esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment Results
### Evaluation Metric  
For the evaluation of the experimental results, we used the nlgeval library [28] to calculate the BLEU (Bilingual Evaluation Understudy) score [29], METEOR (Metric for Evaluation of Translation with Explicit Ordering) score [30], and ROUGE-L (Recall Oriented Understudy of Gisting Evaluation) score [31]. Additionally, we used cosine similarity between sentence vectors utilizing embeddings from ClinicalBERT [32]. The BLEU score calculates how many n-grams overlap between a human-generated reference sentence and a model-generated sentence in machine translation. The METEOR score is similar to the BLEU score but considers recall in addition to precision, which the BLEU score only focuses on. Precision is the ratio of the number of words that overlap between the model-generated sentence and the reference sentence to the total number of words in the model-generated  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Normal** & **ICH** \\ \hline Unremarkable finding of brain parenchyma & Left frontal subcortical intracerebral and cerebrospinal hemorrhage with surrounding edema \\ \hline \hline \end{tabular}
\end{table}
Table 1: Caption examples. Normal column shows one of the radiologist’s reports on the normal CT scan. ICH column shows one of the radiologist’s reports on the ICH CT scan.  
Figure 3: Preprocessing of sequential 2D CT scan images into 3D images. In the preprocessing, image normalizations and image resize are applied to the CT scan images and the images are stacked into one 3D image following the original order.  
sentence. Recall is the ratio of the number of words that overlap between the reference sentence and the number of words in the reference sentence. The ROUGE-L score is a type of ROUGE-N score that emphasizes n-gram recall between the model-generated and reference sentences, and it utilizes the longest common subsequence. The subsequence in this context does not have to be contiguous.  
ClinicalBERT is a medical language model that pretrained the BERT model with a 1.2-billion-word corpus consisting of various disease-related terms and finetunes it with an EHR corpus of more than 3 million patients. In this paper, we embedded both the model-generated sentences and the reference sentences using ClinicalBERT and then calculated the cosine similarity between each resulting embedded sentence vector. Cosine similarity is calculated using the cosine angle between two vectors, where the value is \(1\) when the directions of the two vectors are identical, \(0\) when their angle is \(90\) degrees, and \(-1\) when their direction is opposite at \(180\) degrees.
# Fault Detection and VSC-HVDC Network Dynamics Analysis for the Faults in Its Host AC Networks
## 4 Performance Analysis
### Comparative Assessment with Existing Techniques  
The HHT was applied to find the frequency, energy attenuation coefficient details, and energy of the high-frequency components for the different modes. The comparison study is presented in terms of the time-frequency representations (TFR) obtained by implementing the HHT and SST [17; 18].  
Figure 5: **(a)** DTE index for AG fault. (b) DTE index for ABG fault. (c) DTE index for AB fault. (d) DTE index for ABC fault.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Location** & **Fault Type** & \(\mathbf{R_{f}}\) **(\(\Omega\))** & **\(\mathbf{\delta}\) (deg)** & \(\mathbf{t_{i}}\) **(s)** & **DTE (p.u.)** & **Time Taken SST (ms)** \\ \hline \multirow{4}{*}{F2} & AG & 90 & 85 & 2.28 & 10.25 & 3.25 \\ \cline{2-7}  & ABG & 45 & 65 & 2.55 & 18.52 & 3.0 \\ \cline{2-7}  & AB & 20 & 35 & 2.65 & 35.5 & 2.5 \\ \cline{2-7}  & ABC & 8 & 70 & 3.15 & 40.253 & 1.8 \\ \hline \multirow{4}{*}{F3} & AG & 90 & 80 & 2.32 & 9.152 & 3.5 \\ \cline{2-7}  & ABG & 65 & 60 & 2.65 & 16.50 & 3.0 \\ \cline{2-7}  & AB & 25 & 45 & 2.75 & 30.05 & 2.8 \\ \cline{2-7}  & ABC & 10 & 80 & 3.32 & 36.83 & 1.5 \\ \hline \multirow{4}{*}{F4} & AG & 100 & 75 & 2.45 & 8.212 & 3.80 \\ \cline{2-7}  & ABG & 75 & 55 & 2.65 & 15.50 & 2.75 \\ \cline{2-7}  & AB & 45 & 40 & 2.75 & 28.83 & 2.9 \\ \cline{2-7}  & ABC & 10 & 90 & 3.25 & 35.35 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tested fault conditions.  
