esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Real-Time Display in VCMS
Figure 3: Example of a patient monitoring display in a developed VCMS: (**a**) full screen of the VCMS that displays real-time information from up to 32 patient monitors and ventilators; (**b**) enlarged thumbnail image in box A among patient monitor thumbnails; (**c**) enlarged thumbnail image in box B among the ventilator thumbnails. Figure 4a shows a display window for event reviews, with detailed alarm information. A tab displays alarm events that occurred for each patient and shows the alarm occurrence, event type, cause of occurrence, and patient ID. An additional tab displays more detailed information when the alarm is clicked. Figure 4b shows the display window of the trend review, which can be used to check the information for various waves and numerical parameters in the time series. It is possible to select the desired parameters, and up to five parameters can be simultaneously checked. Various time zones can be set and displayed in the time-series table, and the numerical information can be checked in the table below using the drag-and-drop or direction keys on the blue line. In addition, the time-series data of the desired parameters can be exported through anonymization within the scope of the authority of the clinical staff. Event and trend reviews provide longitudinal respiratory and biometric information for individual patients, enabling continuous monitoring even if the clinical staff shifts and supporting comprehensive diagnosis because long-term patient monitoring can be seen at a glance. Figure 4: (**a**) Display window for event review with detailed alarm information and (**b**) the trend review display window, in which is possible to check time-series information for various waves and numerical parameters.
# Analysis of Turbulence Parameters of Typhoon Morakot along the Southeast Coast of China
## 2 Introduction of Measurement Procedure and Method
### Data Processing Method
#### 2.2.4 Relationship between Turbulence Intensity and Gust Factor  
The turbulence intensity and gust factor are dependent. Based on typhoon record data, [PERSON] [38] derived the empirical link between the turbulence intensity gust factor, which is written as Formula (11):  
\[G_{u}=1+a{I_{u}}^{b}\ln(T/t_{g}) \tag{11}\]  
where \(T\) is the time interval, and \(t_{g}\) is the gust duration. [PERSON] [38] proposed values of \(a=0.5\) and \(b=1.0\).  
Based on the collected data, [PERSON] et al. [39] updated Formula (11) and proposed \(a=0.62\) and \(b=1.27\). [PERSON] et al. [40] fitted Formula (11) using the collected data of Typhoon Maemi, and the results obtained after fitting were \(a=0.5\) and \(b=1.15\).
###### Abstract  
The increasing demand for solar photovoltaic systems that generate electricity from sunlight stems from their clean and renewable nature. These systems are often deployed in remote areas far from urban centers, making the remote monitoring and early prediction of potential issues in these systems significant areas of research. The objective here is to identify maintenance requirements early and predict potential problems within the system. In this study, a cost-effective Internet of Things-based remote monitoring system for solar photovoltaic energy systems is presented, along with a machine learning-based photovoltaic power estimator. An Internet of Things-compatible data logger developed for this system gathers critical data from the photovoltaic system and transmits them to a server. Real-time visualization of these data is facilitated through web and mobile monitoring interfaces. The measured data encompass current, voltage, and temperature information originating from the photovoltaic generator and battery, alongside environmental parameters such as temperature, radiation, humidity, and pressure. Subsequently, these acquired data are employed for photovoltaic power estimation using machine learning techniques. This enables the estimation of potential issues within the photovoltaic system. In the event of a problem occurring within the photovoltaic system, users are alerted through a mobile application. Early detection and intervention assist in preventing power loss and damage to system components. When evaluating the results according to performance assessment criteria, it was observed that the random forests algorithm yielded the best results with an accuracy rate of 87% among the machine learning methods such as linear regression, support vector machine, decision trees, random forests, and k-nearest neighbor. When prediction models using other algorithms were ranked in terms of success, decision trees exhibited an accuracy rate of 81%, k-nearest neighbor achieved 79%, support vector machine reached 67%, and linear regression achieved 64% accuracy. In conclusion, the developed monitoring and estimation system, when integrated with web and mobile interfaces, has been demonstrated to be suitable for large-scale photovoltaic energy systems.  
