esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Skylmager Hardware  
Figure 10a displays the original Skylmager hardware. At NREL it was found necessary to add an extra SBC for increased computational power-the Odroid C1 by Hardkernel. Heat dissipation is an issue with SBC in Texas summers. One C1 was destroyed by heat and as result a cooling fan was added to the design. The new C2 Odroid has a heat sink to eliminate the overheating issue.  
Figure 9: Microgrid in the Canary Islands.  
Acquiring and fusing the 3-exposure images could be done with just the Raspberry Pi 2. The Pi 3 model is 50% faster than its predecessor; careful optimization of the workflow will allow acquisition, processing, and forecasting with just a Pi 3. This would reduce cost and greatly simplify network connections. Figure 10b shows this configuration with a single Pi 3, plastic case, cooling fan, camera, and WeatherBoard. Images could be pushed to the cloud for processing, however, the necessary bandwidth would be substantial. The sky imagers in La Graciosa are built upon a Raspberri Pi 3 model B with no ancillary boards, and a super wide fish-eye lens (field of view over 180\({}^{\circ}\)). An inexpensive mini PV module was added to record irradiance at the camera locations there.  
Figure 11 shows the equipment deployed on the MET tower at Ft. Sam: a Kipp&Zonen CMP11 pyranometer, the UTSA SkyImager, and a Vaisala WXT520 weather transmitter. Each of the commercial devices costs several thousand dollars; this cost prompted us to search for low-cost substitutes.  
Several inexpensive alternatives to a commercial pyranometer exist [49]. Devices can be added to the GPIO pins on the Raspberry Pi. The Hardkernel Weather-Board 2 shown in Figure 12a can take not only temperature, humidity, and pressure readings (bme280 Application-Specific Integrated Circuit ASIC), but also measures light in the Visible, Infra-Red, and Ultra-Violet bands (si1132 ASIC). There is a Python interface for data retrieval. After calibration and conversion of Lux to W/m\({}^{2}\), this provides irradiance measurements and limited weather data in real time. Another ancillary device that will  
Figure 11: CMP11 pyranometer, UTSA SkylImager, Vaisala Weather Station.  
Figure 10: (**a**) Original configuration Raspberry Pi 2/Odroid C1, (**b**) Current hardware with a single Pi 3 Model B.  
be useful during initial deployment of the SkylImager is a GPS locator. At $20, it looks like a small mouse for a desktop computer and plugs into a USB port. The Linux programs gpsmon and cgps can be installed on Raspbian and used to take readings of the exact position using the latest GPS satellite data. These are just two of many environmental sensors that can be connected to the Raspberry Pi. Figure 12b shows the new PiNoIR camera, which captures infrared light as well as visible. This would allow for increased contrast between low-level cumulus and high-level cirrus clouds composed of ice crystals. The SkylImager can be used for additional tasks such as air quality monitoring. Figure 12c shows the $25 MQ-131 ozone detection sensor, for example.
# Adaptive Rate-Compatible Non-Binary LDPC Coding Scheme for the B5G Mobile System
## 2 The Construction of RC-NB-LDPC Codes
### Design and Implementation of the RC-NB-LDPC Code  
The check matrix of the RC-NB-LDPC code has a QC structure, so the coding algorithm can adopt the QC-structured fast-coding method. The FPGA implementation based on the QC-structured fast-coding method will be described in detail below.
## 1 Introduction
Vegetation indices were often used as dependent variable and climate as independent variable to explore the dependence of vegetation greenness on climate change [7]. Most of such studies have a common implicit assumption of negligible effects of land-cover change. While the assumption might be justified for areas with slow and insignificant land-cover changes, it is not valid for areas with fast and extensive land-cover changes driven by the profound economic growth [6]. For areas with fast land-cover changes, ignoring the land-cover changes will bias the equation derived for greenness and related ecosystem parameters such as phenology [4; 7; 26]. To address the climate effects on vegetation greenness while insulating the impacts of land-cover/use changes on greenness, we carefully chose large patches of the temperate forest and grassland without land-cover/use changes in Northern China ranging from wet temperate to dry temperate zones, and investigated the changes in greenness of these patches, the changes in climate and its seasonality, and responses of greenness dynamics to changes in climate. Forest and grassland in Northern China have different mechanisms in their responses to climate changes because of the diverse root-to-shoot ratio, the species composition, and the phenology and longevity among others [9; 11; 27; 28]. An amplified wetting trend in northern high-latitude region was highlighted due to the enhancement of poleward atmospheric moisture transport [29]. We hypothesize the following: temperate forests and grasslands exhibit divergent greenness responses to changing climate; compared to well-developed root systems of forests, the shallow-root grasslands are more sensitive to enhanced summer precipitation [30]; greenness of temperate forests and grasslands responds divergently to asymmetric warming diurnally and seasonally [19; 31; 32]; and species composition further differentiates the direction and/or magnitude of forest greenness [12].
