esa-sceva/satcom-chunk-collection huggingface dataset:
# Post Quantum Cryptographic Keys Generated with Physical Unclonable Functions
## 4 PUF-Based Key Distribution for PQC
### PKI Architecture with PUF-Based Key Distribution and LWE  
The PUF-based key pair generation scheme with LWE cryptography, as presented in the previous section, can be integrated in a PKI securing a network of i clients. Figure 7 shows two client devices communicating directly, either by exchanging secret keys through KEM or DSA. The client devices independently generate the seed \(\mathbf{a_{(i)}}\), while the PUFs and their images are used for the independent generation of the vectors \(\mathbf{s_{(i)}}\) and \(\mathbf{s_{(i)}}\). The role of the CA is to check the validity of the vectors \(\mathbf{t_{(i)}}\), and to transmit both the seeds \(\mathbf{a_{(i)}}\) the vectors \(\mathbf{t_{(i)}}\) to the RA, which maintain a ledger with valid public keys. Such an architecture is secured assuming the following conditions:  
1. The enrollment process in which the PUFs are characterized to generate their image is accurate and not compromised by the opponent.
2. The database stored in the CA that contains the image of the PUFs for the i client devices is protected from external and internal attacks.
3. The PUFs embedded in each client device are reliable, unclonable, and tamper resistant.
4. The key generation process, KEM, and DSA are protected from side channel analysis.  
As we experimentally verified that the latencies of the key generation process from the PUFs are low enough, such a protocol can be used to change the key pairs after each encryption cycle. Therefore, the potential loss of the secret keys during an encryption/decryption cycle has minimum impact as different keys will be used during the subsequent cycles.  
Figure 7: PUF-based public key infrastructure (PKI) with LWE cryptography. The use of PUFs in the PKI network does not impact the user experience during peer-to-peer secure communication. The ability to generate one time use key pairs, and authenticate each client device at each transaction, enhances the root of trust. Such PUF based architectures are only valid if the latencies are kept below a few seconds.
## 3 Results and Discussion
### Hydroonic-Plant-Growth Promotion Effect of L-Phe Treated With AAGD  
To investigate the effects of 80 mM L-Phe treated with AAGD on plant growth, a radish sprout was placed in L-Phe treated with AAGD under the standard condition. The radish sprout was planted in a pristine 80 mM with L-Phe as a control. Figure 6 demonstrates the length of the radish sprout after hydroponic growth in pristine L-Phe and L-Phe treated with AAGD diluted to various concentrations.  
The length of the radish sprout did not change in the case of L-Phe treated with AAGD compared with the pristine L-Phe, indicating that plasma-treated L-Phe under the present condition was not harmful to the plant. Moreover, plasma-treated L-Phe diluted two- and four-fold promotes plant growth resulting in the increase in length of about 30% and 40%, respectively, compared to the control. The observed promotion effect was weaker than that reported elsewhere (up to 99% increase in plant length), where L-Phe was treated with oxygen radicals employing NEAPRS [18]. The difference in the promotion effect could be explained by the difference in concentrations of reactive species formed in L-Phe after the treatment using NEAPRS and AAGD [14; 18; 19]. Treatment using AAGD mostly forms NOx radicals in the liquid phase, whereas the treatment with NEAPRS using an Ar and O2 mixture,  
Figure 5: (**a**) DUV absorption spectra and (**b**) intensity of absorption peak at 200 nm of DI water treated with AAGD at various humidity levels.  
reported elsewhere, resulted in the presence of mostly oxygen reactive species in the liquid phase. Additionally, L-Phe presented in the pristine solution was decomposed through the interaction with plasma and participated in chemical reactions, leading to the formation of different chemicals after treatment with AAGD or NEAPRS. A possible significant difference in chemical composition between L-Phe treated with AAGD and NEAPRS could be responsible for the decrease of the promotion effect in the case of treatment with AAGD. Treatment with NEAPRS is limited to laboratory use due to high equipment and processing costs, and problems with scaling. On the other hand, treatment of flowing liquid using AAGD does not require process gases (such as He or Ar) and could be scaled up for the production of larger amounts of liquids, which is promising for practical use due to the low cost of the treatment despite a reduced plant-growth promotion effect.
