esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Parameters Sensitivity Analysis  
In this subsection, we will discuss the parameter sensitivity of our model using the truncated IND PINE dataset with 10 labeled samples selected from every class. There are two parameters in PCSSR algorithm, \(\lambda_{1}\) and \(\lambda_{2}\). \(\lambda_{1}\) controls the sparsity of \(W\) while \(\lambda_{2}\) controls the effect of class structure regularizer. We repeat 50 runs for each fixed parameter configuration and present the average results. For example, in Figure 10a, we use a fixed \(\lambda_{1}\) value and varies \(\lambda_{2}\) value to observe the classification results. For each \(\lambda_{2}\) value to be observed, we repeat 50 runs, calculate the classification accuracy during each run, and finally obtain the average accuracy. During the experiment, we first keep \(\lambda_{1}\) equal to \(1\times 10^{-4}\) and vary the value of \(\lambda_{2}\) from \(1\times 10^{-5}\) to \(1\times 10^{-4}\) with the step of \(1\times 10^{-5}\). As we can see from Figure 10a, the algorithm reaches the optimal performance when \(\lambda_{2}\) equals \(7\times 10^{-5}\). Then we fix \(\lambda_{2}\) and let \(\lambda_{1}\) change. As illustrated in Figure 10b,c, the OA basically keeps the same when \(\lambda_{1}\) is between \(1\times 10^{-5}\) to \(1\times 10^{-4}\), and drops when \(\lambda_{1}\) is larger. The result shows that sparsity and probabilistic structure both matter in the classification process, though the variety of performance isn't so great when parameters change.
# Semantically Guided Enhanced Fusion for Intent Detection and Slot Filling
## 3 Methodology
### Classifiers  
The slot filling classifier is specifically designed to accurately predict the class of slots in a given sentence. This task is achieved through a dual approach: the conditional random fields (CRFs) layer and the fully connected (FC) layer. Together, they play a crucial role in identifying diverse slot filling types. The CRFs model is a well-suited choice for sequence annotation tasks, as it aims to assign labels to individual units within a sequence. In our research, the global word vectors generated by BERT are fed into the FC layer to construct a labeled sequence \(y=\{y_{1},y_{2},\,\dots,\,y_{n}\}\). Then, the CRFs layer calculates the observed conditional probability of a given labeled sequence \(y\) based on the hidden vector \(H_{L}\) of BERT. Following maximum likelihood estimation, the prediction result \(L_{CRF}\) is shown in the following equation:  
\[p(y|H_{L})=\frac{\prod_{i=1}^{n}T(y_{i-1},y_{i},H_{L})}{\sum_{y^{\prime}\in Y }\prod_{i=1}^{n}T\left(y^{\prime}_{i-1},y^{\prime}_{i^{\prime}},H_{L}\right)} \tag{10}\]  
\[L_{CRF}=-\sum_{i=1}^{M}\log(p(y^{(i)}|H_{L})) \tag{11}\]  
where \(T(\cdot)\) denotes the transition function. Given an input sequence \(H_{L}\) and a pair of consecutive labels, for instance, \(y_{i-1}\) and \(y_{i}\), this function computes the percentage probability of the chosen label sequence relative to the entire set of possibilities. Here, \(Y\) signifies the predefined set of labels, formulated according to the BIO labeling scheme.  
An intent detection classifier is designed to predict the intent type expressed in a sentence. In this paper, the [CLS] vector outputted by BERT is used as sentence vector. It is an approach that has shown significant performance in several natural language processing tasks owing to the richness of semantic information of the vectors [33]. Subsequently, the sentence vector is fed into the FC layer for sentence intent prediction.
