esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Loss Function  
The loss function is composed of three parts: the Probability Map Loss, \(L_{p}\), the Binary Image Loss, \(L_{b}\), and the Threshold Map Loss, \(L_{t}\). The total loss is the weighted sum of these three losses. Specifically, \(\alpha\) and \(\beta\) are set to 1 and 10, respectively.  
\[L=L_{p}+\alpha L_{b}+\beta L_{t} \tag{9}\]The losses \(L_{p}\) and \(L_{b}\) utilize binary cross-entropy loss. To address the issue of unbalanced positive-negative sample numbers, a hard negative mining strategy can be adopted within the binary cross-entropy loss. This involves prioritizing the selection of difficult-to-classify negative samples for sampling, which aims to improve the model's performance on an imbalanced dataset.  
\[L_{p}=L_{b}=\sum_{i\in S_{l}}y_{i}\log x_{i}+(1-y_{i})\log(1-x_{i}) \tag{10}\]  
Amongst the sampled collection \(S_{l}\), the ratio of positive to negative samples is 1:3. This collection includes all positive samples, as well as the top \(k\) negative samples with the highest probability values, where \(k\) is three times the number of positive samples. \(L_{t}\) represents the sum of the L1 distances between the predicted results and the labels within the expanded text polygon area, defined as follows:  
\[L_{t}=\sum_{i\in R_{d}}|{y_{i}}^{*}-{x_{i}}^{*}|, \tag{11}\]  
where \(R_{d}\) represents the set of indices of pixels within the dilated polygonal area \(G_{d}\), and \(y^{*}\) denotes the label of the threshold map.
# Algorithm for Extraction of Reflection Waves in Single-Well Imaging Based on MC-ConvTasNet
## 2 Theory and Method
### Network Architecture
#### 2.2.2 Conv2d (Spatial Feature Extraction)
\right]_{2\times L}\), \(\mathbf{k}^{(b)}\in\mathbf{R}^{1\times L}\) is a filter that is shared across all signal channels toensure the same mapping. The b-th ICD between the n-th pair of signal channels in the CSG can be expressed by the following formula [24]:  
\[D_{n}^{(b)}=\sum_{c=1}^{2}\mathbf{h}_{c}\cdot\left(\mathbf{y}_{n_{c}}\odot \mathbf{k}^{(b)}\right), \tag{9}\]  
where \(D_{n}^{(b)}\) is one of the elements in the n-th row of the b-th ICD matrix; \(\mathbf{h}_{c}\in\mathbf{R}^{1\times L},c=1,2\) is a window function designed to smooth out the ICD and prevent potential spectrum leakage; and \(\odot\) represents the Hadamard product. During training, \(\mathbf{h}_{1}\) is fixed as a matrix filled with ones, while \(\mathbf{h}_{2}\) is initialized to \(-1\) and optimized with the other parameters. The ICD in the study of [PERSON] et al. [24] was originally proposed for a circular microphone array. The farther apart the two channels in a channel pair are, the greater the phase difference. To extract phase difference information from eight receivers in a linear receiver array, we select four channel pairs, namely (1, 5), (2, 6), (3, 7) and (4, 8). In MC-ConvTasNet, the spatial information between the four pairs of channels is extracted by a 2-D convolution layer. The number of convolution kernels is 33, the convolution kernel size is (16, 2), the step size is (8, 1) and the expansion is (1, 4).
# A Survey on Hardware Accelerators for Large Language Models
## 6 In-Memory Hardware Accelerators
### X-Former  
In 2023, [PERSON] et al. presented a novel in-memory hardware acceleration to speed up Transformer networks, called X-Former [35]. X-Former is a hybrid spatial in-memory hardware accelerator that consists of both NVM and CMOS processing elements to execute Transformer workloads efficiently.  
X-Former primarily consists of a projection engine equipped with NVM processing tiles for executing MV MStatic operations, and an attention engine with CMOS processing tiles for executing MV MDynamic operations. The main difference compared to other in-memory architectures is that the weights of all the layers are stored in the projection engine to prevent reprogramming the NVM tiles, while the attention engine is optimized to only process the largest self-attention layer due to area constraints.  