solar photovoltaic power; low-cost; remote monitoring; Internet of Things; power estimation; machine learning +
Footnote †: journal: Applied Sciences
# Mechanical and Conductive Behavior of Graphite Filled Polysulfone-Based Composites
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.3 Compression Test  
This test was performed using ZWICK/ROELL Z020 (Group, Ulm, Germany), a compression testing machine designed specifically to test the strength and deformation characteristics of a material under compressive (pressing) force. The test specimens were prepared according to the ISO standards and were shaped into rectangles using a hot press process. Figure 6 shows that using AG as a filler achieved the greatest outcomes for us. These results can also be explained by the SEM micrographs since it was clear that there was good dispersion between AG and the matrix, allowing the mechanical loads to be transferred in the composite easily. The results obtained using NG and EG are similar to each other. Using NG as a filler, there was poor dispersion of the graphite in the matrix and the compressive strength started to decrease as the filler content was raised above 30 wt.%. However, we could not get the same kind of outcomes with EG as we did with AG due to its porosity.  
As a result, we may assume from the graphs for PSU/AG and PSU/EG that increasing the filler content up to 40 wt.% increased the composite's compression strength before it began to decline. The mechanical properties were decreasing, which was previously explained by the differing properties of the filler and the matrix. Tables 3 and 4 show the mechanical properties of the composite compared to each other and the Young's Modulus of the composites. As we can see, using AG as a filler gave us the best mechanical properties overall compared to NG and EG.  
Figure 6: The compression test of PSU based composites filled with 30 (1), 40 (2), 50 (3), 60 (4) and 70 (5) wt.% of NG (a), AG (b) and EG (c).
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 2 Materials and Methods
### Methods
#### 2.3.1. Svm  
Support vector machine (SVM) is a type of generalized linear classifier for binary classification by supervised learning, and has been widely used for remote sensing [37; 40] and computer graphics [41; 42]. It can find an optimal hyperplane in the feature space and divide the data into two categories (Figure 8).  
[PERSON] et al. confirmed the oil spill detection potential of Full Polar L-band SAR images based on SVM [43]. [PERSON] et al. successfully used the SVM method to carry out oil spill classification in Fully Polarimetric SAR images, and achieved a better performance than with an artificial neural network (ANN) and maximum likelihood classification (ML) [44]. [PERSON] et al. attempted to use the SVM method to screen the approximate position of oil films in the wave area of shipborne radar images, and achieved good results [37]. In view of their work, we used the SVM method to classify the waves of shipborne radar, and placed SVM in the first step of oil film recognition.  
Sixty-eight images with oil films, in the same cruise and sea conditions, were selected as training samples to distinguish the effective wave echo pixels from those that were invalid. Four wave pixels and background pixels with a 5x5 window were selected as the training samples. The mean gray value of each window was taken as the sample value to classify the pre-processed images as (Figure 9).  
Figure 8: Optimal hyperplane for binary classification.  
Figure 7: Flow of oil film classification. SVM—support vector machine.  
Figure 9: Classification of Figure 6e. Long-range shoreline echoes have been deleted.
# Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
## 4 Modelling the Noise
### Autoregressive Models  
An autoregressive process of order \(p\), abbreviated as AR(\(p\)), is a stochastic process where \(h\) is a linear combination of \(p\) terms:  
\[X_{t}=-\sum_{k=1}^{p}h_{k}X_{t-k}+E_{t},\]  
where \(h_{k}\) are constants. The process has \(p+1\) parameters: \(\left\{h_{k}\right\}_{k=1}^{p}\) and \(\sigma\). The estimators \(\widehat{h}_{i}\) and \(\widehat{\sigma}^{2}=\widehat{\rho}(0)+\sum_{k=1}^{p}\widehat{\rho}(k) \widehat{h}_{k}\) are found by linear algebra [37]. Alternatively, the estimators can be found by least squares regression [45]. The parameter estimation procedure costs, at most, \(O((p+1)^{2}+np)\) operations, where \(n\) is the number of observations.  
As the Laplace transformation of \(X_{t}\) suggests the autocorrelation function of an AR(\(p\)) process is formally the sum of \(p\) exponentially decaying terms, thus, \(\widehat{\rho}(h)\) exhibits exponential decay or oscillatory behaviour [32], while \(\widehat{\rho}_{\mathrm{P}}(h)\) vanishes for lags \(p<h\).