# Fracture Toughness of Hybrid Components with Selective Laser Melting 18 Ni300 Steel Parts
## 1 Introduction
[14] verified that the tensile and fatigue behavior in mechanical tests of SLM manufactured H13 specimens compared with the results to those conventionally produced showed tensile strength of as-built specimens to be up to 30% lower than conventional material. Fatigue strength was also significantly lower than reference data. These results were related to the high roughness of the 3D-printed surface and the presence of high tensile residual stresses. [PERSON] et al. [15] realized impact tests in a three different conditions: 3D-printed, 3D-printed heat-treated and conventional heat-treated specimens in H13 tool steel. These authors observed that the H13 steel is so brittle and that it substantially limits direct use of the printed part. Proper heat treatment must be carried out after SLM process in order to obtain desired properties. Studies regarding hybrid parts are not common. [PERSON] et al. [16] studied the fracture behavior of additively manufactured 18 Ni300/H13 hybrid hard steels. These authors observed a presence of an interface between the different steels. On the other hand, tensile tests of hybrid as-printed sample show that the fracture occurs in the H13 steel, away from the interface. The authors of this research [17] also studied the morphologic and fatigue strength of additively manufactured 18 Ni300/H13 and 18 Ni300/AlSI420 hybrid samples. The presence of an interface between the different steels was also observed. The fatigue strength has a lower influence due to the substrate (H13 or AISI420) and the hybrid samples show a lower fatigue life when compared with the as-printed samples. Moreover, the fatigue crack initiation, propagation and final fracture were localized in the as-printed material, being different of the [PERSON] et al. [16] results because these authors used an untreated H13 steel in the as-printed samples. [PERSON] et al. [18] studied 18 Ni300 marage steel clads deposited onto an AISI 304 substrate. These authors observed that the dilution with the substrate is as low as 10 to 28%, which warrantsthe adhesion of marage to AISI 304 steel, and they developed a statistical analysis and mathematical model that can be used to select the process parameters to produce high-density parts, reaching a average density value of 98.2%. No publications about the fracture toughness of conventional steels with 18 Ni300 steel implants built by SLM were found in the literature, justifying the importance of the current investigation in order to understand the influence on material fracture of the geometric shape, the presence of defects and load mode. The results of this study can lead to a better understanding of mechanical design and reliability of mechanical projects of conventional steels with 18 Ni300 steel implants built by SLM. Therefore, the main objective of this work is to study the fracture toughness of both fully 3D-printed specimens and hybrid specimens composed of a conventional steel substrate and a steel implant, both 3D-printed by SLM.
## 1 Introduction  
Ultra-dense networks (UDNs) are a critical technology for future wireless communication systems, aiming to enhance network capacity [1]. The core concept of UDNs involves the dense deployment of numerous base stations (BSs) and small cells. However, dense BSs and small cells will lead to serious inter-cell interference [2]. In recent years, an effective solution to solve the interference problem has been proposed as a cell-free (CF) network [3], where multiple BSs work in unison to serve each user, with these BSs under the control of a central node. This setup eliminates cell boundaries and effectively reduces inter-cell interference, thereby enhancing network capacity [4]. However, the dense deployment of BSs in CF networks can lead to increased implementation costs [5].  
Fortunately, a reconfigurable intelligent surface (RIS), the revolutionary new technique, offers an energy-efficient and cost-effective approach for establishing favorable propagation conditions between BSs and users [6, 7, 8]. An RIS is a thin film made of electromagnetic reconfigurable material, composed of mass passive reflecting elements. Specifically composed of sub-wavelength meta-material particles, the elements of an RIS are connected via multiple positive-intrinsic-negative (PIN) diodes [9]. Thanks to these programmable components being assembled inside, RISs can be considered as passive reconfigurable-phased arrays that require no additional active power sources. They rely solely on the programmable elements' intelligent reflections to boost the performance of wireless networks. The unique design of RISs offers significant advantages in wireless communication systems. By manipulating the reflections of incident signals, RISs can enhance signal strength, reduce interference, and extend coverage in challenging environments. This flexibility in configuring the propagation conditions opens up new possibilities for network design and optimization, enabling more efficient and reliable wireless connectivity.  
It is entirely conceivable that RIS integration into a CF network, which we are calling an RIS-CF network, would capture all of the key benefits of both techniques, as shown in Figure 1. In such a network, the BSs are connected to the central processing unit (CPU) via error-free forward links, and all BSs cooperate fully under the control of the CPU to serve all users with RIS assistance. Different from the large-scale phased array antennas realized by phase shifters in the CF system, RISs eliminate the need for complex digital phase shifter circuits, significantly reducing energy consumption and signal processing complexity [10]. RISs provide a low-cost way to increase network capacity and achieve broader coverage. Therefore, compared to the traditional CF systems, an RIS-CF system only needs lower power consumption to achieve the same level of quality of service (QoS). This reduction in power consumption is a key advantage that sets the RIS-CF system apart from traditional CF networks. Furthermore, the integration of RIS technology into CF networks offers a new dimension for enhancing network performance.
