esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology and Proposed Model
### Data Pre-Processing
#### 3.2.2 Data Encoding  
Since most machine learning algorithms cannot directly handle categorical features, these features must be transformed into numerical representations to be used in an ML model. Several categorical features, including \"Business Travel\", \"Department\", \"Education Field\", \"Gender\", \"Job Role\", \"Marital Status\", \"Overtime\", and \"Attrition\", are included in the original IBM dataset and must be transformed into numerical values. Table 2 shows the transformation of categorical feature values into numerical values.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute** & **Data Type** & **Description** \\ \hline Avg-Monthly-Hrs & integer & The average number of hours worked per month. \\ Department & Categorical & The department of employee. \\ Filed-Complaint & integer & If the employee filed a formal complaint in the last 3 years. \\ Last-Evaluation & Float & Score for the most recent evaluation of the employee, which ranges from 0 to 1. \\ N-Projects & integer & The number of projects that an employee works on. \\ Recently-Promoted & integer & If a worker has received a promotion in the last three years. \\ Salary-Level & Categorical & The employee’s salary level, categorized as low, medium, or high. \\ Satisfaction & Float & Score for employee satisfaction with the company, which ranges from 0 to 1. \\ Tenure & integer & Number of years in the company. \\ Status (target) & Categorical & Current employment status, (Employed/left). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attributes list of the Kaggle dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Categorical Feature** & **Description of Transformed into Numerical Values** \\ \hline Business Travel & 0 = Non travel, 1 = Travel Rarely, 2 = Travel Frequently \\ Department & 0 = Human Resources, 1 = Research and Development, 2 = Sales \\ Education field & 0 = other, 1 = Life Sciences, 2 = Medical, 3 = Marketing, 4 = Life Sciences, \\ \multicolumn{2}{c}{5 = Human Resources} \\ Gender & 0 = Female, 1 = Male \\  & 0 = Sales Executive, 1 = Research Scientist, 2 = Laboratory Technician, \\ Job Role & 3 = Manufacturing Director, 4 = Healthcare Representative, 5 = human resources, \\  & 6 = Sales Representative, 7 = Research Director, 8 = manager \\ Marital Status & 0 = Divorced, 2 = Married, 1 = Single \\ Over Time & 0 = No, 1 = Yes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Transformation of categorical feature values into numerical values.  
In the Kaggle dataset, there are three categorical features: \"Department\", \"Salary Level\", and \"Status\". These features need to be converted into numerical values. For example, for the \"Status\" attribute, \"employed\" is represented as 0, while \"left\" is represented as 1.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 4 Materials and Methods
### Timing Synchronization Method
#### 4.2.2 Accumulated Correlation Peak Detection Algorithm  
We improve the CCPD algorithm above and propose a new timing synchronization algorithm that performs better. This is called the accumulated correlation peak detection (ACPD) algorithm.  
Figure 4: The amplitude variation of the correlation value \(Corr(n)\).  
Similar to the CCPD algorithm, the correlation value \(Corr(n)\) is obtained first. Next, \(m\) correlation values are accumulated with an interval of \(N\) to obtain \(CorrSum(n)\), which can be described as  
\[CorrSum(n)=\sum_{i=0}^{m-1}|Corr(n-i*N)|. \tag{11}\]  
Figure 5: The flowchart of the continuous correlation peak detection (CCPD) algorithm.  
The synchronization position is obtained by detecting the peak of \(CorrSum(n)\). The amplitude variation of \(CorrSum(n)\) is shown in Figure 6.  
