esa-sceva/satcom-chunk-collection huggingface dataset:
# _microorganisms_
## 3 Results and Discussion  
### Standard Curves for Real-Time qPCR  
The primers used for quantitative PCR (qPCR) were designed from the odorant gene sequences deposited with GenBank after the pure isolation of the odorant-producing cyanobacteria from Paldang Lake and the sequences in the NCBI database (Table 1). After isolation, pure cultures were obtained. Sequences of the odorant synthase genes were deposited with GenBank (accession numbers: MT360266, MT515744, and ON365769-ON365771). The odorous gene sequences from the isolated cyanobacterial sequences (_Pseudanabaena_ sp. and _Anabaena_ sp.) and NCBI databases were used to establish the primer design for a real-time qPCR assay of _mic_ and _geo_ using the SYBR Green System (Table 1). To prepare the standards for gene quantification, DNA extracted from the _Pseudanabaena_ sp. (#ON365771) and Anabaena crassa (#ON365770) was used. The extracted DNA was PCR-amplified using the designed primers, and the PCR products were cloned using TOPclonerTM TA (Enzymonics). After cloning, the molecular weight of the plasmid was determined, the copy number was calculated, and tenfold serial dilutions were prepared (10-10\({}^{7}\) copies/\(\upmu\)L) and quantified using real-time qPCR.  
The two standard curves (Figure 2) demonstrated strong linearity with high correlation coefficients (R\({}^{2}\) = 0.9999). The efficiency (E) of the real-time qPCR was calculated as E = (10-1/S -1), where S is the slope of the standard curve [8; 17]. The E values for _mic_ and _geo_ were 102.9% and 99.9%, respectively (Figure 2). These high correlation coefficients and efficiencies indicated that the qPCR using these standard curves were reasonable and that the detection limit of this qPCR analysis was \(<\)10 copies/mL.  
Figure 2: Standard curves for the real-time qPCR-based analysis of (**A**) _mic_ and (**B**) _geo_ based on data obtained using SYBR Green real-time qPCR.
## 3 Security Issues Facing CVs and Solutions
### Vanet
#### 3.4.2 Privacy Protection and Effective Pseudonym Change Strategy  
Privacy protection (e.g., vehicle's location and trajectory) has been a top challenge in VANET [73]. Many approaches have been proposed to implement effective strategies of pseudonym changing [74; 75; 76; 77; 78]. However, the simple changing of a pseudonym is not sufficient to defend against pseudonym linking attacks, especially semantic linking, which can predict the next position of a vehicle and link the vehicle's new pseudonym and the original one [79]. Currently, encryption or radio silence is suggested to defend against the semantic linking attack. However, encryption is ineffective against internal passive adversaries [74], and radio silence may negatively affect safety-related applications in VANET [80]. Therefore, a highly effective and reliable pseudonym change strategy is still needed to protect a vehicle's privacy from semantic linking attack. In addition, a unified framework and metric is needed to quantify the privacy protection level, which will contribute to evaluating new privacy protection mechanisms [81; 82; 83].
# Determining the Optimal Positions of Infill Balise Groups for ERTMS/ETCS Level 1 Applications
## 2 Materials and Methods
### Output
If infill can be provided by IF_1, the train will follow the red trajectory, and if a new MA is available at the EOA, the train must slow down all the way to RS and will follow the violet trajectory, leading to the largest additional runtime. When transferring the theoretical optimal results to the real world or when dealing with more than one type of train the infrastructure shall be optimized for, the question arises of how significant the impact of (slightly) shifting the positions of the infill balise groups on the additional runtime might be. This question can be answered by calculating the weighted additional runtime for each possible and allowed pos  
Figure 4: Example of the two-dimensional plot of the trajectories. The direction of travel is from left to right. The colors correspond to receiving infill information at IF_3 (green), IF_2 (yellow), IF_1 (red), or at the EOA (purple). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unit** & **Variable** & **Data Type** \\ \hline Infill Positions & \([\mathrm{m}]\) & infill\_positions & list of integers \\ Weighted Additional Runtime & \([\mathrm{s}]\) &
\begin{tabular}{c} additional\_- \\ runtime \\ \end{tabular} & float \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Output parameters of the algorithm as the result of the optimization. groups and determining the increase in weighted additional runtime when moving away from the globally optimal positioning. Graphically, Figure 5 allows one to accomplish exactly that. This three-dimensional shape was produced by the same input parameters as before and depicts the weighted additional runtime on the vertical axis. Please note that this axis does not start at 0 s for readability reasons. The two horizontal axes represent the positions of the two infill balise groups to be positioned. The third infill balise group was fixed at position 1500 m as before to allow a timely update of the MA before reaching the IP. From the shape of this contour, it is obvious that we deal with a clear global minimum of the additional runtime. By further investigating, the slope around this optimum is quite low, indicating a broad range of positions where practically no significant increase in the additional runtime is to be expected. We conclude that we deal with a good-natured system behavior, which helps when planning the positions of the infill balise groups as restrictions apply. These restrictions in real-world applications might stem from switches, large metal objects, and other constraints.