# LTDA-MAC v2.0: Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks +
## 3 Simulation Results  
### Simulation Setup  
This section evaluates the performance of the LTDA-MAC protocol equipped with the proposed schedule optimization algorithm applied to the pipeline monitoring scenarios considered in [8], similar to that depicted in Figure 1. The maximum sea depth is 500 m. The pipeline at 480 m depth is connected to the platform at the sea surface through a riser, whose shape is modeled as a quarter-circle. Two different pipeline lengths are simulated: 2 km and 20 km. Initially, 11 nodes (1 sink + 10 sensor nodes) are spread across the length of the pipeline at equidistant points. Afterwards, 50 sets of node positions are generated to include random horizontal perturbations (0-20 m and 0-200 m for the two scenarios, respectively) in random directions from the initial equidistant points. This approach yields a statistical spread of channel conditions with variations in received signal and interference power, thus providing statistically more valid results. The wideband multipath channel with 24 kHz centre frequency and 7.2 kHz bandwidth was modeled using the BELLHOP beam tracing method described in [23]. Without loss of generality, these simulations use a threshold for interfering link detection of 0 dB Signal-to-Noise Ratio (SNR), i.e., all links with SNR \(\geq\) 0 dB were marked with 1 in the interference matrix \(\mathbf{I}\), while all of the wanted links in the linear topology also have SNR \(>\) 0 dB. The SNR is calculated using the analytical ambient noise model with 10 m/s wind speed and 0.5 shipping activity factor [23]. The other parameters for the 2 km and 20 km pipeline scenarios, respectively, are:  
1. 140 dB re \(\mu\)Pa\({}^{2}\)m\({}^{2}\) source level, 200 ms data packets, 50 ms REQ packets, 25 ms guard interval;
2. 170 dB re \(\mu\)Pa\({}^{2}\)m\({}^{2}\) source level, 500 ms data packets, 100 ms REQ packets, 100 ms guard interval.
# Heat Transfer Performance and Operation Scheme of the Deeply Buried Pipe Energy Pile Group
## 1 Introduction
[PERSON] [29] and others investigated the long-term hydrothermal response and the thermal storage performance related to unsaturated soil energy pile groups during high-temperature injection periods. Upon comparing with conventional buried tube heat exchangers, it was observed that the heat extraction efficiency of the pile group increased by approximately 1.2 times. They also established a heat transfer model for pile groups, and simulation results indicated that the enhanced energy pile groups had higher heat transfer capabilities than ordinary groups, while the thermal impact area around the piles increased. Utilizing the heat transfer characteristics of GSHP as a foundation, [PERSON] [30] and others, constructed a comprehensive physical model for a 3U pile foundation heat exchanger group, subsequently conducting simulations to analyze the heat transfer processes involved. The findings indicated that the thermal conductivity of the pile foundation heat exchangers surpassed that of the surrounding soil, resulting in enhanced heat transfer efficiency. Additionally, it was noted that the heat flux per meter of the pile foundation heat exchanger displayed a gradual decrease throughout the study period. Contemporary investigations into subterranean energy infrastructures predominantly center on GSHP and on energy inside-buried pipe energy piles [31; 32; 33; 34; 35; 36], with less emphasis on the deeply buried pipe energy pile (DBP-EP) [14; 15; 16]. The structural construction of DBP-EP differs from that of the inside-buried pipe energy piles. Through the installation of a borehole heat exchanger (BHE) within the pile foundation, penetrating to depths of hundreds of meters, the heat exchange capacity of the pile structure is amplified, thereby augmenting the overall heat exchange capability [15; 37]. While there is considerable analysis on the heat transfer between piles and soil for groups of DBP-EP, there is yet no analysis on the intermittent operation of systems predominantly using DBP-EP. This paper focused on DBP-EP as the subject of study. By conducting on-site in situ experiments, we evaluated the heat transfer characteristics during operation, through nu merical models for the individual and the DBP-EP group, comparing different operational schemes. Our study delved into the impact of intermittent operational modes on the heat transfer dynamics of the DBP-EP group. We propose meticulously devised operational strategies aimed at optimizing efficiency while minimizing energy consumption. Furthermore, our findings offer valuable theoretical insights into the extraction and utilization of shallow geothermal resources from energy piles.
