esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of the SOA Formation in the Reaction of Furfural with Atmospheric Oxidants
## 2 Experimental
### Methods  
#### 2.2.1 Gas-Phase Product Study  
The experimental system used in this study has been described in previous works [4; 16] and thus it will be only briefly described here. Product experiments were carried out at room temperature (297 \(\pm\) 2 K) and atmospheric pressure (700 \(\pm\) 1 Torr) with synthetic air in two different reaction chambers: (1) a 50 L Pyrex(r) glass cell with in situ FTIR equipment (Nicolet 6700, Thermo Fisher Scientific, Waltham, MA, USA) equipped with six actinic fluorescent lamps (\(\lambda_{\text{max}}\) = 360 nm), (2) Teflon(r) (Wilmington, DE, USA) reactor of 150 or 500 L, the latter was employed for the study with the nitrate radical. These Teflon(r) reactors were surrounded by UV-Vis lamps and equipped with ports for sampling onto a 30/50 \(\upmu\)m solid-phase microextraction (SPME) fiber of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS). The fiber was exposed in the reactor during 60 s and then thermally desorbed during 15 min at 250 \({}^{\circ}\)C in the injection port of the gas chromatograph. The compounds were separated by a capillary column (30 m \(\times\) 0.3 mm \(\times\) 1 \(\upmu\)m, Tracsil TRB-1701 Teknokroma, Barcelona, Spain). Detection was performed by mass spectrometry in electron ionization mode (EI) with a time of flight analyzer, GC-TOFMS (AccuTOF GCv Jeol, Tokyo, Japan). For the study of each reaction at least 2 sets of experiments were performed following the reaction at about 45 min.  
For GC-TOFMS analysis, products were identified according to their retention time and/or by comparison of their mass spectra with the database. In the quantitative product analysis previous calibrations were carried out by introducing a known concentration of a commercial sample into the reactor (S1: Annex I in Supplementary Materials). For FTIR analysis, a spectral subtraction procedure was used to obtain the concentrations of furfural and products as a function of the reaction time. Identification was carried out by comparing reaction samples with calibrated reference IR spectra. Quantification of species was carried out by calibrated reference procedure (S2: Annex II in Supplementary Materials). For each experiment, over 30-45 spectra were recorded with a spectral resolution of 1 cm\({}^{-1}\).  
The initial concentrations of reactants in this set of experiments were as follows: (in parts per million, ppm): furfural, 10-40 for FTIR and 2-8 for GC-TOFMS; Cl\({}_{2}\), 20 for FTIR and 2-7 for GC-TOFMS; CH\({}_{3}\)ONO and NO 7-10 for FTIR and 3-5 for GC-TOFMS; N\({}_{2}\)O\({}_{5}\), 10 for FTIR and 20 for GC-TOFMS experiments.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 3 The Experience
### Hardware and Software  
The stations deployed on the bridge are called GeoGuard Monitoring Units (GMU). They have been specifically designed and realized by GReD and algoWatt for monitoring applications in different contexts.  
They are schematically composed of:  
* Tallysman GNSS antenna and cables;
* Solar panel (30 W, 50 W, or 80 W) to provide electric power when it is not available on site;
* Box containing a battery (providing autonomy for 5-7 days), a GNSS receiver, memory and a transmission apparatus (based on u-blox SARA-U201 module);
* Low-cost, single-frequency, multi-constellation GNSS receiver (u-blox NEO-M8T) or low-cost, dual-frequency, multi-constellation GNSS receiver (u-blox ZED-P9T).  
Data are accumulated and transmission is usually performed every 5 or 15 min; in case one transmission fails, it is repeated at the next slot. Data are received by the GeoGuard analysis center, are identified and validated, and are then passed to the computing engine, which is in charge of estimating the positions of the points with a chosen latency, in this case, daily or hourly. All the data discussed in the paper have been processed by GReD using its own proprietary GNSS software, Breva(r).  
