 0.5 & 0.786 & 0.749 & 0.767 & Deformable FPN \\ \hline [PERSON] et al. [33] & 2017 & UNLV & 0.5 & 0.823 & 0.907 & 0.863 & Faster R-CNN \\ \hline [PERSON] et al. [34] & 2020 & ICDAR-2019 & 0.5 & 0.987 & 0.946 & 0.966 & CDe-Net \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table detection performance evaluation across various datasets. The double horizontal line divides the employed datasets.  
Figure 13: Visual illustration of IOU in object detection methods. The bounding box with blue color represents the ground-truth whereas the bounding box with red color denotes the predicted bounding box. Considering the IOU threshold set to 0.5, only the first two predictions from the left will be considered true positives whereas the rest of them will be treated as false positives.
# Using Machine Learning to Detect Vault (Anti-Forensic) Apps
## 2 Related Work
[18] proposed a deep learning-based system called DECADE for detecting and analyzing content-hiding apps (vault apps) on Android devices. Their study developed an automatic recognition framework using advanced machine learning techniques in combination with text and image features extracted from app descriptions and screenshots. DECADE successfully classifies apps into hidden and non-hidden categories, achieving high accuracy rates and providing a mechanism with which to extract hidden user data for investigative purposes. The system also highlights the challenges in detecting cryptographic and anti-forensic vault applications, providing insights into the broader picture of content-hiding applications in both legal and illegal markets. To summarize, whilst machine learning is data-driven (therefore, the more data points, the better the results), good results can be obtained with relatively small datasets. As noted, [PERSON] et al. [16] reported an accuracy of 0.880 using 1000 samples each of malware and benign apps. Interestingly, and closer to our problem space, [PERSON] et al. [5] stated an accuracy of 0.933 using only 102 samples across vault and non-vault apps. In contrast, [PERSON] and [PERSON] [5] achieved an improved F-measure of 0.997 at a cost of a several-magnitude increase in dataset size (11,000 malware and benign Android apps). [PERSON] et al. [19] noted several challenges and open issues in the area of Android malware detection. They highlighted, in particular, the need for (1) accurate ground truth and correct label annotations and (2) effective feature engineering via meaningful feature extraction from apps. We hope that this work contributes to accomplishing both aims, at least in the malware subdomain of vault apps.
## 4 Topology Control
### Pre-Deployment
#### 4.2.2 Construct \(k-Connected\) Networks
There are several papers leveraging this theorem. This theory can also be used at the post-deployment stage; it will be discussed in Section 4.3. The work in [27] presents a distributed algorithm by locally constructing \(k-connected\) components and then joining two components by \(k\) disjoint edges between them to form one larger \(k-connected\) component. The work in [75] proposes an algorithm to construct a new fault-tolerant network by using some basic graphs. For instance, starting from really simple networks having 2, 3 and 5 nodes, it is able to construct a network that has cardinality \(N\) in the form \(2^{i}3^{j}5^{k}\); the resulting graph will tolerate \(i+2j+2k-1\) faults. (3) Spanning Tree:  
The spanning tree is a tree that connects all vertexes in the graph. One graph may have more than one spanning tree, while the one has minimal total weight, called the minimal spanning tree (MST). Since it is a tree, it is a \(1-connected\) network. Therefore, if one only emphasizes the \(1-connected\) and minimal cost (e.g., energy), it is modeled as an MST problem, which can be solved by using the well-known Kruskal and Prim algorithm [76] and a distributed algorithm in [77]. MST can be solved with \(O(|V|)\) by a linear time randomized algorithm [78]. However, for \(k=2\), the problem of finding the minimal \(k-connected\) spanning sub-graph is NP-hard and has \(1+\varepsilon-approximation\) solutions [79]. There is no polynomial time approximation less than \(1+\varepsilon\) for any given positive constant \(\varepsilon\). Let the number \(d_{MST}\) be defined as the maximum possible degree of a minimum-degree MST spanning points from the space (e.g., if in Euclidean plane, \(d_{MST}\) is five); the algorithm for \(2-connected\) and \(2-edge-connected\) is \(2d_{MST}-approximation\)[80].
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
###### Abstract
Using two types of Planet satellite imagery we tracked surface water inundation variations across a 625 km\({}^{2}\) study region in the Yukon Flats, north central Alaska during summer 2016 as follows: First, we developed a non-binary water classification method for Planet imagery (adapted from [29]) and validated it with concurrent WorldView-2 satellite images; second, we analyzed sub-seasonal patterns in lake and river surface areas and their sensitivity to measured discharge variations in the Yukon River; finally, we assess Planet's anticipated future imaging capabilities and discuss some potential applications and limitations that must be addressed before CubeSat imagery can be widely used in hydrologic research.