## 2 Related Work
Obtained from the simulation results and a QoS investigation, the suggestion dramatically improved TCP performance in MANETs. However, it is unable to distinguish between packet losses caused by MANET-specific characteristics and losses caused by congestion. For effective void management in the MANET, [PERSON] and [PERSON] [28] have shown geographic routing with a Hybrid Firefly Algorithm (FF) and Galactic Swarm Optimization (GSO). The GSO was utilized in the suggested hybrid design because it has a quick convergence rate. Numerous exploration and exploitation cycle patterns have been used in this approach. Additionally, FF offers exploitation fine-tuning because it is utilized for local search. The data connection layer's MAC protocol used an optimized channel access procedure. By transmitting a data packet, this proposed HFFGSO supports the network layer routers in their route search. Although this hybrid routing technique proved effective, the total architecture's calculation time was found to be lengthy. The Cross-Layer and Energy-Aware AODV (CLEA-AODV) routing has been demonstrated by [PERSON] et al. [29] to enhance Flying Network performance. Due to their direct influence on the routing process, the suggested methodology concentrates on the least energy, summation energy, and hop count aspects as cost indicators to discover the best route in the network. The residual energy and threshold values were used to compute the routing path by expending the least amount of energy and cumulative output. Conversely, this architecture varies quickly due to its greater mobility. From the overall literature, it is clear that the continuous movement and unpredictability of the nodes causes a dynamic change in the topology of the network. Consequently, routing between the nodes is regarded as one of the most significant challenges in MANETs. Attaining optimal routes between the nodes is the primary task of routing protocols. Recently, it has been stated that issues cannot be resolved using the conventional layered design. There has been a drop in the procedures that depend on cross-layer communication across various levels in an attempt to enhance the effectiveness of MANETs. Therefore, a novel cross-layer design called ECTSA is proposed in this article.
## 6 Error Analysis and Model Reliability
### Unreliable Predictions in Poor-Performing Data Cohorts  
It is generally understood that error is often not evenly distributed among the entire dataset, and a subset (or equivalently, a \"cohort\") of data could have worse accuracy than others. However, the underperforming data cohorts are sometimes difficult to find, and various approaches have been proposed in the literature to discover them as summarized by [27]. In this study, we utilize the method adopted in the Python package InterpretML [35], which involves training a single decision tree on the dataset, with scaled feature values of the data samples as features, and prediction error as labels. Figure 12 demonstrates an example of such a decision tree, trained with data samples from the same model analysed in Section 6.1 and visualized by InterpretML (installed from raiwidgets 0.36.0). The value in each node represents the MAE of the data that lies within the node, and the filling level of each node represents the proportion of errors associated with that node, coloured by magnitude of error. The figure shows that while the MAE of all data samples is 0.67, the cohort of data with feature values \(-0.31<U_{10}\leq-0.16\) and \(\mathit{Hour}\leq 7.50\) is subject to a higher MAE of 0.90. This cohort comprises 13.5% of all data, but contributes to 18.22% of all error. The example shows that a single decision tree can effectively cluster the data and divide it into high- and poor-performing cohorts.  
Assuming no significant model and data drift in the validation set, the data and predictions in the validation set should have similar statistics as in the training set. Therefore, we include a second unreliable prediction identification in MSB, by training a single decision tree on the prediction error in the training set, and identify a high-error data cohort and its corresponding feature values. When data samples in validation are identified as lying within the high error cohort by their feature values, the predictions from these samples are considered unreliable. A high-error data cohort is defined as having RMSE twice as high  
Figure 12: High-error data cohort identified by a single decision tree on training set, visualized by InterpretML ErrorAnalysisBoard. Training window: 18 January–22 March 2023; trained variable: dew point temperature. Site: Archerfield Airport.