X-Former proposes a sequence blocking dataflow, which overlaps the computations of the two processing elements and reduces the total execution time. X-Former has also a dedicated bus-based interconnect network for data transfers between the Projection engine and the attention engine that is used to reduce energy consumption and latency. Compared to the previous in-memory schemes, X-Former avoids frequent reprogramming by mapping static operations to NVM crossbars while processing the dynamic operations using in-memory CMOS processing elements.  
Based on the performance evaluation, it is shown that X-Former achieves improvements of up to 85\(\times\) and 7.5\(\times\) in latency and energy over a NVIDIA GeForce GTX 1060 GPU and improvements of up to 10.7\(\times\) and 4.6\(\times\) in latency and energy over a state-of-the-art in-memory NVM accelerator.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing  
In traditional wireless sensor networks, cluster heads act as aggregators for cluster-based routing. The formation of cluster head in the wireless sensor network is based on the Time Division Multiple Access (TDMA) scheme as followed in the Low Energy Adpative Clustering Hierarchy (LEACH) protocol. However, in terms of an IoT-based routing protocol where downpan communication is used, RPL routing protocol is used. In RPL, the DODAG structure is used for routing among the flowpan sensor nodes, which are unique as compared to traditional sensor routing. The novelty of our work is introducing the concept of an aggregator in RPL using the energy as objective function, which is different from the cluster formation in LEACH routing protocol in sensor networks.  
Secondly, data compression is widely used in sensor routing. However, such data compression for reducing the redundant data transmission with reduced power consumption is not introduced in aggregator in RPL routing for forwarding to the border router. So, towards reducing the dimension of data, redundant data, loss of data, and the number of transmissions, compressed sensing is introduced in aggregator in RPL routing protocol, which is the second novelty. This makes the compressed sensing-based data aggregation novel and unique in RPL-based routing resulting in optimized and energy-efficient routing in the IoT-based environment. Details on aggregator selection and compressed sensing are discussed below.
## 2 Materials and Methods
### Data Analysis  
Data were analyzed with SPSS software v.25 (SPSS Inc., Chicago, IL, USA). Normality was determined by the Shapiro-Wilk test. To determine differences in actEMG variables and peak torque of force during the maximal voluntary isometric contraction for each muscle tested between the ACL and NI groups, the independent samples _t_-test or the U-Mann-Whitney test was used, based on the distribution of the data. Statistical analysis was composed of two parts: (a) Comparative analysis between ACL and NI, considering the laterality of the injured limb. Thus, the DL of players with the ACL injury in DL was compared with the DL of NI players, and the NDL of players with the ACL injury in NDL was compared with the NDL of NI players. (b) Comparative analysis between the limbs within each player. Thus, players were analyzed separately in three categories: players from the ACL group with an injury in the DL (ACL_DL), players from the ACL group with an injury in the NDL (ACL_NDL), and players from the NI group. The mean, the standard deviation (SD), the 95% confidence interval (CI) for the difference in means, and the effect size were obtained. Effect size of actEMG and torque variables between groups and between limbs was determined using Sawilowsky classification [51]. The following values were used to distinguish the levels of effect size: 0.01 to 0.19 (very small), 0.20 to 0.49 (small), 0.50 to 0.79 (medium), 0.80 to 1.19 (large), 1.20 to 1.99 (very large), and more than 2.00 (huge).  
The level of statistical significance was set at \(p\) < 0.05.
## 3 Results and Discussion
### Sampling Location and Instrumentation  
#### 3.1.1 Dilution Tunnel Versus Tailpipe  
Figure 3 summarizes the differences between the SPN instruments connected at the tailpipe and the dilution tunnel for both >23 nm and >10 nm for both vehicles. The comparison is presented as the ratio of the SPN measured in the dilution tunnel (CVS) to the tailpipe (TP) measurement minus one. Thus, positive differences denote higher CVS measurements compared to TP, while negative differences denote the opposite. For emission levels > 5 \(\times\) 10\({}^{9}\) #/km, the differences were \(-\)35% to 20%, with the instrument at the dilution tunnel measuring lower levels on average. The results are in good agreement with the literature, where differences of \(\pm\)30% have been reported [37; 38]. The differences can be attributed to uncertainties of the exhaust flow determination (5%) and agglomeration particle losses (10%) when the concentration is high (>10\({}^{7}\) #/cm\({}^{3}\)) (e.g., during cold start) [36]. The thermophoretic losses are negligible for exhaust gas temperatures up to 200 \({}^{\circ}\)C, as our tailpipe sampling was performed at 150 \({}^{\circ}\)C. They can reach 10% at exhaust gas temperatures close to 400 \({}^{\circ}\)C. High differences (20%) can also be seen when the particle size distributions peak at the cut-off size of the instrument (around 23 nm or 10 nm).  