Results are then collected and provided to the customer in digital and graphical form in a dedicated area of the GeoGuard system. If anomalous motion is detected, a warning and, in the case of persistence, an alarm are issued in the data center and are shared by email, instant messaging applications and/or SMS. The types of solutions presently provided as a continuous service are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Interval** & **Modality** & **Latency** \\ \hline
1 h (24 solutions per day) & Relative & 15 min after the end of the hour, for early warning \\
2 h (12 solutions per day) & Relative & 15 min after the end of 2 h, to confirm the warning \\
24 h (daily solution) & Relative/Absolute & Delivered after midnight \\ \hline \hline \end{tabular}
\end{table}
Table 1: Solutions continuously provided for the monitored bridge.  
Figure 4: Monitoring network deployed on the bridge, together with an example of installation.
## Appendix C Question/Answering Optimization and Prompt Engineering
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Language** & **Italian** & **English** \\ \hline Question & \begin{tabular}{l} Cosa tratta l’Allegato LI del decreto \\ legislativo 9 Aprile 2008 n.81? \\ \end{tabular} & \begin{tabular}{l} What does Annex LI of Legislative Decree \\ No. 81 of 9 April 2008 address? \\ \end{tabular} \\ \hline ChatGPT-4+ctx answer & \
\begin{tabular}{l} L’Allegato LI del Decreto Legislativo 9 aprile \\ 2008 n.81 ratta le “Modalita di svolgimento \\ della sorveglianza sanitaria”. La sorveglianza \\ sanitaria e un componente fondamentale \\ della nutela della salute e sicurezza dei \\ lavoratori. Essa comprende visite mediche \\ preventive ed periodiche, nonche la verifica \\ dell’idoneita alla specifica mansione. Questo \\ allegato contiene quindi indicazioni precise \\ su come questa sorveglianza sanitaria \\ dovrebbe essere svolta, descrivendo ad \\ esempio, i criteri per la programmazione \\ delle visite mediche, le modalita di \\ conservazione e gestione dei dati sanitari, etc. \\ \end{tabular} &
\begin{tabular}{l} Annex LI of Legislative Decree No. 81 of 9 \\ April 2008 addresses the “Methods of \\ carrying out health surveillance”. Health \\ surveillance is a fundamental component \\ of the protection of workers’ health and \\ safety. It includes preventive and periodic \\ medical examinations, as well as the \\ assessment of fitness for specific tasks. \\ This annex provides detailed guidelines \\ on how health surveillance should be \\ carried out, describing, for example, the criteria for scheduling medical visits, the \\ methods for storing and managing health \\ data, etc. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of bad replies from LLMs.
## 2 Materials and Methods
### Experiments with HD PMA
#### 2.5.2 Studying the Effect of Magnetic Field Parameters on the Efficiency of Distant Interaction  
An HD PMA (donor sample) was incubated together with water (acceptor sample) at room temperature (23-24 \({}^{\circ}\)C) for 0 and 60 min. As in Section 2.4, the donor or acceptor sample (18 mL) was poured into a separate identical cubic optical glass cuvette. Then, the cuvettes were placed adjacent to each other in a controlled magnetic field generation device (depicted in Figure 1). Several such incubations were performed at different parameters of magnetic field:  
1. CMF (combination of SMF of 60 uT and AMF of 100 nT, which is formed according to the sinusoidal regime at 48.5 Hz)
2. CMF (combination of SMF of 60 uT and AMF of 100 nT, which is formed according to the sinusoidal regime at 12.6 Hz)
3. SMF of 60 uT
4. \"Magnetic vacuum\" (\(\sim\)<20 nT)  
Additionally, as a control, incubation of some samples was carried out in GMF, outside a controlled magnetic field generation device. In this case, the cuvettes were placed on a laboratory bench either adjacent to each other or at least 2 m away from each other as well as from any ferromagnetic materials. The magnetic field parameters during this experiment were as follows: the AMF component with an amplitude and frequency of approximately 50 nT and 50 Hz, respectively; the SMF component was slightly less than 45 uT.  
