esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
(1) The SUCS increases linearly with increasing static pre-stress when the dynamic loading rate ranges from 2 \(\times\) 10\({}^{-4}\) to 2 \(\times\) 10\({}^{-3}\) s\({}^{-1}\). In addition, the higher the dynamic strain rate, the greater the differences in SUCS under different static pre-stresses.  
(2) When the static pre-stress is less than 40-50% of the UCS, both SUCS and elastic modulus are logarithmically increased with the dynamic loading rate. The SUCS is independent of the dynamic loading rate when the static pre-stress exceeds 60-70% of the UCS.  
(3) For the same dynamic loading rate, the released elastic energy density decreases linearly as the static pre-stress increases, and the dissipated energy density increases. Under low static pre-stress (40% of the UCS) and a high dynamic loading rate (2 \(\times\) 10\({}^{-2}\) s\({}^{-1}\)), the released elastic energy density of specimen is high (257 kJ/m\({}^{3}\)), while the dissipated energy density is low (37.5 kJ/m\({}^{3}\)), which may lead to a greater hazard of rock bursts.  
(4) More cracks, rock fragments, fine particles and even ejected powder can be observed at a higher dynamic loading rate. Failure mode is transitioned from an axial tensile failure to a shear failure. However, the failure modes of different static pre-stresses are basically the same under the same dynamic loading rate.  
(5) Strain localization can be used to identify the crack initiation and propagation processes. The failure mode is closely related to the strain localization zone. The onset of strain localization tends to occur earlier (from post-peak to the peak stress) with the increases in dynamic loading rate, indicating that the strain localization has a significant rate dependence.  
[PERSON] provided methodology and funding; [PERSON] and [PERSON] contributed to the experimental operation; [PERSON] analyzed data and wrote the paper; [PERSON] and [PERSON] reviewed the final paper and made important suggestions and recommendations for paper revision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. 51991393) and the National Natural Science Foundation of China (grant no. 52079134).
## 2 Data and Methods  
### Study Area  
China is located in East Asia (3 deg51'N\(-\)53 deg33'N and 73 deg29'E\(-\)135 deg04'E) and has an approximate land area of 9.63 \(\times\) 10\({}^{6}\) km\({}^{2}\) and a sea area of 3.00 \(\times\) 10\({}^{6}\) km\({}^{2}\)[35]. The main plain regions in China include the Northeast Plain (NEP), the North China Plain (NCP), and the middle-lower reaches of the Yangtze River Plain (MLRYR); the main plateau regions include the Inner Mongolia Plateau, the Loess Plateau (LP), the Qinghai-Tibet Plateau (QTP), and the Yun-Gui Plateau (YGP). The main basins in China include the Sichuan Basin (SCB), the Dzungarian Basin, and the Tarim Basin (Figure 1a). Based on the regional characteristics of China, the major crop production areas (croplands) can be divided into nine subregions ([[http://www.resdc.cn/data.aspx?DATAID=275](http://www.resdc.cn/data.aspx?DATAID=275)]([http://www.resdc.cn/data.aspx?DATAID=275](http://www.resdc.cn/data.aspx?DATAID=275)), accessed on 21 October 2021): the NEP, NCP, MLRYR, YGP, SCB, LP, QTP, southern China (SC), and arid and semiarid northern China (ASANC) (Figure 1c).  
The land-cover map used herein was derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) land-cover-type product (MCD12C1) and was based on the International Geosphere-Biosphere Programme (IGBP) classification scheme. The MCD12C1 product released in 2019 was used to classify different land-cover types without considering the land-cover-type changes that occurred during the 2001-2019 study period, and the proportions of different land-cover types were calculated (Table 1). The evergreen needleleaf forest (ENF) and evergreen broadleaf forest (EBF) land-cover types were mainly located in southwestern China; the deciduous needleleaf forest (DNF) and deciduous broadleaf forest (DBF) land-cover types were mainly located in northeastern China; mixed forests (MF) were mainly located in northeastern, southwestern (YGP), and southeastern China; closed shrulands (CS) were mainly located in southwestern (YGP) China; open shrulands (OS) were mainly located in western (QTP and Tarim Basin) and northern (Inner Mongolia Plateau) China; woody savannas (WS) were mainly located in southwestern (YGP) and  
Figure 1: (**a**) Topographic and geographic zones of China; (**b**) land-cover types of China; and (**c**) the nine major crop production areas of China.  
