esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems
## 4 Implementation and Experimental Results
### A Case Illustration  
Based on the different datasets, two types of Product Relationship Networks (PRN) are constructed. A network based on the transaction-based dataset (TBD) is called a transaction-based network (TBN), while one based on the customer-based dataset (CBD) is called a customer-based network (CBN). For the sake of simplicity, only the CBN experiment is reported in this section, and the performance of TBN and CBN will be compared in Section 4.3. When the minimum support count in the Apriori algorithm is set to 50, the product association matrix for CBN can be generated. Based on the matrix, the connection among all products in CBN is visualized in Figure 5. Note that the product item name is shown in the graph instead of the product item number.  
Based on the matrix, the closeness centrality and betweenness centrality for all products in CBN can be evaluated using Equations (2) and (3). Next, each product item is classified as an attraction item, an opportunity item, or a trivial item according to Definitions 1 to 3. When \(\alpha=10\) and \(\beta=5\), there are 29 attraction items, 26 opportunity items, and 55 trivial items for CBN. Table 3 shows the classification result for CBN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**T\_ID** & **C\_ID** & **Product Item** \\ \hline
1 & A02577 & \(i_{16}\), \(i_{32}\), \(i_{37}\), \(i_{42}\), \(i_{55}\), \(i_{61}\), \(i_{71}\), \(i_{77}\) \\
2 & A06359 & \(i_{16}\), \(i_{74}\), \(i_{86}\) \\
3 & A11223 & \(i_{15}\), \(i_{89}\), \(i_{97}\), \(i_{99}\) \\  & \(\ldots\) & \(\ldots\) \\
4998 & A60825 & \(i_{86}\) \\
4999 & A69390 & \(i_{9}\), \(i_{10}\), \(i_{56}\), \(i_{86}\), \(i_{108}\) \\
5000 & A71889 & \(i_{8}\), \(i_{15}\), \(i_{27}\), \(i_{28}\), \(i_{59}\), \(i_{91}\), \(i_{98}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated purchase dataset.  
Figure 5: Customer-based network (CBN).
# Evaluation and Improvement of Internet Printing Protocol Based on HCPN Model Detection Method
## 1 Introduction  
Quocirca's 2022 Global Print Security Analysis Report [1] showed that many organizations were struggling to meet the print security needs of today's hybrid (office and home) work environment. Due to the COVID-19 pandemic, office printing volume declined significantly in the past years, but 44 percent of people expect this to increase in the next 12 months, while 41 percent expect home printing volume to increase. This continued reliance on printing, especially in larger organizations managing a hybrid workforce, will require more effective print security measures to protect and manage devices, documents, and networks.  
The communication between the host and peripheral devices requires the support of a protocol. The most common network printing protocols are Line Printer Daemon protocol (LPD), AppSocket, and IPP. IPP is the preferred protocol because it uses two-way communication, which provides more feedback information and control options. Compared with other protocols, the main advantages of IPP were the following:  
* It provides the current status of job processing (pages currently being printed) and more detailed information (such as querying completed and canceled jobs);
* The IEEE-ISTO Printer Working Group (PWG) is responsible for standardization. Using the protocol, we can strengthen the control of authentication/authorization, the encryption of transmitted data, access level, and other security-related options;
* With the latest developments of the IPP standard (\"IPP Everywhere [2]\"), driver-less printing is possible.  
Additionally, IPP has been the basis for several printer logo certification programs, including AirPrint, IPP Everywhere, and Mopria Alliance, and is supported by over 98 percent of today's printers [3]. Therefore, inspired by the colored Petri net theory [4; 5; 6] and the Dolev-Yao attack method [7; 8], and based on the CPN Tools model detection tool [9], this paper focused on the formal modeling and security assessment as well as the mining protocol vulnerabilities, and we suggested a targeted security improvement scheme and applied CPN tools to verify the security of the proposed scheme.  
The structure of this paper is the following: Section 1 is the introduction. Section 2 explains the basic concepts and theories involved in this paper. Section 3 analyzes the interactive message flow of the 0-RTT authentication, establishes the original model, and verifies its consistency with the original protocol performance. Section 4 introduces the improved Dolev-Yao attacker model to evaluate the security of the original protocol model and analyzes the loopholes in the authentication process according to the state-space results. Section 5 proposes an improved authentication scheme by introducing random number signatures to enhance the server's ability to resist multiple replays and using the CPN model detection tool to verify the effectiveness of the new scheme. Section 6 provides a discussion and Section 7 provides summary and outlook for the future.