# Performance Analysis of Downlink 5G Networks in Realistic Environments
## 4 Capacity Bounds Analysis
### Multiservice Capacity Bounds
\(\varepsilon_{lk}\) is the interference factor between user \(i\) in service \(l\) and user \(j\) in service \(k\) (i.e., inter-class interference factor). \(n_{1}\) is the number of connections of service type \(l\). \(n_{k}\) is the number of connections of type \(k\). \(V_{l}\) is the activity factor of service type \(l\). \(V_{k}\) is the activity factor of service type \(k\). By substituting the value of \(\varphi\) of (34) into (28), and considering factor F in (26), we obtain the following:  
\[\left(\frac{E_{b}}{N_{0}}\right)_{k}=\frac{SF_{k}\cdot P_{Rk}}{N_{t}+\frac{1}{ \text{F}}\left[(n_{k}-1)V_{k}\cdot\varepsilon_{k}\cdot P_{Rk}+\sum_{l\
eq k,\ l=1}^{s}n_{l}\cdot V_{l} \cdot\varepsilon_{lk}\cdot P_{Rl}\right]} \tag{35}\]  
By the definition of \(S_{i}\) given by (24), (35) can be rewritten as follows:  
\[S_{k}\cdot P_{Rk}=N_{t}+\frac{1}{\text{F}}\left[(n_{k}-1)V_{k}\cdot\varepsilon _{k}\cdot P_{Rk}+\sum_{l\
eq k,\ l=1}^{s}n_{l}\cdot V_{l}\cdot\varepsilon_{lk} \cdot P_{Rl}\right] \tag{36}\]  
This can be further rewritten as follows:  
\[N_{t}=S_{k}\cdot P_{Rk}-\frac{1}{\text{F}}\left[(n_{k}-1)V_{k}\cdot\varepsilon _{k}\cdot P_{Rk}+\sum_{l\
eq k,\ l=1}^{s}n_{l}\cdot V_{l}\cdot\varepsilon_{lk} \cdot P_{Rl}\right] \tag{37}\]
# Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications
## 2 Related Work
The authors of [32] addressed route planning in a navigation system that finds an optimal route from the source to the target location. They proposed a real-time cache-aided route planning system based on mobile edge computing with the aim of reducing the communication delay between the access network and the remote central server and the computational time of route planning queries. The authors of [33] proposed a caching-enabled VEC scheme for jointly optimizing task caching and computation offloading in a VEC system; task caching was shown to reduce response latency but increase energy consumption. They then formulated an optimization problem that minimizes the weighted sum of the service time and energy consumption in the caching-assisted VEC system and used a genetic algorithm to solve the problem. The authors of [34] presented a comprehensive review and analysis of the vehicle routing problem (VRP). They mainly reviewed machine learning-assisted VRP modeling and optimization approaches.
###### Abstract  
In this study, we provide a comparison of radiofrequency electromagnetic field exposure level maps as determined using two approaches: a broadband meter (NARDA EMR-300) equipped with an isotropic probe in the range of 100 kHz to 3 GHz, and a Personal Exposimeter (Satimo EME Spy 140) in the range of 88 MHz to 5.8 GHz. The aim of this research was to determine the necessary adjustments to the measurements made with personal exposures to obtain RF-EMF exposure maps equivalent to those made with broadband meters. We evaluated different possibilities to obtain the best equivalence of measurements between both devices. For this purpose, the datasets obtained in both cases were analyzed, as well as the possible correction factors. First, the possibility of establishing a single or double correction factor depending on the existence (or lack thereof) of a line of sight with respect to the base stations was analyzed by minimizing the average value of the error between the values of the broadband meter and the corrected values of the personal exposure meter. Due to the differences observed in the exposure maps, a second procedure was carried out, in which a genetic algorithm was used to determine the ratio between the measurements from both methods (the broadband meter and personal exposure meter), depending on the existence (or lack thereof) of a line of sight, and we compared the exposure maps generated using kriging interpolation.  
