esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Reverse Bias Characteristic of a PV Module Due to Voltage Mismatch Conditions
### The Operation of Bypass Diodes by Partial Shading  
The partial shading was simulated to confirm the open-circuit voltage, through which reverse current flows by the operation of bypass diodes. In order to operate the bypass diode completely, a black sheet that does not transmit light was attached to the front glass of the PV module (solar cell area). The irradiance of the light source device was fixed at 1000 W/m\({}^{2}\). In addition, the number of operation of the bypass diodes was changed to confirm the electrical characteristics of the PV module according to the number of operation of the bypass diodes (three bypass diodes in the experimental PV module). The experimental results are shown in Figure 4. In the case of the normal condition (=no partial shading), the reverse current flowed to the PV module at 37.32 V. In the case of the operation of one, two, or three bypass diodes, the reverse current flowed at 36.71 V, 36.15 V, or 35.56 V, respectively. A voltage drop of 0.4-0.6 V, which is the operating voltage of a bypass diode, occurred sequentially. The voltage difference due to the operation of the bypass diodes was up to 1.76 V.  
Despite the operation of the bypass diodes, the difference of open-circuit voltage, through which reverse current flows, was small. The PV module I-V curves, before and after the operation of the bypass diodes, are shown in Figure 5. The measured module I-V curves were for the case when all bypass diodes were not operated, and bypass diode operated sequentially. Bypass diodes operate when there is shading on some solar cells in the PV module because shading causes a potential difference of solar cells. Additionally, bypass diodes are connected in parallel with dozens of solar cells that are connected in a series. Therefore, when the bypass diodes are conducted, the voltage produced by the connected solar cells decreases to about 0.4-0.6 V, which is the operating voltage of the bypass diode. Even if the maximum power voltage of the PV module is reduced to about 1/3 by the operation of one  
Figure 3: I–V (Current-Voltage) Characteristics of a PV module due to a non-uniformity of irradiance.  
Figure 2: Open-circuit voltage and reverse current due to a non-uniformity of irradiance.  
bypass diode, the open-circuit voltage difference due to the operation of a bypass diode is small, as shown in Figure 5.  
According to the I-V curve of the PV module of Figure 5, a minute current flows into the solar cells, but no reverse current flows in the voltage range below the open-circuit voltage when bypass diodes are operated. This could be considered a state in which solar cells have a voltage, while the bypass diodes are operating. Therefore, the reverse current does not flow until the open-circuit voltage (=forward operating voltage) of solar cells and the reverse current flow beyond the open-circuit voltage of solar cells. For this reason, when the bypass diode is operating by partial shading, the voltage mismatch is within 2 V, so there is little or very little reverse current flow to PV modules.
# Machine Learning Techniques for Non-Terrestrial Networks
## 3 General Aspects of NTN  
In this section, general aspects related to the NTN deployment are reported in terms of characteristics of the ground, aerial and space elements and their parameters. In Figure 1, it is reported the generic NTN architecture. The user terminal (i.e., the UE) is able to transparently connect to a terrestrial node as well as to one of the NTE, through the service link. The NTE is then connected to the 5G core network (5 GC) through the feeder link to the ground station, namely NTN gateway. In the same figure, it is reported the possibility to have a multi-hop connectivity through the inter-satellite link (ISL) with one of the NTEs (i.e., any satellite but also to an UAV or a HAPS).  
Depending on the NTE, the experienced radio parameters can be different. In Table 1, the most important parameters of NTN are reported. In particular, for each NTE, its altitude from the ground, the typical size of its beam footprint, the maximum distance \(D_{max}\) between the NTE and UE located at cell edge at its minimum elevation angle (typical 10 deg) and the maximum round trip time (RTT) delay with respect to the gNB are reported. In the RTT evaluation, only the propagation delay is considered. Depending of the location of the gNB, RTT can be double the service link propagation delay (regenerative case) or double the service and feeder link propagation delay (transparent case). These two cases are better defined in Section 4.  