# Experimental Assessment of the Effect of Vertical Earthquake Motion on Underground Metro Station
## 2 Shaking Table Tests
### Instrumentation  
The locations of these instruments were all based on numerous prior numerical analyses using finite element method [31; 32], which are shown in Figure 3.  
Figure 3: Layouts of sensors for soil-station model: (**a**) accelerometers of side wall and soil across the whole depth in observation plane-1; (**b**) accelerometers of soil adjacent to station in observation plane-2; (**c**) soil pressure cells in observation plane-3; (**d**) strain gauges; (**e**) photos of strain gauge, accelerometers, and soil pressure cells (all dimensions in mm).  
Five accelerometers (AM-1, AM-2, AM-3, AM-4, and AM-5), as shown in Figure 3a, were placed in the middle between the center of the model soil and the wall of the flexible-wall container to capture the acceleration responses with soil depth. In considering the symmetry, Figure 3a only shows one-half of the whole container-soil-structure system while the accelerometers are completely displayed.  
As displayed in Figure 3a,b three accelerometers (AN-1, AN-2, and AN-3) were arrayed in the model soil adjacent to the side wall, which are set to investigate the effects of the underground metro station on the soil acceleration.  
Three more accelerometers (AW-1, AW-2, and AW-3) were installed near the previous ones on the side wall to record its acceleration. The depths of the accelerometers AW-1, AW-2, and AW-3 correspond to the depths of the base slab, middle slab, and ceiling slab, respectively. The comparison of the acceleration responses between the two groups of accelerometers is expected to investigate the effect of RVH on the dynamic interaction, which is a most interesting topic for the soil-structure system.  
Soil pressure cells PL1-PL11 on the left side wall and PR1-PR11 on the right side wall were arrayed to investigate the amplitude and distribution of the dynamic soil normal stress on the two walls, Figure 3c. The interval between any two adjacent cells was 48 mm, which allowed to capture the essential data and to represent the complex stress distribution along the side wall as stated by the early finite element analyses.  
Strain gauges, plotted in Figure 3d, were arrayed to investigate the values and spatial distribution of the structural strain, especially focusing near the joints between different structural components as the ends of the beam, column, side wall, and slab. In Figure 3d the strain gauges S23 and S24 were located on the top of the column (Z1 in Figure 2b), which was just below the longitudinal beam (ZL2 in Figure 2b).  
Figure 3e shows the selected photos of various sensors. The experimental procedure is shown in Figure 4.
## 4 _Bt_ Lower Limit
### Measurement Error
#### 4.1.3 Dynamic Stress Error  
The DPLL suffers from the dynamic stress error when the receiver-satellite line-of-sight (LOS) range varies rapidly, for example, if the receiver moves fast. The endurable order of the dynamic stress is related to the order of the DPLL as described in Table 1. The dynamic stress error of the DPLL is incurred by the dynamic component that has higher order than the trackable component of the DPLL and can be generally modeled as [2]:  
\[\theta_{\mathrm{e}}=\frac{d^{n}R/dt^{n}}{\omega_{0}^{n}}[\mathrm{deg}] \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Oscillator Type** & \(h_{0}\) **[s]** & \(h_{-1}\) **[-]** & \(h_{-2}\) **[1/s]** \\ \hline Temperature-compensated crystal oscillator (TCXO) & \(1.00\times 10^{-21}\) & \(1.00\times 10^{-20}\) & \(2.00\times 10^{-20}\) \\ Oven-controlled crystal oscillator (OCXO) & \(2.51\times 10^{-26}\) & \(2.51\times 10^{-23}\) & \(2.51\times 10^{-22}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Clock parameters for the calculation of the Allan deviation oscillator phase noise.  
where \(n\) is the loop filter order and \(d^{n}R/dt^{n}\) is the LOS dynamic stress [\(\deg/s^{n}\)] of the \(n\)-th order DPLL. The typically used second-/ third-order DPLLs for the GNSS receiver suffer from the \(d^{2}R/dt^{2}\) and \(d^{3}R/dt^{3}\), which are the acceleration stress and jerk stress, respectively.
