esa-sceva/satcom-chunk-collection huggingface dataset:
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
###### Abstract  
This article focuses on a beacon-less Inter-satellite optical wireless communication (Is-OWC) system. This system uses the same wavelength for the transmitted and received laser beams and uses part of communication light beam as beacon light instead of an additional high divergent beacon laser. Thus, a part of transmitted light will be reflected in optical path and leaked into acquisition, tracking and targeting (ATP) subsystem. This reflected light will seriously interfere with the spot position measurement of received light. To solve this problem, we propose a new method based on intensity modulation and cyclic cross-correlation (CCR). Experiment results show that even when the reflected and received beams differ in intensity by 91.7 times, we can still accurately detect the received light spot position by using the proposed method, where the maximum error is 0.026 mrad and the root mean square error is as low as 0.012 mrad.  
s 2019 9 3032 24 July 2019 27 July 2019
# SAViP: Semantic-Aware Vulnerability Prediction for Binary Programs with Neural Networks
## 4 Evaluation
### Ablation Study  
Although we have demonstrated the effectiveness of our model, it is also important to determine which feature contributes the most to the result. To this end, we conduct an ablation study to analyze the role of each kind of feature. We evaluate the results from two perspectives: recall and the average training time per epoch. Figure 9 shows the training curves under different settings, and Table 5 provides a more direct representation of their differences with various evaluation metrics. The column called GAP shows the impact of each missing feature on the recall rate of model testing, using percentages to increase the contrast.  
From the results, removing semantic features (SM-F) reduces the recall by 8.8%, which shows that this part of the model improves recall the most. However, it is also the most time-consuming part, as the training time difference for an epoch reaches 6.18 s. The addition of statistical features (STA-F) and structural features (STR-F) has similar effects on performance, with recall differences of less than 5%. However, structure feature extraction involves the GNN, which leads to more time consumption (5 s) than statistical feature extraction.  
Figure 8: Model performance for different CWEs. The Top 10 accuracy of SAViP reaches 100% except for CWE134 and CWE401. For recall, SAViP outperforms V-Fuzz for all CWEs.  
Figure 7: Performance comparison of SAViP and V-Fuzz. For both the Top-K accuracies and recall, SAViP performs better than V-Fuzz.  
As mentioned above, the ablation study shows that semantic features contribute the most to our vulnerability prediction model. This proves that semantic learning can penetrate deeply into basic blocks and can extract more complex details than statistical information.
## 3 Results  
### LSTM Architecture  
Extensive investigation and tryouts were conducted with the aim of identifying the optimal specifications and hyper-parameter values for the LSTM architecture. The selected architecture is illustrated in Figure 8.  
The three LSTM cells are constructed by 10 memory units each. The first and second fully connected layers have 10 nodes. All the aforementioned layers use Rectified Linear Unit (ReLU) activation [66], and each one is followed by a dropout layer with 50% drop rate. The output layer has six nodes, one for each class and uses the softmax activation [67]. The total number of trainable parameters sums up to 12,701.  
Figure 7: Schematic of the Machine Learning (ML) pipeline proposed in the present study.  
Training of the model ranged from 25 to 50 epochs. A typical plot of the training and validation loss decrease over the epochs is shown in Figure 9.
## 4 Results
### Development Insights and Technical Achievements
#### 4.1.3 Algorithm Implementation and Platform Integration  
During the implementation phase of the fall detection system, several technical and operational challenges were encountered. One of the primary difficulties was minimizing false positives--situations where regular movements, such as sitting down quickly or jumping, were mistakenly identified as falls. To address this, the training dataset was carefully curated, including a wide range of daily activities alongside both real and simulated fall events, which allowed the algorithm to better distinguish between them.  
Once new data is securely transmitted to the ICIPRO Cloud, the system computes the magnitude of acceleration using the Euclidean formula and immediately applies the pretrained model to classify the event as either a fall or a non-fall. If a fall is detected, the platform automatically triggers email alerts to notify caregivers or healthcare providers, enabling a rapid response and ensuring continuous passive monitoring without requiring user input.
## 2 Materials and Methods
### BiGRU-DNN Module  
The Gated Recurrent Unit network (GRU) is a cyclic neural network RNN that solves the loss of long-distance information [30; 31; 32; 33]. It is mostly used to process timing information and solve the problems of gradient explosion and gradient disappearance in the RNN structure. The most important thing is that it can memorize the information effectively and give up the memory of redundant information. The Bidirectional Gated Recurrent Unit network (BiGRU) is composed of a forward GRU and a reverse GRU, which contains all the information of the forward and backward directions, and its structure is shown in Figure 1.  