## 4 Proposed Solution
### Lightweight Dynamic Communication Protocol
The selection of a different SN as the RN for every round of non-direct data transfer helps to maintain the energy balance in the WSN, thus keeping the WSN homogeneous, which is very important for dynamic network topology. This dynamic RN selection can be performed by proper DS travel route planning, using an offline optimization algorithm. An algorithm to perform LoRa wireless link configuration, packet construction, data processing, and synchronization functionality for the LDAP is implemented both in the SN and the DS. Figure 6 shows the software algorithms implemented in the SN and DS. In Figure 6, the SN performs the initialization and configuration at the start, then reads the sensor and waits for a message from the DS for data upload to the DS. After the initialization phase, the DS starts communication with the SN in a loop and tries to acquire sensor data from the SN database, as shown in Figure 6. If no SN responds in a specific time, it requests a SN with the lowest RSSI (Received Signal Strength Indicator) and highest energy level to relay the missing SN's data. Figure 6 shows the dynamic RN implementation algorithm for the SN. Dimensioning of the WSN and planning the DS travel path depends on the WARM implementation, network configuration, and LoRa link quality described in Section 6. \begin{table}
\begin{tabular}{|c|c c|c|c|c|c|c|c|c|} \hline \multirow{2}{*}{Tag} & **RT** & **RID** & **ST** & **SID** & **MID** & **SQN** & **PLT** & **PLL** & **PL** & **EOM** \\ \hline \multirow{2}{*}{Tag} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\  & & & & & & & & & & \\ \hline \multirow{2}{*}{Size (byte)} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} & \multirow{2}{*}{} \\ \hline \end{tabular}
\end{table} 
Table 3: IoT-WARM Communication packet description of LDAP. Figure 4: Implemented message format of LDAP. Figure 5: Date transfer protocol (**a**) from the SN to DS, (**b**) from the SN to DS through the RN, (**c**) from the SN to RN.
# Medical Image Segmentation Network Based on Dual-Encoder Interactive Fusion
## 4 Experiments and Results
### Ablation Experiments  
For ascertaining the effectiveness of specific components within the DEFI-Net architecture, a series of ablation experiments were performed across various datasets. These experiments focused on evaluating the impact of disabling the GLIL and GLFF modules. A baseline network was established by directly concatenating the outputs of the encoder to assess the contributions of these modules. As demonstrated in Table 4, integrating the GLIL module significantly enhanced segmentation performance, as evidenced by notable improvements in DSC and HD on the Synapse and ACDC datasets, and in DSC and IoU on the Kvasir-SEG dataset. These results highlight the critical role of encoder interactions and confirm that the GLIL module effectively integrates complementary features from both CNN and Transformer architectures. Conversely, when the GLIL module was disabled and replaced with the GLFF module, there was an improvement in segmentation accuracy, indicating that the GLFF module effectively amalgamates multi-scale encoder features to augment model performance. Ultimately, the highest segmentation performance was realized when all proposed modules were integrated, further validating the synergistic effect of combining the GLIL and GLFF components within the DEFI-Net framework.