radiofrequency electromagnetic field (RF-EMF); personal exposure; exposimeter; risk perception; spot measurements 2025  
25 June 2025  
30 June 2025  
Submitted: 3 July 2025
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 3 Background and Attack Model
### Threat Model
#### 3.2.1 DoS Attack  
A DoS attack is an attack method that utilizes the message priority of the CAN bus for arbitration. Its purpose is to occupy the resources of the target bus by frequently sending high-priority messages (0x000) to the target bus and to increase the bus load rate so that other normal packets cannot be transmitted. This affects the normal response of the car's  
Figure 1: CAN bus 2.0A structure.  
Figure 2: Three attack models on the in-vehicle network.  
electronic system, and even causes the car to lose control. In the car-hacking dataset, the DoS attack packets are injected into the CAN bus at a time interval of 0.4 ms, with an interval of 10 s. For a period of time, the CAN bus is filled with a large number of DoS attack packets, so that our model can perform detection better.
## 2 Materials and Methods
### Methodology
#### 2.2.3 Same Coordinate System  
As the MODIS image was in a sinusoidal projection system, Landsat-8 OLI was in the projected coordinate system and GF1, GF2, GF4 were in geographic coordinate system WGS 1984 50N (latitude/longitude format). For fusion purposes, all imagery were brought into the same coordinate system, i.e., WGS 1984 50N in projected format.
## 2 Functioning Principles and General Concepts for Transduction
### Functioning Principles
Transconductance values in the mS range have been reported for OECTs fabricated with solution-processed materials [32]. Moreover, through the optimization of the transistors' geometry, peak transconductance values at zero gate voltage have been obtained [33] allowing the device to be operated efficiently even at very low voltages. This capability is remarkably adapted for biosensing applications which involve fragile samples maintained under bias for long periods of time [34]. In addition, it guarantees low power consumption. Besides, the switching speed of OECTs is now lower than 1 ms, which is far above expectations for biosensing applications. However, OECT transconductance exhibits a non-monotonic dependence on gate voltage (bell-shaped curve). This behavior appears to be a general trend for OECTs as it has been reported many times independently from the device's geometry, materials used (electrodes, conductive polymers) or elaboration technology [35]. To account for this behavior, two distinct origins have been proposed  
Figure 1: Qualitative OECT functioning mechanism. **(a)** Situation at zero gate voltage. Current flows through the device with respect to the intrinsic conductivity of the p-doped OSC. **(b)** Situation under application of a positive bias to the gate electrode. Cations penetrate in the OSC’s bulk and compensate the dopants’ negative charges resulting in the lowering of the polymer conductivity. Adapted from [30] and reproduced with permission from John Wiley and Sons, Copyright © 2007.  
in the recent literature. The first one points out the role of contact resistance in OECTs operating in the depletion mode. For example, the model developed by [PERSON] et al. shows that the dependence of contact resistance with gate voltage can lead to a bell-shaped transconductance [36]. The second one suggests that disorder in the OSC causes non-linearities in the transfer curves of OECTs. Based on a hopping transport model applied to disordered organic materials, [PERSON] et al. showed that non-monotonic transconductance occurs even in the absence of contact resistance [35]. While the former explanation implies that this behavior is the result of poor electrical contact to the channel, the latter makes it an intrinsic property of OECTs, suggesting that for the development of new materials and devices, one should both optimize thin films' interfaces and provide high transconductances over a broad operating voltage range.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 2 Related Work
### Cross-Modal Learning in Anomaly Detection  
Cross-modal learning has emerged as a pivotal approach for enhancing anomaly detection by leveraging complementary information from diverse modalities. Early works predominantly focused on single-modal data, such as visual or temporal features, but faced limitations in capturing complex anomaly patterns. Recent advancements in multimodal pre-trained models have spurred interest in cross-modal frameworks. For instance, [PERSON] et al. [9] proposed a deep structured cross-modal anomaly detection framework, projecting features from heterogeneous modalities (e.g., images and text) into a consensus latent space to identify inconsistencies. This method highlights the significance of nonlinear correlations across modalities, which linear models often fail to capture.  
