esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 4 Conclusions  
This paper proposes an ML-based modeling method and uses this method to establish an empirical prediction model of TEC for parts of Europe. It has been proved that the monthly mean values curve of TEC predicted by the model proposed in this paper is highly consistent with the observed curve of TEC. Furthermore, it is found that the prediction  
Figure 19: Spatial distribution of TECs predicted by CCIR, URSI and the proposed model in May 2009 (low solar activity year): (**a**) CCIR; (**b**) URSI; (**c**) Prop.  
Figure 20: Spatial distribution of TECs predicted by CCIR, URSI and the proposed model in June 2014 (high solar activity year): (**a**) CCIR; (**b**) URSI; (**c**) Prop.  
accuracy of the proposed model is significantly better than that of the IRI model with CCIR and URSI coefficients by comparing the observed TECs and the predicted TECs calculated by the IRI model and the proposed model, respectively. The improvement is 38.63% and 35.79%, respectively, which confirms the effectiveness of the proposed model.  
This model is proposed for parts of Europe. Later we hope that it can have better effects and provide early warning for natural disasters and sudden magnetic storms by collecting more observed data, expanding the application scope, and improving the prediction accuracy. We will further explore the modeling methods for other regions to improve and enhance this model, which is compared with different models to validate the proposed model's effectiveness and correctness based on more collected data such as GNSS TEC. In addition, we will finish the STFM of TEC based on this paper and make more efforts to improve the temporal and spatial resolution of the model and achieve a more accurate and detailed prediction of TEC. At the same time, we hope that this modeling idea can be applied to modeling other ionospheric parameters, such as the peak height of electron density in the F2 layer (hmF2), the maximum electron density in the F2 layer (NmF2), etc., and can provide a reference for modeling in other directions.  
Conceptualization, [PERSON] and J.W.; methodology, [PERSON] and J.W.; software, Y.L., C.Y. and J.W.; validation, Y.L., J.W. and H.F.; formal analysis, Y.L., C.Y. and J.W.; investigation, Y.L.; resources, J.W. and Y.Z; data curation, Y.L. and J.W.; writing--original draft preparation, Y.L. and J.W.; writing--review and editing, [PERSON], C.Y., Y.Z. and H.F; visualization, Y.L.; supervision, H.F.; project administration, J.W.; funding acquisition, C.Y. and J.W. 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 (No. 62031008) and the State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System (No. CEMEE2022G0201, CEMEE-002-2020224).  
Not applicable.  
The authors declare no conflict of interest.
# Poisoning Attacks against Communication and Computing Task Classification and Detection Techniques
## 2 Literature Review and Paper Contributions
### Poisoning Attacks  
In recent years, the data poisoning literature has addressed attacks in a range of scenarios as well as diverse applications, with threat models ranging from attackers having only data access to attackers controlling the entire training process. Poisoning attacks were extensively discussed for other scenarios concerning computer vision (CP) [27; 28; 29] and natural language processing (NLP) [30; 31; 32]. In general, AML in wireless communications is still poorly explored, although adverse wireless channels and introduced distortions are inherent in mobile radio communication.  
Some works discussed the topic in a general way that can be applied to several classification problems. For example, in [33], the authors discussed their novel poison threat algorithm to generate adversarial examples that deceive ML classifiers such as k-means and spatial clustering. Another proposed work [34] dealt with fair ML classification under these attacks and the high poisoning vulnerability of this classifier.  
To launch the attacks against 2C task classification, a variety of techniques can be borrowed from wireless research works. Some of them are conventional wireless attacks. Jamming, for example, might prevent an ML model from being trained correctly on data provided by a given source. This strategy is used to change some training examples, resulting in data poisoning [35]. Another example would be wireless medium eavesdropping to obtain model features, resulting in stealing the trained model or jeopardizing data privacy. In [36], the authors considered class sniffing and quantity inference attacks. These attacks find the class (the label) of a certain example and determine the composition proportion of the training labels owned by selected clients, respectively.  