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 5 Discussion  
### The Impact of ISOL  
The localization method via the supervised learning-based attention mechanism, which is an inaccurate localization focusing on only part of the object region and losing other information, is substantially worse than our prior-knowledge-based ISOL. The accuracy and integrity of the location of the object region directly affect feature extraction and are also reflected in classification accuracy. We conducted a number of ablation experiments on CUB-200-2011 in 5-way 1-shot and 5-way 5-shot settings to demonstrate that our ISOL outperforms the non-localization approach and attention mechanism. The data in Table 2 demonstrates that, when compared to the other two methods, our MDPN, which is based on instance segmentation, has the highest accuracy. We choose CBAM for two reasons when evaluating the impact of attention mechanisms: (1) it is a plug-and-play module, and (2) it combines CAM and SAM [47]. CAM concentrates on channel, while SAM concentrates on spatial. CBAM can produce better results than CAM, Grad-CAM, and SENet [48], which exclusively concentrate on channels.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Backbone**} & \multicolumn{2}{c}{**CUB-200-2011**} \\ \cline{3-4}  & & **5-Way 1-Shot** & **5-Way 5-Shot** \\ \hline ProtoNet [15] (2017) * & resnet12 & 66.09 \(\pm\) 0.92 & 82.50 \(\pm\) 0.58 \\ cosine classifier [42] (2019) * & resnet12 & 67.30 \(\pm\) 0.86 & 84.75 \(\pm\) 0.60 \\ MatchingNet [14] (2016) * & resnet12 & 71.87 \(\pm\) 0.85 & 85.08 \(\pm\) 0.57 \\ FEAT [40] (2020) * & resnet12 & 73.27 \(\pm\) 0.22 & 85.77 \(\pm\) 0.14 \\ DeepEMD [39] (2020) * & resnet12 & 75.65 \(\pm\) 0.83 & 88.69 \(\pm\) 0.50 \\ DMN4 [43] (2022) & resnet12 & 82.95 \(\pm\) 0.75 & 90.46 \(\pm\) 0.46 \\ RENet [44] (2021) & resnet12 & 79.49 \(\pm\) 0.44 & 91.11 \(\pm\) 0.24 \\ DPGN [26] (2020) & resnet12 & 75.71 \(\pm\) 0.47 & 91.48 \(\pm\) 0.33 \\ EPNet [45] (2020) & resnet12 & 82.85 \(\pm\) 0.81 & 91.32 \(\pm\) 0.41 \\ ECKPN [46] (2021) & resnet12 & 77.43 \(\pm\) 0.54 & 92.21 \(\pm\) 0.41 \\ FRN [41] (2021) & resnet12 & 82.12 \(\pm\) 0.85 & 92.49 \(\pm\) 0.43 \\
**MDPN (ours)** & **resnet12** & **83.55 \(\pm\) 0.33** & **92.72 \(\pm\) 0.17** \\ \hline \hline \end{tabular}
* Denotes that is reported from [26].  
\end{table}
Table 1: The accuracy (%) of 5-way 1-shot and 5-way 5-shot settings on CUB-200-2011. The best outcomes are highlighted in bold.
# Cyberattacks and Security of Cloud Computing: A Complete Guideline
## 7 Countermeasures for Threats in Cloud Computing
### Digital Signatures and Message Digests
#### 7.6.1 Message Digests  
Message digests, created using well-known hash functions such as MD5 or SHA, encrypt messages to safeguard data integrity during transmission.
# Software Defined Networks in Wireless Sensor Architectures
## 4 Emerging Sensor Networks: Software Defined Sensor Networks
### Software Defined Sensor Networks Architecture  
Physically, SDN and SDSN architectures are similar in terms of management since the first ones are governed by a controller and the second ones by a sensor control server. The procedure of deploying a new task or application is through reprogramming sensor nodes with the code that a user requires to be run within the network. In addition to the high applicability of WSN (temperature sensing, seismic movement, pollution measurement, etc.), SDSN incorporates a mechanism to change the network configuration with roles. The Sensor Control Server (SCS) is responsible for setting the value of the current role. Then, another entity called a scenario compiler (also placed in the server) generates the role program according to the application that will be used [51; 52], and, after that, it will be sent to sensors through wireless communication.  