As can be seen from Figure 6, the peak value of \(CorrSum(n)\) first increases and then decreases. If the position of the maximum correlation peak can be found, the position of the frame header can be calculated accordingly. The process of the ACPD algorithm is as follows:  
1. Perform correlation operation on the received signal \(r(n)\) and the ZC sequence to obtain the correlation value \(Corr(n)\);
2. Accumulate \(m\) correlation values with an interval of \(N\) to obtain \(CorrSum(n)\);
3. Set a correlation window with a period of \(N\), where the parameter \(Counter\) (\(1\leq Counter\leq N\)) indicates the relative position of each point in the correlation window, and record the relative position \(MaxPos\) of each correlation peak in the corresponding correlation window and its peak value (denoted as \(PeakValue\)), as well as that of the one before (denoted as \(PeakValueBefore\));
4. If the \(MaxPos\) of the current correlation window is the same as that of the one before (denoted as \(MaxPosBefore\)), then add one to the parameter \(MaxPosTimes\); otherwise, set it to zero;
5. If \(MaxPosTimes\) is greater than \(Threshold\), and \(PeakValue\) is smaller than \(PeakValueBefore\), the received signal has been synchronized successfully. The parameter \(Threshold\) is the threshold value of the number of detected correlation peaks, which varies with the number \(m\) of ZC sequences. Next, calculate the frame header position of the received signal according to \(MaxPos\). It is easy to calculate the distance between the synchronization decision point and the frame header as \((m+2)*N-MaxPos\).  
The flowchart of the ACPD algorithm is shown in Figure 7.  
Figure 6: The amplitude variation of \(CorrSum(n)\).
# Minimally-Invasive Neural Interface for Distributed Wireless Electrocorticogram Recording Systems
###### Abstract  
This paper presents a minimally-invasive neural interface for distributed wireless electrocorticogram (ECoG) recording systems. The proposed interface equips all necessary components for ECoG recording, such as the high performance front-end integrated circuits, a fabricated flexible microelectrode array, and wireless communication inside a miniaturized custom-made platform. The multiple units of the interface systems can be deployed to cover a broad range of the target brain region and transmit signals via a built-in intra-skin communication (ISCOM) module. The core integrated circuit (IC) consists of 16-channel, low-power push-pull double-gated preamplifiers, in-channel successive approximation register analog-to-digital converters (SAR ADC) with a single-clocked bootstrapping switch and a time-delayed control unit, an ISCOM module for wireless data transfer through the skin instead of a power-hungry RF wireless transmitter, and a monolithic voltage/current reference generator to support the aforementioned analog and mixed-signal circuit blocks. The IC was fabricated using 250 nm CMOS processes in an area of 3.2 \(\times\) 0.9 mm\({}^{2}\) and achieved the low-power operation of 2.5 \(\upmu\)W per channel. Input-referred noise was measured as 5.62 \(\upmu\)V\({}_{\text{rms}}\) for 10 Hz to 10 kHz and ENOB of 7.21 at 31.25 kS/s. The implemented system successfully recorded multi-channel neural activities in vivo from a primate and demonstrated modular expandability using the ISCOM with power consumption of 160 \(\upmu\)W.  
Electrocorticogram (ECoG); low-power; low-noise; neural recording; push-pull double-gated amplifier; intra-skin communication (ISCOM); neural interface +
Footnote †: journal: Sensors
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
## 3 Identity-Based Encryption Scheme
### Our IBE Construction
\(\textbf{IBE.Dec}(MPK,\mathbf{s}\mathbf{k}_{id},\mathbf{c}_{id})\): Input \(MPK\), user private key \(\mathbf{s}\mathbf{k}_{id}\), and ciphertext \(\mathbf{c}_{id}\). Compute \(\mathbf{s}_{id}^{\prime T}\cdot\mathbf{c}_{id}\) and denote the result as \(msg^{\prime}=\mathbf{s}\mathbf{k}_{id}^{T}\cdot\mathbf{c}_{id}\in\mathbb{Z}_{Q}\). When \(\left|\left|msg^{\prime}-\left|\frac{Q}{2}\right|\right|\right|<\left|\frac{Q}{ 4}\right|\), output \(msg=1\); otherwise, \(||msg^{\prime}||<\left|\frac{Q}{4}\right|\), output \(msg=0\).