southeastern China; savannas (SA) were mainly located in northeastern China; and steppes (ST) were mainly located in northeastern, northern (Inner Mongolia Plateau), northwestern (Tarim Basin), and southwestern (QTP) China. Croplands were mainly located in northeastern (NEP), northern (NCP), and southwestern (SCB) China. Urban and built-up (UB) areas were dispersed across China. Barren or sparsely vegetated (BSV) areas were mainly located in the northwestern part of China (Figure 1b). The percentage of the cultivated area in each crop production area is 58.47% (NEP), 82.26% (NCP), 44.01% (MLRYR), 31.53% (SC), 28.70% (YGP), 26.20% (SCB), 44.42% (LP), 7.752% (ASANC), and 0.70% (QTP).
## 2 Engineering Geological Conditions
### Hydrogeological Conditions  
The planned tunneling site passes through the limestone stratum in the Lower Permian Qixia Formation. As the field investigation revealed, the underground water in the tunneling area is mainly carbonate karst fissure water, according to the lithology and water-bearing characteristics of the aquifer (layer) group. The main manifestations of karst are dissolution pores and cracks. According to the in situ exploration and drilling, the dissolution pores and cracks of rock core are generally 1-6 cm in size, precipitation of calcite crystals is present, and the karst caves are not exposed. Investigation of the ground surface and tunnel exit cliffs found no development of karst caves, as shown in Figure 3a.  
Figure 2: Photos of the surrounding rocks in the entrance and trunk sections: (**a**) surrounding rocks at the portal and (**b**) surrounding rocks in the trunk section.  
The new tunnel is constructed nearly parallel to the slope orientation. The bottom elevation of the tunnel is higher than the groundwater level, and its overburden is shallow. Its top is a mountain slope zone, which is beneficial for ground surface drainage. The surface drainage is mostly discharged through overland runoff, while only a small part is infiltrated to replenish the groundwater. High-to-low migration is achieved via karst fissures and conduits. An in situ investigation of the existing tunnels showed that the dampening and dripping of local cavern walls are seen occasionally, as shown in Figure 3b. Hence, the possibility of water intrush in the tunnel is small, and there is no major spring resource near the exploration area.  
According to the water sample test results of the groundwater and environmental water, regional hydrogeological data, and local experience in Liupanshui, the groundwater, environmental water, and soil layers in the tunnel site are slightly corrosive to concrete structure. In the present exploration, no groundwater was found in the two boreholes, and one surface water sample was collected for the laboratory analysis of water quality. As the results show, the surface water was slightly corrosive to both the concrete structure and rebar.
## 4 Results and Analysis
### Performance Investigation of Traditional Models
#### 4.1.2 Results of Standard AI Models Combined with Pre-Processing Techniques
By comparing each model with the linear trend line of the line y = x, it is clear that the linear trend line of the QM-EMD-Elman model is closest to the line y = x, while the EMD-Elman model is the following closest. This shows that the QM-EMD-Elman model has better prediction accuracy than the other model. In Figure 12, the NSE indicator is analyzed separately because it is the indicator that best reflects a model's strengths and weaknesses [44]. As can be seen in the figure, the NSE indicators of the Elman model, EMD-Elman model, and QM-EMD-Elman model show an increasing trend, and it can also be seen that the QM-EMD-Elman model has the best prediction results among the three models. As can be seen from Figure 13, the prediction accuracy of the Elman model, the EMD-Elman model, and the QM-EMD-Elman model was further evaluated using Taylor plots. Taylor diagrams are based on the geometrical relationships between the correlation coefficient, standard deviation, and the root mean square error [17]. Taylor plots precisely and effectively reflect the properties of a prediction model, indicating when results are closer to the observed values, and are thus better prediction [45]. The graph shows that the predicted values of the QM-EMD-Elman model are closest to the observed values. Figure 12: Trend changes in the NSE indicator for the three models. Figure 13: Taylor diagram.
