esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Cross Layer Adaptation of Check Intervals
Packet loss does not happen if the next packet reception fits in the interval given by [\(\tau_{new\_check}\), \(\tau_{new\_check}\) + DUTY_ON_TIME]. This is the time during which the radio is kept in receive mode within each check interval. If the packet arrives at time within this interval, the BoX-MAC protocol extends the duration of the activity period to guarantee the complete reception of the packet regardless of the time of reception of a packet, called \(\tau\). Otherwise, if \(D(i_{k})>\text{DUTY\_ON\_TIME}\), then the packet is lost. Consider the BoX-MAC behavior presented in Figure 4, where the reception of the packets is shifted to the next round of communication. CLAC can solve this problem by adapting the check intervals of the nodes as shown in Figure 5. We show two possible situations: On the left, an ideal situation where the nodes do not incur contention delays. CLAC adapts the check interval of each node to match the time to send and receive each packet. On the right, a more realistic scenario where network delays do exist. In this case, CLAC shifts the check interval of each node according to the delay introduced by the packet in order to wake up precisely at the packet arrival time. We can ensure that the packet is successfully received since the radio is maintained on during the time needed to complete its reception. As indicated, CLAC uses \(\gamma(i_{k})\) to express the delay experienced by a packet at the node \(i_{k}\). This value is specific to each node and it depends on several factors: the network topology, the duty cycle, the frequency with which the application generates traffic, _etc_. We have analytically computed the value of \(\gamma\) by analyzing two different multi-hop scenarios: a linear and a tree-based data collection topologies.
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 3 Combination of Face Detection with EVM  
### General Design of MC-EVM  
The proposed solution needs to be implemented to work with low computational capabilities (even for smartphone devices). It uses a pre-trained TensorFlow Lite model for face detection to find the correct coordinates of the face in the picture, crop the frame to keep only the region of interest, and provide it for the EVM process to proceed with the pulse monitoring procedure (refer to Figure 1). Therefore, the proposed solution has two main processes, the Tensorflow-based model for face detection, and the EVM process which is used to amplify the small changes in color in the captured video frames. Figure 1 shows the general design of the proposed solution with all the needed blocks to implement and their connections and the partial results of the main four steps of MC-EVM. The proposed solution lies in the following steps (Figure 1):  
1. Capture video frames using a camera;
2. Provide the frame for the face detection process to find the correct coordinates for the detected face;
3. Crop the face/forehead (according to preference) using the detected face in the video frame;
4. Provide the cropped face to the EVM process to magnify the color changes;
5. Extract the red component of the RGB magnified picture;
6. Calculate the mean value of all red components of the RGB picture;
7. Find the peaks where the mean value of the red color exceeds a given threshold;
8. Calculate the number of peaks in a small period of time, i.e., 5 s, and map them to a scale of one minute. Figure 1: General design and the results of the main four steps of the MC-EVM. (**a**) Capture the frames from the camera sensor; (**b**) Detect the face and crop it; (**c**) Perform the EVM process on the cropped picture; (**d**) Extract the color changes over time and calculate the pulse by counting the color change peaks. ```
Data: videoFrames \(\leftarrow\) capturedVideo; # the current captured video Result: Pulse; # the calculated pulse dataLength \(\leftarrow\) sizeOf(videoFrames); fps \(\leftarrow\) detectVideoFPS(videoFrames); # reads the video \"fps\", usually 30 croppedVideo \(\leftarrow\) []; fori \(\leftarrow\) 1, dataLength do: ifisFaceDetectionBusy() then: latestPosition \(\leftarrow\) getLatestFaceCoordinates(); else: latestPosition \(\leftarrow\) startThread(getFaceCoordinates(videoFrames[i])); end  croppedFrame \(\leftarrow\) cropFrame(videoFrames[i], latestPosition); croppedVideo.append(croppedFrame); end  magnifiedVideo \(\leftarrow\) performEVM(croppedVideo); meanRed \(\leftarrow\) []; fori \(\leftarrow\) 1, dataLength do: # get the frame' red color mean and append it to the array meanRed.append(mean(magnifiedVideo[i].R)); end  peaks \(\leftarrow\) findPeaks(meanRed); Pulse \(\leftarrow\) sizeOf(peaks) \(\times\) 60/(sizeOf(magnifiedVideo)/fps); return Pulse;
```  
**Algorithm 1**Calibrate EVM with face detection  
Algorithm 1 shows how to conduct movement compensation for the small-scale movements of users after recording the video for the EVM process.