Finally, it is worth noting the difference in the propagation path loss between TN and NTN. In the case of TN, the main source of attenuation in radio signal propagation is due to the blockage of hills and buildings (of course the distances are reduced with respect to those in NTN). In the case of NTN, we can consider line-of-sight (LoS) propagation conditions for low-altitude NTEs (e.g., UAV and HAPS), while for high-altitude NTEs (e.g., MEO and GEO), the radio signal propagation is dominated by atmospheric path loss due to gases and rain fades. See [27; 28] for details and parameters for the link budget.  
Figure 1: Reference architecture of the non-terrestrial network.
## 3 System Principle  
### Method Overview  
We provide a brief overview of the system first. In the offline training phase, at the transmitter, Pulse Width Modulation (PWM)is used to control three red, green, and blue diodes of the RGB-LED. Then, the light emitted by the red, green, and blue diodes is mixed in RGB-LED light bulbs to become white light. At the receiver, smartphone cameras can be used. The LED project image is captured with stripes by the CMOS sensor because of the rolling shutter mechanism. Due to special modulation, different LED project stripe images have different features, so we can call them the LED-IDs. Then, we use image processing technology to extract features to facilitate counting and extracting. In the process of image processing, the color LED projection is separated into three channels: R, G, and B. Next, according to our selected features, feature data will be extracted through image processing such as binarization, edge detection, etc. Then, according to the acquired feature data, a machine learning algorithm is applied to build the classifier. At last, exploring the boundary and completing the construction of the LED-ID library is done.  
In the online training phase: First, when setting up the system, select the appropriate ID from the LED-ID library according to the required number and modulate the LED with the parameters of ID pairs in the library. The web page or other information corresponding to the ID is stored in the cloud server. Once the user's achievement is recognized, the cloud content can be downloaded from the network. The schematic diagram of the method is given in Figure 3. What is more, in practical applications, the receiving end is not necessarily a cellphone camera, but also an on-board camera, a robot camera, etc.  
Figure 2: Average color perceived by the eye in a critical duration.  
In the next part of this section, we will select six representative RGB-LED-IDs to illustrate signal modulation, image processing, classifier establishment, and the online identification of our system.
# Bias Effects on g- and s-Factors in Westcott Convention
## 1 Introduction
Here, it should be noted that neutron temperature characterizing a Maxwell distribution at sample position is not the same as sample temperature, since neutron absorption reactions distort the neutron energy distribution, and the leakage rate of neutrons through a sample guide hole depends on neutron energy. Therefore, sample temperature should be distinguished from neutron temperature. Sample temperature affects the shape of a resonance peak, since it is broadened due to Doppler effect. A bias effect by sample temperature might be noticeable for isotopes with resonances around the cut-off energy of the joining function. However, the sample temperature effect has not been quantitatively reported, so far. In this study, bias effects on Westcott g- and s-factors are investigated. The s-factor calculated by using the joining function based on a full Monte Carlo simulation is compared with those using traditional joining functions in [PERSON]'s report. The g- and s-factors are calculated for sample temperatures 300 K and 1000 K, for neutron temperatures ranging from 20 K to 1600 K, and for two 1/v isotopes (\({}^{197}\)Au, \({}^{59}\)Co) and six non-1/v isotopes (\({}^{241}\)Am, \({}^{151}\)Eu, \({}^{103}\)Rh, \({}^{115}\)In, \({}^{177}\)Hf, \({}^{226}\)Ra). The bias size for the reaction rate is also evaluated in the case of an epithermal neutron index r = 0.1, for these eight isotopes. The formula and calculation methods in this paper can be utilized for any isotope. Although neutron self-absorption correction is known to be an important bias [13], this correction is outside the scope of this work.