Logically, SDSNs are based on three software-definable layers in order to fulfill their functionality as Figure 5 depicts. The physical layer (data plane) is composed of sensor nodes and it additionally contains the Software Defined Radio (SDR) that is used to control the media access. The networking layer (control plane) is responsible for data transmission across the network and also the SDN concept (controller) is located within it. Finally, the application layer manages the role programs, sensing tasks and the operative system that controls sensor nodes.  
Figure 5: SDSN layers.
# Natural Motion for Energy Saving in Robotic and Mechatronic Systems
## 2 Mechanical Design for Natural Motion
### Discussion of Elastic Elements Configurations  
Both serial and parallel configurations can be implemented with springs of variable or non-variable stiffness, as well as linear or non-linear behavior. Variable compliance allows the online tuning of spring parameters. Therefore, control systems that act on the stiffness to perform varying operations and to change system dynamics can be adopted, as in References [16; 58; 77; 78]. As a drawback, variable compliance springs need a more complex actuator design and sensory feedback. On the contrary, configurations based on non-variable stiffness are easier to implement, the springs can be designed and tuned offline but are suitable only for non-varying tasks and operations. Figure 3: (**a**) Mono-articular and (**b**) bi-articular parallel springs mounted on a robotic manipulator. The serial configuration (adopted for example in References [42; 50; 79]) provides the actuator with a compliance that can be employed to decrease contact shocks and to reduce force peaks due to impacts, for example, In human-robot interaction [46]. On the other hand, the parallel configuration [65; 80; 81; 82] results in simpler mechanism and mathematics formulation [78]. Parallel compliance does not enlarge the configuration space of the robotic system and has a well-posed quadratic cost function for energy consumption minimization with respect to the compliance coefficients [83]. Moreover, the serial arrangement of springs and motors limits the operational speed due to uncontrolled robot deflection when performing high-speed tasks. Comparisons between serial and parallel configurations can be found in References [84; 85; 86; 87; 88]. [PERSON] et al. in Reference [87], provide a comparison between stiff actuators, parallel elastic actuators and serial elastic actuators in terms of power as well as mechanical and electrical energy consumption. As test case, a sinusoidal motion is imposed to a pendulum load. By means of simulations they demonstrate that, if the stiffness of the elastic actuators is properly tuned, it is possible to reduce energy up to 78% compared to rigid actuator using serial elastic actuators and up to 20% using the parallel ones. Although the results of this study demonstrate that a serial spring arrangement outperforms the parallel one from an energetic point of view, the majority of the works dealing with natural motion adopt parallel springs because of their easiness of installation and control. An evidence of this is provided by Table 1, where the spring installation configuration adopted in the reviewed papers are highlighted.
## 6 From Single-Point to Multi-Point Equalization
### Modal Equalization  
Modal equalization has also been proposed at low frequencies [4; 157]. Modal equalization aims to control excessively long decays in listening rooms caused by low-frequency modes, minimizing the audibility of these resonances. Modal equalization balances the rate of sound decay of the low-frequency modes to correspond to the reverberation time at mid and high frequencies. This is not an RRE technique by itself, but it can be used with conventional magnitude equalization to optimize the reproduced sound quality. In [157], two methods for implementing active modal equalization are proposed. The first approach considers a single loudspeaker and filters the sound such that the mode decay rates are controlled (e.g., using a filter with couples of zeros placed in correspondence to the poles responsible for the resonances). The second approach implements modal equalization by one or more secondary loudspeakers. A correction filter is considered for each secondary loudspeaker in order to produce a compensatory sound. The first approach was studied in depth, and different techniques for identifying the modes, estimating their parameters, and designing the equalizer are presented. Estimation of the modal decay parameters is based on the nonlinear optimization of the model for exponential decay plus stationary noise floor presented in [158].