For emission levels < 5 \(\times\) 10\({}^{9}\) #/km, the differences were >30%, with the instrument at the dilution tunnel measuring higher. The main reason was the higher background levels. For example, a 0.5 #/cm\({}^{3}\) background (allowed in the regulation and typical for our systems) would translate to 6 \(\times\) 10\({}^{9}\) #/km at the dilution tunnel and 5 \(\times\) 10\({}^{8}\) #/km at the tailpipe, which is a >1000% difference. It should be mentioned that the current SPN\({}_{23,CVS}\) limit is 6 \(\times\) 10\({}^{11}\) #/km, thus two orders of magnitude higher, and the high relative differences at levels < 5 \(\times\) 10\({}^{9}\) #/km were small or negligible in absolute values. These levels were also close to the lower detection limit of SPN systems, thus adding to the uncertainty of the instruments [36].  
There are two cases with high differences between the 10 nm counters at the tailpipe and the dilution tunnel (not shown in the figure). One was the first active regeneration event of vehicle #1 at 130 km/h, and the other was the first active regeneration of vehicle #2 at the pre-conditioning WLTC. The differences between the tailpipe and dilution tunnel had a difference of a factor of 4-6 at emission levels of around \(10^{12}\) #/km, higher than the 30% that would be acceptable. These two cases will be discussed in the next paragraphs. First, a regeneration event where no high differences were seen will be discussed, and then the two regeneration cases with high differences will be presented.
## 3 Materials and Methods
### Analysis of the Volatile Fraction
#### 3.4.4 SPME-GC-MS Analysis of UM  
To analyze the volatile composition of UM, an SPME device from Supelco (Bellefonte, PA, USA) with 1 cm fiber coated with 50/30 \(\upmu\)m DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) was used. The operative conditions for the sampling were the following: equilibration time of 30 min, and sampling time of 60 min at 35 \({}^{\circ}\)C. Lastly, the SPME fiber was inserted into the injector of the GC-MS system, maintained at 250 \({}^{\circ}\)C and operating as below. A gas chromatograph equipped with a FID and coupled with a mass spectrometer (Clarus 500 model Perkin Elmer-Waltham, MA, USA) was used. The capillary column was a Varian Factor Four VF-1, and the optimized temperature program was the following: from 70 \({}^{\circ}\)C to 120 \({}^{\circ}\)C at 6 \({}^{\circ}\)C/min; from 120 \({}^{\circ}\)C to 220 \({}^{\circ}\)C at 7 \({}^{\circ}\)C/min and held for 10 min. The components were identified by comparison between their calculated linear retention indices (LRIs) and those relating to a mix of \(n\)-alkanes. Furthermore, the matching of their mass spectra against commercial libraries (NIST) was performed. All analyses were conducted in triplicate and the results were expressed as average percentages calculated by peak area normalization from GC-FID chromatograms, without the use of an internal standard or correction factors.
## 6 JCS Sidelink Performance Evaluation Using CCM-SPS
### JCS Sidelike Performances with Dynamic Packet Sizes
#### 6.3.2 Optimization Performance Evaluation of Q-Learning-Based CCM-SPS  
The proposed CCM-SPS can optimize JCS performance metrics by introducing the Q-learning method in order to control the repetition times of the reserved resources by adjusting a decreasing _RC_ step size and suppressing the consecutive collisions probability.  
Figure 12 illustrates the CCM-SPS's range sensing performance evaluation on an empirical CDF of root CRLB with different pack sizes in the case of density = 50, 150, 250 veh/km. The performances of traditional SB-SPS with the same configuration are also given as a comparison. As for the CDF curve of the root CRLB Range, CCM-SPS's performance curve increases faster than traditional SB-SPS at a given packet size and vehicle density, giving a high probability of a small CRLB value. Meanwhile, with an increase in packet size, the larger the packet size, the more CRLBs. As the vehicle density increases, CCM-SPS has the advantage of being able to maintain a smaller CRLB value than SB-SPS.  
