esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Cone Penetration Testing Apparatus with a Textured Shaft
## 2 Laboratory Experiments
### Experimental Setup  
The test samples are prepared using Jumunijn standard sand, and the physical properties of the sand are summarized in Table 1. The initial relative density of approximately 64% and a sample height of 70 cm are achieved using the air-pluviation and layered compaction methods. Figure 3 illustrates the entire process from sample preparation to the assembly of the cone penetration test apparatus. The detailed procedures are explained here.  
1. Sand is placed to a depth of 100 mm using the air-pluviation method, followed by compaction using a 2.5 kg circular tamping rod dropped from a height of 350 mm. The sand is compacted in seven layers, each tamped 100 times. The surface is then leveled to ensure even contact between the sample surface and the bottom of the loading plate [Figure 3a-c].
2. After securing the hydraulic cylinders to the loading plate, the plate is installed on top of the sand sample [Figure 3d].
3. The vertical actuator for cone penetration and pull-out and the hollow load cell for vertical load measurement are attached to the reaction frame, which is secured to the model chamber using a bolt-nut system [Figure 3e,f].
4. An aluminum connecting rod is installed in the center of the hollow load cell, and the upper part of cone is attached using bolts and nuts [Figure 3g]. A wire displacement sensor is installed to the bottom housing cover of the vertical actuator, and then the wire is connected to the upper housing cover [Figure 3h].
5. The hydraulic cylinder control device is adjusted to apply a predetermined vertical load by opening the hydraulic cylinder pump [Figure 3i].
6. Using the LabVIEW2021 program, the cone penetration depth and speed are controlled, and the load and vertical displacement are monitored in real-time during the test. The data are automatically saved after the experiment [Figure 3j].  
Figure 2: Schematic diagram of a miniature cone with a textured shaft. The height of surface asperity in shearing direction A is increased during penetration testing. In contrast, shearing direction B decreases the height of surface asperity.
## 1 Introduction
Hence, the occurrence of memory access streams with wide strides that exceed the size of the correlated granularity is an issue that should be addressed. To this end, our model analyzes such streams based on the page granularity using the second module. When three memory requests from the last-level cache occur for the same memory page, we conduct a pattern matching process for deltas among the delta histories stored in the history table. If the same delta pattern exists in the history table, our prefetching model generates prefetching candidates and sends prefetching requests to the LLC. These steps of triggering a particular prefetching operation make the first and second modules more conservative. As a result, the two modules will be expected to yield a higher prefetch accuracy ratio but a lower coverage ratio. To cover a wider memory stream range, our model performs prefetch operations more aggressively using the third module. However, pursuing a high prefetching coverage ratio without generating the proper candidates negatively impacts the overall system performance. To maintain a high prefetching accuracy, our model changes its prefetching strategy more aggressively when memory accesses are requested across different correlated lines within the same page granularity. To conduct an aggressive prefetching, even if only one delta exists on any page, a prefetch operation is triggered by the analyzed correlation in the history table. To evaluate our proposed model, we implement an in-house simulator that supports the complete cache hierarchy and main memory layers. The simulator uses the memory traces generated by Intel's Pin framework. We configure our prefetch buffer using embedded DRAM (eDRAM) [14], which is a promising technology that fills in the gap between on-core and off-core memory latency. The prefetch buffer is placed between the LLC and DRAM main memory. The prefetched data will be placed from the DRAM main memory to the eDRAM-based prefetch buffer. A comparative analysis was conducted regarding the execution time and energy consumption with other state-of-the-art prefetchers, and other system configurations were evaluated in the same environment. As a result, our proposed model achieved an average improvement in the execution time of 15% compared to both the global history buffer (GHB) [1] and best-offset (BO) prefetchers [2,3] and 6% and 2% compared to the Markov prefetcher and variable-length delta prefetcher (VLDP), respectively. In addition, our proposed model reduced the energy consumption by approximately 18% compared to both the GHB and BO prefetchers, and by 6% and 2.3% compared to the Markov prefetcher and VLDP, respectively. The rest of this paper is organized as follows. The background and related studies are described in Section 2. In Section 3, the details of our proposed model and its internal architecture and operation methodologies are elucidated. Furthermore, in Section 4, evaluation results and an analysis based on a simulation are described. Finally, Section 5 provides a summary and some concluding remarks regarding this paper.
