esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
FAPROTAX is a promising tool for predicting ecological relevant functions of bacterial and archaeal taxa derived from 16S rRNA amplicon sequencing. The database was initially developed to predict the function of marine species using standard microbiological references. This study, however, has attempted to access the application of FAPROTAX in soil environments. We hypothesized that FAPROTAX was compatible with terrestrial ecosystems. The potential use of FAPROTAX to assign ecological functions of soil bacteria was investigated using meta-analysis and our newly designed experiments. Soil samples from two major terrestrial ecosystems, including agricultural land and forest, were collected. Bacterial taxonomy was analyzed using Illumina sequencing of the 16S rRNA gene and ecological functions of the soil bacteria were assigned by FAPROTAX. The presence of all functionally assigned OTUs (Operation Taxonomic Units) in soil were manually checked using peer-reviewed articles as well as standard microbiology books. Overall, we showed that sample source was not a predominant factor that limited the application of FAPROTAX, but poor taxonomic identification was. The proportion of assigned taxa between aquatic and non-aquatic ecosystems was not significantly different (\(p>0.05\)). There were strong and significant correlations (\(\sigma=0.90\)-0.95, \(p<0.01\)) between the number of OTUs assigned to genus or order level and the number of functionally assigned OTUs. After manual verification, we found that more than 97% of the FAPROTAX assigned OTUs have previously been detected and potentially performed functions in agricultural and forest soils. We further provided information regarding taxa capable of N-fixation, P and K solubilization, which are three main important elements in soil systems and can be integrated with FAPROTAX to increase the proportion of functionally assigned OTUs. Consequently, we concluded that FAPROTAX can be used for a fast-functional screening or grouping of 16S derived bacterial data from terrestrial ecosystems and its performance could be enhanced through improving the taxonomic and functional reference databases.  
soil; FAPROTAX; functional annotation; bacterial function (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 2 Dataset
### MASATI (MAritime SATellite Imagery) Dataset  
Another public dataset called MASATI [26] is used in our experiments to assess the effectiveness of the proposed approach. The MASATI dataset was initially constructed for the classification of scenes with ships in satellite RGB images [27]. Then, the dataset was extended for the detection of ships [28; 29] by increasing the number of images and providing ship location annotations. The spatial resolution of images is \(512\times 512\) and the length of each ship spans from 20 to 100. Despite differences in spectral range and partial occlusion of objects compared to our primary dataset, the MASATI dataset shares key characteristics such as small target size and off-center object placement. This makes the MASATI dataset suitable for validating our AP landmine classification framework. Among seven classes in the MASATI dataset, we select three for our study: \"Coast\", \"Coast & ship\", and \"Sea\". Each class contains approximately 1000 samples. Images from the first two classes are used to train models for binary classification, distinguishing between the presence and absence of ships. This task closely resembles image-level AP landmine detection, where the model learns to make a binary decision regarding the presence of a small, off-center AP landmine in the image. The role of the third class will be discussed in Section 4. Example images from all three classes are shown in Figure 3.
## 4 Results  
### Threshold Gradient and Suffrosion  
The permeability in soils obeys the [PERSON] law. The flux velocity \(v\) which is velocity the rate of flow of water is calculated as a product of coefficient of permeability k and hydraulic gradient i. The hydraulic gradient is a head difference inversely proportional to length. The studies under permeability of soils lead to find that not all types of soil behave [PERSON]'s law. Figure 3 presents the relationships between the hydraulic gradient and the flow velocity in non-Darcian flow, proposed by [PERSON] [28]. The RCA tests results have shown existence of non-Darcian flow. For engineering applications, non-Darcian flow is simplified to Darcian flow when the seepage of water starts from a certain gradient, which is called the threshold gradient. The existence of the threshold gradient comes from the assumption that when the gradient value is less than the threshold gradient, the flow rate may dramatically decrease, which follows non-Darcian flow [28].  
