esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection of Moisture Content Conditions Based on a Test Specimen  
Prior to the testing evaluation of the waterproofing performance of resource-recycling adhesive gel based on the response to a humid environment, the moisture environment was set by adjusting the functionality of the test surface, where the adhesive gel was applied, to create a humid background surface. The moisture condition of the test surface was referenced to the state of the background specified in Section 3.1.6 of the Construction Standard Specification (KCS) 41 40 01 for waterproofing construction [16], and the surface humidity condition was set to below 30% for methods applicable in a humid state. Thus, humidity conditions were set to 0% for dry conditions, 10%, 20%, and 30% for functional moisture conditions, and 50%, 70%, and 100% for simulating very humid conditions as a means to test the adhesive performance limitation of the SPRG specimens. The SPRG was immediately applied to the humid conditioned substrate surface, and the test specimens were prepared under conditions identical to those of actual construction in a humid environment (23 \(\pm\) 1 \({}^{\circ}\)C, 65-85% RH). The moisture condition and composition status of the test specimens used in the testing evaluation are shown in Figure 3 below.
## 3 Distributed Optical Fibre Sensing Applied to Geo-Hydrological Applications
### Soil Leves and Embankments Monitoring
#### 3.1.3 Distributed Pressure Sensing for Soil Levees and Embankments  
To the best of our knowledge, the measurement of pressure in soil with real distributed fibre-optic sensors has not yet been proved with the accuracy, resolution, and dynamic range required by soil levees monitoring. In fact, safe prescriptions recommend for such pressure sensing systems to be capable of detecting variations on the order of 50-100 Pa (corresponding to approx. 0.5-1 cm of water level) over a range up to 50-100 kPa (5-10 m of water level) with a spatial resolution of some tens of centimetres or lower.  
Since the very early works on fibre-optic hydrophones [155; 156; 157], it was clear that thick coatings may improve the pressure sensitivity of fibres by converting lateral pressure into strain [158], but the results are still inadequate. Regarding specific implementations, the best pressure-sensitivity reported was obtained in a dual-layer coated fibre interrogated by means of a BOTDA scheme [159]. A Brillouin shift sensitivity of about \(-2\) MHz/MPa was measured for this special fibre--an almost threefold enhancement with respect to bare fibres, whose sensitivity is \(-0.742\) MHz/MPa. Considering the required pressure sensitivity mentioned above (100 Pa), a resolution of some tens of Hz of frequency shift should be required--far too challenging for current systems [160].  
It is worth mentioning here that a distributed pressure sensor based on the POTDR measurement of stress birefringence in a side-hole fibre was also proposed [161]. The resolution was indeed quite poor for the needs of soil levees monitoring.  
Despite that, the scientific and general interest about the topic is very high, mainly for the several collateral applications that may benefit from real distributed pressure sensing with high sensitivity.
## 4 Results and Discussion  
### Performance of YOLO Models on DAWN Dataset  
Table 2 shows the training results of the YOLOv5, YOLOv7, and YOLOv9 models with original hyperparameters, GWO, ARO, and CLEO optimization algorithms in mAP type using the DAWN dataset. Figure 13 shows the graphical results of all training using the DAWN dataset after 50 epochs. The results of other studies using the DAWN dataset mentioned in this paper are also given. When the table is examined, it is seen that the training results of the YOLOv5, YOLOv7, and YOLOv9 models with the original hyperparameters are 60.20%, 68.50%, and 68.00%, respectively. Different results are seen when the training results after optimizing the hyperparameters of the YOLO models using the optimization algorithms are examined. For example, while CLEO provides the best performance improvement in YOLOv5 and YOLOv7, GWO is the best for YOLOv9. YOLOv5 + CLEO has a 63.90% mAP value with a 6.146% performance improvement. YOLOv7 + CLEO has 72.80% mAP with a 