Figure 11: PRR vs. distance performance of SB-SPS with different pack sizes in case of density = 50, 150, 250 veh/km.  
vehicle density becomes dense, the update delay increases in both schemes, but CCM-SPS can maintain a high probability of a small update delay value. Therefore, the proposed CCM-SPS can achieve optimal communication qualities, such as high reliability and low latency, better than the traditional scheme.  
In view of the above discussion results, it is significant that CCM-SPS can fulfill comprehensive performance enhancements on both sensing and communication without affecting the cost of each other. CCM-SPS makes resource reservation feasible for sidelink JCS access after FD detects the dynamic SINR from the echo signal. According to reward function (19) related to \(RC\) and \(N_{col}\), the JCS vehicle agent can learn from the dynamic network environment and feed back a corresponding reward in order to optimize the \(\Delta RC\) selection actions in the reservation process through the Q-learning approach. As a result, the effective resource allocation for both sensing and communication can simultaneously be realized by using CCM-SPS. Therefore, the CCM-SPS algorithm is capable of supporting dynamic data traffic service scenarios such as the V2X network in intelligent transportation systems.
# Gearbox Fault Diagnosis Based on Multi-Sensor and Multi-Channel Decision-Level Fusion Based on SDP
## 3 Fitting Fault Identification Based on SDP Technology
### Experimental Data Acquisition
In addition, the gear pitting status was indicated by zero, one, and two for normal, single pitting, and double pitting states, respectively. After several experiments, the Y-direction of sensor one, the X-direction of sensor two, and the Y-direction of sensor three were clearly distinguished in these experimental data, therefore, these three channels were selected for each sensor. There were 1000 sets of experimental data, and each set of data consisted of 4096 points. Among them, 800 sets of data were selected to be the training set, the remaining 200 sets were used as the test set, then another 100 sets of experimental data were randomly generated as the validation set for detecting the gear fault diagnosis status. Finally, the accurate values that were obtained from the validation data set were adopted as the evidence body of the DS evidence theory for the sake of achieving decision-level fusion.
## 3 Preliminary of Millimeter Radar
### The Principle of Ranging of FMCW Radar  
FMCW radar transmits continuous waves with frequency changes in the frequency scanning period. There is a certain frequency difference between the echo reflected by the object and the transmitted signal. The distance information between the target and the radar can be obtained by measuring the frequency difference.  
Let \(f_{S}(t)\), \(f_{R}(t)\) be the frequency variation functions of sending and receiving signals, respectively, and \(f_{0}\) be the initial frequency. Assuming that the relative speed \(v_{r}\) is zero. The relationship on the rising edge of the signal is as follows:  
\[f_{S}(t)=f_{0}+\frac{f_{c}}{t_{c}}\cdot t \tag{1}\]  
\[f_{R}=f_{S}(t-\tau) \tag{2}\]  
There is a beat frequency function:  
\[f_{b}=f_{S}(t)-f_{R}(t) \tag{3}\]  
Hence, we can draw the Figure 1 below, where \(t_{c}\) is half of the frequency sweeping cycle, \(f_{c}\) is the frequency sweeping bandwidth, and \(\tau\) is the time from signal transmission to reception.  
Because of \(R=\frac{\tau\cdot c}{2}\), from the geometric relationship \(\frac{f_{b}}{\tau}=\frac{f_{c}}{t_{c}}\) in Figure 1, it can be concluded that:  
\[R=\frac{c}{2}\cdot\frac{t_{c}}{f_{c}}\cdot f_{b} \tag{4}\]  
where \(c\) is the speed of light.  
To sum up, the detection range of the FMCW radar is determined by Equation (4), and the key parameter is the frequency difference between transmission and reception, which directly affects the selection of ADC sampling rate. Although the scanning bandwidth also has an impact on the detection distance, it does not affect the selection of ADC when it is a fixed value.
# Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
## 2 Study Area and Data
### Geostationary Satellite Data  
COMS is Korea's first multi-purpose geostationary satellite, and is equipped with a Meteorological Imager (MI) sensor observing East Asia every 15 min [33]. COMS MI provides data at one visible, shortwave infrared (SWIR), water vapor (WV), and two IR channels [22; 29] (Table 1). COMS MI also provides level 2 products such as cloud analysis (CA) and cloud top temperature and heights (CTTP), which quantify the inner and upper properties of clouds. CA contains COT, measuring the optical depth of clouds with a scaling from 0 to 100; and cloud phase (CP), providing information on whether the major portion of a near cloud top is water, ice, mixed, unknown, or if the sky is clear [33; 34; 35]. CTTP provides information on cloud top conditions, including cloud top temperature (CTT), cloud top height (CTH), and cloud top pressure (CTP) [33; 34; 35]. The spatial resolution of the visible channel is 1 km, while the other products have a resolution of 4 km. COMS MI products were downloaded through the National Meteorological Satellite Center (NMSC) webpage ([[http://nmsc.kma.go.kr/](http://nmsc.kma.go.kr/)]([http://nmsc.kma.go.kr/](http://nmsc.kma.go.kr/))).  
Himawari-8, a replacement of the Japanese Multifunctional Transport Satellite 2 (MTSAT-2), was launched by Japan Meteorological Agency (JMA) on 7 October 2014. Its major instrument is the Advanced Himawari Imager (AHI) that has 16 bands with a high spatial resolution (500 m-2 km), and scans Northeast Asia every 10 min [36; 37; 38]. The bands consist of three visible channels with 0.5-1 km resolution and thirteen IR channels with 1-2 km resolution (Table 1). In this study, we used only 14 bands from the site ([ftp://hmwr829 gr.cr.chiba-u.ac.jp/](ftp://hmwr829 gr.cr.chiba-u.ac.jp/)) because there are not yet publicly available cloud products from Himawari-8. COMS data from April 2011 to July 2017 and Himawari-8 data from July 2015 to July 2017 were used in this study.
## 1 Introduction
Previous research on SVFCWs indicates its applicability in treating the organic matter in winery wastewater. However, additional treatment using adsorption media might be required to continuously remove nitrogen and phosphorus during cold weather and after periods of low or no flow of wastewater. Recent research has shown promising results for this type of wastewater for the sorption of ammonia while using the natural zeolite mineral, clinoptilolite [50], and nitrate using tire chips [51]. Sorption allows for the removal of these nutrients, while microbial communities build up and become adequate to treat the nitrogen completely; during periods of low flow of wastewater through the media, these inexpensive materials can be microbially regenerated in-place, which allows for its continued use [51; 52; 53]. Additionally, oyster shells with a composition of 95% calcium carbonate [54] are added to provide pH buffering [51]. In a prior study, scrap tire chips (1-1.5 cm particle size) were found to leach small amounts of bioavailable organic carbon that supported denitrification [51]. Although low concentrations of zinc, selenium, manganese, antimony, and cobalt were detected, other metals of concern were below the detection limits (see [PERSON] et al. [51] for a review of toxicology studies). Moreover, tire chips have been approved for use in onsite drain field applications in several states [55]. Engineered nano-enhanced media are becoming commercially available to adsorb phosphorus and they can be easily regenerated, and the phosphorus recovered as a fertilizer. For this research, PO4 Sponge, by MetaMateria Technologies, Columbus, OH, was used. This adsorption media is a nano-enhanced iron foam that is composed of iron oxide nanocrystals of oxyhydroxide. Its alumino-silicate bonded porous structure gives it increased porosity and contact time, high adsorption rates, and an adsorption capacity ranging from 20-50 mg phosphorus/g media [56]. This research investigates the utility of an SVFCW with nitrogen and phosphorus adsorption media to treat winery wastewater. Performance data were collected from bench-scale treatment systems under normal operating conditions, in reduced temperatures, and after periods of no-flow of wastewater.
# Research on a Fast Image-Matching Algorithm Based on Nonlinear Filtering
## 2 Theoretical Analysis
### Downsampling  
In the traditional SIFT matching algorithm, the author [PERSON] uses averaging to downsample the image 2 to construct the scale space, which results in the loss of some of the feature information of the image and also suppresses the high-frequency information of the image. The average downsampling method may lead to distortion of the edges of the image. By changing the average sampling to maximum downsampling, the computation can be performed quickly with limited computational resources, and the important features of the image can be preserved by using maximum downsampling, as shown in Figure 1:
## 3 UbiPriSEQ: System Requirements
### QoS  
Satisfactory QoS is the primary goal for network design. QoS metrics include throughput, packet loss, bit error rate, throughput, latency, jitter, and others. While all QoS metrics are important, latency has become a bottleneck due to emerging interactive, real-time, and safety-critical applications, for example, online gaming, augmented reality, 4K video streaming, drone control, and remote surgery.