From the obtained results through TFRs for symmetrical and asymmetrical faults, it is noticed that SST provides better TFRs information as compared to HHT. The DTE was computed further, and the detection time computation is presented in Table 4.  
For each fault, the time taken by the SST was quite lower compared with the time taken by the HHT algorithm to detect the fault because the SST squeezes the obtained scalogram, and only the filtered components are present through the SST to extract the most relevant and accurate information from the signals.  
Upon computing the computation burden from EMD and the SST algorithm on the relaying algorithm, the decision regarding fault detection can be accomplished within one cycle time period. Thus, with the proposed integrated logic, an accurate, fast, and reliable decision can be made for the distance relay.
## 1 Introduction
So, the crop yield predictability of various AI models under unexpected weather conditions should be examined objectively, but previous AI studies did not focus on a comparative evaluation of AI models concerning rapid climate change. In this study, we conducted comprehensive performance tests for multiple AI models to predict corn yield under extreme weather conditions in the Midwestern US. Six different AI models included multivariate adaptive regression spline (MARS), SVM, RF, extremely randomized tree (ERT), artificial neural network (ANN), and DNN. We configured each model through the optimization of hyperparameters and compared the validation statistics for drought and heatwave cases. For this, we prepared the input data, including satellite-based vegetation indices and meteorological data for July and August (the dominant growth period). Then, we constructed a match-up database using high-resolution maps for crop types and selected cases of extreme weather conditions such as drought and heatwave. Two types of blind tests, such as leave-one-year-out and 10-fold cross-validation, were conducted to evaluate the accuracy of these models under conditions of drought and heatwave. This study concentrated on corn yields in the Midwestern US in 2006-2015.
# A UWB-Driven Self-Actuated Projector Platform for Interactive Augmented Reality Applications
## 3 System Design
### Design of UWB-Driven Self-Actuated Projector Platform and Input Device  
The USAP platform was built with the Turtlebot3 waffle-pi robot [49] as a mobile base. A tower-like structure was formed to create portions for the components, such as the projector, the wireless receiver device, the UWB tag, and the power source. Figure 1(a) describes the USAP platform, and Table 1 elaborates the specifications of its Components, where a commercial-off-the-shelf projector is placed on the top of the USAP platform and connected to a wireless receiver through an HDMI connection to display content at 720p resolution. The USAP platform navigates between points whenever it receives a new target point utilizing the UWB positioning system. Figure 3 explains the robot operating system (ROS)-based [50] pipeline to navigate the USAP.  
A hand-held input device, shown in Figure 2b, is equipped with a standalone UWB tag to track the user's hand position. Additionally, it integrates a microcontroller to detect push button presses and update its status to the server PC through Wi-Fi communication. The designed input device allows the user to perform pointing, selecting, dragging and dropping, and swiping (based on the user's hand motion patterns). Both the USAP platform and hand-held input device can run on average more than two hours before replacement of batteries are required.  
Figure 1: Ultra-wideband (UWB) wireless sensor network installation configuration.  
Figure 2: System hardware: (**a**) UWB-driven self-actuated projector platform; (**b**) hand-held input device.
## 4 CLAP Correlation and Information Sharing Services
### Information Sharing and Exporting
#### 4.3.1 Alarm Sharing  
CLAP supports sending alarms (in JSON or STIX formats) via RabbitMQ ([[https://www.rabbitmq.com/](https://www.rabbitmq.com/)]([https://www.rabbitmq.com/](https://www.rabbitmq.com/)), accessed on 5 June 2021), a widely used open-source message broker that supports multiple messaging protocols. It also supports sending alarms via the Data Distribution Service (DDS), commonly used for real-time systems. Events generated by the XL-SIEM agent from the data collected by the sensors are sent in JSON format to a RabbitMQ server.  
RabbitMQ is used to create queues and provide secure communications between some of the CLAP components. RabbitMQ works as a passive component; therefore, other tools will connect to RabbitMQ to exchange information using the queues. RabbitMQ needs to be configured to provide secure communications and to create the elements (queues and exchanges) needed for the communications among CLAP components.  