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 3 Propose Scheme
### Last Two-Hop Route Optimization with Reinforcement Learning
#### 3.5.2 Update of \(Q\)-Values  
For each one-hop neighbor, a node maintains a value, \(ls(c,x)\), which shows the link status between node \(c\) and \(x\). For simplicity, we use hello reception ratio to estimate the link status. However, the estimation can be improved by taking into account the vehicle velocity in the link status evaluation. The \(Q\)-Table is updated upon the reception of hello messages. Each node needs to maintain a \(Q\)-value for each one-hop neighbor, the destination (the traffic source node for the TCP ACK messages) node, and the edge nodes located in two-hop distance. \(Q\)-values are broadcasted by each node using hello messages. Each \(Q\)-value is initialized to 0. Upon reception of a hello message from node \(x\), node \(c\) updates the corresponding \(Q\)-value to the node \(r\) as  
\[Q_{c}(r,x) \leftarrow \hat{\alpha}\times ls(c,x)\times\big{\{}\hat{R}+\gamma\times \max_{y\in N_{x}}Q_{x}(r,y)\big{\}} \tag{6}\] \[+\ (1-\hat{\alpha})\times Q_{c}(r,x).\]  
The learning rate (\(\hat{\alpha}\)) is 0.7, and the discount factor (\(\gamma\)) is 0.9. \(\max_{y\in N_{x}}Q_{x}(r,y)\) is the maximal \(Q\)-value of \(x\) to node \(r\). The reward \(\hat{R}\) is calculated as  
\[\hat{R}=\begin{cases}1,&\text{if }c\in N_{r}\\ 0,&\text{otherwise}\end{cases} \tag{7}\]  
where \(N_{r}\) denotes the one-hop neighbor set of node \(r\). When node \(c\) is a neighbor of node \(r\), the reward is 1 and otherwise 0. Please note that only one \(Q\)-value is maintained for each pair of state and action. Upon reception of a hello message, the corresponding \(Q\)-value is updated according to Equation (6) which discounts the reward with the increase of number of hops. This ensure that the hop count is considered in the route selection. The quality of each link is also taken into account by discounting the reward with the link status value [\(ls(c,x)\)]. As a result, a \(Q\)-value represents the evaluation of a next packet forwarder candidate with the consideration of multi-hop performance. Therefore, the proposed scheme is able to optimize the route to the reference node. Figure 5 shows an example for \(Q\)-value update where \(ls(\cdot,\cdot)\) is 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Environment** & **The Entire Vehicular Ad Hoc Network** \\ \hline Agent & Each packet \(P(o,r)\) \\ State of the agent & Each node in the network \\ State space & The set of all nodes in the network \\ Actions & Selecting an one-hop neighbor as the next hop \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Q\)-learning model.
## 2 Materials and Methods
### Test Procedure and Data Acquisition  
Three test scenarios were performed as follows. In each scenario, the bogie was travelling from the end labelled \(S_{0}\) to the one labelled \(S_{1}\). In the first scenario, the rails were without any squats. In the second scenario, 1 mm depth squats were generated. In the third scenario, the depth of the squat was increased to 4 mm. Every test scenario was run three times. The vibration signal was measured with three accelerometers installed on one rod of the point machine. To capture the vibration signals from all three accelerometers, the DAQ9174 data-acquisition platform was used to feed the data directly to a computer for storage. The measuring system operated at a sampling frequency of 51.2 kHz. The speed of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Range** & **Sensitivity** & **Destruction Limit** & **Resonant Frequency** \\  & **(Hz)** & **(mV/g)** & **(g)** & **(kHz)** \\ \hline
608A & 0.5—10,000 & 10.2 & 50 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the accelerometer used.  
Figure 3: Sensors mounted on the point machine in the testbed.  
Figure 2: Diagram of the testbed and sensors placement.  
the bogie was measured using a customised tachometer consisting of a Hall effect sensor A3144 and multiple neodymium magnets. An Arduino Uno unit was used to transmit the revolutions per minute (RPM) of one of the front wheels to the computer via WiFi. The signals collected from a 4 mm test case are visualised in Figure 4.