Firstly, the data is fed into the Forward Network and the Backward Network through the Input Layer. After being processed by the Forward Network and the Backward Network,  
Figure 1: BiGRU structure.  
the data is spliced in the Output Layer. The results after the Forward Network and the Backward Network processing will be used as inputs to the next layer of the proposed model. In this paper, the output dimension of the BiGRU model will be set as two times the input dimension. This can effectively reduce the complexity of the model. Its output is shown in Formula (8):  
\[h_{i}=\overset{\rightarrow}{[h_{i},\overset{\leftarrow}{h_{i}}]} \tag{8}\]  
The \(\overset{\rightarrow}{h_{i}},\overset{\leftarrow}{h_{i}}\) in Formula (8) denote the results after processing of the Forward Network and the Backward Network, respectively.  
Deep neural network (DNN) model is an ordered combination of multiple perceptual machines and is a commonly used deep learning network, which is also called Multilayer perceptron. In this paper, the DNN model is used to fit the output of the previous layer. In this model, DNN has two hidden layers and uses RELU as the activation function. The general Formula (9) for DNN calculation is as follows:  
\[f(x)=\sigma(WX+b) \tag{9}\]
# Integrating Knowledge Graphs into Distribution Grid Decision Support Systems +
## 5 GridKG--Graph-Based Representation of Distribution Grid
### System Events
#### 5.3.2 Example D  
This case is related to outage events that occur in a specific service area and are related to a specific _Element-Asset_ that satisfies particular conditions. For example, we can ask for a list of outages associated with _Switches_ that went through maintenance activities during a specific period of time. An additional condition may narrow the query down to only switches on paths that have at least fifty customers downstream.  
The _Cypehr_ query is shown below. It asks for a list of switches that were involved in outages caused by _Adverse Weather_ and subject to maintenance activity _XYZ_ between _time1_ and _time2_.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Id** & **Time** & **Primary Cause** & **Secondary Cause** \\ \hline
1 & 1997-12-07 14:23 & Unknown & – \\
2 & 1997-06-24 23:59 & Defective Equip. & Electrical Failure \\
3 & 1997-06-28 22:04 & Unknown & – \\
4 & 1998-05-13 06:06 & Unknown & – \\
5 & 1999-09-10 18:08 & Unknown & – \\
6 & 2000-08-14 10:53 & Foreign Intrf. & Wildlife (bird/animal) \\
7 & 1998-07-27 18:38 & Foreign Intrf. & Wildlife (bird/animal) \\
8 & 1998-08-09 02:25 & Unknown & – \\
9 & 1996-07-30 08:15 & Foreign Intrf. & Wildlife (bird/animal) \\
10 & 1997-06-28 21:53 & Unknown & – \\ \hline \end{tabular}
\end{table}
Table 1: Details of 10 outages (selected information).  
Figure 5: Details of 10 outage events downstream of a specific _Element-Asset_.  
MATCH (switch:Element[ComponentType: \"Switch\", ServiceAreaName: \"St. Paul\"]) -[:hasOutageEvent] -(oe:OutageEvent[OutageCause: 'Adverse Weather']) MATCH (switch)-[:Connection*.]->(downstream_element:Element) WHERE switchNumCustomer>50 AND downstream_element.MaintenanceTime>time1 AND downstream_element.MaintenanceTime<time2 AND downstream_element.maintenanceType=\"XYZ\" RETURN switch, downstream_element
## 3 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **FSR (nm)** & & **Sensitivity (nm/RIU)** & **Q Factor** & **LOD (RIU)** \\ \hline Radius (\(\mu\)m) & 50 \(\pm\) 0.02 & 75 \(\pm\) 0.02 & 100 \(\pm\) 0.02 & & & \\ Numerical calculations & \(\sim\)4.2 & \(\sim\)2.8 & \(\sim\)2.1 & \(\sim\)110 & - & - \\ Experimental data & \(\sim\)3.64 & \(\sim\)2.42 & \(\sim\)1.82 & \(\sim\)112.5 & 1.7154 \(\times\) 104  & 7.99 \(\times\) 10\({}^{-4}\) \\ Relative percent error (\%) & \(\sim\)15.4 & \(\sim\)15.7 & \(\sim\)15.3 & \(\sim\)2.2 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of numerical and experimental results obtained for RR device. Figure 4: Experimental demonstration of an RR structure: (**a**) transmission spectrum of an RR structure with R = 100 nm in the presence of n = 1.295, 1.305, and 1.315; (**b**) resonance wavelength versus ambient refractive index. The arrows signify the redshift in the resonance wavelength as the refractive index of the liquids increases. the sensor's sensitivity and its capability to identify subtle variations in the surrounding medium. Various factors influence the LOD of an RR sensor, including intrinsic noise, the signal-to-noise ratio, and the Q factor of the resonator. A higher Q factor, signaling a more distinct resonance and a narrower linewidth, typically results in a lower LOD. The LOD of our sensing device is 7.99 \(\times\) 