CLAP gives the possibility to enable external and internal authentication mechanisms for the clients connecting to the RabbitMQ. When having just the external mechanism enabled, clients will not be able to connect without a signed certificate. Once authenticated, clients can check the queue activity by accessing to the RabbitMQ web administration page.  
The proposed solution sends alarms generated by the XL-SIEM engine to a particular queue, which will be accessed by the Real-time anomaly detector tool for further analysis and visualization.
# Named Entity Recognition Based on Multi-Class Label Prompt Selection and Core Entity Replacement
## 4 Experimental Results and Analysis
### Effectiveness on K-Shot  
The number of shots is a very critical parameter in further improving model performance. Within certain limits, increasing the number of shots improves the stability of convergence, but may lead to a limitation in the generalization ability of the model. In order to verify the effect of the number of shots on the MPSCER-NER model, the effect of \(k\)-shots on the _Precision_, _Recall_, and \(F1\) of the MPSCER-NER model on the CoNLL-2003 dataset (\(k\)-shots, \(k\) = 25, 50, 100) is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Hyper-Parameter** & **Value** \\ \hline
1 & Learning rate & \(2\times 10^{-5}\) \\
2 & Batch size & 64 \\
3 & Epoch & 50 \\
4 & Dropout & 0.5 \\
5 & Maxnoincre & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental hyper-parameter settings.  
As can be seen from the figure, the _Precision_, _Recall_, and \(F1\) of the model gradually increase as the number of shots becomes larger. The _Precision_, _Recall_, and \(F1\) of the model are maximized when the number of shots is taken as 100. More samples of the entity feature information are learned by the model. The models are exposed to different contexts and different forms of domain knowledge. The ability of the model to recognize entity classes is enhanced.
## 5 Implemented Example Use Cases and Configuration Details
### Event Awareness  
In the Road Works Warning use case (the application concept is depicted in Figure 3), the simulation infrastructure was instructed to send DENM (Decentralized Environmental Notification) messages [16] about road works activities that are performed on a multi-lane  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Artery’s Storyboard** & **Proposed Framework** \\ \hline Data source & All SUMO entities & Based on the local environment model \\ \hline Sensor model & All vehicles in the simulation are visible & Based on V2X communication (CAM, DENM and CPM) and 2D sensor models \\ \hline Configuration interface & Python-based & XML-based \\ \hline Main concept & Filter-Effect concept & Filter-Effect concept \\ \hline Effect prioritization & Based on effect stack & All effects are applied sequentially \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of application simulation frameworks in Artery.  
road segment. We expect these messages to increase driver awareness, meaning that the drivers can prepare better for such situations; they can decrease speed and start lane change maneuvers in time. Eventually, this leads to fewer traffic jams and safer transportation in general.  
The road works use case was modeled via a standing vehicle in SUMO (velocity is 0 or very low), as depicted in Figure 4. Using this method, the regular non-V2X vehicles could not foresee this event, which caused traffic jam events behind the standing vehicle due to the late lane changes. Using the DENM information, the V2X-capable road users could detect the road event in time. In this simplified model, as the vehicles received the DENM message, they started the lane change immediately. The scenario was simulated in both urban and highway scenarios. The showcase results of the model are discussed in Section 6.
# A Deep Learning Approach for Intrusion Detection Systems in Cloud Computing Environments
## 4 Experimental Result
### CSE-CICIDS2018 Scenario 2 Results  
Further investigation of the CSE-CICIDS2018 dataset reveals scenario 2, which poses a complex classification challenge involving \"Benign\", \"DoS_GoldenEye\", and \"DoS_Slowloris\" traffic. This section explores the model's ability to effectively differentiate and categorize these distinct traffic types, bringing on the analytical findings presented in Tables 7 and 8.  
In scenario 2, Table 8 provides an overview of the model's performance metrics, showcasing its accuracy of 99.947% and consistent precision, recall, and F1-score values. These metrics demonstrate the model's reliable and stable classification capabilities in various network traffic types, indicating a nuanced understanding and effective detection ability in this scenario.