# Design and Simulation of a Vision-Based Automatic Trout Fish-Processing Robot
## 2 Material and Methods
### Machine Vision and Dimension Determination  
This system is designed, simulated, and fabricated to process trout in an online condition. The main part of the system is the image processing setup in which the images are captured and the cutting points and dimensions are extracted using the developed algorithm. To develop the cutting point determination algorithm, an imaging case with a light-emitting diode (LED) was designed. We applied a video camera (Basler, daA1280-54 uc, USB3) for the online process. To capture the images, as the fish was passing in front of the camera sensor, the signal was sent to the programmable logic controller (PLC) and the frame capturing command was sent to the MATLAB software to store the frame for fish segmentation and dimension extraction. As soon as the dimensions were extracted, they were sent to the controlling unit with the TCP/IP protocol. The machine vision components, segmentation process, and cutting point determination are presented in Figure 1.  
The cutting points and extracted dimensions are presented in Figure 2. The head and belly-cutting regions will be considered as the operational points of the system. L\({}_{1}\) to L\({}_{4}\) are head-cutting point, belly-cutting start point, the body length without tail, and total length, respectively. The operational width of the trout is shown as W.  
Figure 1: The image processing setup: (**a**) pure blue background, (**b**) the video camera, (**c**) inside of the imaging case with the sample, (**d**) segmented fish, and (**e**) trout fin shear to define the cutting points and functioning lengths.
# Quo Vadis Solar Energy Research?
## 4 Discussion
### Recent Trends in Solar Energy Research (2019-2020)
Cluster one (red) is devoted to chemical processes and materials science involving solar energy, as it can be derived from its most common keywords referring to photocatalysis (2 nd position in the cluster), nanoparticles (3 rd position), hydrogen production (4 th position), graphene (7 th position), titanium dioxide (9 th position) and other keywords in the cluster, such as quantum dots, perovskites or composites. Cluster two (green) is devoted to generation technologies and simulation as it can be derived for common keywords in the cluster, such as system (2 nd most common), photovoltaics (3 rd position), design (5 th position), energy efficiency (6 th position), optimization (7 th position), renewable energy (8 th position) or models (10 th position). Other keywords in the cluster, such as CSP (concentrating solar power), CPV (concentrated photovoltaics), power-generation, wind energy or plants are also relevant. Cluster 3 (blue) is devoted to heat and storage, as it can be derived from its most common keywords, referring to desalination (2 nd position), phase change materials (3 rd position), heat transfer (4 th position), thermal energy storage (7 th position), thermal performance (8 th position) or heat (9 th position). However, this is also due to other keywords, such as molten salts or nanofluids. \begin{table}
\begin{tabular}{c c c c} Rank & **Cluster \#1** & **Cluster \#2** & **Cluster \#3** \\
1 & Water (793–4264) & Solar energy (2993–11,206) & Performance (1619–7464) \\
2 & Photocatalysis (589–3128) & System (1109–4693) & Desalination (357–1726) \\
3 & Nanoparticles (535–2658) & Photovoltaics (950–3460) & PCM (337–1415) \\
4 & Hydrogen production (521–2766) & Energy (911–4051) & Heat transfer (298–1328) \\
5 & Energy conversion (456–2199) & Design (817–3652) & Enhancement (255–1345) \\
6 & Solar cells (393–1408) & Energy efficiency (747–3332) & Nanofluids (240–1251) \\
7 & Graphene (366–1738) & Optimization (732–3608) & Thermal energy storage (233–1082) \\
8 & Efficient (363–1845) & Renewable energy (721–2738) & Thermal performance (206–859) \\
9 & TiO\({}_{2}\) (353–1814) & Energy storage (525–2316) & Heat (187–909) \\
10 & Degradation (323–1741) & Models (498–1875) & Parabolic trough collector (181–909) \\  & & & \\ \end{tabular}
\end{table} 
Table 4: Most common keywords from Solar Energy research in 2019–2020. It is worth mentioning that concentrating solar power (also known as CSP) had a much lower relevance in research publications in 2019-2020 (208 occurrences), compared to photovoltaics (PV) with 950 appearances. Concentrating photovoltaics (CPV) had a much lower impact, with 52 appearances. Among CSP related keywords, parabolic trough collectors were dominant, with 181 occurrences.