6.277% performance increase. YOLOv9 + GWO has 72.60% mAP with a 6.764% performance increase. According to Table 2, while the best mAP value is YOLOv7 + CLEO, the best performance improvement is realized with YOLOv9 + GWO in the YOLOv9 algorithm. Table 3 shows the precision, recall, and F1 score values obtained from the confusion matrix (e.g., \"Figure 14\"). Figure 14 is a confusion matrix value resulting from the training of only one algorithm. The values in Table 3 are generated by calculating the values of the 12 confusion matrix values one by one. These values are calculated separately for the six objects in the DAWN dataset, and different results are obtained for each applied model. An example of graphical data showing all the changes that occurred throughout the training is shown in Figures 15 and 16. When the values are analyzed according to the average of all objects in Table 3, the best model for the F1 score is YOLOv7 + ARO. Since the detection and positioning of the object are more important in autonomous driving, mAP values are primarily considered. Therefore, it is observed that the YOLOv7 + CLEO and YOLOv9 + GWO models are superior to the other models in object detection and localization. \begin{table}
\begin{tabular}{l c} \hline \hline
**Model** & **DAWN (mAP (\%))** \\ \hline YOLOv3 + AWBLP [3] & 47.79 \\ pre-trained YOLO-v5 [10] & 34.50 \\ RetinaResnet50 [19] & 32.75 \\ YOLOv3 + RetinaResnet50 [19] & 25.68 \\ RetinaResnet50 + SSD [19] & 26.20 \\ YOLOv3 + RetinaResnet50 + SSD [19] & 25.03 \\ YOLOv5 & 60.20 \\ YOLOv5 + GWO (Ours) & 62.30 \\ YOLOv5 + ARO (Ours) & 60.10 \\ YOLOv5 + CLEO (Ours) & 63.90 \\ YOLOv7 & 68.50 \\ YOLOv7 + GWO (Ours) & 70.60 \\ YOLOv7 + ARO (Ours) & 71.20 \\ YOLOv7 + CLEO (Ours) & 72.80 \\ YOLOv9 & 68.00 \\ YOLOv9 + GWO (Ours) & 72.60 \\ YOLOv9 + ARO (Ours) & 70.80 \\ YOLOv9 + CLEO (Ours) & 70.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The mAP (%) comparison of the DAWN dataset. Figure 13: mAP results for 50 epochs on the DAWN dataset.
###### Abstract  
The response of steel moment frames is estimated by first considering that the mass matrix is the concentrated type (\(M_{L}\)) and then consistent type (\(M_{C}\)). The effect of considering more than one element per beam is also evaluated. Low-, mid- and high-rise frames, modeled as complex-2D-MDOF systems, are used in the numerical study. Results indicate that if \(M_{L}\) is used, depending upon the response parameter under consideration, the structural model, the seismic intensity and the structural location, the response can be significantly overestimated, precisely calculated, or significantly underestimated. Axial loads at columns, on an average basis, are significantly overestimated (up to 60%), while lateral drifts and flexural moments at beams are precisely calculated. Inter-story shears and flexural moments at columns, on average, are underestimated by up to 15% and 35%, respectively; however, underestimations of up to 60% can be seen for some individual strong motions. Similarly, if just one element per beam is used in the structural modeling, inter-story shears and axial loads on columns are overestimated, on average, by up to 21% and 95%, respectively, while the lateral drifts are precisely calculated. Flexural moments at columns and beams can be considerably underestimated (on average up to 14% and 35%, respectively), but underestimations larger than 50% can be seen for some individual cases. Hence, there is no error in terms of lateral drifts if \(M_{L}\) or one element per beam is used, but significant errors can be introduced in the design due to the overestimation and underestimation of the design forces. It is strongly suggested to use \(M_{C}\) and at least two elements per beam in the structural modeling.  
steel moment frames; concentrated mass matrix; consistent mass matrix; inelastic seismic behavior; multi-degree of freedom systems Article  
Effect of the Distribution of Mass and Structural Member Discretization on the Seismic Response of Steel Buildings  
[PERSON]  
Facultad de Ingenieria, Universidad Autonoma de Sinaloa, Ciudad Universitaria, Cullacan 80000, Mexico; [EMAIL_ADDRESS] (F.V.-B.); [EMAIL_ADDRESS] (M.D.L.-T.); [EMAIL_ADDRESS] (E.B.); [EMAIL_ADDRESS] (J.B.); [PERSON]DDRESS] (R.C.); [EMAIL_ADDRESS] (J.M.L.-G.); [EMAIL_ADDRESS] (J.A.S.)