## 2 Description of 2-DoF Azimuth Thruster  
A prototype of a 2-DoF azimuth thruster was fabricated, as shown in Figure 2. The system consists of a thruster and two servo motors and has a gimbal mechanism for 2-DoF rotational motion. A counter mass is used so that the center of mass and the rotational axis intersect. The longitudinal thrust that is generated by the thruster and the tilting angles of the two servo motors are system inputs, and the actual thrust force and reaction torque are outputs of the system model. The thruster (RCD-MI60, 24 VDC, RHINCODON, China) has a maximum thrust force of 5.5 kgf. The duct diameter is 95 mm, and the weight in air is 850 g. The gimbal mechanism is actuated by two BLDC (Brushless DC) motors (EC-max 30, 24 VDC, Maxon, Switzerland). The total weight of azimuth thruster system is 8.56 kg.  
The generated thrust force and reaction moment are shown in Figure 3. The \(X\)-\(Y\)-\(Z\) frame is a fixed frame, and the \(x\)-\(y\)-\(z\) frame is a moving frame that is attached to the tilting thruster. The thrust force is bent and dispersed due to rotational motion, and it can be decomposed into \(f_{x},f_{y}\), and \(f_{z}\) in the moving frame. These forces can be converted to \(f_{X},f_{Y}\), and \(f_{Z}\) in the fixed frame using a rotation matrix. In this study, the frequency response of forces was analyzed in the moving frame. The reaction moment was analyzed within the framework of the gimbal mechanism. The reaction moment \(M_{Z}\) generated by the rotational motion of the 1 st axis is defined in the fixed frame, and the reaction moment \(M_{y}\) for the rotational motion of the 2 nd axis is defined in the moving frame.  
Figure 2: Design drawing of 2-DoF azimuth thruster.  
A diagram of the experimental apparatus is shown in Figure 4. In order to measure forces and moments, the 2-DoF azimuth thruster unit was installed at the end of a cantilever with a 6-DoF force/torque sensor (RFT80-6A01, ROBOTUS, Gyeonggi, Korea) attached at the clamped end. The thruster is driven by a pulse-width modulation (PWM) signal with a range of 55-95%, and the BLDC motors are driven by position controllers (EPOS2 25/2, Maxon, Switzerland). A real-time controller (NI CompactRIO, National Instruments Corp., Austin, TX, USA) controls the actuation and receives the values from the force/torque sensor. The measured forces and torques from the sensor are converted to the values of the system origin through the adjoint matrix of the frame transformation.
## 9 Porosity Evaluation and Mathematical Model Enhancement  
Porosity is defined as the existence of small voids in the materials. In fact, air bubbles are common and inevitable in composites. Their presence has a negative effect on the mechanical properties. In fact, 1% air bubble presence in composite materials reduces the tension strength by 3%, bending strength by 30%, and impact strength by 8% [23]. Therefore, each industry defines its own acceptable threshold. In aerospace, for example, the maximum allowable porosity is 2% [24].  
A quantitative analysis of the material porosity was then essential. The density of the composite material was defined as follows:  
\[\rho_{comp}=\frac{\rho_{f}V_{f}+\rho_{m}V_{m}}{V_{f}+V_{m}} \tag{30}\]  
where \(V_{f}\) and \(V_{m}\) are the volumes of fiber and matrix, respectively. Due to the presence of porosity, Equation (30) can be rewritten as  
\[\rho_{comp}=\frac{\rho_{f}V_{f}+\rho_{m}V_{m}}{V_{f}+V_{m}+V_{p}} \tag{31}\]  
where \(V_{p}\) is the volume of pores in the material.  