## A Pattern Construction Scheme for Neural Network-Based Cognitive Communication  
**[PERSON] * and [PERSON]**  
Faculty of Electricity and Electronics, Istanbul Technical University (iTu), 34469 Istanbul, Turkey;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +90-212-285-6900; Fax: +90-216-346-6187.  
_Received: 1 December 2010; in revised form: 21 December 2010 / Accepted: 22 December 2010 / Published: 7 January 2011_  
**Abstract:** Inefficient utilization of the frequency spectrum due to conventional regulatory limitations and physical performance limiting factors, mainly the Signal to Noise Ratio (SNR), are prominent restrictions in digital wireless communication. Pattern Based Communication System (PBCS) is an adaptive and perceptual communication method based on a Cognitive Radio (CR) approach. It intends an SNR oriented cognition mechanism in the physical layer for improvement of Link Spectral Efficiency (LSE). The key to this system is construction of optimal communication signals, which consist of encoded data in different pattern forms (waveforms) depending on spectral availabilities. The signals distorted in the communication medium are recovered according to the pre-trained pattern glossary by the perceptual receiver. In this study, we have shown that it is possible to improve the bandwidth efficiency when largely uncorrelated signal patterns are chosen in order to form a glossary that represents symbols for different length data groups and the information can be recovered by the Artificial Neural Network (ANN) in the receiver site.  
**Keywords:** cognitive radio; pattern recognition; spectrum management; noise immunity; neural network  
**MSC Codes:** 68T10
## 2 Materials and Methods
### Sample Preparation and Gamma-Ray Measurement  
Sediments were dried at 100 \({}^{\circ}\)C for about 24 h in a drying oven, and thereafter passed through a sieve with 2 mm openings to remove pebbles or hay, and mixed well. Sediments were packed into a polypropylene container (PP-U9, As one Co. Ltd., Japan, 50 mm diameter \(\times\) 25 mm high) to a height of 20 mm, weighing 50-60 g. Gamma-ray measurements were conducted using a low-background Ge detector (GEM-110225, EG&G Ortec, Gaithersburg, MD, USA) in 2012. A new Ge detector (GEM 30-70, EG&G Ortec, Gaithersburg, MD, USA) was utilized for samples collected in 2013-2016.  
The radiocesium concentration in dissolved, and particulate components in river water are usually separated using a 0.45 \(\upmu\)m membrane filter. Then, the radiocesium in dissolved component is concentrated by several methods such as evaporation [27], co-precipitation [28; 29; 30], and Prussian blue-impregnated ion-exchange methods [31]. In the present work, we have applied a simple evaporation method to unfiltered water sample and radiocesium concentration was determined by measuring gamma-ray using a well-type Ge detector which has high sensitivity for small amount of samples. This method was previously utilized to determine uranium and radium concentrations in groundwater [32]. Unfiltered water sample (1 L) was measured accurately with a volumetric flask, and dried on a 250 \(\times\) 250 \(\times\) 2 mm Teflon sheet-lined steel tray on a hot plate at 120 \({}^{\circ}\)C for about 6 h. After drying process, the Teflon sheet was rolled up to a 13 mm diameter \(\times\) 40 mm long size and pushed into a polyethylene tube. Gamma-ray measurements were performed using a low-background well-type Ge detector (GWL 120230-S, EG&G Ortec) which has a well port of 14 mm diameter \(\times\) 40 mm deep. This well-type Ge detector was equipped with an anticoincidence system composed of plastic scintillators, to reduce the cosmic-ray background level [33]. The typical gamma-ray measuring time was 80,000 s.  
The detection efficiencies of three Ge detectors were determined using a set of volume calibration sources (code No. MX033U8 PP) manufactured by the Japan Radioisotope Association (JRIA). The detection efficiencies were previously reported, taking into account the coincidence-summing effect for \({}^{134}\)Cs and self-absorption [34]. The detection limits of the present Ge spectrometers for the  
Figure 2: River sampling sites in the Minami-Soma City and the Iitate Village. Colors indicate the initial \({}^{137}\)Cs deposition normalized on 2 July 2011 [7].  