All experiments were carried out in 6 independent repetitions. After a 60-min incubation, an aliquot was taken from the cuvette with the acceptor sample, which was then used to assess the activity of solutions by cell chemiluminescencence assay.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 4 Global Applications of Flood Sensors
During the floods, in situ sensors, social sensors, and satellite remote sensing sensors are not utilized in the most effective way, especially in urban areas in bad weather [143], and unscientific land-use planning and poor-quality flood defenses directly exacerbate the impacts of the floods [144]. A study of Thailand's flood resilience building, published in 2018, noted that Thailand more than likely continued to opt for constructive measures to mitigate the floods after the 2011 floods [145], but in reality, the Chao Phraya River floods caused such high casualties and economic losses, imperfect national institutions and flood policies also need to bear much of the blame. On the contrary, the performance of developed countries in the face of large-scale floods has demonstrated significant advantages in flood prevention infrastructure, early warning systems, social media messaging, and the use of ground-based and telemetry data, etc. The 2010-2011 floods in Queensland, Australia, were one of the worst natural disasters in its history, affecting the daily lives of more than 170,000 people. Thanks to the timely and accurate ALERT flood warning system, evacuation measures, and Facebook disaster information dissemination [146], the actual number of deaths (35) was kept at a low level, while 25 flood-scoping products were developed by collecting data from various parts of the affected area, as well as more than 600 satellite images and derived data obtained from the US, UK, and Germany, which compensated for the lack of data from ground-based sensors and helped the smooth management of floods [147]. Learning from the lessons of its \"Flood of the Century\" in 2002, Germany invested heavily in the development of constructive and non-constructive measures that played a key role in the 2013 flood event. The International Charter for Space and Major Disasters was activated in the immediate aftermath of the floods, and with the help of meteorological radar and Earth observation satellites, the Deutscher Wetterdienst (DWD) issued timely and accurate warnings that were continuously updated to the public and relevant organizations in virtually all regions affected by the floods. Social media and drone technology [148] also played a significant role in the flood management process. Statistics indicate that, ultimately, only 5% and 3% of companies were affected by not receiving any flood warnings [149].
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 2 Literature Review
### Blockchain Types  
Blockchain technology consists of various types: public, private, and consortium blockchains. Public blockchains, like Bitcoin and Ethereum, are open to everyone, thus enabling participation, transaction validation, and ledger upkeep. They emphasize transparency and security, but their consensus mechanisms can lead to scalability and transaction speed issues [76]. Private blockchains are restricted networks that only allow certain entities or individuals to participate [77]. Examples include Hyperledger Fabric and Quorum [78; 79]. These are often used by businesses to ensure data privacy and control. They offer efficiency, scalability, and faster transactions compared to public blockchains, as they do not face the same computational limitations [79].  
Consortium blockchains are networks where a group of organizations jointly governs the system. Notable examples are R3 Corda [80] and Hyperledger Besu [81]. In these setups, participating entities hold partial control, and consensus is achieved through predefined mechanisms. These blockchains offer a middle ground between fully open public blockchains and tightly controlled private ones. They are ideal for industries needing collaborative information sharing while upholding trust among participants [82]. The choice of blockchain type depends on specific requirements, such as desired levels of transparency, privacy, scalability, governance, and the nature of participating entities.
# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
###### Abstract  
Near-infrared (874-1734 nm) hyperspectral imaging technology combined with chemometrics was used to identify parental and hybrid okra seeds. A total of 1740 okra seeds of three different varieties, which contained the male parent xiaolusi, the female parent xianzhi, and the hybrid seed penzai, were collected, and all of the samples were randomly divided into the calibration set and the prediction set in a ratio of 2:1. Principal component analysis (PCA) was applied to explore the separability of different seeds based on the spectral characteristics of okra seeds. Fourteen and 86 characteristic wavelengths were extracted by using the successive projection algorithm (SPA) and competitive adaptive reweighted sampling (CARS), respectively. Another 14 characteristic wavelengths were extracted by using CARS combined with SPA. Partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were developed based on the characteristic wavelength and full-band spectroscopy. The experimental results showed that the SVM discriminant model worked well and that the correct recognition rate was over 93.62% based on full-band spectroscopy. As for the discriminative model that was based on characteristic wavelength, the SVM model based on the CARS algorithm was better than the other two models. Combining the CARS+SVM calibration model and image processing technology, a pseudo-color map of sample prediction was generated, which could intuitively identify the species of okra seeds. The whole process provided a new idea for agricultural breeding in the rapid screening and identification of hybrid okra seeds.  