To exclude the possibility of unexpected suffosion during the tests, which could disrupt collected data, the occurrence of suffosion by [PERSON] and Lau [45; 46; 47; 48] was analyzed. The results of the above analysis are presented in Figure 4. It has been concluded that suffosion occurs only in the blend whose particle size is 0.05-16 mm and only at a higher grain diameter, which is indicated by the occurrence of the crushing of the sample during the test. Nevertheless, the top perforated cover does not allow the grains to escape from the filtration hoop.  
Figure 4: Results of suffosion analysis in respect to Kenney and Lau method [45] for the three blends of the RCA.  
Figure 3: The relationship between the flux velocity and the hydraulic gradient in non-Darcian flow [28].
# On the Security of Offloading Post-Processing
###### Abstract  
Quantum key distribution (QKD) has been researched for almost four decades and is currently making its way to commercial applications. However, deployment of the technology at scale is challenging because of the very particular nature of QKD and its physical limitations. Among other issues, QKD is computationally intensive in the post-processing phase, and devices are therefore complex and power hungry, which leads to problems in certain application scenarios. In this work, we study the possibility to offload computationally intensive parts in the QKD post-processing stack in a secure way to untrusted hardware. We show how error correction can be securely offloaded for discrete-variable QKD to a single untrusted server and that the same method cannot be used for long-distance continuous-variable QKD. Furthermore, we analyze possibilities for multi-server protocols to be used for error correction and privacy amplification. Even in cases where it is not possible to offload to an external server, being able to delegate computation to untrusted hardware components on the device itself could improve the cost and certification effort for device manufacturers.  
quantum key distribution; post-processing; secure offloading; secure outsourcing; information reconciliation; privacy amplification 2023/  
are focusing on algorithmic improvements to reduce computational effort (c.f. [1; 2; 3; 4]) or extending the local computational resources with specialized hardware. They leverage equipment from high-performance computing or graphics processing units [5; 6; 7] or even develop dedicated hardware designs as co-processing units for local installation [8; 9].
## 4 Discussion
An average error of 2-3 cm was obtained, compared to significantly chosen points (dam edge, road marks, etc.) measured with the help of GNSS (Global Navigation Satellite System) technology. The points determined with the help of GNSS can be the subject of field measurements and office jobs that would involve several days of measurements and office calculations for an equivalent study area (approximately 2 hectares). This would include grid point verification and compensation, point density by planimetric displacement method, and complex calculations. It must be considered that, following some field activities (UAV flights) that totaled 1 h of fieldwork, information was obtained with a tolerable error for the preparation of analyses, simulations, and GIS modeling for flood studies. Also, a DEM of the study area was obtained, which proved useful in the subsequent preparation of the simulation and flood studies. The generated 3D model respects the relief and level curves that exist in the area. Research, articles, case studies, and works previously published by specialists in this field of study have focused on the separate study of the way in which useful geospatial information can be obtained with the help of terrestrial measurement technologies or GIS analysis with predetermined information. The main topic that this study proposed to analyze is related to the efficient and fast way in which information related to floods can be obtained from the ground, with the help of UAV, because in the case of an intervention in the event of a disaster, time is the most considerable challenge. The contributions of photogrammetry and topography to the field of preventing the effects of natural disasters have been known for several years. However, there are certain regions (the studied one is an example) where the authorities are still skeptical about the use of UAV information in the case of rapid intervention, complaining of a certain dubious accuracy. Our study was concerned with an important area located in the immediate vicinity of the city of Cluj-Napoca, Romania. We therefore studied an extremely populated area (approximately 450,000 inhabitants in the metropolitan area), Floresti Commune, an urban locality with the largest population in Romania, according to the most recent population census (approximately 55,000 inhabitants). Although the operation of UAVs (Unmanned Aerial Vehicles) could also present certain risks, especially in the case of fully autonomous flights, these risks can be avoided by announcing the flights in advance. They have the advantage of durability.