## 2 Materials and Methods
### Field Spectral Measurements  
#### 2.2.1 Spectral Measurements and Processing  
An automated multi-angle spectrometer system named AMSPEC II [45] was installed on the same EC flux tower at a height of 32 m during the growing period of summer maize. It was used to measure continuous multi-angle remote sensing observations of top-of-canopy radiance and reflectance. The AMSPEC II comprises tower observation and a computer control modules. The tower part consists of a spectrometer (Unispec-DC, PP System, Amesbury, MA, USA), a pan-tilt unit (D-46, FLIR Commercial Systems, Inc), and a high-resolution digital camera (NetCam SC SD500 BN). The Unispec-DC features 256 consecutive bands ranging from 350 to 1200 nm with a spectral resolution of 3 nm and a full-width half maximum (FWHM) spectral response of 10 nm. To enable sampling under various sky conditions, the Unispec-DC is equipped with two channels for measuring solar irradiance (upward probe) and vegetation radiance (downward probe) synchronously with a frequency of 5 s all day. Its instantaneous field of view (IFOV) is 20\({}^{\circ}\) and it can collect the canopy spectral information around the tower with a viewing azimuth range of 0 to 330\({}^{\circ}\) and zenith angle of 43 to 78\({}^{\circ}\) using the pan-tilt unit. A complete horizontal and vertical rotation of the pan-tilt unit takes 15 minutes with a 10\({}^{\circ}\) angular spacing. More detailed information about this system can be found in [45].  
The spectral data during the growing period of summer maize (from June to October 2015) collected above were used in this study. The missing observations from 17 August 2015 to 1 September 2015 were due to an instrument failure. The discrepancies in light sensitivity were corrected using a cross-calibration method using the reflectance of a standardized reference whiteboard [42] and dark current (dc) was rectified by the relationship between the dc measured when blocking off the light from both probe channels and the sensor's temperature measured by the internal thermometer of Unispec-DC [45]. In addition, by comparing the reflected spectral curves, it was found that the anomaly values originated from the areas of non-vegetated areas near the tower and distant background observations. They were eliminated by removing the measurements in a range of the viewing azimuth (174-226\({}^{\circ}\)) and viewing zenith (>64\({}^{\circ}\)). Finally, multi-angle measurements included complete information on vegetation characteristics from different observation directions, which could cause directional impacts on the intrinsic spectrum compared with Nadir observations [45]. Therefore, the RossThick-LiSparse (RTLS) bidirectional reflectance distribution function (BRDF) model was further used to rectify vegetation indices to normalize the data into uniform sun-sensor geometry and minimize the directional effects. The BRDF model, which comprises RossThick volumetric and LiSparse geometric kernels, depicts the reflectance as the linear superimposition of multiple basic BRDF shapes [46]. Its specific formulation is given in [47].
_J. Sens. Actuator Netw._**2013**, \(2\), 717-760; doi:10.3390/jsan2040717  
DONAN Access  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan/  
_Review_  
**Software Updating in Wireless Sensor Networks:**  
**A Survey and Lacunae**  
**[PERSON] 1,* and [PERSON] 2**  
* Callan Institute/Department of Computer Science, National University of Ireland Maynooth, Maynooth, Co. Kildare, Ireland
* MISL, Department of Computer Science, University College Cork, Co. Cork, Ireland; E-Mail: [EMAIL_ADDRESS]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +353-1-708-3936; Fax: +353-1-708-3848.  
_Received: 18 September 2013; in revised form: 1 November 2013 / Accepted: 1 November 2013 / Published: 14 November 2013_  
**Abstract:** Wireless Sensor Networks are moving out of the laboratory and into the field. For a number of reasons there is often a need to update sensor node software, or node configuration, after deployment. The need for _over-the-air_ updates is driven both by the scale of deployments, and by the remoteness and inaccessibility of sensor nodes. This need has been recognized since the early days of sensor networks, and research results from the related areas of mobile networking and distributed systems have been applied to this area. In order to avoid any manual intervention, the update process needs to be autonomous. This paper presents a comprehensive survey of software updating in Wireless Sensor Networks, and analyses the features required to make these updates autonomous. A new taxonomy of software update features and a new model for fault detection and recovery are presented. The paper concludes by identifying the _lacunae_ relating to autonomous software updates, providing direction for future research.  