## 2 Related Work
### Topic Identification and Text Categorization  
Topic identification and text categorization are fundamental tasks in NLP and information retrieval [28]. Topic identification extracts the main idea from a text by classifying or grouping it into predefined categories [29,30]. Its outcome provides an understanding of the content, facilitating tasks like summarizing and recommendation. Text categorization is used to analyze and organize text into categories based on their content [31,32]. Its application is crucial for email filtering, fake news detection, domain identification, and sentiment analysis [33, 34, 35, 36]. Topic identification in interviews refers to the automated categorization of interview transcripts into predefined categories [37]. It can be performed with statistical approaches, latent semantic approaches, traditional ML algorithms, rule-based systems, and Deep Learning (DL) frameworks [29,38,39]. NLP techniques develop the ML models' ability to categorize text [40,41]. Regarding ML techniques in topic identification, approaches range from statistical (e.g., Term Frequency-Inverse Document Frequency), latent semantic (e.g., Latent Dirichlet Allocation, Latent Semantic Analysis) [42,43], to traditional ML algorithms like Naive Bayes, for its simplicity and computational efficiency in large datasets, and Support Vector Machines for their ability to effectively handle complex data [44, 45, 46]. Rule-based systems and decision trees also play a significant role, offering either predefined sets of rules or tree-like models for topic identification, respectively [47]. Moving to DL approaches, frameworks like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Transformers have become prominent [48]. Their capability to capture intricate contextual relationships within the text and complex patterns, improves the performance and reliability of topic identification [49,50].
# Visual-Based Localization Using Pictorial Planar Objects in Indoor Environment
## 1 Introduction
### Visual Matching and Mapping  
Different approaches and methods for producing the location information from camera captured images have been developed. Research projects on VBL [1] have shown a big diversity on the tradeoff between operation simplicity and result quality. VBL methods can be divided into two general classes: matching only methods and matching with mapping methods.  
Matching only VBL can be modeled as an image look up and retrieval problem [3]. This class of methods produces a camera's current location by searching for similar images from a known image collection when compared with scenes of the camera view. The camera location is given by the image(s) with the most resemblance to a view using image matching techniques. The location is a simple lookup without any mapping or positional conversion. Due to the limited precision, this class of methods can be used by some simple, approximate context-aware applications, such as mobile scenic guide and ROI (region of Interest) searching.  
To achieve more accurate localization, researchers have studied VBL using the matching with mapping approach. Such methods generate the precise location and orientation of a camera by mapping the camera view to a 3D position using the spatial relationship between the camera and its view on the 3D map. The 3D map may be built either prior to runtime or on-the-fly depending on the application scenario. The location produced by such mapping-based methods can be very precise due to the adoption of sophisticated 2D-to-3D conversions. These methods are better for indoor navigation and route planning of moving devices since the location produced from VBL is a precise 3D coordinate on the 3D map.
# Simulation of Hybrid Photovoltaic Solar Assisted Loop Heat Pipe/Heat Pump System
## 3 Model Development
### Model of LHP Mode
#### 3.1.1 Model of PV/T Evaporator  
Heat transfer process of the PV/T collector is shown in Figure 3, with schematics of heat transfer illustrated in Figure 3a. After shining into PV cells, part of the sunlight was converted into electrical energy, part was lost to the external environment, and the rest was converted into heat energy absorbed by the refrigerant. In a steady-state condition, the PV/T evaporator's internal energy did not change with time, as such, the heat collected by collector per unit time can be calculated as follows:  
\[Q_{e}=Q_{s}-P_{pv}-(Q_{con}+Q_{rad}) \tag{1}\]  
\(Q_{e}\) was further evaluated by using Equations (2) and (3) [20]:  
\[Q_{e}=E_{s}A_{P}\tau_{g}(\alpha-\beta\eta_{pv})-U_{L}E_{s}A_{p}(T_{pv}-T_{a}) \tag{2}\]  
According to the heat transfer resistance model of the PV/T-collector shown in Figure 3b, the total heat loss coefficient between PV cells and environment, \(U_{L}\), was defined by:  
\[U_{L}=(\frac{1}{h_{con}+h_{rad}}+R_{glass}+R_{EVA})^{-1} \tag{3}\]  
where \(R_{glass}\) and \(R_{EVA}\) are the heat transfer resistances of armored glass and EVA film used in silicon cells, respectively. \(h_{con}\) and \(h_{rad}\) are the respective heat loss coefficients of the convective and radiation heat transfer between the surface of PV/T and ambient air, and can be evaluated by using Equations (4) and (5) [21], separately:  
\[h_{con}=2.8+3.0u_{w} \tag{4}\]  
\[h_{rad}=\varepsilon\sigma({T_{pv}}^{2}+{T_{a}}^{2})(T_{pv}+T_{a}) \tag{5}\]  
Figure 3: Heat transfer process of the photovoltaic/thermal (PV/T) collector. (**a**) Schematics of heat transfer in the PV/T collector; (**b**) Heat transfer resistance model of the PV/T collector.  