## 4 Discussion
### Stock Assessment with an Acoustic Camera  
Besides identification and measurement errors, an acoustic camera could have other limitations resulting in an under or overestimation of fish abundance. Immobile individuals on the bottom are not counted because they can be confused with rocks. Such an error would cause underestimation of actual abundance. This bias could be minimized bycombining stationary and mobile surveys because stationary surveys allow time to observe fish movement. However, stationary surveys produce another bias because fish can leave the acoustic camera field-of-view then return to the field, leading to multiple counts on the same individual and resulting in an overestimation of abundance. Over-counting was minimized by measuring individuals, but errors could result from counting fish of the same size.  
Quantification of counting bias was not possible during the present study because visual counting was not possible in French Guiana relative to studies in other areas [12; 14]. However, the repeatability of the method showed that the results are reliable, and its reproducibility could be used to study inter or intra annual variation of the total abundance of a demersal species with a P900-130 BlueView acoustic camera.  
Even if goliath groupers show strong home-site fidelity, remaining on the same site year-round, it would not have been possible to estimate the number of individuals using mark/recapture data because study sites were fished, and many fishers did not participate in this study. The acoustic camera, even with its limitations, is a powerful tool to quantify the abundance of marine species [48]. In this study, it was clear that the efficiency of the acoustic camera was much greater than that of fishing.  
By combining mobile and stationary surveys, it was possible to improve the detection of individuals (16% more individuals) and, therefore to minimize the counting bias on immobile individuals [29].  
Abundance estimated with the acoustic camera followed the same trend as counting derived from fishing (higher abundance in the Grand Connetable MPA than Remire islands; higher abundance in 2011 than in 2012). Since there was no other possible methodology to study abundance in turbid and shallow waters and that estimation via fishing method \"failed\", acoustic cameras appeared as the most appropriate tool to perform spatial and temporal surveys of demersal fish populations in the turbid environment of French Guiana. Abundances found for goliath groupers in September, between 163 and 217 individuals around rocky areas in front of Cayenne, need to be confirmed at different times of the year. Multiple seasonal surveys with the acoustic camera would accurately estimate intra-annual variation of abundance around the islands. Therefore, seasonal acoustic surveys would improve data for stock assessments. Such an approach may also provide information on the most efficient time (most representative of the standing population) to take the survey if deployment of the acoustic camera is limited.
# Research on Map-SLAM Fusion Localization Algorithm for Unmanned Vehicle
## 4 Experimental Verification
### Experiments Results in Closed Loop Path  
As shown in Figure 13, The data set was collected at the south gate of the University of Electronic Science and Technology for the location experiment. The data were collected using the same experimental platform as in the previous section. The platform was equipped with GNSS terminals, cameras and other equipment to collect the dataset in data collection vehicle mode to obtain image data and GNSS position data during vehicle operation.  
Compared to the original slam algorithm, our algorithm error is greatly improved, and the lane line positioning algorithm is able to constrain the lateral position of the vehicle relative to the lane line with good accuracy, eliminating most of the vehicle lateral position error. As shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Local Average** & **Local Maximum** & **Global Average** \\  & **Positioning Error/m** & **Positioning Error/m** & **Positioning Error/m** \\ \hline Original SLAM algorithm & 1.3 & 2.1 & 5.94 \\ Lane line localization algorithm & 0.25 & 0.44 & None \\ Combined with map SLAM & 0.16 & 0.36 & 1.35 \\ relocation algorithm & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Error comparison.  
Figure 12: Lateral localization error.
