esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Programs
### Design of Specim with Densely Arranged, Small Diameter Reinforcements  
In this study, we investigate the deformation performance and failure conditions using two RC specimens with different arrangements of steel bars but same longitudinal reinforcement ratios under cyclic loading, as illustrated in Figure 1. The experiments employed different loading devices, resulting in effective heights (measured from the bottom of the pier to the horizontal force point) of 1350 mm and 1600 mm for the two RC column specimens, and overall column heights of 1750 mm and 2565 mm. Both specimens had a square cross-section of 400 mm \(\times\) 400 mm and footings with dimensions of 700 mm in height, 1300 mm in width, and 1100 mm in depth.  
As illustrated in Figure 0(a), the RC column specimen utilized SD295A reinforcement bars, featuring 16 D13 bars as longitudinal reinforcement and D6 bars as hoop reinforcement, spaced at 50 mm intervals from the column base. The longitudinal reinforcement ratio and hoop reinforcement volume ratio for this case, referred to as Case-1, was 1.27% and 0.79%.  
In contrast, Case-2, depicted in Figure 0(b), involved an RC column specimen with the same longitudinal reinforcement ratio as Case-1 but incorporated thinner D10 deformed reinforcement bars in a denser arrangement. This design used 28 D10 SD295A reinforcement bars, maintaining a constant 40 mm cover concrete thickness and 50 mm intervals between hoop reinforcement in the vertical direction. The longitudinal reinforcement ratio and hoop reinforcement volume ratio for Case-2 were approximately the same as in Case-1, at 1.25% and 0.79%.  
\[I_{rc}=I_{c}+\left(\frac{E_{s}}{E_{c}}-1\right)\sum_{i=1}^{n}\left(I_{si}+A_{ i}{y_{i}}^{2}\right) \tag{1}\]  
In the study of the bending resistance characteristics of RC columns, we utilized Equation (1) to calculate the moments of inertia for the cross-sections of two RC columns  
Figure 1: Reinforcement details of the RC columns specimens (units: mm). (**a**) Case-1. (**b**) Case-2.  
with different rebar diameters and arrangements. Case-1, which utilized large-diameter and low-density arrangement, resulted in a moment of inertia of \(3.11\times 10^{9}\) mm\({}^{4}\). Conversely, Case-2, which used small-diameter and high-density arrangement, had a moment of inertia of \(3.13\times 10^{9}\) mm\({}^{4}\). Not only were the reinforcement ratios essentially identical for both cases, but their moments of inertia were also roughly on par with each other.  
In summary, Table 1 presents the specific parameters for each experimental subject. The concrete used was standard Portland cement with an aggregate maximum particle size of 20 mm. Furthermore, as per the Road Bridge Specifications [19], the elastic moduli for the longitudinal reinforcements and stirrups were set at 210 Gpa, while that of the concrete was 28.0 Gpa.
## 4 Applications and Market Adoption
NFC defines a subhundred-millisecond setup, two-way wireless technology based on the principle of magnetic inductive coupling, which allows for very short range (typically, below 20 cm). Therefore, NFC offers very limited device physical location flexibility in comparison with BLE, while it also offers significantly reduced security threat scope. Since transactions in the aforementioned application space are critical from a security viewpoint, the use of BLE would require the Passkey Entry or an adequate Out of Band pairing method. Given the properties of NFC, it constitutes a powerful candidate as Out of Band technology for BLE pairing, which additionally avoids the need to enter a key by the user. This combination of BLE and NFC provides advantages of both technologies. Note that NFC does not impact on BLE use cases whereby communication range is greater than a few tens of centimeters. BLE is also adequate for industrial environments, which are challenging due to multipath fading and radio interference from machinery [20]. Inherited from classic Bluetooth, BLE accounts with adaptive frequency hopping, which offers a robust solution to the aforementioned problems. Frequency hopping is also used in industrial standards such as WirelessHART and ISA 100.11a, and is a component of the recently published IEEE 802.15.4e specification [21].
# COMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
## 2 Materials and Methods
### Complementary Masking Module  
Based on the relationship between the relatively low- and high-level representations, the CMM masks the background noise at the low-level representation through the abstracted high-level representation. After obtaining the denoised representation, we propagate the complementary information to the decoder representation and the next decoder. Here, we denote the i-th encoder block representation \(E_{i}^{(j)}\) in the j-th skip connection path, where \(i\in\{1,2,3,4\}\) and \(j\in\{1,2\}\). \(D_{i}^{(j)}\) indicates the i-th decoder block representation in the j-th decoder, where \(i\in\{3,4\}\) and \(j\in\{1,2\}\). In the CMM, the encoder output \(E_{i}^{(j)}\in\mathbb{R}^{C_{i}\times H_{i}\times W_{i}}\) is masked by the decoder output \(D_{i+1}^{(j)}\in\mathbb{R}^{C_{i+1}\times H_{i+1}\times W_{i+1}}\), as follows:  
\[mask=\sigma(\mathcal{F}(Up(D_{i+1}^{(j)})))\in\mathbb{R}^{1\times H_{i}\times W _{i}} \tag{1}\]  
\[\mathcal{C}=\mathcal{F}(E_{i}^{(j)})\otimes mask\]  
Here, \(Up(\cdot)\) and \(\mathcal{F}(\cdot)\) indicate the bilinear upsampling and post-activated convolutional operations (i.e., Conv2D-Batch normalization-ReLU) [34], respectively. We apply a dual post-activation convolutional operation to \(E_{4}^{(1)}\) for the first decoder representation \(D_{4}^{(1)}\). To exclude the background noise, we apply \(\mathcal{F}(\cdot)\) to the upsampled decoder representation \(Up(D_{i+1}^{(j)})\) to obtain the _mask_ that high-level representation is channel-wise aggregated. Subsequently, we employ a sigmoid function \(\sigma\) to discriminate the mask information and eliminate the background noise in \(E_{i}^{(j)}\) through _mask_. Following the masking, a post-activated convolutional operation is applied to complementary representation \(\mathcal{C}\) to refine the features in each path of low- and high-level. These features are fused with the original CMM input features (\(E_{i}^{(j)}\) and \(D_{i+1}^{(j)}\)) to propagate the next CMM and decoder as follows:  
\[E_{i}^{(j)}=\mathcal{F}(\mathcal{C})+E_{i}^{(j)},\ \ D_{i}^{(j)}=\mathcal{F}( \mathcal{C})+Up(D_{i+1}^{(j)}) \tag{2}\]  
For an efficient computation, we include two CMMs in a single decoder. Then, to progressively improve and propagate the complementarity of the output propagated to the next decoder CMM, we designed a cascading multi-decoder structure. We describe an experiment in Section 3.4.1 in which we investigated the effects of progressive improvement in complementarity. Finally, we obtain five deep supervision maps [24]: two decoder path  
Figure 1: Overall architecture of proposed COMMA.  
outputs (\(DS_{1}\) and \(DS_{2}\)) and three skip-connected path outputs (\(DS_{3}\), \(DS_{4}\), and \(DS_{5}\)). Each deep supervision map is upsampled to the ground truth size to compute the model loss.