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
## 3 Results
### Results of Model Verification  
The verification of the model was done according to methodology presented in Section 2.3. The verification was done for 84 positions of the standard ring. Figures 6 and 7 present the comparison between the results of the experimental probe head error identification and the simulation using the developed model for \(A=75^{\circ}\) and \(B=80^{\circ}\) (Figure 5) and \(A=-100^{\circ}\) and \(B=30^{\circ}\) (Figure 6).  
For the first position the mean absolute difference between the real values of the probe head errors (determined experimentally) and the simulated ones was equal to 0.0002 mm and for the second position 0.0001 mm. The authors assumed that the PE value may be considered as simulated correctly if it differs from the corresponding value obtained experimentally not more than \(\pm 3\times\) standard deviation assigned to the considered point. Out of 64 simulated values of PE for each position, 61 (95.31%) were properly simulated for the first position and 64 (100%) for the second position (according to assumption presented in Section 2.3).
## 4 Materials and Methods
### RNA Isolation and Sequencing  
Eight days of submerged and semi-solid fermentation of _P. dalae_ NBP-49626 were selected for transcriptome analysis. Total RNA was extracted from the frozen mycelia samples with TRIzol Reagent(r)(Invitrogen, Carlsbad, CA, USA) according to the instructions and digested using DNase I for genomic DNA removal. The RNA quality was evaluated with a NanoDrop(tm) One UV-Vis spectrophotometer (Thermo Fisher Scientific, Lenexa, KS, USA). The messenger RNA (mRNA) was purified from total RNA using a Dynabeads(tm) mRNA Purification Kit (Cat#61006, Invitrogen, USA), then mixed and cleaved into 200-700 nt with fragmentation buffer with the MGIEasy RNA Library Prep Kit V3.1 (Cat# 1000005276, MGI Tech Co., Ltd., Shenzhen, China). First-strand cDNA was synthesized using the cleaved RNA fragments as templates with random hexamer primers and reverse transcriptase, followed by second-strand cDNA synthesis using DNA Polymerase I and RNase H. The synthesized cDNA was end-repaired, A-tailed, and ligated to sequencing adapters according to the library construction protocol. The cDNA fragments were amplified using PCR and purified using MGIEasy DNA Clean beads (Cat#100005279, MGI, Shenzhen, China). The library was analyzed using an Agilent Technologies 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Double-stranded PCR products were heat-denatured and circularized using the split oligo sequence in the MGIEasy Circularization Module (Cat#1000005260, MGI, China). Single-strand circular DNA was used as the final library. Qualified libraries were sequenced on the DNBSEQ-T7 RS platform (MGI, China), generating 150 bp paired-end reads. Three replicates were employed at all time points, except for the initial day, when only two replicates were utilized, as this represented the early stage of growth with a lower number of organisms. One replicate failed during the RNA extraction on the seventh day of liquid fermentation. A total of 45 replicates were successfully completed for RNA extraction and sequencing during an 8-day period, using two different growth modes.
## 4 Engineering Application
### Construction Procedure of Arch Cover Method  
The construction sequence of symmetry is mainly adopted in the excavation of the Shikui Road arch cover pilot tunnel. First of all, a side pilot tunnel is excavated, then follows a middle pilot tunnel, and a supporting structure is timely constructed to prevent excessive settlement. Necessary monitoring measures shall also be taken during the construction to adjust the excavation footage and support scheme according to the monitoring data. The construction scheme is shown in Table 2. The specific construction process is as follows:  
1. The side pilot tunnels 1 and 2 of the main body are grouted and reinforced with advanced small conduits, which are then excavated and supported in time. The mortar and foot lock anchor bolts are set at the arch foot, and a crown beam is set when the side pilot tunnel is through.
2. Advanced small tremie grouting reinforcement is carried out for pilot tunnel No. 3 and No. 4 in the main body, and then excavation is carried out, support is to be provided in time, and plain concrete is backfilled in the empty part of the side pilot tunnel.
3. The side walls of side pilot tunnel No. 1 and No. 2 are dismantled, and secondary primary support is provided at the same time.