**Keywords:** WSN; software update; configuration update; fault detection; reliability; recovery; management; autonomous
## 2 Basic Principles of RNS Arithmetic  
Residue Number System (RNS) arithmetic is based on abstract algebra and number theory [29; 30].  
In the set of integers \(\mathbb{Z}\), an RNS is defined by a set of pairwise coprime natural moduli \(m_{1},m_{2},\ldots,m_{k}\) where \(m_{i}>2\) for \(i=1,2,\ldots,k\) and \(k\geq 2\). In this system, an integer \(X\in\mathbb{Z}\) is represented by the \(k\)-tuple \((\chi_{1},\chi_{2},\ldots,\chi_{k})\), where \(\chi_{i}=|X|_{m_{i}}\) is the residue of \(X\) modulo \(m_{i}\), i.e., \(\chi_{i}\) belongs to the set \(\mathbb{Z}_{m_{i}}=\{0,1,\ldots,m_{i}-1\}\), ensuring \(X\equiv\chi_{i}\pmod{m_{i}}\) for all \(i=1,2,\ldots,k\)An RNS allows the unique representation of up to \(M_{k}=\prod_{i=1}^{k}m_{i}\) integers through their residue codes. The number range is typically defined by \(\mathbb{Z}_{M_{k}}=\{0,1,\ldots,M_{k}-1\}\).
###### Abstract  
Resonant waveguide gratings (RWG) are widely used as on-chip refracometers due to their relatively high sensitivity to ambient refractive index changes, their possibility of parallel high-throughput detection and their easy fabrication. In the last two decades, efforts have been made to integrate RWG sensors onto fiber facets, although practical application is still hindered by the limited resonant peak intensity caused by the low coupling efficiency between the reflected beam and the fiber mode. In this work, we propose a new compact RWG fiber-optic sensor with an additional Fabry-Perot cavity, which is directly integrated onto the tip of a single-mode fiber. By introducing such a resonant structure, a strongly enhanced peak reflectance and improved figure of merit are achieved, while, at the same time, the grating size can be greatly reduced, thus allowing for spatial multiplexing of many sensors on a tip of a single multi-core fiber. This paves the way for the development of probe-like reflective fiber-tip RWG sensors, which are of great interest for multi-channel biochemical sensing and for real-time medical diagnostics.  
resonant waveguide grating; fiber sensor; F-P cavity +
Footnote †: journal: Applied Sciences
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating Their Impact on Water Ecosystem  
The exponential growth observed in the installation of residential, commercial, and industrial photovoltaic systems has resulted in a debate on the potential environmental risks associated with such a widespread diffusion of this renewable energy technology [50]. As a matter of fact, the debate is backed by assumptions which are often unfounded. Nevertheless, these might risk slowing down the energy transition process, as has been recently discussed, for example, by [PERSON] and coauthors with respect to the problem of special waste that would be produced by the diffusion of this type of technology [51]. On the other hand, photovoltaic systems built on lands used for agricultural crops or, as discussed here, floating plants built on water basins show their own peculiarities, since in both such cases, the problem is to evaluate the potential negative effects that such installations may have on complex environmental and ecological systems. Given that both agrivoltaics and floating photovoltaics are rather new application sectors, it should not seem strange that the points of view on the topic can still be very different. As an example, [PERSON] and coauthors list in their work a series of negative elements related to the diffusion of solar photovoltaics [52] and in the case of floating photovoltaics, environmental modeling works have, for example, suggested negative effects on phytoplankton concentrations, which increase with the area of the reservoir covered by the photovoltaic system [53]. However, in most of the cases reported and discussed in the literature, floating photovoltaic systems do not currently have any type of monitoring system installed that could allow us to actually evaluate possible negative (or positive) effects on the environment, which makes the discussion rather undefined [54; 55]. The problem is, therefore, determining which are the water parameters that need to be kept under control at the installation site of a floating photovoltaic system, and also, how to carry out the sampling itself. As a matter of fact, all the guidelines that have been published so far up to now on this topic [56; 57] suggest carrying out a series of measurements ex ante, during plant operation and after its decommission, evaluating the water parameters reported in Table 1.
