esa-sceva/satcom-chunk-collection huggingface dataset:
# Channel Measurement and Modeling for 5G Urban Microcellular Scenarios
## 4 Evaluations  
### Power Delay Profiles  
Figure 3a shows a typical result of one measurement run on Track Rx1 in the form of averaged power delay profiles (APDPs) evolving over time. During the measurement run, the RX moved away from the TX, starting at 25 m distance and ending at 50 m. Each APDP has been obtained by averaging over \(K=~{}250\) \"instantaneous power delay profiles\" \(|h_{k}(t)|^{2}\) at consecutive RX points according to  
\[P(\tau)=\frac{1}{K}\sum_{k=1}^{K}\left|h_{k}(\tau)\right|^{2}, \tag{1}\]  
where \(P(\tau)\) denotes the APDP and \(h_{k}(\tau)\) is the \(k\)th CIR. \(K=~{}250\) corresponds to a spatial averaging over 10 cm or 20 wavelengths. Comparative investigations with different factors have shown that the dominant effect of this kind of averaging on the given data is the reduction of thermal noise  
Figure 2: Top view of 3D models used for ray tracing. **(a)** OS-CC scenario; **(b)** “street canyon, city center” (SC-CC) scenario.  
and phase noise. It is clearly visible that the first resolvable multipath component (MPC) is subject to strong fading. Based on geometric considerations, the distinct regular structure can clearly be attributed to the superposition of the direct path and the ground reflection, which cannot be resolved with 250 MHz bandwidth for larger distances. Further MPCs can be observed up to 800 ns delay. They are also subject to fading on a scale above 50 cm. However, overall, the APDP is relatively sparse. The MPCs will lead to time dispersion on the one hand. On the other hand, the related propagation paths can be very beneficial if the LOS path is blocked.  
Figure 2(b) illustrates the corresponding simulated APDPs. The first MPC and its fading structure are well reproduced by the ray tracer and further strong MPCs are identified. However, in the measurements, they underlie a stronger fading on a time scale of several seconds. This indicates that the ray tracer is able to predict arriving path clusters, but not potential intra-cluster paths from a structured surface, e.g., outer walls with windows inwards. The granularity of the 3D model is of the order of 1 m and does not account for such structures. Furthermore, scattering, blockage and dynamic changes of the environment caused by cars and pedestrians cannot be considered. The ray tracer also tends to overestimate the strength of MPCs arriving with long delays, which gives rise to further investigations. The reason may be partially diffuse rather than ideal specular reflections. However, it is important to note that the results have been achieved without calibration of the ray tracer, but based on the simple assumptions mentioned above.
## 2 Summary of the Paper
### A Compressed Sensing Based Ultra-Wideband System  
The concept of the compressed sensing based UWB system is illustrated in Figure 26. The core of the system lies in compressed sensing [19, 20, 21, 31]. For example, for an RF bandwidth of 5 GHz, a sampling rate of 125 MHz is sufficient for a measured channel impulse response indoors. This example has demonstrated the power of the CS principle to reduce the receiver complexity by simplifying the mixed signal processing. Related concept is used in radar [28] and imaging [32].  
Figure 3: Experimental systems.
# Non Data-Aided SNR Estimation for UAV OFDM Systems
## 3 The Proposed NDA SNR Estimation Method
Therefore, this method can not only extract the mean square of the noise power but also can avoid estimating the maximum delay of the multipath channel of the signals. For extracting the maximum platform, first calculate the average of the data sequence after denoising, then all the parts being larger than the average are supposed to be the maximum platform parts. The maximum platform and the minimum platform extracted by the proposed method are corresponding to the root mean square of real noise power and total power in Figure 3. Hence, the proposed method is effective. In order to use less data and get more accurate results, averaging \(y_{acc}\) relative to the minimum and maximum platforms to get the root mean square of the noise power and total power. Figure 3: The characteristic curve of \(y_{acc}\) sequence after the wavelet transform.
## 3 Performance Implication of NVM Based Storage
### Effects of Prefetching  
Prefetching is a widely used technique to improve storage system performances. Specifically, prefetching reads not only the currently requested page but also a certain number of adjacent pages in storage, and stores them in the memory. Note that Linux's prefetching reads up to 32 adjacent pages while handling read I/Os. Prefetching is effective in slow storage systems like HDD as the access latency of HDD is not sensitive to the amount of data to be transferred but storage accesses can be decreased if prefetched data are actually used. Note that file accesses usually show sequential patterns, so prefetching is effective in the HDD storage. In this section, we analyze the effectiveness of prefetching when NVM storage is adopted. As the storage access time becomes close to the memory access time, the effectiveness of prefetching obviously decreases. Furthermore, prefetching may waste the effective memory space if the prefetched data are not actually used.  