# Mechanism of Combustion Noise Influenced by Pilot Injection in PPCI Diesel Engines
## 4 Conclusions  
In the present study, the influences of pilot injection on combustion noise were analyzed using in-cylinder pressure data measured in a single-cylinder diesel engine. The mechanism of pilot and main injection parameters' effects on combustion noise was captured by means of CPLs, SWT, and SWPT. By means of the analysis and results provided above, conclusions can be stated as follows:  
Figure 11: Main combustion noise energy of seven sub-signals. (**a**) Pilot injection timing = \(-9^{\circ}\) ATDC; (**b**) pilot injection timing = \(-14^{\circ}\) ATDC; (**c**) pilot injection timing = \(-19^{\circ}\) ATDC; (**d**) pilot injection timing = \(-24^{\circ}\) ATDC.  
Figure 12: Overall combustion noise energy.  
1. Generally, main injection is the significant factor that affects combustion noise in low and mid frequency segments, and an increase of the pilot mass results in mitigation of combustion noise. Pilot injection plays an essential role in high frequency combustion noise, and larger pilot injection quantities lead to higher combustion noise in the high-frequency domain.
2. Advancing pilot injection timing will attenuate pilot combustion noise energy in mid and high frequency domains, but will strengthen that in the low-frequency range. Additionally, an earlier pilot injection timing also leads to higher main combustion noise energy in almost the whole frequency spectrum.
3. From the results of SWPT, pilot and main injection combustion impacts on high-frequency combustion noise are concentrated in the frequency band of 1125-2250 Hz and 3375-4500 Hz, and 1125-2250 Hz, respectively.
4. According to the view of overall combustion noise energy, a large pilot mass with advanced pilot injection timing and small pilot quantity with retarded pilot injection timing is considered as the optimal pilot injection strategy for the mitigation of combustion noise.  
[PERSON] proposed the primary research concept. [PERSON] developed the algorithm and preformed combustion noise analysis. [PERSON] did the programing work, analyzed the calculated results, and wrote this paper. [PERSON] performed the experiments and processed the data. [PERSON] managed the research project.  
This research was funded by the National Natural Science Foundation of China (grant number 51509051), and Marine Power Research and Development Program (grant number DE0302)  
The experiments presented here had been performed in the Combustion and Power Engineering Laboratory at Kyoto University.  
The author declares no conflict of interest.
## 2 Materials and Methods
### Spectrum Calculation  
We estimated the power spectral density (PSD) to assess the frequency content of EEG signals and the presence of theta peaks, which has been identified as a prerequisite for a meaningful interpretation of PAC, although checked in less than half of the papers reviewed in [25]. We used the EEGLAB function spectopo applying the Welch method, with a one-second-long Hamming window, 50% overlap, and oversampling by zero-padding to twice the window size.
## Results
### Electrochemical Analysis
#### 3.2.2 Cyclic Voltammetry  
For cyclic voltammetry analyses, linearly voltage sweeps from 0.02 mV/s to 0.1 mV/s were applied to cells containing NMC 622 electrodes. The resulting current signals were recorded as shown in Figure 5. The first two cycles with a scan rate of 0.02 mV/s were removed due to influence of SEI formation on the lithium-ions' diffusion. A hysteresis loop was recorded of current with a voltage increase and decrease from 3 V to 4.3 V. It is noticeable that for all cells, a current peak appears during charge and discharge process, which corresponds to the presence of a redox reaction on the cathode side. For each thickness, the current maximum increases from 0.001 A to 0.004 A with higher scan rates. Besides, the current peaks are located closer to each other, with an increasing scan rate for cells with structured cathodes in comparison to cells with unstructured cathodes, indicating better lithium-ion diffusion kinetics. In opposition to other cathodes, the cell with unstructured cathodes and a thickness of 250 \(\upmu\)m thickness showed smaller current maxima when the scan rates reached values above 0.06 mV/s. This effect was less significant for cells with structured cathodes, showing a maximal current which stopped rising at scan rates of 0.09 mV/s and 0.1 mV/s.  