## 3 Results and Discussion  
[PERSON] et al. (2020) showed that the biofortification of potatoes was not effective. The amount of accumulated iodine was too low to cover the daily iodine requirement of an adult (150 ug/day) [46]. The lack of a positive iodine accumulation in the potato for these authors could have been due to the use of an inappropriate chemical analysis procedure. The method used could result in iodine loss during mineralisation in nitric acid. The use of alkaline extraction in TMAH (Tetramethylammonium hydroxide), according to the European Union analytical standard, should have a positive effect on the accumulation of iodine in potatoes. [PERSON] et al. (2020) evaluated the effectiveness of the iodine biofortification of potato tubers. The application of KI via soil and the application of KIO\({}_{3}\) via foliar feeding were tested in a three-year field experiment. Both the soil and foliar feeding iodine application methods resulted in potato tubers with an increased content of this element without a decrease in starch and sugar content. The highest efficiency of iodine biofortification was recorded for the foliar spraying of KIO\({}_{3}\). The obtained iodine level in 100 g of potatoes could be sufficient to cover up to 25% of the recommended daily intake of this element [47].  
Our research confirms that the applied biofiltration method using inorganic and organic forms of iodine, i.e., iodoquinolines, can cover approximately 46.02% (KIO\({}_{3}\)), 18.88% (8-OH-7-I-SQSA) and 16.02% (5-Cl-7-I-8-Q) of the daily iodine requirement (RDA) if a 260 g daily portion of potatoes is consumed.
# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 4 Results  
### Experiment Preparation  
In order to verify the reliability and generalizability of the method, fusion experiments were selected from four satellites with different feature types, as shown in the Table 1. The four satellites were GeoEye-1 (GE-1), WorldView-4 (WV-4), Gaofen-7 (GF-7), and Gaofen Multi-Mode (GFDM), where the spatial resolutions of the PAN images were all at the sub-meter scale, between 0.31 and 0.8 m, and the spatial resolution of the MS images were at the meter scale, between 1.24 and 3.2 m. The feature types of the four scenes were urban, plants and water, agricultural land, and desert, covering some of the features that frequently appear during satellite observation of Earth. The PAN image block size was 2048 pixels, and the MS image fast size was 512 pixels. The GE-1 and WV-4 data were from the standard fusion dataset [24], GF-7 and GFDM were from the China Resource Satellite observation data, and each dataset was preprocessed using the exact alignment method [25; 26].  
In the subsequent experiments, for the experimental mode with references, the MS images were down-sampled to 128 \(\times\) 128 pixels, the PAN images were down-sampled to \(512\times 512\) pixels, and the original MS images were used as the reference data. The experiments were performed with the original-sized data for the experimental mode without references, and the experimental effects of the different methods were evaluated using the method without references.  
Five classic fusion algorithms were used as comparison methods: (1) GIHS fusion algorithm [14; 27]; (2) SFIM fusion algorithm [27; 28]; (3) Brovey fusion algorithm [14; 28]; (4) GS fusion algorithm [13; 27]; and (5) PCA fusion algorithm [27; 29].
## 3 Field and Lab Ballast Data Preparation
### Methodology  
#### 3.2.1 Field Data Acquisition  
Ballast images obtained from a variety of field sites introduce different ballast types and field conditions to the dataset. In total, 144 field ballast images were collected from six distinct railroad sites, including the Transportation Technology Center (TTC) High Tonnage Loop (HTL) in Pueblo, Colorado; Burlington Northern Santa Fe (BNSF) track near Kansas City, Missouri; Union Pacific (UP) track in Martinon, Illinois; Canadian National (CN) track in Champaign, Illinois; Norfolk Southern (NS) track in Champaign, Illinois; NS track near Sandusky, Ohio; and NS track near Kendallville, Indiana. These field images encompass a variety of ballast characteristics including type, color, degradation level, size distribution, and particle arrangement. Field ballast images from railroad field sites were collected with a self-invented Ballast Scanning Vehicle (BSV) equipped with a line scan camera and an area scan camera in an automated manner [10]. Additionally, aggregate images sourced from various aggregate producers (i.e., quarries) serve as a supplementary contribution, enhancing the dataset's inclusivity. These images depict clean aggregate stockpiles comprising medium- to large-sized particles, which are either slightly separated or densely stacked. Images from aggregate quarries were collected with smartphone cameras, and detailed camera settings were introduced in the previous work [7].