# The IoT/IoE Integrated Security & Safety System of Pompeii Archeological Park
## 7 The Telecommunication System
It supports the use of smart handheld terminals equipped with interactive displays and processing capabilities, allowing staff to access critical information and system functionalities in real time. This ensures a coordinated, informed, and efficient response to any situation. Furthermore, the DMR network is designed to be interoperable with the regional civil protection and security infrastructure, making it possible to integrate into a unified response network during large-scale emergencies or critical events. In addition to the wired and DMR-based systems, a dedicated Wi-Fi communication infrastructure has also been established. This network is intended to provide high-speed wireless connectivity not only for internal operational needs but also for visitors and staff, enhancing both user experience and internal communication. As with the other network components, the design and deployment of the Wi-Fi system had to take into consideration the environmental and cultural constraints of the archeological context. To address this, a proper genetic algorithm [7; 25; 26] was applied to identify optimal access point placement and network configuration. This resulted in an infrastructure that delivers broad and reliable coverage while minimizing visual and structural impact, once again achieving optimal performance and efficiency in terms of cost-benefit. Overall, the telecommunication infrastructure--composed of the fiber optic backbone, DMR system, and Wi-Fi network--represents a state-of-the-art technological ecosystem, carefully designed and implemented to operate within a highly delicate historical environment. It supports not only daily operational needs and security and safety management  
Figure 7: Scheme of the wired telecommunication infrastructure. but also offers the scalability and flexibility required for future expansion and integration with evolving digital services. Great attention has been given to service continuity and disaster recovery aspects, both for this component of the project and for all other components.
# WLAN Aware Cognitive Medium Access Control Protocol for IoT Applications
## 6 Simulation Results
### Network Lifetime  
Figure 16 illustrates the lifetime of IoT nodes for the standard and the proposed WAC-MAC protocol. The graph shows the number of beacon intervals required to deplete the energy of a sensor node. For the standard protocol, all nodes deplete their energy within 17 BI. On the other hand, WAC-MAC extends the network lifetime of IoT nodes to 27 BI. This improvement is due to the added function of sensing in the proposed WAC-MAC, as a result of which the sensor nodes transit to the sleep mode if WLAN user is active during the sensing period. This results in the energy conservation of IoT sensor nodes, which would otherwise be wasted due to unsuccessful transmission.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 2 Related Work
### Deep Multi-View Clustering  
Deep multi-view clustering methods aim to leverage data obtained from multiple modalities to uncover the inherent clustering structure. Some approaches are based on traditional clustering methods. [PERSON] et al. [48] proposed a centroids-guided clustering method. They use the view-specific cluster centroids as guiding signals to guide deep representation learning. This approach incorporates information from different perspectives, but does not take into account common information between views, and it is also difficult to cluster large-scale datasets. Other approaches can be divided into four categories including CCA-based methods [49; 50; 51], spectral-based methods [52], graph-based methods [53; 54] and self-expressive-based methods [55; 56; 57; 58; 59; 60]. The self-expressive-based methods have gained significant attention due to their ability to capture the intrinsic relationships within data and their good performance. Due to the richness of information contained in multi-view data, many approaches focus on the consistency and specificity of the data in each view. For instance, [PERSON] et al. [18] proposed a framework called MvDSCN, which consists a diversity module and a universality module, and the final results are obtained by applying spectral clustering using the common affinity matrix. [PERSON] et al. [55] designed a network called DMSC-UDL to learn local and global information by different self-expressive matrices, and the shared self-expressive matrix was used to obtain the final results. [PERSON] et al. [59] proposed a network called SIB-MSC, which captured view-specific and view-common information by using different autoencoders. They also introduced mutual information as regularization terms to isolate view-specific spaces to be complementary to the shared view spaces. They use the view-common affinity matrices to perform spectral clustering to obtain the final results. However, most of them mainly focus on how to obtain the commonness and specificity between each view and often face limitations when it comes to handling large-scale datasets.