## 6 Evaluation
### Evaluation Results
#### 6.2.4 Rate of Ability for Stray Nodes to Communicate in Multiple Hops  
If a stray node can communicate with the leader node, it can return to the group. Therefore, it is desirable that communication with a stray node can be established in multiple hops. Figure 16 shows the rate of the ability for stray nodes to communicate in multiple hops. This rate is hereafter referred to as a \"stray node communication ability rate.\" This rate is calculated as follows.  
\[\text{SNCAR}=\ \frac{Number\ of\ communication\ attempts\ that\ nodes\ outside\ the\ area\ have\ made}{Number\ of\ departures\ from\ the\ area} \tag{14}\]  
Number of communication attempts that nodes outside the area have \(\text{made}=\ \sum_{i=1}^{N}i_{NCAO}\) (15)  
\[\text{Number of times that a node has gone outside the area}=\ \sum_{i=1}^{N}i_{NTWO} \tag{16}\]  
where \(i_{NCAO}\) is the number of communication attempts that node \(i\) has made outside the area, and \(i_{NTWO}\) is the number of moments when node \(i\) went outside the area. Figure 16 indicates that stray nodes can communicate in multiple hops at a certain probability. It also shows that, as the number of nodes increased, the stray node communication ability also increased. An increase in the number of nodes results in an increase in the node density, which means that there are more relay nodes available. Figure 16a shows that, in the case of AR-Proposal, 60% of stray nodes were able to communicate. In Figure 16b, RR-Proposal took a higher communication ability rate than RR-Basic but the rate was less than half of the rate provided by AR-Proposal. Even when multiple hops are involved, the autonomous response-type stray node detection protocol provides a higher rate than other protocols.  
Figure 16: _Cont._These results indicate that using CSMA/CA only cannot avoid contention sufficiently and thus cannot detect stray nodes efficiently. The best protocol for detecting stray nodes is the autonomous response-type stray node detection protocol. However, as mentioned in Section 4, the request-response-type stray node detection protocol can reduce the number of communication attempts. In an environment in which terminals have limited power supply, it is important to reduce the number of communication attempts. In such an environment, the request-response-type stray node detection protocol can be a preferred protocol.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 4 Methods  
In wilderness formation tasks, it is common for backpack computers to preprocess images captured by wearable image sensors. This typically involves using the 2D Fourier Transform before applying intelligent processing to the images. The 2D Fourier Transform requires performing 1D Fourier Transforms on the rows and columns of the image individually. That means the calculation process comprises three steps, namely: (1) performing the Fourier Transformation according to Formula (5), (2) transposing the result obtained in step 1, and (3) performing another Fourier Transformation using Formula (5).  
\[\begin{bmatrix}1&1&1&\cdots&1\\ 1&\omega&\omega^{2}&\cdots&\omega^{N-1}\\ \vdots&\vdots&\vdots&&\vdots\\ 1&\omega^{N-1}&\omega^{2(N-1)}&\cdots&\omega^{(N-1)^{2}}\end{bmatrix}\times \begin{bmatrix}x[0]&x[1]&\cdots&x[N-1]\\ x[1]&x[2]&\cdots&x[2(N-1)]\\ \vdots&\vdots&&\vdots\\ x[N-1]&x[2(N-1)]&\cdots&x\left[(N-1)^{2}\right]\end{bmatrix}=\begin{bmatrix} y[0]&y[1]&\cdots&y[N-1]\\ y[1]&y[2]&\cdots&y[2(N-1)]\\ \vdots&\vdots&&\vdots\\ y[N-1]&y[2(N-1)]&\cdots&y\left[(N-1)^{2}\right]\end{bmatrix} \tag{5}\]  
Considering that the forward and inverse transformations are commonly used in pairs in engineering applications, the inverse transformation algorithm adds only a conjugationoperation based on the forward transformation algorithm, thus having little influence on the software architecture. Moreover, the inverse transform can intuitively demonstrate the effects of the forward transform, helping to validate the correctness of the transform algorithms. Therefore, the image preprocessing process is designed for both forward and inverse transformations.  
In this section, two algorithms based on NPUs were designed to accelerate the application of the 2D Fourier Transform in image processing for formation task scenarios. One uses the direct matrix multiplication, while the other uses the divide-and-conquer method.
## 6 Experimental Validation
### Validation of Barometric Sensor Height Monitoring  
Here, we verify the corrective effect of the BP neural network training model on the barometric pressure sensor. Firstly, the air pressure sensor is used to measure the air pressure value of the first floor, and the air pressure value of the first floor is used to zero. Then, the height difference is obtained according to the subsequent measurement of the air pressure value, the floor height is 3.5 m, and the measurement results are shown in Table 4.  
According to the experimental results, it can be obtained that the relative height obtained after temperature correction and the actual height error is within \(\pm\)20 cm, and the leveling accuracy meets the design requirements of this system, and during the experiment, all data are accurately transmitted to the cloud server.
# Grouped Contrastive Learning of Self-Supervised Sentence Representation
## 2 Related Work  
### Representation Learning Based on Contrastive Learning  
Contrastive learning obtains promising results in representation learning [14; 17; 22]. Generally, a Siamese network is used to construct the contrastive framework and conduct contrastive training [14].  
In the computer vision domain, contrastive learning achieves significant improvement in the representation of image. SimCLR [14] uses an encoder and projection head as the contrastive framework, which advances the state-of-the-art results for image representation. BYOL [17] designs a momentum encoder to avoid collapsing on contrastive training, which obtains an encouraging result. SimSiam [16] uses BYOL's contrastive framework, but it changes the way that a network's parameters are updated. Surprisingly, stop-gradient not only solves the issue of the model's collapse but also obtains comparable results in STS tasks.  
Contrastive learning achieves promising results for image representation. Therefore, researchers have started to adopt contrastive learning to obtain high-quality sentence representation. CERT [23] augments a sentence by back-translation and performs contrastive training using the contrastive framework of MoCo. CMV-BERT [24] adopts different tokenizers to conduct sentence augmentation and performs contrastive training on the framework of the SimSiam. ConSERT [25] performs data augmentation (such as token shuffling, adversarial attack, cutoff, and dropout) on word vectors of BERT to obtain positive samples and achieve encouraging results. More details about contrastive learning in sentence representation are shown in Table 1.  
While great success has been achieved by contrastive learning in sentence representation, deficiencies still exist in the aforementioned methods, hampering the improvement of performance. The details are shown below: (1) Most methods directly utilize the framework of computer vision as the pipeline for contrastive learning. Therefore, it could hamper contrastive training because text data are discrete and sparse compared to image data. (2) The well-performing pre-trained models (such as BERT) are adopted as the backbone network of contrastive learning. Consequently, they cannot evaluate the performance of a lightweight model on sentence representation using contrastive learning. After all, the pre-trained model works well in NLP tasks. (3) Improper data augmentation could change the original semantics of a sentence. Most methods use discrete data augmentation to produce positive samples to perform contrastive training, which could deteriorate the original semantics. Different from the aforementioned methods, we design a dedicated contrastive learning framework for sentence representation, namely, GCLSR. To obtain high-quality positive samples, GCLSR uses partial-word vector augmentation, a continuous form of data augmentation, which can maintain more of the original semantics of sentences. Further, GCLSR uses a lightweight model TextCNN to explore the effectiveness of contrastive learning on sentence representation.
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Domestic Water Heating (DWH)  
The studies show that the annual average per hospital bed varies between 29 m\({}^{3}\)/bed and 47 m\({}^{3}\)/bed in Europe and between 36 and 55 m\({}^{3}\)/bed in the USA [27]. In Greece, the annual consumption of water varies between 32.85 and 43.8 m\({}^{3}\)/bed [28]. [PERSON] (2010) calculated that the average consumption of DWH in a hospital per year was between 40 and 60 m\({}^{3}\)/bed [29].  
[PERSON] et al. (2007) studied the empirical assessment non-residential building stock in Greece, energy consumption, emissions, and potential energy savings [30]. Their energy conservation measures proposed were: installation of solar collectors for DWH production, addition of thermal insulation of exposed external walls in hotels and healthcare, replacement of old inefficient boilers, installation of building management systems, and regular maintenance of central heating boilers.  
In order to quantify the economic and environmental impact from energy saving by optimizing DWH facilities, a total of 10 hospitals and 50 healthcare centres were analysed for a three-year period.  
Figure 3 shows the potential average energy saving per built surface area (m\({}^{2}\)) through the optimization of the DWH facilities, in healthcare buildings with less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds). A potential average saving between 0.65 kWh and 0.80 kWh per built surface area is detected, in a payback period of less than three years.  
Here are the specific energy saving measures used: timers with programmed disconnection for DWH system production by means of water heaters, or storage tanks, in order to interrupt operation when no activity takes place. Installation of low consumption taps and tanks with double push buttons to reduce cold water for human consumption (CWHC) consumption.
## 5 Experimental Results
### Learning Behavior
The results showed 21 behavior transitions with z-scores significantly greater than 1.96, indicating statistical significance. Similarly, Table 8 presents the results for the low-achievement group, with 20 behavior transitions having z-scores significantly greater than 1.96, indicating significance. Figure 4 shows that high achievers, after starting a task, tend to engage in selective dialogue with the AI chatbot and repeat this dialogue (Q \(\rightarrow\) H\(\rightarrow\) AS\(\rightarrow\)AS), demonstrating targeted and in-depth help-seeking behavior. Additionally, these students shift to watching instructional videos (V\(\rightarrow\) I) or reading-task clues (V\(\rightarrow\) L) when needed, then seek help from the AI chatbot again (I\(\rightarrow\) H, L\(\rightarrow\) H), leading to selective dialogue (H\(\rightarrow\) AS), often repeating the process to ensure deep understanding and thorough problem resolution. After these interactions, they typically start new tasks (V\(\rightarrow\) Q). In contrast, low achievers also seek help from the AI chatbot after starting tasks (Q\(\rightarrow\) H) but engage more frequently in free dialogue (H\(\rightarrow\) AF) and free questioning (AS\(\rightarrow\) AF). After watching videos or reading task clues, they typically proceed directly to answering questions (L\(\rightarrow\) S) rather than seeking further help. In summary, comparing the behavioral patterns of high and low achievers, we find that regardless of achievement level, students actively seek help from the AI chatbot during AI chatbot-supported digital game-based learning. However, high achievers exhibit more systematic and in-depth learning strategies, engaging in multiple interactions and repeated confirmations to master task essentials, while low achievers rely more on immediate feedback and external assistance.
## 4 Conclusions  
MLLC ceramics with different amounts of CaTiO\({}_{3}\) additives were prepared using the conventional solid-state method. The effects of CaTiO\({}_{3}\) contents on the structures, sintering behaviors, and MWDPs were studied. A mixed-phase structure composed of Mg\({}_{2}\)SiO\({}_{4}\), LiTiO\({}_{2}\), Li\({}_{2}\)TiO\({}_{3}\), Li\({}_{2}\)MgSiO\({}_{4}\), SiO\({}_{2}\), and CaTiO\({}_{3}\) was detected using the XRD pattern. The relative density significantly increased after adding CaTiO\({}_{3}\), leading to a significantly increased \(Q\)\(\times\)\(f\). The dielectric constant and \(Q\)\(\times\)\(f\) first increased and then decreased with the increase in the sintering temperature, and achieved a maximum value when the MLLC11.5 ceramics were sintered at 800 \({}^{\circ}\)C with an \(\varepsilon_{r}\) of 14.13 and \(Q\)\(\times\)\(f\) of 54,581 GHz (at 8.06 GHz). The \(\tau_{f}\) increased linearly with the increase in the CaTiO\({}_{3}\) content and reached three near-zero values of \(-5\), +2.59, and +5.81 ppm/\({}^{\circ}\)C for MLLC9.5, MLLC10.5, and MLLC11.5 compositions, respectively. The MLLC11.5 composition could be co-fired without any reaction with Ag powders at 800 \({}^{\circ}\)C, indicating LTCC application. In the investigation of the GPS antenna, the MLLC11.5 composition exhibited good potential for antenna application.  
Conceptualization, Z.W. and Y.C.; methodology, Z.W. and F.P.; validation, Z.W. and Y.C.; investigation, Z.W., F.P. and L.L.; resources, Y.C.; data curation, Z.W. and Y.C.; writing--original draft preparation, Z.W.; writing--review and editing, Z.W., Q.D., R.T. and Y.C.; visualization, J.A. and H.Z.; supervision, Y.C.; project administration, Y.C.; and funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Province Key Research and Development Project (Grant Nos. 2019C05002, 2020C05004, 2021C01183), National Natural Science Foundation of China (Grant No. 11902292), Postdoctoral Program of Zhejiang Province (Grant No. ZJ2020018), Natural Science Foundation of Zhejiang Province (Grant Nos. LQ20E030008, LQ20E050014, LQ20A020005, LGG20E050011) and National Key Research and Development Program of China (Grant No. 2020 YFC2007101).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 7: GPS antenna application. (**a**) Designed sample, (**b**) front face of the GPS antenna, (**c**) back face of the GPS antenna, (**d**) return loss (S11), (**e**) experimental process of GPS signal, and (**f**) C/N0 and number of satellites.
## 4 Experiments and Analysis
### Comparison of Various Object Detection Algorithms  
In a comprehensive analysis comparing the YOLOv8-E network with existing mainstream and newer algorithms, as documented in Table 4, YOLOv8-E demonstrated superior performance. Specifically, YOLOv8-E achieved the highest [EMAIL_ADDRESS] and mAP@95 scores. YOLOv8-E not only maintains high accuracy but also a small model size of only 5.6 MB, ranking second only to YOLOv5n among the compared models and reducing the size by 0.4 MB compared to YOLOv8n. Additionally, YOLOv8-E demands reduced computational  
Figure 14: Heat map comparison of different improved models.  
overhead, exhibiting 7.0 GFLOPs, which is 1.1 lower than YOLOv8n, making it suitable for integration into embedded mobile devices. In terms of computational proficiency, YOLOv8-E surpasses classical algorithms such as Faster R-CNN (370.2 GFLOPs) and SSD (162 GFLOPs) by a considerable margin. YOLOv3-tiny, YOLOv6n, and YOLOv7-tiny fall short of YOLOv8-E in relation to the accuracy, model size, and GFLOPs, while YOLOv5n, though slightly smaller in size, has considerably lower [EMAIL_ADDRESS] and mAP@95 scores. The two-stage detector Faster R-CNN exhibits the lowest detection accuracy and computational efficiency, and SSD, while achieving an [EMAIL_ADDRESS] of 71.2%, has an excessive model size that hinders real-time detection. Compared to the newer YOLOv9c model, YOLOv8-E not only improves [EMAIL_ADDRESS] and mAP@95 by 4.2% and 1.3%, respectively, but also boasts a smaller model size and lower GFLOPs. Lastly, RT-DETR-R50 is still inferior to YOLOv8-E in terms of detection accuracy and computational efficiency, indicating that both YOLOv9c and RT-DETR-R18 require significant processing resources. Overall, YOLOv8-E emerges as a superior choice for eggplant disease detection due to its balanced performance in accuracy, model size, and computational efficiency.  
To further verify the superiority of our algorithm in terms of detection performance, we compared its detection effects on different eggplant disease categories with other algorithms and recorded the [EMAIL_ADDRESS] for each category, as shown in Table 5. When using YOLOv8-E to detect various eggplant disease categories, the [EMAIL_ADDRESS] for healthy, fruit borers, fruit rot, and yellow spot disease reached 79.5%, 81.6%, 78.6%, and 79.3%, respectively, representing improvements of 1.7%, 8.1%, 6.3%, and 7.3% over the YOLOv8 model. Moreover, YOLOv8-E outperforms other models in detecting all types of eggplant disease. This further demonstrates the superiority of the YOLOv8-E in terms of eggplant disease detection performance.