# A Modular Experimentation Methodology for 5G Deployments: The 5 GENESIS Approach
## 5 Experimental Setup
### 5G Deployment at UMA Campus  
This section describes the 5G deployment at UMA campus, where the experiments have been executed. 3 GPP TS 37.340 [53] considers different options for the deployment of 5G networks. The options range from purely 5G solutions, deployed independently from the existing network, to hybrid solutions, which combine part of the existing infrastructure with 5G NR.  
Because of the high costs of building native 5G solutions, most operators currently prefer to lean on and evolve from the existing LTE infrastructure [35; 36]. In particular, the combination of 4G and 5G radio access connected to an LTE EPC is known as NSA operation Option 3x architecture. This option is also called eUTRA New Radio-Dual Connectivity (EN-DC). Dual Connectivity (DC) was introduced in 3 GPP in order to allow a UE to simultaneously transmit and receive data on multiple component carriers from/to two cell groups via a master eNB (MeNB) and a secondary eNB (SeNB). In Option 3x, DC is used in order to allow a device to connect to both 4G and 5G NR.  
For this reason, the initial UMA pilot also follows the NSA Option 3x architecture. In Option 3x, the UE is connected to an eNB that acts as MeNB, and to a gNB that acts as SeNB, as shown in Figure 2. As said, there is no 5 GC in this option and the gNB does not connect to the Mobility Management Entity (MME) in the EPC. The gNB connects to the eNB to receive requests to activate 5G bearers via X2 interface. Data bearers can be handled by the MeNB or SeNB, or split between these two. For the measurements that are discussed in this paper, the data are handled by the SeNB only (the gNB), which is the most popular Option 3x variant.  
Figure 3 shows the four paired gNBs/eNBs currently deployed in the UMA campus, along with other infrastructure components, i.e., the server rack hosting the EPC from Athonet, the Baseband Unit (BBU) from Nokia, and the main data center, in which OpenStack has been adopted as the Virtual Infrastructure Manager (VIM) and Open Source MANO (OSM) as the solution for NFV management and orchestration. Moreover, the mobile edge solution provided by Telefonica is based on OpenNebula and OSM. More details regarding the testbed components can be also found at [54].
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 3 Results and Discussion
### Position Ten Dial Gauges  
To evaluate the accuracy of the pointer positioning algorithm in this paper, the above 10 images were also selected as experimental data. Table 4 compares the experimental result of manual marking, traditional Hough line detection, the algorithm of [9] and our algorithm. The recognition angle is the angle between the pointer and the zero-scale line.  
As can be seen from Table 4, the average absolute error of the traditional Hough line detection is 0.34, and the average running time is 1432.31 ms. The algorithm [9] has an average absolute error of 0.144 and an average running time of 250.08 ms. The average absolute error of our algorithm is 0.06, and the average running time is 143.57 ms. In Table 4, it should be noted that image 8 and image 9 have small errors in our algorithm, while the error in the Hough line detection and the algorithm of [9] is large. As shown in Figure 9, there are more labels on the dials of these three images, and the damping fluid just covers part of the scale values. Thus, our algorithm is barely disturbed by background images.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & **Manual** & \multicolumn{4}{c}{**Hough Transform**} & \multicolumn{4}{c}{**Algorithm of [9]**} & \multicolumn{4}{c}{**Ours**} \\ \cline{2-10}  & **Measured** & **Recognition** & **Absolute** & **Time** & **Recognition** & **Absolute** & **Time** & **Recognition** & **Absolute** & **Time** \\  & **Vales/** & **F** & **Error/** & **/ms** & **Values/** & **Error/** & **/ms** & **Values/** & **Error/** & **/ms** \\ \hline
1 & 171.2 & 171.74 & 0.54 & 1548.23 & 171.31 &
# Purification and Characterization of Authentic 30S Ribosomal Precursors Induced by Heat Shock
## 2 Results
### Ribosome Synthesis at 45 \({}^{\circ}\)C  
Before purification of the 21S particles, we checked to ensure that 70S ribosomes could be correctly neosynthesized at this high temperature. Since bS20 proteins repress their own synthesis [25], we cloned an _rpsT_-strep open reading frame deprived of regulatory sequences into the pBAD24 vector under the control of the _paraB_ promoter. This ensured that bS20-strep would not repress its own synthesis but would instead repress the chromosomally encoded bS20. We grew the cells at 30 \({}^{\circ}\)C in LB medium containing glucose to block bS20:strep synthesis, then collected them by centrifugation and re-suspended them in LB medium. We divided the cells into two groups and cultivated them at either 30 or 45 \({}^{\circ}\)C until an OD\({}_{600}\) of 0.8. We induced the production of bS20:strep by adding 1 mM L-arabinose. We then collected and broke the cells, and the crude extracts were applied to streptactin columns (see Section 4 Materials and Methods). The eluted fractions of both groups (induced at 30 or 45 \({}^{\circ}\)C) show similar profiles (Figure 1C, lane 4), revealing the presence of 70S ribosomes. We therefore showed that ribosome synthesis is not compromised, and that mature ribosomes can form during heat stress of 45 \({}^{\circ}\)C. This is not surprising, since ribosome synthesis is mostly dependent on growth rates, and these are almost identical under both temperature conditions [26].  