## 1 Introduction
### Motivation and Objective  
Satellite imagery of marine, coastal, and inland water reflectance is now routinely used for measuring parameters such as the concentrations of chlorophyll a, a proxy for phytoplankton biomass, and suspended particulate matter, important for sediment transport applications. The satellite data are used for regulatory monitoring of the aquatic environment, e.g., via the European Union Water Framework and Marine Strategy Framework Directives [1; 2], and for providing a scientific basis for coastal zone decision-making, e.g., via the assessment of impacts of human activities and constructions [3]. However, end-users of the data require reliable information on data quality, and validation of the satellite data at the level of water reflectance is particularly crucial. This is because of the large errors that may occur during the data calibration and processing, particularly during the atmospheric correction steps [4].  
This validation is best achieved by a \"match-up\" of in situ measurements of water (surface) reflectance made at the same time as the satellite measurement [5], and experience over the last 10 years showed that only autonomous in situ systems can provide sufficient data for this purpose. In particular, AERONET-OC [6], a federated network of multispectral robotically pointed radiometers on offshore platforms all over the world, proved to be the main source of validation data [7] for spaceborne optical missions such as ENVISAT/MERIS, MODIS/AQUA, VIIRS, Sentinel-3/OLCL, Sentinel-2/MSI, Landsat-8/OLI, etc. However, the radiometer adopted within AERONET-OC is only multispectral and cannot adequately cover the spectral bands of all recent and future optical spaceborne missions without spectral interpolation/extrapolation/modelling [8] and associated uncertainties. The WATERHYPERNET network is, therefore, being developed, based closely on the concept of the successful AERONET-OC network [6] but with the essential advantage of a hyperspectral radiometer, thus enabling the validation of all visible and near-infrared bands of all present and future satellite missions providing water reflectance data.  
The objective of the present paper is to describe the measurement system, called PANTHYR (pan-and-tilt hyperspectral radiometer) that was developed for use within the WATERHYPERNET network. This measurement system consists of two commercial off-the-shelf (COTS) hyperspectral radiometers, mounted on a COTS pan-and-tilt (PT) pointing system, controlled by a single-board-computer and associated custom-designed electronics which provide power, pointing instructions, and data archiving and transmission.
## 3 Materials and Methods
### The Classification Model  
A Multi-Layer Perceptron (MLP) was employed as the classification model. The MLP was chosen for its ability to model complex and non-linear relationships. An MLP is ideal for highly precise tasks such as breast cancer classification. The MLP consisted of multiple layers. Each layer performed the following operations:  
\[z^{(l)}=W^{(l)}a^{(l-1)}+b^{(l)} \tag{1}\]  
\[a^{(l)}=f(z^{(l)}) \tag{2}\]  
where \(W^{(l)}\) represents the weights, \(b^{(l)}\) the biases and \(f\) the activation function. We applied ReLU [31], Logistic and Tanh activation functions for the hidden layers. The output layer used the sigmoid function, which produced a probability score for binary classification:  
\[\hat{y}=\sigma(z)=\frac{1}{1+e^{-z}} \tag{3}\]  
The model's parameters were optimized using the cross-entropy loss function:  
\[J(\theta)=-\frac{1}{m}\sum_{i=1}^{m}\left[y^{(i)}\log\hat{y}^{(i)}+(1-y^{(i)}) \log(1-\hat{y}^{(i)})\right] \tag{4}\]  
where \(y^{(i)}\) is the true label, \(\hat{y}^{(i)}\) is the predicted probability and \(m\) is the number of samples.
## 2 Study Area and Method
### Model Parameters and Input Data
#### 2.3.8 Reference NPP  
To achieve the spatiotemporal simulation of the forest carbon cycle, the InTEC model needs to correct the initial NPP by reference NPP. Therefore, this study used the boreal ecosystem productivity simulator (BEPS) model to simulate the NPP of Zhejiang Province in 2004 and took this as the reference NPP. The NPP simulation process based on the BEPS model refers to [PERSON] et al. [53].