# State Estimation of Over-Sensored Systems Applied to a Low-Cost Robotic Manipulator
## 4 State Estimation
### Link Model Estimation
#### 4.1.4 Full Model  
Combining the measurements of all available sensors produces the estimates from Figures 13-15.  
Figure 11: Evolution of Rotation Vector 2 when testing the link model using only encoder measurements.  
Figure 12: Evolution of Rotation Vector 3 when testing the link model using only encoder measurements.  
Figure 10: Evolution of Rotation Vector 1 when testing the link model using only encoder measurements.  
Clearly, the full model detects the overshoot of \(\theta_{2}\), as well as the smallvalues of the \(x\) and \(y\) components of the rotation vectors that the encoders cannot measure. Furthermore, this model is capable of estimating the value of \(\theta_{1}\) since it limits the relative orientations of adjacent links and attributes changes along the \(x\) axis of Links 2 and 3 to the first encoder's measurements. This gives the model a level of awareness that it lacks when using only the gyroscopes and the accelerometers, making the system fully observable.  
Table 3 displays a summary of the previously discussed errors of the link model when using individual sensors.  
Figure 14: Evolution of Rotation Vector 2 when testing the link model using all measurements.  
Figure 13: Evolution of Rotation Vector 1 when testing the link model using all measurements.  
Figure 15: Evolution of Rotation Vector 3 when testing the link model using all measurements.
# Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
## 3 LLMs for Computer Science Studies: Advantages and Challenges
### Ethical Challenges in Using LLMs in Computer Science Studies
#### 3.3.3 Equity  
The guidelines documents from Cornell University's Center for Teaching Innovation [19] and the University of California, Berkeley Academic Senate [68] have both highlighted critical equity considerations when integrating generative AI into teaching and learning contexts. While these tools have the potential to enhance learning experiences, they also risk exacerbating existing inequalities if not thoughtfully implemented.  
Key concerns include disparities in access to technology, varying levels of digital literacy, and inherent biases in AI models that can disproportionately disadvantage students from under-represented or marginalized backgrounds. For under-served institutions, the challenge of investing in technology and ensuring equitable access policies is particularly acute [25]. Equitable access to AI tools is crucial to prevent the widening of educational inequalities.  
Specifically, equity concerns arise regarding the ability of students to access tools like ChatGPT during examinations. These concerns are amplified if some students utilize the paid version of ChatGPT, which offers significantly more sophistication and accuracy than the free version [68]. Such disparities compromise fairness, as the paid version allows for longer prompts and responses improving the depth and quality of feedback. This creates an uneven playing field, further disadvantaging students who cannot afford paid subscriptions.  
Both documents [19; 68] have advocated for a mindful and inclusive approach to the use of AI in education. They urge educators to critically evaluate the ethical implications of AI, ensure equitable access to resources, and foster digital literacy among all students. Addressing these equity concerns is essential to creating a more inclusive learning environment that leverages the capabilities of AI without reinforcing systemic inequities.  
Reflecting on these studies, it becomes evident that fostering educational integrity and encouraging self-guided work are critical. Institutions that enforce rigorous standards for independent assignments can deter reliance on AI for academic shortcuts. Such policies not only enhance students' skills but also promote authentic learning experiences. As subsequent sections will cover, implementing robust technical methods to detect and prevent plagiarism is essential for maintaining academic integrity and preparing future software developers.
# Guided Spatial Transformers for Facial Expression Recognition
## 5 Results
### Transfer-Learning on FER-2013  
We applied the most competitive strategies used in AffectNet for the dataset of FER-2013. Although the task to solve is essentially the same, the conditions are more challenging in this dataset.  
The first difference with AffectNet is that the original size of the images in FER-2013 is 48 \(\times\) 48. The reduced resolution of these images makes it more problematic to detect faces to extract landmarks. A number that supports this statement is the amount of misdetected faces in each dataset. In AffectNet, we lost only 1.6% of the faces, whereas in FER-2013, we have 13.57% of misdetections. Another handicap of this dataset is the variety of data as there are images without faces, with cartoons, occlusions, etc. The reader can find a more detailed analysis of errors in the Appendix C.  
Nonetheless, we decided to use it to discover if our methodology also works under challenging conditions. As we can see in Figure 8, the saliency-mask strategy reaches a statistically significant accuracy compared to the 'Baseline-STN'. The landmark-based strategies overpass the baseline model and follow tendencies similar to the experiments with AffectNet although they did not achieve statistically significant results probably because of the limited amount of evaluation data and the increment in the number of misdetected faces.  
Comparing the green results with the red results of Figure 8, we can conclude that the use of pre-trained weights on AffectNet benefits all the models, especially the STN trained with saliency maps. This saliency-based model gets an increment of 1.08 points compared to its version trained 'from scratch'. It seems that TL alleviates the reduced size of the dataset and enlarges the performance gap between our proposals and the baseline.  
In general, the performance of our models for this second dataset also demonstrates certain robustness as the accuracy of the landmarks-based models does not decrease in a relevant manner despite losing approximately 14% of the training images. More interesting are the results obtained with the saliency-based model. This strategy beats all the landmark versions, enhancing the advantage of this method that always returns a saliency map, facilitating the functioning of the Spatial Transformer to learn the relevant regions on the image.  
Figure 8: Quantitative evaluation of the most relevant strategies on FER-2013. In red, the results training from scratch; in green applying Transfer-Learning on AffectNet weights. Table with values and the v2 and v3 versions of the binary-masks in Appendix A, Table A1.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 6 Robustness Test
\(\circ\), \(\circ\)\(\circ\)\(\circ\)\(\circ\)\(\circ\)\(\circ\) indicate the prediction performance of BA-SVR model and ANN model by DM  
In terms of medium-term prediction performance, 17 of the 18 sample stock indexes selected in this paper show that the prediction performance of BA-SVR model is better than that of RF model. Among the 18 stock indexes, 13 results show that the prediction performance of BA-SVR model is better than ANN model. Results in Tables 15 and 16 show that compared with short-term prediction, BA-SVR model performs better than ANN model and RF model in predicting mid-term stock index price. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & **Evaluation Index** & **BA-SVR** & **ANN** & RF \\ \hline \multirow{4}{*}{CSI 800} & RMSE & 0.1256 & 0.0877 & 0.0893 \\ \cline{2-5}  & MAE & 0.0992 & 0.0726 & 0.0732 \\ \cline{2-5}  & MAPE & 0.1744 & 0.1304 & 0.1275 \\ \hline \multirow{4}{*}{SZCI} & RMSE & 0.1641 ***\({}^{\circ\circ}\) & 0.1077 & 0.1023 \\ \cline{2-5}  & MAE & 0.1347 ***\({}^{\circ\circ}\) & 0.0792 & 0.0828 \\ \cline{2-5}  & MAPE & 0.2130 ***\({}^{\circ\circ}\) & 0.1184 & 0.1322 \\ \hline \multirow{4}{*}{SZSE B shares} & RMSE & 0.1385 & 0.1442 & 0.1165 \\ \cline{2-5}  & MAE & 0.1117 & 0.1277 & 0.0998 \\ \cline{2-5}  & MAPE & 0.1464 & 0.1660 & 0.1339 \\ \hline \multirow{4}{*}{SZSE} & RMSE & 0.1308 & 0.1098 & 0.1069 \\ \cline{2-5}  & MAE & 0.1104 & 0.0937 & 0.0880 \\ \cline{2-5}  & MAPE & 0.1700 & 0.1464 & 0.1406 \\ \hline \multirow{4}{*}{SZSE A shares} & RMSE & 0.1290 & 0.1136 & 0.1078 \\ \cline{2-5}  & MAE & 0.1093 & 0.0941 & 0.0881 \\ \cline{2-5}  & MAPE & 0.1685 & 0.1504 & 0.1422 \\ \hline \multirow{4}{*}{SME} & RMSE & 0.1572 ***\({}^{\circ\circ}\) & 0.1121 & 0.1022 \\ \cline{2-5}  & MAE & 0.1226 ***\({}^{\circ\circ}\) & 0.0913 & 0.0812 \\ \cline{2-5}  & MAPE & 0.1920 ***\({}^{\circ\circ}\) & 0.1436 & 0.1303 \\ \hline \multirow{4}{*}{SME 300} & RMSE & 0.1683 ***\({}^{\circ\circ}\) & 0.1109 & 0.0978 \\ \cline{2-5}  & MAE & 0.1462 ***\({}^{\circ\circ}\) & 0.0863 & 0.0789 \\ \cline{2-5}  & MAPE & 0.2385 ***\({}^{\circ\circ}\) & 0.1454 & 0.1279 \\ \hline \multirow{4}{*}{SME CI} & RMSE & 0.1485 ***\({}^{\circ\circ}\) & 0.0890 & 0.0922 \\ \cline{2-5}  & MAE & 0.1214 ***\({}^{\circ\circ}\) & 0.0729 & 0.0767 \\ \cline{1-1} \cline{2-5}  & MAPE & 0.1938 ***\({}^{\circ\circ}\) & 0.1194 & 0.1253 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: _Cont._