It is worth mentioning that a large group of works consider attacks on ML algorithms using other ML algorithms or hybrid attacks (conventional or ML attacks). One recent work [37] discussed generative adversarial networks (GANs) in 5G communication systems. AML attacks on wireless communication are categorized and summarized in [38].
# Investigation of Nanogrids for Improved Navy Installation Energy Resilience
## 3 Methodology
### Step 4: Calculate Resilience
#### 3.4.1 Step 4.1: Simulate Nanogrid Systems  
Using the equations developed by [PERSON] et al. [22], a simulation can be developed to examine various nanogrid configurations. We modified an existing MATLAB simulation tool [32] to develop the Case Study (Section 4). Other implementations of [PERSON] et al. have been developed [5].  
The simulation implements a simple power flow analysis that matches generation and storage with loads to identify periods of time where insufficient capacity is available to support critical loads. Input parameters include sizing information for PV area, battery capacity and power output, generator power output, amount of fuel storage available, solar irradiance data, load data, and initial conditions of all electrical hardware. Output parameters include load shedding information (which load(s) is/are shed, and for how long) among others. The power flow analysis takes into account the charge state (modeling charge and discharge rates, capacity, etc.) of the ESS, the amount of fuel available for generators and when fuel resupply may occur, load profiles, load shedding strategies (which loads to shed first when insufficient power is available to support all loads), and other considerations. The output of the simulation is then used along with individual MI scores of the critical loads to calculate \(M_{\text{s}}\) as shown in Equation (1).  
We advocate using a one hour time step in the simulations which is sufficient for the high level systems engineering analysis of energy resilience conducted in our methodology. Practitioners can choose to develop higher fidelity models based on their individual situations although we have observed in our professional practice that most military installation energy managers can make decisions using data with one hour time steps.  
While it is possible to simulate across one or multiple calendar years using a Monte Carlo approach or similar (as [PERSON] et al. did [22]) to develop a broader picture of load shedding under different load and solar irradiance conditions (e.g., diurnal temperature and solar irradiance swings, seasonal swings, etc.), we recommend that one particular time period with the highest loads and lowest solar irradiance is specifically chosen. This greatly reduces computation time and also focuses the analysis on the worst case scenario of high load and low availability of solar power. The reason for this focus is that threats from adversaries are likely to choose the time of greatest electrical infrastructure vulnerability to attack. This is unique to military energy resilience and microgrids versus civilian energy systems that generally do not have nation-state or terrorist adversaries looking to cause damage to national security.
# Optimizing Service Placement for Microservice Architecture in Clouds
## 4 Placement Algorithm
### Placement Finding  
As we discussed before, in order to partition the application, the threshold \(\alpha\) is required by the algorithm. However, giving an appropriate deterministic threshold \(\alpha\) is difficult, as it cannot guarantee that the algorithm can find a placement solution through the randomized partition and the heuristic packing under a certain threshold \(\alpha\). Intuitively, the higher threshold \(\alpha\) results in less parts in the partition, which leads to less traffic rate between different parts. Thus, we introduce a simple algorithm to find a better threshold \(\alpha\) by enumerating from large to small. The algorithm is shown in Algorithm 5. At the beginning, the value of \(\alpha\) is 1.0. To adjust the thresholds, we set a step value \(\Delta\), and the default value is 0.1, which can be customized by users. In each iteration, with the threshold \(\alpha\), the algorithm first partitions the given application \(\mathcal{S}\) based on the binary partition algorithm or k partition algorithm. Note that the algorithm records the latest partition results to avoid multiple repeated partition. It then tries to pack all parts in the partition into machines based on the heuristic packing algorithm to find a placement solution for the application.  