## 2 Methodology, System Description, and Problem Formulation
### System Description
#### 2.2.3 Fairness-Oriented Transmission and Computing Resource Allocation
The MEC server's cached tiles are classified based on \(H(n,\,m)\): let \(B_{q}=\left\{(n,m)\right|H(n,m)=q\right\}\) denote the set of tiles cached by the MEC server and hit by \(q\) users, where \(\left|B_{q}\right|\in\left\{0,1,\ldots,\left(k+1\right)^{2}\right\}\), \(q\in\left\{0,1,2,\ldots,l\right\}\). To make efficient use of the MEC server's computing resources, the computing resource \(W_{q}\) allocated to \(B_{q}\) satisfies:  
\[W_{q}=\frac{W_{\mathrm{M}}}{\sum_{q=1}^{l}\left|B_{q}\right|}\cdot\left|B_{q}\right| \tag{2}\]  
where \(W_{\mathrm{M}}\) represents the computing capacity of the MEC server, in bit/s. In the analysis in the next section, we will find that one advantage of using this computing resource allocation strategy is that the computation completion times for all \(B_{q}\) are the same, meaning that the computing offloading is fair for the users.
# Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
###### Abstract  
A UAV network composed of multiple UAVs allows a wide operating radius and various tasks to be performed. However, a UAV network mostly suffers from high probability of transmission failure due to interference or mobility. Also, nodes connected to the network often experience connection loss and segment loss caused by frequent node mobility and routing update. Since congestion is not the only cause of data loss in UAV networks, the TCP congestion control should not be run if there is a possibility of transient link instability unless a reduction in transmission speed is required. For this reason, we propose an algorithm to improve the transmission performance of UAV network through TCP with Slow-Start threshold (Sethresh) value adjusted. The adjustment algorithm is called Adaptive Sethresh Reviser for flying Ad hoc Network (\(ASRAN\)) that quickly restores unnecessary decrease of transmission speed in UAV network.  
transmission control protocol; UAV network; ad hoc; wireless network +
Footnote †: journal: Applied  
_Article_  
Adaptive TCP Transmission Adjustment for UAV Network Infrastructure
# Parallel-Based Corpus Annotation for Malay Health Documents
## 5 Discussion (Challenges)
### Co-Reference in Translation  
The next challenge lies in the use of co-reference during the translation process from English to Malay. Co-reference refers to the use of words or phrases that point to the same concept or entity within a sentence or text. For example, in the sentence that can be seen in Table 8, the pronoun \"it\" could be used later in the text to refer back to Table 8. The use of co-reference is crucial in the translation process as it aids in maintaining consistency and clarity.  
Co-references can become significantly complex, especially within lengthy and nuanced texts. For instance, a document might initially mention \"sakit perut\" and subsequently use pronouns like \"ia\" in other parts of the text to refer to \"sakit perut\". In such scenarios, the NER system must be adept enough to recognize that \"ia\" is indeed referring to the initial mention of \"sakit perut\".  
Effectively leveraging co-reference in the translation process necessitates a deep understanding of the structure and semantics of both languages. The system must recognize and maintain co-reference throughout the translation process while ensuring that the final translation remains accurate and comprehensible [30]. This requires advanced techniques in natural language processing and machine learning, as well as a good understanding of both languages' cultural and social contexts.  
\begin{table}
\begin{tabular}{c c} \hline
**English Text** & **Malay Text** \\ \hline Stomachache or abdominal pain is a common complaint. & Sakit perut atau sakit pada abdomen merupakan perkara biasa. \\ Abdominal pain by itself is not a disease but is a symptom of a variety of underlying disorders. Causes of Abdominal pain: & Ia bukan sejenis penyakit tetapi merupakan gejala bagi penyakit lain. Sebab-sebab Sakit Abdomen Sebab-sebab psychogenic \\ Psychogenic causes—The Abdominal pain occurs in non-diseased organs. Pain is believed to arise from stress, anxiety, and depression. & Sakit perut atau sakit pada abdomen merupakan perkara biasa. \\ \hline \end{tabular}
\end{table}
Table 8: Examples of the sentences translated to Malay using co-reference.  
Furthermore, in many instances, the source and target languages might have different co-reference rules and conventions. For example, Malay might have different ways of referring to entities or concepts compared to English. Thus, the system must be capable of adapting the co-reference from the source language to the target language in a natural and accurate manner. This is often challenging and necessitates ongoing research and development.  
Addressing these challenges requires careful consideration and the development of methodologies that account for synonyms, resolve entity categorization ambiguities, and accurately handle co-reference during translation. By addressing these challenges, the Malay Health Document Annotated Corpus can be further refined and serve as a valuable resource for training and evaluating NLP models in the Malay language.