The integration of large language models (LLMs) and vision-language models (VLMs) has further revolutionized zero-shot anomaly detection. [PERSON] et al. [21] introduced LAVAD, a training-free paradigm that generates textual descriptions for video frames and employs LLMs for temporal aggregation and anomaly scoring. Their work underscores the potential of semantic alignment between visual and textual features in detecting context-dependent anomalies. Similarly, [PERSON] et al. [22] developed FiLo, combining fine-grained anomaly descriptions from LLMs with position-enhanced localization via multimodal encoders. FiLo addresses the challenge of generic anomaly descriptions by leveraging domain-specific knowledge, achieving state-of-the-art performance in both detection and localization.  
Recent studies also emphasize temporal dynamics in cross-modal learning. [PERSON] and [PERSON] [23] proposed VLAVAD, which maps high-dimensional visual features to low-dimensional semantic spaces using a video-language model. Their Sequence State Space Module (S3M) captures temporal inconsistencies in semantic features, overcoming the limitations of static frame analysis. These methods collectively demonstrate that cross-modal learning not only improves detection accuracy but also enhances interpretability by grounding anomalies in semantic contexts. However, challenges remain in balancing computational efficiency and generalization, particularly when scaling to dynamic, real-world scenarios with heterogeneous data streams.
## 3 Materials and Methods
### Robotic Arm: Automatic Control  
The trained barcode detector model guides the robotic arm while in auto mode to rotate the item in front of the camera installed near the scanner until the barcode is found. Once detected, the rotation is stopped, and the arm gripper is guided with a PID controller in the horizontal and vertical axes while moving slowly toward the scanner. This is achieved by running the PID code, where the required thrust in the \(x\)-axis and \(y\)-axis is calculated; then, the results are written continuously to a text file that can be accessed by the Python code controlling the arm at the same time. This is achieved through the following steps:  
1. A camera is used to stream frames to the system once the robotic arm's auto mode is activated.
2. \(K_{p}\), \(K_{i}\), and \(K_{d}\) parameters are initialized. These represent the proportional control, derivative control, and integral control, respectively. The PID controller response for each parameter is calculated as shown in (15)-(17) for an input signal \(S_{IN}(t)\) and output \(S_{OUT}(t)\). \[S_{OUT}(t)=K_{p}S_{IN}(t)\] (15) \[S_{OUT}(t)=K_{i}\int_{0}^{t}S_{IN}(\tau)d\tau\] (16) \[S_{OUT}(t)=K_{d}\frac{d}{dt}S_{IN}(t)\] (17)
3. We use the three parameters to implement the controller block as shown in Figure 11 for the discrete signal per each frame in the camera stream sequence.
4. The controller is used in the feedback loop shown in Figure 12. We want to have the center of the barcode be aligned with the scanner. The actual location of the scanner with respect to the center of the camera is mapped into the camera frame, ignoringthe difference in depth between the sensor of the camera and the sensor of the scanner as shown in Figure 13.  
We set the system input to zero since we want the barcode centroid to align with the center of the scanner. However, we allow a suppression threshold for the difference between both, so if the _distance_ signal in the figure is less than 20 pixels, it is set to zero. This is because the detection system does not return perfect feedback, as the bounding box around the barcode keeps changing slightly, due to the small fluctuations in the scene.  
5. The closed-loop with the PID is applied twice--once for each axis--so the distance is calculated in only one dimension.