# Microsphere Polymers in Molecular Imprinting: Current and Future Perspectives
## 3 Preparation of Molecularly Imprinted Microspheres
### Precipitation Polymerisation
In MPP, the coagulation level increased along with an increase in the speed of agitation, as increased collision frequencies among the particles allowed them to coagulate. However, another hurdle that precipitation polymerisation has to face is the low rate of the reaction as a result of using a low monomer concentration, making the polymerisation process relatively slow. Furthermore, a large amount of solvent might adversely impact the template-monomer complex, shifting the equilibrium to the uncomplexed form and decreasing the amount of imprinted binding sites. To overcome this, some studies [51; 53; 54] have successfully prepared MIMs by modifying the conventional precipitation polymerisation method, so that very high monomer concentrations could be added (\(\geq\)25-40 \(v\)/\(v\)%), by using appropriate solvent or solvent mixtures. The solvent needs to be thermodynamically poor, i.e., it either has a large molar volume or has a significantly different solubility parameter compared to the polymer. It may be a problem to dissolve the monomer, but this can be resolved by using the right co-solvents. Therefore, the polymer will be swollen mainly in the co-solvent, while the incompatible solvent is expected to segregate the growing particles. The use of a good co-solvent results in smoothly dispersed microspheres, while segmented microparticles are obtained when using poorer co-solvents [51]. Recent studies showed that the preparation of MIMs using precipitation polymerisation has successfully led to different templates [55; 56; 58; 59; 60; 61; 90; 91; 92; 93; 94] and sensors [107], and extracted various analytes from natural ingredients [95; 111; 112; 113], biological samples [62; 63; 64; 65; 108], foods [96; 97; 109; 114; 115] and wastewater [98; 99]. As an easy, simple and commonly used strategy for producing MIMs, precipitation polymerisation has great potential for developing advanced strategies in combination with other approaches (molecularly imprinted solid phase extraction (MISPE), biochemical sensors, high performance liquid chromatography (HPLC), etc.) (see Table 1). Figure 1: The mechanism of precipitation polymerisation.
# Sample Reduction Strategies for Protein Secondary Structure Prediction
## 3 Materials and Methods  
### Dataset  
To further validate our method, we applied it to the non-homologous CB513 dataset constructed by Reference [26], which contains 513 protein chains and 84,119 amino acids. This dataset is one of the standard benchmarks in protein secondary structure prediction to assess the accuracy of algorithms [27]. It contains protein sequences and structure label assignments obtained using the DSSP program [28] starting from the structure information in Protein Data Bank (PDB) [29]. The DSSP convention is used to map 8-state representation of secondary structure labels into 3-state by applying the following conversion rule: H, G, I to H; E, B to E; S, T, to L.
# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 3 Results
### Irradiance Decomposition  
The results presented in Table 8 indicate that the Skartveit model surpasses the \(\text{NWP}_{\text{DHI}}\) in accuracy. In the MAE and RMSE metrics, models such as Engerer, DISC and Dirint are inferior to \(\text{NWP}_{\text{DHI}}\), so they are discarded from all analysis. The Skartveit model shows an average MAE of 98 [\(\text{W}/\text{m}^{2}\)] and an \(R^{2}\) of 0.41, notably outperforming the outcomes of \(\text{NWP}_{\text{DHI}}\), which only obtained a \(R^{2}\) of 0.12.  
\begin{table}
\begin{tabular}{c l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Metric**} & \multicolumn{1}{c}{**Predictors**} & \multicolumn{5}{c}{**Station**} & \multicolumn{2}{c}{**Mean**} \\  & & **0** & **1** & **2** & **3** & **4** & **5** & **8** & \\ \hline \multirow{3}{*}{MAE} & Calibration\({}_{\text{MAE}}\) & 80 & 89 & 105 & 96 & 185 & 128 & 110 & 113 \\  & Calibration\({}_{\text{MSE}}\) & 82 & 100 & 110 & 97 & 194 & 127 & 118 & 119 \\  & \(\text{NWP}_{\text{GHI}}\) & 81 & 93 & 108 & 102 & 217 & 127 & 114 & 120 \\ \hline \multirow{3}{*}{RMSE} & Calibration\({}_{\text{MAE}}\) & 124 & 158 & 149 & 134 & 267 & 189 & 155 & 168 \\  & Calibration\({}_{\text{MSE}}\) & 124 & 155 & 148 & 134 & 257 & 188 & 157 & 166 \\  & \(\text{NWP}_{\text{GHI}}\) & 123 & 162 & 149 & 140 & 310 & 186 & 158 & 175 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Calibration results of \(\text{NWP}_{\text{GHI}}\). RMSE and MAE are measured in [\(\text{W}/\text{m}^{2}\)].  
Although the [PERSON] model does not consistently improve the forecast across all seasons, on average, it demonstrates significant improvements in the MAE, RMSE, and \(R^{2}\) metrics. Therefore, Skartveit is selected as the Best\({}_{\text{DHI}}\) for future testing in photovoltaic power forecasting models.