## 2 Materials and Methods
### Calibration and Experimental Protocol
#### 2.3.4 Experiment 3: Synthetic EO Data Constraint  
The goal of this experiment was to test whether the MDF approach tested above could provide robust estimates of crop N status during a critical management period, allowing effective intervention. We evaluated the performance of the multi-treatment parameter calibration for estimating yield when constraining DALEC-Crop with synthetic EO-derived LAI observations during the recommended period of N fertilization--coinciding with the middle of the tilleering to stem elongation growth stages (i.e., GS 25-35) [46]. We produced a synthetic EO LAI times-series by, first, generating daily DALEC-Crop LAI estimates during this N application period. This DALEC-Crop LAI time-series was resampled from a daily to a 5-day interval to matches the potential revisit time of the European Space Agency's (ESA) Sentinel-2 dual-satellite constellation. We further added random noise to this synthetic time-series to match the expected uncertainty of Sentinel-2 wheat LAI retrievals, which has previously been quantified as having an NRMSE of 17% [15]. The resultant time-series comprised of 7 (first season) and 5 (second season) synthetic observations with a 5-day temporal resolution. This time-series was then used to calibrate the slope and intercept coefficients for the LNA linear fit and the model yield predictive performance was evaluated for all plots.
## 4 Implementation
### Model for Dataset Noise Reduction and Purification  
This section describes how to construct the noise reduction model used in the proposed method. This paper uses all six classifier models described in Section 2.2: Decision Tree, Random Forest, SVM, Naive-Bayes, K-NN, and Logistic Regression. Each model showsdifferent performance depending on the data distribution and their features. Combining these models allows consistent performance even for datasets with various distributions and characteristics. This paper constructs a hard-voting ensemble model using the above six classifier models. The hard-voting method in the ensemble model filters only the results of the unanimous classification of the six internal models. This ensemble model selects only data classified as an attack (6 voted) or normal (0 voted) by all internal models, and all remaining data (1 to 5 voted) is classified as noise data and removed. This extreme filtering has the purest form of the decision boundary.
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 3 Proposed System Model  
In this paper, the proposed system model uses the architecture of the cloud radio access network that enables the functional separation of a traditional base station (BS) into two parts: the remote radio head (RRH) and the baseband unit (BBU) as shown in Figure 1.
## 4 Numerical Analysis
### Analysis of Dynamic Response of Concrete Slab Track  
The dynamic behaviors of the deformed concrete slab track due to the impact load were investigated. The concrete track system on the earthwork in Korea is composed of a track concrete layer and a hydraulically stabilized base, as shown in Figure 18.  
The track concrete layer and the hydraulically stabilized base were constructed with reinforced concrete and plain concrete, respectively. Thus, the track in the numerical modeling can be divided into the TCL (track concrete layer) layer and the HSB (hydraulically stabilized base) layer, and the vertical stresses of these two layers were analyzed. A correlation between vertical stresses of the track and allowable stresses suggested in the Korean Railroad Design Standard (KR C-14040) was also analyzed [26]. The numerical analysis was performed by increasing the train speed from 100 to 300 km/h in 100 km/h increments. Total time taken to pass the model varied from 1.5 s to 4.5 s depending on the train speed, so as to ensure the train would completely pass the track system.  
Figure 19 shows the vertical stresses of the TCL and HSB layers according to sections of the transition zone. The stress was reduced in the settlement section and then increased due to the impact load at 46 to 62 m from the bridge-earthwork boundary where rail deflection was restored. In addition, distances between the impact load applied point and the bridge-earthwork boundary were increased as the train speed increases as shown in Figure 20. The distance when the train speed increases from 200 to 300 km/h is 2.2 times farther nonlinearly than when it increases from 100 to 200 km/h.  
Figure 19: Vertical stresses of the concrete slab track. (**a**) Track concrete layer (TCL) layer; (**b**) Hydraulically stabilized base (HSB) layer.  