seed classification; near-infrared spectroscopy; hybrid okra seeds; chemometrics Article
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 2 Materials and Methods
### Stronger Backbone Network  
Through experimental comparison, it has been observed that DCNv2 offers a wider range of feature-level sampling compared to its predecessors, such as DCN and Conv, when extracting feature maps. Moreover, DCNv2 incorporates a modulation mechanism  
Figure 1: Overall structure visualization of DCN-YOLO model.  
that not only learns offsets for the samples but also allows them to be modulated by the learned feature magnitude. This enables DCNv2 to have control over its sample space and relative impact. Given these advantages, for this paper, we chose DCNv2 to upgrade and improve the model. Two main basic modules are derived from DCNv2, and their internal structures are illustrated in detail in Figure 2. The D-EALN module follows the concept of an efficient aggregation network with a multi-branch structure. Each branch maintains consistent input and output channels, and the features are concatenated together using the concat operation while ensuring that the longest gradient path remains unchanged. Additionally, a \(1\times 1\) convolutional layer is applied to compress the channels, enhancing the network's robustness. In the D-MP module, the left branch employs Maxpool for spatial downsampling and incorporates a \(1\times 1\) convolutional layer for channel compression. On the other hand, the right branch begins with a \(1\times 1\) convolutional layer for channel compression, followed by the DCN block for downsampling. Finally, the outputs of the two branches are concatenated using the concat operation.
# A New Passive Islanding Detection Solution Based on Accumulated Phase Angle Drift
## 5 Simulation Test  
The simulation is based on Matlab Simscape multidomain physical systems. The simulation model shown in Figure 6 consists in a 30 MVA gas turbine synchronous machine (SM), local series RL load, connection cables, CBs, 110 kV/10 kV transformer, and the equivalent main grid source of 50 Hz system. The SM system included an exciter with IEEE type AC1A voltage regulator and the gas turbine with governor. The local load and the main grid could be switched off with a Step On signal. The detailed parameters of the simulation model are shown in Table 2.
## 2 Optical Coherent Detection Technique
### Heart Rate Detection with Self Mixing Optical Heterodyne Technique  
The self mixing interferometer consists of a Mach-Zehnder interferometer, as shown in Figure 6. Unlike homodyne detection, the frequency of the fundamental optical signal is not same as the local oscillator signal (\(f_{0}\
eq f_{LO}\)) in heterodyne detection. Therefore, the modulation frequency \(f_{m}\) is detected at the receiver due to optical beating. The self-mixing optical heterodyne detection technique makes use of AOM in the test arm of the interferometer.  
Figure 4: Structure of AOM.  
Figure 5: Optical heterodyne detection.  
Figure 7 shows the detection of the \(HR\) signal with the self-mixing heterodyne detection scheme. The test arm of the Mach-Zehnder interferometer consists of an AOM. The optical signal first becomes modulated by the RF frequency \(f_{m}\) given to the AOM. Further, this signal is subjected to another modulation by reflection from the chest wall of the test subject. The chest wall vibrations are due to the \(HR\) frequency \(f_{H}\). The optical signal after reflection from the chest wall has a frequency of \(f_{s}\pm f_{m}\pm f_{H}\). The fundamental optical signal passes through the reference arm of interferometer. The fundamental optical signal and bio-modulated optical signal orthogonally combine at the 3 dB coupler. In this case, in both arms of the interferometer, optical signals travel the same path distance. The total path difference in the two orthogonal arms becomes zero. The zero-path difference in both orthogonal arms leads to a zero phase difference between the two signals. Therefore, the phase of the modulated signal with frequency \(f_{s}\pm f_{m}\pm f_{H}\) is matched to local oscillator signal with frequency \(f_{s}\) and the phase of both the beams are locked [31].  