In order to measure the chemical diffusion coefficient of lithium-ions in NMC 622 cathodes according to the Randles-Sevcik equation, which will be further discussed in the next section, all maximum currents were selected from CV analyses and plotted in Figure 6 against the square root of the corresponding scan rates. The recorded currents ranged from \(-\)0.004 A to 0.005 A. Figure 6 shows a linear relation between the two variables for cells, with cathodes having film thicknesses of 91 \(\upmu\)m and 151 \(\upmu\)m. However, when applying cathodes with 250 \(\upmu\)m thickness, a linear correlation no longer exists when the scan rate is higher than 0.06 mV/s (square root of the scan rate higher as 0.008 V1/2-s-1/2). Therefore, in order to ensure the accuracy of the calculation, the slope for cathode with 250 \(\upmu\)m thickness was calculated excluding the last four points ranging from 0.0835 to 0.0998 V1/2-s-1/2.  
Figure 5: CV analyses of cells containing unstructured cathodes and structured cathodes with different pitch distances (film thicknesses: 91 μm and 250 μm).
## 3 Mobile Mapping Systems
### Custom MMS Configurations  
Numerous studies demonstrated custom MMS configurations for specific applications, e.g., a multi-camera MMS [85] has been developed by the Finnish Geospatial Research Institute (FGI) for urban mapping. A customized MMS with one rotary LiDAR on a side, one MEM LiDAR in front, a LadyBug V5 multi-camera, GNSS, and IMU is shown in Figure 2a. Two custom MMS developed by FGI are demonstrated in Figure 2b,c. In this figure, LiDAR-based MMS without a camera is demonstrated. Portable MMS designs for flexible usage by an operator without a need for a car platform are an interesting topic that is followed by many research works; e.g., the FGI roamer could be installed on a backpack. A portable LiDAR-based MMS was demonstrated by [PERSON] et al. [111]. A camera-based MMS constructed on the visual SLAM principle was demonstrated by [PERSON] et al. [112]. An open-source handheld MMS solution was described by [PERSON] [113]. These are just a few examples of the vast number of custom MMSs.
# Privacy Protection Based on Special Identifiers of Intersection Base Computing Technology
## 2 Materials and Methods
### Paillier Homomorphic Encryption System  
Paillier homomorphic encryption [11]: Paillier homomorphic encryption is a public key encryption method that satisfies the addition of homomorphism, and the scheme has been proved secure, specifically described as follows:  
Key generation: Given the security parameter \(\kappa\), generate two \(\kappa\) primes \(p,q\) and \(p\
eq q\) (this property guarantees that two primes have the same length), and compute \(N=pq\), \(\lambda=lcm(p-1,q-1)\).  
Key generation: Given safety parameter \(\kappa\), generate two prime numbers \(p,q\) and \(p\
eq q\) that are particularly specifically large \(\kappa\) (this property ensures that two prime numbers have the same length), and compute \(N=pq\), \(\lambda=lcm(p-1,q-1)\). In defining the Fractional Division Functions \(L(x)=\frac{x-1}{N}\), select a positive integer \(g\in Z_{N}^{*}(\text{such as }:\ g=n+1)\), making \(\gcd(L(g^{\lambda}\text{mod}N^{2}),N)=1\). Then, the public key of the system is pk = \((g,N)\), and the private key is sk = \(\lambda\). Paillier's plaintext space and ciphertext space are \(Z_{N}\) and \(Z_{N^{2}}^{*}\). In the following text, the encryption algorithm and decryption algorithm are denoted as \(E\) and \(D\).  
Encryption process: To encrypt plaintext \(m\in Z_{N}\), select the random number \(r\in Z_{N^{\prime}}^{*}\) and calculate the ciphertext:  
\[C=g^{m}r^{N}\text{mod}N^{2}\]  
Decryption process: For the ciphertext \(C\in Z_{N^{2}}^{*}\), calculate:  
\[m=\frac{L(c^{\lambda}\text{mod}N^{2})}{L(g^{\lambda}\text{mod}N^{2})}\text{ mod}N\]  
Additive homomorphism:  
\[\begin{array}{l}E(m_{1})\times E(m_{2})=g^{m_{1}}r_{1}^{N}g^{m_{2}}r_{2}^{N} \text{mod}N^{2}\\ =g^{m_{1}+m_{2}}(r_{1}r_{2})^{N}\text{mod}N^{2}\\ =E(m_{1}+m_{2})\text{mod}N^{2}\end{array}\]  
Scalar multiplication: \(E(m_{1})^{m_{2}}\text{mod}N^{2}=E(m_{1}m_{2})\).