4. Advanced small tremie grouting reinforcement is carried out for pilot tunnel No. 3 and No. 4 in the main body; then, excavation is carried out, support is to be provided in time, and plain concrete is backfilled in the empty part of the side pilot tunnel.
5. The side walls of side pilot tunnel No. 1 and No. 2 are dismantled, and secondary primary support is provided at the same time.
6. According to the measured data, the middle partition is removed at a certain distance, a waterproof layer is laid on the bottom of the crown beam, and a secondary lining structure is constructed in time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Construction Steps** & **Diagrammatic Sketch** & **Construction Steps** & **Diagrammatic Sketch** \\ \hline \hline  
\end{tabular}
\end{table}
Table 2: Schematic diagram of arch cover method construction process.  
7. The lower soil mass is excavated layer by layer, and the side walls are initially supported, and at the same time, the side wall is provided with anchor cables and steel supports. After reaching the tunnel bottom elevation, a bottom cushion is constructed.
8. The waterproof layer is paved, the secondary lining structure and medium plate beam column are constructed, and then the internal structure of the station is constructed.
## 3 System Model  
### Scenario  
As shown in Figure 1, we consider a UAV-assisted post-disaster early warning scenario with a set \(\mathcal{N}\) of \(N\) ground nodes randomly distributed on the ground, a hovering UAV, and a ground control station (GCS). The ground nodes are responsible for sensing the environmental information data of the target area, and the UAV takes off from the GCS and collects the data from each ground node. During the flying and collection process, if the remaining energy is insufficient, the UAV should return to the control station for recharging to ensure the completion of the mission. For the ease of deployment, we assume that the UAV flies at a fixed flight velocity \(v_{u}\) and a fixed altitude \(h\). In addition, we require the UAV to fly directly above the ground node to collect data. Therefore, the UAV has \(N+1\) predetermined waypoints, which are denoted by the set \(\mathcal{P}^{\cap}=\left\{p_{0}^{u},p_{1}^{u},...,p_{N}^{u}\right\}\), where \(p_{0}^{u}\) is the position of GCS and \(p_{n}^{u},n\in\mathcal{N}\) is the position of the \(n\)-th ground node. The trajectory of the UAV is approximated by the sequence \(p^{u}=\)\(\left\{p_{0}^{u},...,p^{u}(i),...,p_{0}^{u}\right\}\), where \(p^{u}(i)\), \(p^{u}(i)\in\mathcal{P}^{\cap}\) denotes the hover point of the \(i\)-th decision.
## 3 Relevant Background
### IEEE 802.1 Qbv Protocol  
The IEEE 802.1 Qbv protocol defines the time-aware shaping (TAS) technique to control traffic transmissions between TSN elements using a gating mechanism [17]. Each switching egress port has eight priority queues, as depicted in Figure 2, each of which has a gate with either an open or a closed states. Changing the gate state is controlled by the GCL under guaranteed synchronization for all selected switches in the transmission path. Only one priority frame from a specific queue can be transmitted during a related open interval by the first-in first-out (FIFO) mechanism. TT gates are prioritized over other gates to send the scheduled frames if they are open simultaneously. However, if the open gates correspond to unscheduled flows, i.e., AVB and BE traffic, the credit-based shaper (CBS) is implemented between the gates.  
The capable switch described in IEEE 802.1 Qbv must simultaneously realize fabric switching, filtering, and traffic policy selection for flows arriving from the ingress ports [19]. Fabric switching and filtering processes redirect the arrival of traffic to the appropriate priority queue, as depicted in Figure 2. Using the GCL timing schedule, the queued frames are selected to the egress port and then to the physical link. The frames experience related delay components during the transmission process to achieve these switching functions, as differentiated in Figure 2.  
Figure 1: Example of a simple time-sensitive network (TSN) architecture: TSN components, unicast and multicast transmission, and alternative routes.  
Figure 2: An IEEE 802.1 Qbv capable switch with multiple input and output ports, showing delay components between adjacent nodes.
# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 7 Conclusions  
In this study, a new construction cost prediction method was developed to address the problem of strong interaction among construction cost data. Based on the exploration and improvement of the BiLSTM network in the field of deep learning, the temporal interactions in the data are captured at a deep level, and the HAM module is added to the network, which enhances the ability of the network to capture the interactions among the features and then constructs an effective construction project cost prediction model. In addition, to select the feature indicators that have greater impacts on the cost as model inputs, GRA was used to assess the importance of the factors related to influencing the cost, and the most valuable feature indicators were screened according to the principle of proportionality. To avoid the tediousness and uncertainty of manual parameter tuning, an improved particle swarm optimisation algorithm was designed to find the optimal hyperparameter combination for the model automatically. Through comparative experiments and analyses, the performance of the method in construction cost prediction work was verified.  
Even though the results obtained with the improved model are very positive compared to the BiLSTM network, it must be recognised that it has limitations. Firstly, since HAM contains two attention modules, it embodies a powerful feature extraction capability, which will pay more computational resources accordingly. Secondly, IPSO is committed to improving the algorithm's ability to find the global optimal solution, and we do not reduce the number of iterations and running time of the algorithm relative to the original algorithm.  
In future research, we will devote ourselves to overcoming these limitations by focusing on an improved method to simplify the model structure in the feature extraction problem, and a hyperparameter optimisation method that combines computing accuracy and computing speed in the hyperparameter optimisation problem. In addition, for the construction cost prediction problem, not only is an accurate prediction model needed, but also the selection of characteristic indexes is equally important. We will analyse the influencing factors of the cost in depth to contribute more value to the development of this field.  
Conceptualisation, C.W.; methodology, J.Q.; validation, J.Q.; investigation, J.Q.; data curation, J.Q.; writing--original draft preparation, J.Q. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62072363) and the Natural Science Foundation of Shaanxi Province (No. 2019M-167).  
Not applicable.  
The data used in this study are available at (www.gldzb.com, accessed on 18 January 2024) and (www.gldjc.com, accessed on 18 January 2024).  
The authors declare no conflicts of interest.
## 3 Results  
### Study Area 1--Colli Alti e Bassi Fault (CAB)  
#### 3.1.1 Topographic Analysis of CAB  
The graphs in Figure 8 show a comparison between the measures of the Total Topographic Offset (TTO = blue line), the Height of the preserved Fault Scarp (HFS = red line) and the 30 October 2016 Coseismic Ruptures (net displacement) from Remote Analysis (CRRA= violet line) measured on the CAB in the sample area 1.  
The estimated values of these features (on the Y axis, in meters) are plotted against the along-strike distance of each measurement point (abscissa) from the northern tip point of the fault-scarp outcrop (assumed as X = 0).  
Dots in the Figure 8 graph mark the values, manually measured on the control points, respectively, for the height of the fault scarp, measured with hand ruler (green dots), and the 30 October 2016 coseismic ruptures (net displacement) from the manual survey (yellow dots). The good agreement between the field data and the assessments obtained from the topographic analysis on the virtual outcrop, confirms the reliability of the adopted method.  
Figure 8: (**a**) Outcrop image of the CAB fault; (**b**) Comparison of different types of topographic profiles; blue line: height of long term slope; red line: effective present fault scarp; violet: coseismic net displacement. Both along-strike distance (abscissa) and height values (ordinate) are expressed in meters. Colored dots refer to field measurements collected at control points.  
The TTO and HFS curves show very similar trends but differ in several points because the former includes the part of the scarp removed by erosion.  
Interestingly, the CRRA curve, even if characterized by lower values (higher observed net slip = 1.25 m), shows a good correlation with TTO and HFS curves, as regards the position of the maxima and minima. This remarkable consistence suggests that the along-strike height profile of the current fault scarp derives from the add up of a certain number of coseismic displacements, each characterized by slip-profiles similar to the one caused by the mainshock of the 2016 sequence.