# Prediction of Arsenic Removal from Contaminated Water Using Artificial Neural Network Model
## 1 Introduction  
Heavy metals, discharged even at low concentrations into natural water bodies, have threatening impacts on human life and the environment. One of such toxic heavy metals is arsenic (_As_), which is considered an environmental hazard [1]. The immediate symptoms of acute _As_ poisoning include abdominal pain and vomiting. It can lead to death in extreme cases. Long-term exposure can cause diabetes, pulmonary disease, cardiovascular disease, cancer, and skin lesions [2]. Various technologies, such as coagulation-filtration [3], membrane separation [4; 5], ion exchange [6], adsorption [7; 8], and hybrid membrane systems [9; 10] have been employed to remove _As_ from contaminated water. Among these methods, adsorption is probably the most effective separation method for the removal of hazardous heavy metals such as _As_ from water. The adsorption process has long been used in the water and wastewater industries for its ease of handling, minimal sludge production, cost-effectiveness, and regeneration capability [11]. An integral part of the application of an adsorbent in removing a heavy metal ion from a contaminated aqueous solution is developing a process model. The traditional means of modeling adsorption is to obtain the parameters of the kinetic and isotherm models using experimental data at optimum conditions. However, the optimum values of the adsorption variables, such as pH, adsorbent dosage, adsorbate initial concentration, contact time, andtemperature, are investigated independently [7; 12; 13; 14]. Considering the analytical error, uncertainty, and independent investigation associated with the traditional experimental work, different artificial intelligence (AI)-based machine learning (ML) models are being used to correlate all input variables to the output parameter (contaminant removal percentage) directly. A few examples of such applications are presented in Table 1.  
The mostly used ML algorithm for modeling various adsorption processes is the artificial neural network (ANN) [28; 29]. It has been used all across the world for classification and prediction purposes in a wide range of real-time adsorption applications [14; 15; 16; 17; 18; 22; 23; 24; 25; 26; 27]. The ANN correlates the input(s) to the output(s) with nodes arranged in single or multiple hidden layers. The nodes in one layer are connected with weight functions to the nodes in the next layer. An activation function is used to non-linearly map the inputs to the outputs. In addition to ANN, other machine learning algorithms (MLAs) such as decision trees, support vector regression, random forest, genetic model, particle swarm optimization, and adaptive network-based fuzzy inference system (ANFIS) have been used for modeling various adsorption processes [18; 19; 20; 21; 22; 25; 26; 27]. Among these MLAs, the ANFIS has the advantages of the ability to capture the non-linear structure of a process, adaptation
## 1 Introduction
[14] experimentally and numerically investigated the flexural strength and stiffness of an invented reinforced concrete beam with a new reinforcement system comprising additional steel bracings and steel plates, arranged to auto-balance the compression and tension forces developed in the member due to an increasing applied load in a three-point flexure test. The newly invented reinforcement system increased the flexural strength and the stiffness of the member to 300% compared with an ordinary reinforced concrete beam. LS-DYNA was used for the numerical simulations. [PERSON] et al. [15] suggested a new method to increase the flexural capacity of reinforced concrete beams in a simply supported form. They adopted laboratory work together with numerical modeling and the results revealed an increase in the load-bearing capacity and the stiffness of the structural elements. A stress block method was proposed by [PERSON] [16], which was verified using a database that consisted of 52 singly tested reinforced high-strength concrete beams with a concrete strength above 55 MPa. The proposed model was compared with models of various design codes and proposals of researchers found in the literature. [PERSON] and [PERSON] [17] proposed a reinforcement technique in a beam to increase the bending capacity that supports a sealed rubber tube with a diameter twice that of the reinforcement bar covering the slanted part to separate it from the concrete of the beam. [PERSON] and [PERSON] [18] investigated experimental and numerical analyses of the flexural behavior of concrete beams reinforced with high-strength prestressing strands. For the same reinforcement amount, beams reinforced with high-strength strands exhibited slightly smaller service stiffness than those reinforced with conventional reinforcing bars. The influence of key factors, including the degree of reinforcement, concrete strength, steel yield strength, compression steel ratio, and confining pressure, were studied by [PERSON] et al. [19], based on a theoretical method. They developed a new method of beam design called \"concurrent flexural strength and deformability design\", which would allow both strength and deformability requirements to be considered simultaneously. The process of optimizing the design of flexural strength and ductility of reinforced concrete beams depends mainly on conducting laboratory tests on both concrete mixtures and steel reinforcements, which are costly and time-consuming. In this study, we construct a reliable theoretical tool to efficiently predict and optimize the responses of a reinforced concrete beam without depending on only laboratory tests. For this purpose, the machine learning approach is presented. We utilize numerical modeling using ATENA software with the support of the Box-Behnken design method to construct 13 models of reinforced concrete beams to build the surrogate models.