```
Input: service-based application \(\mathcal{S}\), vectors of available resources on each machine \(\{V_{1},V_{2},...,V_{M}\}\) Output: a placement solution \(X\)
1\(X\leftarrow[x_{ij}=0]_{N\times M}\);
2\(\alpha\gets 1.0\);
3\(\Delta\gets 0.1\);
4 while\(\alpha\geq 0.0\)do
5\(P\leftarrow\textbf{Binary\_Partition}(\mathcal{S},\alpha)\); /* Or \(P\leftarrow\textbf{K\_Partition}(\mathcal{S},\alpha)\); */
6\(X^{\prime}\leftarrow\textbf{Heuristic\_Packing}(P,\{V_{1},V_{2},...,V_{M}\})\);
7 if\(X^{\prime}\
eq null\)then
8 Calculate \(X\) according to \(X^{\prime}\) and \(P\);
9 return\(X\);
10
11 end if
12\(\alpha\leftarrow\alpha-\Delta\);
13
14 end while return\(null\);
```  
**Algorithm 5**Placement Finding  
Next, we discuss the time complexity of the algorithm we proposed. We assume the number of services is \(n\); the number of edges in the service graph is \(m\) (i.e., the number of the traffic rates \(t_{ij}>0\)); the number of machines is \(M\). For a service-based application, it can be partitioned up to \(n\) parts. For each partition, we perform the contraction algorithm \(n\) times, and the time complexity of the contraction algorithm is \(O(m\text{log}^{2}n)\). As we record the latest partition results to avoid multiple repeated partition, the time complexity of the overall partition is \(O(n^{2}m\text{log}^{2}n)\). To the heuristic packing, the time complexity is \(O(nM+n^{2})\) as the overall time complexity of calculating the factor \(tf\) is \(O(n^{2})\). Let \(C=\frac{1}{\mathcal{S}}\) denote the number of iterations. The overall time complexity of the proposed algorithm is \((n^{2}m\text{log}^{2}n+CnM+Cn^{2})\).
# Microbial Biopolymers: From Production to Environmental Applications--A Review
## 2 Microbial Polymers
### Microbial \(\beta\)-Glucan  
Traditionally, based on raw materials, the isolation of \(\beta\)-glucan is grouped into cereals and non-cereals with varying compositional structures. Additionally, certain microorganisms can also create exopolysaccharideaccharides, that may be able to meet demand on an industrial scale. Exopolysaccharides from microorganisms are advantageous replacements for natural plant polysaccharides because of their distinctive metabolic characteristics. The source from which beta-glucan was acquired affects its characteristics. Industrial scale-ups of \(\beta\)-glucan have some constraints--notably, lengthy extraction times and high costs--associated with the process [136]. This microbial biopolymer exists both intracellularly (typically as a storage unit of carbohydrates) and extracellularly (generally as a capsule or as slime layers and biofilms). Furthermore, there are two types of microbial beta-glucans: linear and branched. Singular \(\beta\) (1-3) or \(\beta\) (1-6) glucose units may make up linear beta-glucans. Branched beta-glucans are identified by the combination of \(\beta\) (1-3) and \(\beta\) (1-6) connections [137]. [PERSON] et al. [136] summed up the production of \(\beta\)-glucan using different processes and compared the yields and production times. The biological activity of obtained \(\beta\)-glucan depends upon raw material, manufacturing processes, and purifying techniques [17; 18; 32]. \(\beta\)-glucan is a significant antifungal agent for crop protection. [PERSON] et al. [138] reported the valuable impact of \(\beta\)-glucan in plant defense pathways, resistance against disease-causing microorganisms, and in response to changing environmental circumstances. [PERSON] and [PERSON] [139] observed the antifungal effect of \(\beta\)-glucan against the destructive fungus _P. aphaattermatum_, which mainly harms crucial crop plants. Similarly, [PERSON] et al. [62] and [PERSON] and [PERSON] [140] reported the antifungal efficacy of \(\beta\)-D glucan against the _Saccharomyces cerevisiae_ strain and in the prevention of rhizome rot disease of turmeric, respectively. [PERSON] [141] examined the impact of \(\beta\)-glucan against environmental toxins like mycotoxin, aflatoxin, and depleted uranium.