Figure 1: Ribosome purification and subsequent synthesis under heat stress. **(A)** Protein extracts from the Escherichia coli E6006 strain were either loaded onto streptactin columns and eluted with desthiob biotin (for Strep), or loaded onto NiTa resins and eluted with imidazole (for His). MW, molecular weight marker. **(B)** Purified ribosomes were loaded onto a streptactin resin, then flow-through (50S) and elution (30S) were analyzed. **(C)** MG1655 (pE7867) cells were grown overnight at 30 \({}^{\circ}\)C in LB medium containing 10 mM glucose, then inoculated in LB medium containing arabinose (lane 1) or glucose (2), and grown at 45 \({}^{\circ}\)C to an OD600 of 0.8. The same was done just in arabinose (1 mM), and grown at either 30 \({}^{\circ}\)C (3) or 45 \({}^{\circ}\)C (4). The ribosomes were loaded onto streptactin columns, and the eluted proteins were analyzed.
# SAHA: A String Adaptive Hash Table for Analytical Databases
## 1 Introduction
### Our Solutions and Contributions  
To address these problems, we present SAHA, a string adaptive hash table for analytical databases. It reinterprets short string keys as an array of integers and stores them directly inside slots without any indirection. For long string keys, it saves the computed hash value along with the string data pointer in each slot to lower the frequency of long string comparisons. SAHA precomputes the hash values in each vectorized processing step of group and join operators, and uses these hash values directly in the following operations. This reduces the cache pollution when probing empty slots during hash table insertion and lookup, especially for long strings. SAHA also provides a highly optimized key dispatcher based on string length. It can adapt to different string datasets and choose the best suited hash table implementation dynamically. The main contributions of our work are summarized as follows:  
1. **Optimized String Dispatcher.** We implement a string key dispatcher based on the length of a string. It is used to categorize string datasets into subsets with predefined string length distributions. The dispatcher is optimized with special memory loading techniques so that it does not introduce performance regressions to the underlying hash table.
2. **Hybrid String Hash Tables.** We design five implementations of hash tables for strings with different lengths. Each implementation is optimized for one distribution of string length. All implementations are united as one hash table with the standard hash table interface but can adapt to string data. It has the benefit of low peak memory usage because the sub hash tables grow independently, which is smoother than growing as a whole.
3. **Pre-hashing and Vectorized Programming Interface.** We identify the chance of precomputing the hash values of records before doing vectorized processing in the hash table. This optimizes the cache utilization when the current batch is filled with long strings. We propose a new programming interface accepting callbacks to insertion and lookup functions so that additional information such as the precomputed hash values can be calculated inside the hash table.
4. **Extensive Experiments.** We carry out extensive experiments using both real-world and synthetic datasets to validate the performance of SAHA. Our experiments look into the key performance factors of a hash table, including the hashing functions, the computational time and the effectiveness of main memory utilization. The results reveal that SAHA outperforms the state-of-the-art hash tables in all cases by one to five times, as shown in Figure 4.  
The rest of this paper is organized as follows. Section 2 discusses the related works. Section 3 describes the main constructs of SAHA with its optimizations. Section 4 shows the vectorized specific optimization of SAHA and its supporting programming interface. Section 5 contains experimental results. Finally, Section 6 gives the concluding remarks.