# A Unified PUF and Crypto Core Exploiting the Metastability in Latches
## 4 ChaCha20  
The ChaCha20 cipher is implemented using a permutation matrix using a nonce, key, counter, and some constants [31; 32]. This cryptography function is based on the Salsa20 algorithm [33]. ChaCha20 is used in TLS 1.3 to provide another solution different from the cipher based on the AES [30]. Figure 9 illustrates the architecture of the implemented ChaCha20 core. The architecture uses four quarter round (QR) operators to permute the operation regs in the block function highlighted in blue. Additionally, a finite state machine, highlighted in red, controls the permutation of the operational regs depending on the actual round (column or diagonal). Therefore, a column or diagonal round is finished in one cycle. In the last round, the values of the initial and operation regs are added to obtain the keystream. Finally, a _crypto function_, highlighted in green, applies an XOR operation between the _plain text_ and the keystream. The PUF is implemented in the _initial regs_, replacing the regs with latches. The signal _mode_ switches between the memory and PUF operations in the latches. In addition, a 32-bit _challenge_ is used to select one of the two branches of each latch to obtain the response of the PUF.  
Figure 8: Strategies and constraints in the PUF implementation.  
Figure 9: Architecture of the ChaCha20 crypto core [31].  
Figure 10 depicts the quarter round (QR) operation implemented in the ChaCha20 crypto core. The inputs (a,b, c, and d) each denote one stage of the operational matrix. Each stage of the initial and operational matrix consists of a 32-bit register. The results (A, B, C, and D) are stored in the same position as those used earlier in the round. The QR operator is implemented using an add, rotate, and XOR operations [41]. The rotate operation, highlighted in red, is implemented using a wire permutation to reduce the area required.  
Figure 11 shows the organization of the initial stages, column, and diagonal rounds of the ChaCha20 matrix. The initial stages are stored in the _initial regs_, using 128 bits of constants, 256 bits of the key, 96 bits of nonce, and 32 bits of counter. However, the counter stage is implemented using the latches described in Section 3.3. ChaCha20 works in a normal function when the latches are used to store the counter's value. On the other hand, the latches in the metastable mode generate a random number depending on the process variations inside the latches. In addition, the ChaCha20 core can initialize a pseudo-key derivation using the value of the latches in metastability mode, permuting the key and the nonce used in the ChaCha20 algorithm for the ChaCha20-Poly1305 AEAD.
## 5 Conclusions  
The results suggest that the Trimble R12 GNSS receiver, when mounted on a harvester, is capable of achieving a high level of positioning accuracy, with 86% of the observations meeting the maximum horizontal precision of 8 mm. The analysis of the geometric quality of the GNSS signals showed that the presence of a canopy may impact the performance of the Trimble R12; this is evidenced by the lower number of satellites used in determining 'Inaccurate' positions and the higher values of GDOP, PDOP, VDOP, and HDOP. Canopy cover estimated from the viewshed analysis of the DSM was found to be a significant predictor of PDOP and the maximum possible deviation from the true position in northing or easting directions. Despite the greater computational demands of the viewshed analysis of the DSM, these findings suggest that it provides more precise results when evaluating the impact of canopy cover on the harvester GNSS's positioning.  