## 2 Test Scheme Design and Implementation
### Loading Device and Acquisition System  
The testing was conducted using the JN-020 1000 kN high-performance universal testing machine. The testing machine is equipped with an advanced electrohydraulic servo system, providing a maximum load capacity of 1000 kN. It is capable of delivering precise measurements and acquiring data under various loading conditions, as shown in Figure 2. The testing machine is equipped with high-precision sensors and controllers, allowing real-time monitoring and recording of the load parameters during the testing process.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**ID** & **P1** & **P2** & **P3** \\ \hline beam length (m) & 3.2 & 3.2 & 3.2 \\ calculate span (m) & 3.0 & 3.0 & 3.0 \\ height (mm) & 240 & 240 & 240 \\ width (mm) & 150 & 150 & 150 \\ prestressed bar & — & 7\(\Phi^{5}\) & 7\(\Phi^{5}\) \\ upper steel bar (mm) & 2\(\Phi\)12 & 2\(\Phi\)12 & 2\(\Phi\)12 \\ lower steel bar (mm) & 2\(\Phi\)16 & 2\(\Phi\)16 & 2\(\Phi\)16 \\ stirrup (mm) & \(\Phi\)8@200 & \(\Phi\)8@200 & \(\Phi\)8@200 \\ eccentricity (mm) & 0 & curve & curve \\ concrete strength & C40 & C40 & C40 \\ \hline \hline \end{tabular} Note: “—” means no prestressed bar.  
\end{table}
Table 1: Structural parameters of reinforced concrete test beams.  
Figure 1: Construction of reinforced concrete test beams (mm).  
Figure 2: Loading device and equipment layout. The red arrows in the figure represent the physical image of the corresponding device. Serial number (1)–(8) indicates the number of the acoustic emission sensor.  
Magnetic flux sensors were embedded inside the test beam during the pouring process, and they were connected to a magnetic field detector during testing. The internal prestressing of the beam was monitored in real time using the non-destructive testing technique of magnetic flux-force measurement. To comprehensively investigate the entire process of loading-induced failure in reinforced concrete beams, five strain gauges were installed on the test beam: one on the top surface, one on the bottom surface, and three on the sides, connected to strain gauge channels 1, 2, 3, 4, and 5, respectively. Furthermore, a dial gauge was installed at the mid-span of the bottom surface of the test beam to measure its deflection, as shown in Figure 2, for accurate positioning.  
The AE detection system used the AE-DS5 series full-information acoustic emission signal analyzer, produced by Beijing Soft Island Technology Co., Ltd. It operated on a 220 V AC power supply and consisted of 16 AE signal channels and 8 additional external parameter channels. For this experiment, 8 channels were used, and RS-2A type sensors were employed as AE sensors. The 8 sensors were positioned around the perimeter of the rectangular test beam, as shown in Figures 2 and 3. To effectively reduce external mechanical noise from impacts and friction [30], rubber pads were placed at the contact points between the loading hammer and the test beam. Additionally, the AE instrument was grounded.
# Deep Learning-Powered System for Real-Time Digital Meter Reading on Edge Devices
## 2 Materials and Methods
### Automatic Meter Reading Pipeline
#### 2.2.3 Text Recognition  
Text recognition models that could recognize alphanumeric characters and special characters, such as dots, minus signs, etc., and allowed conversion to the open neural network exchange (ONNX) format were first identified. Facebook's Rosetta [15] is an efficient text recognition model, based on the ResNet18 [14] architecture. Connectionist temporal classification (CTC) loss is used to train the sequence model, which allows the system to recognize words of arbitrary length and out-of-vocabulary words (words that were not seen during training). The framework introduced by [PERSON] et al. [16] from _Clova AI Research_[17] allows training of this and other models based on the combination of four modules: transformation, feature extraction, sequence modeling and prediction. The CRNN [4] is an algorithm for image-based sequence recognition tasks, which combines a CNN for the extraction of visual features and an RNN for processing the feature map to recognize sequences of characters. Furthermore, the addition of CTC allows the length of the output sequence to not be necessarily equal to the length of the input image and helps to handle challenges such as variations in font, size, and orientation, and overlapping characters. [PERSON] and [PERSON] [18] reproduced the implementation of this model in the _Scene Text Recognition Model Hub_[19].  