# Static and Moving Target Imaging Using Harmonic Radar
## 2 Harmonic Radar
### Description of Harmonic Radar  
The block diagram of our harmonic radar system is shown in Figure 2. The system transmits a stepped frequency waveform over the 800-1000 MHz range and processes returns at the fundamental and the second harmonic 1.6-2 GHz frequency ranges.  
The signal is generated by a National Instruments (NI) 5651 synthesizer card. Next, a coupler is used to tap off a small amount of the signal that is used as the reference. The signal is then filtered by two diplexers, each consisting of a high pass filter and a low pass filter allowing the separation of the fundamental frequency from the harmonics. The cutoff frequencies of the high and low pass filters are both set to 1.3 GHz. The cutoff of 1.3 GHz was chosen to lie between the highest fundamental frequency and the lowest second harmonic frequency. The highest fundamental (\(n=1\)) frequency is 1 GHz and the lowest second harmonic (\(n=2\)) frequency is 1.6 GHz. A power amplifier (PA) is used to amplify the signal to 10 W (+40 dBm). After amplification, additional harmonics are generated,  
Figure 2: Block diagram of the harmonic radar system.  
so additional diplexers are utilized for suppression. Since the power at this point is 10 W, high-power cavity diplexers are used. Each diplexer provides \(>\)80 dB of rejection in the unwanted frequency range while only attenuating the desired signal by less than 0.5 dB. The receiver is very sensitive to any harmonic signals, including harmonics generated by the transmitter. For this reason, two diplexers are used to further reduce the harmonics. An isolator is used between the diplexers to isolate the power amplifier from reverse traveling waves. Possible sources of reverse traveling wave are signals entering the transmit antenna and mismatches between the diplexer and antenna.  
In the receiver, a diplexer is used after the antenna to separate the fundamental and the second harmonic. The fundamental is sent directly into a LeCroy oscilloscope for digitization. Following the diplexer, the second harmonic is amplified in a low noise amplifier (LNA), filtered through four additional diplexers, amplified in another LNA, followed by a medium power amplifier (MPA). A MPA is used for the final amplification stage to reduce distortions. A 3-dB attenuator is added between the MPA and the LNA to improve the impedance match between them and to reduce reflections. A high-speed LeCroy oscilloscope is used to digitize the reference signal from the coupler, the second harmonic from the receiver, and the fundamental response. The oscilloscope is set to sample the signals at 10 GS/s.  
Specifications of the harmonic radar system are presented in Table 1.
## 4 Discussion
### Comparative Analysis with Previous Research  
The novelty of this research, seen from the literature review/previous research, is that no one has measured the incubation process of a university in an effort to accelerate the commercialization of research results, and no one has used the DEA model (BCC with output oriented) in measuring the efficiency level of the incubation process in universities, as well as generate a death valley framework and look at which processes aim to speed up time to market and which can reduce investment costs.  
This model has used techniques such as Data Envelopment Analysis (DEA) to measure and predict commercialization performance, but it has limitations because it is only a benchmarking tool that evaluates relative efficiencies or a black box that provides no modeling information.
# Comparative Analysis of BERT and GPT for Classifying Crisis News with Sudan Conflict as an Example
## 4 Methodologies  
This study evaluated the text classification performance of two classification frameworks (Figure 1): SL-MLG and MLC. The experiment setup involved testing GPT and BERT models under multiple configurations to assess their ability to classify news articles related to the Sudan Conflict.  
For SL-MLG, both models are prompted to choose a single label that best fits the article. If the selected label appears among the expert-assigned labels, it is considered correct. This approach simplifies evaluation while still using multiple labels as the ground truth. In contrast, the MLC framework allows models to assign one or more relevant categories to each article, capturing the complexity of real-world reporting where news often spans multiple categories. The GPT and BERT models were tested across both frameworks to compare their classification performance on Sudan Conflict articles.  