\({}^{137}\)Cs measurements were 5 Bq\(\cdot\)kg\({}^{-1}\) for sediment with Ge detectors, and 2 m Bq\(\cdot\)L\({}^{-1}\) for water with the well-type Ge detector.
## 3 Materials and Methods  
### Split City Port Features  
The Republic of Croatia is a maritime country with a thousand-year tradition. Due to the extremely indented coast with a length of 6278 km and 1244 islands, islets, rocks, and reefs, maritime transport is of particular importance for the coastal and island populations [46].  
Split is the second-largest city in the Republic of Croatia and the largest Croatian city on the eastern coast of the Adriatic Sea [46]. Split is an important cultural and traffic center, the second-largest Croatian port, and the third-largest passenger port in the Mediterranean. In 2024, the city of Split was visited by 1,040,300 tourists and had 3,130,060 overnight stays [47]. The port of Split is the most important passenger port in Croatia; it connects the large islands of the Croatian coast. Moreover, it also takes part in international passenger traffic from Italy. In addition, the port has a cargo port with a relatively modest local hinterland consisting of the Dalmatia region and parts of Bosnia and Herzegovina. The port of Split is a port open to international public traffic, and according to its size and significance, it is a port of special (international) economic interest for the Republic of Croatia [48]. Figure 3 shows the plan of the City Port of Split. The land area of responsibility of the Split Port Authority is marked in gray.  
Emissions of harmful gases into the environment in the Split waterway are generated by vessels that maintain state ferry lines, high-speed shipping lines, and long-shore and international lines. Furthermore, large cruise ships, fast private and public ships, and recreational vessels that use the nautical port within the City Port of Split also contribute to emissions of harmful gases. Figure 4 shows the type of vessels that sail into the City Port of Split [50]. Ferry transport accounts for one part of the emissions of harmful gases generated in the Split waterway. Observing the planned quotas for fuel procurement of the state operator Jadrolinija in 2023 [51], it is evident that the analyzed ferry fleet accounts for 60.8%. The remaining part of the procurement refers to high-speed shipping lines and international lines. In the case of electrification of all vessels entering the City Port of Split, an analysis of energy needs should be performed for each group of vessels separately.  
Figure 4: Types of vessels using the City Port of Split [48].  
Figure 3: Plan of the City Port of Split [49].  
The study [52] analyzes the inventory of pollutants generated by line vessels during the maneuvering and hoteling phase in the area of the City Port of Split. The total inventory of harmful effects on the environment in the observed three-year period is as follows: non-methane volatile organic compounds (NMVOC) 38.31 (t), PM 33.97 (t), CO\({}_{2}\) 38,908.95 (t), NOX 675.17 (t), SO\({}_{2}\) 368.15 (t). In the case of a transition to fully electric propulsion, where ESS is charged via RES, there would be zero emissions.
## 3 Results and Discussion
### Tensile Test
Similar to the observations for the RSW titanium alloy cases, the unequal-thickness RSW joints for AISI 304 (case H) exhibited the lowest strain before failure compared to the equal-thickness RSW joints for cases D and G. This was due to the reasons previously explained for the titanium RSW joints. Figure 4: Shear strength of RSW specimens of titanium alloy cases: A (1–1 mm), B (0.5–0.5 mm), and C (1–0.5 mm). Figure 5: Shear strength of RSW specimens of stainless steel cases: D (1–1 mm), G (0.5–0.5 mm), and H (1–0.5 mm). Regarding the shear strength of the titanium-stainless steel 304 dissimilar joint, case J, which includes dissimilar stainless steel (0.5 mm) and titanium (1 mm), exhibited the highest shear strength of 183.9 MPa, as illustrated in Figure 6, whereas case E, consisting of stainless steel (1 mm) and titanium (1 mm), demonstrated the lowest tensile shear strength of 114.97 MPa. Furthermore, case F (SS (1 mm) and Ti (0.5 mm)) and case I (SS (0.5 mm) and Ti (0.5 mm)) demonstrated shear strength of 128.96 MPa and 156.2 MPa, respectively.