10\({}^{-4}\) RIU. Table 2 provides a performance comparison of the RR structure fabricated in this work with those reported in previously published papers. It is important to note that this comparison encompasses RR designs utilizing various waveguide configurations, such as ridge, slot, and hybrid plasmonic, across different material platforms, each of which can enhance the sensitivity of the device. The sensitivity and LOD of those from the previous works, as reported in Table
## 4 Experimental Section
### Ablation Studies
#### 4.8.2 Transfer Learning  
To examine the influence of transfer learning on BTAM model, an experiment is created to test the model directly after training on CWQS dataset without utilizing transfer learning. Figure 10 shows a comparison of the results with the model using transfer learning.  
As shown in Figure 10, the model improves its \(F1\) value by \(11.4\%\) when using transfer learning versus not using transfer learning, which is a more significant improvement. The model that used transfer learning was initially trained on the transfer learning dataset before being trained on the experimental dataset, allowing the model to gain preliminary knowledge of simple questions. The model that does not use transfer learning can only be trained on the experimental dataset, which contains nearly \(70\%\) less training data than the former, so the results are lower.
## 3 Study on the ECD Model of the Irregular Eccentric Wellbore
### Validation of Computational Results  
The ECD calculation model is used to simulate and calculate the ECD in the wellbore and at the bottom of the well during the cementing process in the 7X 7x2 oilfield in Eastern China. The displacement scheme is shown in Table 3, and the wellbore and related fluid parameters are shown in Table 4.  
The bottomhole ECD and total well ECD can be calculated using calculation models that consider conventional, coupled temperature-pressure and irregular wellbore diameter factors, respectively. The calculation results are shown in Figure 8. In the figure, compared with the conventional model and the model that only considers temperature and pressure, the new model's calculation results consider the temperature, pressure, and radius changes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inlet Velocity (m/s)** & **Model Calculation (Pa)** & **Experimental Result (Pa)** & **Deviation (\%)** \\ \hline
0.18 & 24,043.35 & 21,858.351 & 9.09 \\
0.3 & 27,829.73 & 26,313.37 & 5.45 \\
0.4 & 30,270.05 & 29,501.259 & 2.54 \\
0.5 & 32,334.30 & 32,301.236 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of model calculations and experimental results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item Number** & **Type of Fluid** & \
\begin{tabular}{c} **Injection Flow Rate** \\ **(m3/min)** \\ \end{tabular} &
\begin{tabular}{c} **Injection Volume** \\ **(m3)** \\ \end{tabular} \\ \hline
1 & Spacer & 1.8 & 30 \\
2 & Leading cement & 1.0 & 18.0 \\
3 & Tailing cement & 1.0 & 34.0 \\
4 & Displacement fluid 1 & 1.2 & 4.8 \\
5 & Displacement fluid 2 & 1.2 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The displacement scheme.  
They can more accurately calculate the change in the displacement interface position caused by the irregular wellbore.
# All about Delay-Tolerant Networking (DTN) Contributions to Future Internet
## 3 Related Work
### Information-Centric Networking (ICN) and Named Data Networking (NDN)
#### 3.2.2 DTN-ICN in Emergency Scenarios  
The possibility of utilizing the ICN approach in emergency scenarios where the DTN concept is a key aspect has been highlighted by several research groups [79; 80] and studies.  
This approach can help re-establish communication in scenarios where communication infrastructures are unavailable.  
Along these lines, the authors in [75] introduce a CCN-Oriented Notification Service (CNS) that leverages ICN to facilitate efficient disaster management by minimizing administrative overhead, thus reducing network congestion, latency, and enhancing security during communication establishment. They perform large-scale simulations with real-world disaster traces and compare the CNS with IP-based solutions. The authors in [81] introduce Delay-Tolerant ICN for Disaster Management (DID), which is a framework designed to address the challenges of communication resilience in disaster scenarios. DID focuses on enabling interest-based content retrieval among fragmented networks. The work of [82] presents an orchestration framework that integrates ICN/DTN with long-term evolution standards for public security applications, thereby offering a radio access network to end users.  