To review the effectiveness of prefetching in NVM storage, we execute the IOzone benchmark under HDD and NVM storage with/without prefetching and measure the throughput. Figures 8-10 depict the measured throughput of RAMDisk, HDD, and PCMdisk, respectively, with prefetching (denoted as \"ra_on\") and without prefetching (denoted as \"ra_off\"). As shown in Figure 8, prefetching is effective in read performances when the HDD storage is adopted. However, it does not affect the performance of RAMDisk and PCMdisk as shown in Figures 9 and 10. This implies that prefetching is not effective any longer in RAMDisk or PCMdisk.  
Figure 6: Performance effects of the buffer cache when storage is the RAMDisk.  
Figure 7: Performance comparison of the buffer cache with Least Recently Used (LRU) and admission-controlled LRU when storage is the RAMDisk.  
Figure 8: HDD performance when the prefetching option is turned on/off.  
Figure 10: PCMdisk performance when the prefetching option is turned on/off.  
Figure 9: RAMDisk performance when the prefetching option is turned on/off.
## 4 Discussion
### Insights from the Case Studies  
By analyzing the scores obtained by Company Alpha and Company Beta, it is possible to identify weaknesses that may guide the practitioners for improving the overall resilience maturity level. Considering Company Alpha, it is characterized by a low level of maturity for \"Risk Management\" (D1). All the sub-dimensions of \"Risk Management\" (D1) must be improved since they obtained a score of 2 or 1. Also, \"Transparency and Visibility\" (D4) and \"Collaboration and Relationships\" (D5) should be improved to pursue a higher resilience maturity. Regarding \"Transparency and Visibility\" (D4), more efforts should be directed towards \"Digitalization\" (S4.2) since it is evaluated as \"Fully Immuting\". On the other hand, depending on the priority of Company Alpha, either one of the sub-dimensions to \"Collaboration and Relationships\" (D5) can be improved. Working on the former dimensions and sub-dimensions will allow Company Alpha to be more resilient.  
Despite the higher maturity level of Company Beta, there is still room for improvement. Indeed, \"Agility, Flexibility, and Adaptability\" (D2) is evaluated with a low score (2.67). In this dimension, Company Beta should work on \"Product and Process Strategies\" (S2.3), which is the sub-dimension characterized by the lowest maturity (2). Next, Company Beta could work on the sub-dimension \"Backup Suppliers and Supplier Fortification\" (S3.1) since it is the sub-dimension with the lowest score (2). This will assist Company Beta in pursuing a higher level of resilience maturity.  
Despite being significantly different, the two companies did not struggle with the application of the MaMoReS. This possibly provides some hints on the potential external applicability and validity of the model. Indeed, the companies presented significant differences in size and sector. On the other hand, the obtained results cannot be generalized. However, it is possible to assume that similar maturity levels are expected from companies operating in the same sectors and with similar sizes.
###### Abstract
These requirements make deep learning algorithms very challenging in the context of medical images where, typically, only a limited number of images are available. A lot of time and experience are required to annotate medical images, and that is where transfer learning can play a significant role: It allows for the use of a pre-trained architecture that was previously fitted to images of the same domain. Thus, transfer learning is particularly suitable for addressing the DR classification domain, where there is a lack of images to accurately train a CNN from scratch. Several studies have been done to classify DR by using CNN, either by using transfer learning or by introducing novel architectures [21; 22; 23; 24; 25], but to the best of our knowledge, there have not been any reviews that survey the existing transfer learning techniques to classify DR images. To answer this call, in this paper, we discuss state-of-the-art DR image classification models that use the transfer learning of deep CNNs. Moreover, we discuss some important open questions to better apply transfer learning in the DR domain. More in detail, we discuss state-of-the-art models and techniques that were published from 2015 to mid-2019. We used the following descriptors: \"diabetic retinopathy,\" \"convolutional neural  
Figure 1: Random samples of different grades of diabetic retinopathy. (**a**) grade 0, (**b**) grade 1, (**c**) grade 2, (**d**) grade 3, and (**e**) grade 4.  
networks,\" \"transfer learning,\" and \"image classification\" to cover the primary studies that address the classification of DR images by using transfer learning. These keywords were entered into the most well-known academic databases, namely Scopus and PubMed. Two filters were used to produce the results: The first filter excluded any paper that was not about DR, which reduced the results from 172 papers to 31 papers; the second filter excluded any paper that was not about transfer learning, which resulted in 18 papers that were about transfer learning applied to DR. This paper is organized as follows: Section 2 gives an overview of a CNN structure. Section 3 discusses various CNN architectures that are commonly used in transfer learning. Section 4 provides a brief description of the main DR datasets that are available for public use. Section 5 provides a review of papers on the usage of transfer learning in classifying DR. Section 6 presents the discussion, while Section 7 presents open research questions. Finally, Section 8 concludes the paper.
## 4 The Components of FAGD-Net
### Loss Function  
In this paper, we treat the grasp detection problem as a regression task, framing it as the optimization of the minimum error between the predicted grasp and its corresponding label. To achieves this, we employ the L1 loss function, which has the inherent advantage of diminished sensitivity to outliers, encompassing instances that are notably large or small. This characteristic renders the L1 loss function robust and stable for our training model. The mathematical representation of the L1 loss function is denoted as Equation (12).  
\[L1\ \text{loss}=\left\{\begin{aligned} 0.5x^{2},& \quad\text{if}\ |x|<1\\ |x|-0.5&\quad\text{otherwise}\end{aligned}\right. \tag{12}\]  
In each prediction task, the loss for grasping position prediction can be expressed as Equation (13).  
\[L_{p}=-\frac{1}{N}\sum_{i}^{N}L1\ \text{loss}\Big{(}p_{i}-\hat{p_{i}}\Big{)} \tag{13}\]  
Figure 4: The feature Fusion Pyramidal Module.  
where \(p_{i}\) and \(\hat{\vec{p}_{i}}\) represent the ground truth and predicted values of the grasp quality score, respectively.  
The loss for prediction of the grasping angle can be expressed as Equations (14) and (15).  
\[L_{\text{cos}2\theta}=-\frac{1}{N}\sum\limits_{i}^{N}L1\ \text{loss}\bigg{(} \cos 2\theta_{i}-\cos 2\hat{\vec{\theta}_{i}}\bigg{)} \tag{14}\]  
\[L_{\text{sin}2\theta}=-\frac{1}{N}\sum\limits_{i}^{N}L1\ \text{loss}\bigg{(} \sin 2\theta_{i}-\sin 2\hat{\vec{\theta}_{i}}\bigg{)} \tag{15}\]  
where \(\cos 2\theta\) and \(\cos 2\hat{\vec{\theta}_{i}}\) represent the ground truth and predicted values of the grasp angle score, respectively. Similarly, \(\sin 2\theta_{i}\) and \(\sin 2\hat{\vec{\theta}_{i}}\) are also the ground truth and predicted values of the grasp angle score.  
While the loss for the predicted grasp width can be expressed as Equation (16).  
\[L_{w}=-\frac{1}{N}\sum\limits_{i}^{N}L1\ \text{loss}\Big{(}w_{i}-\hat{\vec{w}_{i }}\Big{)} \tag{16}\]  
where \(w_{i}\) and \(\hat{\vec{w}_{i}}\) represent the ground truth and predicted values of the grasp width score, respectively.  
Thus, the total loss for grasping detection can be expressed as the function in Equation (17):  
\[L_{total}=L_{p}+L_{w}+L_{\text{cos}2\theta}+L_{\text{sin}2\theta} \tag{17}\]
# Method for Handling Massive IoT Traffic in 5G Networks
## 9 Simulation Model
The analytical probability, \(a_{x}\) is defined in (12). To find the a-posteriori probability \(a_{x}\) for simulation model, a record of the occurrences of a certain number of IoT packets packed inside a large packet is kept. For this purpose, a counter is deployed for recording arrivals at every stage of the aggregation buffer \(x\). This means that a record of the number of instances where the Coxian process goes into a specific stage \(x\) is maintained. The counters designed for this purpose are denoted as \(c_{x}\) for \(1\leq x<r\) stages and defined in such a way that \(c_{x}\) counts the number of times the large aggregated and multiplexed packet contained \(x\) packets or more. Thus \(c_{x}\) is also the frequency instances that a particular stage \(x\) has been reached by the Coxian process. Probability of \(a_{x}\) is found with the help of value at counter \(c_{x}\). For example, to determine the probability \(a_{1}\), the value at counter \(c_{2}\) is considered. This figures out the likelihood of arrival of second packet with respect to value at counter \(c_{1}\) (i.e., the total number of arrivals). So, \(a_{x}\) is calculated as:  
\[a_{x}=\left\{ \begin{array}{ll}1,&x=0\\ \frac{c_{x+1}}{c_{x}},&1\leq x<r\\ 0,&x\geq r\end{array} \right. \tag{12}\]
## 4 Conclusions  
Underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water-air interface is also highly desirable. In this scenario, communications between UAVs and AUVs across the water-interface can be implemented by VLC or OCC. As the OCC supports wide FOV, several AUVs can receive signals from a UAV. The data received by the UAV can be repeated and retransmitted to neighbor UAVs, which in turn retransmit to ground stations via VLC, IR or RF links. Previously, we successfully demonstrated water-to-air OCC. However, the reverse transmission (i.e., air-to-water) using OCC had not been analyzed. In this work, we proposed and experimentally demonstrated a wide FOV air-to-water OCC system using CuDNNLSTM, which is a library of GPU-accelerated primitives designed for deep neural networks for speeding up the training and inference procedures. It is worth mentioning that in this proof-of-concept experiment, the controlled water tank neglects some effects of real-life open-water situations, such as sunlight interference, biofouling, or multi-path fading. We used tap water, not salt water. Experimental results showed that the proposed air-to-water OCC system can support 7.2 kbit/s transmission through a still water surface, and 6.6 kbit/s transmission through a wavy water surface; this performance fulfills the HD-FEC BER of 3.8 \(\times\) 10\({}^{-3}\). We also compared the proposed CuDNNLSTM algorithm with the PPSNN, showing that CuDNNLSTM outperforms the PPSNN. In addition, we also compared the model training times needed when using CuDNNLSTM and LSTMNN running in Python using the same computer. The time required for CuDNNLSTM was 264.89 s, while that for LSTMNN was 2120.13 s, which was about eight times larger. Finally, we evaluated the stability of our trained CuDNNLSTM  
Figure 11: **(a–c)** The captured RS pattern when the distance between the LED panel and the CMOS camera increases.  
model. The losses of the training and validation data were a good match with each other. The losses decreased rapidly after a few epochs, and became nearly zero after 200 epochs.  
Data curation, Y.-J.C., Y.-H.L. and G.-L.S.; Funding acquisition, C.-W.C.; Investigation, Y.-J.C.; Writing--original draft, Y.-J.C.; Writing--review and editing, C.-W.C. and C.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by National Science and Technology Council, Taiwan, under Grant NSTC-113-2221-E-A49-055-MY3, NSTC-112-2221-E-A49-102-MY3, NSTC-113-2622-8-A49-013-SB, NSTC-113-2640-E-A49-006, NSTC-113-2218-E-011-009.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### HPCC Implementation  
Usually, for the construction of an HPCC, devices with server or workstation characteristics are used. As previously mentioned, the choice of devices depends on the budget and scalability; however, on this occasion, since there was no high budget, we opted for hardware or devices with the characteristics shown in Table 2. For greater ease of adaptability at the time of configuration, it is recommended that all the equipment be similar or the same in model, processing, and storage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Node** & **Node RAM** & **Processor** & **HDD** & **Graphic Card** & **No. CPUs** & **CUDA Cores** & **GC RAM** \\ \hline Master node & 16 & Intel i7-7700K & 240 GB & GTX 1050 ti & 4 & 768 & 4 GB \\ Node01 & 16 & Intel i7-7700K & 500 GB & GTX 1050 ti & 4 & 768 & 4 GB \\ Node02 & 16 & Intel i7-7700K & 500 GB & GTX 1050 ti & 4 & 768 & 4 GB \\ Workstation & 32 & AMD Ryzen 9500X & 1 TB & RTX 3070 & 12 & 5888 & 8 GB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the computers used to implement the CPU cluster.  
Figure 3: Logical configuration of the HPCC.  
An HPCC can be made up of different computer or device architectures. For this implementation, it is important to mention that not all the computers or nodes were of the same brand model (they were assembled), which is a typical and common situation in most cases where one wants to assemble or configure an HPCC with equipment or devices that are no longer used or are collected from different areas of a research center, university, or company.  
Table 2 describes the hardware characteristics of each of the computers used for the implementation of this HPCC.  
The Ubuntu Server 18.04 Operating System was installed on each of the computers, which allowed users to interact with the tools of the HPCC. In order for the infrastructure to function correctly, the aforementioned network services were installed and configured to be able to manage both the users and the applications necessary for user interaction with the HPCC; it is also important to mention that the applications must co-exist or must be compatible for the processing to be carried out correctly. In the event that any of the applications are not compatible or have missing dependencies, the executions or processes that need to be carried out in a distributed or parallel manner cannot be run within an HPCC.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 2 Regional Geology and Tectonics
### Mineral Potential  
The Khyber district possesses a wide variety of mineral resources. These deposits include marble, limestone, dolomite, soapstone, silica sand, barite, mica, and graphite. Semiprecious stones are also found at certain places within the district. Marble deposits are found in the Mullagbri, Sultan Khel, Ghundai Sar, and Loe Shalman areas of the Khyber district. Stratigraphically, the rocks containing marble extend continuously for about 14 km between Ghundai Sar in the south and Shahid Mena in the north. Limestone is abundantly available in the area and is the main source of building material for the area. The greater parts of almost all the high mountains of the area extending from Loe Shilman in the north to Aka Khel in the south are composed of limestone. Sizable reserves of dolomite are available in the Khyber district in the Shilman and Mullagori area (Figure 2). Some coal and fluorite presence has also been reported in the literature, and [46] also reported the presence of localized lead mineralization in carbonate rocks of Jamrud, Khyber, Pakistan.
# Generalized Sketches for Streaming Sets
## 2 Related Work
[2; 12] used different sampling methods to enlarge the estimation range of LPC. [PERSON] and [PERSON] [56] developed a sketch method, FM, which uses a register initialized to zero for cardinality estimation. To further improve the performance of FM, sketch methods LogLog [4], HyperLogLog [5], RoughEstimator [7], and HLL-TaiLCut+ [57] were developed to use a list of \(m\) registers and compress the register size from 32 bits to 5 or 4 bits. [PERSON] [6] developed a sketch method, _MinCount_, (also known as bottom-\(k\) sketch [58]) which stores the \(k\) minimum hash values of elements in the stream to estimate the stream cardinality. [PERSON] [59] developed another order-statistics-based cardinality estimation method which hashes the data stream's elements into \(k\) registers at random, and each register stores the minimum hash value of elements hashed into it. [PERSON] [53] developed a martingale-based estimator to further improve the accuracy of the above sketch methods, such as LPC, HyperLogLog, and MinCount. [PERSON] et al. [60] extended HyperLogLog to sliding windows. In addition to sketch methods, two sampling methods [61; 62] were also developed for cardinality estimation. Recently, considerable attention [8; 9; 10; 11] has been given to developing fast sketch methods to monitor the cardinalities of network hosts over high-speed links. [PERSON] [13] developed methods to estimate the cardinality of set unions and intersections from MinCount sketches. [PERSON] et al. [15] developed a method combining MinHash and HyperLogLog to estimate set intersection cardinalities. These two methods fail to solve the problem studied in this paper, because MinCount does not belong to the LSH family, and cannot be used for sub-linear time similarity search, and MinHash fails to deal with high-speed data streams due to its high time complexity. **LSH for clustering and feature tracking. ** LSH can also support other analytical tasks, such as clustering and feature tracking. [PERSON] et al. [63] proposed the MR-PGDLSH algorithm to reduce the overhead of frequent communication between nodes through LSH, so as to solve the problem of excessive communication overhead faced by the partition-based K-means clustering algorithm in the big data environment. [PERSON] et al. [64; 65] proposed the DENCAST algorithm to process large-scale high-dimensional data using LSH, which is more efficient than the most advanced distributed clustering algorithm. [PERSON] et al. [66] used LSH-based k-nearest neighbor matching to generate feature correspondence, and then used a ratio test method to remove outliers from the previous matching set, and finally achieved better results. [PERSON] et al. [67] proposed a perceptual hash algorithm based on deep learning and LSH to generate edge feature samples to improve the robustness of HRRS image authentication.