Conceptualization, K.V. and L.S.; methodology, E.L.; formal analysis, E.L., A.K., H.K. and E.H.; investigation, E.L.; resources, L.S., J.R. and Y.N.; writing--original draft preparation, E.L.; writing--review and editing, E.L., K.V., A.K., H.K., E.H., L.S., J.R. and Y.N.; visualization, E.L.; project administration, K.V.; funding acquisition, J.R. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Academy of Finland Flagship Programme (Forest-Human-machine Interplay (UNITE)) [grant No 337653], Density4 Trees [Decision No 331708], the Natural Resources Institute Project \"CCFBASIS\", and project BOFORI (KA4002) within the Karelia CBC Programme financed by the European Union.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors wish to thank Metsa Group, Ponsse Plc, and Motoajo Ltd. for help and collaboration in collecting the measurements for this work.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 4 Experiments and Discussion
### Real Image Experimental Results and Discussion  
Real surveillance images were captured by a CMOS (complementary metal-oxide-semiconductor) sensor with a 3 s exposure time. The detector has \(10\) K \(\times\) 10 K pixels, \(10^{\circ}\times 10^{\circ}\) field of view and a 12-bit gray level. To see the experimental results more intuitively, we cropped the image to \(1024\times 1024\) pixels. The results of nonuniform background elimination are shown in Figure 11, where (a-c) are original surveillance images and (d-o) refer to those obtained after eliminating the nonuniform backgrounds in various ways.  
Unlike the simulation experiment, in the real image experiment, we cannot obtain images without nonuniform backgrounds that only contain stars and space targets. As a result, we cannot evaluate the effect of nonuniform background elimination in terms of RMSE.
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Existing Problems and Solutions
#### 2.3.2 Solution  
Taking all of the above factors into consideration, this paper proposes an acceptable and feasible piles foundation underpinning design plan to solve this problem. It makes fully consideration on the metro route line design, construction cost and the safety of the bridge structure. According to the underpinning and reinforcement plan, two new supported piles and a bearing platform should be built before the construction of the shield tunnel, after those new foundation structures were finished, connected the new bearing platform with the original one, and used small hydraulic equipment to cut the original bridge piles off, and completed the bridge structure system stress-conversion. In order to minimize the impact of construction on the bridge structure, a series of monitoring devicessteel strands, the underpinning bearing platform design and prestressed steel strands, arrangement are presented in Figure 2.  
Figure 2: Design plan of the underpinning bearing platform.  
Piles sealing and backfilling construction  
Before the bridge piles foundation underpinning construction was completed, the last step is sealing piles and backfilling.  
In this process, the new underpinning foundation bearing platform would be connected with the new pile cap by pouring concrete into the connecting area under the new building bearing platform both sides. When the force given by the connecting area concrete reaches the designed strength, the supporting jacks can be slowly unloaded. Then the excavating area should be backfilled and the bridge foundation construction can be completed.  
Shield tunnel that crosses through the existing bridge piles foundation could be started after the sealing piles and pit backfilling process. Checking the whole bridge foundation structure underpinning construction qualities comprehensively before shield machine approached. Field monitoring data from the bearing cap and bridge pile such as settlements, displacements and stress changes are critically vital. According to the design requirement if there is a settlement that over 2 mm/day or the cumulative value is over \(\pm\)15 mm, the shield tunneling construction would be ceased until find the causing reason and make it corrected. The briefly schematic diagram of the piles foundation underpinning construction is illustrated in Figure 4.  
Figure 3: Construction process of the pile foundation underpinning.
## 3 Array System Overview
### Single Element of SA Array
The introduction of the semi-cylindrical metallic superstrate results in an improvement of approximately \(5\) dBi in the realized gain compared to the standalone slotted patch antenna. The metallic reflector also effectively preserves circular polarization at \(412\) MHz within a \(2\) MHz bandwidth, as demonstrated by the Axial Ratio (AR) behavior shown in Figure 5.  
The realized gain value is confirmed by the measured results shown in Figures 6 and 7, which present the realized gain for both RHCP and LHCP ports, in both principal planes. The HPBW is equal to \(75^{\circ}\), while the measured SLL is approximately \(-10\) dB in both cases. Figure 4: Measured reflection coefficient for SA element. Figure 5: Measured Axial Ratio for SA element. Figure 6: The radiation pattern of the SA element for the RHCP port. The polarization isolation between the co-polar and cross-polar components in the E plane and H plane is approximately 12 dB, which is sufficient to ensure proper separation between orthogonally polarized signal components.