Furthermore, the notion of ICN \"data mules\" [83; 84; 81; 85], which deliver information in a publish/subscribe manner to different fragments of the network with predetermined, fixed, or random paths, has also been examined. In this context, additional features have been introduced to ICN, such as scoping and prioritizing messages/interests according to specific attributes. These attributes may include (i) user-defined priorities; (ii) content lifetime and validity [86]; (iii) criticality (e.g., broadcasting emergency messages [81] or the preprocessing and delivery of medical images for healthcare workers [87; 88]); (iv) popularity [85]; or (v) reputation score [89].
# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 3 Materials and Methods
### Target Device  
The target device for deployment is an Arduino Nano 33 BLE Sense [38], featuring a Cortex-M4F 64 MHz processor, chosen for its compact size and versatility across various applications. With 256 kB of RAM and 1024 kB of ROM, the device has limited computational resources. To optimize model deployment, we used post-training quantization provided by the Edge Impulse platform, utilizing implementations from the TensorFlow Lite Micro library [39]. This technique reduces the precision of a model's internal representations by converting 32-bit floating-point parameters into lower-precision int8, significantly reducing the model's memory (ROM) requirements. This optimization makes deployment on a constrained device more feasible and accelerates computation, enabling quicker predictions and responses. Post-training quantization is thus a crucial step, preserving core functionality and accuracy while adapting the models to the device's limited resources.  
Given the deployment on the Arduino Nano 33 BLE Sense, additional metrics, such as inference time, memory usage, and storage footprint, were considered. These factors are essential to ensuring the models' efficiency and smooth performance on the device. By evaluating classification metrics, computational efficiency, and suitability for the tiny  
\begin{table}
\begin{tabular}{c c} \hline \hline
**CNN Model Proposed by Edge Impule** \\ \hline Input(shape=(975, )) \\ \hline Reshape(975 / 13, 13)) \\ \hline Conv1D(8, kernel\_size=3, activation=’relu’) \\ \hline MaxPooling1D(pool\_size=2) \\ \hline Dropout(25\%) \\ \hline Conv1D(16, kernel\_size=3, activation=’relu’) \\ \hline MaxPooling1D(pool\_size=2) \\ \hline Dropout(25\%) \\ \hline Dense(classes, activation=’softmax’) \\ \hline \hline \end{tabular}
\end{table}
Table 6: CNN model proposed by Edge Impule.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Learning Rate** & **Epochs** & **Batch Size** \\ \hline
**Custom CNN** & \(6\times 10^{-3}\) & 300 & 32 \\ \hline
**CNN Edge Impulse** & \(6\times 10^{-3}\) & 100 & 16 \\ \hline
**Custom LSTM** & \(6\times 10^{-3}\) & 100 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Optimal hyperparameters showing the highest accuracy.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Our LSTM Model** \\ \hline Dense(8, activation=’relu’) \\ \hline Dropout(40\%) \\ \hline Dense(classes, activation=’softmax’) \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._device, we made informed decisions regarding model selection. Edge Impulse offers a functionality that estimates the performance of the model on the target device before deployment, facilitating a more informed deployment process.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 4 The Proposed Detection Scheme
### Step 4: Attack Detection  
In the previous step, the Ryan-Joiner test is applied to measure the R-J correlation coefficient. The values of this correlation coefficient will allow us to determine the probability that an attacker is present or not, so that observations with R-J values that fall below a threshold value representing the R-J normal critical value will identify an abnormal behavior. The threshold values are computed using formulas in Equation (4), Equation (5), or Equation (6) given in Section 2.3.  
* If \(R-J_{calculated}\geq~{}R-J_{critical}\), the assumption of normality is approved and consequently, we can conclude the absence of malicious behavior.