Figure 6: Self-mixing heterodyne detection with M–Z interferometer.  
Figure 7: Self-mixing heterodyne detection with human test subject.  
The modulated bio signal in the test arm of the interferometer is given by,  
\[E_{s,t}=A_{s,t}Exp\ [-j[\left(\omega_{s}\pm\omega_{m}\pm\omega_{H}\right)t+\Phi_{s,t }]] \tag{18}\]  
The fundamental optical signal in the reference arm of the interferometer is given by  
\[E_{s,r}=A_{s,r}Exp\ [-j[\omega_{s}t+\Phi_{s,r}]] \tag{19}\]  
The output power of the photodetector is given by,  
\[P(t)=P_{s,t}+P_{s,r}+2\sqrt{P_{s,t}P_{s,r}}\cos(2\pi f_{IF}t+\Phi_{s,t}-\Phi_{s,r}) \tag{20}\]  
The intermediate frequency signal is given by \(f_{IF}\), which is the difference between the frequency of the test and the reference signals.  
\[f_{IF}=(f_{s}\pm f_{m}\pm f_{H})-f_{s} \tag{21}\]  
\(f_{IF}\) provides the heart frequency with the modulation frequency of AOM at the photo detector,  
\[f_{IF}\ =f_{m}\pm f_{H} \tag{22}\]  
The overall phase shift observed at the detector is the phase difference of the modulating signal in the test arm and the fundamental signal in the reference arm [32]  
\[\Phi=\Phi_{s,t}-\Phi_{s,r} \tag{23}\]
# A Direct Search Algorithm for Global Optimization
## 2 Direct Search Methods and Global Optimization
### Global Optimization Approaches  
Local optimization methods are the best option for convex, or at least unimodal, optimization problems. However, most real-world problems are not convex, and the solution provided by a local method is not guaranteed to be global. Unfortunately, even if derivatives are available, it is not possible in general to prove that a local optimum is global. A rigorous global optimization algorithm for a multimodal cost function requires in-depth exploration of the search space, and this is only possible for functions with a small number of variables, because the computational time for space exploration increases exponentially with the dimension of the search space.  
In order to circumvent the curse of dimensionality, many global optimization algorithms use random search [28] and heuristics [29; 30]. Some successful classes of global optimization algorithms are simulated annealing algorithms [31], evolutionary algorithms [32], estimation of distribution algorithms [33], particle swarm optimization algorithms [34; 35], differential evolution algorithms [36; 37], tabu search algorithms [38; 39] or ant colony optimization algorithms [40; 41].  
In recent years, there has been a great interest in derivative-free methods for global optimization; see the monographs [42; 43; 44; 45] and the references therein. In particular, there are two classes of algorithms that are of special interest. The first class is Lipschitz optimization methods. Lipschitz methods substitute the cost function by an auxiliary function that underestimates it. The auxiliary function is linear piecewise and is obtained by partitioning the search space and repeatedly using the Lipschitz property [47; 48; 49; 50]. An interesting Lipschitz method is DIRECT (DIviding RECTangles), that was introduced in [51]. In this method, the cost function is evaluated at several sample points by using all possible weights on local _versus_ global search expressed by the Lipschitz constant. The DIRECT algorithm has gained popularity due to its simplicity. However, it has two weaknesses. First, it is slow in reaching the solution with high accuracy, and second, it spends an excessive number of evaluations exploring local minima. Some variants have been developed to improve these weaknesses [52; 53; 54; 55].  
Another class of global optimization methods uses a filled function to avoid local minima. The basic idea is to design an auxiliary function, called the filled function, such that when the algorithm reaches a local minimum, the minimization of the filled function provides a point that moves away from the basin of the local minimum. The filled function method was introduced by [PERSON] [56; 57]. It has had increasing interest in filled methods in the last few years [58; 59; 60].