The BERT models were evaluated in two settings: one using default hyperparameters and another with hyperparameter tuning to optimize classification accuracy. The GPT models, on the other hand, were tested under three strategies: Zero-Shot learning, RAG  
Figure 1: Workflow diagram for comparative evaluation of BERT and GPT in SL-MLG and MLC.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Train** & **Validation** & **Test** \\ \hline Unlawful Detention & 7 & 4 & 7 \\ Human trafficking & 0 & 0 & 0 \\ Ensalvement & 1 & 0 & 0 \\ Willful killing of civilians & 138 & 24 & 21 \\ Mass execution & 14 & 2 & 1 \\ Kidnapping & 19 & 2 & 3 \\ Extrajudicial killing & 42 & 5 & 4 \\ Forced disappearance & 13 & 1 & 1 \\ Damage/destruction of civilian infrastructure & 78 & 12 & 9 \\ Damage/destruction/looking of cultural heritage & 5 & 1 & 1 \\ Military operations (battle, shelling) & 193 & 37 & 27 \\ Gender-based or other conflict-related sexual violence & 3 & 0 & 0 \\ Violent crackdowns on protesters/opponents & 25 & 6 & 6 \\ Indiscriminate use of weapons & 75 & 16 & 8 \\ Torture or indications of torture & 15 & 3 & 1 \\ Persecution (political, racial, etc.) & 6 & 1 & 1 \\ Movement of military/equipment & 11 & 1 & 0 \\ Total Occurrences & 645 & 115 & 90 \\ Total Rows & 323 & 57 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of articles per incident category and dataset split. “Total Occurrences” are the sum of the category assignments; “Total Rows” are the number of unique articles per split.  
without category definitions, and RAG with ICL, with expert-defined category descriptions provided. These configurations allowed for a comparative analysis of model adaptability and classification efficiency under different learning conditions.  
For SL-MLG, classification performance was measured using accuracy alone, while for MLC, it was evaluated using accuracy, precision, recall, and F1-score. By systematically analyzing these models across different setups, this study aimed to identify the best approach for classifying news into single and multiple categories.
# Integrating Machine Learning Techniques for Enhanced Safety and Crime Analysis in Maryland
## 7 Disclosure: Crime Reporting Bias and Ethical Considerations
### Ethical Implications of Model Deployment  
The potential ethical implications of deploying our predictive model are profound and complex. In particular, there is a risk that the use of predictive policing tools might reinforce existing societal disparities, such as those based on race, gender, or economic status. To address these issues, we propose several safeguards:  
* Ethical oversight: The implementation of a continuous ethical review process involving diverse stakeholders is necessary to oversee the deployment and operation of the model.
* Transparency: This is a commitment to maintaining transparency about how the model is used and the impact that it has, including regular public reports on its performance and outcomes.
* Community Engagement: Involving community representatives in the monitoring and evaluation process is important to ensure that the model's use aligns with community needs and ethical standards.  
While our model provides significant insights and tools for crime prediction, it is crucial that its deployment in real-world scenarios is handled with the utmost responsibility. We are committed to ongoing research into the ethical use of crime prediction technology  
Figure 11: SHAP analysis of feature importance.  
and to refining our models to ensure that they serve the public good without causing unintended harm.  
The study was initiated by [PERSON], who also played a crucial role in validating the results and refining the methodology. [PERSON] was responsible for developing the methodology, model development, validation, and data analysis, and for generating the results. [PERSON] contributed to data collection and analysis. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Center for Multi-Modal Mobility in Urban, Rural, and Tribal Areas (CMMM).  
Not applicable.  
This project was funded by the Center for Multi-Modal Mobility in Urban, Rural, and Tribal Areas (CMMM).  
The data supporting the findings of this study are openly available, in accordance with MDPI's data-sharing policies. The dataset has been organized into a publicly accessible Google document, which can be shared upon request to ensure the transparency and reproducibility of the research results. Interested researchers may contact the corresponding author to gain access to the data. No new data were created during this study; the analysis is based on pre-existing data that adhere to privacy and ethical standards.  
This project was funded by the Center for Multi-Modal Mobility in Urban, Rural, and Tribal Areas (CMMM). We are grateful for their support, which was crucial in enabling this research.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision