esa-sceva/satcom-chunk-collection huggingface dataset:
# Mail Inspection Based on Terahertz Time-Domain Spectroscopy
## 3 Sample Scan Strategies and Visualization
Two examples of the implemented visualization possibilities are shown in Figure 13: (a) shows an extension of the already described traffic-light format (green-yellow-red) classification with an orange visualization inserted. Depending on the similarity of the measurement points spectrum with one of the spectra of the database, this false-color-coding is used. Figure 13b shows a discrimination visualization example, which uses black-to-color indication of different substances. With this, not only a true-positive, but a substance-specific classification is visualized, which provides the user with more information about the content of the investigated sample. Figure 12: Procedure of result dependent search strategy and suspect handling. First, random points on the sample surface are inspected (**a**). In case of a suspect (not clearly positive), the surrounding is scanned (**b**,**c**). In case of surrounding measurement points with negative results, the suspect is declared negative (**d**). In this case, a fast-scanning system was used in contrast to Figure 11. Therefore, lines of points result instead of single measurement points. Figure 13: Two examples of the visualization possibilities. (**a**) Detection indication from green (no substance of database identified) over yellow/orange (uncertain result) to red (positive identification of a substance in the database). (**b**) Depending on the specific substance identified at the point of measurement, a color code is used, enabling the discrimination of substances. From black (no detection) to dark color (low similarity) to color (strong similarity).
# Docker Performance Evaluation across Operating Systems
## 2 Docker Architectures
### Docker on Linux
#### 2.1.2 Mount Namespaces  
Docker uses mount namespaces to separate the container file system [19]. Each mount namespace specifies which mount points can be seen by the container. This namespace provides isolation between the host and the container and between multiple containers. The original implementation of mount namespaces has shown that the isolation it provided was sometimes too excessive. This issue was fixed by providing mount propagation types [20].
The two electrodes are placed on the sclera at the two sides of the eyeball. In this paper, we present the design of such an on-sclera implant-based TcES system.  
An implantable medical device (IMD) has to be small and thin for the comfort of the patients. Battery has to be eliminated due to its bulky size and limited lifetime [3]. Wireless power transfer using inductive coupling has been widely used for power transfer and data telemetry in IMD applications such as retinal prosthesis and neural stimulation and recording [4; 5; 6; 7]. To achieve high power transfer efficiency and high data rate, multi-channels were proposed in [8; 9; 10; 11] to transfer the power and data separately using two or more pairs of coils. In [12] and [13], 3-coil and 4-coil coupled power links were proposed, respectively, to enhance the coupling efficiency. For the TcES application, data rate does not need to be high and system volume has to be minimized. To reduce system volume, we propose that the power link and the data link should share only one pair of coils. On-chip coil [14; 15] could be used to further reduce the size, but the power transfer efficiency and the voltage gain would be significantly deteriorated due to poor quality factor and weak coupling with the primary coil. Hence, off-chip coil is adopted.  
The human tissue specific absorption rate (SAR) increases with the coupling frequency. Therefore, inductive power links with 13.56 MHz or lower ISM bands are commonly used [16; 17]. However, lower coupling frequency means larger matching and filtering capacitors that lead to a larger implant size. In [15], 40.68 MHz in the ISM band was proposed as a reasonable frequency in achieving good power transfer efficiency for the power link of IMDs. When compared with using 13.56 MHz, the matching and filtering capacitors at 40.68 MHz are reduced by 9 and 3 times, respectively. Hence, 40.68 MHz was finally chosen as the power coupling frequency.  
This paper is organized as follows. Section 2 gives an overview of the system and the design of the power and data links, including control methods for power boosting and regulation. Detailed circuit implementation is presented in Section 3. Off-chip circuitry and coil design are discussed in Section 4. Measurement results of the fabricated chip are given in Section 5, and conclusions are drawn in Section 6.
# Proof-of-Concept of a Millimeter-Wave Integrated Heterogeneous Network for 5G Cellular
## 3 mmWave Beamforming Antenna and Backhaul  
Highly directional antennas are required to compensate the millimeter-wave propagation, which has a higher path loss than lower band communication systems, like LTE or Wi-Fi. These highly directional antennas should also be steerable to support access and a reconfigurable backhaul link. A range of concepts and designs for such antennas have been proposed [18; 19; 20; 21]. In the next two subsections, we discuss the most practical approaches.  
Different requirements are placed on the mmWave antennas in backhaul and access. For small cell backhauling, medium distances of several hundred meters in static configurations are most important, while access links require fast beamsteering to track mobile users.  
The key performance indicators for backhaul links are the data rate, power consumption and cost. The cost can be divided into capital expenditure (CAPEX) and operational expenditure (OPEX). While CAPEX is driven by the price of the device and the cost of installation, OPEX is driven by power consumption, licensing costs and required servicing.
# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 3 Materials and Methods  
### Experimental Method  
#### 3.1.1 Experimental Field  
The field trial was conducted in mid-October 2020 at the agricultural experimental base of Jilin University. The corn variety was Xianyu 335. The average plant height was 310 cm. The average plant spacing was 28 cm and the average moisture content of the corn kernels at harvest was 27%. The model of moisture meter was MA50/1.R, which has an accuracy of \(1\times 10^{-6}\).  
As shown in Figure 2, the harvester used for the field experiment was a modified John Deere 1075 harvester, based on the original frame. The harvester was converted to a corn kernel harvester with a single longitudinal axial threshing system. The harvester control system was a Wecon LX3V-1412 MR PLC, which was made in China. Magneto-electric speed sensors were installed at the threshing cylinder and engine shaft end to detect the harvester's speed and the threshing cylinder's speed.  
The corn harvester operated on 6 rows, with an effective cutting width of 3280 mm, a cylinder speed of 300 to 900 r/min, a concave clearance of 20 to 40 mm, and an operating speed of 2 to 8 km/h. In this experiment, the corn row distance was 600 mm.
## 4 Analysis of Numerical Simulation Results  
Before conducting excavation simulations and monitoring using FLAC3D, the displacement of the model's four boundaries was set to 0, and the displacement of the bottom boundary was also set to 0. The top of the model was set as a free surface, and constitutive models, material parameters, and initial conditions were assigned. Prior to designing the backfill schemes, the ore body within the model was excavated using FLAC3D software. The results revealed large-scale subsidence within the mining area, with the maximum surface subsidence reaching 17.63 cm. The subsidence was primarily concentrated in the YZS mining area, and the extent of the collapse is illustrated in the right diagonal cross-section shown in Figure 6. Since the YH and BJ mining areas have already been backfilled, with backfill ratios set at 94% and 90%, respectively, and considering the preliminary geological exploration of the mine and the experience of similar local mines in terms of backfill materials and ratios, two backfill schemes were designed. Figure 6a: YZS backfilled at 90%, YH at 94%, and BJ at 90%. Figure 6b: YZS backfilled at 100%, YH at 94%, and BJ at 90%.
## 2 Related Work  
In recent years, various experimental platforms and datasets have been proposed to support IoT and cybersecurity research. However, most of them focused on industrial or urban environments, often assuming stable connectivity, abundant energy sources, and access to data centers. Simulation tools such as GNS3 [6] and CupCarbon [7] offer flexibility for designing wireless sensor networks, but they do not integrate real hardware and fail to reflect the unpredictability of wireless communication.  
While AI-based Intrusion-Detection Systems (IDS) have shown promise in adapting to evolving threat landscapes [8,9], these tools are typically trained on datasets generated in controlled, urban-like environments, ignoring the specific conditions of rural deployments [10, 11, 12]. In addition, existing research laboratories in IoT, cybersecurity, and AI are mostly designed for urban or industrial settings [13,14], with access to high-speed networks and data centers. This gap highlights the need for an experimental environment that mimics real-world rural conditions, especially when using AI algorithms [15].  
One of the most significant gaps is the lack of datasets that represent real traffic in rural IoT networks. Most datasets commonly used for intrusion detection, such as CIC-IDS2017 [14] and UNSW-NB15 [16], were created in enterprise environments and do not consider the unique conditions of rural deployments. Other datasets, such as ToN-IoT [12] or Edge-IIoTset [17], were developed for industrial contexts and still lack the characteristics that make rural environments unique, such as long communication distances, sporadic connectivity or limited power supply. Several documents, such as the LoRaWAN Security Whitepaper [18], explore vulnerabilities and security measures in LoRaWAN but do not provide real datasets for evaluating attacks in rural networks. While LoRaWAN offers solutions for low-power and long-range transmission, these networks are highly affected by environmental conditions, terrain, and interference.  
In addition, several simulation tools [19,20] have been developed to facilitate IoT experimentation. Tools for generating IoT network traffic can be categorized into three types: hardware-based, simulation-based, and hybrid. Hardware-based solutions use real IoT devices, offering a high level of realism. Simulation-based tools, such as CupCarbon-Lab [7] and GNS3 [6], use device virtualization, simulating their behavior, but these tools sacrifice some degree of realism. Hybrid approaches, such as GothX [21] (an evolution of the Gotham Testbed [22]), combine real and virtual devices to balance the advantages and limitations of previous approaches. However, the literature lacks comprehensive reviews focused exclusively on rural IoT traffic generation, indicating a shortage of specialized tools for this purpose.
## 2 System and Channel Model
### Phase Noise Model  
Most of the SIM FSO systems use a coherent demodulation approach, which means that the carrier phase needs to be appropriately synchronized in the receiver. In such systems, the received optical signal is first converted to an electrical signal whose phase is then tracked via a PLL. However, PLL devices are not perfect [5; 6]; this is mainly because the local oscillator in the electrical demodulator unit does not generate an ideal sinusoidal waveform, which would be required for perfect synchronization. This sort of imperfection is usually denoted as a phase noise effect, and characterized by a Tikhonov distribution with a probability density function (PDF):  
\[f_{T}(\theta)=\frac{e^{v\,\cos\theta}}{2\pi\,I_{0}(v)},\,-\pi\leq\theta<\pi\,, \tag{2}\]  
where \(I_{0}(\cdot)\) denotes the zero-order modified Bessel function of the first kind (Equation (9.6.16) of [PERSON] and [PERSON], [10]), and \(v\) represents the signal-to-noise (SNR) of the phase-locked loop (PLL) used for carrier synchronization.
## 2 Theoretical Background and Hypotheses Development
### Stimulus (S): Social Robot (MOCCA)'s Features  
#### 2.3.1 Perceived Anthropomorphism--Intimacy (PAI)  
With the advancement of AI, social robots are increasingly exhibiting anthropomorphic features [13]. The more these robots demonstrate behaviors resembling human interaction, the more users interpret their actions through the lens of anthropomorphism [38]. Anthropomorphism refers to the attribution of human traits, emotions, and intentions to non-human entities [39; 40]. Social robots display anthropomorphic characteristics through their human-like social behaviors during interactions with users, such as engaging in emotionally expressive dialogue, exhibiting personality traits similar to those of humans, and forming social relationships [29]. These features enable social robots to assume diverse roles, including that of an assistant, friend, or companion, resulting in a human-like interaction experience [14]. This foundation supports the development of more intimate user-robot relationships.  
MOCCA not only exhibits anthropomorphic characteristics but also retains information from previous interactions and tailors its responses to users. This ability enhances emotional resonance, promotes the formation of emotional bonds, and increases users' sense of intimacy with the robot [41]. Intimacy is defined as the willingness to share personal information, preferences, and even deeply private thoughts [42]. In human relationships, intimacy typically develops over time through repeated emotional exchanges and interactions. Similarly, MOCCA can build and maintain databases of user preferences, behaviors, and previous conversations, forming episodic memory. Consequently, users may come to perceive MOCCA not simply as a programmed machine, but rather as an intimate partner capable of understanding and responding to their emotional needs.
# Performance Optimization of Underwater Communication Links at Different Ranges for AIS Relay to AUV
## 7 Underwater AIS Relay
### Description of the Application  
Since GPS signals cannot be received underwater, AUVs need to surface to calibrate their navigational devices. To avoid collisions with ships during the surfacing process, AUVs need to know the position of ships. A solution to this problem is to communicate the positions of ships to AUVs. Fortunately, ships often communicate their positions and other information through the AIS. This system is used to avoid collisions between ships and survey the maritime traffic. Therefore, it makes sense to extend it to underwater vehicles, such as submarines and AUVs.  
By having a surface buoy or a ship, it is possible to collect data about the surrounding ships and then forward them to the AUV fleet. Extending AIS to submarines has already been proposed as an application of JANUS protocol [31,32]. JANUS aims to enable communication between heterogeneous devices by using a common protocol and is, therefore, used to integrate the AUVs and submarines into the AIS system. Using this protocol, any ship can communicate with the AUVs. In some scenarios where we want to secure the integrity of the AUVs as much as possible, it may be preferable to separate the AUVs from the AIS system and create a private communication link between the AUVs and a trusted buoy.
## 3 Materials and Methodology
### Field Measurement  
A Continuously Operating Reference Station (CORS) system was used to monitor the ground's deformation. Based on GPS navigation and positioning technology, several GPS reference stations were fixed at a certain distance above the working face and connected with a data center, a computer, data communication, and the internet to form a network. The backfill working face entered into production in November 2017 and was fully stopped in December 2018. Four surface movement and deformation measuring lines related to the backfill working face were set up, adhering to the \"Coal Mine Survey Regulations\", ensuring reliable data. The arrangement of the ground deformation measuring lines E, N, S and W is illustrated in Figure 6.
## 2 Theoretical Analysis of Energy Spectrum Study
### Energy Balance Analysis
#### 2.3.2 Governing Equations  
As shown in Figure 4, mechanical models were plotted for the original SDOF element, the SDOF element connected to a single dashpot, and the SDOF element connected to the TVMD. The TVMD consists of a tuning spring \(k_{d}\) in series connection with the configuration of an inerter \(m_{in}\) and a dashpot  
Figure 3: Mechanical model of the inerter.  
\(c_{d}\) spaced in parallel. Subject to the base seismic excitation \(\vec{x_{g}}\), the differential equation of motion of the original SDOF element can be expressed as follows:  
\[\vec{mx}+cx+kx=-\vec{mx_{g}}, \tag{9}\]  
where \(m\), \(c\), and \(k\) are the mass, damping coefficient, and stiffness and \(x\), \(x\), and \(\vec{x}\) are the displacement, velocity, and acceleration of the primary SDOF element relative to the ground, respectively. If an extra dashpot \(c_{d}\) is inserted between the mass and the ground for vibration control, the added damping coefficient \(c_{d}\) is added to \(c\) to modify the governing equation.  
In terms of the SDOF element connected to the TVMD, an extra degree of freedom, i.e., deformation of the dashpot (\(x_{d}\)) is added so that the governing equation is modified as follows:  
\[\left\{\begin{array}{c}\vec{mx}+cx+kx+k_{d}(x-x_{d})=-\vec{mx_{g}}\\ \vec{m_{in}x_{d}}+c_{d}x_{d}=k_{d}(x-x_{d})\end{array}\right., \tag{10}\]  
where \(x_{d}\) and \(\vec{x_{d}}\) are the velocity and acceleration of the dashpot in the TVMD. The considered control devices, including the single dashpot and the TVMD, can be described in a dimensionless form by the following equation:  
\[\mu=\frac{m_{in}}{m},\ \xi_{d}=\frac{c_{d}}{2\sqrt{km}},\ \kappa=\frac{k_{d}}{k}, \tag{11}\]  
where \(\mu\), \(\xi_{d}\), and \(\kappa\) are the inertance-mass ratio, nominal damping ratio, and stiffness ratio, respectively.
## 3 Studies of PCMs Incorporated in Passive Systems  
### Experimental Studies  
In recent years, many experimental studies have been carried out to evaluate the performance of buildings incorporating building materials/PCMs composites. In this section, these studies are divided into two categories according to the scale involved (material or system).
# Student Dropout Prediction for University with High Precision and Recall
## 3 Data Characteristics
### Features  
There were many features, but the existing works tell us that not all data help predict dropouts. We analyzed the data with permutation importance and SHAP analysis to reduce the features and increase the prediction performance. Some of the data had to be encoded to ordinal before applying permutation importance and SHAP analysis, and one-hot encoding was used to encode the categorical data. We ran XGBoost ten times to measure the permutation importance, and Table 5 shows the top five features that showed the highest importance; however, the numbers were not high.  
Figure 1 shows the results of the SHAP analysis. The blue and red colors indicate the low and high relationships between the input and output of the model, respectively. Just like the permutation importance, it also shows that the dropout rate is correlated with completed course credits and grade ranking. One interesting point is that the login count of the main university website shows a high correlation with the dropout rate.  
We performed principal component analysis and autoencoder denoising on the data, but it had little effect on the result. We ignored the unknown category and used the SimpleInputer scikit-learn package to fill in the missing values with the median of the feature. Note that we added facility usage history along with academic records. The complete list of features used in the prediction is included in Table 6. There were five feature classes, which are as follows: academic data, academic records, personal information, facility use history, and website use history.  
Figure 1: SHAP of Characteristics of Feature Set with Respect to Dropout Information.  
\begin{table}
\begin{tabular}{c c c} \hline
**Rank** & **Feature Name** & **Permutation Importance** \\ \hline
1 & Grade Ranking & 0.0177 \\ \hline
2 & Completed Credits & 0.0070 \\ \hline
3 & Department & 0.0043 \\ \hline
4 & Grade Average & 0.0040 \\ \hline
5 & Univ. Main Site Login Count & 0.0040 \\ \hline \end{tabular}
\end{table}
Table 5: Permutation Importance of Input Data.
###### Abstract  
The bulk backscattering ratio (\(\tilde{b_{bp}}\)) is commonly used as a descriptor of the bulk real refractive index of the particulate assemblage in natural waters. Based on numerical simulations, we analyze the impact of modeled structural heterogeneity of phytoplankton cells on \(\tilde{b_{bp}}\). \(\tilde{b_{bp}}\) is modeled considering viruses, heterotrophic bacteria, phytoplankton, organic detritus, and minerals. Three case studies are defined according to the relative abundance of the components. Two case studies represent typical situations in open ocean, oligotrophic waters, and phytoplankton bloom. The third case study is typical of coastal waters with the presence of minerals. Phytoplankton cells are modeled by a two-layered spherical geometry representing a chloroplast surrounding the cytoplasm. The \(\tilde{b_{bp}}\) values are higher when structural heterogeneity is considered because the contribution of coated spheres to light backscattering is higher than homogeneous spheres. The impact of heterogeneity is; however, strongly conditioned by the hyperbolic slope \(\xi\) of the particle size distribution. Even if the relative abundance of phytoplankton is small (\(<\)1%), \(\tilde{b_{bp}}\) increases by about 58% (for \(\xi=4\) and for oligotrophic waters), when the heterogeneity is taken into account, in comparison with a particulate population composed only of homogeneous spheres. As expected, heterogeneity has a much smaller impact (about 12% for \(\xi=4\)) on \(\tilde{b_{bp}}\) in the presence of suspended minerals, whose increased light scattering overwhelms that of phytoplankton.  
ocean optics; backscattering ratio; phytoplankton; coated-sphere model; bulk refractive index; seawater component +
Footnote †: journal: Applied Physics Letters
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 5 Simulation Model
### Metrics  
We define the following metrics to give an intensive performance analysis.  
* Deadline Miss Ratio (DMR): It is the ratio of the number of deadline missed requests to the number of submitted requests. The primary objective of a real-time scheduling algorithm is to minimize the DMR.
* Average Response Time (ART): It is the average duration for getting the response from the server after submitting a request. ART measures the responsiveness of the system. The primary objective of a non-real-time scheduling algorithm is to minimize the ART.
* Average Stretch (AS): It is the ratio of the response time to the service time of the corresponding data item. It is a widely used metric in heterogeneous database environments. The primary objective of a stretch optimal scheduling algorithm is to minimize the AS.
* Average Encode Length (AEL): It is the average size of an encoded packet. It measures how a scheduling algorithm exploits the coding opportunity. The larger AEL mean more coding benefit.
* Average Broadcast Productivity (ABP): It is the average number of requests satisfied per broadcast. It measures the productivity of a broadcasted packet/item. A large ABP implies the efficient bandwidth use.