* If \(R-J_{calculated}<R-J_{critical}\), the normality assumption is rejected and we detect the existence of a routing attack. A notification is generated as soon as the R-J coefficient value is below the pre-defined threshold corresponding to Ryan-Joiner the critical values.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
## 3 Method and Analysis
### Thermal Imaging, Panels Settlement, and Fault Detection
\begin{table}
\begin{tabular}{l l} \hline
**Specification** & **Specification** \\ \hline Drone & DJI Matrice 300 \\ Visual Camera & DJI Zenmuse H20T \\ Visual Camera Resolution & 4000 \(\times\) 3000 \\ Thermal Camera & DJI Zenmuse H20T \\ Thermal Camera Resolution & 640 \(\times\) 512 \\ Operating Range (0C) & \(-\)25 to 153 \\ Sensitivity (NedT) & \textless{}50 mK \\ \hline \end{tabular}
\end{table} 
Table 3: Actual and predicted results of faults of Site-1. thermal images acquired, panels were chosen, and individual panel images were captured at a resolution of 24 \(\times\) 40 pixels and 96 dpi. In the real situation, 127 bypass diode faults and 38 hotspot faults were detected throughout Site-1. By making use of these determinations, a sample of the system was tried to be created and the sample in question includes a total of 415 images, including all images of defective panels and 250 randomly selected no-fault panel images. Hierarchical clustering of images was performed on the Orange platform. The image transformation was made with the help of the image embedding algorithm, then the distance metrics were applied for calculating the distances. Once the data are passed to the hierarchical clustering, the widget displays a dendrogram, a tree-like clustering structure. According to the branches of the dendrogram, the data are divided into several clusters. Table 4 shows the number of clusters needed to group each state under consideration with different distance metrics. Comparative representation of the actual and the predicted values is given in Table 5.  
Table 5 reveals that the cosine distance metric yields the most successful results. While all three metrics achieved a 100% success rate in detecting hotspot fault, the algorithm should exhibit greater sensitivity to differentiate between a defect-free panel and bypass diode failure. This observation is consistent with the recorded results, which indicate a correct prediction rate of 96.9% for bypass failure malfunctions and 99.6% for panels without faults.
## 3 Analysis of I2O WBAN Systems
### Path Loss Model  
The human body is a natural lossy environment, which therefore leads to high attenuation for signal transmission. The PL model of homogeneous human tissue/organ has been described in [14]. For the heterogeneous human body, the Tx antenna moves through the different layers (tissues) and the PL is obtained. When the antenna is placed in a specific layer, the surrounding layers differ from that containing the antenna, leading to deviations between the simulated and calculated PL values. A semi-empirical PL formula in dB between two implant devices can be expressed as [5,14,15,25]:  
\[PL_{dB}\left(d\right)=PL_{dB}\left(d_{ref}\right)+10n\ log_{10}\left(\frac{d} {d_{ref}}\right)+S_{dB},\ d\geq d_{ref} \tag{1}\]  
where \(PL_{dB}\left(d_{ref}\right)\) is the PL value at the reference distance \(d_{ref}\) (5 mm in this paper). \(S_{dB}\) is the shadow fading parameter expressed in decibels (dB), which follows a normal distribution \(S\)\(\sim\)\(N\) (0, \(\sigma_{s}\)) where \(\sigma_{s}\) is the standard deviation, since the logarithm of a lognormal distribution is normally distributed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Skin** & **SAT** & **BT** & **Muscle** & **Liver** \\ \hline \(\epsilon_{r}\) & 38 & 10.8 & 5.15 & 52.7 & 43 \\ \(\sigma\) [S/m] & 1.46 & 0.27 & 0.14 & 1.74 & 1.69 \\ tan\(\delta\) & 0.28262 & 0.14524 & 0.19535 & 0.24194 & 0.28751 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Conductivity (\(\epsilon_{r}\)), Relative Permititivity \(\sigma\) and Loss Tangent tan\(\delta\)[24].  
Figure 3: The 3D human body model and dipole antennas. **(left)** front view; **(right)** vertical view.  
Moreover, \(\sigma_{s}\) reflects the degree of the shadow fading strength [25]. The probability density function of the shadow fading effect \(S_{dB}\) can be expressed as:  
\[P_{S_{dB}}=\frac{1}{\sigma\sqrt{2\pi}}exp\left(-\frac{S_{dB}^{2}}{2\sigma_{log}^ {2}}\right) \tag{2}\]  
The variable \(d\) is the separation distance between the Tx and Rx; \(n\) is the path loss exponent which depends on the propagation media. MATLAB(r) least square fit computation has been implemented to yield a fitted PL as seen in Figure 4. The simulation results are summarized in Table 3. Closer agreement than many proposed PL models [15; 16] is obtained between the derived PL model and the simulations using CST with an average deviation of 2.93 dB.