# A Multiple-Step, Randomly Delayed, Robust Cubature Kalman Filter for Spacecraft-Relative Navigation
## 2 Preliminaries
### Non-Gaussian Noise in Measurements  
A Gaussian distribution is often adopted to describe the distribution of sensor noise. However, the sensor noise does not satisfy the Gaussian assumption in practice. Furthermore, the sensors used in vision-based spacecraft-relative navigation are often perturbed by outliers, so they generally regarded as giving contaminated Gaussian distributions [30], and the probability density function (PDF) is expressed as follows:  
\[p(v)=(1-\varepsilon)\frac{1}{\sqrt{2\pi\sigma_{1}}}\exp\left(-\frac{(v/\sigma_ {1})^{2}}{2}\right)+\varepsilon\frac{1}{\sqrt{2\pi\sigma_{2}}}\exp\left(-\frac {(v/\sigma_{2})^{2}}{2}\right) \tag{7}\]  
where \(\varepsilon\in[0,1]\) is the perturbing parameter that denotes contamination probability. \(\sigma_{1}\) and \(\sigma_{2}\) are standard deviations of the individual Gaussian distributions, which are satisfied with \(\|\sigma_{2}\|>\|\sigma_{1}\|\). Figure 2 presents an example that compares the Gaussian distribution and the contaminated Gaussian distribution with \(\varepsilon=0.05\), \(\sigma_{1}=1\) and \(\sigma_{2}=7.5\sigma_{1}\). Obviously, the contaminated Gaussian distribution exhibits more clutter.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 2 Related Works and Applications
### Participants  
* Smartphone (S): It is the user's smartphone, to which various sensors (wearable devices) are connected to monitor the patient's health condition. The smartphone periodically transmits encrypted records to the healthcare provider and stores them in the cloud server.
* Healthcare provider (H): It refers to an institution that provides and records medical practice. H manages the user's health status monitored through the S, and performs medical treatment when users visit.
* Emergency center (E): An institution that carries out medical practice in case of emergency.
* Vehicle (including ambulances, V): V means vehicles or helicopters that transport users. It may or may not have medical equipment installed.
* Cloud server (CS): CS is a storage space. It stores the user's medical records (medical practices performed by H, the patient's conditions measured by S's sensors, etc.) and video files taken by drones. This is assumed to be a semi-trusted party.
* First responders (F): F stands for police, fire engines, rescue helicopters, etc.
* Drone (D): The drone is registered in the CC in advance.
* Control center (CC): This is a kind of trusted third party (TTP), which controls security-related matters such as the generation and management of encryption keys and the registration of participants. Hence, CC knows all the security keys for every participant.
# Influence of Linear Springing on the Fatigue Damage of Ultra Large Ore Carriers
## 5 Results and Analysis
### Influence of Navigation Speed  
Figure 11 shows the fatigue damage ratios of four different OCs at four different speeds: 5 kn, 7.5 kn, 11.25 kn, and 15 kn. It is found that the fatigue damage ratios of the 400,000 DWT OC, 388,000 DWT OC, and 250,000 DWT OC are close to each other at different navigational speeds. The corresponding average fatigue damage ratios for these three OCs are 40.74% for 15 kn, 41.11% for 12.25 kn, 14.98% for 7.5 kn, and 3.17% for 5 kn, respectively. The corresponding damage ratios of the 300,000 DWT OC at different speeds are 28.43%, 49.88%, 17.79%, and 3.9%. For all four OCs, the sum of the fatigue damage ratios at 15 kn, 11.25 kn, and 7.5 kn is close to 97%. It indicates that the fatigue damages resulting from springing mainly occur in the medium and high speed conditions. The fatigue damage at low speeds is very small. This is because the encountering frequency is much lower than the first order natural frequency of the hull.