As mentioned previously by many researchers, the performance of PV cells could deteriorate with increases in its working temperature. The dependences of crystalline silicon cells' electric efficiency, \(\eta_{pv}\), was evaluated by [PERSON] et al. [22]:  
\[\eta_{pv}=\eta_{0}[1-0.00045(t_{pv}-25)] \tag{6}\]  
where \(\eta_{0}\) is the electric efficiency of PV cells at standard conditions (i.e., \(E_{s}\) = 1000 W/m\({}^{2}\), \(t_{pv}\) = 25 \({}^{\circ}\)C).  
In the PV/T evaporator, the refrigerant that absorbed heat from solar irradiation experienced changes of phase state. The heat transfer coefficient of two-phase region was calculated by [PERSON] [23]:  
\[h_{tp}=3.4X_{tt}{}^{-0.45}h_{l} \tag{7}\]  
where the heat transfer coefficient of liquid refrigerant was given by:  
\[h_{l}=0.023R_{el}{}^{0.8}p_{r}{}^{0.4}\frac{\lambda_{l}}{d} \tag{8}\]
###### Abstract  
The aim of this study is to identify a method that can maximize implant primary stability (IPS) and bone density under the controlled drilling conditions of the same diameter and length in low-density bones through an ex vivo study. A total of 87 dental implants were placed with standard drilling, under-drilling, and osseedensification drilling in 13 fresh porcine sternums. The Periotest value and the implant stability quotient were measured to evaluate the primary stability. The difference in the Hounsfield unit (HU) between the hole and peripheral bone up to a distance of 1 mm was measured. Osseedensification and under-drilling technique increased the IPS, compared with conventional drilling technique with statistical significance under the drilling conditions of the same diameter and length. Osseedensification technique with the counter-clockwise direction had higher HU gaps than the standard drilling and osseedensification technique with clockwise direction. Due to the effect of bone densification, the gap of HU was increased by a minimum of 43 HU and a maximum of 180 HU. Within the limitations of this ex vivo study, it was found that the osseedensification technique with counter-clockwise direction is effective to increase IPS and bone mineral density in low-density bone.  
biomechanics; bone density; dental implants; osseedensification; osseedensification
## 3 The Clustering Protocol  
The main steps of the clustering protocol in this paper are as follows:  
1. Calculate the number \(k\) of clusters required according to the calculation formula of the network optimal cluster number introduced in Section 2.3.
2. Through the AGNES (AGglomerative NESting) algorithm with balanced energy consumption optimization, we can build the required \(k\) clusters.
3. Implement the selection mechanism of the CH in each cluster, then we can implement the D-CHs division of the energy balance strategy and node dormancy mechanism for the large cluster area before and after the death of the first node, respectively.
4. Data transmission and energy update.  
To minimize the total energy consumption and balance the energy consumption of the nodes in the network, we perform the node death decision after each round of data transmission in the network (once the node dies, return to Step 1; otherwise, return to Step 3). In addition, Steps 1, 2, and 3 are collectively called the preparation phase of the protocol. Step 4 is called the stabilization phase of the protocol.  