## 2 Polarization Density and the Cavity Quality (Q) Factor  
The Q factor indicates the amount of the stored energy inside a resonator for a given round trip loss. A high Q value usually signifies that the resonator is low loss, i.e., has a low loss factor, which means that high energy can be trapped efficiently in the resonator. The Q factor depends on the resonator length, the reflectivities of the resonator walls, the frequency of the propagating wave, the total absorption coefficient of the interaction medium between the resonator walls, and any sort of diffraction or scattering loss that may take place inside a resonator. The Q factor of a resonator is defined as:  
\[CAVITY\;QUALITY\;(Q)FACTOR=2\pi\frac{\textit{Stored\;energy}}{\textit{Energy \;dissipated\;per\;round\;trip}}=fT_{rt}\frac{2\pi}{\zeta}=\frac{4 fL\pi}{\zeta c}. \tag{1}\]  
\(T_{rt}:\textit{Round\;trip\;time}\), \(f:\textit{Wave\;frequency}\), \(\zeta:\textit{Fractional\;power\;loss\;per\;round\;trip}\)\(c:\textit{Speed\;of\;light}\), \(L:\textit{Cavity\;length}\)  
The electric energy density in a resonator depends on the magnitude of the electric field and the polarizability of the interaction medium. For a given resonator configuration, the stored electric energy density in a homogenous isotropic interaction medium is given as [7]:  
\[W_{e}=\textit{Stored\;energy\;density}=\frac{1}{2}ED=\frac{1}{2}E(\varepsilon _{\infty}E+P)=\frac{1}{2}\varepsilon_{\infty}E^{2}+\frac{1}{2}EP. \tag{2}\]\(D:\) Electric flux density, \(P:\) Polarization density, \(E:\) Electric field intensity, \(\varepsilon_{\infty}:\) Background (infinite spectral band) permittivity  
The electrical polarization density (\(P\)) in an interaction medium is a microscopic parameter that indicates both the volume density of electrons and the displacement of an electron with respect to the position of the nucleus [3]. For a given electron density of a medium, the more an electron is allowed to displace from its initial position (and the nucleus) the higher the polarization density becomes. It is a measure of the electrical excitability of a material by an incident electromagnetic wave [1]. Polarization density is an important parameter for energy storage in a resonator as more energy can be stored in a cavity with a highly polarizable interaction medium. This is because, as each electron displaces further from the nucleus, their potential energy becomes higher, and an abundance of electrons in the medium will lead to a higher potential energy being stored in the medium [4]. For a highly non-linear medium, the polarization density itself depends on the magnitude of the electric field [1,4]. For these reasons, we will frequently use the term polarization density in our analyses.
# FTUC: A Flooding Tree Uneven Clustering Protocol for a Wireless Sensor Network
## 5 FTUC Algorithm
### Inter-Cluster Communication  
Once the final cluster heads are elected, they need to establish a multi-hop communications route to transmit the data to the base station. In order to do so, each final cluster head (fCHi) needs to find the best next hop (fCHi) until reaching the base station.  
In this process, each fCH broadcasts a'search cluster message' using its maximum radio communication range. Then, for all received messages j, fCHi calculates the cost function p(i,j) by using Equation (11).  
\[\mathrm{p(i,j)}=\alpha\times\frac{1}{\mathrm{E_{c}}}+\beta\times\mathrm{Dist( fCHj,\;BS)}+\gamma\times\mathrm{Dist(fCHi,\;fCHj)} \tag{11}\]  
where \(\alpha,\;\beta,\;\gamma\) are weighting factors that should respect the following condition: \(\alpha+\beta+\gamma=1\). Finally, each final cluster head uses a greedy algorithm to choose the minimum cost function value fCHj as the next hop.  
For example, if fCHi needs to find its optimal next-hop, and there are three fCH (fCHa, fCHb, and fCHc) in its communication range, and Dist(fCHi,BS) = d1. Firstly, fCHi broadcasts a'search cluster message' to fCHa, fCHb, and fCHc including the d1 value. When those fCHs receive this message they compare their own Dist(fCH,BS) to d1. If we consider that fCHa and fCHb are closer to the BS, then they start to compute Dist(fCHi,fCHa) and Dist(fCHi,fCHb), respectively. fCHc cannot be chosen as the next hop since it is farther from the BS. In order to choose between fCHa and fCHb, they use their residual energy and use the calculated distance to fCHi to compute the cost function p(i,j) (using Equation (11)) and send their results to fCHi. If we consider that the p(a,i) value is less than p(b,i), then fCHi will choose fCHa as the next-hop.  
As can be seen from this process, there is a greater probability for a cluster head to be the next hop node when it has more residual energy, it is closer to the BS, and the distance between the two cluster heads is short.  
Using simulations, we determined that the best compromise in a network lifetime is obtained with \(\alpha=0.4,\;\beta=0.3,\;\mathrm{and}\;\gamma=0.3\). Giving a highest priority to the cluster head node residual energy enables one to have a better balanced communications load.