Figure 18: Track system of high-speed railway in Korea.  
Variations of stresses at the impact load point according to train speeds are shown in Figure 21. It was found that the stress acting on the concrete track increased with the train speed. The stresses of the TCL and HSB layers were distributed from 0.73 to 1.13 MPa and from 0.24 to 0.32 MPa, respectively. Therefore, the stress increase of the TCL layer, which has a linear trend, is greater than that of the HSB layer when the train speed increases.  
The maximum stress of the TCL and HSB layers at the speed of 300 km/h is 1.13 MPa and 0.78 MPa, which satisfies the allowable standard of 1.7 MPa and 1.57 MPa.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling  
Using AI for propagation modeling offers several advantages when compared to empirical models [21], such as:  
* Adaptability: can adapt to different scenarios and technologies such as LPWAN and particularly LoRaWAN, making them suitable for various applications, from urban to rural areas and indoor spaces;
* Accuracy: can outperform empirical models in their accuracy as they learn from real-world data while using environmental information to their advantage;
* Cost and time efficiency: can reduce the time required to generate a propagation estimation.  
Based on the method of feature extraction that is used, this section divides the AI into machine learning and deep learning algorithms and addresses each individually.
# Refinement Orders for Quantitative Information Flow and Differential Privacy +
## 7 Lattice Properties
### Max-Case Refinement  
In the case of \(\sqsubseteq^{\text{max}}\), \"equivalent\" channels are those producing the same posteriors (or more generally the same convex hull of posteriors). However, in contrast to \(\sqsubseteq^{\text{avg}}\), if we identify such channels that is if we represent a channel only by the convex hull of its posteriors, then \(\sqsubseteq^{\text{max}}\) becomes a lattice.  
First, note that given a finite set of posteriors \(P=\{\delta^{y}|y\}\), such that \(\pi\in ch\,\{\delta^{y}\}_{y}\), i.e., such that \(\pi=\sum_{y}a_{y}\delta^{y}\), it is easy to construct a channel \(C\) producing each posterior \(\delta^{y}\) with output probability \(a_{y}\). It suffices to take \(C_{x,y};=\delta^{y}_{x}a_{y}/\pi_{x}\).  
Thus, \(A\lor^{\text{max}}B\) can be simply constructed by taking the intersection of the convex hulls of the posteriors of \(A,B\). This intersection is a convex polytope itself, so it has (finitely many) extreme points, so we can construct \(A\lor^{\text{max}}B\) as the channel having exactly those as posteriors. \(A\land^{\text{max}}B\), on the other hand, can be constructed as the channel having as posteriors the union of those of \(A\) and \(B\).  
Note that computing the intersection of polytopes is NP-hard in general [21], so \(A\lor^{\text{max}}B\) might be hard to construct. However, efficient special cases do exist [22]; we leave the study of the hardness of \(\lor^{\text{max}}\) as future work.
## 2 Materials and Methods  
### Dataset Construction  
Currently, there is no unified public dataset for transmission-line engineering vehicles. Therefore, in this study, the experimental data were autonomously collected by drones, resulting in 718 images of engineering vehicles captured at different times and under different lighting conditions. The images had a resolution of 1920 \(\times\) 1080, and the data categories included trucks, excavators, cranes, and loaders. Vehicle targets were annotated using the LabelImg tool v.1.4.0 and saved in Pascal VOC format as XML files, which were then converted to the YOLO format required for training.  
To enrich data diversity and avoid overfitting during the training process, data augmentation methods as shown in Figure 1 were applied to expand the samples. As a result,2154 images were obtained in total. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio, resulting in 1724 training images, 215 validation images, and 215 test images. Figure 2 provides a detailed overview of the dataset. It can be observed that the data categories were imbalanced, with fewer large targets and more small targets in the images. This distribution is consistent with the main characteristics of drone images and further enhances the research significance of vehicle detection in transmission-line scenarios.