We pre-trained Rosetta on the SynthText (ST) [20] dataset and employed the CRNN with weights pre-trained on the MJSynth (MJ) [21] and ST datasets. Both models are also fine-tuned with the SINATRA dataset. For data augmentation of the input images, we adjust the contrast and brightness, add Gaussian blur, perform zooms and rotations, and pad each side by 0 to 10% to mimic faulty detections. The images are resized to the input shape of each model: \(32\times 100\) for Rosetta and \(32\times 128\) for CRNN, each. The models are trained for the 94-character set, which consists of mixed-case alphanumeric characters with punctuation.
# An Efficient Encoding Algorithm Using Local Path on Huffman Encoding Algorithm for Compression
## 2 Technical Background
### Huffman Encoding Algorithm
In cases where a small number of symbols are used and there are small differences in the probability of characters, the efficiency of Huffman encoding is reduced. Thus, arithmetic coding is needed to handle this problem.
## 2 The Evolution of Wireless Communication Networks
Table 3 gives a summary of the different generations of wireless networks and their different parameters starting from 1G to 6G. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Parameters** & **1G** & **2G** & **3G** & **4G** & **5G** & **6G** \\ \hline Year deployed & 1980-1986 & 1990–1992 & 2000–2001 & 2010–2016 & 2019–2020 & Expected by 2030 \\ \hline Organization & AMPS & GSM, 3 GPP, 3 GPP2 & 3 GPP2 & 3 GPP, LTE & 3 GPP, ATSC 3.0 & ITU-T standards \\ \hline Bandwidth & Analog & 25 MHz & 25 MHz & 100 MHz & 30–300 GHz & 300 GHz-10 THz \\ \hline Network speed & 2 Kbps & 64 Kbps & 8 Mbps & 50 Mbps & 10 Gbps & 100 Tbps \\ \hline Switching & Circuit & Circuit, Packet & Packet & All Packet & All Packet & All Packet \\ \hline \multirow{2}{*}{Service} & \multirow{2}{*}{Voice Only} & Voice, SMS, MMS, & Voice, video, & Video streaming, VoIP, & IoT, smart automation, smart & Smart cognitive sensing, smart \\  & & WAP & data & internet browsing & city, and wearables & healthcare, and hybrid \\ \hline Handoff & Horizontal & Horizontal & Horizontal & Horizontal and Vertical & Horizontal and Vertical \\ \hline Internet connectivity & Nil & Narrowband & Broadband & Ultra-broadband & Wireless WWW & Terrestrial wireless WWW \\ \hline Main Network & PSTN & PSTN & Packet Network & Internet & Tactile Internet & Cognitive Internet \\ \hline Mobility capacity & & & 200 km/h & 350 km/h & 500 km/h & 1000 km/h \\ \hline Peak frequencies & 900 MHz & & & 6 GHz & 90 GHz & 10 THz \\ \hline Peak data rate & 2.4 Kbps & 14.4 Kbps & 2 Mbps & 128 Mbps & 20 Gbps & 1 Tbps \\ \hline Peak spectral efficiency & & & & 15 ps/Hz & 30 ps/Hz & 100 ps/Hz \\ \hline Latency & - & - & 100 ms & 10 ms & 1 ms & 01 ms \\ \hline Reliability & - & - & - & 99.9\% & 99.999\% & 99.9999\% \\ \hline \multirow{2}{*}{System multiplexing Architecture} & \multirow{2}{*}{FDMA} & \multirow{2}{*}{TDMA and CDMA} & \multirow{2}{*}{CDMA} & Massive MIMO, & Massive MIMO, & Intelligent sources, OFDM, BDMA, \\  & & & & LTE, and MC-CDMA & mm (Kive, NOMA, and & and Beamspace \\  & & & & PD/CD-NOMA & & Future RAT, FFS, Ultra-URLLC, \\ \hline \multirow{2}{*}{Key Technologies} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & & & & & \\ \hline \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\  & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: Evolution of mobile network from 1G to 6G in terms of internet service, bandwidth, applications, speed of network, multiplexing and spectrum algorithm, and year of deployment.