## 1 Introduction  
Lempel-Ziv compression [1; 2; 3] is based on string factorization. Two different factorization processes exist with no memory constraints. With the first one (LZ1) [2], each factor is independent from the others since it extends by one character the longest match with a substring to its left in the input string. With the second one (LZ2) [3], each factor is instead the extension by one character of the longest match with one of the previous factors. This computational difference implies that while LZ1 compression has efficient parallel algorithms [4; 5] LZ2 compression is hard to parallelize [6]. This difference is maintained when bounded memory versions of Lempel-Ziv compression are considered [5; 7; 8]. On the other hand, parallel decompression is possible for both approaches [9]. Moreover, distributed algorithms for the LZ1 and LZ2 methods approximating in practice their compression effectiveness have been realized [8; 10; 11; 12]. The LZ2 method is less effective than LZ1 but it is more scalable if we use a tree architecture. In Section 2, we describe the Lempel-Ziv compression techniques and in Section 3, we present the bounded memory versions. In Sections 4 and and 5 we discuss how Lempel-Ziv data compression and decompression can be implemented on parallel and distributed systems, respectively. The Storer's extension [13] for image compression is discussed in Section 6. Conclusions and future work are given in Section 7.
# An NFC on Two-Coil WPT Link for Implantable Biomedical Sensors under Ultra-Weak Coupling
## 5 Experimental Results
### Comparison with the State-of-the-Art  
A comparison of the proposed NFC with other published works is shown in Table 2. We carefully select reference papers that focus on power transmission and communication on the same inductive link. As in this case, the performance of the data link is limited by the power link. As seen in Table 2, our work novelly uses dual carriers and BPSK modulation for both downlink and uplink to achieve reliable communication. Through optimizing the inductive link circuit parameters, the proposed NFC achieves reliable communication (BER \(<\) 1\(\times\) 10\({}^{-7}\)) when the coupling coefficient is five- to 10-times lower than the minimal coupling coefficient achieved by other referenced works. At the same time, the proposed NFC coexists better with the WPT. The proposed NFC not only overcomes the strong interference (\(-\)100 dB SIR) of the power carrier, but also induces less PTE loss and smaller voltage ripples on the power link compared with other referenced works.  
The proposed dual-carrier NFC is able to work under the ultra-weak coupling. In this paper, the target application requires the coupling coefficient to be as low as 0.01. Moreover, we optimize the same dual-carrier system to reliably transmit uplink data under the coupling coefficient as low as 0.003 with a data rate of 2 kbps in [18]. Although the coil size of our prototype may be larger than the coil size of other works, the large coil separation could lead to the same ultra-weak coupling situation. Therefore, this NFC system could be an available option for the data transmission to the recent mm-sized implantable biomedical sensor systems [2].  
Figure 19: (**a**) Inductive link simulation with the human head model in ANSYS HFSS; and (**b**) measured and simulated \(k\) in air and in tissue.
## 5 Test Experiments
### Transit Tilt Error Calibration  
Tests were carried out according to the calibration process mentioned in Section 4.3. The telecentric measurement system was placed as shown in Figure 10, and the emitter and receiver were horizontally placed. As an explanation of the process, the transmitter emits collimated lighting with a green LED to project a shadow on the CMOS sensor in the receiver, and the receiver captures the image of test rod A. The initial position of the central axis of test rod A is recorded as \(M\). The standing axis rotates \(180^{\circ}\), and the green LED light emitted from the transmitter is projected to test rod B; the position of the central axis of test rod B is recorded as \(N\). The offset (\(MN\)) between the central axis of test rod A and B before and after the standing axis rotation was 0.004 mm, and the distance (\(PO_{4}\)) between test rod A and test rod B was 502 mm. Therefore, the transit tilt error (\(i\)) was 3.2 arcseconds according to Equation (9).