Before entering the stabilization phase, each member of each cluster needs to send a set of control message named as _Node_Msg_ to its CH in the form of (_Node_NO_, _Node_Status_). The status is only divided into work and dormancy. According to the received _Node_Msg_, the CH of each cluster allocates a time slot for the member nodes of the cluster that need to work. Then every CH sends a set of control message named as _Schedule_Msg_ to its member nodes that need to work in the form of (_Node_ _NO.1_, _Time Slot1; Node_ _NO.2_, _Time Slot2;...... ). Once entering the stabilization phase, the nodes which have received time slots send their sensed information to their associated CHs, and others are in dormancy. As for the CHs, they are responsible for receiving and processing the information sent by the member nodes and eventually transmitting it to the BS. The time slot allocation of the clustering protocol in this paper is provided in Figure 4. A flowchart of the clustering protocol in this paper is presented in Figure 5.  
Figure 4: The time slot allocation of the clustering protocol.
## 3 Results
### Limitations and Effectiveness of Combining VR and FES  
#### 3.5.1 Effectiveness of Combining VR and FES  
1. Enhanced Motor Function and Dexterity  
Integrating VR and FES has proven highly effective in enhancing motor function and dexterity in stroke patients. Notably, studies by [PERSON] et al. [33], (2018), [PERSON] et al. [35], (2020), and [PERSON] et al. [34], (2021) have reported significant improvements in clinical assessments such as WMFT, BBT, and 9 HPT. These improvements underscore the potential of the combined VR and FES approach to enhance gross and fine motor skills, making it a potent tool in neurorehabilitation.
2. Increased Patient Engagement and Motivation  
VR's immersive nature creates an engaging and motivating environment for patients, which is critical for successful rehabilitation [38]. [PERSON] et al. [32], 2022, and [PERSON] et al. [33], 2018, highlighted high levels of patient engagement and adherence to rehabilitation programs. The interactive and adaptive VR systems kept patients motivated and committed to their therapy, which is essential for achieving significant functional gains.
3. Neurolasticity and Brain Function Improvements  
[PERSON] et al. [35], (2020) demonstrated that integrating EEG with VR and FES improved motor function and enhanced neuroplasticity. EEG data showed significant changes in brain activity patterns, indicating that this combined approach could facilitate recovery. These results support other work that indicates that BCI can play an essential role in cortical reorganization underlying functional improvement [39; 40; 41].
## 2 Experimental Procedure  
### Down-Sized Plasma Nitriding System  
The high-density RF (Radio-Frequency)/DC (Direct Current) plasma nitriding system is downsized by 1/20 as suitable equipment for surface treatment of miniature mechanical elements in the dimensional size range from mm to sub-mm. Figure 1a depicts a table-top plasma nitriding system with a chamber size of \(\upphi\)350 mm \(\times\) 150 mm. This chamber is automatically operated to open and move upward for the experiment setup. Every experimental operation is performed on the touch-panel in the controller. All commands as well as data acquisition are driven by a process computer through wireless communication.  
There are two setting-up modes in this system, for nitriding a single component and for simultaneously nitriding a stack of elements up to 24 pieces. In the following, the former setup is used to describe the nitriding behavior of a single micro-nozzle. The latter is also employed to demonstrate the feasibility of simultaneous nitriding in mass production.  
The hollow cathode device was utilized to increase the N\({}_{2}\)\({}^{+}\) ion and NH-radical densities in the experimental setup to work in both modes. Figure 1b depicts an experimental setup for nitriding of a single micro-nozzle. As also illustrated in Figure 1c, N\({}_{2}\) + H\({}_{2}\) mixture gas was introduced to this DC-biased hollow. Since the DC-bias was also applied to this micro-nozzle, the ignited RF (Radio Frequency)-plasma was also confined inside the micro-nozzle by the hollow cathode effect.  
Figure 1: Down-sized plasma nitriding for surface treatment of the miniature mechanical elements. (**a**) Experimental apparatus, (**b**) hollow cathode to intensify the ion density in N\({}_{2}\) + H\({}_{2}\) plasmas, and (**c**) illustration on the hollow cathode device.