Porosity can be assessed using non-destructive or destructive methods. Non-destructive evaluation methods (NDE) include ultrasonic testing, radiography testing, and flash thermography. Ultrasonic testing is commonly used in the industry. It consists of multiple scans of the material in which spherical pores can be evaluated. Pore spheres that have a diameter smaller than the instrument's resolution cannot be detected [25], which determines its precision. The specimen for X-ray tomography is placed between the X-ray source and the detector on a rotary machine. This method detects defects with a diameter up to 1 \(\upmu\)m [26]. Flash thermography evaluates porosity through thermal diffusivity. This method has recently been combined with infrared cameras that monitor the temperature variation in the material [25]. Destructive evaluation includes density measurement, matrix digestion, and microscopy testing, [27]. Density evaluation consists of a comparison between Archimedes' theoretical density value and the actual density. This method is complicated as it requires a specific knowledge of the material's matrix and fiber characteristics and volumes. Another method is matrix burn digestion, which uses acid to break down the matrix and reduce the sample to its fiber content. Microscopy is a visual characterization of the sample with a 2D cross-section analysis. This method is relatively simpler than the above and is widely used.
## 4 Performance Evaluation
### Delay Overhead  
The Average overhead Delay (or mean delay) is an important metric to consider in order to evaluate the influence of the communication overhead, that is introduced by the proposed protocol for communication security. In our evaluation, this delay includes the MAC layer queuing besides transmission and propagation delays. It is expressed for one packet as the difference between the time of packet reception at the destination (rec_time) and the time of packet emission at the source side (emis_time).  
The Average Delay Overhead (AoD) for a network of N devices is calculated as follows:  
\[\text{AoD}=\sum_{j=1}^{N}{((\sum_{i=1}^{M}{\text{rec\_time}(i)-\text{emis\_time }(i))}/M)}/N \tag{3}\]  
\(M\): number of packets (overhead) introduced in the authentication process of device \(j\).
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 3 Results and Discussion
### Application Performance and Scalability of Al-S957  
To evaluate the application potential of Al-S957, real photovoltaic industry wastewater was studied, with its composition shown in Figure 9. When the effluent fluoride concentration is controlled below 1 mg/L, Al-S957 can treat 170 BV of industrial wastewater, with only a minimal reduction in treated water volume after five adsorption-regeneration cycles. At the same time, the 10 wt% AlCl\({}_{3}\)-6H\({}_{2}\)O solution is demonstrated to be an effective desorption agent. For comparison, the fixed-bed column experimental results for Al-D001 and Al-D412 are presented in Figure S4. During the initial adsorption cycle, Al-D412 and Al-D001 treated only 80 BV and 7 BV of the real wastewater. Moreover, after multiple regeneration cycles, the treated water volumes for both materials also declined. These findings further confirm the superior fluoride removal performance of Al-S957 in treating real industrial wastewater.  
Figure 8: Mechanism of Al-S957 adsorbing fluoride.  
To further assess the scalability and practical applicability of Al-S957 for industrial water treatment, a preliminary evaluation of its synthesis was performed. The required raw materials are commercially available, and a detailed cost analysis (Table S5) estimates the treatment cost at approximately USD 0.52 per liter of photovoltaic wastewater, confirming the economic viability. Environmentally, the synthesis avoids the use of toxic or hazardous reagents, and the aluminum species involved pose minimal environmental risk. Regarding reproducibility, the S957 resin consistently exhibits stable aluminum ion chelation performance across multiple synthesis batches. Taken together, these factors suggest that Al-S957 is a promising candidate for practical industrial-scale water treatment applications.
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
## 3 Results  
### Search Results  
The review process is shown in Figure 1. The search yielded 411 (PubMed 147 articles, SCOPUS 56 articles, PEDro 13 articles, REHABDATA 12 articles, and Web of Science 183 articles) results. After removing duplicates, 177 articles were reviewed. Of these articles, about 53 were selected for review of titles and abstracts based on relevance, language, and intervention. Ten out of the 53 papers were excluded based on relevance and patient diagnoses. The remaining 43 papers were selected for the full text review. This list was then narrowed down to a final list of 22 papers. Of the 22 studies included in this review, four articles targeted only the lower extremity [41; 42; 43; 44], 17 studies targeted only the upper extremity [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61], and one targeted both upper and lower extremities [62]. There were 14 randomized controlled trials (RCT) [42; 43; 47; 49; 50; 51; 53; 55; 57; 58; 59; 60; 61; 62], seven quasi-experimental design, [41; 44; 45; 46; 48; 54; 59], and one single subject design [56]. The details of each included study are summarized below in Table 1. The vibration device used, vibration parameter, and protocols are summarized in Table 2.  
Figure 1: Summary of literature review process.