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 5 Experiments  
### Experimental Data and Environment  
In this section, we present a benchmark dataset for vessel detection, tracking, and data fusion, which is used to evaluate the proposed augmented reality (AR) architecture for waterway traffic situation awareness. The dataset comprises 30 videos along with corresponding AIS data, all collected from the Wuhu section of the Yangtze River waterway in China. The AIS and video data were captured using network cameras and AIS receivers located at the #4 navigation marker of the Wugong Railway Bridge. The data were transmitted to the local system via an SSH remote tunnel. The scope of the dataset is illustrated in Figure 4.  
During the data collection process, due to the inherent time delay in AIS data transmission, the actual AIS data collected correspond to moments before and after the video start timestamp. To mitigate the errors caused by this time delay, we first calculate the vessel's position at the video start timestamp using interpolation. Then, we improve the fusion accuracy by setting up equal-time interval sampling. Our experimental dataset is available at [[https://github.com/20221095/AIS-CV](https://github.com/20221095/AIS-CV)]([https://github.com/20221095/AIS-CV](https://github.com/20221095/AIS-CV)) (accessed on 28 December 2024).  
The AIS data collected for this experiment comprise over 100,000 records spanning from 22-24 October 2024. These records include information such as vessel MMSI, heading, speed, latitude, longitude, and vessel type. The main vessel categories are cargo ships, tankers, yachts, fixed navigation aids, and tugboats. Since the majority of vessels in the waterway are cargo ships and tankers, other vessel types, such as yachts, fixed navigation aids, and tugboats, are categorized as \"Unknown\" to mitigate the imbalance caused by significant differences in the number of records across categories. The AIS data were filtered based on completeness and the camera detection range to eliminate entries with missing key data, outliers, and corrupted records. After filtering, we obtained 28,216 AIS records for cargo vessels, 22,584 for tankers, and 8431 for unknown vessels. A sample of the collected AIS data is shown in the Table 1 below.  
This study extracts frames from waterway monitoring video, saving one image every 60 frames based on the video length. Intervals were used to create an image dataset, consisting of 56,874 images. The dataset includes three vessel categories: Cargo, Tanker, and Unknown, with each image having a resolution of 1980 \(\times\) 1080. The dataset is divided into training, validation, and test sets at a ratio of 8:2:1.  
The operating system used in this study is Linux, version 5.4.0-200-generic, with a CPU version of E5-2698 v4 @ 2.20 GHz, a Tesla V100-DGXS-32 GB GPU with 32 GB of memory, and an Intel(R) Xeon(R) CPU. The programming language is Python 3.10.15, the deep learning framework is PyTorch 2.5.0, and the CUDA version is 12.4.
## 4 Data Transfer Solutions
The other generally available ad hoc solutions had more or less issues as well. In the last decade, several wireless standards have been developed, many of them specially tailored to serve the requirements of IoT applications and LPWANs (low-power wide-area networks) [58]. The most widely used and well-known protocol is LoRa [59]. The first LoRa standard was issued in 2015 [60] and became widespread in the next few years. LoRa targets applications which require very low data rates (even a few bytes a day, down to 146.1 bps) and have limited energy. The most remarkable property of this protocol is the great link budget (up to 150 dB), which allows long-range communication and challenging propagation circumstances with high attenuation. LoRa offers modulation settings with different data rates to balance between power consumption and link budget. LoRa can operate on 169, 433, 868, and 915 MHz, based on the local regulations. Although LoRa can be used alone with local receivers (gateways) for experimentation and gives independence from service providers, LoRaWAN gives much convenience through well-designed upper network layers and optional cloud support. Different commercial LoRaWAN networks are operated worldwide in several countries and provide good coverage. SigFox [58] was issued a few years later than LoRa and offers a promising commercial communication solution for low-power, low-data-rate applications. It uses a 100 or 600 bps data rate and provides a link budget even better than LoRa (around 160 dB). SigFox operates from 862 to 928 MHz. NB-IoT [61] is operated by the cellular providers as a part of the LTE (4G) network. It operates from 700 to 2100 MHz and authorizes the device with a SIM card, similarly to any cellphone. NB-IoT has the highest maximum link budget (164 dB) out of the last three technologies and provides the highest bandwidth (26 kbps). The future of bolus sensors is obviously in using one of these IoT communication protocols.